Glossary of Quantitative and Qualitative Market Research Terms

Introduction

Market research is a critical component in understanding consumer behaviour, market trends, and the competitive landscape. By employing a combination of quantitative and qualitative research methods, businesses can gather comprehensive data to inform their strategies and decisions. This glossary provides an extensive list of terms used in both quantitative and qualitative market research, offering detailed explanations to help professionals navigate and apply these concepts effectively. Understanding these terms is essential for conducting robust research and deriving meaningful insights.

Quantitative Market Research Terms

1. Census:

A census involves collecting data from every member of a population. It provides a comprehensive overview, allowing for detailed analysis and insights into the entire group. This method is often used in government surveys and large-scale studies.

2. Closed-Ended Questions:

Closed-ended questions in surveys offer respondents a fixed set of responses, making the data easier to quantify and analyse. These questions are typically used in quantitative research to gather specific information efficiently.

3. Cross-Tabulation (Cross-Tab):

Cross-tabulation is a statistical tool used to examine relationships between two or more variables by dividing data into subgroups. It helps identify patterns and correlations within the data, providing deeper insights into survey results.

4. Data Cleaning:

Data cleaning involves detecting and correcting or removing errors and inconsistencies from datasets. This crucial step ensures the accuracy and reliability of the data, leading to more valid research findings.

5. Data Collection:

Data collection is the systematic process of gathering information for a specific purpose. Methods include surveys, questionnaires, and online polls, aimed at acquiring accurate and relevant data for analysis.

6. Data Mining:

Data mining is the practice of examining large datasets to generate new information. It involves using statistical techniques and algorithms to discover patterns, correlations, and trends within the data.

7. Descriptive Statistics:

Descriptive statistics summarize and describe the main features of a dataset. Common measures include mean, median, mode, and standard deviation, providing a basic understanding of the data distribution.

8. Likert Scale:

The Likert scale is a psychometric scale used in surveys to measure respondents’ attitudes or behaviours. It typically involves a series of statements with a range of response options, such as “strongly agree” to “strongly disagree.”

9. Longitudinal Study:

A longitudinal study involves repeated observations of the same variables over an extended period. This approach helps identify changes and trends, offering valuable insights into long-term effects and developments.

10. Margin of Error:

The margin of error quantifies the amount of random sampling error in survey results. It indicates the range within which the true population parameter is expected to fall, providing a measure of the survey’s precision.

11. Mean:

The mean, or average, is a measure of central tendency calculated by summing all values in a dataset and dividing by the number of values. It provides a simple representation of the data’s central point.

12. Median:

The median is the middle value in a sorted list of numbers. It is a robust measure of central tendency, especially useful when the data distribution is skewed or contains outliers.

13. Mode:

The mode is the value that appears most frequently in a dataset. It is a simple measure of central tendency, highlighting the most common occurrence within the data.

14. Multivariate Analysis:

Multivariate analysis involves examining the relationships between three or more variables simultaneously. It helps researchers understand complex interactions and dependencies within the data.

15. Population:

The population in market research refers to the entire group being studied. Understanding the population is crucial for designing representative samples and ensuring the validity of research findings.

16. Probability Sampling:

Probability sampling is a technique where each member of the population has a known, non-zero chance of being selected. This method ensures the sample is representative, enhancing the reliability of the results.

17. Random Sampling:

Random sampling involves selecting participants from the population so that each individual has an equal chance of being chosen. This method reduces selection bias, contributing to more accurate and generalizable results.

18. Regression Analysis:

Regression analysis is a set of statistical techniques used to estimate the relationships among variables. It helps identify which factors significantly impact the outcome, enabling predictive modelling and analysis.

19. Response Rate:

The response rate is the percentage of people who complete a survey out of the total number invited. A higher response rate typically indicates more reliable and valid survey results.

20. Sample:

A sample is a subset of the population selected for study. Sampling allows researchers to make inferences about the population without studying every member, saving time and resources.

21. Standard Deviation:

Standard deviation measures the amount of variation or dispersion in a set of values. A low standard deviation indicates that values are close to the mean, while a high standard deviation suggests greater variability.

22. Survey:

A survey is a method of collecting data from respondents to gain insights into various topics of interest. Surveys can be conducted online, by phone, or in person, and are widely used in market research.

23. Validity:

Validity refers to the extent to which a concept, conclusion, or measurement is well-founded and accurately represents the real world. High validity indicates that the research results are credible and trustworthy.

24. Variable:

A variable is any characteristic, number, or quantity that can be measured or quantified. Variables are fundamental in research as they represent the traits or attributes being studied.

25. Confidence Interval:

A confidence interval is a range of values, derived from sample data, that is likely to contain the value of an unknown population parameter. It provides an estimate of the uncertainty around the sample statistic.

26. Correlation Coefficient:

The correlation coefficient is a statistical measure that describes the strength and direction of a relationship between two variables. Values range from -1 to +1, indicating negative, positive, or no correlation.

27. Hypothesis Testing:

Hypothesis testing is a method of making decisions using data. It involves making an initial assumption (the null hypothesis) and testing whether the sample data provide sufficient evidence to reject this assumption.

