Data Analysis
- Created by: Natalie
- Created on: 15-01-14 17:07
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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
- Spearman's rho
- 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.
- Central tendency
- 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.
- Quantitative
- Key points
- Probability and significance
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