quantitative data example in research paper

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]

• Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
• Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
• Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
• Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
• Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng | Published: May 18, 2022

Related Articles

A healthcare giant successfully introduces the most effective drug dosage through rigorous statistical modeling, saving countless lives. A marketing team predicts consumer trends with uncanny accuracy, tailoring campaigns for maximum impact.

Table of Contents

These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

• Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

• Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

• Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

However, if you have data from multiple sources, collecting and cleaning it can be a cumbersome task. This is where Hevo Data steps in. With Hevo, extracting, transforming, and loading data from source to destination becomes a seamless task, eliminating the need for manual coding. This not only saves valuable time but also enhances the overall efficiency of data analysis and visualization, empowering users to derive insights quickly and with precision

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources (40+ free sources), we help you not only export data from sources & load data to the destinations but also transform & enrich your data, & make it analysis-ready.

Start for free now!

Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article, which is to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

• Descriptive Statistics
• Inferential Statistics

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers obtained from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

• Mean:   This calculates the numerical average of a set of values.
• Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
• Mode: This is used to find the most commonly occurring value in a dataset.
• Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
• Frequency: This indicates the number of times a value is found.
• Range: This shows the highest and lowest values in a dataset.
• Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
• Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values, and descriptive statistics is all about explaining details of a specific dataset using numbers, but it does not explain the motives behind the numbers; hence, a need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

There are various statistical analysis methods used within inferential statistics; a few are discussed below.

• Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs help understand the nuances of a dataset and factors that may influence a data point.
• Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis, and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
• Monte Carlo Simulation: Monte Carlo simulation, also known as the Monte Carlo method, is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. Data analysts use it to perform advanced risk analyses to help forecast future events and make decisions accordingly.
• Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
• Factor Analysis:   A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
• Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis, where these groups or cohorts usually have common characteristics or similarities within a defined period.
• MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank higher than the others in the process. 
• Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different; that is, data points within a cluster will look like each other and different from data points in other clusters.
• Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different times, like weekly and monthly email sign-ups, to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
• SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

• Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
• Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

• Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
• Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

• Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
• This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

• Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

• Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

• Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
• The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

• Data Analysis and Modeling: 4 Critical Differences
• Exploratory Data Analysis Simplified 101
• 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

Ofem Eteng is a seasoned technical content writer with over 12 years of experience. He has held pivotal roles such as System Analyst (DevOps) at Dagbs Nigeria Limited and Full-Stack Developer at Pedoquasphere International Limited. He specializes in data science, data analytics and cutting-edge technologies, making him an expert in the data industry.

No-code Data Pipeline for your Data Warehouse

• Data Analysis
• Data Warehouse
• Quantitative Data Analysis

Continue Reading

Muskan Kesharwani

Top 10 Data Quality Tools for Ensuring High Data Standards

Arun Chaudhary

Fivetran vs RudderStack Comparison

Roopa Madhuri G

Fivetran vs Airbyte: A Comprehensive Comparison for 2024

I want to read this e-book.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Quantitative Methods
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

Need Help Locating Statistics?

Resources for locating data and statistics can be found here:

Statistics & Data Research Guide

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

• << Previous: Qualitative Methods
• Next: Insiderness >>
• Last Updated: Aug 21, 2024 8:54 AM
• URL: https://libguides.usc.edu/writingguide

Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations .

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

77 Comments

Hi, I have read your article. Such a brilliant post you have created.

Thank you for the feedback. Good luck with your quantitative analysis.

Thank you so much.

Thank you so much. I learnt much well. I love your summaries of the concepts. I had love you to explain how to input data using SPSS

Very useful, I have got the concept

Amazing and simple way of breaking down quantitative methods.

This is beautiful….especially for non-statisticians. I have skimmed through but I wish to read again. and please include me in other articles of the same nature when you do post. I am interested. I am sure, I could easily learn from you and get off the fear that I have had in the past. Thank you sincerely.

Send me every new information you might have.

i need every new information

Thank you for the blog. It is quite informative. Dr Peter Nemaenzhe PhD

It is wonderful. l’ve understood some of the concepts in a more compréhensive manner

Your article is so good! However, I am still a bit lost. I am doing a secondary research on Gun control in the US and increase in crime rates and I am not sure which analysis method I should use?

Based on the given learning points, this is inferential analysis, thus, use ‘t-tests, ANOVA, correlation and regression analysis’

Well explained notes. Am an MPH student and currently working on my thesis proposal, this has really helped me understand some of the things I didn’t know.

I like your page..helpful

wonderful i got my concept crystal clear. thankyou!!

This is really helpful , thank you

Thank you so much this helped

Wonderfully explained

thank u so much, it was so informative

THANKYOU, this was very informative and very helpful

This is great GRADACOACH I am not a statistician but I require more of this in my thesis

Include me in your posts.

This is so great and fully useful. I would like to thank you again and again.

Glad to read this article. I’ve read lot of articles but this article is clear on all concepts. Thanks for sharing.

Thank you so much. This is a very good foundation and intro into quantitative data analysis. Appreciate!

You have a very impressive, simple but concise explanation of data analysis for Quantitative Research here. This is a God-send link for me to appreciate research more. Thank you so much!

Avery good presentation followed by the write up. yes you simplified statistics to make sense even to a layman like me. Thank so much keep it up. The presenter did ell too. i would like more of this for Qualitative and exhaust more of the test example like the Anova.

This is a very helpful article, couldn’t have been clearer. Thank you.

Awesome and phenomenal information.Well done

The video with the accompanying article is super helpful to demystify this topic. Very well done. Thank you so much.

thank you so much, your presentation helped me a lot

I don’t know how should I express that ur article is saviour for me 🥺😍

It is well defined information and thanks for sharing. It helps me a lot in understanding the statistical data.

I gain a lot and thanks for sharing brilliant ideas, so wish to be linked on your email update.

Very helpful and clear .Thank you Gradcoach.

Thank for sharing this article, well organized and information presented are very clear.

VERY INTERESTING AND SUPPORTIVE TO NEW RESEARCHERS LIKE ME. AT LEAST SOME BASICS ABOUT QUANTITATIVE.

An outstanding, well explained and helpful article. This will help me so much with my data analysis for my research project. Thank you!

wow this has just simplified everything i was scared of how i am gonna analyse my data but thanks to you i will be able to do so

simple and constant direction to research. thanks

This is helpful

Great writing!! Comprehensive and very helpful.

Do you provide any assistance for other steps of research methodology like making research problem testing hypothesis report and thesis writing?

Thank you so much for such useful article!

Amazing article. So nicely explained. Wow

Very insightfull. Thanks

I am doing a quality improvement project to determine if the implementation of a protocol will change prescribing habits. Would this be a t-test?

The is a very helpful blog, however, I’m still not sure how to analyze my data collected. I’m doing a research on “Free Education at the University of Guyana”

tnx. fruitful blog!

So I am writing exams and would like to know how do establish which method of data analysis to use from the below research questions: I am a bit lost as to how I determine the data analysis method from the research questions.

Do female employees report higher job satisfaction than male employees with similar job descriptions across the South African telecommunications sector? – I though that maybe Chi Square could be used here. – Is there a gender difference in talented employees’ actual turnover decisions across the South African telecommunications sector? T-tests or Correlation in this one. – Is there a gender difference in the cost of actual turnover decisions across the South African telecommunications sector? T-tests or Correlation in this one. – What practical recommendations can be made to the management of South African telecommunications companies on leveraging gender to mitigate employee turnover decisions?

Your assistance will be appreciated if I could get a response as early as possible tomorrow

This was quite helpful. Thank you so much.

wow I got a lot from this article, thank you very much, keep it up

Thanks for yhe guidance. Can you send me this guidance on my email? To enable offline reading?

Thank you very much, this service is very helpful.

Every novice researcher needs to read this article as it puts things so clear and easy to follow. Its been very helpful.

Wonderful!!!! you explained everything in a way that anyone can learn. Thank you!!

I really enjoyed reading though this. Very easy to follow. Thank you

Many thanks for your useful lecture, I would be really appreciated if you could possibly share with me the PPT of presentation related to Data type?

Thank you very much for sharing, I got much from this article

This is a very informative write-up. Kindly include me in your latest posts.

Very interesting mostly for social scientists

Thank you so much, very helpfull

You’re welcome 🙂

woow, its great, its very informative and well understood because of your way of writing like teaching in front of me in simple languages.

I have been struggling to understand a lot of these concepts. Thank you for the informative piece which is written with outstanding clarity.

very informative article. Easy to understand

Beautiful read, much needed.

Always greet intro and summary. I learn so much from GradCoach

Quite informative. Simple and clear summary.

I thoroughly enjoyed reading your informative and inspiring piece. Your profound insights into this topic truly provide a better understanding of its complexity. I agree with the points you raised, especially when you delved into the specifics of the article. In my opinion, that aspect is often overlooked and deserves further attention.

