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what is unit of analysis in research

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Unit of Analysis: Definition, Types & Examples

A unit of analysis is what you discuss after your research, probably what you would regard to be the primary emphasis of your research.

The unit of analysis is the people or things whose qualities will be measured. The unit of analysis is an essential part of a research project. It’s the main thing that a researcher looks at in his research.

A unit of analysis is the object about which you hope to have something to say at the end of your analysis, perhaps the major subject of your research.

In this blog post, we will explore and clarify the concept of the “unit of analysis,” including its definition, various types, and a concluding perspective on its significance.

What is a unit of analysis?

A unit of analysis is the thing you want to discuss after your research, probably what you would regard to be the primary emphasis of your research.

The researcher plans to comment on the primary topic or object in the research as a unit of analysis. The research question plays a significant role in determining it. The “who” or “what” that the researcher is interested in investigating is, to put it simply, the unit of analysis.

In his 2001 book Man, the State, and War, Waltz divides the world into three distinct spheres of study: the individual, the state, and war.

Understanding the reasoning behind the unit of analysis is vital. The likelihood of fruitful research increases if the rationale is understood. An individual, group, organization, nation, social phenomenon, etc., are a few examples.

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Types of “unit of analysis”

In business research, there are almost unlimited types of possible analytical units. Data analytics and data analysis are closely related processes that involve extracting insights from data to make informed decisions. Even though the most typical unit of analysis is the individual, many research questions can be more precisely answered by looking at other types of units. Let’s find out, 

1. Individual Level

The most prevalent unit of analysis in business research is the individual. These are the primary analytical units. The researcher may be interested in looking into:

  • Employee actions
  • Perceptions
  • Attitudes or opinions.

Employees may come from wealthy or low-income families, as well as from rural or metropolitan areas.

A researcher might investigate if personnel from rural areas are more likely to arrive on time than those from urban areas. Additionally, he can check whether workers from rural areas who come from poorer families arrive on time compared to those from rural areas who come from wealthy families.

Each time, the individual (employee) serving as the analytical unit is discussed and explained. Employee analysis as a unit of analysis can shed light on issues in business, including customer and human resource behavior.

For example, employee work satisfaction and consumer purchasing patterns impact business, making research into these topics vital.

Psychologists typically concentrate on research on individuals. This research may significantly aid a firm’s success, as individuals’ knowledge and experiences reveal vital information. Thus, individuals are heavily utilized in business research.

2. Aggregates Level

Social science research does not usually focus on people. However, by combining individuals’ reactions, social scientists frequently describe and explain social interactions, communities, and groupings. Additionally, they research the collective of individuals, including communities, groups, and countries.

Aggregate levels can be divided into Groups (groups with an ad hoc structure) and Organizations (groups with a formal organization).

The following levels of the unit of analysis are made up of groups of people. A group is defined as two or more individuals who interact, share common traits, and feel connected to one another. 

Many definitions also emphasize interdependence or objective resemblance (Turner, 1982; Platow, Grace, & Smithson, 2011) and those who identify as group members (Reicher, 1982) .

As a result, society and gangs serve as examples of groups. According to Webster’s Online Dictionary (2012), they can resemble some clubs but be far less formal.

Siblings, identical twins, family, and small group functioning are examples of studies with many units of analysis.

In such circumstances, a whole group might be compared to another. Families, gender-specific groups, pals, Facebook groups, and work departments can all be groups.

By analyzing groups, researchers can learn how they form and how age, experience, class, and gender affect them. When aggregated, an individual’s data describes the group they belong to.

Sociologists study groups like economists and businesspeople to form teams to complete projects. They continually research groups and group behavior.

Organizations

The next level of the unit of analysis is organizations, which are groups of people set up formally. Organizations could include businesses, religious groups, parts of the military, colleges, academic departments, supermarkets, business groups, and so on.

The social organization includes things like sexual composition, styles of leadership, organizational structure, systems of communication, and so on. (Susan & Wheelan, 2005; Chapais & Berman, 2004) . (Lim, Putnam, and Robert, 2010) say that well-known social organizations and religious institutions are among them.

Moody, White, and Douglas (2003) say social organizations are hierarchical. Hasmath, Hildebrandt, and Hsu (2016) say social organizations can take different forms. For example, they can be made by institutions like schools or governments.

Sociology, economics, political science, psychology, management, and organizational communication are some social science fields that study organizations (Douma & Schreuder, 2013) .

Organizations are different from groups in that they are more formal and have better organization. A researcher might want to study a company to generalize its results to the whole population of companies.

One way to look at an organization is by the number of employees, the net annual revenue, the net assets, the number of projects, and so on. He might want to know if big companies hire more or fewer women than small companies.

Organization researchers might be interested in how companies like Reliance, Amazon, and HCL affect our social and economic lives. People who work in business often study business organizations.

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3. Social Level

The social level has 2 types,

Social Artifacts Level

Things are studied alongside humans. Social artifacts are human-made objects from diverse communities. Social artifacts are items, representations, assemblages, institutions, knowledge, and conceptual frameworks used to convey, interpret, or achieve a goal (IGI Global, 2017).

Cultural artifacts are anything humans generate that reveals their culture (Watts, 1981).

Social artifacts include books, newspapers, advertising, websites, technical devices, films, photographs, paintings, clothes, poems, jokes, students’ late excuses, scientific breakthroughs, furniture, machines, structures, etc. Infinite.

Humans build social objects for social behavior. As people or groups suggest a population in business research, each social object implies a class of items.

Same-class goods include business books, magazines, articles, and case studies. A business magazine’s quantity of articles, frequency, price, content, and editor in a research study may be characterized.

Then, a linked magazine’s population might be evaluated for description and explanation. Marx W. Wartofsky (1979) defined artifacts as primary artifacts utilized in production (like a camera), secondary artifacts connected to primary artifacts (like a camera user manual), and tertiary objects related to representations of secondary artifacts (like a camera user-manual sculpture).

The scientific study of an artifact reveals its creators and users. The artifact researcher may be interested in advertising, marketing, distribution, buying, etc.

Social Interaction Level

Social artifacts include social interaction. Such as:

  • Eye contact with a coworker
  • Buying something in a store
  • Friendship decisions
  • Road accidents
  • Airline hijackings
  • Professional counseling
  • Whatsapp messaging

A researcher might study youthful employees’ smartphone addictions. Some addictions may involve social media, while others involve online games and movies that inhibit connection.

Smartphone addictions are examined as a societal phenomenon. Observation units are probably individuals (employees).

Anthropologists typically study social artifacts. They may be interested in the social order. A researcher who examines social interactions may be interested in how broader societal structures and factors impact daily behavior, festivals, and weddings.

LEARN ABOUT: Level of Analysis

Even though there is no perfect way to do research, it is generally agreed that researchers should try to find a unit of analysis that keeps the context needed to make sense of the data.

Researchers should consider the details of their research when deciding on the unit of analysis. 

They should remember that consistent use of these units throughout the analysis process (from coding to developing categories and themes to interpreting the data) is essential to gaining insight from qualitative data and protecting the reliability of the results.

QuestionPro does much more than merely serve as survey software. We have a solution for every sector of the economy and every kind of issue. We also have systems for managing data, such as our research repository, Insights Hub.

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Unit of analysis: definition, types, examples, and more

Last updated

16 April 2023

Reviewed by

Cathy Heath

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  • What is a unit of analysis?

A unit of analysis is an object of study within a research project. It is the smallest unit a researcher can use to identify and describe a phenomenon—the 'what' or 'who' the researcher wants to study. 

For example, suppose a consultancy firm is hired to train the sales team in a solar company that is struggling to meet its targets. To evaluate their performance after the training, the unit of analysis would be the sales team—it's the main focus of the study. 

Different methods, such as surveys , interviews, or sales data analysis, can be used to evaluate the sales team's performance and determine the effectiveness of the training.

  • Units of observation vs. units of analysis

A unit of observation refers to the actual items or units being measured or collected during the research. In contrast, a unit of analysis is the entity that a researcher can comment on or make conclusions about at the end of the study.

In the example of the solar company sales team, the unit of observation would be the individual sales transactions or deals made by the sales team members. In contrast, the unit of analysis would be the sales team as a whole.

The firm may observe and collect data on individual sales transactions, but the ultimate conclusion would be based on the sales team's overall performance, as this is the entity that the firm is hired to improve.

In some studies, the unit of observation may be the same as the unit of analysis, but researchers need to define both clearly to themselves and their audiences.

  • Unit of analysis types

Below are the main types of units of analysis:

Individuals – These are the smallest levels of analysis.

Groups – These are people who interact with each other.

Artifacts –These are material objects created by humans that a researcher can study using empirical methods.

Geographical units – These are smaller than a nation and range from a province to a neighborhood.

Social interactions – These are formal or informal interactions between society members.

  • Importance of selecting the correct unit of analysis in research

Selecting the correct unit of analysis helps reveal more about the subject you are studying and how to continue with the research. It also helps determine the information you should use in the study. For instance, if a researcher has a large sample, the unit of analysis will help decide whether to focus on the whole population or a subset of it.

  • Examples of a unit of analysis

Here are examples of a unit of analysis:

Individuals – A person, an animal, etc.

Groups – Gangs, roommates, etc. 

Artifacts – Phones, photos, books, etc.  

Geographical units – Provinces, counties, states, or specific areas such as neighborhoods, city blocks, or townships

Social interaction – Friendships, romantic relationships, etc.

  • Factors to consider when selecting a unit of analysis

The main things to consider when choosing a unit of analysis are:

Research questions and hypotheses

Research questions can be descriptive if the study seeks to describe what exists or what is going on.

It can be relational if the study seeks to look at the relationship between variables. Or, it can be causal if the research aims at determining whether one or more variables affect or cause one or more outcome variables.

Your study's research question and hypothesis should guide you in choosing the correct unit of analysis.

Data availability and quality

Consider the nature of the data collected and the time spent observing each participant or studying their behavior. You should also consider the scale used to measure variables.

Some studies involve measuring every variable on a one-to-one scale, while others use variables with discrete values. All these influence the selection of a unit of analysis.

Feasibility and practicality

Look at your study and think about the unit of analysis that would be feasible and practical.

Theoretical framework and research design

The theoretical framework is crucial in research as it introduces and describes the theory explaining why the problem under research exists. As a structure that supports the theory of a study, it is a critical consideration when choosing the unit of analysis. Moreover, consider the overall strategy for collecting responses to your research questions.

  • Common mistakes when choosing a unit of analysis

Below are common errors that occur when selecting a unit of analysis:

Reductionism

This error occurs when a researcher uses data from a lower-level unit of analysis to make claims about a higher-level unit of analysis. This includes using individual-level data to make claims about groups.

However, claiming that Rosa Parks started the movement would be reductionist. There are other factors behind the rise and success of the US civil rights movement. These include the Supreme Court’s historic decision to desegregate schools, protests over legalized racial segregation, and the formation of groups such as the Student Nonviolent Coordinating Committee (SNCC). In short, the movement is attributable to various political, social, and economic factors.  

Ecological fallacy

This mistake occurs when researchers use data from a higher-level unit of analysis to make claims about one lower-level unit of analysis. It usually occurs when only group-level data is collected, but the researcher makes claims about individuals.

For instance, let's say a study seeks to understand whether addictions to electronic gadgets are more common in certain universities than others.

