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Child Care and Early Education Research Connections

Descriptive research studies.

Descriptive research is a type of research that is used to describe the characteristics of a population. It collects data that are used to answer a wide range of what, when, and how questions pertaining to a particular population or group. For example, descriptive studies might be used to answer questions such as: What percentage of Head Start teachers have a bachelor's degree or higher? What is the average reading ability of 5-year-olds when they first enter kindergarten? What kinds of math activities are used in early childhood programs? When do children first receive regular child care from someone other than their parents? When are children with developmental disabilities first diagnosed and when do they first receive services? What factors do programs consider when making decisions about the type of assessments that will be used to assess the skills of the children in their programs? How do the types of services children receive from their early childhood program change as children age?

Descriptive research does not answer questions about why a certain phenomenon occurs or what the causes are. Answers to such questions are best obtained from  randomized and quasi-experimental studies . However, data from descriptive studies can be used to examine the relationships (correlations) among variables. While the findings from correlational analyses are not evidence of causality, they can help to distinguish variables that may be important in explaining a phenomenon from those that are not. Thus, descriptive research is often used to generate hypotheses that should be tested using more rigorous designs.

A variety of data collection methods may be used alone or in combination to answer the types of questions guiding descriptive research. Some of the more common methods include surveys, interviews, observations, case studies, and portfolios. The data collected through these methods can be either quantitative or qualitative. Quantitative data are typically analyzed and presenting using  descriptive statistics . Using quantitative data, researchers may describe the characteristics of a sample or population in terms of percentages (e.g., percentage of population that belong to different racial/ethnic groups, percentage of low-income families that receive different government services) or averages (e.g., average household income, average scores of reading, mathematics and language assessments). Quantitative data, such as narrative data collected as part of a case study, may be used to organize, classify, and used to identify patterns of behaviors, attitudes, and other characteristics of groups.

Descriptive studies have an important role in early care and education research. Studies such as the  National Survey of Early Care and Education  and the  National Household Education Surveys Program  have greatly increased our knowledge of the supply of and demand for child care in the U.S. The  Head Start Family and Child Experiences Survey  and the  Early Childhood Longitudinal Study Program  have provided researchers, policy makers and practitioners with rich information about school readiness skills of children in the U.S.

Each of the methods used to collect descriptive data have their own strengths and limitations. The following are some of the strengths and limitations of descriptive research studies in general.

Study participants are questioned or observed in a natural setting (e.g., their homes, child care or educational settings).

Study data can be used to identify the prevalence of particular problems and the need for new or additional services to address these problems.

Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research."

Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples.

Limitations:

Descriptive studies cannot be used to establish cause and effect relationships.

Respondents may not be truthful when answering survey questions or may give socially desirable responses.

The choice and wording of questions on a questionnaire may influence the descriptive findings.

Depending on the type and size of sample, the findings may not be generalizable or produce an accurate description of the population of interest.

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5.8: Descriptive Research

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Learning Objectives

• Differentiate between descriptive, experimental, and correlational research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are case studies, naturalistic observation, and surveys.

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Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

To learn more about Krista and Tatiana, watch this video about their lives as conjoined twins.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

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Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 1).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 2). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this module: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

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Think It Over

A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

clinical or case study:  observational research study focusing on one or a few people

correlational research:  tests whether a relationship exists between two or more variables

descriptive research:  research studies that do not test specific relationships between variables; they are used to describe general or specific behaviors and attributes that are observed and measured

experimental research:  tests a hypothesis to determine cause and effect relationships

generalize inferring that the results for a sample apply to the larger population

inter-rater reliability:  measure of agreement among observers on how they record and classify a particular event

naturalistic observation:  observation of behavior in its natural setting

observer bias:  when observations may be skewed to align with observer expectations

population:  overall group of individuals that the researchers are interested in

sample:  subset of individuals selected from the larger population

survey:  list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

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• Approaches to Research. Authored by : OpenStax College. Located at : http://cnx.org/contents/[email protected]:iMyFZJzg@5/Approaches-to-Research . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/contents/[email protected]
• Descriptive Research. Provided by : Boundless. Located at : https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/researching-psychology-2/types-of-research-studies-27/descriptive-research-124-12659/ . License : CC BY-SA: Attribution-ShareAlike

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments. The main categories of psychological research are descriptive, correlational, and experimental research. Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions.

Research studies that do not test specific relationships between variables are called  descriptive studies . For this method, the research question or hypothesis can be about a single variable (e.g., How accurate are people’s first impressions?) or can be a broad and exploratory question (e.g., What is it like to be a working mother diagnosed with depression?). The variable of the study is measured and reported without any further relationship analysis. A researcher might choose this method if they only needed to report information, such as a tally, an average, or a list of responses. Descriptive research can answer interesting and important questions, but what it cannot do is answer questions about relationships between variables.

Video 2.4.1.  Descriptive Research Design  provides explanation and examples for quantitative descriptive research. A closed-captioned version of this video is available here .

Descriptive research is distinct from  correlational research , in which researchers formally test whether a relationship exists between two or more variables.  Experimental research  goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about causal relationships between variables. We will discuss each of these methods more in-depth later.

Table 2.4.1. Comparison of research design methods

Candela Citations

• Descriptive Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/edpsy/chapter/descriptive-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Descriptive Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/adolescent/chapter/descriptive-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Educational Psychology Copyright © 2020 by Nicole Arduini-Van Hoose is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Descriptive Research: Definition, Characteristics, Methods + Examples

Suppose an apparel brand wants to understand the fashion purchasing trends among New York’s buyers, then it must conduct a demographic survey of the specific region, gather population data, and then conduct descriptive research on this demographic segment.

The study will then uncover details on “what is the purchasing pattern of New York buyers,” but will not cover any investigative information about “ why ” the patterns exist. Because for the apparel brand trying to break into this market, understanding the nature of their market is the study’s main goal. Let’s talk about it.

What is descriptive research?

Descriptive research is a research method describing the characteristics of the population or phenomenon studied. This descriptive methodology focuses more on the “what” of the research subject than the “why” of the research subject.

The method primarily focuses on describing the nature of a demographic segment without focusing on “why” a particular phenomenon occurs. In other words, it “describes” the research subject without covering “why” it happens.

Characteristics of descriptive research

The term descriptive research then refers to research questions, the design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity.

Some distinctive characteristics of descriptive research are:

• Quantitative research: It is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature.
• Uncontrolled variables: In it, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher.
• Cross-sectional studies: It is generally a cross-sectional study where different sections belonging to the same group are studied.
• The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research.

Applications of descriptive research with examples

A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey , though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research? To understand the end objective of research goals, below are some ways organizations currently use descriptive research today:

• Define respondent characteristics: The aim of using close-ended questions is to draw concrete conclusions about the respondents. This could be the need to derive patterns, traits, and behaviors of the respondents. It could also be to understand from a respondent their attitude, or opinion about the phenomenon. For example, understand millennials and the hours per week they spend browsing the internet. All this information helps the organization researching to make informed business decisions.
• Measure data trends: Researchers measure data trends over time with a descriptive research design’s statistical capabilities. Consider if an apparel company researches different demographics like age groups from 24-35 and 36-45 on a new range launch of autumn wear. If one of those groups doesn’t take too well to the new launch, it provides insight into what clothes are like and what is not. The brand drops the clothes and apparel that customers don’t like.
• Conduct comparisons: Organizations also use a descriptive research design to understand how different groups respond to a specific product or service. For example, an apparel brand creates a survey asking general questions that measure the brand’s image. The same study also asks demographic questions like age, income, gender, geographical location, geographic segmentation , etc. This consumer research helps the organization understand what aspects of the brand appeal to the population and what aspects do not. It also helps make product or marketing fixes or even create a new product line to cater to high-growth potential groups.
• Validate existing conditions: Researchers widely use descriptive research to help ascertain the research object’s prevailing conditions and underlying patterns. Due to the non-invasive research method and the use of quantitative observation and some aspects of qualitative observation , researchers observe each variable and conduct an in-depth analysis . Researchers also use it to validate any existing conditions that may be prevalent in a population.
• Conduct research at different times: The analysis can be conducted at different periods to ascertain any similarities or differences. This also allows any number of variables to be evaluated. For verification, studies on prevailing conditions can also be repeated to draw trends.

Advantages of descriptive research

Some of the significant advantages of descriptive research are:

• Data collection: A researcher can conduct descriptive research using specific methods like observational method, case study method, and survey method. Between these three, all primary data collection methods are covered, which provides a lot of information. This can be used for future research or even for developing a hypothesis for your research object.
• Varied: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough.
• Natural environment: Descriptive research allows for the research to be conducted in the respondent’s natural environment, which ensures that high-quality and honest data is collected.
• Quick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive.

Descriptive research methods

There are three distinctive methods to conduct descriptive research. They are:

Observational method

The observational method is the most effective method to conduct this research, and researchers make use of both quantitative and qualitative observations.

A quantitative observation is the objective collection of data primarily focused on numbers and values. It suggests “associated with, of or depicted in terms of a quantity.” Results of quantitative observation are derived using statistical and numerical analysis methods. It implies observation of any entity associated with a numeric value such as age, shape, weight, volume, scale, etc. For example, the researcher can track if current customers will refer the brand using a simple Net Promoter Score question .

Qualitative observation doesn’t involve measurements or numbers but instead just monitoring characteristics. In this case, the researcher observes the respondents from a distance. Since the respondents are in a comfortable environment, the characteristics observed are natural and effective. In a descriptive research design, the researcher can choose to be either a complete observer, an observer as a participant, a participant as an observer, or a full participant. For example, in a supermarket, a researcher can from afar monitor and track the customers’ selection and purchasing trends. This offers a more in-depth insight into the purchasing experience of the customer.

Case study method

Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they can’t make accurate predictions because there could be a bias on the researcher’s part. The other reason why case studies are not a reliable way of conducting descriptive research is that there could be an atypical respondent in the survey. Describing them leads to weak generalizations and moving away from external validity.

Survey research

In survey research, respondents answer through surveys or questionnaires or polls . They are a popular market research tool to collect feedback from respondents. A study to gather useful data should have the right survey questions. It should be a balanced mix of open-ended questions and close ended-questions . The survey method can be conducted online or offline, making it the go-to option for descriptive research where the sample size is enormous.

Examples of descriptive research

Some examples of descriptive research are:

• A specialty food group launching a new range of barbecue rubs would like to understand what flavors of rubs are favored by different people. To understand the preferred flavor palette, they conduct this type of research study using various methods like observational methods in supermarkets. By also surveying while collecting in-depth demographic information, offers insights about the preference of different markets. This can also help tailor make the rubs and spreads to various preferred meats in that demographic. Conducting this type of research helps the organization tweak their business model and amplify marketing in core markets.
• Another example of where this research can be used is if a school district wishes to evaluate teachers’ attitudes about using technology in the classroom. By conducting surveys and observing their comfortableness using technology through observational methods, the researcher can gauge what they can help understand if a full-fledged implementation can face an issue. This also helps in understanding if the students are impacted in any way with this change.

Some other research problems and research questions that can lead to descriptive research are:

• Market researchers want to observe the habits of consumers.
• A company wants to evaluate the morale of its staff.
• A school district wants to understand if students will access online lessons rather than textbooks.
• To understand if its wellness questionnaire programs enhance the overall health of the employees.

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Descriptive Epidemiology

Introduction

The image above illustrates the ten essential functions of public health. Epidemiology plays a particularly important role for three of the functions: monitoring, investigating, and evaluating. The 10 Essential Public Health Services describe the public health activities that all communities should undertake. Public health systems should

• Monitor health status to identify and solve community health problems.
• Diagnose and investigate health problems and health hazards in the community.
• Inform, educate, and empower people about health issues.
• Mobilize community partnerships and action to identify and solve health problems.
• Develop policies and plans that support individual and community health efforts.
• Enforce laws and regulations that protect health and ensure safety.
• Link people to needed personal health services and assure the provision of health care when otherwise unavailable.
• Assure competent public and personal health care workforce.
• Evaluate effectiveness, accessibility, and quality of personal and population-based health services.
• Research for new insights and innovative solutions to health problems.

