Data Analysis

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  • Data Analysis
    • Probability and significance
      • Probability = likelihood that a pattern of results could arise by chance.
      • If probability extremely unlikely, then result is statistically significant.
      • Inferential tests determine whether chance or real trend in data.
      • Probability levels represent acceptable level of risk (e.g. p is less or equal to 0.05) of making a type 1 error.
      • More important research, more stringent significant levels.
      • Type 1 error = null hypothesis rejected when true.
      • Type 2 error = null hypothesis accepted when false.
    • Inferential tests
      • Spearman's rho
        • Correlation
        • Repeated Measures
        • Ordinal data
      • Chi-Square
        • Differences between two sets of data
        • Independent groups
        • Nominal data
      • Mann-Whitney U
        • Differences between two sets of data
        • Independent groups
        • Ordinal data
      • Wilcoxon T
        • Differences between two sets of data
        • Repeated measures
        • Ordinal data
    • Descriptive statistics
      • Central tendency
        • Indicates average score
        • Mean = sum of all scores divided by number of scores. Unrepresentative if extreme scores.
        • Median = middle value in ordered list of scores. Not affected by extreme scores but not as sensitive as mean.
        • Mode = most common value. Not useful if there are many modes in a set of scores.
      • Measures of dispersion
        • Indicate spread of scores.
        • Range = difference between highest and lowest score. Not representative if extreme scores.
        • Standard deviation = spread of data around mean. Precise measure but influence of extreme scores not taken into account.
      • Graphs
        • Bar chart = illustration of frequency, height of bar represents frequency.
        • Scattergram = illustration of correlation, suitable for correlational data. Indicates strength of correlation and direction.
    • Qualitative data
      • Key points
        • Quantitative methods not relevant to 'real life'.
        • Qualitative represents world as seen by individual.
        • Emphasises collection of subjective.
        • Data sets tend to be large.
        • Qualitative data cannot be reduced to numbers.
        • Can be examined for themes.
      • Methods of analysis
        • Coding using top-down approach (thematic analysis) = codes represent ideas / themes from existing theory.
        • Coding using bottom-up approach (grounded theory) = codes emerge from data.
        • Behavioural categories used to summarise data.
        • Reflexivity indicates attitudes and biases of researcher.
        • Validity demonstrated by triangulation
        • Reliability checked by inter-rater reliability.
      • Quantitative versus qualitative
        • Quantitative
          • Easy to analyse and produces neat conclusions
          • Oversimplifies reality and human experience.
        • Qualitative
          • Represents true complexities of behaviour through rich detail of thoughts, feelings etc.
          • More difficult to detect patterns and subjects to bias of subjectivity.


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