28. Inferential Statistics:

Inferential statistics involve methods that use sample data to make generalizations about a population. Techniques include estimation, hypothesis testing, and regression analysis.

29. Non-Probability Sampling:

Non-probability sampling is a sampling technique where not all members of the population have a chance of participating. This method includes convenience sampling, judgmental sampling, and quota sampling.

30. Outlier:

An outlier is a data point that differs significantly from other observations. Outliers can indicate variability in measurement, experimental errors, or novel findings.

31. Panel Study:

A panel study is a type of longitudinal study where data are collected from the same individuals (the panel) at multiple points in time. This method tracks changes over time within the same sample group.

32. Quota Sampling:

Quota sampling involves selecting a sample that reflects the characteristics of the population. Researchers ensure that certain traits or segments are represented proportionately to their occurrence in the population.

33. Reliability:

Reliability refers to the consistency of a measure. A research instrument is considered reliable if it produces the same results under consistent conditions.

34. Sampling Frame:

A sampling frame is a list or database from which a sample is drawn. It should accurately represent the population to ensure the sample is representative.

35. Stratified Sampling:

Stratified sampling involves dividing the population into subgroups (strata) and sampling from each stratum. This method ensures that specific segments of the population are adequately represented.

36. T-Test:


A t-test is a statistical test used to compare the means of two groups. It determines whether the differences between the groups are statistically significant.

Quantitative Market Research Terms

1. Case Study:

A case study is an in-depth exploration of a single instance or event, providing detailed insights and understanding. This method allows researchers to investigate complex issues in real-life contexts.

2. Content Analysis:

Content analysis involves studying documents and communication artifacts to interpret and quantify their content. This method can be applied to texts, images, audio, and video, revealing patterns and themes.

3. Ethnography:

Ethnography is a qualitative research method that involves observing and interviewing participants in their real-life environment. It aims to understand cultural and social dynamics from the participants’ perspectives.

4. Focus Group:

A focus group is a moderated discussion with a small group of participants, used to gather opinions, beliefs, and attitudes about a specific topic. This method provides rich, qualitative data and insights.

5. Grounded Theory:

Grounded theory is a research method that involves building theory from data, rather than testing existing theories. It focuses on generating new theories through systematic data collection and analysis.

6. In-Depth Interview:

In-depth interviews are qualitative research techniques involving intensive individual interviews with a small number of respondents. These interviews explore respondents’ perspectives on a particular topic in detail.

7. Narrative Analysis:

Narrative analysis is a method of interpreting the stories and personal accounts provided by research participants. It focuses on understanding the meaning and significance of these narratives.

8. Observational Research:

Observational research involves observing ongoing behaviour in a natural setting. This method provides real-time insights into how people interact with their environment and each other.

9. Open-Ended Questions:

Open-ended questions in surveys allow respondents to answer in their own words, providing richer and more detailed responses. These questions are essential in qualitative research for exploring participants’ thoughts and feelings.

10. Phenomenology:

Phenomenology is a qualitative research approach focused on studying individuals’ lived experiences. It aims to understand the essence of experiences and how they are perceived by participants.

11. Purposive Sampling:

Purposive sampling is a non-probability sampling method where the researcher selects participants based on specific characteristics or qualities. This method ensures that the sample includes relevant and informative individuals.

12. Saturation:

Saturation in qualitative research is the point where no new information or themes are observed in the data. Reaching saturation indicates that the data collection is complete and comprehensive.

13. Semi-Structured Interview:

Semi-structured interviews combine pre-determined questions with the flexibility to explore topics in more depth. This approach balances consistency with the opportunity for deeper insights.

14. Thematic Analysis:

Thematic analysis is a method for identifying, analysing, and reporting patterns (themes) within data. It is widely used in qualitative research to interpret complex data and uncover underlying meanings.

15. Transcription:

Transcription is the process of converting audio or video recordings of interviews or discussions into text format. Accurate transcription is essential for thorough analysis and interpretation of qualitative data.

16. Triangulation:

Triangulation uses multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena. This approach enhances the validity and reliability of the research findings.

17. Unstructured Interview:

Unstructured interviews do not follow a specific set of questions, allowing the conversation to flow naturally. This method is useful for exploring topics in-depth and understanding participants’ perspectives.

18. Visual Data:

Visual data includes images, videos, or other visual formats used in qualitative research. Analysing visual data can reveal insights that might not be apparent through text-based data alone.

19. Coding:

Coding in qualitative research involves categorising and labelling data to identify different themes and patterns. This process helps organise and interpret complex data sets.

20. Memoing:

Memoing is the practice of recording reflective notes about what the researcher is learning from the data. These memos capture insights, ideas, and observations that inform the research analysis.

 

Summary

This comprehensive glossary covers essential terms in both quantitative and qualitative market research. From data collection methods like surveys and focus groups to analytical techniques such as regression analysis and thematic analysis, these terms provide a foundation for understanding and conducting effective market research. By familiarising yourself with these concepts, you can enhance the accuracy and depth of your research, leading to more informed business decisions and strategies. Whether you are a seasoned researcher or new to the field, this glossary is a valuable resource for navigating the complexities of market research.