1. Non-parametric test used to assess whether a relationship exists between two categorical variables.
    Chi-square test of independence
  2. Frequencies expected for a contingency table if the null hypothesis of no relationship between variables is true.
    Expected frequencies
  3. Actual frequencies seen in a contingency table.
    Observed frequencies
  4. Correction to the chi-square statistic used when the expected frequency for any cell of a contingency table is less than 10.
    Yates’ correction
  5. Non-parametric procedure to test the significance of the difference in proportion; used when any expected cell frequency is less than 5 or a cell has no observations; can only be used with a 2X2 table.
    Fisher’s exact test
  6. Index describing the magnitude of relationship between two dichotomous variables.
    Phi coefficient
  7. Index describing the magnitude of relationship between nominal variables when the contingency table is bigger than 2X2.
    Cramer’s V
  8. Test to determine the fit of the data to hypothesized population values or a hypothesized model.
    Chi-square goodness of fit test
  9. Non-parametric test used to test the difference between two independent groups based on ranked scores.
    Mann-Whitney U-test
  10. Non-parametric test used to test the differences between three or more independent groups based on ranked scores.
    Kruskal-Wallis test
  11. Non-parametric test for comparing differences in proportions when the values are derived from paired groups.
    McNemar test
  12. Non-parametric tests for population differences in proportions; used when the dependent variable is dichotomous and the design is within subjects.
    Cochran’s Q test
  13. Non-parametric test for comparing two paired groups based on the relative ranking of values between the pairs.
    Wilcoxon signed ranks test
  14. Non-parametric analog of ANOVA, used with paired groups or repeated measures.
    Friedman test
Card Set