Specification: Quantitative and qualitative data; the distinction between qualitative and quantitative data collection techniques. Primary and secondary data, including meta-analysis. 

Quantitative data

Quantitative data is numerical data that can be statistically analysed and converted easily into a graphical format. Experiments, structured observations, correlations and closed/rating‐scale questions from questionnaires all produce quantitative data. 

Evaluation of quantitative data

A strength of quantitative data is that it is easy to analyse statistically. When large amounts of numerical data are generated it is relatively easy to conduct descriptive statistics or inferential tests of significance which allow for comparisons and trends to be identified between groups. Since established mathematical procedures are in place for this type of analysis it makes quantitative data more objective.

A disadvantage of quantitative data is its lack of representativeness. Since this type of data is often generated from closed questions, the responses gained are narrow in their scope towards explaining complex human behaviour. This means that, in comparison to qualitative data, the numerical findings can often lack meaning and context. As such, it may not be a true representation of real life and thus lacks validity.

Qualitative data

Qualitative data is non‐numerical, language‐based data expressed in words which is collected through semi‐structured or unstructured interviews and open questions in a questionnaire. It allows researchers to develop an insight into the unique nature of human experiences, opinions and feelings.

Evaluation of qualitative data

A strength of obtaining qualitative data is the rich detail obtained by the researcher. Since participants can develop their responses freely this provides the investigator with meaningful insights into the human condition. Because of this, the external validity of findings is enhanced as they are more likely to represent an accurate real‐world view.

A limitation of qualitative data is that it can be subjective. Due to the rich, and often lengthy, detail of participants’ responses, interpretations of this type of data can often rely on the opinions and judgements of the researcher. This means that any preconceptions that the researcher holds may act to bias any conclusions drawn.

Primary data

Primary data refers to data that has been collected for a specific reason and reported by the original researcher. It is data that the participant reports directly to the researcher (often via an interview /questionnaire) or is witnessed first‐hand (via an observation/experiment). Primary data is sometimes referred to as field research. 

Evaluation of primary data

A strength of primary data is its authenticity. This is because it is collected with the sole purpose of being for a specific investigation. Since the data collection is designed to suit the aims of the research, this enables the researcher to exert a high level of control. This is advantageous as there is a greater probability that the data generated will fit the aims of the investigation, reducing any wasted time on behalf of the researcher and ensuring that the information prepared for analysis is relevant.

There are limitations of gathering primary data. Designing and carrying out a psychological study can take a long period of time and considerable effort. This means that expenses can accrue due to the time investment needed on behalf of the researchers in addition to any equipment that needs to be purchased. Therefore, in comparison to primary data, secondary data which already exists can save the researcher time, effort and money.

Secondary data

Secondary data is information that was collected by other researchers for a purpose other than the investigation in which it is currently being used. In other words, it is data which already exists. Examples include Government reports like the census or statistics about mental health from the NHS. It is sometimes referred to as desk research because the significance of the data is already known.

Evaluation of secondary data

There are many strengths to using secondary data. Since the information already exists in the public domain, it means that it is much less time consuming and expensive to collect. This means that researchers can find the information they desire with very little effort. This makes the collection and use of secondary data much easier when compared with primary data.

A limitation of using secondary data involves concerns over accuracy. Given the information was not gathered to meet the specific aim of the research, it stands to reason that there may be significant variability in the quality of the data. This means that much of the data may be of little or no value to the researchers.

Meta-analysis

Meta‐analysis refers to a process whereby investigators combine findings from multiple studies (secondary data) on a specific phenomenon to make an overall analysis of trends and patterns arising across research. This can include a qualitative review of previous research or a statistical, quantitative analysis to test for significance of effect size. An example of a meta‐analysis from developmental psychology is that conducted by van Ijzendoorn et al., investigating cross‐cultural variations in attachment. In total, their analysis examined 32 studies from eight different countries that had used Ainsworth’s strange situation. In total, the results of over 1,990 infants were included in the analysis.

Evaluation of meta-analysis

There are advantages of adopting a meta‐analysis methodology. Since the results are combined from many studies, rather than just one, the conclusions drawn will be based on a larger sample which provides greater confidence for generalisation. This, therefore, serves to increase the validity of the patterns and trends identified.

There are issues of bias associated with meta‐analyses. Since the researcher is selecting data from research which has already taken place, they may choose to omit certain findings from their investigation. This could be particularly true if the previous findings showed no significant results or were inconclusive. As a result, the findings and conclusions from the meta‐analysis will be biased as they do not accurately represent all of the relevant data on the topic.

Possible exam questions

1. Define what is meant by quantitative data. (1 mark)

2. Explain one difference between primary and secondary data. (2 marks)

3. Suggest two reasons why behaviourists do not collect qualitative data in their investigations. (2 marks)

4. Dr Khanom and Dr Begum are researchers interested in investigating attitudes including racial prejudice. They decided that a self‐report questionnaire, comprising 20 items with a number of fixed responses for each, would be the best methodology for their study. Identify the type of data Dr Khanom and Dr Begum will be collecting. Provide justification for your choice. (2 marks)

5. Psychologists sometimes collect quantitative data. Outline one study in which a psychologist collected quantitative data. In your answer, explain how the data collection technique was quantitative. (3 marks)