Research Methods- Data analysis: Kinds of data

Question 1

Qualitative data -

Answer 1

Data that is expressed in words and non-numerical (although qualitative data may be converted to numbers for the purposes of analysis).

Question 2

Quantitative data -

Answer 2

Data that can be counted, usually given as numbers.

Question 3

Primary data -

Answer 3

Information that has been obtained firsthand by the researcher for the purposes of a research project. In psychology, such data is often gathered directly from participants as part of an experiment, self-report or observation.

Question 4

Secondary data -

Answer 4

Information that has already been collected by someone else and so pre-dates the current research project. In psychology, such data might include the work of other psychologists or government statistics.

Question 5

Meta-analysis -

Answer 5

Research about research’, refers to the process of combining results from a number of studies on a particular topic to provide an overall view. This may involve a qualitative review of conclusions and/or a quantitative analysis of the results producing an effect size.

Question 6

What is qualitative data?

Answer 6

Qualitative data is expressed in words and includes descriptions of thoughts, feelings, and opinions, such as interview transcripts, diary extracts, or observational notes.

Question 6

What is an example of qualitative data?

Answer 6

A transcript from an interview or notes from a counseling session are examples of qualitative data.

Question 6

What is quantitative data?

Answer 6

Quantitative data is expressed numerically and includes individual scores or measurements, such as the number of words recalled in a memory experiment.

Question 6

What is an example of quantitative data?

Answer 6

The number of hours participants spend on social media per day or their scores on a depression scale are examples of quantitative data.

Question 6

What is the difference between qualitative and quantitative data?

Answer 6

Qualitative data is descriptive and expressed in words, while quantitative data is numerical and can be analyzed statistically.

Question 7

Which type of data is better, qualitative or quantitative?

Answer 7

Neither is inherently better; the choice depends on the research aims. Qualitative data provides depth and detail, while quantitative data allows for statistical analysis and comparisons.

Question 8

Can qualitative data be converted into quantitative data?

Answer 8

Yes, qualitative data can sometimes be converted into numerical form, such as counting the frequency of specific themes or responses.

Question 9

What is primary data?

Answer 9

Primary data is original data collected specifically for the research, such as data from experiments, questionnaires, interviews, or observations.

Question 10

What is an example of primary data?

Answer 10

Responses from a survey conducted by the researcher or results from a laboratory experiment are examples of primary data.

Question 11

What is secondary data?

Answer 11

Secondary data is data collected by someone else, such as government statistics, journal articles, or organizational records.

Question 12

What is an example of secondary data?

Answer 12

Census data, employee absence records, or published research findings are examples of secondary data.

Question 13

What is a strength of qualitative data?

Answer 13

It provides rich, detailed insights into participants’ thoughts, feelings, and experiences, offering greater external validity.

Question 14

What is a limitation of qualitative data?

Answer 14

It is difficult to analyze statistically, and conclusions may rely on subjective interpretations, which can introduce bias.

Question 15

What is a strength of quantitative data?

Answer 15

It is easy to analyze statistically, allowing for objective comparisons between groups and clear patterns to be identified.

Question 16

What is a limitation of quantitative data?

Answer 16

It may lack depth and fail to capture the complexity of real-life experiences, providing a narrower scope of meaning.

Question 17

What is a strength of primary data?

Answer 17

It is tailored to the researcher’s specific needs and provides authentic, firsthand information from participants.

Question 18

What is a strength of secondary data?

Answer 18

It is often inexpensive, easily accessible, and may already include statistical analysis, saving time and effort.

Question 18

What is a limitation of primary data?

Answer 18

It requires significant time, effort, and resources to collect, making it more labor-intensive than secondary data.

Question 19

What is an example of converting qualitative data to quantitative data?

Answer 19

Counting the number of times a specific theme (e.g., “stress”) appears in interview transcripts to create numerical data.

Question 19

What is a limitation of secondary data?

Answer 19

It may vary in quality, accuracy, and relevance, and may not fully align with the researcher's objectives.

Question 20

What is an advantage of using both qualitative and quantitative data?

Answer 20

Combining both types of data can provide a more comprehensive understanding of the research topic, balancing depth with statistical rigor.

Question 21

What is a limitation of relying solely on secondary data?

Answer 21

The data may be outdated, incomplete, or not fully relevant to the research question, limiting its usefulness.

Question 22

What is an example of a research method that collects qualitative data?

Answer 22

Unstructured interviews or open-ended questionnaire responses are methods that collect qualitative data.

Question 23

What is an example of a research method that collects quantitative data?

Answer 23

Surveys with closed-ended questions or experiments measuring numerical outcomes collect quantitative data.

Question 24

What is the importance of external validity in qualitative data?

Answer 24

High external validity means the findings are more applicable to real-life situations, as qualitative data captures participants' authentic experiences.

Question 25

What is the importance of objectivity in quantitative data?

Answer 25

Objectivity reduces bias, ensuring that the data and conclusions are based on measurable, numerical evidence rather than subjective interpretations.

Question 26

What is an example of a research scenario where qualitative data would be more useful?

Answer 26

Exploring participants' personal experiences of grief would benefit from qualitative data to capture the depth and complexity of their emotions.

Question 27

What is an example of a research scenario where quantitative data would be more useful?

Answer 27

Measuring the effectiveness of a new drug by comparing symptom reduction across treatment groups would require quantitative data for statistical analysis.

Question 28

What is a limitation of using primary data in large-scale studies?

Answer 28

Collecting primary data for large-scale studies can be time-consuming and resource-intensive, making it less practical than using secondary data.

Question 29

What is a strength of using secondary data in exploratory research?

Answer 29

Secondary data can provide a quick and cost-effective way to identify trends or patterns, guiding the direction of further research.

Question 30

What is an example of a situation where secondary data might be insufficient?

Answer 30

If the research question requires specific, tailored information that existing datasets do not provide, primary data collection would be necessary.

Question 31

What is the role of bias in qualitative data analysis?

Answer 31

Bias can arise from the researcher's subjective interpretations, especially if they have preconceived expectations about the findings.

Question 32

What is an example of a research method that combines qualitative and quantitative data?

Answer 32

A mixed-methods study might use surveys (quantitative) to measure behavior frequency and follow-up interviews (qualitative) to explore participants' motivations.

Question 32

What is the role of statistical analysis in quantitative data?

Answer 32

Statistical analysis allows researchers to identify patterns, test hypotheses, and draw objective conclusions from numerical data.