Absolutely!!! Thank you

Thank you very much for this post. It made me to understand how to do my data analysis.

its nice work and excellent job ,you have made my work easier

Wow! So explicit. Well done.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• The Chicago School
• The Chicago School Library
• Research Guides

Quantitative Research Methods

What is quantitative research, about this guide, introduction, quantitative research methodologies.

• Key Resources
• Quantitative Software
• Finding Qualitative Studies

 The purpose of this guide is to provide a starting point for learning about quantitative research. In this guide, you'll find:

• Resources on diverse types of quantitative research.
• An overview of resources for data, methods & analysis
• Popular quantitative software options
• Information on how to find quantitative studies

Research involving the collection of data in numerical form for quantitative analysis. The numerical data can be durations, scores, counts of incidents, ratings, or scales. Quantitative data can be collected in either controlled or naturalistic environments, in laboratories or field studies, from special populations or from samples of the general population. The defining factor is that numbers result from the process, whether the initial data collection produced numerical values, or whether non-numerical values were subsequently converted to numbers as part of the analysis process, as in content analysis.

Citation: Garwood, J. (2006). Quantitative research. In V. Jupp (Ed.), The SAGE dictionary of social research methods. (pp. 251-252). London, England: SAGE Publications. doi:10.4135/9780857020116

Watch the following video to learn more about Quantitative Research:

(Video best viewed in Edge and Chrome browsers, or click here to view in the Sage Research Methods Database)

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y, and Z are also playing a role.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses.

• Next: Key Resources >>
• Last Updated: Aug 20, 2024 5:29 PM
• URL: https://library.thechicagoschool.edu/quantitative

Quantitative Research: Examples of Research Questions and Solutions

Are you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way.

Understanding Quantitative Research Questions

Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:

• What is the relationship between class size and student academic performance?
• Does the use of technology in the classroom improve learning outcomes?
• How does parental involvement affect student achievement?
• What is the effect of a new drug treatment on reducing blood pressure?
• Is there a correlation between physical activity levels and the risk of cardiovascular disease?
• How does socioeconomic status influence access to healthcare services?
• What factors influence consumer purchasing behavior?
• Is there a relationship between advertising expenditure and sales revenue?
• How do demographic variables affect brand loyalty?

Stats Camp: Your Solution to Mastering Quantitative Research Methodologies

At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way.

Bringing Your Own Data

One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed.

Courses Offered at StatsCamp.org

• Latent Profile Analysis Course : Learn how to identify subgroups, or profiles, within a heterogeneous population based on patterns of responses to multiple observed variables.
• Bayesian Statistics Course : A comprehensive introduction to Bayesian data analysis, a powerful statistical approach for inference and decision-making. Through a series of engaging lectures and hands-on exercises, participants will learn how to apply Bayesian methods to a wide range of research questions and data types.
• Structural Equation Modeling (SEM) Course : Dive into advanced statistical techniques for modeling complex relationships among variables.
• Multilevel Modeling Course : A in-depth exploration of this advanced statistical technique, designed to analyze data with nested structures or hierarchies. Whether you’re studying individuals within groups, schools within districts, or any other nested data structure, multilevel modeling provides the tools to account for the dependencies inherent in such data.

As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research!

Follow Us On Social! Facebook | Instagram | X

933 San Mateo Blvd NE #500, Albuquerque, NM 87108

4414 82 nd Street #212-121 Lubbock, TX 79424

Monday – Friday: 9:00 AM – 5:00 PM

© Copyright 2003 - 2024 | All Rights Reserved Stats Camp Foundation 501(c)(3) Non-Profit Organization.

Quantitative Research

• Reference work entry
• First Online: 13 January 2019
• Cite this reference work entry

• Leigh A. Wilson 2 , 3  

5148 Accesses

4 Citations

Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Writing Quantitative Research Studies

Qualitative Research Methods

Babbie ER. The practice of social research. 14th ed. Belmont: Wadsworth Cengage; 2016.

Google Scholar  

Descartes. Cited in Halverston, W. (1976). In: A concise introduction to philosophy, 3rd ed. New York: Random House; 1637.

Doll R, Hill AB. The mortality of doctors in relation to their smoking habits. BMJ. 1954;328(7455):1529–33. https://doi.org/10.1136/bmj.328.7455.1529 .

Article   Google Scholar  

Liamputtong P. Research methods in health: foundations for evidence-based practice. 3rd ed. Melbourne: Oxford University Press; 2017.

McNabb DE. Research methods in public administration and nonprofit management: quantitative and qualitative approaches. 2nd ed. New York: Armonk; 2007.

Merriam-Webster. Dictionary. http://www.merriam-webster.com . Accessed 20th December 2017.

Olesen Larsen P, von Ins M. The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. Scientometrics. 2010;84(3):575–603.

Pannucci CJ, Wilkins EG. Identifying and avoiding bias in research. Plast Reconstr Surg. 2010;126(2):619–25. https://doi.org/10.1097/PRS.0b013e3181de24bc .

Petrie A, Sabin C. Medical statistics at a glance. 2nd ed. London: Blackwell Publishing; 2005.

Portney LG, Watkins MP. Foundations of clinical research: applications to practice. 3rd ed. New Jersey: Pearson Publishing; 2009.

Sheehan J. Aspects of research methodology. Nurse Educ Today. 1986;6:193–203.

Wilson LA, Black DA. Health, science research and research methods. Sydney: McGraw Hill; 2013.

Download references

Author information

Authors and affiliations.

School of Science and Health, Western Sydney University, Penrith, NSW, Australia

Leigh A. Wilson

Faculty of Health Science, Discipline of Behavioural and Social Sciences in Health, University of Sydney, Lidcombe, NSW, Australia

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Leigh A. Wilson .

Editor information

Editors and affiliations.

Pranee Liamputtong

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this entry

Cite this entry.

Wilson, L.A. (2019). Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_54

Download citation

DOI : https://doi.org/10.1007/978-981-10-5251-4_54

Published : 13 January 2019

Publisher Name : Springer, Singapore

Print ISBN : 978-981-10-5250-7

Online ISBN : 978-981-10-5251-4

eBook Packages : Social Sciences Reference Module Humanities and Social Sciences Reference Module Business, Economics and Social Sciences

Share this entry

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• Current issue
• Write for Us
• BMJ Journals

You are here

• Volume 21, Issue 4
• How to appraise quantitative research
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

This article has a correction. Please see:

• Correction: How to appraise quantitative research - April 01, 2019

• Xabi Cathala 1 ,
• Calvin Moorley 2
• 1 Institute of Vocational Learning , School of Health and Social Care, London South Bank University , London , UK
• 2 Nursing Research and Diversity in Care , School of Health and Social Care, London South Bank University , London , UK
• Correspondence to Mr Xabi Cathala, Institute of Vocational Learning, School of Health and Social Care, London South Bank University London UK ; cathalax{at}lsbu.ac.uk and Dr Calvin Moorley, Nursing Research and Diversity in Care, School of Health and Social Care, London South Bank University, London SE1 0AA, UK; Moorleyc{at}lsbu.ac.uk

https://doi.org/10.1136/eb-2018-102996

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Introduction

Some nurses feel that they lack the necessary skills to read a research paper and to then decide if they should implement the findings into their practice. This is particularly the case when considering the results of quantitative research, which often contains the results of statistical testing. However, nurses have a professional responsibility to critique research to improve their practice, care and patient safety. 1  This article provides a step by step guide on how to critically appraise a quantitative paper.

Title, keywords and the authors

The authors’ names may not mean much, but knowing the following will be helpful:

Their position, for example, academic, researcher or healthcare practitioner.

Their qualification, both professional, for example, a nurse or physiotherapist and academic (eg, degree, masters, doctorate).

This can indicate how the research has been conducted and the authors’ competence on the subject. Basically, do you want to read a paper on quantum physics written by a plumber?

The abstract is a resume of the article and should contain:

Introduction.

Research question/hypothesis.

Methods including sample design, tests used and the statistical analysis (of course! Remember we love numbers).

Main findings.

Conclusion.

The subheadings in the abstract will vary depending on the journal. An abstract should not usually be more than 300 words but this varies depending on specific journal requirements. If the above information is contained in the abstract, it can give you an idea about whether the study is relevant to your area of practice. However, before deciding if the results of a research paper are relevant to your practice, it is important to review the overall quality of the article. This can only be done by reading and critically appraising the entire article.

The introduction

Example: the effect of paracetamol on levels of pain.

My hypothesis is that A has an effect on B, for example, paracetamol has an effect on levels of pain.

My null hypothesis is that A has no effect on B, for example, paracetamol has no effect on pain.

My study will test the null hypothesis and if the null hypothesis is validated then the hypothesis is false (A has no effect on B). This means paracetamol has no effect on the level of pain. If the null hypothesis is rejected then the hypothesis is true (A has an effect on B). This means that paracetamol has an effect on the level of pain.