The researcher moves on and obtains data on the percentage of gadget-addicted students from different universities around the country. But looking at the data, the researcher notes that universities with engineering programs have more cases of gadget additions than campuses without the programs.

Concluding that engineering students are more likely to become addicted to their electronic gadgets would be inappropriate. The data available is only about gadget addiction rates by universities; thus, one can only make conclusions about institutions, not individual students at those universities.

Making claims about students while the data available is about the university puts the researcher at risk of committing an ecological fallacy.

  • The lowdown

A unit of analysis is what you would consider the primary emphasis of your study. It is what you want to discuss after your study. Researchers should determine a unit of analysis that keeps the context required to make sense of the data. They should also keep the unit of analysis in mind throughout the analysis process to protect the reliability of the results.

What is the most common unit of analysis?

The individual is the most prevalent unit of analysis.

Can the unit of analysis and the unit of observation be one?

Some situations have the same unit of analysis and observation. For instance, let's say a tutor is hired to improve the oral French proficiency of a student who finds it difficult. A few months later, the tutor wants to evaluate the student's proficiency based on what they have taught them for the time period. In this case, the student is both the unit of analysis and the unit of observation.

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  • Unit of Analysis: Definition, Types & Examples

Emmanuel

Introduction

A unit of analysis is the smallest level of analysis for a research project. It’s important to choose the right unit of analysis because it helps you make more accurate conclusions about your data.

What Is a Unit of Analysis?

A unit of analysis is the smallest element in a data set that can be used to identify and describe a phenomenon or the smallest unit that can be used to gather data about a subject. The unit of analysis will determine how you will define your variables, which are the things that you measure in your data. 

If you want to understand why people buy a particular product, you should choose a unit of analysis that focuses on buying behavior. This means choosing a unit of analysis that is relevant to your research topic and question .

For example, if you want to study the needs of soldiers in a war zone, you will need to choose an appropriate unit of analysis for this study: soldiers or the war zone. In this case, choosing the right unit of analysis would be important because it could help you decide if your research design is appropriate for this particular subject and situation.

Why is Choosing the Right Unit of Analysis Important?

The unit of analysis is important because it helps you understand what you are trying to find out about your subject, and it also helps you to make decisions about how to proceed with your research.

Choosing the right unit of analysis is also important because it determines what information you’re going to use in your research. If you have a small sample, then you’ll have to choose whether or not to focus on the entire population or just a subset of it. 

If you have a large sample, then you’ll be able to find out more about specific groups within your population. For example, if you want to understand why people buy certain types of products, then you should choose a unit of analysis that focuses on buying behavior. 

This means choosing a unit of analysis that is relevant to your research topic and question.

Unit of Analysis vs Unit of Observation

Unit of analysis is a term used to refer to a particular part of a data set that can be analyzed. For example, in the case of a survey, the unit of analysis is an individual: the person who was selected to take part in the survey. 

Unit of analysis is used in the social sciences to refer to the individuals or groups that have been studied. It can also be referred to as the unit of observation.

Unit of observation refers to a specific person or group in the study being observed by the researcher. An example would be a particular town, census tract, state, or other geographical location being studied by researchers conducting research on crime rates in that area.

Unit of analysis refers to the individual or group being studied by the researcher. An example would be an entire town being analyzed for crime rates over time.

Types of “Unit of Analysis”

The unit of analysis is a way to understand and study a phenomenon. There are four main types of unit of analysis: individuals, groups, artifacts (books, photos, newspapers), and geographical units (towns, census tracts, states).

  • Individuals are the smallest level of analysis. For example, an individual may be a person or an animal. A group can be composed of individuals or a collection of people who interact with each other. For example, an individual might go to college with other individuals or a family might live together as roommates. 
  • An artifact is anything that can be studied using empirical methods—including books and photos but also any physical object like knives or phones. 
  • A geographical unit is smaller than an entire country but larger than just one city block or neighborhood; it may be smaller than just two houses but larger than just two houses in the same street. 
  • Social interactions include dyadic relations (such as friendships or romantic relationships) and divorces among many other things such as arrests.

Examples of Each Type of Unit of Analysis

  • Individuals are the smallest unit of analysis. An individual is a person, animal, or thing. For example, an individual can be a person or a building.
  • Artifacts are the next largest units of analysis. An artifact is something produced by human beings and is not alive. For example, a child’s toy is an artifact. Artifacts can include any material object that was produced by human activity and which has meaning to someone. Artifacts can be tangible or intangible and may be produced intentionally or accidentally.
  • Geographical units are large geographic areas such as states, counties, provinces, etc. Geographical units may also refer to specific locations within these areas such as cities or townships. 
  • Social interaction refers to interactions between members of society (e.g., family members interacting with each other). Social interaction includes both formal interactions (such as attending school) and informal interactions (such as talking on the phone).

How Does a Social Scientist Choose a Unit of Analysis?

Social scientists choose a unit of analysis based on the purpose of their research, their research question, and the type of data they have. For example, if they are trying to understand the relationship between a person’s personality and their behavior, they would choose to study personality traits.

For example, if a researcher wanted to study the effects of legalizing marijuana on crime rates, they may choose to use administrative data from police departments. However, if they wanted to study how culture influences crime rates, they might use survey data from smaller groups of people who are further removed from the influence of culture (e.g., individuals living in different areas or countries).

Factors to Consider When Choosing a Unit of Analysis

The unit of analysis is the object or person that you are studying, and it determines what kind of data you are collecting and how you will analyze it.

Factors to consider when choosing a unit of analysis include:

  • What is your purpose for studying this topic? Is it for a research paper or an article? If so, which type of paper do you want to write?
  • What is the most appropriate unit for your study? If you are studying a specific event or period of time, this may be obvious. But if your focus is broader, such as all social sciences or all human development, then you need to determine how broad your scope should be before beginning any research process (see question one above) so that you know where to start in order for it to be effective (see question three below).
  • How do other people define their units? This can be helpful when trying to understand what other people mean when they use certain terms like “social science” or “human development” because they may define those terms differently than what you would expect them to.
  • The nature of the data collected. Is it quantitative or qualitative? If it’s qualitative, what kind of data is collected? How much time was spent observing each participant/examining their behavior?
  • The scale used to measure variables. Is every variable measured on a one-to-one scale (like measurements between people)? Or do some variables only take on discrete values (like yes/no questions)?

The unit of analysis is the smallest part of a data set that you analyze. It’s important to remember that your data is made up of more than just one unit—you have lots of different units in your dataset, and each of those units has its own characteristics that you need to think about when you’re trying to analyze it.

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The Unit of Analysis Explained

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  • By DiscoverPhDs
  • October 3, 2020

Unit of Analysis

The unit of analysis refers to the main parameter that you’re investigating in your research project or study. Example of the different types of unit analysis that may be used in a project include:

  • Individual people
  • Groups of people
  • Objects such as photographs, newspapers and books
  • Geographical unit based on parameters such as cities or counties
  • Social parameters such as births, deaths, divorces

The unit of analysis is named as such because the unit type is determined based on the actual data analysis that you perform in your project or study.

For example, if your research is based around data on exam grades for students at two different universities, then the unit of analysis is the data for the individual student due to each student having an exam score associated with them.

Conversely if your study is based on comparing noise level data between two different lecture halls full of students, then your unit of analysis here is the collective group of students in each hall rather than any data associated with an individual student.

In the same research study involving the same students, you may perform different types of analysis and this will be reflected by having different units of analysis. In the example of student exam scores, if you’re comparing individual exam grades then the unit of analysis is the individual student.

On the other hand, if you’re comparing the average exam grade between two universities, then the unit of analysis is now the group of students as you’re comparing the average of the group rather than individual exam grades.

These different levels of hierarchies of units of analysis can become complex with multiple levels. In fact, its complexity has led to a new field of statistical analysis that’s commonly known as hierarchical modelling.

As a researcher, you need to be clear on what your specific research questio n is. Based on this, you can define each data, observation or other variable and how they make up your dataset.

A clarity of your research question will help you identify your analysis units and the appropriate sample size needed to obtain a meaningful result (and is this a random sample/sampling unit or something else).

In developing your research method, you need to consider whether you’ll need any repeated observation of each measurement. You also need to consider whether you’re working with qualitative data/qualitative research or if this is quantitative content analysis.

The unit of analysis of your study is the specifically ‘who’ or what’ it is that your analysing – for example are you analysing the individual student, the group of students or even the whole university. You may have to consider a different unit of analysis based on the concept you’re considering, even if working with the same observation data set.

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Choosing the Right Unit of Analysis for Your Research Project

Table of content.

  • Understanding the Unit of Analysis in Research
  • Factors to Consider When Selecting the Right Unit of Analysis
  • Common Mistakes to Avoid

A research project is like setting out on a voyage through uncharted territory; the unit of analysis is your compass, guiding every decision from methodology to interpretation.

It’s the beating heart of your data collection and the lens through which you view your findings. With deep-seated experience in research methodologies , our expertise recognizes that choosing an appropriate unit of analysis not only anchors your study but illuminates paths towards meaningful conclusions.

The right choice empowers researchers to extract patterns, answer pivotal questions, and offer insights into complex phenomena. But tread carefully—selecting an ill-suited unit can distort results or obscure significant relationships within data.

Remember this: A well-chosen unit of analysis acts as a beacon for accuracy and relevance throughout your scholarly inquiry. Continue reading to unlock the strategies for selecting this cornerstone of research design with precision—your project’s success depends on it.

Engage with us as we delve deeper into this critical aspect of research mastery.

Key Takeaways

  • Your research questions and hypotheses drive the choice of your unit of analysis, shaping how you collect and interpret data.
  • Avoid common mistakes like reductionism , which oversimplifies complex issues, and the ecological fallacy , where group-level findings are wrongly applied to individuals.
  • Consider the availability and quality of data when selecting your unit of analysis to ensure your research is feasible and conclusions are valid.
  • Differentiate between units of analysis (what you’re analyzing) and units of observation (what or who you’re observing) for clarity in your study.
  • Ensure that your chosen unit aligns with both the theoretical framework and practical considerations such as time and resources.

The unit of analysis in research refers to the level at which data is collected and analyzed. It is essential for researchers to understand the different types of units of analysis, as well as their significance in shaping the research process and outcomes.

Definition and Importance

With resonio, the unit of analysis you choose lays the groundwork for your market research focus. Whether it’s individuals, organizations, or specific events, resonio’s platform facilitates targeted data collection and analysis to address your unique research questions. Our tool simplifies this selection process, ensuring that you can efficiently zero in on the most relevant unit for insightful and actionable results.

This crucial component serves as a navigational aid for your market research. The market research tool not only guides you in data collection but also in selecting the most effective sampling methods and approaches to hypothesis testing. Getting robust and reliable data, ensuring your research is both effective and straightforward.

Choosing the right unit of analysis is crucial, as it defines your research’s direction. resonio makes this easier, ensuring your choice aligns with your theoretical approach and data collection methods, thereby enhancing the validity and reliability of your results.

Additionally, resonio aids in steering clear of errors like reductionism and ecological fallacy, ensuring your conclusions match the data’s level of analysis

Difference between Unit of Analysis and Unit of Observation

Understanding the difference between the unit of analysis and observation is key. Let us clarify this distinction: the unit of analysis is what you’ll ultimately analyze, while the unit of observation is what you observe or measure during the study.

