Types of data

• Quantitative data is data in numerical form (eg stats).

  • Recording of variables collect qualitative data which include scales, tally, measures of time or amount of time something happens. Descriptive statistics then summarise the quantitative data and these descriptive stats are then displayed on tables and graphs.
• Qualitative data is data in the form of words. These involve descriptions, thoughts and feelings.

  • Content analysis converts large amounts of qualitative date into quantitative data. To turn observations and interviews into quantitative data, behavioural categories must be created then tallied.
• Strengths of qualitative data:

  • Data is rich in detail due to the amount of data collected and the use of open-ended question (so participants are not limited to a response). This give the data higher validity.
• Limitations of qualitative data:

  • Potentially biased as researchers know what they are looking for.
  • Challenging to summarise due to the extensive amount of research.
  • Reduced reliability due to the open-ended questions being more variable.
• Strengths of quantitative data:

  • Objectively measured reducing the likelihood of results being due to chance. This increases scientific credibility.
  • Descriptive statistics allow for data to be summarised and displayed on graphs, charts and tables.
  • More reliable due to the high chance of getting the same results if the study was replicated.
• Limitations of quantitative data:

  • Limited detail and depth. It only focuses on individual behaviours and what can be mathematically measured.
• Primary data is when the researcher is responsible for generating data. This is also known as 'first hand' or 'original' data. This is collected to answer the research question. Common ways to collect this data is in the form of experiments, observations, interviews, questionnaires and case studies.
• Secondary data, also known as 'second-hand' data is when researchers use information that was previously collected by a third party and build off of it to answer a current research question. Examples of this include government reports, census data, and news articles.
• Strengths of primary data:

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

  • Time consuming and costly. This includes having to pay participants for their time and other researcher for their work. Setting up an experiment means having to also pay for materials.
• Strengths of secondary data:

  • Already exists and is often analysed, reducing the amount of time and costs needed in conducting research.
• Limitations of secondary data:

  • Decreased validity as data is not collected to directly answer the research question. The data used may not be appropriate to answer the question.
  • Decreased validity as the research has no role in the data collection process and so cannot ensure the data collected before was free from bias or is the result of extraneous variables.
• Meta-analysis is a process that collects and combines the results of a range of previously published studies asking similar research questions. The data collected is then compared and reviewed together and this section of the review can include statistically combining the data to produce an overall effect size and conclusion.
• Strengths of meta analysis:

  • Large sample size of meta analysis produces more statistically powerful results compared to studies with smaller numbers of participants.
  • Looks at the overall pattern of results across many studies. This means a small study containing bias or lack of control will not change the overall pattern of results, making the analysis more trustworthy than an individual study.
  • Studies testing the same variable in various contexts (eg across cultures) may reveal unexpected relationships.
• Limitations of meta-analysis:

  • Weakness of secondary data. The researcher has no control over the quality of the data collected.
  • Studies showing statistically significant results are more likely to be published, and so included in a meta-analysis, while non-significant results are unlikely to be submitted for publication.
  • Choice of what studies to include / exclude may be biased.