qualitative + quantitative data / primary + secondary data

• Quantitative data


  Data in the form of numbers. The recording of variables collects quantitative data. Examples include reaction times using a stopwatch. Descriptive statistics (averages and ranges) summarise quantitative data, and these descriptive statistics are then displayed on tables and graphs.
• Qualitative data


  Data in the form of words, meaning descriptions of behavior, thoughts and feelings. Content analysis converts large amounts of qualitative data into quantitative data. To turn observations and interviews into quantitative data behavioural categories can be created and then tallied.
• When to use quantitative and qualitative data

  Quantitative data is used in experimental and observational research. Qualitative data is used in case studies, open-question interviews and questionnaires.

  Studies can collect a combination of both quantitative and qualitative techniques in research. If both methods agree, this increases credibility.
• Strengths of quantitative data

  • Objectively measured, reducing the likelihood of bias. This increases scientific credibility.
  • Descriptive statistics allows quantitative data to be summarised and then displayed on graphs, charts and tables.
  • Quantitative data tends to be more reliable - because of the limited number of responses, there is a higher chance of getting the same findings if the study is repeated.
• Weaknesses of quantitative data

  • The limited number of qualitative research responses results in data lacking depth and detail, focusing only on individual features of behavior and only on what can be mathematically measured.
• Strengths of qualitative data

  • Seen as richer in detail because they collect more information, and use of open-ended questions means participants are not limited in the responses they can give, meaning qualitative data has higher validity.
• Weaknesses of qualitative data

  • Qualitative data gathered by the researcher can be open to interpretation and potentially biased.
  • Due to the extensive range of data collected, it can be challenging to summarise.
  • As the questions that produce qualitative data are open-ended, this tends to be more variable, reducing the reliability of qualitative research.
• Example showing the difference in information gained between quantitative and qualitative data

  • Milgram study - quantitative findings vs observational recordings of participants
• Primary data


  The researcher is responsible for generating the data, also known as 'first hand' or 'original' data. Primary data is created to answer the research question. Common ways to collect primary data are the researcher conducting experiments, observations, interviews, questionnaires and case studies.
• Secondary data


  'second-hand' data, this is when researchers use information previously collected by a third party, such as another researcher or organisation. This secondary data was initially collected for a reason other than to answer the current research question. Examples of secondary data are government or business statistics and records or previously published studies.
• Advantages of primary data

  • Increased validity as the data is collected to answer the research question directly. The experiment or observation is designed to test the intended variable directly.
  • Increased validity as the researcher can control the data collection process carefully.
• Disadvantages of primary data

  • Collecting original data from participants is both time-consuming for the researcher and potentially expensive. Costs include paying participants for their time and other researchers for their work. Setting up an experiment also includes paying for materials.
• Advantages of secondary data

  • Secondary data already exists and is often already analysed; this can dramatically reduce both the time needed to conduct research and the cost involved in conducting a study involving participants.
• Disadvantages of secondary data


  • Decreased validity as the data is not collected to answer the research question directly. The data may not be appropriate to answer the researcher's research question.
  • Decreased validity as the researcher had no role in the data collection process, so cannot ensure that the data collected was free from bias or the result of extraneous variables.
• Meta-analysis


  A process that collects and combines results of a range of previously published studies asking similar research questions. The data collected is compared and reviewed together, and part of this review can include statistically combining all the data to produce an overall effect size and conclusion.
• Advantages of meta-analysis

  • The large sample size of meta-analysis produces results that are more statistically powerful than studies with a small number of participants.
  • As meta-analysis looks at the overall pattern of results across many studies, a small number of individual studies that are affected by bias or lack of control will not change the overall pattern of results, making meta-analysis more trustworthy than any individual study.
  • Studies testing the same variable in various contexts be compared, revealing unexpected relationships.
• Disadvantages of meta-analysis

  • A meta-analysis has all the weaknesses of secondary data; the researcher has no control over the quality of the data collected. Also, included studies are conducted to answer particular research questions, so may not be comparable.
  • Studies that show a statistically significant result are more likely to be published, while non-significant results are unlikely to be submitted for publication.
  • The choice of which studies to include/exclude could be biased.