Background/literature review

The literature review should include reference to recent and relevant research in the area. It should summarise what is already known about the topic and why the research study is needed and state what the study will contribute to new knowledge. 5 The literature review should be up to date, usually 5–8 years, but it will depend on the topic and sometimes it is acceptable to include older (seminal) studies.

Methodology

In quantitative studies, the data analysis varies between studies depending on the type of design used. For example, descriptive, correlative or experimental studies all vary. A descriptive study will describe the pattern of a topic related to one or more variable. 6 A correlational study examines the link (correlation) between two variables 7  and focuses on how a variable will react to a change of another variable. In experimental studies, the researchers manipulate variables looking at outcomes 8  and the sample is commonly assigned into different groups (known as randomisation) to determine the effect (causal) of a condition (independent variable) on a certain outcome. This is a common method used in clinical trials.

There should be sufficient detail provided in the methods section for you to replicate the study (should you want to). To enable you to do this, the following sections are normally included:

Overview and rationale for the methodology.

Participants or sample.

Data collection tools.

Methods of data analysis.

Ethical issues.

Data collection should be clearly explained and the article should discuss how this process was undertaken. Data collection should be systematic, objective, precise, repeatable, valid and reliable. Any tool (eg, a questionnaire) used for data collection should have been piloted (or pretested and/or adjusted) to ensure the quality, validity and reliability of the tool. 9 The participants (the sample) and any randomisation technique used should be identified. The sample size is central in quantitative research, as the findings should be able to be generalised for the wider population. 10 The data analysis can be done manually or more complex analyses performed using computer software sometimes with advice of a statistician. From this analysis, results like mode, mean, median, p value, CI and so on are always presented in a numerical format.

The author(s) should present the results clearly. These may be presented in graphs, charts or tables alongside some text. You should perform your own critique of the data analysis process; just because a paper has been published, it does not mean it is perfect. Your findings may be different from the author’s. Through critical analysis the reader may find an error in the study process that authors have not seen or highlighted. These errors can change the study result or change a study you thought was strong to weak. To help you critique a quantitative research paper, some guidance on understanding statistical terminology is provided in  table 1 .

• View inline

Some basic guidance for understanding statistics

Quantitative studies examine the relationship between variables, and the p value illustrates this objectively.  11  If the p value is less than 0.05, the null hypothesis is rejected and the hypothesis is accepted and the study will say there is a significant difference. If the p value is more than 0.05, the null hypothesis is accepted then the hypothesis is rejected. The study will say there is no significant difference. As a general rule, a p value of less than 0.05 means, the hypothesis is accepted and if it is more than 0.05 the hypothesis is rejected.

The CI is a number between 0 and 1 or is written as a per cent, demonstrating the level of confidence the reader can have in the result. 12  The CI is calculated by subtracting the p value to 1 (1–p). If there is a p value of 0.05, the CI will be 1–0.05=0.95=95%. A CI over 95% means, we can be confident the result is statistically significant. A CI below 95% means, the result is not statistically significant. The p values and CI highlight the confidence and robustness of a result.

Discussion, recommendations and conclusion

The final section of the paper is where the authors discuss their results and link them to other literature in the area (some of which may have been included in the literature review at the start of the paper). This reminds the reader of what is already known, what the study has found and what new information it adds. The discussion should demonstrate how the authors interpreted their results and how they contribute to new knowledge in the area. Implications for practice and future research should also be highlighted in this section of the paper.

A few other areas you may find helpful are:

Limitations of the study.

Conflicts of interest.

Table 2 provides a useful tool to help you apply the learning in this paper to the critiquing of quantitative research papers.

Quantitative paper appraisal checklist

• 1. ↵ Nursing and Midwifery Council , 2015 . The code: standard of conduct, performance and ethics for nurses and midwives https://www.nmc.org.uk/globalassets/sitedocuments/nmc-publications/nmc-code.pdf ( accessed 21.8.18 ).
• Gerrish K ,
• Moorley C ,
• Tunariu A , et al
• Shorten A ,

Competing interests None declared.

Patient consent Not required.

Provenance and peer review Commissioned; internally peer reviewed.

Correction notice This article has been updated since its original publication to update p values from 0.5 to 0.05 throughout.

Linked Articles

• Miscellaneous Correction: How to appraise quantitative research BMJ Publishing Group Ltd and RCN Publishing Company Ltd Evidence-Based Nursing 2019; 22 62-62 Published Online First: 31 Jan 2019. doi: 10.1136/eb-2018-102996corr1

Read the full text or download the PDF:

• How it works

Quantitative Research Questionnaire – Types & Examples

Published by Alvin Nicolas at August 20th, 2024 , Revised On August 21, 2024

Research is usually done to provide solutions to an ongoing problem. Wherever the researchers see a gap, they tend to launch research to enhance their knowledge and to provide solutions to the needs of others. If they want to research from a subjective point of view, they consider qualitative research. On the other hand, when they research from an objective point of view, they tend to consider quantitative research.

There’s a fine line between subjectivity and objectivity. Qualitative research, related to subjectivity, assesses individuals’ personal opinions and experiences, while quantitative research, associated with objectivity, collects numerical data to derive results. However, the best medium to collect data in quantitative research is a questionnaire.

Let’s discuss what a quantitative research questionnaire is, its types, methods of writing questions, and types of survey questions. By thoroughly understanding these key essential terms, you can efficiently create a professional and well-organised quantitative research questionnaire.

What is a Quantitative Research Questionnaire?

Quantitative research questionnaires are preferably used during quantitative research. They are a well-structured set of questions designed specifically to gather specific, close-ended participant responses. This allows the researchers to gather numerical data and obtain a deep understanding of a particular event or problem.

As you know, qualitative research questionnaires contain open-ended questions that allow the participants to express themselves freely, while quantitative research questionnaires contain close-ended and specific questions, such as multiple-choice and Likert scales, to assess individuals’ behaviour.

Quantitative research questionnaires are usually used in research in various fields, such as psychology, medicine, chemistry, and economics.

Let’s see how you can write quantitative research questions by going through some examples:

• How much do British people consume fast food per week?
• What is the percentage of students living in hostels in London?

Types of Quantitative Research Questions With Examples

After learning what a quantitative research questionnaire is and what quantitative research questions look like, it’s time to thoroughly discuss the different types of quantitative research questions to explore this topic more.

Dichotomous Questions

Dichotomous questions are those with a margin for only two possible answers. They are usually used when the answers are “Yes/No” or “True/False.” These questions significantly simplify the research process and help collect simple responses.

Example: Have you ever visited Istanbul?

Multiple Choice Questions

Multiple-choice questions have a list of possible answers for the participants to choose from. They help assess people’s general knowledge, and the data gathered by multiple-choice questions can be easily analysed.

Example: Which of the following is the capital of France?

Multiple Answer Questions

Multiple-answer questions are similar to multiple-choice questions. However, there are multiple answers for participants to choose from. They are used when the questions can’t have a single, specific answer.

Example: Which of the following movie genres are your favourite?

Likert Scale Questions

Likert scale questions are used when the preferences and emotions of the participants are measured from one extreme to another. The scales are usually applied to measure likelihood, frequency, satisfaction, and agreement. The Likert scale has only five options to choose from.

Example: How satisfied are you with your job?

Semantic Differential Questions

Similar to Likert scales, semantic differential questions are also used to measure the emotions and attitudes of participants. The only difference is that instead of using extreme options such as strongly agree and strongly disagree, opposites of a particular choice are given to reduce bias.

Example: Please rate the services of our company.

Rank Order Questions

Rank-order questions are usually used to measure the preferences and choices of the participants efficiently. In this, multiple choices are given, and participants are asked to rank them according to their perspective. This helps to create a good participant profile.

Example: Rank the given books according to your interest.

Matrix Questions

Matrix questions are similar to Likert scales. In Likert scales, participants’ responses are measured through separate questions, while in matrix questions, multiple questions are compiled in a single row to simplify the data collection method efficiently.

Example: Rate the following activities that you do in daily life.

How To Write Quantitative Research Questions?

Quantitative research questions allow researchers to gather empirical data to answer their research problems. As we have discussed the different types of quantitative research questions above, it’s time to learn how to write the perfect quantitative research questions for a questionnaire and streamline your research process.

Here are the steps to follow to write quantitative research questions efficiently.

Step 1: Determine the Research Goals

The first step in writing quantitative research questions is to determine your research goals. Determining and confirming your research goals significantly helps you understand what kind of questions you need to create and for what grade. Efficiently determining the research goals also reduces the need for further modifications in the questionnaire.

Step 2: Be Mindful About the Variables

There are two variables in the questions: independent and dependent. It is essential to decide what would be the dependent variable in your questions and what would be the independent. It significantly helps to understand where to emphasise and where not. It also reduces the probability of additional and vague questions.