For example, in using resonio for educational research, individual test scores are the units of analysis, while the students providing these scores are the units of observation.

This distinction is essential as it clarifies the specific aspect under scrutiny and what will yield measurable data. It also emphasizes that researchers must carefully consider both elements to ensure their alignment with research questions and objectives .

Types of Units of Analysis: Individual, Aggregates, and Social

Choosing the right unit of analysis for a research project is critical. The types of units of analysis include individual, aggregates, and social.

  • Individual: This type focuses on analyzing the attributes and characteristics of individual units, such as people or specific objects.
  • Aggregates: Aggregates involve analyzing groups or collections of individual units, such as neighborhoods, organizations, or communities.
  • Social: Social units of analysis emphasize analyzing broader social entities, such as cultures, societies, or institutions.

When selecting the right unit of analysis for a research project, researchers must consider various factors such as their research questions and hypotheses , data availability and quality, feasibility and practicality, as well as the theoretical framework and research design .

Each of these factors plays a crucial role in determining the most appropriate unit of analysis for the study.

Research Questions and Hypotheses

The research questions and hypotheses play a crucial role in determining the appropriate unit of analysis for a research project. They guide the researcher in identifying what exactly needs to be studied and analyzed, thereby influencing the selection of the most relevant unit of analysis.

The alignment between the research questions/hypotheses and the unit of analysis is essential to ensure that the study’s focus meets its intended objectives. Furthermore, clear research questions and hypotheses help define specific parameters for data collection and analysis, directly impacting which unit of analysis will best serve the study’s purpose.

It’s important to carefully consider how each research question or hypothesis relates to different potential units of analysis , as this connection will shape not only what you are studying but also how you will study it .

Data Availability and Quality

When considering the unit of analysis for a research project, researchers must take into account the availability and quality of data. The chosen unit of analysis should align with the available data sources to ensure that meaningful and accurate conclusions can be drawn.

Researchers need to evaluate whether the necessary data at the chosen level of analysis is accessible and reliable. Ensuring high-quality data will contribute to the validity and reliability of the study , enabling researchers to make sound interpretations and draw robust conclusions from their findings.

Choosing a unit of analysis without considering data availability and quality may lead to limitations in conducting thorough analysis or drawing valid conclusions. It is crucial for researchers to assess both factors before finalizing their selection, as it directly impacts the feasibility, accuracy, and rigor of their research project.

Feasibility and Practicality

When considering the feasibility and practicality of a unit of analysis for a research project, it is essential to assess the availability and quality of data related to the chosen unit.

Researchers should also evaluate whether the selected unit aligns with their theoretical framework and research design. The practical aspects such as time, resources, and potential challenges associated with analyzing the chosen unit must be thoroughly considered before finalizing the decision.

Moreover, it is crucial to ensure that the selected unit of analysis is feasible within the scope of the research questions and hypotheses. Additionally, researchers need to determine if the chosen unit can be effectively studied based on existing literature and sampling techniques utilized in similar studies.

By carefully evaluating these factors, researchers can make informed decisions regarding which unit of analysis will best suit their research goals.

Theoretical Framework and Research Design

The theoretical framework and research design establish the structure for a study based on existing theories and concepts. It guides the selection of the unit of analysis by providing a foundation for understanding how variables interact and influence one another.

Theoretical frameworks help to shape research questions , hypotheses, and data collection methods, ensuring that the chosen unit of analysis aligns with the study’s objectives. Research design serves as a blueprint outlining the procedures and techniques used to gather and analyze data, allowing researchers to make informed decisions regarding their unit of analysis while considering feasibility, practicality, and data availability .

Researchers often make the mistake of reductionism, where they oversimplify complex phenomena by focusing on one aspect. Another common mistake is the ecological fallacy, where conclusions about individual behavior are made based on group-level data.

Reductionism

Reductionism occurs when a researcher oversimplifies a complex phenomenon by analyzing it at too basic a level. This can lead to the loss of important nuances and details critical for understanding the broader context.

For instance, studying individual test scores without considering external factors like teaching quality or student motivation is reductionist. By focusing solely on one aspect, researchers miss out on comprehensive insights that may impact their findings.

In research projects, reductionism limits the depth of analysis and may result in skewed conclusions that don’t accurately reflect the real-world complexities. It’s essential for researchers to avoid reductionism by carefully selecting an appropriate unit of analysis that allows for a holistic understanding of the phenomenon under study.

Ecological Fallacy

The ecological fallacy involves making conclusions about individuals based on group-level data . This occurs when researchers mistakenly assume that relationships observed at the aggregate level also apply to individuals within that group.

For example, if a study finds a correlation between high levels of education and income at the city level, it doesn’t mean the same relationship applies to every individual within that city.

This fallacy can lead to erroneous generalizations and inaccurate assumptions about individuals based on broader trends. It is crucial for researchers to be mindful of this potential pitfall when selecting their unit of analysis, ensuring that their findings accurately represent the specific characteristics and behaviors of the individuals or entities under investigation.

Selecting the appropriate unit of analysis is critical for a research project’s success, shaping its focus and scope. Researchers must carefully align the chosen unit with their study objectives to ensure relevance.

The impact on findings and conclusions from this choice cannot be understated. Correctly choosing the unit of analysis can considerably influence the direction and outcomes of a research undertaking.

Robert Koch

I write about AI, SEO, Tech, and Innovation. Led by curiosity, I stay ahead of AI advancements. I aim for clarity and understand the necessity of change, taking guidance from Shaw: 'Progress is impossible without change,' and living by Welch's words: 'Change before you have to'.

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One of the most important ideas in a research project is the unit of analysis . The unit of analysis is the major entity that you are analyzing in your study. For instance, any of the following could be a unit of analysis in a study:

  • individuals
  • artifacts (books, photos, newspapers)
  • geographical units (town, census tract, state)
  • social interactions (dyadic relations, divorces, arrests)

Why is it called the ‘unit of analysis’ and not something else (like, the unit of sampling)? Because it is the analysis you do in your study that determines what the unit is . For instance, if you are comparing the children in two classrooms on achievement test scores, the unit is the individual child because you have a score for each child. On the other hand, if you are comparing the two classes on classroom climate, your unit of analysis is the group, in this case the classroom, because you only have a classroom climate score for the class as a whole and not for each individual student. For different analyses in the same study you may have different units of analysis. If you decide to base an analysis on student scores, the individual is the unit. But you might decide to compare average classroom performance. In this case, since the data that goes into the analysis is the average itself (and not the individuals’ scores) the unit of analysis is actually the group. Even though you had data at the student level, you use aggregates in the analysis. In many areas of social research these hierarchies of analysis units have become particularly important and have spawned a whole area of statistical analysis sometimes referred to as hierarchical modeling . This is true in education, for instance, where we often compare classroom performance but collected achievement data at the individual student level.

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Measurement and Units of Analysis

24 Units of Analysis and Units of Observation

Another point to consider when designing a research project, and which might differ slightly in qualitative and quantitative studies, has to do with units of analysis and units of observation. These two items concern what you, the researcher, actually observe in the course of your data collection and what you hope to be able to say about those observations. Table 3.1 provides a summary of the differences between units of analysis and observation.

A unit of analysis is the entity that you wish to be able to say something about at the end of your study, probably what you would consider to be the main focus of your study. A unit of observation is the item (or items) that you actually observe, measure, or collect in the course of trying to learn something about your unit of analysis. In a given study, the unit of observation might be the same as the unit of analysis, but that is not always the case. Further, units of analysis are not required to be the same as units of observation. What is required, however, is for researchers to be clear about how they define their units of analysis and observation, both to themselves and to their audiences. More specifically, your unit of analysis will be determined by your research question. Your unit of observation, on the other hand, is determined largely by the method of data collection that you use to answer that research question.

To demonstrate these differences, let us look at the topic of students’ addictions to their cell phones. We will consider first how different kinds of research questions about this topic will yield different units of analysis. Then we will think about how those questions might be answered and with what kinds of data. This leads us to a variety of units of observation.

If I were to ask, “Which students are most likely to be addicted to their cell phones?” our unit of analysis would be the individual. We might mail a survey to students on a university or college campus, with the aim to classify individuals according to their membership in certain social classes and, in turn, to see how membership in those classes correlate with addiction to cell phones. For example, we might find that students studying media, males, and students with high socioeconomic status are all more likely than other students to become addicted to their cell phones. Alternatively, we could ask, “How do students’ cell phone addictions differ and how are they similar?  In this case, we could conduct observations of addicted students and record when, where, why, and how they use their cell phones.  In both cases, one using a survey and the other using observations, data are collected from individual students. Thus, the unit of observation in both examples is the individual. But the units of analysis differ in the two studies.  In the first one, our aim is to describe the characteristics of individuals.  We may then make generalisations about the populations to which these individuals belong, but our unit of analysis is still the individual.  In the second study, we will observe individuals in order to describe some social phenomenon, in this case, types of cell phone addictions. Consequently, our unit of analysis would be the social phenomenon.

Another common unit of analysis in sociological inquiry is groups. Groups of course vary in size, and almost no group is too small or too large to be of interest to sociologists. Families, friendship groups, and street gangs make up some of the more common microlevel groups examined by sociologists. Employees in an organization, professionals in a particular domain (e.g., chefs, lawyers, sociologists), and members of clubs (e.g., Girl Guides, Rotary, Red Hat Society) are all meso-level groups that sociologists might study. Finally, at the macro level, sociologists sometimes examine citizens of entire nations or residents of different continents or other regions.

A study of student addictions to their cell phones at the group level might consider whether certain types of social clubs have more or fewer cell phone-addicted members than other sorts of clubs. Perhaps we would find that clubs that emphasize physical fitness, such as the rugby club and the scuba club, have fewer cell phone-addicted members than clubs that emphasize cerebral activity, such as the chess club and the sociology club. Our unit of analysis in this example is groups. If we had instead asked whether people who join cerebral clubs are more likely to be cell phone-addicted than those who join social clubs, then our unit of analysis would have been individuals. In either case, however, our unit of observation would be individuals.

Organizations are yet another potential unit of analysis that social scientists might wish to say something about. Organizations include entities like corporations, colleges and universities, and even night clubs. At the organization level, a study of students’ cell phone addictions might ask, “How do different colleges address the problem of cell phone addiction?” In this case, our interest lies not in the experience of individual students but instead in the campus-to-campus differences in confronting cell phone addictions. A researcher conducting a study of this type might examine schools’ written policies and procedures, so his unit of observation would be documents. However, because he ultimately wishes to describe differences across campuses, the college would be his unit of analysis.

Social phenomena are also a potential unit of analysis. Many sociologists study a variety of social interactions and social problems that fall under this category. Examples include social problems like murder or rape; interactions such as counselling sessions, Facebook chatting, or wrestling; and other social phenomena such as voting and even cell phone use or misuse. A researcher interested in students’ cell phone addictions could ask, “What are the various types of cell phone addictions that exist among students?” Perhaps the researcher will discover that some addictions are primarily centred around social media such as chat rooms, Facebook, or texting while other addictions centre single-player games that discourage interaction with others. The resultant typology of cell phone addictions would tell us something about the social phenomenon (unit of analysis) being studied. As in several of the preceding examples, however, the unit of observation would likely be individual people.

