Data handling & analysis

• Content analysis


  A technique for analysing quantitative data of various kinds
• Process of content analysis

  Sampling - deciding what material to use and how much to use it

  Pilot study - becoming familiar with the types of material

  Coding units - deciding how to categorise the analysed material
• Thematic analysis


  Looking for any consistent ideas, concepts or patterns within a source
• Strengths of content analysis

  - cheap and not time-consuming

  - makes complex text easier to analyse

  - produces objective, quantitative data

  - easier to compare findings from similar studies
• Weaknesses of content analysis

  - can easily create bias

  - low validity as documents can be easily misinterpreted

  - language used may not be familiar to the psychologists

  - reductionism means complexity of qualitative data is lost
• Case studies


  A form of observational research that gives a detailed study of an individual or small group of people
• Are case studies considered longitudinal or snapshot?

  Longitudinal
• Strengths of case studies

  - in depth data

  - holistic

  - useful to investigate rare conditions/behaviours

  - used when experiments are unethical
• Weaknesses of case studies

  - difficult to generalise

  - may not be replicable or objective

  - difficult to say what things were like beforehand
• quantitative data

  data in the form of numbers
• strengths of quantitative data

  • more likely to be objective
  • easier to analyse and compare ppts
• weaknesses of quantitative data

  • lacks depth and detail
  • may be reductionist
• qualitative data 


  data in the form of words
• strengths of qualitative data

  • ppts can express themselves in their own words
  • more in depth and detailed
• weaknesses of qualitative data

  • more difficult to compare ppts
  • requires interpretation so subjective
• 3 levels of measurement 


  nominal, ordinal and interval
• nominal data

  putting data into categories - no numerical value
• ordinal data 


  data in rank order
• Interval data

  Knowing the differences between data
• 2 ways psychologists analyse their data

  descriptive statistics and inferential statistics
• descriptive statistics 


  describes the results we have without having to list the raw results
• inferential statistics 


  helps us decide if we should accept the alternative or null hypothesis
• measures of central tendency

  mean, median and mode
• measures of dispersion

  range and standard deviation
• standard deviation


  a measure that shows to what the extent the values in a data set deviate from the mean
• what does a small standard deviation tell us?

  all ppts in the study behaved or responded in the same way
• what does a large standard deviation tell us?

  there was a lot of variety in the way participants behaved or responded
• describe the 68-95-99.7 rule

  68% of population fall within 1 SD either side of the mean, 95% within 2 SD and 99.7% within 3 SD
• Bar charts

  • Used for discrete data
  • show differences
  • the bars should not touch
• histograms

  • use continuous data
  • show how grouped data is spread
  • the bars must touch
• line graphs

  • used for continuous data which is not grouped
  • show how a variable changes
• scattergraphs

  • used for correlations
  • show the strength of a relationship between 2 variables
  • show whether the relationship is positive or negative