 Test adopted depends on a number of factors: ◦ Design of study (e.g. within or between-participants?) ◦ Number of variables ◦ The nature of the data (levels of measurement) ◦ Sample characteristics (e.g. normally distributed?) ◦ Inferences to be made (e.g. investigating a relationship or looking for differences?)  Main distinction = parametric or non-parametric

 Use if: 1.Level of measurement on DV is scale 2.The data is normally distributed 3.Homogeneity of variance is met

 Many different types exist  Some common examples ◦ Independent samples t-tests ◦ Paired samples t-tests ◦ One-way between groups ANOVA ◦ Repeated measures ANOVA

 Use if you are comparing two unrelated groups ◦ E.g. comparing males and females in anxiety levels  Uses a non-repeated/between-groups design

 Use if you are comparing two related groups ◦ E.g. testing people’s change in attitudes over time (test at time 1 and again at time 2)  This uses a repeated/within-participants design

 If you have more than two experimental groups or conditions (with scale data), you will need to use an ANOVA ◦ Analysis of Variance (also compares means)  If design is within-participants use a repeated measures ANOVA  Between-groups use a between groups ANOVA ◦ e.g. testing three occupation groups on their levels of job satisfaction  If differences with more variables involved consider the two way ANOVAs and MANOVAs.

 Used when you want to see if there is a relationship between two variables  Select Pearson’s r for normally distributed scale variables  Select Spearman’s rho or Kendall’s Tau if data is ordinal or not normally distributed

 Can you predict one variable from others  Linear: Lets you look at relationships between TWO variables at a time  Multiple: Lets you look at relationships between three or more variables at a time

 Like parametric tests, depends on whether your groups or conditions are related or unrelated (between- or within-participants)  Also depends on the type of data obtained  When data are unrelated and nominal use the chi-square test ◦ E.g. looking at how many men or women work

 Tests for two related samples ◦ The Wilcoxon (T) signed ranks test ◦ (equivalent to dependent t-test)  Tests for two unrelated samples ◦ The Mann-Whitney (U) test ◦ (equivalent to independent t-test)

 Non-parametric equivalent to one-way ANOVA is ◦ Kruskal-Wallis  For a repeated ANOVA, equivalent is ◦ Friedman’s test

 For more detail in terms of running the tests and reporting each one please see the zipped folder on moodle which goes through it step by step.