Types of Data

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

Qualitative Data Eval: Offers researchers much more richness of detail than quantitative data this also means it has greater external validity. However it is difficult to analyse and is difficult to summarise statistically. This means conclusions often rely on the subjective interpretations of the researcher which may be subject to bias.

Quantitative Data: Data that can be counted, usually given as numbers.

Quantitative Data Eval: Relatively simple to analyse and so comparisons between groups can be easily drawn. Data in numerical form tends to be more objective and less open to bias. However it is much narrower in meaning and detail so it may fail to represent ‘real life’.

Primary Data: Information that has been obtained first-hand by a 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 of observation.

Primary Data Eval: Authentic data obtained from the participants themselves for the purpose of a particular investigation. However this requires time and effort on the part of the researcher as it can take considerable planning, preparation and resources.

Secondary Data: 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.

Secondary Data Eval: May be inexpensive and easily accessed requiring minimal effort. The researcher may find that the desired information already exists and so there is no need to conduct primary data collection. However there may be substantial variation in the quality and accuracy of secondary data. Info could be outdated or incomplete and so may not match the researchers needs or objectives.

Meta-Analysis: The process of combining the findings from a number of studies on a particular topic. The aim is to produce an overall statistical conclusion (the effect size) based on a range of studies. A meta-analysis should not be confused with a review where a number of studies are compared and discussed.