Disease surveillance systems and health data sources provide the raw information necessary to monitor trends in health and disease. Descriptive epidemiology provides a way of organizing and analyzing these data in order to understand variations in disease frequency geographically and over time, and how disease (or health) varies among people based on a host of personal characteristics (person, place, and time). This makes it possible to identify trends in health and disease and also provides a means of planning resources for populations. In addition, descriptive epidemiology is important for generating hypotheses (possible explanations) about the determinants of health and disease. By generating hypotheses, descriptive epidemiology also provides the starting point for analytic epidemiology, which formally tests associations between potential determinants and health or disease outcomes. Specific tasks of descriptive epidemiology are the following:

• Monitoring and reporting on the health status and health related behaviors in populations
• Identifying emerging health problems
• Alerting us to potential threats from bioterrorism
• Establishing public health priorities for a population
• Evaluating the effectiveness of intervention programs and
• Exploring potential associations between "risk factors" and health outcomes in order to generate hypotheses about the determinants of disease.

Learning Objectives

After successfully completing this unit, the student will be able to:

• Explain the role of descriptive studies for identifying problems and establishing hypotheses.
• Explain how the characteristics of person, place, & time are used to formulate hypotheses in acute disease outbreaks and in studies of chronic diseases.
• Identify case reports and case series and explain their uses and their limitations.
• Describe the design features of an ecologic study and discuss their strengths and weaknesses.
• Explain the concept of ecologic fallacy both in general and in the context of a study. Identify the strengths and limitations of an ecologic study.
• Describe the design features of a cross-sectional study and describe their uses, strengths, and limitations.

Hypothesis Formulation – Characteristics of Person, Place, and Time

Descriptive epidemiology searches for patterns by examining characteristics of person, place, & time . These characteristics are carefully considered when a disease outbreak occurs, because they provide important clues regarding the source of the outbreak.

Hypotheses about the determinants of disease arise from considering the characteristics of person, place, and time and looking for differences, similarities, and correlations. Consider the following examples:

• Differences : if the frequency of disease differs in two circumstances, it may be caused by a factor that differs between the two circumstances. For example , there was a substantial difference in the incidence of stomach cancer in Japan & the US. There are also substantial differences in genetics and diet. Perhaps these factors are related to stomach cancer.
• Similarities : if a high frequency of disease is found in several different circumstances & one can identify a common factor, then the common factor may be responsible. Example : AIDS in IV drug users, recipients of transfusions, & hemophiliacs suggests the possibility that HIV can be transmitted via blood or blood products.
• Correlations: If the frequency of disease varies in relation to some factor, then that factor may be a cause of the disease. Example: differences in coronary heart disease vary with cigarettes consumption.

Descriptive epidemiology provides a way of organizing and analyzing data on health and disease in order to understand variations in disease frequency geographically and over time and how disease varies among people based on a host of personal characteristics (person, place, and time). Epidemiology had its origins in the desire to understand the determinants of acute infectious diseases, but its methods and applicability have expanded to include chronic diseases as well.

Descriptive Epidemiology for Infectious Disease Outbreaks

Outbreaks generally come to the attention of state or local health departments in one of two ways:

• Astute individuals (citizens, physicians, nurses, laboratory workers) will sometimes notice cases of disease occurring close together with respect to time and/or location or they will notice several individuals with unusual features of disease and report them to health authorities.
• Public health surveillance systems collect data on 'reportable diseases'. Requirements for reporting infectious diseases in Massachusetts are described in 105 CMR 300.000 (Link to Reportable Diseases, Surveillance, and Isolation and Quarantine Requirements).

Clues About the Source of an Outbreak of Infectious Disease

When an outbreak occurs, one of the first things that should be considered is what is known about that particular disease. How can the disease be transmitted? In what settings is it commonly found? What is the incubation period? There are many good summaries available online. For example, Massachusetts DPH provides this link to a PDF fact sheet for Hepatitis A, which provide a very succinct summary. With this background information in mind, the initial task is to begin to characterize the cases in terms of personal characteristics, location, and time (when did they become ill and where might they have been exposed given the incubation period for that disease. In sense, we are looking for the common element that explains why all of these people became ill. What do they have in common?

"Person"

Information about the cases is typically recorded in a "line listing," a grid on which information for each case is summarized with a separate column for each variable. Demographic information is always relevant, e.g., age, sex, and address, because they are often the characteristics most strongly related to exposure and to the risk of disease. In the beginning of an investigation a small number of cases will be interviewed to look for some common link. These are referred to as "hypothesis-generating interviews." Depending on the means by which the disease is generally transmitted, the investigator might also want to know about other personal characteristics, such as travel, occupation, leisure activities, use of medications, tobacco, drugs. What did these victims have in common? Where did they do their grocery shopping? What restaurants had they gone to in the past month or so? Had they traveled? Had they been exposed to other people who had been ill? Other characteristics will be more specific to the disease under investigation and the setting of the outbreak. For example, if you were investigating an outbreak of hepatitis B, you should consider the usual high-risk exposures for that infection, such as intravenous drug use, sexual contacts, and health care employment. Of course, with an outbreak of foodborne illness (such as hepatitis A), it would be important to ask many questions about possible food exposures. Where do you generally eat your meals? Do you ever eat at restaurants or obtain foods from sources outside the home? Hypothesis generating interviews may quickly reveal some commonalities that provide clues about the possible sources.

"Place"

Assessment of an outbreak by place provides information on the geographic extent of a problem and may also show clusters or patterns that provide clues to the identity and origins of the problem. A simple and useful technique for looking at geographic patterns is to plot, on a "spot map" of the area, where the affected people live, work, or may have been exposed. A spot map of cases may show clusters or patterns that reflect water supplies, wind currents, or proximity to a restaurant or grocery store.

In 1854 there was an epidemic of cholera in the Broad Street area of London. John Snow determined the residence or place of business of the victims and plotted them on a street map (the stacked black disks on the map below). He noted that the cases were clustered around the Broad Street community pump. It was also noteworthy that there were large numbers of workers in a local workhouse and a brewery, but none of these workers were affected - the workhouse and brewery each had their own well.

On a spot map within a hospital, nursing home, or other such facility, clustering usually indicates either a focal source or person-to-person spread, while the scattering of cases throughout a facility is more consistent with a common source such as a dining hall. In studying an outbreak of surgical wound infections in a hospital, we might plot cases by operating room, recovery room, and ward room to look for clustering.

• Link to more on the outbreak of cholera in the Broad Street area of London
• Link to an enlarged version of Snow's spot map

"Time"

When investigating the source of an outbreak of infectious disease, Investigators record the date of onset of disease for each of the victims and then plot the onset of new cases over time to create what is referred to as an epidemic curve . The epidemic curve for an outbreak of hepatitis A is shown in the illustration below. Begriming in late April, the number of new cases rises to a peak of twelve new cases reported on May 12, and then the number of new cases gradually drops back to zero by May 21. Knowing that the incubation period for hepatitis A averages about 28-30 days, the investigators concluded that this was a point source epidemic because the cluster of new cases all occurred within the span of a single incubation period (see explanation on the next page). This, in conjunction with other information, provided important clues that helped shape their hypotheses about the source of the outbreak.

Video Summary: Person, Place, and Time (10:42)

Epidemic Curves

An "epidemic curve" shows the frequency of new cases over time based on the date of onset of disease. The shape of the curve in relation to the incubation period for a particular disease can give clues about the source. There are three basic types of epidemic curve.

Point source outbreaks (epidemics) involve a common source, such as contaminated food or an infected food handler, and all the exposures tend to occur in a relatively brief period. Consequently, point source outbreaks tend to have epidemic curves with a rapid increase in cases followed by a somewhat slower decline, and all of the cases tend to fall within one incubation period.  The graph above from a hepatitis outbreak is an example of a point source epidemic. The incubation period for hepatitis ranges from 15-50 days, with an average of about 28-30 days. In a point source epidemic of hepatitis A you would expect the rise and fall of new cases to occur within about a 30 day span of time, which is what is seen in the graph below.

Continuous common source epidemics may also rise to a peak and then fall, but the cases do not all occur within the span of a single incubation period. This implies that there is an ongoing source of contamination. The down slope of the curve may be very sharp if the common source is removed or gradual if the outbreak is allowed to exhaust itself. The epidemic curve below is from the cholera outbreak in the Broad Street area of London in 1854 that was investigated by Dr. John Snow. Cholera has an incubation period of 1-3 days, and even though residents began to flee when the outbreak erupted, you can see that this outbreak lasted for more than a single incubation period. This suggests an ongoing source of infection, in this case the Broad Street pump.

Propagated (or progressive source) epidemic . The epidemic curve shown below is from an outbreak of measles that began with a single index case who infected a number of other individuals. (The incubation period for measles averages 10 days with a range of 7-18 days.) One or more of the people infected in the initial wave infected a group of people who become the second wave of infection. So here transmission is person-to-person, rather than from a common source. Propagated epidemic curves usually have a series of successively larger peaks, which are one incubation period apart. The successive waves tend to involve more and more people, until the pool of susceptible people is exhausted or control measures are implemented. This is an ideal example, however; in reality, most of these epidemics do not produce the classic pattern.

For some outbreaks the descriptive information is all that is needed to figure out the source, and control measures can be undertaken rapidly. In other cases, this descriptive information (person, place, and time) helps generate hypotheses about the source, but it isn't obvious what the source is. When this occurs, it is necessary to test the hypotheses by conducting an analytical study, i.e. either a case-control study or a cohort study. This means collecting data and analyzing it in order to identify the source. After the hepatitis outbreak in Marshfield, DPH conducted a case-control study. After an outbreak of Giardia in Milton, MA, a retrospective cohort study was conducted. However, it is important to recognize that you can't test a hypothesis unless you have one to test. So, the descriptive studies that generate hypotheses are essential.

Use the graph below to answer this "Quiz Me."

(Optional) - Two Methods for Creating an Epidemic Curve in Excel

Method 1 - video

Method 2 - video

(Optional) - Steps in the Investigation of a Disease Outbreak

Most outbreak investigations involve the following steps:

• Preparation for the investigation
• Verifying the diagnosis and establishing the existence of an outbreak
• Establishing a case definition and finding cases
• Conducting descriptive epidemiology to determine the personal characteristics of the cases, changes in disease frequency over time, and differences in disease frequency based on location.
• Developing hypotheses about the cause or source
• Evaluating the hypotheses & refining the hypotheses and conducting additional studies if necessary
• Implementing control and prevention measures
• Communicating the findings

Some of these steps may be conducted simultaneously, and the order may vary depending on the circumstances. For example, if new cases are continuing to occur and there are steps that can be taken to control the outbreak and prevent more cases, then certainly control and prevention measures would take top priority.

Optional Additional Resources

General Information on Outbreak Investigations

For an overview of outbreak investigations for foodborne illness see the CDC web page linked here. Other good general sources of information on how to conduct outbreak investigations can be found in the University of North Carolina (UNC) online Focus on Field Epidemiology series. The following links to online articles may be of interest:

Issue #1: Overview of Outbreak Investigations

Issue #2: Anatomy and Physiology of an Outbreak Team

Issue #3: Embarking on an Outbreak Investigation

Issue #4: Case Finding and Line Listing: A Guide for Investigators

Issue #5: Epidemic Curves Ahead with a Focus Flash on Creating an Epidemic Curve in Excel

Issue #6:Hypothesis Generation During Outbreaks

Issue #1: Hypothesis-Generating Interviews

Issue #2: Developing a Questionnaire

Issue #3: Interviewing Techniques

Another good general resource is "Hepatitis in Sparta." This is an online interactive teaching case that thrusts the student into the role of investigator trying to determine the source for an outbreak of hepatitis cases in the town of Sparta.

Descriptive Epidemiology for Chronic Diseases

The same questions about person, time, and place can be applied to chronic diseases.  Who are the people who have the disease? What are their characteristics? What is their occupation? Where do they live and work? How did disease occurrence vary over time?

Personal Characteristics

Personal characteristics also provide clues about the causes of chronic diseases. Many disease vary in relation to age and gender, but many other characteristics are also important, such as occupation, diet, sexual activity, travel history, and personal behaviors (exercise, smoking, etc.)

Age-specific Rates of Disease

Because so many diseases vary in relation to disease, one frequently sees disease rates categorized this way - so-called "age-specific rates of disease." Mortality rates are very low in the youngest age groups & similar in males and females. In adulthood the mortality rates rise sharply and become higher in males. Although the mortality rate continues to rise into old age, the gender difference begins to narrow. One might describe this as a chronic, progressive disease in which the gender differences raise the question of whether sex hormones play a role, particularly since females begin to catch up after menopause occurs.

