Types of data

• what are the two types of data?

  qualitative, quantitative, primary and secondary data
• quantitative data


  numerical data, e.g. reaction time or number of mistakes.
• strength of quantitative data

  easier to analyse. can draw graphs an calculate averages. can 'eyeball' data and see patterns at a glance.
• limitation of quantitative data

  oversimplifies behaviour. e.g. using rating scale to express feelings. means that individual meanings are lost.
• qualitative data


  non-numerical data expressed in words, e.g. extract from a diary.
• strength of qualitative data

  represents complexities. more detail included, e.g. explaining your feelings. can also include information that is unexpected.
• limitation of qualitative data

  less easy to analyse. large amount of detail is difficult to summarise. difficult to draw conclusions, many 'ifs and buts'.
• primary data


  'first-hand' data collected for the purpose of the investigation.
• strength of primary data

  fits the job. study designed to extract only the data needed. information is directly relevant to research aims.
• limitation of primary data

  requires time and effort. design may involve planning and preparation. secondary data can be assessed within minutes.
• secondary data


  collected by someone other than the person who is conducting the research, e.g. taken from journals, articles, books, websites or government records.
• strength of secondary data

  inexpensive. the desired information may already exist. requires minimal effort making it inexpensive.
• limitation of secondary data.

  quality may be poor. information may be outdated or incomplete. challenges the validity of the conclusions.
• meta-analysis


  a type of secondary data that involved combining data from a large number of studies. calculation of effect size.
• strength of meta-analysis


  increases validity of conclusions. the eventual sample size is much larger than individual samples. increases the extent to which generalisations can be made.
• limitation of meta-analysis


  publication bias. researchers may not select all relevant studies, leaving out negative or non-significant results. data may be biased because it only represents some of the data and incorrect conclusions are drawn.