Step 3: Choose the Right Type of Question

It is also important to determine the right type of questions to add to your questionnaire. Whether you want Likert scales, rank-order questions, or multiple-answer questions, choosing the right type of questions will help you measure individuals’ responses efficiently and accurately.

Step 4: Use Easy and Clear Language

Another thing to keep in mind while writing questions for a quantitative research questionnaire is to use easy and clear language. As you know, quantitative research is done to measure specific and simple responses in empirical form, and using easy and understandable language in questions makes a huge difference.

Step 5: Be Specific About The Topic

Always be mindful and specific about your topic. Avoid writing questions that divert from your topic because they can cause participants to lose interest. Use the basic terms of your selected topic and gradually go deep. Also, remember to align your topic and questions with your research objectives and goals.

Step 6: Appropriately Write Your Questions

When you have considered all the above-discussed things, it’s time to write your questions appropriately. Don’t just haste in writing. Think twice about the result of a question and then consider writing it in the questionnaire. Remember to be precise while writing. Avoid overwriting.

Step 7: Gather Feedback From Peers

When you have finished writing questions, gather feedback from your researcher peers. Write down all the suggestions and feedback given by your peers. Don’t panic over the criticism of your questions. Remember that it’s still time to make necessary changes to the questionnaire before launching your campaign.

Step 8: Refine and Finalise the Questions

After gathering peer feedback, make necessary and appropriate changes to your questions. Be mindful of your research goals and topic. Try to modify your questions according to them. Also, be mindful of the theme and colour scheme of the questionnaire that you decided on. After refining the questions, finalise your questionnaire.

Types of Survey Questionnaires in Quantitative Research

Quantitative research questionnaires have close-ended questions that allow the researchers to measure accurate and specific responses from the participants. They don’t contain open-ended questions like qualitative research, where the response is measured by interviews and focus groups. Good combinations of questions are used in the quantitative research survey .

However, here are the types of surveys in quantitative research:

Descriptive Survey

The descriptive survey is used to obtain information about a particular variable. It is used to associate a quantity and quantify research variables. The questions associated with descriptive surveys mostly start with “What is” and “How much”.

Example: A descriptive survey to measure how much money children spend to buy toys.

Comparative Survey

A comparative survey is used to establish a comparison between one or more dependable variables and two or more comparison groups. This survey aims to form a comparative relation between the variables under study. The structure of the question in a comparative survey is, “What is the difference in [dependable variable] between [two or more groups]?”.

Example: A comparative survey on the difference in political awareness between Eastern and Western citizens.

Relationship-Based Survey

Relationship-based survey is used to understand the relationship or association between two or more independent and dependent variables. Cause and effect between two or more variables is measured in the relationship-based survey. The structure of questions in a relationship-based survey is, “What is the relation [between or among] [independent variable] and [dependable variable]?”.

Example: What is the relationship between education and lifestyle in America?

Advantages & Disadvantages of Questionnaires in Quantitative Research

Quantitative research questionnaires are an excellent tool to collect data and information about the responses of individuals. Quantitative research comes with various advantages, but along with advantages, it also has its disadvantages. Check the table below to learn about the advantages and disadvantages of a quantitative research questionnaire.

Quantitative Research Questionnaire Example

Here is an example of a quantitative research questionnaire to help you get the idea and create an efficient and well-developed questionnaire for your research:

Warm welcome, and thank you for participating in our survey. Please provide your response to the questions below. Your esteemed response will significantly help us to achieve our research goals and provide effective solutions to society.