Finally, a number of social scientists examine policies and principles, the last type of unit of analysis we will consider here. Studies that analyse policies and principles typically rely on documents as the unit of observation. Perhaps a researcher has been hired by a college to help it write an effective policy against cell phone use in the classroom. In this case, the researcher might gather all previously written policies from campuses all over the country and compare policies at campuses where the use of cell phones in classroom is low to policies at campuses where the use of cell phones in the classroom is high.

In sum, there are many potential units of analysis that a sociologist might examine, but some of the most common units include the following:

  • Individuals
  • Organizations
  • Social phenomena
  • Policies and principles
Table 4.1 Units of analysis and units of observation: A hypothetical study of students’ addictions to cell phones
Which students are most likely to be addicted to their cell phones? Individuals Survey of students on campus Individuals Media majors, men, and students with high socioeconomic status are all more likely than other students to become addicted to their cell phones.
Do certain types of social clubs have more cell phone -addicted members than other sorts of clubs? Groups Survey of students on campus Individuals Clubs with a scholarly focus have more cell phone-addicted members than more socially focused clubs.
How do different colleges address the problem of addiction to cell phones? Organizations Content analysis of policies Documents Campuses without policies prohibiting cell phone use in the classroom have high levels of cell phone addiction.
What are the various types of cell phone addictions that exist among students? Social phenomena Observations of students Individuals There are two main types of cell phone addictions: social and antisocial
What are the most effective policies against cell phone addiction? Policies and principles Content analysis of policies and student records Documents Policies that require students with cell phone addictions to attend group counselling for a minimum of one semester have been found to treat addictions more effectively than those that call for expulsion of addicted students.

Text Attributions

  • This chapter has been adapted from Chapter 5.2 in Principles of Sociological Inquiry , which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor. © Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License .

An Introduction to Research Methods in Sociology Copyright © 2019 by Valerie A. Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Design Review

A discussion of qualitative & quantitative research design, qualitative data analysis: the unit of analysis.

what is unit of analysis in research

As discussed in two earlier articles in Research Design Review (see “The Important Role of ‘Buckets’ in Qualitative Data Analysis” and “Finding Connections & Making Sense of Qualitative Data” ), the selection of the unit of analysis is one of the first steps in the qualitative data analysis process. The “unit of analysis” refers to the portion of content that will be the basis for decisions made during the development of codes. For example, in textual content analyses, the unit of analysis may be at the level of a word, a sentence (Milne & Adler, 1999), a paragraph, an article or chapter, an entire edition or volume, a complete response to an interview question, entire diaries from research participants, or some other level of text. The unit of analysis may not be defined by the content per se but rather by a characteristic of the content originator (e.g., person’s age), or the unit of analysis might be at the individual level with, for example, each participant in an in-depth interview (IDI) study treated as a case. Whatever the unit of analysis, the researcher will make coding decisions based on various elements of the content, including length, complexity, manifest meanings, and latent meanings based on such nebulous variables as the person’s tone or manner.

Deciding on the unit of analysis is a very important decision because it guides the development of codes as well as the coding process. If a weak unit of analysis is chosen, one of two outcomes may result: 1) If the unit chosen is too precise (i.e., at too much of a micro-level than what is actually needed), the researcher will set in motion an analysis that may miss important contextual information and may require more time and cost than if a broader unit of analysis had been chosen. An example of a too-precise unit of analysis might be small elements of content such as individual words. 2) If the unit chosen is too imprecise (i.e., at a very high macro-level), important connections and contextual meanings in the content at smaller (individual) units may be missed, leading to erroneous categorization and interpretation of the data. An example of a too-imprecise unit of analysis might be the entire set of diaries written by 25 participants in an IDI research study, or all the comments made by teenagers on an online support forum. Keep in mind, however, that what is deemed too precise or imprecise will vary across qualitative studies, making it difficult to prescribe the “right” solution for all situations.

Although there is no perfect prescription for every study, it is generally understood that researchers should strive for a unit of analysis that retains the context necessary to derive meaning from the data. For this reason, and if all other things are equal, the qualitative researcher should probably err on the side of using a broader, more contextually based unit of analysis rather than a narrowly focused level of analysis (e.g., sentences). This does not mean that supra-macro-level units, such as the entire set of transcripts from an IDI study, are appropriate; and, to the contrary, these very imprecise units, which will obscure meanings and nuances at the individual level, should be avoided. It does mean, however, that units of analysis defined as the entirety of a research interview or focus group discussion are more likely to provide the researcher with contextual entities by which reasonable and valid meanings can be obtained and analyzed across all cases.

In the end, the researcher needs to consider the particular circumstances of the study and define the unit of analysis keeping in mind that broad, contextually rich units of analysis — maintained throughout coding, category and theme development, and interpretation — are crucial to deriving meaning in qualitative data and ensuring the integrity of research outcomes.

Milne, M. J., & Adler, R. W. (1999). Exploring the reliability of social and environmental disclosures content analysis. Accounting, Auditing & Accountability Journal , 12 (2), 237–256.

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Science-Education-Research

Prof. Keith S. Taber's site

Unit of analysis

A topic in research methodology

Unit of analysis is a term used in experimental research, and refers to how the data will be conceptualised and grouped during analysis. For example, if research investigates school learning, the unit of analysis might be the lesson, or the learner, or the teacher, or the curriculum subject? This will depend upon the research question being investigated, and should be established at the outset of the research  before data collection commences.

"An important term used in discussing experimental research is ' unit of analysis '. An experiment may, for example, be comparing outcomes between different learners, different classes, different year groups, or different schools… It is important at the outset of an experimental study to clarify what the unit of analysis is, and this should be explicit in research reports so that readers are aware what is being compared" ( Taber, 2019 , p.72)

"The unit of analysis refers to the types of 'things' that will be characterised and perhaps compared in a study. In educational research the unit of analysis could be a student, a lesson, a class, a teacher, a school, a group within a class, a question asked, an explanation given, a conversational exchange, a test script, a scheme of work, a lesson plan, etc. That is, we might characterise and compare different students; we might characterise and compare different lessons; we might characterise and compare different classes, etc.

  • So, in a study looking at teacher beliefs about pedagogy , the unit of analysis is likely to be the teacher .
  • In a study of the relationship between school ethos and exclusion rates, the unit of analysis is likely to be the school .
  • In a study of student understanding of creation myths in different cultures, the unit of analysis is likely to be the student .
  • In a study on the effect of gender on school science group work, the unit of analysis is likely to be the group (although a group does not have a gender, and so the gender composition of the group will need to be seen as the 'independent' (or input) variable" ( Taber, 2013 , p.254).

Units of analysis in experiments

In experimental research, in a 'true' experiment , the units of analysis must be randomly assigned to conditions:

what is unit of analysis in research

So, for example, if a researcher has to compare two existing classes, then the unit if analysis should be the class, not the individual learners (which has consequences for the ability to use statistics to test for statistically different outcomes). "If the units of analysis are schools, it may be difficult to enrol a large enough number of schools into the sample for the statistical methods to be used – especially in those national contexts that rely on schools responding to invitations to volunteer (this is less of a problem when research access is granted at regional/district or state level)" ( Taber, 2019 , p.74).

"So one might consider 50 students who were to be part of a study where it was intended to use individual student test results as a measure of learning to explore whether some teaching approach brought about greater learning than some other teaching approach. If it is possible to randomly assign the 50 students into two groups of 25, then there are 25 ' units of analysis ' [n=25] in each group. However, if the researchers are required to work with existing classes then the most randomisation that is possible is to assign whole classes to the two conditions. This would mean the units of analysis were whole classes (one in each condition). To consider this a true experiment (meeting the requirement of randomisation , see Figure) there would need to be one measure of learning from each class, but it would be difficult to use statistics to infer anything useful when comparing just two values" ( Taber, 2019 , p.84)

"A random control trial ( R.C.T. ) is an experiment where the units of analysis are randomly assigned to different conditions, and statistical methods are used to determine whether any overall difference in the measured outcomes in those conditions is (probably) due to the intervention….A R.C.T. is referred to as a ' true experiment ' because there is randomisation of the ' units of analysis ' (people, classes, schools, etc.) to conditions" ( Taber, 2019 , p.73).

Sources cited:

Taber, K. S. (2013).  Classroom-based Research and Evidence-based Practice: An introduction (2nd ed.).  London: Sage.

  • Taber, K. S. (2019). Experimental research into teaching innovations: responding to methodological and ethical challenges . Studies in Science Education . doi:10.1080/03057267.2019.1658058

what is unit of analysis in research

My introduction to educational research:

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Data & statistics for journalists: unit of analysis.

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The  unit of analysis  is the entity that you're analyzing. It's called this because it's your analysis (what you want to examine) that determines what this unit is, rather than the data itself.

For instance, let's say that you have a dataset with 40 students, divided between two classrooms of 20 students each, and a test score for each student. You can analyze this data in several ways:

  • Individual unit of analysis: Compare the test scores of each student to the other students. (You're analyzing students, individuals.)
  • Group unit of analysis:  Compare the average test score of the two classrooms. (You're analyzing the classrooms, comparing two groups of individuals.)

Knowing your unit of analysis is helpful, because it helps you determine what kind of data you need. The other piece of this puzzle is whether you need  macrodata  (aggregated data) or  microdata.

Microdata & Macrodata

So what is the difference between  macrodata  (aggregated data) and  microdata ?

  • MICRODATA Contains a record for every individual (e.g., person, company, etc.) in the survey/study. Source for US Census microdata:  IPUMS
  • MACRODATA  (Aggregated Data) Higher-level data compiled from smaller (individual) units of data. For example, Census data in Social Explorer  has been aggregated to preserve the confidentiality of individual respondents. Source for US Census macrodata: Social Explorer  
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7.3 Unit of analysis and unit of observation

Learning objectives.

  • Define units of analysis and units of observation, and describe the two common errors people make when they confuse the two

Another point to consider when designing a research project, and which might differ slightly in qualitative and quantitative studies, has to do with units of analysis and units of observation. These two items concern what you, the researcher, actually observe in the course of your data collection and what you hope to be able to say about those observations. A unit of analysis is the entity that you wish to be able to say something about at the end of your study, probably what you’d consider to be the main focus of your study. A unit of observation is the item (or items) that you actually observe, measure, or collect in the course of trying to learn something about your unit of analysis.

In a given study, the unit of observation might be the same as the unit of analysis, but that is not always the case. For example, a study on electronic gadget addiction may interview undergraduate students (our unit of observation) for the purpose of saying something about undergraduate students (our unit of analysis) and their gadget addiction. Perhaps, if we were investigating gadget addiction in elementary school children (our unit of analysis), we might collect observations from teachers and parents (our units of observation) because younger children may not report their behavior accurately. In this case and many others, units of analysis are not the same as units of observation. What is required, however, is for researchers to be clear about how they define their units of analysis and observation, both to themselves and to their audiences.

young boy peering through binoculars in a desert

More specifically, your unit of analysis will be determined by your research question. Your unit of observation, on the other hand, is determined largely by the method of data collection that you use to answer that research question. We’ll take a closer look at methods of data collection later on in the textbook. For now, let’s consider again a study addressing students’ addictions to electronic gadgets. We’ll consider first how different kinds of research questions about this topic will yield different units of analysis. Then, we’ll think about how those questions might be answered and with what kinds of data. This leads us to a variety of units of observation.

