research methods

• Experimental method


  The manipulation of an independent variable (IV) to have an effect on the dependent variable (DV) which is measured and stated in results
• Directional hypothesis


  States the direction of the impact of independent variable (IV) on the dependent variable (DV)
• Non-directional hypothesis


  Does not state the direction of the relationship between the IV and the DV
• Sampling methods


  • Opportunity sampling
  • Random sampling
  • Systematic sampling
  • Stratified sampling
  • Volunteer sampling
• Taking a stratified sample


  1. Identify the strata

  2. Calculate the required proportion for each stratum based on the size of the target population

  3. Select the sample at random from each stratum

  4. Use a random selection method e.g. using a computer
• Advantage of stratified sampling

  • The sample is able to be more representative of the target population compared to other types of sampling
• Operationalisation


  The process by which a researcher defines how a concept is measured, observed, or manipulated within a particular study
• Independent variable (IV)


  The variable that is manipulated to observe its effect on the DV
• Dependent variable (DV)


  The variable that is being measured and is affected by the IV
• Counterbalancing


  A solution to the problem of order effects caused by a repeated measures design, where half of the participants do conditions in one order and the other half in the opposite order
• Aim


  Tells us what the study is investigating
• Hypothesis


  A statement that predicts the relationship between the IV and the DV
• Types of experiments


  • Laboratory
  • Field
  • Quasi
  • Natural
• Purpose of peer review

  • To allocate research funding to worthwhile projects
  • To ensure the research is of good quality and relevant
  • To suggest improvements so that faulty or incorrect data is not released to the public
• Positive skew


  When plotted on a graph, the data has a long tail on the right
• Negative skew


  When plotted on a graph, the data has a long tail on the left
• Strengths of content analysis

  • High mundane realism and external validity as what is being studied is already out there in the real world
  • Produces a large data set of both qualitative and quantitative data that is easy to analyse
• Limitations of content analysis

  • Causality cannot be established as it merely describes the data
  • As it only describes the data it cannot extract any deeper meaning or explanation for the data patterns arising
• Limitations of case studies

  • As it only concerns one person it is not really generalisable to wider populations
  • Retrospective studies may rely on memory which can be unreliable
  • They are time consuming
• Reliability


  A measure of consistency, for example if a particular measurement is replicable then that measurement is described as being reliable
• Assessing validity


  • Face validity: A measure is scrutinised to determine whether it appears to measure what it is supposed to
  • Concurrent validity: Checking the extent to which a psychological measure relates to an existing and well-established similar one
• Face validity


  A measure is scrutinised to determine whether it appears to measure what it is supposed to
• Concurrent validity


  Checking the extent to which a psychological measure relates to an existing and well-established similar one
• Factors that help decide which inferential statistical test to use


  • The level of data that was collected
  • Whether the design of the study is related or unrelated
  • Whether a difference or correlation is being measured
• Levels of measurement


  • Nominal data
  • Interval data
  • Ordinal data
• Nominal data


  Data that describes characteristics or groups, for example ethnicity, car brand, place of birth. There's no ranking or natural order.
• Ordinal data


  Data that, like nominal data, describes characteristics or groups, for example political orientation or income level. Unlike nominal data, it can be ordered or ranked, e.g. left-of-centre -> centre -> right-of-centre, or low income, middle income and high income.
• Interval data


  Numerical data such as credit ratings, temperature, IQ. Numerical data doesn't have a meaningful zero point - for example, the temperature can be 0C but that doesn't mean there's no temperature or heat.
• Single-blind procedure


  Only the participant isn't aware of the aims, procedures and conditions of the study - the researcher is.
• Double-blind procedure


  Neither the researcher nor the participant are aware of the aims, procedures and conditions of the study.
• Unstructured observation


  Continuous recording where everything the researcher sees is written down.
• Structured observation


  Has a predetermined list of behaviours and sampling methods with which the researcher quantifies their observation with (e.g., notes the number of times a participant crosses their arms).
• Correlation


  Variables are simply measured not manipulated. There is no cause and effect relationship found, only an association is found.
• Experiment


  There is a dependent variable and an independent variable, allowing for cause and effect relationships to be found.
• Measures of central tendency

  • Mode
  • Median
  • Mean
• Primary data


  Obtained firsthand by the researcher
• Secondary data


  Has already been collected by someone else other than the researcher
• Things a researcher should think about when constructing a questionnaire

  • Clarity - the researcher should make sure it is clear what each of the questions are asking
  • Analysis - the questionnaire should be written in a way that can be easily analysed
  • Sequencing questions- the researcher should think about the order of questions, maybe easy ones first then harder ones last to build up the confidence of the participants
• Strengths of conducting an unstructured interview


  • Lots of data is collected which has more depth and detail
  • It can be tailored to individuals giving more insight into the subjective experience of the person being interviewed
• Types of observation


  • Naturalistic
  • Controlled
  • Overt
  • Covert
  • Participant
  • Non-participant