Types of data

Qualitative data

- Expressed in words, rather than numbers or statistics, and may take the form of a written description of the thoughts, feelings, and opinions of participants (or a written account of what the researcher saw in the case of an observation)

Quantitative data

- Expressed numerically. Collection techniques providing this usually gather numerical data in the form of individual scores from participants such as the number of words a person was able to recall in a memory experiment. Data is open to being analysed statistically and can be converted into graphs, charts, etc

Primary data

- Refers to original data that has been collected specifically for the purpose of the investigation by the researcher. It is data that arrives first-hand from the participants themselves

- Data that is gathered by conducting an experiment, questionnaire, interview, or observation would be classed as this

Secondary data

- Data that has been collected by someone other than the person who is conducting the research. It is data that already exists before the psychologist begins their research or investigation. Has already been subject to statistical testing and therefore significance is known

Examples of secondary data

- May be located in journal articles, books, or websites. Can also be statistical information held by the government (population records, etc.)

Examples of primary data

- Data that is gathered by conducting an experiment, questionnaire, interview, or observation would be classed as this

Evaluation of qualitative data: Strengths

- Offers much more richness of detail than quantitative data. It is much broader in scope and gives the participant/respondent the opportunity to more fully report their thoughts, feelings, and opinions on a given subject

- Tends to have greater external validity than quantitative data; it provides the researcher with a more meaningful insight into the participant's worldview

Evaluation of qualitative data: Weaknesses

- Often difficult to analyse. It tends not to lend itself to being summarised statistically so that patterns and comparisons within and between data may be hard to identify

- Conclusions often rely on the subjective interpretations of the researcher and these may be subject to bias, particularly if the researcher has preconceptions about what he/she is expecting to find

Evaluation of quantitative data: Strengths

- Relatively simple to analyse, therefore comparisons between groups can be easily drawn.Also, data in numerical form tends to be more objective and less open to bias

Evaluation of quantitative data: Weaknesses

- Much narrower in meaning and detail than qualitative data. It thus may fail to represent 'real life'

Evaluation of primary data: Strengths

- Authentic data obtained from the participants themselves for the purpose of a particular investigation. Questionnaires and interviews, i.e. can be designed in such a way that they specifically target the information that the researcher requires; it fits the job

Evaluation of primary data: Weaknesses

- Requires time and effort on the part of the researcher. Conducting an experiment, i.e. requires planning, preparation, and resources. Whereas with secondary data, it may be accessed within a matter of minutes

Evaluation of secondary data: Strengths

- May be inexpensive and easily accessed requiring minimal effort. When examining this data, the researcher may find that the desired information already exists and so there is no need to conduct primary data collection

Evaluation of secondary data: Weaknesses

- May be a substantial variation in the quality and accuracy of this data. Information might first appear to be valuable and promising but, on further investigation, may be outdated or incomplete

- The content of the data may not quite match the researcher's needs or objectives. This may challenge the validity of any conclusions

Meta-analysis

- A procedure for statistically combining the results of many different research studies. Uses secondary data

Strengths of meta-analysis

- Allows us to create a larger, more varied sample and results can then be generalised across much larger populations, increasing validity

Weaknesses of meta-analysis

- May be prone to publication bias. The researcher may not select all relevant studies, choosing to leave out those studies with negative or non-significant results. Therefore the conclusions will be biased because they only represent some of the relevant data