# Data Analysis PSYA4

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## Introduction Inferential tests

Significance and Probability

• Inferential tests provide a means of assessing whether any pattern in data collected is meaningful or significant.
• They enable us to make inferences form the research sample to the population.
• Probability= likelihood that a pattern of results could arise by chance.
• Probability levels represent acceptable level of risk or of making a Type 1 error.
• More important research requires more strigent significance levels
• Type 1 error= null hypothesis rejected when true.
• Type 2 error= null hypothesis accepted when false.

Inferential tests

• Significance of observed value determined in table of critical values.
• Degree of Freedom normally the amount of participants in the study.
• One tailed test is a directional hypothesis
• Two tailed test is a non-directional hypothesis
• Significance level is usually p(equal to or more than) 0.05 (5%)
• Different research designs and levels of measurement (nominal, ordinal, interval) require different tests
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## Inferential tests- Spearman's Rho

Used when...

• Hypothesis states correlational between two variables.
• Each person is measured on both variables.
• Data is at least ordinal (i.e not norminal)
• With repeated measurres and matched pairs
• Scatter line graph is used
• Observed value is greater than or same as the critical value.
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## Inferential tests- Chi-Square

Used when...

• Hypothesis states differences between two conditions or association between two variables
• Data is independant.
• Data in frequencies. (nominal)
• Expected frequencies in each cell must not fall below 5.
• Bar graph is used
• Observed value equal to or greater than the critical value
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## Inferential tests- Mann-Whitney U-Test

Used when...

• Hypothesis states difference between two sets of data.
• Independant groups design
• Data at least ordinal (i.e not nominal)
• Bar chart is used
• Observed value is equal to or less than the critical
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## Inferential tests- Wilcoxon T Test

Used when...

• Hypothesis states difference between two sets of data
• Related design (repeated measures or matched pairs)
• Data at least ordinal (i.e not norminal)
• Line of scatter graph is used.
• Observed value is equal to or less than the Critical value.
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## Descriptive Statistics- Central Tendency

• Indicates typical or 'average' score
• Mean = sum of all scores divided by number of scores however it is unrepresentative of extreme scores
• Median = middle value in ordered list of scores. Not affected by extreme scores but not as sensitive of all scores than the mean.
• Mode = most common value. Not useful is there are many modes in a set of scores.
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## Descriptive Statistics- 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.
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## Descriptive Statistics- Graphs

• Bar Chart= Illustration of frequency, height of bar represents frequency.
• Scattergram= illustration of correlation, suitable for correlational data. Indicated strength of correlation and direction (positive or negative).
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## Qualitive Data

Key Points

• Quantitive methods not relevant to 'real life'
• Qualitive methods represent world as seen by individual (subjective)
• Data sets tend to be large but few participants
• Qualitive data connot be reduced to numbers
• Reflexivity indicates attitudes and biases of researcher.
• Validity demonstrated by triangulation.

Qualitive analysis

• Summarised by identifying themes in data.
• Inductive (bottom-up) approach so themes emerge, although sometimes deductive (top-down)
• Iterative process- imposing order on the data, reflexting participants perspective.
• 1) Consider data
• 2) Break into meaningful units
• 3) Code each unit
• 4) Create categories themes
• 5) Check themes using new data set
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## Qualitive V Quantitive

• Quantitive easy to analyse and produces neat conclusions
• But oversimplifies reality and human experience.
• Qualitive sata represents true complexities of behaviour through rich detail of thoughts, feelings etc.
• But more difficult to detect patterns and subject to bias of subjectivity.
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