17-20 21-24 25-28 29-32 ii) What is your gender? Male Female Other Prefer not to say ii) Have you graduated? Yes No iii) Are you employed? iv) Are you married? Yes No   Part 2: Provide your honest response.  Question 1: I have tried online shopping. Strongly Disagree Disagree Neutral  Agree Strongly Agree Question 2: I have good experience with online shopping. Strongly Disagree Disagree Neutral Agree Strongly Agree Question 3: I have a bad experience with online shopping. Question 4: I received my order on time.  Question 5: I like physical shopping more.  Frequently Asked Questions What is a quantitative research questionnaire. A quantitative research questionnaire is a well-structured set of questions designed specifically to gather specific and close-ended participant responses. What is the difference between qualitative and quantitative research? The difference between qualitative and quantitative research is subjectivity and objectivity. Subjectivity is associated with qualitative research, while objectivity is associated with quantitative research.  What are the advantages of a quantitative research questionnaire? It is quick and efficient. There is less risk of research bias and subjectivity. It is particular and simple. You May Also Like Content analysis is used to identify specific words, patterns, concepts, themes, phrases, or sentences within the content in the recorded communication. A survey includes questions relevant to the research topic. The participants are selected, and the questionnaire is distributed to collect the data. Inductive and deductive reasoning takes into account assumptions and incidents. Here is all you need to know about inductive vs deductive reasoning. USEFUL LINKS LEARNING RESOURCES COMPANY DETAILS How It Works Pardon Our Interruption As you were browsing something about your browser made us think you were a bot. There are a few reasons this might happen: You've disabled JavaScript in your web browser. You're a power user moving through this website with super-human speed. You've disabled cookies in your web browser. A third-party browser plugin, such as Ghostery or NoScript, is preventing JavaScript from running. Additional information is available in this support article . To regain access, please make sure that cookies and JavaScript are enabled before reloading the page. Extract insights from Customer & Employee Interviews. At Scale. Kinds of data in qualitative research examples. Home » Kinds of data in qualitative research examples Qualitative data types play a crucial role in understanding the complexities of human behavior and perspectives. Through methods such as interviews, focus groups, and content analysis, researchers gather rich descriptive data that often reveals in-depth insights hidden from quantitative approaches. This qualitative data provides a context that facilitates a deeper understanding of social phenomena, cultural narratives, and individual experiences. The diversity of qualitative data types encompasses various forms such as text, audio, and video. Each format has unique advantages, allowing researchers to capture nuances that often escape traditional numeric data. By engaging thoroughly with qualitative data types, researchers can illuminate patterns and themes that foster a comprehensive understanding of their subject matter, ultimately driving more informed decision-making and innovation in their respective fields. Qualitative Data Types: Unstructured Data Unstructured data is a core element of qualitative research, consisting of information that lacks a predefined format or structure. This type of data often arises from sources like interviews, open-ended survey responses, and social media posts. Unlike structured data, which is easily organized and analyzed, unstructured data requires more nuanced methods to derive meaningful insights. To effectively utilize unstructured data, researchers often engage in several key processes. First, they must collect data through methods such as in-depth interviews or focus groups, where participants share thoughts and feelings freely. Second, data analysis involves thematic coding to identify patterns and trends. Finally, interpreting the findings provides a deeper understanding of participant experiences and attitudes. Understanding how to navigate unstructured data is essential, as it offers rich, contextual insights into human behavior, which are invaluable for qualitative research. Interviews and Focus Groups: Exploring Human Experiences Interviews and focus groups provide a rich tapestry of qualitative data types, delving into the complexities of human experiences. Through one-on-one interviews, researchers can deeply engage with individuals, unearthing their personal narratives, emotions, and perceptions. This method allows for an intimate understanding of how personal backgrounds and circumstances shape viewpoints, making it invaluable for discussing topics like leadership and organizational culture. Focus groups, on the other hand, offer a dynamic setting where participants engage in collective discussions. This format encourages interaction and the sharing of diverse perspectives, revealing how group dynamics influence ideas and attitudes. By capturing the nuances of participant interactions, researchers can gather insights that are often overlooked in isolated interviews. Both methods enrich the data collected and highlight the intricate human experiences that underpin qualitative research. Open-Ended Surveys: Capturing Nuanced Responses Open-ended surveys are a valuable tool for qualitative research, providing in-depth insights and capturing nuanced responses from participants. Unlike closed-ended questions that require fixed answers, open-ended queries allow respondents to express their feelings and thoughts freely. This flexibility often leads to richer data, revealing complexities that pre-defined options may overlook. In administering open-ended surveys, researchers can adopt several strategies. First, crafting thoughtful questions encourages detailed responses, making it essential to consider wording carefully. Secondly, offering anonymity may increase honesty, as participants feel safer sharing candid opinions. Lastly, thorough analysis of the collected data can uncover trends and themes, contributing significantly to qualitative data types. This process informs decision-making and enhances understanding of the subject matter, making open-ended surveys an indispensable method in qualitative research. Qualitative Data Types: Structured Data In qualitative research, structured data is a crucial element that contributes to a comprehensive understanding of various phenomena. This type of data often consists of predetermined response categories and fixed formats, making it easier to analyze and interpret. For instance, surveys with multiple-choice questions or rating scales collect structured data, allowing researchers to quantify responses and identify trends. The clarity of structured data enables researchers to draw conclusions effectively and create visually appealing reports. Structured data types can be categorized into several forms, including quantitative surveys, checklists, and guided interviews. Each form serves a specific purpose in research. Quantitative surveys help in gathering numerical data quickly, while checklists ensure that key areas are covered during observations. Guided interviews provide a framework for discussions, promoting consistency in responses. Collectively, these structured data types enhance the richness of qualitative research, making it a valuable tool for uncovering insights in various fields. Observational Data: Recording In-Field Behaviors Observational data plays a crucial role in understanding in-field behaviors, serving as a direct reflection of interactions and responses in real-world settings. This method involves systematically recording behaviors as they occur, allowing researchers to capture authentic reactions and dynamics that might otherwise go unnoticed. By focusing on qualitative data types, researchers gain deep insights into participants' actions, expressions, and contextual factors influencing their behavior. To effectively utilize observational data, researchers should consider several key aspects. First, defining the specific behaviors of interest ensures that observations remain focused and relevant. Next, utilizing a structured observation checklist can help maintain consistency and detail in the data collected. Finally, documenting the context of behaviors, such as environmental factors and participant interactions, enriches the data's qualitative depth, ultimately leading to more accurate interpretations and conclusions. Through these practices, observational data becomes a powerful tool in qualitative research, offering valuable insights into human behavior. Document Analysis: Deriving Insights from Existing Texts Document analysis plays a crucial role in qualitative research by extracting meaningful insights from existing texts. This method involves examining various forms of qualitative data types, such as interview transcripts, documents, and historical records. By identifying themes and patterns within these texts, researchers can derive valuable insights that contribute to understanding broader contexts or specific phenomena. One effective approach in document analysis is to focus on keywords, recurring topics, and underlying sentiments. Through systematic coding and categorization, researchers can pinpoint significant trends and expert opinions that inform their inquiries. This analytical process not only unveils insights about risks and challenges but also highlights gaps in current knowledge. Ultimately, a rigorous document analysis provides a solid foundation for qualitative research, paving the way for more in-depth exploration and understanding of complex issues. Conclusion: Understanding Qualitative Data Types in Research Understanding qualitative data types is crucial for any research endeavor. These data types, including text, audio, and video, provide rich insights that quantitative data cannot. By recognizing the different forms of qualitative data, researchers can choose the best methods for analysis and draw meaningful conclusions. Each type of data serves unique purposes, allowing for a comprehensive understanding of participants and their experiences. In conclusion, mastery of qualitative data types enhances the overall quality and impact of research. An effective approach ensures that biases are minimized and insights are maximized. Qualitative research thrives on the depth of understanding it provides, making it essential for formulating strategies that resonate with audiences. Embracing these concepts can significantly improve the effectiveness of research initiatives. Turn interviews into actionable insights On this Page Coding for qualitative research: AI-powered tools You may also like, best computer-aided qualitative data analysis software. Best AI qualitative analysis programs for 2024 Best tools for qualitative data collection in 2024. Unlock Insights from Interviews 10x faster Request demo Get started for free Integrations What's new? In-Product Prompts Participant Management Interview Studies Prototype Testing Card Sorting Tree Testing Live Website Testing Automated Reports Templates Gallery Choose from our library of pre-built mazes to copy, customize, and share with your own users Browse all templates Financial Services Tech & Software Product Designers Product Managers User Researchers By use case Concept & Idea Validation Wireframe & Usability Test Content & Copy Testing Feedback & Satisfaction Content Hub Educational resources for product, research and design teams Explore all resources Question Bank Research Maturity Model Guides & Reports Help Center Future of User Research Report The Optimal Path Podcast User Research Aug 19, 2024 • 17 minutes read Qualitative research examples: How to unlock, rich, descriptive insights Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need. Armin Tanovic Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points. There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact. In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them. Qualitative UX research made easy Conduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making. 7 Qualitative research methods: An overview There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented. Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them. Method User interviews Focus groups Ethnographic research Qualitative observation Case study research Secondary research Open-ended surveys to extract descriptive insights. 1. User interviews A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives. Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights. When to use user interviews Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way. Interviews can be used at any stage in the product design and development process, being particularly helpful during: The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not How to conduct user interviews: The basics Draft questions based on your research objectives Recruit relevant research participants and schedule interviews Conduct the interview and transcribe responses Analyze the interview responses to extract insights Use your findings to inform design, product, and business decisions 💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform. User interviews: A qualitative research example Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'. You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide. One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?” Most respondents answer with a resounding and passionate ‘no’ with one of them expanding: “For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.” You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles. You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs. 2. Focus groups A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group. When to use focus groups You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews. Focus groups can be used before, during, and after a product launch. They’re ideal: Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas Post-launch: To evaluate and understand the collective opinion of your product’s user experience When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product How to conduct focus group studies: The basics Draft prompts to spark conversation, or a series of questions based on your UX research objectives Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session Conduct the focus group by talking and listening to users, then transcribe responses Analyze focus group responses and extract insights Use your findings to inform design decisions The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights. Focus groups: A qualitative research example You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles. However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle. Time to talk to users. You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why. A dialog develops: Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.” Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.” Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.” Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.” Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next. A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees. 3. Ethnographic research Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives. Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations. When to use ethnographic research Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process: Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product. Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice. How to conduct ethnographic research: Recruit users who are reflective of your audience Meet with them in their natural environment, and tell them to behave as they usually would Take down field notes as they interact with your product Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study Collect all your data and analyze it for insights While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants. ⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants. Ethnographic research: A qualitative research example You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO. The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface. You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?” The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.” You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details. 4. Qualitative observation Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research. When to qualitative observation Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during: Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product. Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments How to conduct qualitative observation: Recruit users who regularly use your product Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab Observe them and take down field notes based on what you notice Qualitative observation: An qualitative research example You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study. Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website. Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform. 5. Case study research Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users. When to use case studies Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it. As for specific phases in the product design and development process: Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations How to conduct case studies: Outline an objective for your case study such as examining specific user tasks or the overall user journey Select qualitative research methods such as interviews, ethnographic studies, or observations Collect and analyze your data for comprehensive insights Include your findings in a report with proposed solutions Case study research: A qualitative research example Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns. To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow. With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points. Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns. 6. Secondary research Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective. Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application. When to use secondary research Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include: Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market Design and development phase: See what solutions are working in other contexts for an idea of how to build yours Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process. How to conduct secondary research: Outline your UX research objective Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors Analyze your data by identifying recurring patterns and themes for insights Secondary research: A qualitative research example SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research. Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached. After checking your other social media channels and review sites, the issue pops up again. With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security. By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users. 7. Open-ended surveys Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses. When to use open-ended surveys User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful: When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it. How to conduct open-ended surveys: Design your survey and draft out a list of survey questions Distribute your surveys to respondents Analyze survey participant responses for key themes and patterns Use your findings to inform your design process Open-ended surveys: A qualitative research example You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions. To better understand if users find the new feature helpful, you create an open-ended, in-app survey. The questions you ask your users: “What has been your experience with our new predictive analytics feature?" “Do you find it easy or difficult to rework your routes based on our predictive suggestions?” “Does the predictive analytics feature make planning routes easier? Why or why not?” Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time. To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly. 💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports. Qualitative research vs. quantitative research: What’s the difference? Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different. Here are some of the key differences between qualitative research and quantitative research . Research type Qualitative research . Quantitative research Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation. Conduct qualitative research with Maze You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why. So, what about how —how do you identify your 'what' and your 'why'? The answer is with a user research tool like Maze. Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster. Frequently asked questions about qualitative research examples What is qualitative research? Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions. Can a study be both qualitative and quantitative? Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights. For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers. Is qualitative or quantitative research better? The choice between qualitative and quantitative research depends upon your research goals and objectives. Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions. What are some approaches to qualitative research? There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research: Grounded theory: Researchers study a topic and develop theories inductively Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved Ethnography: Researchers immerse themselves in organizations to understand how they operate Information Author Services Initiatives You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader. All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess . Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers. Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal. Original Submission Date Received: . Active Journals Find a Journal Proceedings Series For Authors For Reviewers For Editors For Librarians For Publishers For Societies For Conference Organizers Open Access Policy Institutional Open Access Program Special Issues Guidelines Editorial Process Research and Publication Ethics Article Processing Charges Testimonials Preprints.org SciProfiles Encyclopedia Article Menu Subscribe SciFeed Recommended Articles Google Scholar on Google Scholar Table of Contents Find support for a specific problem in the support section of our website. Please let us know what you think of our products and services. Visit our dedicated information section to learn more about MDPI. JSmol Viewer Progress in remote sensing and gis-based fdi research based on quantitative and qualitative analysis. 1. Introduction 2. research methods and data, 2.1. research methods, 2.2. data sources and screening, 2.3. data processing, 3. subject categories and publication trends, 3.1. subject evolution, 3.2. trends in the number and cited times of published papers, 4. the intellectual structure, 4.1. quantitative analysis, 4.2. qualitative analysis, 4.2.1. macro-environmental research at national, regional, and city scales, 4.2.2. global industrial development and layout, 4.2.3. research on global value chains, 4.2.4. micro-information geography of tncs, 4.2.5. internationalization and commercialization of geo-information industry, 4.2.6. multiple data and interdisciplinary approaches, 5. discussions and conclusions, data availability statement, acknowledgments, conflicts of interest. 1 (accessed on 13 July 2024). One date of launch is missing from the data set, but this has a minimal impact on the overall trend. 2 , accessed on 13 July 2024) is selected as the primary quantitative analysis tool in this paper. Friedmann, J. The world city hypothesis. Dev. Chang. 1986 , 17 , 69–83. [ Google Scholar ] [ CrossRef ] Sassen, S. The Global City: New York, London, Tokyo ; Princeton University Press: Princeton, NJ, USA, 2001. [ Google Scholar ] Scott, A.J. Global City-Regions: Trends, Theory, Policy ; Oxford University Press: Oxford, UK, 2001. [ Google Scholar ] Gregory, D.; Johnston, R.; Pratt, G.; Watts, M.; Whatmore, S. The Dictionary of Human Geography ; Wiley-Blackwell: New York, NY, USA, 2009; pp. 395–396, 771–772. [ Google Scholar ] Dicken, P. Global Shift: Mapping the Changing Contours of the World Economy , 7th ed.; Guilford Press: New York, NY, USA, 2015. [ Google Scholar ] Coe, N.M.; Hess, M.; Yeung, H.W.; Dicken, P.; Henderson, J. ‘Globalizing’regional development: A global production networks perspective. Trans. Inst. Br. Geogr. 2004 , 29 , 468–484. [ Google Scholar ] [ CrossRef ] Baker, J.C.; Williamson, R.A. Satellite imagery activism: Sharpening the focus on tropical deforestation. Singap. J. Trop. Geogr. 2006 , 27 , 4–14. [ Google Scholar ] [ CrossRef ] Charles, K.P.; Adolfo, C. Mascarenhas. Remote sensing in development. Science 1981 , 214 , 139–145. [ Google Scholar ] Torraco, R.J. Writing integrative literature reviews: Guidelines and examples. Hum. Resour. Dev. Rev. 2005 , 4 , 356–367. [ Google Scholar ] [ CrossRef ] Snyder, H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res. 2019 , 104 , 333–339. [ Google Scholar ] [ CrossRef ] Torraco, R.J. Writing integrative literature reviews: Using the past and present to explore the future. Hum. Resour. Dev. Rev. 2016 , 15 , 404–428. [ Google Scholar ] [ CrossRef ] Watson, R.T.; Webster, J. Analysing the past to prepare for the future: Writing a literature review a roadmap for release 2.0. J. Decis. Syst. 2020 , 29 , 129–147. [ Google Scholar ] [ CrossRef ] Onwuegbuzie, A.J.; Leech, N.L.; Collins, K.M.T. Qualitative analysis techniques for the review of the literature. Qual. Rep. 2012 , 17 , 1–28. [ Google Scholar ] [ CrossRef ] Su, D.Z. GIS-based urban modelling: Practices, problems, and prospects. Int. J. Geogr. Inf. Sci. 1998 , 12 , 651–671. [ Google Scholar ] [ CrossRef ] Rozas, L.W.; Klein, W.C. The Value and Purpose of the Traditional Qualitative Literature Review. J. Evid.