Table - Death Rates from Coronary Artery Disease (Age-Specific Rates)

Differences by Race and Ethnicity

In addition to age and gender one might want to examine how disease rates differ with respect to other characteristics, such as race. The table below summarizes. annual mortality rates per 100,000 in whites and blacks in the United States in 1967. Ethnic and racial differences in disease rates sometimes have a genetic basis, e.g., sickle cell anemia in people of African descent or beta thalassemia in people of Mediterranean descent, but in other cases racial differences are due to environmental or socioeconomic factors.

• Link to more on sickle cell anemia
• Link to more on beta thalassemia

Table - Annual Mortality Rates per 100,000 population in the US, 1967

Other Personal Characteristics

Besides age, gender and race/ethnicity, other personal characteristics that might be important to consider are:

• Religious practices, e.g. dietary restrictions or restrictions on drinking alcohol or tobacco use
• Leisure activities, e.g., exercise

Place: Variation by Location

Differences in disease frequency by location provides important clues about the determinants of chronic diseases. Where does the disease tend to occur?

• Does the frequency of disease vary from country to country? Or state to state?
• Does it vary among cities or neighborhoods?
• Does it vary within different parts of a large workplace?

Example 1: Stomach Cancer by Location in the US

These maps show death rates from stomach cancer in females (top) and males (below) in different US counties. The darkness of shading of each county indicates how its stomach cancer rate compares with the national average. The darkest shading indicates rates well above average, and white shading indicates rates below average; the gray shading indicates intermediate levels. Note that rates of stomach cancer tend to be high in counties in the north-central part of the country in both males and females. Investigators speculated that these clusters might correlate with populations of German or Scandinavian descent who have a tradition of eating smoked fish. Could the high rates of stomach cancer be the result of their consumption of smoked fish or other traditional methods of food preservation?

Source: Atlas of Cancer Mortality for U.S. Counties: 1950-1969, TJ Mason et al, PHS, NIH, 1975

Example 2: Differences in Rates of Stomach Cancer in Japan and US

Rates of stomach cancer also vary among countries. Japanese have a higher rate of stomach cancer than Caucasians in California. Is this due to a genetic difference? A dietary difference? The rate among Japanese people diminishes after they move to US, and diminishes even more in their offspring. One possibility is that once the Japanese move here, they begin to shift to an American diet, and this trend is even stronger in their children. Are there important dietary differences? Could consumption of large amounts of smoked fish be a cause of stomach cancer?

Variation in Disease Over Time

• Has the frequency of disease changed over several decades?
• Does frequency of disease vary in a cyclic way that relates to the seasons?
• Has it changed over the course of days?

Changes in disease rate over time can also provide clues for chronic diseases.

Example 1: Annual Mortality from Pulmonary Tuberculosis in England and Wales

Tuberculosis (TB) is one of the great killers of all times. The graph on the right shows the mortality rate from TB from 1855-1955 in England and Wales. The remarkable downward trend began well before the development of antibiotics. The steady improvement was probably a direct result of "the sanitary idea" which resulted in concerted efforts to improve working and living conditions, nutrition, ventilation, and waste management. Also, note the increases in TB mortality that occurred during World War I and World War II. This suggests that nutritional deficiencies, translocation, crowding, and other adverse circumstances associated with war are contributing factors to the causation of TB.

Example 2: Toxic Shock and Rely Tampons

In January 1980 there were several reports of toxic shock syndrome due to infection with Staphylococcus aureus bacteria, and the descriptive epidemiology indicated that the problem was occurring primarily in menstruating women. A CDC task force investigated and eventually traced the outbreak to the introduction of Rely tampons, a super absorbent product marketed by Proctor and Gamble. The monthly cases of toxic shock syndrome in 1980-1981 are shown in the graph below [from A. Reingold et al., Toxic shock syndrome surveillance in the United States, 1980-1981. Ann. Intern. Med 96:875, 1982]. The graph shows that prior to 1978 there were just occasional cases of toxic shock syndrome in the United States. After Rely tampons were introduced in 1978, there was a steady increase in toxic shock cases which peaked at about 125 per month in 1980. Shortly after that, Rely tampons were taken off the market, and the incidence declined sharply.

There were actual two pieces of evidence related to time variations that supported Rely tampons as the cause. First, descriptive epidemiology suggested a link to menstruation, leading doctors to take bacterial cultures from the vagina. This provided a key clue suggesting a link to certain brands of tampons. In addition, the frequency of toxic shock syndrome clearly correlated with the introduction and subsequent removal of Rely tampons from the market.

• Link to more on toxic shock syndrome

Other Factors That Can Produce Changes in Disease Frequency Over Years or Decades

If the frequency of a disease or mortality from a disease changes over time, there are several factors which could be responsible:

• Changes in incidence due to environmental or life-style changes.
• Improvements in diagnosis may increase cases reported even though the incidence may not be changing.
• Changes in record keeping (accuracy) can create what appear to be changes in disease rates.
• Improved treatment may decrease mortality rates
• Changes in the age distribution of a population can produce changes in the overall rate of disease, even though age-specific rates are not changing.

Two Fundamental Types of Study Questions

Specifying the research questions is essential to selection of an appropriate study population, and infinite questions exist. Nevertheless, Keyes and Galea stress two fundamental types of research questions which have important implications selecting an appropriate study design.  

1. Questions whose goal is accurate estimation of population parameters

• What proportion of high school students smoke? Or use drugs?
• What is the frequency of death from coronary artery disease among black and white males and females, and how have those rate changed over the past 20 years?

Questions like these require samples that are representative of the population being studied, that is comparable to the population in their characteristics (and they require adequate sample size in order to minimize sampling error).

2. Questions whose goal is to identify and quantify exposures that have causal effects on health outcomes.

• Does use of cell phones cause cancer?
• Do "brain exercises prevent cognitive decline with advancing age?
• Do childhood vaccinations cause autism?

Questions like these also require an adequate sample size to precisely assess the magnitude of an effect, but they differ from questions aimed at parameter estimation in that that they require making comparisons, e.g., comparing risk between exposed and non-exposed persons. When trying to answer questions like these regarding etiology, it is not so important that the samples be representative of the overall population, but for accurate assessment of the effect the groups being compared must be comparable to each other with respect to other factors that affect the outcome.

Fundamental Study Designs for Both Representative and Purposive Studies

Keyes and Galea identify three fundamental approaches to study design that can be applied regardless of whether one's goal is to take representative samples to estimate population parameters or to take purposive samples in order to determine whether a given exposure or factor causes one or more health outcomes.

• One can study the sample at a particular point in time.
• One can follow the sample forward in time to compare the frequency of health indicators among two or more exposure groups.
• One can examine the retrospective exposure history of a sample.

The second option will only be utilized in analytical studies, which will be covered in a separate module, but the first two options will be seen in the next section describing several types of descritive studies.

Categories of Descriptive Epidemiology

Case reports.

A case report is a detailed description of disease occurrence in a single person. Unusual features of the case may suggest a new hypothesis about the causes or mechanisms of disease.

Example: Acquired Immunodeficiency in an Infant; Possible Transmission by Means of Blood Products

Link to article by Ammann AJ et al: Acquired immunodeficiency in an infant: possible transmission by means of blood products. The Lancet 1:956-958, 1983.

In April 1983 it had not yet been shown that AIDS could be transmitted by blood or blood products. An infant born with Rh incompatibility; required blood products from 18 donors over 8 weeks and subsequently developed unusual recurrent infections with opportunistic agents such as Candida. The infant's T cell count was low, suggesting AIDS. There was no family history of immunodeficiency, but one of the blood donors was found to have died of AIDS. This led the investigators to hypothesize that AIDS could be transmitted by blood transfusion.

Example: Survival after Treatment of Rabies with Induction of Coma.

Link to article by Willoughby R, Jr., et al: N Engl J Med 2005;352:2508-14.

Rabies is almost uniformly fatal once it develops. As of 2005 there had been only four survivors, each of whom received rabies prophylaxis after the bite, but before symptoms developed. Willoughby et al. reported on a 15 year-old girl who rescued and released a bat that had struck an interior window. The bat bit her left index finger. The wound was washed with peroxide, but medical attention was not sought, and no rabies prophylaxis was administered. One month later she began to experience progressive neurological symptoms that were eventually diagnosed as rabies. The mainstay of her treatment was medically induced coma. Eight days later blood tests demonstrated that she had begun to develop an immune response to the rabies virus. Eventually the coma was reversed, and the patient gradually regained consciousness. She had severe neurological deficits, but gradually improved. She was discharged to her home after 76 days. Five months after her initial hospitalization, she was alert and communicative, but had persistent slurred speech and an unsteady gait.

The report by Willoughby et al. is an example of a case report – a detailed description of a single subject. The report is important because it demonstrates that it is possible for victims of rabies to survive, even without post-exposure prophylaxis. However, we have no idea how effective this treatment might be.

Case Series

A case series is a report on the characteristics of a group of subjects who all have a particular disease or condition. Common features among the group may suggest hypotheses about disease causation. Note that the "series" may be small (as in the example below) or it may be large (hundreds or thousands of "cases"). However, the chief limitation is that there is no comparison group. Consequently, common features may suggest hypotheses, but these need to be tested with some sort of analytical study before an association can be accepted as valid.

Example: Pneumocystis carinii pneumonia and mucosal candidiasis in previously healthy homosexual men: evidence of a new acquired cellular immunodeficiency.

Link to article by Gottlieb MS, et al: N Engl J Med 1981;305:1425-1431.

In 1980 –1981 four previously healthy young men were diagnosed with Pneumocystis carinii pneumonia, an unusual "opportunistic" infection that had only been seen in immune compromised people with hereditary disorders or in people with immune compromise due to chemotherapy. The medical histories didn't suggest any preexisting immunodeficiency, but all had decreased immune responses and low T cell counts. These unusual infections suggested the possibility of a previously unknown disease.  It was noted that all four men were sexually active homosexuals, and in the case series which was published in the New England Journal of Medicine the authors speculated that the immune dysfunction was due to a sexually transmitted infectious agent.

This was an extraordinarily important case series (a detailed description of characteristics of a series of people who all have the same disease) that suggested that this new syndrome was associated with sexual activity in male homosexuals. Alerting the medical establishment and proposing a hypothesis was an important milestone in the AIDS epidemic, however, the association could not be securely established based on this small case series. It was not known how many other individuals might be suffering from this new syndrome. It was also not known what the prevalence of homosexuality might be in others with this syndrome or how this might compare to the overall prevalence of homosexuality in the population that gave rise to the cases. As a result, this case series could not securely establish a valid association. Nevertheless, it laid the ground work for subsequent case-control studies and cohort studies (analytic studies) that did establish the risk factors for this disease.

Example: Oral Contraceptives and Hepatocellular Carcinoma?

There had been a number of case reports of liver cancers in young women taking oral contraceptives. A study was undertaken by contacting all of the cancer registries collaborating with the American College of Surgeons. The investigators wanted to collect information on as many of these rare liver tumors as possible across the US.  

Table - Oral Contraceptive Use Among Women Who Developed Liver Cancer

What conclusions can you draw from these data regarding a possible increased risk of liver cancer in woman taking oral contraceptives? Think about it before you look at the answer.

  Answer

Video Summary: Case Reports and Case Series (6:59)

Cross-Sectional Surveys

Cross-sectional surveys assess the prevalence of disease and the prevalence of risk factors at the same point in time and provide a "snapshot" of diseases and risk factors simultaneously in a defined population. For example, US government agencies periodically send out large surveys to random samples of the US population, asking about health status and risk factors and behaviors at that point in time. The Health Interview Survey (HIS) and the National Health and Nutrition Examination Survey (NHANES) are good examples.

The health questionnaires you are asked to fill out when you go to a new physician or being processed for a new job, or prior to entry into military service are similar to cross-sectional surveys in that they ask about the health problems that you have (heart disease? diabetes? asthma?) and your current behaviors and risk factors (e.g., How old are you? Do you smoke? What is your occupation?).

Cross-sectional surveys ask people their current status with respect to both exposures and diseases. This results in two main disadvantages.

• The temporal relationship between exposure and disease outcomes can be unclear, i.e., which came first.
• Cross-sectional studies tend to identify prevalent cases of long duration , since people who die quickly or recover quickly or who are no longer employed in a particular occupation are less likely to be identified.

