Types of data

• When an investigation is conducted, data is collected. This can be in the form of numbers, words, images, sounds etc. There are different ways to describe these types of data.

  • Throughout research methods we have referred to the terms qualitative and quantitative data.
  • These two types of data are very different, are collected in different ways and have different strengths and limitations.
  • We are also going to discuss primary and secondary data which can both be quantitative or qualitative.
• Qualitative data – Data that is expressed in words and is non-numerical
• Primary data – information that has been obtained first hand by a researcher for the purpose of a research project.
• Quantitative data – Data that can be counted – normally given in numbers
• Secondary data – Information that has already been collected by someone else and so predates the current research project.
• Meta-analysis – The process of combining findings from a number of studies on a particular topic. The aim of this is to produce an overall statistical conclusion based on a range of studies.
• Quantitative Data:

  Data that can be counted – normally given in numbers.

  • Expressed numerically.
  • Data can represent how much, how long, how many etc.
  • A DV in an experiment is quantitative.
  • Methods of data collection include the gathering of numerical data in the form of individual scores from p’s such as the number of words one can recall in a memory experiment.

  In an observation a tally of behavioural categories would be quantitative.
• Qualitative Data:

  Data that is expressed in words and is non-numerical.

  • Written description of thoughts, feelings and opinions
  • Written description of what researcher has observed
  • Transcript from interview, diary notes, recordings of counselling sessions
  • Methods of data collection are those concerned with the interpretation of language from for example, an interview or unstructured observation
• Quantitative collection techniques

  • Quantity
  • Deals with numbers
  • Data can be measured
  • Looks at averages and differences between groups
• Qualitative collection techniques

  Quality

  Deals with descriptions

  Data is observed but not measured Attitudes, beliefs, emotions
• Quantitative strengths

  • Easy to analyse, using statistics
  • Conclusions can be easily drawn
• Qualitative strengths

  • Detailed information can provide unexpected insights into thoughts and behaviour because the
  • Answers are not restricted by previous expectations
• Quantitative limits

  • Data may oversimplify reality e.g. questionnaire – people may be forced to choose from a list where nothing reflects their views.
  • The results may be meaningless.
• Qualitative limits

  • Complex so more difficult to analyse
• Primary Data:

  Information observed or collected directly from first-hand experience

  • Refers to original data that has been collected for the purpose of that specific investigation by the researcher.
  • It arrives first hand from the p’s themselves.
  • Includes data gathered by conducting experiments, questionnaires, interviews and observations.
• Secondary Data:

  Information used in research that was collected by someone else for a purpose other than the current one.

  • Has been collected by someone other than the researcher
  • The data already exists before the psychologist begin their research
  • Often secondary data has already been subject to statistical testing and therefore the significance is already known.
  • Includes: Journal articles, books, website, government statistics, population records etc.
• Primary data collection techniques

  • Data collected by the researcher of the current study through any research method e.g questionnaire, observation, experiment.
• Secondary data collection techniques

  • e.g. Government statistics, data held
  • by a hospital or other institution.
• Primary data strengths

  • Researcher has control over the data.
  • Data collection can be designed so it fits the aims and hypotheses of the study.
• Secondary data strengths

  • Simpler and cheaper to access someone else’s data.
• Primary data limits

  • Lengthy and expensive process
• Secondary data limits

  • For some studies, the data may not Exactly fit the needs of the study.
• Meta-analysis

  • The process in which a number of studies are identified which have investigated the same aims/hypothesis.

  In the case of experimental research, results can be statistically analysed to calculate the ‘effect size’. The effect size measures the strength of the relationship between variables across a number of studies. Results can be pooled together and a joint conclusion can be drawn.
• Meta-analysis strengths

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

  Allows us to reach an overall conclusion by having a statistic to represent the findings of different studies.
• Meta-analysis limits

  • The studies may not be truly comparable if the research designs in the studies sampled vary considerably.
  • Putting them all together to calculate an effect size may not be appropriate and thus the conclusions are not always valid.
  • May be subject to publication bias; the researcher may be selective with the studies that they chose to use and deliberately leave others out. This leads to meta-analysis being bias because it only represents some of the relevant data.