types of data

• research data is categorised as either quantitative or qualitative data
• Quantitative data is numerical data that can be statistically analysed. Experiments, observations, correlations and closed/rating scale questions from questionnaires all produce quantitative data.
• qualitative data is data in the form of words and is based on descriptions of behaviour, 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
• 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 data. if both methods agree, credibility is increased (methodological triangulation).
• quantitative data advantages

  • objectively measured, reducing the likelihood of bias. This increases scientific credibility
  • Easy to analyse, using descriptive statistics and statistical tests, enabling conclusions to be easily drawn
• quantitative data disadvantages

  • the limited number of qualitative data results in responses lacking depth and detail
  • Such data may oversimplify reality. For example, a questionnaire with closed questions may force people to tick answers that don’t really represent their feelings, so the conclusions may be meaningless
• qualitative data strengths

  • Provides rich and detailed information about people’s experiences. This can provide unexpected insights into thought and behaviour because the answers are not restricted in responses they give.
  • This means qualitative data has higher validity
• qualitative data weaknesses

  • More difficult to analyse data and draw conclusions
  • the questions are open-ended which reduces the reliability of it
  • it can be expensive and time-consuming to collect and analyse qualitative data.
• Primary data refers to data that has been collected directly by the researcher, solely for the purpose of their investigation.
• common ways of collecting primary data are questionnaires, experiments, observations and case studies
• Secondary data is information that someone else has collected e.g. the work of other psychologists that has been published in journals or government statistics. They are sometimes used by other researchers, as they are often cheaper and more convenient than gathering one’s own primary data.
• secondary data was initially created to answer a different research question to the current one
• primary data strengths

  • The researcher has control over the data. The data collection can be designed to fit the aims and hypothesis of the study- this increases validity
• primary data weaknesses

  • It is a very lengthy and therefore expensive process. Simply designing a study takes a lot of time and then further time is spent recruiting participants, conducting the study and analysing the data
• secondary data strengths

  • It is simpler and cheaper to just access someone else’s data because significantly less time and equipment is needed
  • Such data may have been subjected to statistical testing and thus it is known whether it is significant
• secondary data weaknesses

  • decreased validity as the data is not collected to directly answer the research question. the data may not be appropriate to answer the current question.
  • decreased validity as the researcher had no role in data collection so it can't be ensured that the data collected is free from bias
• A meta-analysis is where researchers combine the findings from multiple studies that ask similar research questions to draw an overall conclusion.
• strengths 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 studies affected by bias will not change the overall pattern of results, making meta-analysis more trustworthy than any individual study
• weaknesses of meta-analysis

  • a meta analysis has all the weaknesses of secondary data; the researcher has no control over the quality of the results collected
  • the choice of which studies to include/exclude could be biased
  • The studies included in the meta-analysis will likely use different research designs, raising the question of whether the data is comparable.