Consider the following example in which a survey was conducted among white male farm workers. The survey asked many questions, but among them were the questions: "Have you been told you have coronary heart disease (CHD)?" And "How would you classify your level of physical activity?" The table below summarizes the findings. 

Table - Current Coronary Heart Disease Among Male Farm Workers

Note that the investigators did not follow these subjects over a period of time, so they did not assess the "incidence" of heart disease. Instead, they asked the subjects questions designed to determine the prevalence of heart disease, i.e., the proportion of the study population that had heart disease at this particular point in time. When they divided the sample into physically active and inactive farmers and computed the prevalence of heart disease in each of these, they found that CHD was much more prevalent among the inactive farmers. However, this was a cross-sectional study that related the prevalence of disease to the prevalence of activity at a point in time. They did not follow subjects over time to track the development of heart disease (i.e., the incidence). Consequently, the temporal relationship between the risk factor of interest (physical inactivity) and the outcome (CHD) is unclear. Had the farmers been physically active prior to developing CHD? Or, did they begin to limit their physical activity after they developed CHD? Consequently physical inactivity could have been either a cause of heart disease, or it could have been a consequence of CHD.

Large cross-sectional surveys are important for monitoring health status and health care needs of the population over time, and they are sometimes useful for suggesting possible associations between risk factors and diseases. However, the temporal relationship between the risk factor and disease is frequently unclear. Under these circumstances, they can generate hypotheses, but these associations need to be tested by appropriate analytical studies.

However, note that under some circumstances, the temporal relationship is clear on a cross-sectional survey. For example, if one conducted a survey of salaries of male and female professors to see if gender was associated with salary inequities, we could regard this as an analytical study, because it is clear that gender was established long before salary level. In this situation the temporal relationship between the "exposure" of interest (gender) and outcome (salary paid) is clear; we know that gender was established before the salary was negotiated. So, in a sense cross-sectional studies (and ecological studies can be thought of as an intermediate category between descriptive and analytic studies.

Video Summary on Cross-Sectional Surveys (8:25)

Ecological Studies (Correlational Studies)

These studies are distinguished by the fact that the unit of observation is not a person; rather it is an entire population or group. In essence, these studies examine the correlation between the average exposure in various populations with the overall frequency of disease within the populations.

In the study below investigators used commerce data to compute the overall consumption of meat by various nations. They then calculated the average (per capita) meat consumption per person by dividing total national meat consumption by the number of people in a given country. There is a clear linear trend; countries with the lowest meat consumption have the lowest rates of colon cancer, and the colon cancer rate among these countries progressively increases as meat consumption increases.

Note that in reality, people's meat consumption probably varied widely within nations, and the exposure that was calculated was an average that assumes that everyone ate the average amount of meat. This average exposure was then correlated with the overall disease frequency in each country. The example here suggests that the frequency of colon cancer increases as meat consumption increases. The characteristic of ecological studies that is most striking is that there is no information about individual people. If the data were summarized in a spread sheet, you would not see individual level data; you would see records with data on average exposure in multiple groups .

Morgenstern notes that, "Individual­ level variables are properties of individuals, and ecologic variables are properties of groups. To be more specific, ecologic measures may be classified into three types:

• Aggregate measures are summaries (e.g. means or proportions) of observations derived from individuals in each group (e.g. the proportion of smokers or median family income).
• Environmental measures are physical characteristics of the place in which members of each group live or work (e.g. air-pollution level or hours of sunlight). Note that each environmental measure has an analogue at the individual level, and these individual exposures, or doses, usually vary among members of each group, though they may remain unmeasured.
• Global measures are attributes of groups or places for which there is no distinct analogue at the individual level. Unlike aggregate and environmental measures (e.g. population density, level of social disorganization. or the existence of a specific law).

Morgenstern goes on to note: "Ecologic study designs may be classified on two dimensions: (a) whether the primary group is measured (exploratory vs analytic study); and (b) whether subjects are grouped by place (multiple-group study), by time (time-trend study), or by place and time (mixed study). Despite several practical advantages of ecologic studies, there are many methodologic problems that severely limit causal inference, including ecologic and cross-level bias, problems of confounder control, within-group misclassification, lack of adequate data, temporal ambiguity, collinearity, and migration across groups."

For a detailed review of ecologic studies see follow the link to an article by Morgenstern H: Ecologic Studies in Epidemiology: Concepts, Principles, and Methods. Annual Review of Public Health 1995;16:61-81.

To see an extraordinary example of an ecologic study, play the video below created by Hans Rosling. This is a magnificent example that examines the correlation between income and life expectancy in the countries of the world over time. It is also a terrific example of a creative, engaging, and powerful way to display a vast quantity of data.

Advantages of Ecological Studies:

• The data required is frequently readily available. Commerce data can be used to estimate a population's total consumption of products (possible risk factors) such as meat, tobacco, fish, etc. So, these studies are quick & inexpensive.
• The " correlation coefficient " or an "r" value provides a measure of how closely the observed data points conform to a straight line. Some authors say that the "r" value is a measure of the association between the risk factor and the disease, but this is incorrect. The slope of the line would be a measure of the strength of association.  (See the course spreadsheet "Epi_Tools. XLSX" for a worksheet that calculates correlation coefficients). The value of a correlation coefficient is from +1 (a perfect positive correlation) and –1 (a perfect negative correlation). See the tabbed activity below for examples.

Limitations of Ecological Studies: It is important to bear in mind that the exposure in correlational studies is the average exposure for an entire population or group. This results in major limitations:

• Since you don't have any information about the risk factor status or the outcome status of individual people, you can't directly link the risk factor to the disease, i.e., it is not clear that the people who ate the most meat were the ones who got colon cancer. This is sometimes referred to as "ecological bias" or the "ecological fallacy."
• Another limitation is that there is no effective way of taking into account, or adjusting for, other factors that influence the outcome (confounding factors). As a result, an apparent correlation, or the lack of a correlation could be misleading. For example, one might find a strong correlation between the average number of hours of TV viewing & the rate of coronary artery disease among different countries. However, this doesn't necessarily mean that TV per se is a risk factor for CAD. There may be a number of other differences between the populations that are associated with higher rates of TV viewing: e.g., greater industrialization, less exercise, greater availability of processed foods and saturated fat, and so forth. And conversely, the lack of a correlation doesn't necessarily imply that there is no association.
• Since the exposure levels represent average exposure in a large number of people, correlational studies can mask more complicated relationships, as illustrated below.

When a correlational study compared per capita alcohol consumption to death rates from coronary heart disease in different countries, it appeared that there was a fairly striking negative correlation.

However, a meta-analysis of prospective cohort studies which determined mortality rates in subjects for whom they had estimates of individual alcohol consumption, showed that there was actually a "J" shaped relationship. The people who drank the most actually had the highest mortality rates; moderate drinkers had the lowest mortality. This relationship was masked in the correlational study, because of the small percentage of people who have more than three drinks per day.

Adapted from: Di Castelnuovo A, Costanzo S, et al.: Alcohol Dosing and Total Mortality in Men and Women:  

An Updated Meta-analysis of 34 Prospective Studies. Arch Intern Med. 2006;166(22):2437-2445.

  Video Summary for Ecological Studies (7:48)

Summary & Self-Check

Descriptive studies are useful for:

Other Resources

• University of North Carolina (UNC) -Torok M and Anderson M: "Focus on Field Epidemiology: Volume 5; Issue 5:Introduction to Public Health Surveillance."
• University of North Carolina (UNC) - Anderson M: "Focus on Field Epidemiology: Volume 5; Issue 6: Public Health Surveillance Systems".
• Trifonov V, Khiabanian H, Rabadan R: Geographic Dependence, Surveillance, and Origins of the 2009 Influenza A (H1N1) Virus. Perspective article in: N. Engl. J. Med. 2009;361(2):115-119.  
• Scallan E, Hoekstra RM, Angulo FJ, et al. Foodborne Illness Acquired in the United States - Major Pathogens. Emerging Infectious Diseases 2011;17(1):7-15. [Volume 17, Number 1, January 2011, pages 7-15]
• Marsden-Haug N, Foster VB, Gould PL, Elbert E, Wang H, Pavlin JA. Code-based syndromic surveillance for influenzalike illness by International Classification of Diseases, ninth revision. Emerg Infect Dis, Feb. 2007;13(2):207-216.

Psychological Research

Descriptive research, learning objectives.

• Differentiate between descriptive, experimental, and correlational research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research, it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not. Table 1 displays a quick overview of the characteristics of each research design.

Table 1. Characteristics of Descriptive, Experimental, and Correlational Research

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are case studies, naturalistic observation, and surveys.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

To learn more about Krista and Tatiana, watch this video about their lives as conjoined twins.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

Figure 1 . Seeing a police car behind you would probably affect your driving behavior. (credit: Michael Gil)

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 1).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 2). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

Figure 2 . (a) Jane Goodall made a career of conducting naturalistic observations of (b) chimpanzee behavior. (credit “Jane Goodall”: modification of work by Erik Hersman; “chimpanzee”: modification of work by “Afrika Force”/Flickr.com)

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.

Figure 3 . Surveys can be administered in a number of ways, including electronically administered research, like the survey shown here. (credit: Robert Nyman)

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this module: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

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Descriptive Statistics | Definitions, Types, Examples

Published on July 9, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Descriptive statistics summarize and organize characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.

In quantitative research , after collecting data, the first step of statistical analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).

The next step is inferential statistics , which help you decide whether your data confirms or refutes your hypothesis and whether it is generalizable to a larger population.

Table of contents

Types of descriptive statistics, frequency distribution, measures of central tendency, measures of variability, univariate descriptive statistics, bivariate descriptive statistics, other interesting articles, frequently asked questions about descriptive statistics.

There are 3 main types of descriptive statistics:

• The distribution concerns the frequency of each value.
• The central tendency concerns the averages of the values.
• The variability or dispersion concerns how spread out the values are.

You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more, in bivariate and multivariate analysis.

• Go to a library
• Watch a movie at a theater
• Visit a national park

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A data set is made up of a distribution of values, or scores. In tables or graphs, you can summarize the frequency of every possible value of a variable in numbers or percentages. This is called a frequency distribution .

• Simple frequency distribution table
• Grouped frequency distribution table

From this table, you can see that more women than men or people with another gender identity took part in the study. In a grouped frequency distribution, you can group numerical response values and add up the number of responses for each group. You can also convert each of these numbers to percentages.

Measures of central tendency estimate the center, or average, of a data set. The mean, median and mode are 3 ways of finding the average.

Here we will demonstrate how to calculate the mean, median, and mode using the first 6 responses of our survey.

The mean , or M , is the most commonly used method for finding the average.

To find the mean, simply add up all response values and divide the sum by the total number of responses. The total number of responses or observations is called N .

The median is the value that’s exactly in the middle of a data set.

To find the median, order each response value from the smallest to the biggest. Then , the median is the number in the middle. If there are two numbers in the middle, find their mean.

The mode is the simply the most popular or most frequent response value. A data set can have no mode, one mode, or more than one mode.

To find the mode, order your data set from lowest to highest and find the response that occurs most frequently.

Measures of variability give you a sense of how spread out the response values are. The range, standard deviation and variance each reflect different aspects of spread.

The range gives you an idea of how far apart the most extreme response scores are. To find the range , simply subtract the lowest value from the highest value.

Standard deviation

The standard deviation ( s or SD ) is the average amount of variability in your dataset. It tells you, on average, how far each score lies from the mean. The larger the standard deviation, the more variable the data set is.

There are six steps for finding the standard deviation:

• List each score and find their mean.
• Subtract the mean from each score to get the deviation from the mean.
• Square each of these deviations.
• Add up all of the squared deviations.
• Divide the sum of the squared deviations by N – 1.
• Find the square root of the number you found.

Step 5: 421.5/5 = 84.3

Step 6: √84.3 = 9.18

The variance is the average of squared deviations from the mean. Variance reflects the degree of spread in the data set. The more spread the data, the larger the variance is in relation to the mean.

To find the variance, simply square the standard deviation. The symbol for variance is s 2 .

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Univariate descriptive statistics focus on only one variable at a time. It’s important to examine data from each variable separately using multiple measures of distribution, central tendency and spread. Programs like SPSS and Excel can be used to easily calculate these.