If we were to explore which students are most likely to be addicted to their electronic gadgets, our unit of analysis would be individual students. We might mail a survey to students on campus, and our aim would be to classify individuals according to their membership in certain social groups in order to see how membership in those classes correlated with gadget addiction. For example, we might find that majors in new media, men, and students with high socioeconomic status are all more likely than other students to become addicted to their electronic gadgets. Another possibility would be to explore how students’ gadget addictions differ and how are they similar. In this case, we could conduct observations of addicted students and record when, where, why, and how they use their gadgets. In both cases, one using a survey and the other using observations, data are collected from individual students. Thus, the unit of observation in both examples is the individual.

Another common unit of analysis in social science inquiry is groups. Groups of course vary in size, and almost no group is too small or too large to be of interest to social scientists. Families, friendship groups, and group therapy participants are some common examples of micro-level groups examined by social scientists. Employees in an organization, professionals in a particular domain (e.g., chefs, lawyers, social workers), and members of clubs (e.g., Girl Scouts, Rotary, Red Hat Society) are all meso-level groups that social scientists might study. Finally, at the macro-level, social scientists sometimes examine citizens of entire nations or residents of different continents or other regions.

A study of student addictions to their electronic gadgets at the group level might consider whether certain types of social clubs have more or fewer gadget-addicted members than other sorts of clubs. Perhaps we would find that clubs that emphasize physical fitness, such as the rugby club and the scuba club, have fewer gadget-addicted members than clubs that emphasize cerebral activity, such as the chess club and the women’s studies club. Our unit of analysis in this example is groups because groups are what we hope to say something about. If we had instead asked whether individuals who join cerebral clubs are more likely to be gadget-addicted than those who join social clubs, then our unit of analysis would have been individuals. In either case, however, our unit of observation would be individuals.

Organizations are yet another potential unit of analysis that social scientists might wish to say something about. Organizations include entities like corporations, colleges and universities, and even nightclubs. At the organization level, a study of students’ electronic gadget addictions might explore how different colleges address the problem of electronic gadget addiction. In this case, our interest lies not in the experience of individual students but instead in the campus-to-campus differences in confronting gadget addictions. A researcher conducting a study of this type might examine schools’ written policies and procedures, so her unit of observation would be documents. However, because she ultimately wishes to describe differences across campuses, the college would be her unit of analysis.

In sum, there are many potential units of analysis that a social worker might examine, but some of the most common units include the following:

  • Individuals
  • Organizations
Table 7.1 Units of analysis and units of observation: An example using a hypothetical study of students’ addictions to electronic gadgets
Which students are most likely to be addicted to their electronic gadgets? Individuals Survey of students on campus Individuals New Media majors, men, and students with high socioeconomic status are all more likely than other students to become addicted to their electronic gadgets.
Do certain types of social clubs have more gadget-addicted members than other sorts of clubs? Groups Survey of students on campus Individuals Clubs with a scholarly focus, such as social work club and the math club, have more gadget-addicted members than clubs with a social focus, such as the 100-bottles-of- beer-on-the-wall club and the knitting club.
How do different colleges address the problem of electronic gadget addiction? Organizations Content analysis of policies Documents Campuses without strong computer science programs are more likely than those with such programs to expel students who have been found to have addictions to their electronic gadgets.
Note Please remember that the findings described here are hypothetical. There is no reason to think that any of the hypothetical findings described here would actually bear out if tested with empirical research.

One common error people make when it comes to both causality and units of analysis is something called the ecological fallacy . This occurs when claims about one lower-level unit of analysis are made based on data from some higher-level unit of analysis. In many cases, this occurs when claims are made about individuals, but only group-level data have been gathered. For example, we might want to understand whether electronic gadget addictions are more common on certain campuses than on others. Perhaps different campuses around the country have provided us with their campus percentage of gadget-addicted students, and we learn from these data that electronic gadget addictions are more common on campuses that have business programs than on campuses without them. We then conclude that business students are more likely than non-business students to become addicted to their electronic gadgets. However, this would be an inappropriate conclusion to draw. Because we only have addiction rates by campus, we can only draw conclusions about campuses, not about the individual students on those campuses. Perhaps the social work majors on the business campuses are the ones that caused the addiction rates on those campuses to be so high. The point is we simply don’t know because we only have campus-level data. By drawing conclusions about students when our data are about campuses, we run the risk of committing the ecological fallacy.

On the other hand, another mistake to be aware of is reductionism. Reductionism occurs when claims about some higher-level unit of analysis are made based on data from some lower-level unit of analysis. In this case, claims about groups or macro-level phenomena are made based on individual-level data. An example of reductionism can be seen in some descriptions of the civil rights movement. On occasion, people have proclaimed that Rosa Parks started the civil rights movement in the United States by refusing to give up her seat to a white person while on a city bus in Montgomery, Alabama, in December 1955. Although it is true that Parks played an invaluable role in the movement, and that her act of civil disobedience gave others courage to stand up against racist policies, beliefs, and actions, to credit Parks with starting the movement is reductionist. Surely the confluence of many factors, from fights over legalized racial segregation to the Supreme Court’s historic decision to desegregate schools in 1954 to the creation of groups such as the Student Nonviolent Coordinating Committee (to name just a few), contributed to the rise and success of the American civil rights movement. In other words, the movement is attributable to many factors—some social, others political and others economic. Did Parks play a role? Of course she did—and a very important one at that. But did she cause the movement? To say yes would be reductionist.

It would be a mistake to conclude from the preceding discussion that researchers should avoid making any claims whatsoever about data or about relationships between levels of analysis. While it is important to be attentive to the possibility for error in causal reasoning about different levels of analysis, this warning should not prevent you from drawing well-reasoned analytic conclusions from your data. The point is to be cautious and conscientious in making conclusions between levels of analysis. Errors in analysis come from a lack of rigor and deviating from the scientific method.

Key Takeaways

  • A unit of analysis is the item you wish to be able to say something about at the end of your study while a unit of observation is the item that you actually observe.
  • When researchers confuse their units of analysis and observation, they may be prone to committing either the ecological fallacy or reductionism.
  • Ecological fallacy- claims about one lower-level unit of analysis are made based on data from some higher-level unit of analysis
  • Reductionism- when claims about some higher-level unit of analysis are made based on data at some lower-level unit of analysis
  • Unit of analysis- entity that a researcher wants to say something about at the end of her study
  • Unit of observation- the item that a researcher actually observes, measures, or collects in the course of trying to learn something about her unit of analysis

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Scientific Inquiry in Social Work Copyright © 2018 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is a unit of analysis?

The unit of analysis is an important concept whether you are conducting quantitative or qualitative research. It is related to another concept – the unit of observation. Though both are often used interchangeably (and can actually mean the same thing in some studies) they are not exactly the same conceptually.

This paper takes a closer look at what a unit of analysis is.

Unit of analysis explained

A unit of analysis is the main subject or entity whom the researcher intends to comment on in the study. It is mainly determined by the research question. Simply put, the unit of analysis is basically the ‘who’ or what’ that the researcher is interested in analyzing. For instance, an individual a group, organization, country, social phenomenon, etc. 

Unit of observation explained

A unit of observation is any item from which data can be collected and measured. The unit of observation determines the data collection and measurement techniques to be used. Just like a unit of analysis, an individual, group, country, social phenomenon, etc can also be a unit of observation.

The examples below highlight the way varying research questions can bring about varying units of analysis. They will also examine how different units of observation can arise due to the types of data used to find answers to the research questions.

Consider the question “Which nation has the brightest chance of winning the forthcoming senior world cup.” Here, the unit of analysis is a country. To answer this question may require sampling the opinions of some soccer aficionados or experts. Hence, a survey can be conducted to aggregate expert views (e.g., coaches, players, analysts, reporters, administrators, etc) all over the world.

The objectives of the survey can include finding out if variables like continent of origin, venue of the tournament, climatic conditions, quality of players, level of preparation, and administrative efficiency play any role in the emergence of the champion. The survey’s findings may indicate that the quality of players, level of preparation, and the efficiency of a country’s soccer administrators are the most important determinants for winning the trophy. 

Suppose an alternative question is asked, say “what are the differences and similarities in the ways countries prepare for the senior world cup.” One way to answer this question (assuming it is a world cup season) is to closely observe the preparation programmes of participating countries, including camping and physical training activities.  

It can be deduced from the above examples that the unit of analysis is different in each case. In the first question, the country is the unit of analysis while in the second, a social phenomenon – preparation programme is the unit of analysis. In both examples, the unit of observation is the same – countries.

As noted in the definitions above, groups can also constitute a unit of analysis. In the question about which country is likely to win the senior world cup, for example, a group survey of a couple of soccer clubs can also be used to elicit responses. In this case, the unit of analysis is a group [say a professional football club].

For organizations, take the senior world cup example mentioned above as an example. Suppose a researcher poses the question “Are the levels of funding provided by soccer associations enough for them to challenge for the world cup?” Note that the main concern here is on soccer administrators and not on the teams of players. To determine the adequacy or otherwise of national teams’ funding, the researcher might need to source for and study various forms of documents. This means that documents are the unit of observation in this scenario. If he decides to make country-by-country comparisons on national team funding, then his unit of analysis will be the countries investigated.

Rules, policies, and principles are yet another form of units of analysis. Policy research, for example, will most likely involve analyzing several documents. Consider a soccer association that employs a lawyer to help draft a code of conduct for players [unit of analysis] preparing for the world cup in a closed camp. To come up with an acceptable code of conduct, the lawyer may decide to study all past code of conduct documents [unit of observation] of the association and maybe how the rules in the code have been observed and otherwise as well as the kind of penalties for the various violations of camp rules.

Unit of analysis and unit of observation as one

It has been suggested above that both concepts can be one and the same in some situations. For instance, a tutor can be hired to improve the oral or spoken English proficiency of a student struggling in that area. After a couple of months, the tutor decides to assess and evaluate the proficiency levels of his or her student based on what has been taught thus far. In this example, the student is both a unit of analysis as well as a unit of observation.

As noted from the discussion above, both the unit of analysis and the unit of observation are research concepts. These units can be individuals, groups, countries, organizations, social phenomena, etc. Though both concepts can be the same in some studies, differences also exist between them in other studies. Because of this confusing tendency, it is necessary that the researcher is as clear as possible when explaining the similarities or differences between both concepts.

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4.3 Unit of analysis and unit of observation

Learning objectives.

  • Define units of analysis and units of observation, and describe the two common errors people make when they confuse the two

Another point to consider when designing a research project, and which might differ slightly in qualitative and quantitative studies, has to do with units of analysis and units of observation. These two items concern what you, the researcher, actually observe in the course of your data collection and what you hope to be able to say about those observations. A unit of analysis is the entity that you wish to be able to say something about at the end of your study, probably what you’d consider to be the main focus of your study. A unit of observation is the item (or items) that you actually observe, measure, or collect in the course of trying to learn something about your unit of analysis.

