# Data Analysis

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• Created by: Natalie
• Created on: 15-01-14 17:07
• 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
• 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.