If you were to only consider the mean as a measure of central tendency, your impression of the “middle” of the data set can be skewed by outliers, unlike the median or mode.

Likewise, while the range is sensitive to outliers , you should also consider the standard deviation and variance to get easily comparable measures of spread.

If you’ve collected data on more than one variable, you can use bivariate or multivariate descriptive statistics to explore whether there are relationships between them.

In bivariate analysis, you simultaneously study the frequency and variability of two variables to see if they vary together. You can also compare the central tendency of the two variables before performing further statistical tests .

Multivariate analysis is the same as bivariate analysis but with more than two variables.

Contingency table

In a contingency table, each cell represents the intersection of two variables. Usually, an independent variable (e.g., gender) appears along the vertical axis and a dependent one appears along the horizontal axis (e.g., activities). You read “across” the table to see how the independent and dependent variables relate to each other.

Interpreting a contingency table is easier when the raw data is converted to percentages. Percentages make each row comparable to the other by making it seem as if each group had only 100 observations or participants. When creating a percentage-based contingency table, you add the N for each independent variable on the end.

From this table, it is more clear that similar proportions of children and adults go to the library over 17 times a year. Additionally, children most commonly went to the library between 5 and 8 times, while for adults, this number was between 13 and 16.

Scatter plots

A scatter plot is a chart that shows you the relationship between two or three variables . It’s a visual representation of the strength of a relationship.

In a scatter plot, you plot one variable along the x-axis and another one along the y-axis. Each data point is represented by a point in the chart.

From your scatter plot, you see that as the number of movies seen at movie theaters increases, the number of visits to the library decreases. Based on your visual assessment of a possible linear relationship, you perform further tests of correlation and regression.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Statistical power
• Pearson correlation
• Degrees of freedom
• Statistical significance

Methodology

• Cluster sampling
• Stratified sampling
• Focus group
• Systematic review
• Ethnography
• Double-Barreled Question

Research bias

• Implicit bias
• Publication bias
• Cognitive bias
• Placebo effect
• Pygmalion effect
• Hindsight bias
• Overconfidence bias

Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population.

The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset.

• Distribution refers to the frequencies of different responses.
• Measures of central tendency give you the average for each response.
• Measures of variability show you the spread or dispersion of your dataset.
• Univariate statistics summarize only one variable  at a time.
• Bivariate statistics compare two variables .
• Multivariate statistics compare more than two variables .

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Psychological Research

Descriptive Research

Learning objectives.

• Differentiate between descriptive, experimental, and correlational research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are case studies, naturalistic observation, and surveys.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

To learn more about Krista and Tatiana, watch this video about their lives as conjoined twins.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 1).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 2). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this module: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

CC licensed content, Original

• Modification and adaptation. Provided by : Lumen Learning. License : CC BY-SA: Attribution-ShareAlike

CC licensed content, Shared previously

• Approaches to Research. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/2-2-approaches-to-research . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction.
• Descriptive Research. Provided by : Boundless. Located at : https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/researching-psychology-2/types-of-research-studies-27/descriptive-research-124-12659/ . License : CC BY-SA: Attribution-ShareAlike

research studies that do not test specific relationships between variables; they are used to describe general or specific behaviors and attributes that are observed and measured

tests whether a relationship exists between two or more variables

tests a hypothesis to determine cause and effect relationships

observational research study focusing on one or a few people

observation of behavior in its natural setting

inferring that the results for a sample apply to the larger population

when observations may be skewed to align with observer expectations

measure of agreement among observers on how they record and classify a particular event

list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

the collection of individuals on which we collect data.

a larger collection of individuals that we would like to generalize our results to.

General Psychology Copyright © by OpenStax and Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

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VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

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Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

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S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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Original research article, testing the effect of descriptive dynamic social norm messages on meatless food purchases in aotearoa new zealand and uk university food outlets.

• 1 Department of Psychology, The University of Sheffield, Sheffield, United Kingdom
• 2 The Grantham Centre for Sustainable Futures, Western Bank, Sheffield, United Kingdom
• 3 Department of Food Science, The University of Otago, Dunedin, New Zealand

A reduction in meat consumption is urgently needed to address multiple harms related to the environment, animal welfare, and human health. Social norm interventions have been found to be feasible and effective at shifting consumer behaviour, however, evidence related to meat reduction behaviour is limited – especially in naturalistic settings. Two social norm interventions were conducted at university food outlets in Aotearoa New Zealand and in the UK, to assess the effect of social norm messages on meat and meatless food purchases. Both interventions consisted of a week-long intervention phase during which descriptive dynamic social norm messages referring to reduced meat intake were displayed in the food outlets (study one and two) and via social media (study two). Meat and meatless food purchases during the interventions were compared to pre- and post-intervention weeks. Surveys were also conducted with a sub-group of customers to assess demographics, dietary habits, and awareness of the social norm message. In both studies, there was no significant effect of the social norm interventions on meat or meatless food purchases, and awareness of the norms message across both studies was low. These findings indicate that social norm interventions alone may be ineffective in encouraging meat reduction. Implications for interventions to reduce meat intake to support pro-environmental food choices are discussed.

1 Introduction

To improve human and environmental health, meat consumption must be reduced, especially in the Global North ( Springmann et al., 2016 ; Willett et al., 2019 ). Meat eating is an entrenched social norm, and shifting meat eating behaviour at the scale required necessitates concerted and sustained behaviour change efforts ( Marteau, 2017 ). However, dietary change is challenging due to various complex and interacting factors, such as taste preferences, habits, and the cultural and social status of meat ( Stoll-Kleemann and Schmidt, 2017 ). These factors present significant barriers to most behaviour change efforts that rely on education or information sharing, necessitating the need for alternative strategies to overcome these barriers.

One potential strategy is the use of social norm interventions. Social norms are perceptions about peer behaviour, which may be descriptive (i.e., referring to the commonness of a behaviour) or injunctive (i.e., referring to the social acceptability or approval of a behaviour) ( Cialdini et al., 1990 ). Multiple studies have shown that exposure to social norm messages can increase pro-environmental behaviour and change dietary behaviours ( Cruwys et al., 2015 ; Farrow et al., 2017 ; Yamin et al., 2019 ). The use of these messages in interventions generally involves exposing participants to normative messages about a behaviour of interest. Participants’ own behaviours or choices are then monitored and compared to participants who were not exposed to normative messages. Such non-deliberative approaches have been recommended to encourage environmentally sustainable behaviours, such as reduced meat intake ( Marteau, 2017 ).

Most studies applying this approach to dietary behaviours have tested the effects of social norm messages under laboratory conditions, with moderate yet consistent effects ( Robinson et al., 2014 ; Robinson, 2015 ). Extending beyond the laboratory, studies conducted in naturalistic settings have also reported favourable effects of descriptive social norm messages on dietary behaviours, though these studies are few in number. For example, Thomas et al. (2017) conducted a study in workplace cafeterias, and found that a descriptive social norm message of “Most people here choose to eat vegetables with their lunch” increased the number of vegetable side-order purchases by 4 percentage points between baseline and intervention.

These types of descriptive social norm messages may be a promising approach for reducing meat intake. In support of this hypothesis, findings from observational data show that participants’ beliefs about the amount of meat other people eat (perceived descriptive norms) was associated with meat and meatless food intake ( Sharps et al., 2021 ). To date, only a limited number of studies have investigated the effect of descriptive social norm messages on meat intake. Alblas et al. (2022) provided participants with a descriptive social norm message about the amount of meat that Dutch residents consume per day and assessed self-reported meat intake over two weeks in low and high habitual meat consumers. Findings showed that regardless of condition (e.g., descriptive social norm or control), high meat consumers reduced meat intake, while low meat consumers increased meat intake over two weeks. While this study was limited by self-report dietary measures which are prone to inaccuracies ( Heitmann and Lissner, 1995 ), a further study in Swedish fast-food outlets which displayed a descriptive social norm message and measured the number of ‘green’ or vegetarian sales, also reported no effects on intake ( Reinholdsson et al., 2022 ). However, a key limitation in both these studies was that the descriptive social norm message presented did not explicitly refer to reduced meat intake. In Alblas et al. (2022) , the average amount of meat consumed was communicated (e.g., “consume meat 1.32 times per day”), and it may not have been clear to participants that these amounts reflected reductions in meat intake. Additionally, the message in Reinholdsson et al. communicated ‘many here choose green’, and reduction of meat was not explicitly communicated. It is possible that consumers did not link this ‘green’ message with reduced meat intake, especially given that many consumers are unaware of the environmental impacts of meat-rich diets (e.g., Macdiarmid et al., 2016 ). Further research which tests the effects of a descriptive social norm message, that explicitly communicates reduced meat intake, is needed.

Indeed, most social norm interventions aiming to reduce meat consumption have used dynamic, rather than descriptive norm messages ( Sparkman and Walton, 2017 ; Sparkman et al., 2020 ; Çoker et al., 2022 ). Dynamic norm messages outline how people’s behaviours have changed over time, for instance, by providing the proportion of people who have reduced their meat intake in recent years. Importantly, studies testing the effects of dynamic social norm messages on meat intake have yielded inconsistent findings. In one study, researchers approached and provided customers in an on-campus café with either a control, dynamic, or static social norm message [the static message stated the proportion of people who have reduced meat intake (3 out of 10) without referring to the recency of the change] ( Sparkman and Walton, 2017 ). Comparison of purchase data showed that customers presented with the dynamic social norm message were more likely to purchase meatless meals compared to customers in the static social norm condition (the comparison between the dynamic social norm and control conditions did not reach significance) ( Sparkman and Walton, 2017 ). A further two studies by the same researchers, which used larger samples, delivered the dynamic social norm message using restaurant menus, and assessed the influence of messages over a longer period of time, also reported modest increases of meatless purchases (by 1–2.5 percentage points; Sparkman et al., 2020 study 1 and 2). However, another study by Sparkman et al. (2020 ; study 4) reported opposite effects; compared to a control message, exposure to a dynamic social norm message reduced meatless purchases and increased meat purchases. Additionally, another study conducted by a different research group in retail café settings reported no significant differences in meat or meatless purchases in response to a dynamic social norm message (“More and more [retail store name] customers are choosing our veggie options”; Çoker et al., 2022 ) (of note, Çoker et al., 2022 was published after this research had been planned). These studies demonstrate that findings on the effectiveness of dynamic social norm messages for reducing meat intake are mixed.

Considering this evidence, it may be that combining descriptive and dynamic elements could leverage the strengths and potential of both approaches to yield promising results. Specifically, descriptive dynamic messages may hold more promise for reducing meat intake for three main reasons. First, the messages explicitly specify the behaviour required (reducing meat intake), contrasting previous usage of descriptive messages to influence meat consumption (e.g., Alblas et al., 2022 ; Reinholdsson et al., 2022 ). Second, the social norm message aligns with previous studies that supported the effects of descriptive messages to increase healthy food choices (e.g., Payne et al., 2015 ; Thomas et al., 2017 ). Finally, the messages signal a durable dietary change. This contrasts to dynamic messages that indicate the recency of the dietary change, which some recipients may doubt the longevity of the change, in turn compromising the persuasiveness of the message.

Therefore, this research tested the effect of descriptive dynamic social norm messages on meat purchases in food outlets in Aotearoa New Zealand (study one, pre-registered: https://osf.io/ku35z/?view_only=bf1288ca34ce4750bcccadced674421a ) and in the UK (study two, pre-registered: https://osf.io/utqaj/?view_only=6604489ca34d422db1fc45f19431c6f5 ).

2 Study one: Aotearoa New Zealand

The aim of study one was to assess the effects of a descriptive dynamic social norm message about peer meat reduction on meat purchases in an Aotearoa New Zealand university food outlet. This study was the first to investigate social norms messaging related to meat reduction in an Aotearoa New Zealand context. It was expected that this intervention would result in a reduction of meat items purchased.

2.1 Context

Aotearoa New Zealand is small archipelago in the south Pacific Ocean, with a population of approximately 5 million in 2020. Following colonisation, an aspiration to become a “Britain of the South” ( Barker, 2012 ) resulted in extensive agricultural development and intensification, facilitating the growth of the meat and dairy industries. Alongside this dominant form of land use arose a national identity and pride as a rural or agricultural nation, aided by the immense economic role played by the meat and dairy industries ( Ballingall and Pambudi, 2017 ) and reflected in high national per capita consumption of these products ( FAOSTAT, 2020 ). Conversely, meat-free diets such as vegetarianism and veganism are in the minority, and have been previously perceived as “unpatriotic” or contrary to “kiwi” (New Zealander) ideals ( Potts and White, 2007 ).

