1. Presenting the Results of a Contingency Table Analysis

2. Verdict x Defendant Physical Attractiveness Mock jurors were significantly more likely to find the defendant guilty when he was unattractive (75.7%) than when he was attractive (65.0%),  2 (1, N = 145) = 6.229, p =.013,  =.207, odds ratio = 2.45, 95% CI [1.20, 4.99]. Please note that with significant results one should emphasize the direction of effect.

3. Nonsignificant Results One should NOT mention direction of effect, unless having tested directional hypotheses.

4. Direction x Device People were significantly more likely to take the stairs when going down (24.3%) than when going up (6.1%),  2 (1, N = 3,217) = 217.22, p <.001,  =.26, odds ratio = 4.90, 95% CI [3.91, 6.13]. SPSS gave me an odds ratio of.204 and a CI of [.163,.256]. I inverted these numbers to get ratios greater than one.

5. Figure 1. Device x Weight

6. Choice of device was significantly associated with weight of patron,  2 (2, N = 3,217) = 11.752, p =.003,  =.06. As shown in Table 1, obese individuals used the stairs considerably less often than did others.

7. Pairwise Comparisons Use of the stairs did not differ significantly between overweight and normal individuals,  2 (1, N = 2,907) = 1.034, p =.31,,  =.02, odds ratio = 1.12, 95% CI [0.90, 1.38]. Individuals of normal weight used the stairs significantly more often than did obese individuals,  2 (1, N = 2,142) = 9.062, p =.003,,  =.065, odds ratio = 1.94, 95% CI [1.25, 3.00].

8. SPSS will mess up if you use a dichotomous predictor that is coded with numbers other than consecutive integers, such as Weight = 1 (obese) and 3 (normal). If you declare Weight to be categorical, SPSS works fine. Overweight individuals used the stairs significantly more often than did obese individuals,  2 (1, N = 1,385) = 11.815, p =.001,  =.092, odds ratio = 2.16, 95% CI [1.38, 3.38].