1. Chapter 4 Selected Nonparemetric Techniques: PARAMETRIC VS. NONPARAMETRIC

2. CHI-SQUARE  The Research Question  Compares actual frequency with expected frequency

3. Expected Frequencies  Equal - null hypothesis  Theoretical - “goodness of fit”  Past Experience  Norms

4. CHI-SQUARE ASSUMPTIONS  Frequency data  Adequate sample size  Independent measures  Theoretical basis for categorization

5. CHI-SQUARE  Sample Size  Problems 2 X 2 tables with expected frequencies less than 5 larger tables  more than 20% of cells with less than 5  empty cell/s

6. CHI-SQUARE Power  40 subjects  2 X 2 design  10 subjects in each cell  probability @.05  moderate effect size  power =.47

7. CHI-SQUARE  Degrees of freedom  (Rows - 1) (Columns - 1)

8. CHI-SQUARE  Continuity Correction  Conditions 2 X 2 Table Expected frequency in a cell of less than 5

9. PHI  Correlation coefficient  Shortcut method for two dichotomous variables  Only appropriate when chi-square significant  Strength of relationship  Most useful in 2 X 2 tables  Values range from 0 to 1

10. CRAMER'S V  Modified Phi  Used with tables larger than 2 X 2

11. Research Question  Do men and women differ in smoking status?

12. SPSS - Chi-square  ANALYZE DESCRIPTIVE STATISTICS  CROSSTABS Columns - comparison groups Rows - outcome measure Statistics  Chi-square  Contingency Coefficient - 2X2 tables  Phi & Cramer’s V Cells  Column percents

13. Example from the Literature

14. McNemar  Dependent measures  two dichotomous measures  same subjects  tests the direction of change  degrees of freedom equal 1

15. Research Question  Is there change over time in perceived quality of life?

16. Creating new variables  Satisfaction with quality of life at age 18 and satisfaction with current quality of life are both rated from a low of 1 to a high of 6.  Recode them into new variables where 1-3 = 0 (low)  4-6 = 1 (high)

17. SPSS  TRANSFORM RECODE  Into Different Variable Give names and labels to new variables After completing transformation, add value labels

18. SPSS - McNemar  ANALYZE NONPARAMETRICS  2 Related Samples

19. Example from the Literature

20. Mann-Whitney U  Analogous to t-test  Ordinal data  Independent groups

21. Kruskal-Wallis H  Analogous to analysis of variance  Two or more groups  Ordinal data

22. Research Question  Is smoking history related to depressed state of mind?  Do the three smoking groups (never, quit, still) differ significantly on how often they report having a depressed state of mind?

23. SPSS-Kruskal-Wallis  ANALYZE NONPARAMETRICS  K independent Samples

24. SPSS- Mann-Whitney U  ANALYZE NONPARAMETRICS  2 independent samples

25. Example from the Literature

26. Wilcoxon matched-pairs  Analogous to paired t-test  two measures  same subjects

27. Friedman matched samples  analogous to repeated measures analysis of variance  Repeated measures design  Subjects serve as their own controls  Subjects exposed to all treatments

28. Research Question  Do the respondents to this survey differ significantly on:  current satisfaction with quality of life, satisfaction with quality of life at age 18, satisfaction with current weight, satisfaction with weight at age 18?

29. SPSS - Friedman  ANALYZE NONPARAMETRICS  K related samples

30. SPSS - Wilcoxon  ANALYZE NONPARAMETRICS  2 Related Samples

31. Example from the Literature