In recent years, however, low meat and meat-free lifestyles have become more common. In 2019, approximately 34% of New Zealanders had either reduced, limited, or eliminated meat from their diets ( Colmar Brunton, 2019 ), and there has been 42% reduction in per capita red meat consumption from 2007–2017 ( Beef and Lamb New Zealand, 2018 ). Due to increasing reports linking meat intake to health problems (e.g., Papier et al., 2021 ), New Zealand’s Ministry of Health recently revised its eating guidelines toward largely plant-based recommendations. Similarly, increasing coverage of meat’s environmental impacts may be especially poignant in a nation that places great value and pride in its natural environments, and in which pro-environmentalism is a fundamental aspect of national identity ( Milfont et al., 2020 ). Approximately 50% of national greenhouse gas emissions come from agricultural production ( Ministry for the Environment, 2023 ), and dairy intensification has been increasingly linked to environmental degradation, especially of freshwater habitats (e.g., Foote et al., 2015 ). Concerns related to ethics and animal welfare in farming practices have also become more frequent, with the recent examples of winter grazing and live export controversies (e.g., Government defers introducing tougher winter grazing rules—again, 2021 ; McClure, 2022 ). Aotearoa New Zealand’s strong historical, cultural, and economic ties to animal agriculture warrant interventions aiming to reduce meat consumption, in order to address its effects on the environment and public health.

2.2 Methods

The study was originally planned to be conducted at the University of Sheffield in the UK, however circumstances due to COVID-19 necessitated a change in location. The study procedures were initially approved by the [anonymised] Ethics Committee, and adjustments to the design and procedure for the new study setting were approved by the New Zealand university’s Human Ethics Committee. Purchase data for all customers was anonymous and recorded by outlets as standard practice. Given the anonymity, informed consent was not obtained for purchase data, however for transparency, a debrief information notice was displayed at participating outlets at the end of the study period. Informed consent was obtained from all survey participants. Data collection for this study took place in May 2021, during the COVID-19 pandemic. However, there were no COVID-related restrictions in effect in Aotearoa New Zealand at the time of data collection.

2.2.1 Research setting

This study was conducted at a prominent New Zealand university. The research setting was a campus café, centrally located at a busy throughway between several lecture theatres, and which typically serves university staff, students, and workers not affiliated with the university. The café serves an array of food items including cakes, scones, plain and filled croissants, sandwiches, sushi, toasted or fresh paninis and wraps, hot pies, salads, and packaged goods (e.g., lasagne, confectionary).

For the purposes of this research, savoury items (i.e., sandwiches, wraps, paninis, calzones, sushi, pies, and packaged lasagne and noodles) were included in the analysis. On average, 73% of offerings on any given day during the research period contained meat or fish, and 27% of offerings were vegetarian; they did not contain meat or fish but may have included eggs and/or dairy. Equivalent meat and meatless foods were priced similarly. On average, approximately 53% of available items were offered every day during the research period.

2.2.2 Design and intervention

The study period was split into three phases: pre-intervention, intervention, and post-intervention, each lasting one week. During the intervention phase, a social norms sign was displayed in the research setting. The sign contained a descriptive dynamic norm message related to national meat reduction based on Colmar Brunton (2019) and Beef and Lamb New Zealand (2018) data. The message (see Figure 1 ) read “Many people in New Zealand have reduced or stopped eating meat for health, environmental, or animal welfare reasons,” and was adapted from similar messaging used by Thomas et al. (2017) and informed by McAlaney et al. (2010) and Miller and Prentice (2016) . Meat reduction rationale (i.e., “…for health, environmental, or animal welfare reasons.”) was included, as norm messages may be more effective if attention is drawn to the significance of, or motivation for peer behaviour ( van der Linden, 2015 ). The sign was designed to be read as clearly as possible, with a simple colour scheme and font choice. Like those used by Thomas et al. (2017) , the sign was A4-sized and placed in a clear, plastic display atop the hot food cabinet (see Figure 2 ) during the intervention phase. During the pre- and post-intervention phases, this social norm signage was not displayed anywhere in the research setting, and there were no other campaigns, initiatives, or events taking place.

Figure 1 . Descriptive dynamic social norms signage, displayed during the intervention phase (study one).

Figure 2 . Study one research setting, including descriptive dynamic social norm signage atop food cabinet (left) during the intervention phase.

For one day during each of the three study phases, a paper-based survey was disseminated to customers to gain insight into their characteristics (e.g., demographics, dietary habits) and purchase experience. To assess awareness of the social norm signage, the survey included a visual multiple-choice question, which showed five images of the various signage in the café and asked which of these the customer had noticed. During the intervention week only, this question included the social norm signage as one of the selectable answer options. Surveys were conducted with a small sub-sample of customers who had made a purchase at the outlet. To account for some customers not wishing to participate in a survey, the target recruitment was approximately 50% of total customers. Participants were required to be aged 18 or over; no further eligibility criteria applied. For one day during each phase, the researcher approached these customers, inviting them to participate in the survey for the chance to win a $50 shopping voucher via a prize draw. The researcher was not present in the café outside of these days.

2.2.3 Measures

2.2.3.1 meat and meatless purchases.

Daily purchase data was collected from the outlet for the duration of the trial. The data collected for this measure included itemised quantities sold and corresponding financial figures from all customers who purchased an item during the trial and was recorded by outlets as standard practice. Purchase data were collected from the university operations manager at the end of the three-week period.

2.2.3.2 Customer characteristics and awareness of message

The customer characteristics survey consisted of 11 questions aiming to identify outlet customer demographics, purchase experience, and dietary habits (adapted from Papies and Hamstra, 2010 and Thomas et al., 2017 ). Demographic items included age, sex, ethnicity, nationality, and staff/student status. This was followed by a series of questions about participants’ purchases, including what was purchased, factors that influenced the purchase, frequency of outlet visitation, and whether the social norms messaging was noticed (during the intervention week only). The survey concluded with two questions aiming to discern participants’ dietary habits and whether they were reducing their meat consumption.

2.2.4 Data analysis

The data analysis was conducted using SPSS version 28 ( IBM Corp, 2021 ). Due to the format of the data obtained from the food outlet, Pearson’s chi squared tests were used to explore differences in purchases. 1 Based on their ingredients and composition, food items were coded as either meat (0) or meatless (1). The number of meat and meatless items sold were compared (a) between pre-intervention and intervention phases, (b) between intervention and post-intervention phases, and (c) between pre-intervention and post-intervention phases. For all tests to account for multiple comparisons the significance level was corrected to p  < 0.017, and effect sizes were estimated using partial eta squared (η p 2 , for overall sales between phases) and odds ratios (for differences in meat and meatless sales between phases). Survey data was used to characterise customer demographics across the three study phases.

2.3 Results

On average, 1,534 items were sold per trial phase, and the average sales per phase were similar, p  = 0.961, ηp 2  = 0.007 (see Table 1 for a breakdown of sales per trial phase). Pearson’s chi squared tests revealed that phase was not associated with a significant difference in items purchased (meat versus meatless); intervention compared to pre-intervention phase; χ 2 (1) = 0.002, p  = 0.960, OR = 1, 95% CI [0.86, 1.17], and to post-intervention phase; χ 2 (1) = 0.207, p  = 0.649, OR = 1.04, 95% CI [0.89, 1.21]. There was also no significant association in items purchased between pre-intervention and post-intervention phases; χ 2 (1) = 0.257, p  = 0.612, OR = 1.04, 95% CI [0.89, 1.21].

Table 1 . Items sold by trial phase (study one).

In total, 66 customers completed the customer characteristics survey, and distribution of participants across the three trial phases was similar (see Table 2 ). Notably, only 6 participants (approximately 26% of all participants) noticed the social norms signage during the intervention phase. Additionally, the majority of customers (approximately 83% in total) were meat consumers, and only a small number of these were currently reducing their meat consumption.

Table 2 . Customer characteristics across the three trial phases (study one).

3 Study two: the UK

The aim of study two was to test the effect of descriptive dynamic social norm messages about meat reduction on meat purchases, at three food outlets in a UK university. Building upon study one, study two incorporated several research sites and message delivery modes. As the message referred to a referent group that has been previously found to be favourably perceived, and referred to data on rates of meat reduction specific to this context ( Patel and Buckland, 2021 ), it was expected that the social norm intervention would reduce purchases of meat items and increase meatless purchases.

3.1 Context

Per capita meat consumption in the UK is higher than the global average ( OECD, 2022 ), with consumption levels exceeding recommendations for optimal human (e.g., National Health Service, 2021 ) and planetary health ( Willett et al., 2019 ). However there is evidence to suggest that alternative low and no meat diets are growing in prevalence (e.g., YouGov, 2019 ), with a decline in meat consumption ( Stewart et al., 2021 ). Whilst this is promising, meat reduction rates must be accelerated to address the issues caused by high per capita meat consumption.

UK university food environments appear to be less meat-centric, especially compared to those in Aotearoa New Zealand at time of writing. This is evident in the number and range of meatless food items available, and the primary researcher’s lived experience in both contexts. Having an adequate variety of available meatless items is important to support any intervention aiming to change food behaviours, including reducing meat consumption (e.g., Stoll-Kleemann and Schmidt, 2017 ), and it is thus likely that the increased range of meatless items available in UK university food outlets increases the potential effectiveness of the intervention.

3.2 Methods

The study procedures were approved by [anonymised] Ethics Committee. Purchase data was anonymous and recorded by outlets as standard practice, so was acquired for all customers making purchases. Given the anonymity, informed consent was not obtained for purchase data, however a debrief information notice was displayed at participating outlets at the end of the study period for transparency. Informed consent was obtained from all survey participants. Data collection took place in February 2022. There were no COVID-19 restrictions in place at the time of data collection; some measures (e.g., mask wearing) were encouraged but not mandatory.

3.2.1 Research setting

This study was conducted at The University of Sheffield, a prominent UK university with an ambitious sustainability strategy ( The University of Sheffield, 2020 ). The study arose through a Living Labs initiative, 2 aiming to utilise and build upon previous research exploring sustainable diets at the University of Sheffield’s Student Union (SSU), a focal point of the university featuring multiple food outlets. This study was conducted at three food outlets at the SSU, chosen based on three criteria. First, outlets had to be operated by the SSU. Second, outlets were required to offer an adequate range of meatless items; at least one quarter of all savoury offerings available were required to be meatless. Third, the purchase data needed to include clear differentiation between meat and meatless purchases. Finally, eligible outlets were discussed and determined following feasibility conversations with university operations and outlet management. As such, the final decision of three study sites was pragmatically determined, based on what the SSU agreed to make available for participation.

The three sites used in this study were, Site A: A café, serving hot and cold drinks and an array of sweets, snacks, and sandwiches; Site B : A burger bar, serving burgers, fries, and drinks; and Site C : An express food shop, serving fast hot foods such as toasted sandwiches, noodle pots, nuggets, baked potatoes, and soup with a self-service ordering system. All sites had a consistent menu that did not differ between days or trial phases. For the purpose of this research, only savoury items were included in the analyses; drinks, packaged snacks, sides, and sweets were excluded. The relative proportion of available meat and meatless offerings differed between sites. At site A, 40% of offerings were meat and 60% were meatless, at Site B, 60% of offerings were meat and 40% were meatless, and at site C, 47% of offerings were meat and 53% were meatless. Equivalent meat and meatless foods were mostly priced identically, with the exception of beef items at sites B and C, which were priced £1 and 50p extra, respectively, as part of an ongoing sustainability initiative.

3.2.2 Design and intervention

Much of the design of this study was similar to study one. However, to address issues around awareness and acceptance of the social norm message reported in study one, several changes were made in study two, including the use of a more credible (based on collected data) and relevant norm message, increased modes of delivery, and using more sites. These changes align with Yamin et al. (2019) ’s recommendations for situated social norm interventions, which highlight the importance of (1) credibility, with the social norm message ideally developed using data from the same target referent group, and (2) the design and strategic placement of messages to optimise visibility and accessibility (e.g., via the use of different message formats). Not only were these changes made to increase the effectiveness of the intervention, but they also allowed for a more complex intervention approach. Complex interventions are those that emphasise real world transferability and feasibility over absolute scientific fidelity ( Craig et al., 2008 ; Skivington et al., 2021 ). In doing so, they often incorporate several components or settings.

