types of data

• Qualitative data


  Data that is expressed in words and is non-numerical.
• Quantitative data


  Data associated with numerics and that can be counted
• Strengths of qualitative data

  1. The data can be in-depth, rich in detail, insightful and therefore not reductionistic.

  2. The data can help us to understand why people behave in a particular way, and could promote further research.
• Strengths of quantitative data

  1. Use of numbers and statistics allows direct comparison of participants in difference conditions. It can also allow comparison if the study is replicated.

  2. Use of numbers and statistics is more objective and scientific, so is more likely to be accepted by the scientific community.
• Limitations of qualitative data

  1. More difficult to analyse and draw conclusions.
• Limitations of quantitative data

  1. The data may oversimplify reality. For example, a questionnaire with closed questions may force to tick answers that don't represent feelings so conclusions maybe meaningless.
• Primary data


  Information that is collected for the first time; used for solving the particular problem under investigation. Only conducted by the researcher.
• Secondary data


  information that already exists somewhere, having been collected for another purpose than the current one.
• Strengths of primary data

  1. Researcher has control over the data.

  2. Data collection can be designed to fit the aims and hypothesis of the study.
• Strengths of secondary data

  1. Simpler and cheaper to access someone else's data.
• Limitations of primary data

  1. Lengthy and expensive process.
• Limitations of secondary data

  1. For some studies, the data may not exactly fit the needs of the study, which lowers the validity of the study.

  2. Variations in quality and aims of research which could affect the validity and create bias.
• Meta-analysis


  A statistical technique that averages the results of two or more studies to see if they investigated the same aims and hypotheses and grouping them together.
• Strengths of meta-analysis


  1. Increased validity of the conclusions drawn as they are based on a wider sample of participants.

  2. Allows us to reach an overall conclusion by having a statistic to represent findings of different studies.
• Limitations of meta-analysis


  1. The studies may not be truly comparable if the research designs in the studies vary considerably.

  2. Putting them all together to calculate an effect size may not be appropriate and thus conclusions are not always valid.

  3. May be subject to publication bias, the researcher may be selective with the studies that they choose to compare and deliberately leave others out. This leads to meta-analysis being bias because it only represents some of the relevant data.
• Strengths of primary data