Open patient_data.sav in SPSS. Test the following hypotheses.
Print and save your output. Write up your results using the examples below as a guide. Be sure to (a) state the null hypothesis being tested, (b) identify and justify the statistical test(s) that are used, (c) present and discuss the values of all relevant statistics, (d) state your alpha level and indicate your decision regarding the null hypothesis, and (e) discuss the implications of your findings for the original hypothesis. Include tables to support your narratives. Turn in your write up and a printout of your output.
Example 1:
To determine whether social class affects support for welfare reform, I tested the null hypothesis that support for welfare reform is independent of social class. A chisquare test of independence was used because such a test is appropriate for determining if the distribution of one categorical variable (support for welfare reform) is independent of the distribution of another categorical variable (social class). However, since chisquare tells us little about the strength or direction of the association between the two variables, taub was also calculated. Taub is appropriate because both variables are ordinal. The analysis produced a chisquare of 19.69. The probability of getting a chisquare this large if the null hypothesis of independence is true is .03. Given that this probability falls below my alpha level of .05, I reject the null hypothesis that support for welfare reform is independent of social class. Further, taub was calculated to be .37 indicating a moderate positive association between the variables, suggesting that as social class increases, support for welfare reform increases. Based on these results, I find support for the original hypothesis that social class affects support for welfare reform.
Example 2:
To determine whether education level affects religious affiliation, I tested the null hypothesis that religious affiliation is independent of education level. A chisquare test of independence was used because such a test is appropriate for determining if the distribution of one categorical variable (religious affiliation) is independent of the distribution of another categorical variable (education level). However, since chisquare tells us little about the strength or direction of the association between the two variables, tau was also calculated. Tau is appropriate because one of the variables is nominal (religious affiliation). The analysis produced a chisquare of 8.3. The probability of getting a chisquare this large if the null hypothesis of independence is true is .04. Given that this probability falls below my alpha level of .05, I reject the null hypothesis that religious affiliation is independent of education level. However, tau was calculated to be .09 indicating only a weak association between the variables. Based on these results, I find limited support for the original hypothesis that education level affects religious affiliation.
Below is a sample bivariate table.
Table 1 

Distribution of Marital Status by Sex of Respondent 


Males 
Females 
Total 


Number 
Percent 
Number 
Percent 
Number 
Percent 
Never Married 
61 
12.3 
78 
7.8 
139 
9.3 
Married 
272 
54.7 
204 
20.3 
476 
31.7 
Widowed 
119 
23.9 
648 
64.6 
767 
51.1 
Separated/Divorced 
45 
9.1 
73 
7.3 
118 
7.9 
Total 
497 
100 
1003 
100 
1500 
100 
Chisquared = 21.7; p < .05. 