In a given study, the unit of observation might be the same as the unit of analysis, but that is not always the case. For example, a study on electronic gadget addiction may interview undergraduate students (our unit of observation) for the purpose of saying something about undergraduate students (our unit of analysis) and their gadget addiction. Perhaps, if we were investigating gadget addiction in elementary school children (our unit of analysis), we might collect observations from teachers and parents (our units of observation) because younger children may not report their behavior accurately. In this case and many others, units of analysis are not the same as units of observation. What is required, however, is for researchers to be clear about how they define their units of analysis and observation, both to themselves and to their audiences.

what is unit of analysis in research

More specifically, your unit of analysis will be determined by your research question. Your unit of observation, on the other hand, is determined largely by the method of data collection that you use to answer that research question. We’ll take a closer look at methods of data collection later on in the textbook. For now, let’s consider again a study addressing students’ addictions to electronic gadgets. We’ll consider first how different kinds of research questions about this topic will yield different units of analysis. Then, we’ll think about how those questions might be answered and with what kinds of data. This leads us to a variety of units of observation.

If we were to explore which students are most likely to be addicted to their electronic gadgets, our unit of analysis would be individual students. We might mail a survey to students on campus, and our aim would be to classify individuals according to their membership in certain social groups in order to see how membership in those classes correlated with gadget addiction. For example, we might find that majors in new media, men, and students with high socioeconomic status are all more likely than other students to become addicted to their electronic gadgets. Another possibility would be to explore how students’ gadget addictions differ and how are they similar. In this case, we could conduct observations of addicted students and record when, where, why, and how they use their gadgets. In both cases, one using a survey and the other using observations, data are collected from individual students. Thus, the unit of observation in both examples is the individual.

Another common unit of analysis in social science inquiry is groups. Groups of course vary in size, and almost no group is too small or too large to be of interest to social scientists. Families, friendship groups, and group therapy participants are some common examples of micro-level groups examined by social scientists. Employees in an organization, professionals in a particular domain (e.g., chefs, lawyers, social workers), and members of clubs (e.g., Girl Scouts, Rotary, Red Hat Society) are all meso-level groups that social scientists might study. Finally, at the macro-level, social scientists sometimes examine policies, citizens of entire nations, or residents of different continents or other regions.

A study of student addictions to their electronic gadgets at the group level might consider whether certain types of social clubs have more or fewer gadget-addicted members than other sorts of clubs. Perhaps we would find that clubs that emphasize physical fitness, such as the rugby club and the scuba club, have fewer gadget-addicted members than clubs that emphasize cerebral activity, such as the chess club and the women’s studies club. Our unit of analysis in this example is groups because groups are what we hope to say something about. If we had instead asked whether individuals who join cerebral clubs are more likely to be gadget-addicted than those who join social clubs, then our unit of analysis would have been individuals. In either case, however, our unit of observation would be individuals.

Organizations are yet another potential unit of analysis that social scientists might wish to say something about. Organizations include entities like corporations, colleges and universities, and even nightclubs. At the organization level, a study of students’ electronic gadget addictions might explore how different colleges address the problem of electronic gadget addiction. In this case, our interest lies not in the experience of individual students but instead in the campus-to-campus differences in confronting gadget addictions. A researcher conducting a study of this type might examine schools’ written policies and procedures, so her unit of observation would be documents. However, because she ultimately wishes to describe differences across campuses, the college would be her unit of analysis.

In sum, there are many potential units of analysis that a social worker might examine, but some of the most common units include the following: i ndividuals, g roups, and o rganizations.

Table 4.1 Units of analysis and units of observation: An example using a hypothetical study of students’ addictions to electronic gadgets
Which students are most likely to be addicted to their electronic gadgets? Individuals Survey of students on campus Individuals New Media majors, men, and students with high socioeconomic status are all more likely than other students to become addicted to their electronic gadgets.
Do certain types of social clubs have more gadget-addicted members than other sorts of clubs? Groups Survey of students on campus Individuals Clubs with a scholarly focus, such as social work club and the math club, have more gadget-addicted members than clubs with a social focus, such as the 100-bottles-of- beer-on-the-wall club and the knitting club.
How do different colleges address the problem of electronic gadget addiction? Organizations Content analysis of policies Documents Campuses without strong computer science programs are more likely than those with such programs to expel students who have been found to have addictions to their electronic gadgets.
Note Please remember that the findings described here are hypothetical. There is no reason to think that any of the hypothetical findings described here would actually bear out if tested with empirical research.

One common error people make when it comes to both causality and units of analysis is something called the ecological fallacy . This occurs when claims about one lower-level unit of analysis are made based on data from some higher-level unit of analysis. In many cases, this occurs when claims are made about individuals, but only group-level data have been gathered. For example, we might want to understand whether electronic gadget addictions are more common on certain campuses than on others. Perhaps different campuses around the country have provided us with their campus percentage of gadget-addicted students, and we learn from these data that electronic gadget addictions are more common on campuses that have business programs than on campuses without them. We then conclude that business students are more likely than non-business students to become addicted to their electronic gadgets. However, this would be an inappropriate conclusion to draw. Because we only have addiction rates by campus, we can only draw conclusions about campuses, not about the individual students on those campuses. Perhaps the social work majors on the business campuses are the ones that caused the addiction rates on those campuses to be so high. The point is we simply don’t know because we only have campus-level data. By drawing conclusions about students when our data are about campuses, we run the risk of committing the ecological fallacy.

On the other hand, another mistake to be aware of is reductionism. Reductionism occurs when claims about some higher-level unit of analysis are made based on data from some lower-level unit of analysis. In this case, claims about groups or macro-level phenomena are made based on individual-level data. An example of reductionism can be seen in some descriptions of the civil rights movement. On occasion, people have proclaimed that Rosa Parks started the civil rights movement in the United States by refusing to give up her seat to a white person while on a city bus in Montgomery, Alabama, in December 1955. Although it is true that Parks played an invaluable role in the movement, and that her act of civil disobedience gave others courage to stand up against racist policies, beliefs, and actions, to credit Parks with starting the movement is reductionist. Surely the confluence of many factors, from fights over legalized racial segregation to the Supreme Court’s historic decision to desegregate schools in 1954 to the creation of groups such as the Student Nonviolent Coordinating Committee (to name just a few), contributed to the rise and success of the American civil rights movement. In other words, the movement is attributable to many factors—some social, others political and others economic. Did Parks play a role? Of course she did—and a very important one at that. But did she cause the movement? To say yes would be reductionist.

It would be a mistake to conclude from the preceding discussion that researchers should avoid making any claims whatsoever about data or about relationships between levels of analysis. While it is important to be attentive to the possibility for error in causal reasoning about different levels of analysis, this warning should not prevent you from drawing well-reasoned analytic conclusions from your data. The point is to be cautious and conscientious in making conclusions between levels of analysis. Errors in analysis come from a lack of rigor and deviating from the scientific method.

Key Takeaways

  • A unit of analysis is the item you wish to be able to say something about at the end of your study while a unit of observation is the item that you actually observe.
  • When researchers confuse their units of analysis and observation, they may be prone to committing either the ecological fallacy or reductionism.
  • Ecological fallacy- claims about one lower-level unit of analysis are made based on data from some higher-level unit of analysis
  • Reductionism- when claims about some higher-level unit of analysis are made based on data at some lower-level unit of analysis
  • Unit of analysis- entity that a researcher wants to say something about at the end of her study
  • Unit of observation- the item that a researcher actually observes, measures, or collects in the course of trying to learn something about her unit of analysis

Image attributions

Binoculars by nightowl CC-0

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is unit of analysis and why is it important for qualitative dissertations

A ll researchers must deal with questions related to their unit of analysis and the related idea of unit of observation. Unit of analysis helps the researcher define what is being studied as well as what aspects are being studied. For dissertations, the importance of this concept is that it provides guardrails to know what is in the scope of your dissertation and what is outside the bounds of what you are examining. More specifically, the unit of analysis describes the level at which you are conducting your study. Are you researching states, universities, schools/colleges, departments, presidents, deans, professors, or students just to name a few levels. If you determine that you are studying universities, this leads to a different focus than if you are studying departments. In this post, I will describe unit of analysis and why it is important for qualitative dissertations.

what is unit of analysis in research

One of the best ways to help identify your unit of analysis is to think about what you want to make recommendations about when you finish your dissertation.

Do you want to recommend new policies at a university level? Then, your unit of analysis is likely organization. If you want to recommend faculty in the natural dbol australia sciences try new teaching approaches, then your unit of analysis is likely a group (a group of science faculty in this example).

Typical units of analysis include individual, group, organizations, social phenomena, and policies/values/principles.  

Doctoral students as novice researchers struggle with defining the unit of analysis for their qualitative research. Often, the challenge is confusion regarding what is their unit of analysis versus their unit of observation.

A unit of observation is the level at which you are collecting data. For example, you might collect data from individuals (such as through interviews), groups (such as observing a class), or documents.

For qualitative researchers, the difference between units of analysis and observation can be particularly fuzzy because they could be the same or different. For instance, you could study student preparation for final exams by interviewing students in which the unit of analysis is individual and the unit of observation is individual.

In this case, both are individual.   On the other hand, you could study student preparation for final exams by interviewing students in which the unit of analysis is the department and the unit of observation is still individual.

In the second study, your goal might be to compare how different departments prepare so your analysis will focus on how the departments are similar or different.  

The reason that unit of analysis and unit of observation are important is that they provide important boundaries for your study particularly your data analysis.

In qualitative research, researchers can struggle to identify what is germane and what is not. By clearly identifying the level and what you are studying, you will be better able to stay focused on what is most important for your dissertation.  

As you define your specific research questions and refine your interests related to your dissertation topic, you will likely narrow your focus. As part of this process, you may find that you thought you were interested at one unit of analysis but now are focusing more on another.

Changing your unit of analysis at this stage of the process is not a problem. Indeed, the unit of analysis can be revisited as much as necessary. In fact, you can change your unit of analysis even at later stages of the process although this is not something I recommend to doctoral students.

The later you are in the dissertation, you will have made some research design and data collection decisions based on your unit of analysis. While you can change and in some circumstances this may well be necessary, but I always suggest discussing this with your dissertation chair first. A change may require you to change your design or collect additional data which can obviously slow your dissertation process.  

A final point that can be useful for dissertation students is to pick a unit of analysis that is commonly used when studying your topic.

As part of your conclusions and recommendations, you will want to draw comparisons between your dissertation and other research on your topic. If your unit of analysis is substantively much different, making these comparisons and injectable steroids otherwise drawing information from the research literature can be unnecessarily complicated.

Moreover, as a new researcher, unit of analysis is probably not the best area to try to be different or unique. Sticking with a commonly used unit of analysis can provide helpful clarity throughout the process and quite simply make the entire dissertation process easier.  

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regreSSHion: Remote Unauthenticated Code Execution Vulnerability in OpenSSH server

Bharat Jogi

Last updated on: July 3, 2024

Table of Contents

About openssh: securing enterprise communications and infrastructure, affected openssh versions:, potential impact of regresshion, immediate steps to mitigate risk, technical details, qualys qid coverage, discover vulnerable assets using qualys cybersecurity asset management (csam), enhance your security posture with qualys vulnerability management, detection, and response (vmdr).

  • Gain exposure visibility and remediation tracking with the regreSSHion Unified Dashboard
  • Automatically Patch regreSSHion vulnerability With Qualys Patch Management

Detect and remediate CVE-2024-6387 with Qualys TotalCloud Container Security

Qualys products and customer responsibilities, frequently asked questions (faqs).

what is unit of analysis in research

The  Qualys Threat Research Unit (TRU)  has discovered a Remote Unauthenticated Code Execution (RCE) vulnerability in OpenSSH’s server (sshd) in glibc-based Linux systems. CVE assigned to this vulnerability is CVE-2024-6387.

