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Statistics in SPSS Lecture 9

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Presentation on theme: "Statistics in SPSS Lecture 9"— Presentation transcript:

1 Statistics in SPSS Lecture 9
Petr Soukup, Charles University in Prague

2 ALTERNATIVES TO T-TESTS (NON-PARAMETRIC TESTS)
2

3 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 3

4 Alternatives in SPSS Analyze-Nonparametric tests-One sample
Analyze-Nonparametric tests-Independent samples Analyze-Nonparametric tests-Paired Example: alternative to ANOVA: Analyze-Nonparametric tests-Independent samples-Compare Medians (Kruskal-Wallis with multiple comparisons) 4

5 CONTINGENCY TABLES 5

6 Contingency table (CT)
Combine at least two (nominal or ordinal) variables in one table Main goal: to analyze relationship between variables Descriptive stats in CT: observed counts and percentages (row, column and total) Analyze-Descriptive statistics-Crosstabs (Cells) 6

7 Test for CT Test of independence: Chi-square
H0: variables are independent (in the whole population) H1: variables are dependent (in the whole population) Analyze-Descriptive statistics-Crosstabs (Statistics) Computation: comparison of observed and expected (by H0) counts Note: for correct computation 80 % of exp. Counts>5 and no zeros 7

8 Contingency coefficient (CC)
Effect size for CT Measurement of relationship between variables Usually computed from chi-square Values between 0 (no relationship) and 1 (perfect relationship) Mostly used coefficient: Cramer’s V Analyze-Descriptive statistics-Crosstabs (Statistics) 8

9 Detailed analysis in CC
For proven relationship (reject H0) Sign scheme for adusted standardized residuals Once again rule of values -2 and +2 Signs instead of residuals and it’s meaning Analyze-Descriptive statistics-Crosstabs (Statistics) and MS Excel tool for sign scheme 9

10 HW 10

11 HW9 Try to analyze relationship between two nominal or ordinal variables by contingency tables. Interpret results. 11

12 Thanks for your attention
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