Week 3 Project – Chi-Square

This assignment focuses on categorical data, and two of the statistics most often used to test hypotheses about categorical data are odds ratios (ORs) and the chi-square. A chi-square is calculated first to identify if two categorical variables are associated with each other, and if they are then an odds ratio is often calculated. The disease-OR refers to the odds in favor of disease in the exposed group divided by the odds in favor of the unexposed group. Chi-square statistics measure the difference between the observed counts and the corresponding expected counts. The expected counts are hypothetical counts that would occur if the null hypothesis were true.

Part 2: Chi-Square

Bain, Willett, Hennekens, Rosner, Belanger, and Speizer (1981) conducted a study of the association between current postmenopausal hormone use and risk of nonfatal myocardial infarction (MI), in which 88 women reporting a diagnosis of MI and 1,873 healthy control subjects were identified from a large population of married female registered nurses aged thirty to fifty-five years. There were 32 women who currently used hormones and had a diagnosis of MI and 56 women reporting a MI and never used hormones. Of the women controls (women who did not report a MI) 825 currently use hormones and 1,048 never used hormones. To test the hypothesis that there is no association between use of postmenopausal hormones and risk of MI, chi-square statistics need to be calculated in SPSS using a 0.05 level of significance. The SPSS data are provided in the link below. The SPSS dataset consists of two variables:

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