-Based Soc. Work. 2010 , 7 , 387–399. [ Google Scholar ] [ CrossRef ] Chen, C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 2006 , 57 , 359–377. [ Google Scholar ] [ CrossRef ] Chen, C. Science map: A systematic review of the literature. J. Data Inf. Sci. 2017 , 2 , 1–40. [ Google Scholar ] Davis, J.; Mengersen, K.; Bennett, S.; Mazerolle, L. Viewing systematic reviews and meta-analysis in social research through different lenses. SpringerPlus 2014 , 3 , 1–9. [ Google Scholar ] [ CrossRef ] [ PubMed ] Svensson, R.B.; Haggar, G.J.A.; Aurum, A.; Hooper, V.J. The application of geographical information systems to multinational finance corporations. Int. J. Bus. Syst. Res. 2009 , 3 , 437–455. [ Google Scholar ] [ CrossRef ] Weber, P.; Chapman, D. Investing in geography: A GIS to support inward investment. Comput. Environ. Urban Syst. 2009 , 33 , 1–14. [ Google Scholar ] [ CrossRef ] Horn, S.A.; Cross, A.R. Japanese production networks in India: Spatial distribution, agglomeration and industry effects. Asia Pac. Bus. Rev. 2016 , 22 , 612–640. [ Google Scholar ] [ CrossRef ] Özdoğan, M.; Baird, I.G.; Dwyer, M.B. The role of remote sensing for understanding large-scale rubber concession expansion in Southern Laos. Land 2018 , 7 , 55. [ Google Scholar ] [ CrossRef ] Wang, X.; Zhang, Y.; Zhang, J.; Fu, C.; Zhang, X. Progress in urban metabolism research and hotspot analysis based on CiteSpace analysis. J. Clean. Prod. 2021 , 281 , 125224. [ Google Scholar ] [ CrossRef ] Chen, C.; Hu, Z.; Liu, S.; Tseng, H. Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace. Expert Opin. Biol. Ther. 2012 , 12 , 593–608. [ Google Scholar ] [ CrossRef ] Seto, K.C.; Kaufmann, R.K.; Woodcock, C.E. Landsat reveals China’s farmland reserves, but they’re vanishing fast. Nature 2000 , 406 , 121. [ Google Scholar ] [ CrossRef ] [ PubMed ] Seto, K.C.; Woodcock, C.E.; Song, C.; Huang, X.; Lu, J.; Kaufmann, R.K. Monitoring land-use change in the Pearl River Delta using Landsat TM. Int. J. Remote Sens. 2002 , 23 , 1985–2004. [ Google Scholar ] [ CrossRef ] Tan, M.; Li, X.; Li, S.; Xin, L.; Wang, X.; Li, Q.; Li, W.; Li, Y.; Xiang, W. Modeling population density based on nighttime light images and land use data in China. Appl. Geogr. 2018 , 90 , 239–247. [ Google Scholar ] [ CrossRef ] Zhao, M.; Cheng, W.; Zhou, C.; Li, M.; Huang, K.; Wang, N. Assessing spatiotemporal characteristics of urbanization dynamics in Southeast Asia using time series of DMSP/OLS nighttime light data. Remote Sens. 2018 , 10 , 47. [ Google Scholar ] [ CrossRef ] Liu, H.Y.; Tang, Y.K.; Chen, X.L.; Poznanska, J. The determinants of Chinese outward FDI in countries along “One Belt One Road”. Emerg. Mark. Financ. Trade 2017 , 53 , 1374–1387. [ Google Scholar ] [ CrossRef ] Du, J.; Zhang, Y. Does one belt one road initiative promote Chinese overseas direct investment? China Econ. Rev. 2018 , 47 , 189–205. [ Google Scholar ] [ CrossRef ] Duan, F.; Ji, Q.; Liu, B.Y.; Fan, Y. Energy investment risk assessment for nations along China’s Belt & Road Initiative. J. Clean. Prod. 2018 , 170 , 535–547. [ Google Scholar ] Huang, Y. Environmental risks and opportunities for countries along the Belt and Road: Location choice of China’s investment. J. Clean. Prod. 2019 , 211 , 14–26. [ Google Scholar ] [ CrossRef ] Yuan, J.; Li, X.; Xu, C.; Zhao, C.; Liu, Y. Investment risk assessment of coal-fired power plants in countries along the Belt and Road initiative based on ANP-Entropy-TODIM method. Energy 2019 , 176 , 623–640. [ Google Scholar ] [ CrossRef ] Hussain, J.; Zhou, K.; Guo, S.; Khan, A. Investment risk and natural resource potential in “Belt & Road Initiative” countries: A multi-criteria decision-making approach. Sci. Total Environ. 2020 , 723 , 137981. [ Google Scholar ] [ PubMed ] Hashemizadeh, A.; Ju, Y.; Bamakan, S.M.H.; Le, H.P. Renewable energy investment risk assessment in belt and road initiative countries under uncertainty conditions. Energy 2021 , 214 , 118923. [ Google Scholar ] [ CrossRef ] Dell’angelo, J.; D’odorico, P.; Rulli, M.C.; Marchand, P. The tragedy of the grabbed commons: Coercion and dispossession in the global land rush. World Dev. 2017 , 92 , 1–12. [ Google Scholar ] [ CrossRef ] D’Odorico, P.; Rulli, M.C.; Dell’Angelo, J.; Davis, K.F. New frontiers of land and water commodification: Socio-environmental controversies of large-scale land acquisitions. Land Degrad. Dev. 2017 , 28 , 2234–2244. [ Google Scholar ] [ CrossRef ] Davis, K.F.; Koo, H.I.; Dell’Angelo, J.; D’Odorico, P.; Estes, L.; Kehoe, L.J.; Kharratzadeh, M.; Kuemmerle, T.; Machava, D.; Pais, A.d.J.R.; et al. Tropical forest loss enhanced by large-scale land acquisitions. Nat. Geosci. 2020 , 13 , 482–488. [ Google Scholar ] [ CrossRef ] Liu, B.; Xue, D.; Zheng, S. Evolution and Influencing Factors of Manufacturing Production Space in the Pearl River Delta—Based on the Perspective of Global City-Region. Land 2023 , 12 , 419. [ Google Scholar ] [ CrossRef ] Tong, Y.; Zhou, H.; Jiang, L. Exploring the transition effects of foreign direct investment on the eco-efficiency of Chinese cities: Based on multi-source data and panel smooth transition regression models. Ecol. Indic. 2021 , 121 , 107073. [ Google Scholar ] [ CrossRef ] Wei, G.; Bi, M.; Liu, X.; Zhang, Z.; He, B.J. Investigating the impact of multi-dimensional urbanization and FDI on carbon emissions in the belt and road initiative region: Direct and spillover effects. J. Clean. Prod. 2023 , 384 , 135608. [ Google Scholar ] [ CrossRef ] Zou, Y.; Lu, Y.; Cheng, Y. The impact of polycentric development on regional gap of energy efficiency: A Chinese provincial perspective. J. Clean. Prod. 2019 , 224 , 838–851. [ Google Scholar ] [ CrossRef ] Schneider, A.; Seto, K.C.; Webster, D.R. Urban growth in Chengdu, Western China: Application of remote sensing to assess planning and policy outcomes. Environ. Plan. B Plan. Des. 2005 , 32 , 323–345. [ Google Scholar ] [ CrossRef ] Su, Y.; Lu, C.; Su, Y.; Wang, Z.; Huang, Y.; Yang, N. Spatio-temporal evolution of urban expansion based on a novel adjusted index and GEE: A case study of central plains urban agglomeration, China. Chin. Geogr. Sci. 2023 , 33 , 736–750. [ Google Scholar ] [ CrossRef ] Cao, R.; Zhu, J.; Tu, W.; Li, Q.; Cao, J.; Liu, B.; Zhang, Q.; Qiu, G. Integrating aerial and street view images for urban land use classification. Remote Sens. 2018 , 10 , 1553. [ Google Scholar ] [ CrossRef ] Tu, W.; Hu, Z.; Li, L.; Cao, J.; Jiang, J.; Li, Q.; Li, Q. Portraying urban functional zones by coupling remote sensing imagery and human sensing data. Remote Sens. 2018 , 10 , 141. [ Google Scholar ] [ CrossRef ] Yu, D.; Wei, Y.D. Spatial data analysis of regional development in Greater Beijing, China, in a GIS environment. Pap. Reg. Sci. 2008 , 87 , 97–119. [ Google Scholar ] [ CrossRef ] Cao, H.; Liu, J.; Chen, J.; Gao, J.; Wang, G.; Zhang, W. Spatiotemporal patterns of urban land use change in typical cities in the Greater Mekong Subregion (GMS). Remote Sens. 2019 , 11 , 801. [ Google Scholar ] [ CrossRef ] Krylov, V.A.; Kenny, E.; Dahyot, R. Automatic discovery and geotagging of objects from street view imagery. Remote Sens. 2018 , 10 , 661. [ Google Scholar ] [ CrossRef ] Huang, X.; Yang, J.; Li, J.; Wen, D. Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery. ISPRS J. Photogramm. Remote Sens. 2021 , 175 , 403–415. [ Google Scholar ] [ CrossRef ] Müller, M.F.; Penny, G.; Niles, M.T.; Ricciardi, V.; Chiarelli, D.D.; Davis, K.F.; Dell’angelo, J.; D’odorico, P.; Rosa, L.; Rulli, M.C.; et al. Impact of transnational land acquisitions on local food security and dietary diversity. Proc. Natl. Acad. Sci. USA 2021 , 118 , e2020535118. [ Google Scholar ] [ CrossRef ] Liu, B.; Xue, D.; Tan, Y. Deciphering the manufacturing production space in global city-regions of developing countries—A case of Pearl River Delta, China. Sustainability 2019 , 11 , 6850. [ Google Scholar ] [ CrossRef ] Zhang, P.; Yang, X.; Chen, H.; Zhao, S. Matching relationship between urban service industry land expansion and economy growth in China. Land 2023 , 12 , 1139. [ Google Scholar ] [ CrossRef ] Cho, K.; Goldstein, B.; Gounaridis, D.; Newell, J.P. Hidden risks of deforestation in global supply chains: A study of natural rubber flows from Sri Lanka to the United States. J. Clean. Prod. 2022 , 349 , 131275. [ Google Scholar ] [ CrossRef ] Shi, F.; Xu, H.; Hsu, W.L.; Lee, Y.C.; Zhu, J. Spatial pattern and influencing factors of outward foreign direct investment enterprises in the Yangtze River Economic Belt of China. Information 2021 , 12 , 381. [ Google Scholar ] [ CrossRef ] Yin, Y.; Liu, Y. Investment suitability assessment based on B&R symbiotic system theory: Location choice of China’s OFDI. Systems 2022 , 10 , 195. [ Google Scholar ] [ CrossRef ] Liu, C.; Yan, S. Transnational technology transfer network in China: Spatial dynamics and its determinants. J. Geogr. Sci. 2022 , 32 , 2383–2414. [ Google Scholar ] [ CrossRef ] Xu, Y.; Zuo, X.L. Technology roadmapping of geo-spatial information and application services industry in China. Forum Sci. Technol. China 2016 , 4 , 30–36. [ Google Scholar ] Robinson, D.K.R.; Mazzucato, M. The evolution of mission-oriented policies: Exploring changing market creating policies in the US and European space sector. Res. Policy 2019 , 48 , 936–948. [ Google Scholar ] [ CrossRef ] Auque, F. The space industry in the context of the European aeronautics and defence sector. Air Space Eur. 2000 , 2 , 22–25. [ Google Scholar ] [ CrossRef ] George, K.W. The economic impacts of the commercial space industry. Space Policy 2019 , 47 , 181–186. [ Google Scholar ] [ CrossRef ] von Maurich, O.; Golkar, A. Data authentication, integrity and confidentiality mechanisms for federated satellite systems. Acta Astronaut. 2018 , 149 , 61–76. [ Google Scholar ] [ CrossRef ] Zelnio, R.J. Whose jurisdiction over the US commercial satellite industry? Factors affecting international security and competition. Space Policy 2007 , 23 , 221–233. [ Google Scholar ] [ CrossRef ] Naqvi, S.A.A.; Naqvi, R.Z. Geographical information systems (GIS) in industry 4.0: Revolution for sustainable development. In Handbook of Smart Materials, Technologies, and Devices: Applications of Industry 4.0 ; Springer International Publishing: Cham, Switzerland, 2021; pp. 1–27. [ Google Scholar ] Kleemann, J.; Baysal, G.; Bulley, H.N.N.; Fürst, C. Assessing driving forces of land use and land cover change by a mixed-method approach in north-eastern Ghana, West Africa. J. Environ. Manag. 2017 , 196 , 411–442. [ Google Scholar ] [ CrossRef ] Chen, W.; Huang, H.; Dong, J.; Zhang, Y.; Tian, Y.; Yang, Z. Social functional mapping of urban green space using remote sensing and social sensing data. ISPRS J. Photogramm. Remote Sens. 2018 , 146 , 436–452. [ Google Scholar ] [ CrossRef ] Seto, K.C.; Kaufmann, R.K. Modeling the drivers of urban land use change in the Pearl River Delta, China: Integrating remote sensing with socioeconomic data. Land Econ. 2003 , 79 , 106–121. [ Google Scholar ] [ CrossRef ] Click here to enlarge figure Step Description Details 1 Topic identification Identify a knowledge domain using the broadest possible terms 2 Data collection Collect data of commonly used sources of scientific literature 3 Terms extract Extract research front terms 4 Time slicing Build time series models over time 5 Outcome layout Analyze domains and generate visualizations The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. Share and Cite Li, Z. Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis. Land 2024 , 13 , 1313. https://doi.org/10.3390/land13081313 Li Z. Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis. Land . 2024; 13(8):1313. https://doi.org/10.3390/land13081313 Li, Zifeng. 2024. "Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis" Land 13, no. 8: 1313. https://doi.org/10.3390/land13081313 Article Metrics Article access statistics, further information, mdpi initiatives, follow mdpi. Subscribe to receive issue release notifications and newsletters from MDPI journals Have a language expert improve your writing Run a free plagiarism check in 10 minutes, generate accurate citations for free. Knowledge Base Methodology Data Collection | Definition, Methods & Examples Data Collection | Definition, Methods & Examples Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023. Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem . While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider: The  aim of the research The type of data that you will collect The methods and procedures you will use to collect, store, and process the data To collect high-quality data that is relevant to your purposes, follow these four steps. Table of contents Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection. Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter? Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data : Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods . Qualitative data is expressed in words and analyzed through interpretations and categorizations. If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data. Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations. Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve. Prevent plagiarism. Run a free check. Based on the data you want to collect, decide which method is best suited for your research. Experimental research is primarily a quantitative method. Interviews , focus groups , and ethnographies are qualitative methods. Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods. Carefully consider what method you will use to gather data that helps you directly answer your research questions. Data collection methods Method When to use How to collect data Experiment To test a causal relationship. Manipulate variables and measure their effects on others. Survey To understand the general characteristics or opinions of a group of people. Distribute a list of questions to a sample online, in person or over-the-phone. Interview/focus group To gain an in-depth understanding of perceptions or opinions on a topic. Verbally ask participants open-ended questions in individual interviews or focus group discussions. Observation To understand something in its natural setting. Measure or survey a sample without trying to affect them. Ethnography To study the culture of a community or organization first-hand. Join and participate in a community and record your observations and reflections. Archival research To understand current or historical events, conditions or practices. Access manuscripts, documents or records from libraries, depositories or the internet. Secondary data collection To analyze data from populations that you can’t access first-hand. Find existing datasets that have already been collected, from sources such as government agencies or research organizations. When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in? For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ). Operationalization Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed. Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure. You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability. You ask their direct employees to provide anonymous feedback on the managers regarding the same topics. You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from. Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection. Standardizing procedures If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study. This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias . This helps ensure the reliability of your data, and you can also use it to replicate the study in the future. Creating a data management plan Before beginning data collection, you should also decide how you will organize and store your data. If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers). If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion. You can prevent loss of data by having an organization system that is routinely backed up. Finally, you can implement your chosen methods to measure or observe the variables you are interested in. The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns. To ensure that high quality data is recorded in a systematic way, here are some best practices: Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study. Double-check manual data entry for errors. If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality. Receive feedback on language, structure, and formatting Professional editors proofread and edit your paper by focusing on: Academic style Vague sentences Style consistency See an example If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples. Student’s  t -distribution Normal distribution Null and Alternative Hypotheses Chi square tests Confidence interval Cluster sampling Stratified sampling Data cleansing Reproducibility vs Replicability Peer review Likert scale Research bias Implicit bias Framing effect Cognitive bias Placebo effect Hawthorne effect Hindsight bias Affect heuristic Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations. When conducting research, collecting original data has significant advantages: You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website) You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods ) However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable. Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail. Reliability and validity are both about how well a method measures something: Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions). Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure). If you are doing experimental research, you also have to consider the internal and external validity of your experiment. Operationalization means turning abstract conceptual ideas into measurable observations. For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations. Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure. In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question . Cite this Scribbr article If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator. Bhandari, P. (2023, June 21). Data Collection | Definition, Methods & Examples. Scribbr. Retrieved August 21, 2024, from https://www.scribbr.com/methodology/data-collection/ Is this article helpful? Pritha Bhandari Other students also liked, qualitative vs. quantitative research | differences, examples & methods, sampling methods | types, techniques & examples, "i thought ai proofreading was useless but..". I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes” 411 COMMENTS Quantitative Data Quantitative data refers to numerical data that can be measured or counted. This type of data is often used in scientific research and is typically collected through methods such as surveys, experiments, and statistical analysis. A Practical Guide to Writing Quantitative and Qualitative Research To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6) 16 and qualitative research ( Table 7) 17, and how to transform these ambiguous research question (s) and hypothesis (es) into clear and good statements. Presenting the Results of Quantitative Analysis Mikaila Mariel Lemonik Arthur. This chapter provides an overview of how to present the results of quantitative analysis, in particular how to create effective tables for displaying quantitative results and how to write quantitative research papers that effectively communicate the methods used and findings of quantitative analysis. A Really Simple Guide to Quantitative Data Analysis The research uses a quantitative approach, mainly quantitative descriptive. [7] For quantitative descriptive data analysis, it begins by entering data into raw data. Quantitative Data Analysis: A Comprehensive Guide Dive into the concept of quantitative data analysis. Understand its steps, benefits and methods, and learn the importance of data analysis in quantitative research. A practical guide to data analysis in general literature reviews This article seeks to describe a systematic method of data analysis appropriate for undergraduate research theses, where the data consists of the results from available published research. We present a step-by-step guide with authentic examples and practical tips. Quantitative Methods Definition Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular ... The Beginner's Guide to Statistical Analysis The Beginner's Guide to Statistical Analysis | 5 Steps & Examples Statistical analysis means investigating trends, patterns, and relationships using quantitative data. It is an important research tool used by scientists, governments, businesses, and other organizations. What Is Quantitative Research? Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations. (PDF) Quantitative Analysis: the guide for beginners quantitative (numbers) and qualitative (words or images) data. The combination of. quantitative and qualitative research methods is called mixed methods. For example, first, numerical data are ... Writing Quantitative Research Studies Summarizing quantitative data and its effective presentation and discussion can be challenging for students and researchers. This chapter provides a framework for adequately reporting findings from quantitative analysis in a research study for those contemplating to write a research paper. The rationale underpinning the reporting methods to ... What Is Quantitative Research? An Overview and Guidelines Abstract. In an era of data-driven decision-making, a comprehensive understanding of quantitative research is indispensable. Current guides often provide fragmented insights, failing to offer a holistic view, while more comprehensive sources remain lengthy and less accessible, hindered by physical and proprietary barriers. Quantitative Data Analysis 101 Learn the basics of quantitative data analysis, including the most popular analysis methods and techniques, explained simply with examples. Qualitative vs. Quantitative Research When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Data Analysis in Quantitative Research Quantitative data analysis serves as part of an essential process of evidence-making in health and social sciences. It is adopted for any types of research question and design whether it is descriptive, explanatory, or causal. However, compared with qualitative counterpart, quantitative data analysis has less flexibility. PDF Introduction to quantitative research While quantitative research is based on numerical data analysed statistically, qual-itative research uses non-numerical data. Qualitative research is actually an umbrella term encompassing a wide range of methods, such as interviews, case studies, ethnographic research and discourse analysis, to name just some examples. What is Quantitative Research? Research involving the collection of data in numerical form for quantitative analysis. The numerical data can be durations, scores, counts of incidents, ratings, or scales. Quantitative data can be collected in either controlled or naturalistic environments, in laboratories or field studies, from special populations or from samples of the ... Examples of Quantitative Research Questions Learn the secrets of quantitative research with examples of quantitative research questions. How to formulate clear and concise inquiries. Quantitative Research Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. (PDF) An Overview of Quantitative Research Methods Quantitative research is a systematic empirical inquiry of observable phenomena through numerical data collection, statistical analysis and mathematical models. How to appraise quantitative research In quantitative studies, the data analysis varies between studies depending on the type of design used. For example, descriptive, correlative or experimental studies all vary. Quantitative Research Questionnaire Quantitative research questions allow researchers to gather empirical data to answer their research problems. As we have discussed the different types of quantitative research questions above, it's time to learn how to write the perfect quantitative research questions for a questionnaire and streamline your research process. Quantitative Research Article Review: Guidelines & Tips Quantitative data analysis is based on precise measurements using structured and validated data-collection instruments. The final research report for a quantitative study is a statistical report with correlations, comparisons of means, and the statistical significance of Reporting Research Results in APA Style The results section of a quantitative research paper is where you summarize your data and report the findings of any relevant statistical analyses. The APA manual provides rigorous guidelines for what to report in quantitative research papers in the fields of psychology, education, and other social sciences. Kinds of data in qualitative research examples Qualitative data types play a crucial role in understanding the complexities of human behavior and perspectives. Through methods such as interviews, focus groups, and content analysis, researchers gather rich descriptive data that often reveals in-depth insights hidden from quantitative approaches. Qualitative Research: 7 Methods and Examples Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need. (PDF) Quantitative Data Analysis Descriptive analysis is a quantitative data analysis approach that assists researchers in presenting data in an easily understood, quantitative format, assisting in the interpretation and ... Information The research findings can provide quantitative decision support for cost estimators in the early estimation stages of residential project investment decision-making. ... Combining the research results of domestic and international scholars with the relatively small sample size of the data in this paper, to maximize data utilization, a grey ... Progress in Remote Sensing and GIS-Based FDI Research Based on ... In recent studies, scholars have employed a range of research methods, integrated various data sources, and utilized a mixed-method approach that combines qualitative and quantitative data analyses from the natural and social sciences [19,65,66,67]. Data Collection Learn how to collect data for your research project, whether it is qualitative or quantitative, and explore the advantages and disadvantages of different methods and examples. assignment essay homework paper phd report speech thesis