As in study one, this study used a three-phase pre-post design (i.e., pre-intervention, intervention, and post-intervention), each with a duration of one week. During the intervention phase, a descriptive dynamic social norm message was displayed in the three research settings. All signage contained the same message; “Most staff and students here have reduced or stopped consuming meat for health, environmental, or animal welfare reasons.” Extending study one’s message and to increase credibility, the signage referred to two data sources to support the social norm message ( Larner et al., 2021 ; Patel and Buckland, 2021 ). These sources included data on the number of people reducing meat intake at the university. All signage used a consistent colour scheme and font and were designed in collaboration with the Student Union’s marketing team. This ensured that norm messages were stylistically consistent and congruent with the usual marketing materials displayed around the building. Student Union and Living Labs branding were included at the bottom of the poster at the request of the marketing team.

The size and placement of the signage differed according to each specific research site and was informed by the feasibility conversations with stakeholders. In site A (café), a large A3-sized poster was placed in a prominent display typically used for marketing ( Figure 3 , Panel 1). In site B (burger bar), 16:9 landscape posters were displayed on digital screens within the bar for a duration of ten seconds within a circulation of other marketing materials ( Figure 3 , Panel 2), and A4-sized posters were placed on individual clipboards holding the menu ( Figure 3 , Panel 3). In site C (express shop), small laminated business-card sized signs were attached to the self-serve screens, which served as the menu and point of decision and purchase. All signage was displayed from the first day until close-of-business on the final day of the intervention phase. The social norm message was also posted on the Student Union’s social media accounts (see Figure 4 for an example) in an attempt to increase the potential reach and visibility of the norm message and add to the authenticity of the intervention by utilising communications channels that would be typically used by the outlets. At the request of the marketing team, the social media posts were accompanied by contextual text (see Figure 4 ). The social media posts were posted on the second day of the intervention phase. During the pre- and post-intervention phases, no social norms signage was displayed anywhere in the sites or posted on social media.

Figure 3 . Examples of the descriptive dynamic social norm message in different research sites of study two; Panel 1. Poster at site A; Panel 2. Digital signage at site B; Panel 3. Menu clipboard at site B.

Figure 4 . An example of the descriptive dynamic social norm messages posted on social media channels (Instagram; study two).

As in study one, a short survey was conducted to gain insight into demographics, dietary habits, experience at the research site(s), and whether customers had noticed the social norms poster. During the final two days of the post-intervention phase, posters containing QR codes were displayed in each of the research sites. When scanned, these QR codes directed customers to an online survey (Qualtrics, Provo, UT). After obtaining consent, eligibility was checked (aged 18 and over) and participants proceeded to answer questions on demographics, dietary habits, experience at the research site(s), and awareness of the social norm message. Upon completion, participants had the opportunity to enter a prize draw for a £50 shopping voucher.

Related to survey recruitment, it is important to highlight two changes that were made to the design of study one. First, due to COVID-19 and safety concerns, in-person survey participant recruitment was avoided, and adverts containing QR codes were used. Second, in an attempt to keep surveys temporally close to the intervention whilst minimising effects on purchasing behaviour, the QR adverts and survey were live for only the final two days of the post-intervention phase.

3.2.3 Measures

3.2.3.1 meat and meatless purchases.

Daily purchase data was collected from the three outlets for the duration of the trial. The data was recorded by outlets as standard practice, and included itemised quantities sold from all customers who purchased any item during the specified period. Purchase data were collected from the university operations managers at the end of the three-week period.

3.2.3.2 Customer characteristics and awareness of social norm message

The customer survey assessed demographics, purchase experience, and dietary habits. Demographic items included age, gender, ethnicity, nationality, and staff/student status. This was followed by a question that queried which of the three participating outlets the participant had visited over the past two weeks. Participants were then presented with questions specific to the outlets specified. These questions assessed how often the outlet(s) were visited, and what was purchased at outlet(s) over the previous two weeks. Participants were then asked about their dietary habits, including whether they were reducing their meat consumption. Finally, all participants were asked whether they recalled seeing the social norms signage over the past two weeks. Those that did recall were asked in what location and format (i.e., in which of the outlets or social media platforms was the sign viewed). Participants were then debriefed.

3.2.4 Data analysis

All data analyses were conducted using SPSS version 28 ( IBM Corp, 2021 ). The data was first cleaned by removing items not intended for analysis. These items included drinks, packaged snacks (e.g., crisps, chocolate), sides (e.g., fries, sauces), and sweet treats (e.g., cakes, cookies). As such, the food items for analysis were savoury items. Based on their ingredients and composition, food items at each outlet were coded as either meat (0) or meatless (1).

A binary logistic regression 3 was used to assess the effect of trial phase, site, and the interaction between the two on sales across all three sites combined. The dependent variable was binary (0 = meat, 1 = meatless), and the experimental variables were entered as categorical variables (phase 1 = preintervention, 2 = intervention, 3 = post-intervention; site = A, B, C). Due to differences in baseline sales, each site was also assessed separately using Pearson’s chi-squared tests to explore differences in purchases. For each food outlet, the number of meat and meatless items sold were compared (a) between pre-intervention and intervention phases, (b) between intervention and post-intervention phases, and (c) between pre-intervention and post-intervention phases. For all tests the significance level was corrected to p  < 0.017 to correct for multiple comparisons, and measures of effect were estimated using odds ratios. Survey data was used to assess customer demographics across the three study phases.

3.3 Results

In total across the three phases 1,121 sales were recorded in site A, 1909 sales were recorded in site B, and 950 sales were recorded in site C. Total sales varied between the three time phases and across sites (see Table 3 ).

Table 3 . Items sold by site and trial phase (study two).

Overall, the binary logistic regression revealed that the overall model correctly predicted 63.3% of sales and was statistically significant when compared to the null model: χ 2 (8) = 153.63, p  < 0.001. Site had a significant effect on sales ( p  < 0.001), but phase did not ( p  = 0.144), and there were no significant interaction effects identified between phase and site ( p  = 0.350). For site, the odds of a meat item being purchased was highest in site C (compared to site A: ß = −0.826, p  < 0.001, odds ratio = 0.438; and site B: ß = −0.98, p  < 0.001, odds ratio = 0.375).

The proportion of meatless items sold for each of the food outlets across the three time phases are shown in Figure 5 . Per site, Pearson’s chi squared tests revealed no significant association in the number of items purchased between the intervention and pre-intervention phase; site A: χ 2 (1) = 2.93, p  = 0.092, OR = 1.29, 95% CI [0.96, 1.72]; site B: χ 2 (1) = 0.02, p  = 0.904, OR = 1.02, 95% CI [0.76, 1.36]; site C: χ 2 (1) = 1.56, p  = 0.212, OR = 1.25, 95% CI [0.88, 1.77]. There were also no significant associations in sales between intervention and post-intervention phases in any of the three outlets; site A: χ 2 (1) = 3.26, p  = 0.071, OR = 0.75, 95% CI [0.55, 1.03]; site B: χ 2 (1) = 0.13, p  = 0.720, OR = 1.05, 95% CI [0.82, 1.34]; site C: χ 2 (1) = 0.22, p  = 0.641, OR = 1.09, 95% CI [0.77, 1.54]. Finally, no significant associations were identified between sales in pre-intervention and post-intervention phases; site A: χ 2 (1) = 0.01, p  = 0.936, OR = 0.99, 95% CI [0.75, 1.31]; site B: χ 2 (1) = 0.28, p  = 0.596, OR = 1.07, 95% CI [0.84, 1.34]; site C: χ 2 (1) = 3.58, p  = 0.059, OR = 1.36, 95% CI [0.98, 1.86].

Figure 5 . Percentage of meatless items sold in each of the three research sites (A: café; B: burger bar; C: express shop) across trial phases (study two).

Completion rates for the customer survey were low ( n  = 13), however the descriptive data is included in the Supplementary material for transparency. Given the low completion rates, responses need to be interpreted with caution. Notably, 6 out of 13 participants reported that they had noticed the social norms signage during the intervention phase.

4 General discussion

Across two studies, sales of meat and meatless items did not significantly change in response to a descriptive dynamic social norm intervention that encouraged reduced meat intake. Study one, conducted in a university food outlet in Aotearoa New Zealand, displayed a social norm message for one week which referred to ‘people in New Zealand’ as the referent group. No significant associations were found between purchases (meat or meatless) and trial phases. Study two extended study one by increasing the visibility of the social norm message (location, delivery methods of messages), referring to a more relevant referent group, basing the social norms message on previously collected dietary data (credible message) and being based in three University food outlets at a UK University which provided a range of meat-free options. However, similar to study one, there were no significant changes in the amount of meat and meatless purchases in response to the social norm message. These studies add to increasing evidence that social norm messages delivered via signs in food outlets and corresponding social media posts are ineffective at reducing meat consumption.

Indeed, several studies have also reported no effects of a social norm message to reduce meat intake ( Sparkman et al., 2020 , study 4; Çoker et al., 2022 ; Reinholdsson et al., 2022 ). However, several other studies reported that exposure to a dynamic social norm message reduced meat purchases ( Sparkman and Walton, 2017 ; Sparkman et al., 2020 , studies 1 and 2). The reasons for the mixed findings are currently unclear, however there are a few potential explanations. One of these concerns engagement with the social norm message. When researchers hand-delivered the social norm messages to customers waiting in line to order food, the intervention was effective at reducing meat intake ( Sparkman and Walton, 2017 ), likely because exposure to the norm message was ensured. However more naturalistic interventions that did not directly involve the researcher in message delivery (e.g., Sparkman et al., 2020 ; Çoker et al., 2022 ; the present research) were ineffective. Additionally, most previous food-related norm interventions used a prescriptive norm – they encouraged rather than discouraged choice ( Mollen et al., 2013 ; Payne et al., 2015 ; Thomas et al., 2017 ; Çoker et al., 2022 ). The message used in the current studies specified meat reduction . It is possible that social norm interventions may be less effective when being used to reduce food intake or discourage choice. Further research directly comparing the effects of social norm messages that encourage (e.g., encourage alternatives to meat such as plant-based foods and pulses) versus discourage food choices (e.g., reduce meat intake) will be beneficial to confirm if any and which types of message framing used in social norm interventions can be effective to encourage reduced meat in favour of environmentally sustainable alternatives.

Notably, there were several changes from study one to study two to maximise the delivery of the social norm intervention in line with key recommendations for social norm intervention designs ( Yamin et al., 2019 ). First, to be effective, it is important to use a credible social norm message ( Burchell et al., 2012 ; Yamin et al., 2019 ). Study one’s social norm message was not informed by dietary data and referred to “people in New Zealand” as the referent group. Whilst Aotearoa New Zealand prides itself on its pro-environmentalism, there is also a concurrent perception that meatless options and lifestyles are an “un-Kiwi” threat to the national identity ( Potts and White, 2008 ). Furthermore, whilst national identity is a fundamental aspect of social identity ( Milfont et al., 2020 ), it is possible that this referent group (“people in New Zealand”) was too general and did not facilitate enough of a social connection or identification with customers at the food outlet. Therefore, to increase the credibility of the social norm message and increase identification with the referent group in study two, the social norm message was informed by dietary data collected from the specific context and referent group ( Patel and Buckland, 2021 ). Another change was the availability of the meatless options. In study one’s food outlet there was a limited range of meatless options (only 27% of total savoury offerings being meatless) and this may have limited the opportunity to observe changes in meat and meatless purchases. Stoll-Kleemann and Schmidt’s (2017) model of influences on meat eating behaviour includes appropriate “plant-based diet friendly” infrastructure as a key external incentive to reduced meat consumption. Therefore, in study two food outlets that offered a range of appealing meatless alternatives were used to evaluate the effects of the social norm message. However, despite these changes, the social norm messages used in study two did not significantly shift food purchasing behaviour in any of the three food outlets.