The vulnerability, which is a signal handler race condition in OpenSSH’s server (sshd), allows unauthenticated remote code execution (RCE) as root on glibc-based Linux systems; that presents a significant security risk. This race condition affects sshd in its default configuration.

Based on searches using Censys and Shodan, we have identified over 14 million potentially vulnerable OpenSSH server instances exposed to the Internet. Anonymized data from Qualys CSAM 3.0 with External Attack Surface Management data reveals that approximately 700,000 external internet-facing instances are vulnerable. This accounts for 31% of all internet-facing instances with OpenSSH in our global customer base. Interestingly, over 0.14% of vulnerable internet-facing instances with OpenSSH service have an End-Of-Life/End-Of-Support version of OpenSSH running.

In our security analysis, we identified that this vulnerability is a regression of the previously patched vulnerability CVE-2006-5051, which was reported in 2006. A regression in this context means that a flaw, once fixed, has reappeared in a subsequent software release, typically due to changes or updates that inadvertently reintroduce the issue. This incident highlights the crucial role of thorough regression testing to prevent the reintroduction of known vulnerabilities into the environment. This regression was introduced in October 2020 (OpenSSH 8.5p1).

Qualys has developed a working exploit for the regreSSHion vulnerability. As part of the disclosure process, we successfully demonstrated the exploit to the OpenSSH team to assist with their understanding and remediation efforts. We do not release our exploits, as we must allow time for patches to be applied. However, even though the exploit is complex, we believe that other independent researchers will be able to replicate our results.

OpenSSH (Open Secure Shell) is a suite of secure networking utilities based on the Secure Shell (SSH) protocol, which is vital for secure communication over unsecured networks. It provides robust encryption to ensure privacy and secure file transfers, making it an essential tool for remote server management and secure data communication. Known for its extensive security and authentication features, OpenSSH supports various encryption technologies and is standard on multiple Unix-like systems, including macOS and Linux.

OpenSSH’s implementation serves as a critical tool for secure communication. Its enterprise value lies in its scalability and the ability to enforce robust access controls and secure automated processes across various environments. This includes everything from automated backups and batch processing to complex DevOps practices, which involve the secure handling of sensitive data across multiple systems and locations. Its continued development and widespread adoption highlight its importance in maintaining the confidentiality and integrity of network communications worldwide.

OpenSSH stands as a benchmark in software security, exemplifying a robust defense-in-depth approach. Despite the recent vulnerability, its overall track record remains exceptionally strong, serving as both a model and an inspiration in the field.

  • OpenSSH versions earlier than 4.4p1 are vulnerable to this signal handler race condition unless they are patched for CVE-2006-5051 and CVE-2008-4109.
  • Versions from 4.4p1 up to, but not including, 8.5p1 are not vulnerable due to a transformative patch for CVE-2006-5051, which made a previously unsafe function secure.
  • The vulnerability resurfaces in versions from 8.5p1 up to, but not including, 9.8p1 due to the accidental removal of a critical component in a function.

OpenBSD systems are unaffected by this bug, as OpenBSD developed a secure mechanism in 2001 that prevents this vulnerability.

This vulnerability, if exploited, could lead to full system compromise where an attacker can execute arbitrary code with the highest privileges, resulting in a complete system takeover, installation of malware, data manipulation, and the creation of backdoors for persistent access. It could facilitate network propagation, allowing attackers to use a compromised system as a foothold to traverse and exploit other vulnerable systems within the organization.

Moreover, gaining root access would enable attackers to bypass critical security mechanisms such as firewalls, intrusion detection systems, and logging mechanisms, further obscuring their activities. This could also result in significant data breaches and leakage, giving attackers access to all data stored on the system, including sensitive or proprietary information that could be stolen or publicly disclosed.

This vulnerability is challenging to exploit due to its remote race condition nature, requiring multiple attempts for a successful attack. This can cause memory corruption and necessitate overcoming Address Space Layout Randomization (ASLR). Advancements in deep learning may significantly increase the exploitation rate, potentially providing attackers with a substantial advantage in leveraging such security flaws.

Addressing the regreSSHion vulnerability in OpenSSH, which enables remote code execution on Linux systems, demands a focused and layered security approach. Here are concise steps and strategic recommendations for enterprises to safeguard against this significant threat:

  • Patch Management : Quickly apply available patches for OpenSSH and prioritize ongoing update processes.
  • Enhanced Access Control : Limit SSH access through network-based controls to minimize the attack risks.
  • Network Segmentation and Intrusion Detection : Divide networks to restrict unauthorized access and lateral movements within critical environments and deploy systems to monitor and alert on unusual activities indicative of exploitation attempts.
  • Custom Assessment and Remediation: Quickly execute mitigation script on required assets. To find out more, check out the FAQ section ‘Are there any mitigations for this vulnerability?’

You can find the technical details of this vulnerability at:  

https://www.qualys.com/2024/07/01/cve-2024-6387/regresshion.txt

Qualys is releasing the QIDs in the table below as they become available, starting with vulnsigs version VULNSIGS-2.6.83-4 and in Linux Cloud Agent manifest version LX_MANIFEST-2.6.83.4-5

513833Alpine Linux 3.20 Security Update for openssh (regreSSHion)Alpine Linux
513832Alpine Linux 3.19 Security Update for openssh (regreSSHion)Alpine Linux
513831Alpine Linux 3.18 Security Update for openssh (regreSSHion)Alpine Linux
513830Alpine Linux 3.17 Security Update for openssh (regreSSHion)Alpine Linux
285635Fedora Security Update for openssh (FEDORA-2024-213f33544e) (regreSSHion)Fedora Security
756591SUSE Enterprise Linux Security Update for openssh (SUSE-SU-2024:2275-1) (regreSSHion)SUSE Enterprise
357791Amazon Linux Security Advisory for openssh : ALAS2023-2024-649 (regreSSHion)Amazon Linux
710942Gentoo Linux OpenSSH Remote Code Execution Vulnerability (GLSA 202407-09) (regreSSHion)Gentoo Linux
6081987VMware Photon OS Security Update for openssh (PHSA-2024-4.0-0642) (regreSSHion)VMware Photon
6081986VMware Photon OS Security Update for openssh (PHSA-2024-5.0-0307) (regreSSHion)VMware Photon
6122971Google Container OS-Optimized OS 101 Security Update for net-misc/openssh (CVE-2024-6387) (regreSSHion)Google Container OS
6122969Google Container OS-Optimized OS 105 Security Update for net-misc/openssh (CVE-2024-6387) (regreSSHion)Google Container OS
6122965Google Container OS-Optimized OS 109 Security Update for net-misc/openssh (CVE-2024-6387) (regreSSHion)Google Container OS
6122961Google Container OS-Optimized OS 113 Security Update for net-misc/openssh (CVE-2024-6387) (regreSSHion)Google Container OS
161766Oracle Enterprise Linux Security Update for openssh (ELSA-2024-12468) (regreSSHion)Oracle Enterprise
691562Free Berkeley Software Distribution (FreeBSD) Security Update for openssh (f1a00122-3797-11ef-b611-84a93843eb75) (regreSSHion)Free Berkeley
200455Debian/Ubuntu Notification for OpenSSH Vulnerability (USN-6859-1) (regreSSHion)Debian/Ubuntu
6007430Debian 11 Security Update for openssh (CVE-2024-6387) (regreSSHion)Debian 11 Security
6007429Debian/Ubuntu Update for openssh (DSA 5724-1) (regreSSHion)Debian/Ubuntu
42046OpenSSH Remote Unauthenticated Code Execution Vulnerability (regreSSHion) OS agnostic
243964Red Hat Update for openssh (RHSA-2024:4312)Red Hat

It is recommended that Qualys customers use OS-specific QIDs to scan for backported packages on supported Linux distributions.

Please check the Qualys Vulnerability Knowledgebase for the full list of coverage for this vulnerability.

The initial and crucial step in managing this critical vulnerability and mitigating associated risks involves pinpointing all assets susceptible to this specific issue. Use CSAM 3.0 with External Attack Surface Management to identify your organization’s internet-facing instances that have vulnerable versions of OpenSSH or are at their End of Life (EOL) or End of Support (EOS).

Identify internet-facing instances with vulnerable versions of OpenSSH

In the following example, we aim to identify all assets running the OpenSSH:

what is unit of analysis in research

Qualys VMDR  offers comprehensive coverage and visibility into vulnerabilities, empowering organizations to rapidly respond to, prioritize, and mitigate the associated risks. Additionally, Qualys customers can leverage Qualys Patch Management to remediate these vulnerabilities effectively.

Leverage the power of Qualys VMDR alongside TruRisk and the Qualys Query Language (QQL) to efficiently identify and prioritize vulnerable assets, effectively addressing the vulnerabilities highlighted above.

Try Qualys VMDR at no cost for 30 days

Use this QQL statement:

what is unit of analysis in research

Gain exposure visibility and remediation tracking with the “regreSSHion” Unified Dashboard

With the Qualys Unified Dashboard, you can track the vulnerability exposure within your organization and view your impacted hosts, their status, distribution across environments, and overall management in real time, allowing you to see your mean time to remediation (MTTR).

what is unit of analysis in research

To make it easier for customers to track and manage regreSSHion vulnerability in their subscriptions, we have created the Manage regreSSHion dashboard , which you can download and import into your subscription.

Automatically Patch “regreSSHion” vulnerability With Qualys Patch Management

We expect vendors to release patches for this vulnerability shortly. Qualys Patch Management can  automatically deploy those patches to vulnerable assets, when available.

Customers can use the “patch now” button found to the right of the vulnerability to add regreSSHion to a patch job. Once patches are released, Qualys will find the relevant patches for this vulnerability and automatically add those patches to a patch job. This will allow customers to deploy those patches to vulnerable devices, all from the Qualys Cloud Platform.

Qualys Patch Management No-Cost 45-Day Trial

Qualys TotalCloud Container Security offers comprehensive coverage and visibility into vulnerabilities across all your container environments, including managed Kubernetes and on-premises Kubernetes. This empowers organizations to rapidly respond to, prioritize, and mitigate associated risks effectively.

Leverage the power of Qualys TotalCloud Container Security and the Qualys Query Language (QQL) to efficiently identify and prioritize vulnerable assets, ensuring prompt and effective remediation of the vulnerabilities highlighted by CVE-2024-6387.

Qualys is cutting the release cycle short for certain products that are deployed on customer premises. At least one of those products depends on a supplier that will publish a fix release shortly. We intend to release fixes for this Severity HIGH CVE in the coming days to ensure that customers are safe from regreSSHion. Once builds have cleared Quality Assurance, we will provide updates to help customers patch.

Will the Qualys Research Team publish exploit code or include proof-of-concept code for this vulnerability?

No, as part of our commitment to responsible disclosure and maintaining high-security standards, we will not publish exploit codes. Given the complexity of this vulnerability, it is crucial to allow organizations to apply patches effectively without the immediate pressure of public exploits.

Are there any mitigations for this vulnerability?

If sshd can’t be updated or recompiled, set LoginGraceTime to 0 in the config file. This exposes sshd to a denial of service by using up all MaxStartups connections, but it prevents the remote code execution risk.

