Statistics in SPSS Lecture 7 Petr Soukup, Charles University in Prague

T-tests 2

T-tests Statistical tests for means Can be used for proportions as well Three tests: for one sample (rare usage) Two independent samples (the most common) Test for paired (dependent) samples 3

T-tests Requirements: Cardinal variable Normally distributed (small samples) For big sample (more than 30 units) normality is not necessary PARAMETRIC TESTS (it is necessary to follow some requirements) 4

T-tests Analyze-Compare Means 1. example – ONE SAMPLE : for mean (income) and for proportion (participation in the last election) 2. example – TWO IND. SAMPLES : no difference for participation in the last election between men and women Note: Cohen’s d for two independent samples (effect size, measurement of substantive significance of results) 5

TESTS & CI 6

Confidence interval (CI) and testing CI can answer the same question as the test: Can be the effect generalized to the population (from the sample)? CI can do more: Estimate unknown parameter for population by the range, so we know the size of the effect ! Warning: Testing is only binary process –statistically significant vs. statistically insignificant 7

CI and testing Comparison of t-test and CI for mean Reco: Use CI instead or with the test 8

ALTERNATIVES TO T-TESTS (NON-PARAMETRIC TESTS) 9

Alternatives to T-tests data Parametric test Nonparametric a) Two-ind. Samples Two ind. sample t-test Man-Whitney test b) Two related samples (paired) Paired t-test Wilcoxon test c) More ind. Samples Analysis of variance Kruskal-Wallis test d) More relateds samples Friedman test 10

HW 11

HW6 Try to test mean (one cardinal variable) by t-test and use the same test also for proportion (one binary variable). Interpret results. 12

Thanks for your attention 13