Of note, in both studies, the awareness of the social norm message was low. In study one, only one norm message was displayed during the intervention phase. Anecdotal observations of customer behaviour by the researcher and café staff independently suggested that many customers seemed to know what they had planned to purchase and did not tend to browse the cabinet or examine surrounding signage. This may be indicative of regular or returning customers who are less inclined to browse for new options ( Sparkman et al., 2020 ), and is likely to have resulted in the relatively small percentage of individuals who reported noticing the norm message during the intervention phase. Low sign awareness also occurred in study two (note that this was based on a low sample size of thirteen survey participants), despite efforts to increase the visibility of the social norm message by increasing the size of posters, placing them in more visible locations, and posting the message on social media channels. Importantly, Mollen et al. (2013) reported that their social norm message was only effective at influencing food choice among participants who had reported seeing it. Sparkman et al. (2020) noted that people are generally not obliged to look at norm messages given that they act as a distraction from their primary goal, at that time, to view and select food options. The potentially limited exposure to norm messages highlights a trade-off inherent in naturalistic field studies. Whilst ecological validity is maximised, it is difficult to ensure exposure to the norm message which can be achieved in controlled laboratory-based studies ( Robinson et al., 2014 ).

There are strengths of the current studies. First, these studies are among the very few that have naturalistically tested the effect of social norm messages on meat and meatless purchases, as well as eating behaviours more generally. While there is a wealth of research assessing the role of social norms on eating behaviours conducted in the laboratory ( Robinson et al., 2014 ), there are very few that have been applied in real world eating contexts, leading to calls for more naturalistic designs (e.g., Robinson, 2015 ). These studies contribute to evidence that applied social norm interventions may not be as consistently effective at changing food behaviours as they are in the laboratory, even when following design recommendations to optimise their behaviour change potential (e.g., those concerning the credibility, design, and placement of norm messages). Furthermore, these studies were conducted in two different countries with different meat-eating contexts, with Aotearoa New Zealand having a more meat-centric culture when compared to the UK. That both studies resulted in similar findings adds strength to the conclusions drawn.

These studies also evidence the possibility of fruitful, collaborative relationships with stakeholders in promoting healthy and sustainable diets. Relative to other types of interventions, social norm interventions are feasible to implement and present low financial risk, and these types of interventions have been found to be more acceptable than others (e.g., menu reformulations, disincentives) to both customers and retail stakeholders ( Graham et al., 2020 ). Additionally, these studies were designed and conducted in close consultation with stakeholders at all stages of the process. Stakeholder engagement has been identified as a key consideration in the success of applied interventions ( Skivington et al., 2021 ), not only to increase the potential effectiveness of the intervention, but to also bring context-specific insights and expertise, boosting real world transferability and ensuring smooth implementation. Prior to any intervention, it is important to first lay the groundwork so that stakeholders understand the importance of the issue and why it requires their investment and involvement ( Graham et al., 2020 ). Only when interventions are acceptable and feasible for stakeholders will they be sustainable for long term implementation.

There were also several limitations that are worth noting. First, COVID-19 and time constraints prevented the collection of dietary data to refer to in the social norm message, and there were also several unplanned events during study two’s intervention week (e.g., severe thunderstorms and staff strikes) which decreased the number of customers in the university’s Student Union during the intervention week, as reflected by fewer overall sales observed across all research sites. These events were impossible to predict and plan for and reflect the challenges of conducting research in naturalistic settings. The intervention duration was also relatively short (1 week), however another UK study with a two-week intervention also reported the same non-significant results ( Çoker et al., 2022 ). Finally, limited information was obtained about the customers in both studies, due to low numbers of customers completing the customer characteristics surveys. As such, the information drawn from the survey results (i.e., percentage of customers that noticed the norms messages) cannot be considered representative and should be cautiously interpreted.

There is no doubt that social norms around meat intake are important for changing meat-centric cultures. However, as our findings and others indicate, naturalistic social norm interventions without researcher involvement in message delivery are relatively ineffective at reducing meat intake. Future research may benefit by testing different wording of the social norms message, modes of message delivery, or exploring alternative behaviour change interventions. Interventions which have shown more promise for reducing meat intake include information provision and labelling (e.g., Brunner et al., 2018 ; Larner et al., 2021 ), reformatting menus so that desirable options (i.e., meatless options) are integrated and not segregated in a separate section (e.g., Bacon and Krpan, 2018 ; Gravert and Kurz, 2019 ) and default interventions that place meatless items as the option automatically received unless otherwise specified (see Meier et al., 2022 for a review). Notably, any intervention needs to be acceptable and feasible for all stakeholders involved, by (1) having minimal operational costs to commercial partners, (2) incorporating components that embed the intervention into usual business practices (e.g., the integration of social media and/or marketing), and (3) having the ability to easily scale ( Attwood et al., 2020 ; Graham et al., 2020 ; Skivington et al., 2021 ).

5 Conclusion

To conclude, the descriptive dynamic social norm interventions reported in this paper did not significantly reduce meat item purchases across two university settings. These results confirm previous studies that social norm messages delivered via signs in naturalistic settings are ineffective for reducing meat purchases. Further research is required to compare and identify the most effective delivery modes and framing of social norm messages, as well as exploring other types of interventions to reduce meat intake for improved human, animal, and planetary outcomes.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the University of Sheffield Psychology Ethics Committee and the University of Otago Human Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

VP: Writing – review & editing, Writing – original draft, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. MM: Resources, Project administration, Writing – review & editing, Conceptualization. NB: Supervision, Writing - original draft, Writing – review & editing, Conceptualization.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Grantham Centre for Sustainable Futures.

Acknowledgments

The authors would like to sincerely thank Professor Helen Kennedy (Department of Sociological Studies, The University of Sheffield) for her valuable insight provided at all stages of this research. Our thanks also go to the various food retail management and marketing staff that assisted with and enabled these studies, especially to the operations managers at both universities. Finally, our sincere gratitude goes to the Grantham Centre for Sustainable Futures for funding this research. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. This work was conducted as part of a PhD thesis, which will shortly be available online.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2024.1260343/full#supplementary-material

1. ^ Deviation from pre-registration as it was realised upon handling the data that an alternative test was more appropriate.

2. ^ https://www.sheffield.ac.uk/sustainability/living-labs

3. ^ Deviation from pre-registration as it was realised upon handling the data that an alternative test was more appropriate.

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Keywords: social norms, meat reduction, food choice, behavioural intervention, sustainability

Citation: Patel V, Mirosa M and Buckland NJ (2024) Testing the effect of descriptive dynamic social norm messages on meatless food purchases in Aotearoa New Zealand and UK university food outlets. Front. Sustain. Food Syst . 8:1260343. doi: 10.3389/fsufs.2024.1260343

Received: 17 July 2023; Accepted: 12 March 2024; Published: 27 March 2024.

Reviewed by:

Copyright © 2024 Patel, Mirosa and Buckland. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nicola J. Buckland, [email protected]

This article is part of the Research Topic

Ethical and Sustainable Food Choice: Drivers and Health Effects

Title: Effect of sustainable competitive advantage on business excellence in the hotel industry

Authors : Deepali Anand; Alka Munjal

Addresses : Amity Business School, Amity University, Sector 125, Noida, UP, India ' Amity Business School, Amity University, Sector 125, Noida, UP, India

Abstract : The purpose of this paper is to establish and study the impact of sustainable competitive advantage (cost leadership and differentiation) on business excellence in star-rated hotels. This will further help the top management define internal strategies that drive the organisation according to the adopted competitive strategy. Under descriptive research through survey method, a sample of 220 executives (top and middle management) from different star-rated hotels in NCR was taken. Structural equation modelling as a technique was adopted to conduct the analysis of the data obtained through the survey. Among the research results, it is worth focusing on the scale validation and testing of hypothesis that accentuated that competitive advantage (cost leadership and differentiation) has a significant impact on the formulation of business excellence strategies. Economies of scale, technology, partnership in the supply chain are important predictors of cost leadership; relation-building, service innovation and quality, brand image are predictors of differentiation. A clear understanding of the hotels on competitive strategies to be followed helps formulate internal business excellence strategies for success and higher profitability.

Keywords : business excellence; cost leadership; differentiation; hotel industry; tourism sector.

DOI : 10.1504/IJBEX.2024.137569

International Journal of Business Excellence, 2024 Vol.32 No.4, pp.545 - 560

Received: 20 Jul 2020 Accepted: 02 Mar 2021 Published online: 26 Mar 2024 *

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Why do women go through menopause? Scientists find fascinating clues in a study of whales.

The existence of menopause in humans has long been a biological conundrum, but scientists are getting a better understanding from a surprising source: whales.

Findings of a new study suggest menopause gives an evolutionary advantage to grandmother whales’ grandchildren. It's a unique insight because very few groups of animals experience menopause.

A paper published Wednesday in the journal Nature looked at a total of 32 whale species, five of which undergo menopause. The findings could offer clues about why humans, the only land-based animals that also goes through menopause, evolved the trait.

“They’ve done a great job of compiling all the evidence,” said Michael Gurven, a professor of anthropology at the University of California, Santa Barbara who studies human evolution and societies. “This paper quite elegantly gets at these very difficult issues.”

Whales might seem very distant from humans, but they have important similarities. Both are mammals, both are long-lived, and both live in family and social groups that help each other.

How long does menopause last? Menopause questions and concerns, answered.

Studying these toothed whale species offers a way to think about human evolution, said Gurven, who was not involved in the study.

In five species of toothed whales – killer whales, beluga whales, narwhals, short-finned pilot whales and false killer whales – the researchers’ findings suggest menopause evolved so grandmothers could help their daughters' offspring, without competing with them for mates.

Only daughters' offspring are aided because in these whales, while the males stay with their family group, they mate with females in other groups. But mothers do tend to give more support to their male offspring than to their female offspring.

Post-reproductive-age females help their family group in many ways. Off the coast of Washington state and British Columbia in Canada, grandmother killer whales catch salmon and "break the fish in half and share that catch with their families. So they're actively feeding their families,” said Darren Croft, a professor of behavioral ecology at the University of Exeter in the United Kingdom and senior author on the paper.

The whale grandmothers also store ecological knowledge about when and where to find food in times of hardship by using the experience they have gained over the lifetime of their environments.

“We see just the same patterns in (human) hunter-gatherer societies,” Croft said. “In times of a drought or in during times of social conflict, the people would turn to the elders of that community. They would have the knowledge.”

The 'grandmother hypothesis'

The researchers’ findings support what’s known as “the grandmother hypothesis .” It states that menopause is evolutionarily useful because while older women are no longer able to have children, they can instead focus their efforts on supporting their children and grandchildren. This means their family lines are more likely to survive, which has the same effect as having more children.

“What we showed is that species with menopause have a much longer time spent to live with their grand offspring, giving them many more opportunities for intergenerational health due to their long life,” said Samuel Ellis, an expert in human social behavior at the University of Exeter and the paper’s first author.

The difference in humans, Gurven said, is that both grandmothers and grandfathers contribute to the well-being of their children and grandchildren.

“In the human story, I think it’s multigenerational cooperation on steroids,” he said.

Though the study doesn’t prove once and for all that the grandmother hypothesis is the reason for menopause in women, it does lay out the evidence, he said. “It’s part of the story, but no one would say it tells the whole story,” Gruven said.

Does menopause lead to a longer life in humans?

There are two proposed pathways for how menopause evolved in humans: the live-long hypothesis and the stop-early hypothesis.

The live-long hypothesis suggests menopause increased total life span, but not how long a woman could have children. That leads to a prediction that species with menopause would live longer but have the same reproductive life span as species without menopause.

In the stop-early hypothesis, the theory is that menopause evolved by shortening the reproductive life span while the total life span remained unchanged. For this to be true, it would be likely that similar species without menopause would have the same life span as those that have menopause, but a shorter reproductive life span.

In looking at species of toothed whales that don’t have menopause and five that do, the researchers' findings make the long-life hypothesis seem most likely.

“This comparative work we’ve been able to do shows that females minimize this competition over reproduction by not also lengthening their reproductive period. Instead, they've evolved a longer lifespan while keeping a shorter reproductive life span,” Croft said.

This appears to be exactly what humans did.

“One of the striking features of this work is the fact that we find this really incredible and rare life-history strategy that we see human societies and in the ocean, but not elsewhere in mammal societies,” he said.

Whale study doesn't reflect men's life spans

The similarities with humans are not across the board, which is good news for men.

No one knows why in humans only females undergo menopause even though both sexes live to be approximately the same ages.

That’s not the case in some of these whales species, where male life spans are typically much shorter than those of females.

“In the killer whale population, for example, females regularly live into their 60s and 70s," Croft said. "The males are all dead by 40.”