Using Qualys Custom Assessment and Remediation (CAR), you can easily apply this mitigation across affected assets in one go. Just follow these easy steps:

1. Go to CAR Library, look for Zero Day Utilities, and import the mitigation script.

what is unit of analysis in research

2. You can approve while importing or later on.

what is unit of analysis in research

3. Execute it across required assets/asset tags.

what is unit of analysis in research

To execute this mitigation, enable your free trial of CAR – https://www.qualys.com/forms/custom-assessment-remediation/

Is this vulnerability remotely exploitable?

Yes, this vulnerability can be exploited remotely and allows unauthenticated remote code execution (RCE) as root, posing a significant security risk.

Why is the vulnerability named “regreSSHion”?

This is a pun/reference to this being a regression bug affecting OpenSSH.

Should organizations patch these vulnerabilities urgently?

Yes, we would encourage organizations to patch this vulnerability urgently, especially on their internet-facing assets.

How will the new security fix be implemented for different versions?

This fix is part of a major update, making it challenging to backport. Consequently, users will have two update options: upgrading to the latest version released on Monday, July 1st (9.8p1) or applying a fix to older versions as outlined in the advisory, which is the approach most vendors will take.

Does this vulnerability affect macOS or Windows?

While it is likely that the vulnerability exists in both macOS and Windows, its exploitability on these platforms remains uncertain. Further analysis is required to determine the specific impact.

How can users identify exploitation attempts of this vulnerability?

Exploitation attempts for this vulnerability can be identified by seeing many many lines of “Timeout before authentication” in the logs.

What is the exposure to Qualys infrastructure?

The Qualys security team has taken immediate steps to protect our corporate infrastructure and products from any impact regarding the exploitation of this vulnerability. At this time, we have not experienced any negative impacts or detected any exploitation attempts. In addition, the Qualys security team has implemented enhanced monitoring and response plans to detect and respond to future exploit attempts. Emergency patching procedures have been initiated to fully remediate the vulnerability. To further help the broader security community, we are sharing our detection logic (see FAQ: “How to identify exploitation attempts of this vulnerability?”) to help customers respond should attacks occur before patching and mitigation efforts are completed.

How can users identify systems vulnerable to the OpenSSH regreSSHion vulnerability?

Users can determine if their systems are vulnerable by verifying the version of the OpenSSH server installed. Systems running affected versions should be considered at risk and prioritized for updates.

Under what circumstances might QID 42046 fail to report accurately?

Accurate detection with QID 42046 requires root privileges, as the command used only runs with root access.

Why is a QID categorized as a confirmed or potential vulnerability?

A QID is reported as confirmed in authenticated scan results because these scans can access detailed information that verifies the vulnerability more reliably. On the other hand, remote unauthenticated scans categorize a QID as potential because they primarily depend on the information presented by the OpenSSH service banner. This banner might display a partial version of details, leading to less definitive conclusions about the presence of a vulnerability.

When will the Qualys Detection Score (QDS) be updated?

As the vulnerability begins to trend across various threat intelligence sources, our QDS will utilize these intelligent feeds for dynamic updates. We expect its effectiveness to reach a score of 90 or above.

Has the threat feed been updated to include the regreSSHion vulnerability?

Yes, the Qualys threat feed is updated when emerging threats are tracked and reported from the dark web and other sources. The update activates as soon as a vulnerability trends across various threat intelligence platforms.

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No record found for this QID or CVE in Qualys KB. Great job on reporting however the data set is not published.

“Immediate Steps to Mitigate Risk” there’s a config-based mitigation, this section is just marketing nonsense and it’s irresponsible of you to hide the actual immediate mitigation in the “technical details”

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In May 2023, the 76th World Health Assembly adopted  Resolution 76.16   on the Health of Indigenous Peoples, requesting the WHO Director-General to develop a  Global Plan of Action for the Health of Indigenous Peoples  in consultations with countries, Indigenous Peoples, relevant United Nations and multilateral system agencies, as well as civil society, academia and other stakeholders. 

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Keynote speaker: Valmaine Toki, member of the Expert Mechanism on the Rights of Indigenous Peoples.   

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Pauliina (Lina) Nykänen-Rettaroli, Senior Technical Lead and Unit Head on Human Rights, WHO Department on Gender, Rights, Equity and Diversity (GRED)

Rodrigo Paillalef, Leading researcher for the development of the situation analysis and Member of the UN Permanent Forum on Indigenous Issues 

​​ Mariam Wallet Aboubakrine , Leading researcher for the development of the situation analysis, Co-Principal Investigator of Ărramăt Project and President of Association Tinhinan 

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  • Predictors of successful neonatal intubation in inexperienced operators: a secondary, non-randomised analysis of the SHINE trial
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  • http://orcid.org/0000-0003-3614-2388 Kate Alison Hodgson 1 , 2 , 3 ,
  • Sharoan Selvakumaran 1 ,
  • Kate Louise Francis 4 ,
  • http://orcid.org/0000-0002-9490-6377 Louise S Owen 1 , 2 , 3 ,
  • Sophie E Newman 5 ,
  • http://orcid.org/0000-0002-4872-3860 Camille Omar Farouk Kamlin 1 , 2 , 3 ,
  • Susan Donath 4 ,
  • http://orcid.org/0000-0002-9111-5027 Calum T Roberts 6 , 7 , 8 ,
  • http://orcid.org/0000-0001-6742-7314 Peter G Davis 1 , 2 , 3 ,
  • http://orcid.org/0000-0003-0212-7956 Brett James Manley 1 , 2 , 3
  • 1 Newborn Research Centre , The Royal Women's Hospital , Parkville , Victoria , Australia
  • 2 Murdoch Children's Research Institute , Parkville , Victoria , Australia
  • 3 Department of Obstetrics, Gynaecology and Newborn Health , The University of Melbourne , Parkville , Victoria , Australia
  • 4 Clinical Epidemiology and Biostatistics Unit , Murdoch Children's Research Institute , Parkville , Victoria , Australia
  • 5 Department of Neonatal Medicine , The Royal Children's Hospital Melbourne , Melbourne , Victoria , Australia
  • 6 Department of Paediatrics , Monash University Faculty of Medicine Nursing and Health Sciences , Clayton , Victoria , Australia
  • 7 The Ritchie Centre , Hudson Institute of Medical Research , Clayton , VIC , Australia
  • 8 Monash Newborn , Monash Children's Hospital , Clayton , Victoria , Australia
  • Correspondence to Dr Kate Alison Hodgson; Kate.Hodgson{at}thewomens.org.au

Objective Neonatal endotracheal intubation is a lifesaving but technically difficult procedure, particularly for inexperienced operators. This secondary analysis in a subgroup of inexperienced operators of the Stabilization with nasal High flow during Intubation of NEonates randomised trial aimed to identify the factors associated with successful intubation on the first attempt without physiological stability of the infant.

Methods In this secondary analysis, demographic factors were compared between infants intubated by inexperienced operators and those intubated by experienced operators. Following this, for inexperienced operators only, predictors of successful intubation without physiological instability were analysed.

Results A total of 251 intubations in 202 infants were included in the primary intention-to-treat analysis of the main trial. Inexperienced operators were more likely to perform intubations in larger and more mature infants in the neonatal intensive care unit where premedications were used. When intubations were performed by inexperienced operators, the use of nasal high flow therapy (nHF) and a higher starting fraction of inspired oxygen were associated with a higher rate of safe, successful intubation on the first attempt. There was a weaker association between premedication use and first attempt success.

Conclusions In inexperienced operators, this secondary, non-randomised analysis suggests that the use of nHF and premedications, and matching the operator to the infant and setting, may be important to optimise neonatal intubation success.

Trial registration number ACTRN12618001498280.

  • Neonatology
  • Intensive Care Units, Neonatal
  • Respiratory Medicine

Data availability statement

Data are available upon reasonable request. The deidentified data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Proposals must undergo review by the Trial Steering Committee of the SHINE trial. Data will be made available for researchers who provide a methodologically sound research proposal as determined by the Trial Steering Committee following Human Research Ethics Committee review and receipt of a signed data access agreement.

https://doi.org/10.1136/archdischild-2024-327081

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X @katehodg18, @calumtheroberts, @drbretty

Contributors KAH and SS conceptualised the study, collected data and drafted and edited the manuscript. KF and SD analysed the data and edited the manuscript. BJM, LSO, COFK, SEN, CTR and PGD collected data, assisted with study design and edited the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. KAH is the guarantor.

Funding Supported by grants from the National Health and Medical Research Council of Australia (Practitioner Fellowship #1157782 to PGD, Investigator Grants #2016662 to BJM, #1175634 to CTR). Vapotherm supplied the equipment for the main trial.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer-reviewed.

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COMMENTS

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    Unit of Analysis: Definition, Types & Examples. The unit of analysis is the people or things whose qualities will be measured. The unit of analysis is an essential part of a research project. It's the main thing that a researcher looks at in his research. A unit of analysis is the object about which you hope to have something to say at the ...

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  3. Unit of Analysis: Definition, Types & Examples

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    Learn what the unit of analysis is and how it affects your research project or study. Find out the different types of units of analysis and how to choose the appropriate one based on your research question and data.

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  23. What is unit of analysis and why is it important for qualitative

    The reason that unit of analysis and unit of observation are important is that they provide important boundaries for your study particularly your data analysis. In qualitative research, researchers can struggle to identify what is germane and what is not. By clearly identifying the level and what you are studying, you will be better able to ...

  24. regreSSHion: Remote Unauthenticated Code Execution Vulnerability in

    The Qualys Threat Research Unit (TRU) has discovered a Remote Unauthenticated Code Execution (RCE) vulnerability in OpenSSH's server (sshd) in glibc-based Linux systems. CVE assigned to this vulnerability is CVE-2024-6387.. The vulnerability, which is a signal handler race condition in OpenSSH's server (sshd), allows unauthenticated remote code execution (RCE) as root on glibc-based Linux ...

  25. Constitution Unit commentary on the 2024 general election

    Throughout the 2024 general election campaign, and after, the Constitution Unit has provided analysis of key constitutional issues and of the parties' policies in relation to these. Catch up on our general election-related publications, manifesto-specific commentary, blog posts, events and media.

  26. SEC.gov

    Each of the three actions announced today originated from the SEC Enforcement Division's Market Abuse Unit's (MAU) Analysis and Detection Center, which uses data analysis tools to detect suspicious trading patterns.

  27. Advancing the development of a situation analysis on the health of

    The objective of this side event is to present the preliminary findings of the literature review and to provide an opportunity for Indigenous Peoples to contribute and input to the ongoing research, which includes analysis of the following components: The key health outcomes for which Indigenous Peoples experience health inequities; The social ...

  28. Predictors of successful neonatal intubation in inexperienced operators

    Objective Neonatal endotracheal intubation is a lifesaving but technically difficult procedure, particularly for inexperienced operators. This secondary analysis in a subgroup of inexperienced operators of the Stabilization with nasal High flow during Intubation of NEonates randomised trial aimed to identify the factors associated with successful intubation on the first attempt without ...

  29. The Daily Show Fan Page

    Host Jon Stewart returns to his place behind the desk for an unvarnished look at the 2024 election, with expert analysis from the Daily Show news team. Extended Interviews. Peter S. Goodman - Extended Interview. The Daily Show. 10m; 06/26/2024; Watch this content. Sharon Lerner - Extended Interview.