SPSS Statistics: Data Analysis
Many factors are associated with satisfaction with one's job. Look at the variable satjob. (Respondents were asked, "On the whole, how satisfied are you with the work you do?" The question was asked of people who work or keep house.)
Is job satisfaction related to education? Income? Age? Explain.
Is the relationship between job satisfaction, education, and income similar for men and women? What do you base your answer on?
Is Job Satisfaction related to Education? Income? Age?
According to an Analysis of Variance (ANOVA), Education, Age and Income are all significant predictors of Job Satisfaction. Age, Income and Education all explained a significant portion of the variance, with p. < .05 for Education and p
Table 1
Job Satisfaction
Men
Women
Age of Respondent
-.124**
-.125**
-.123**
Education
-.068*
-.042
-.93*
Total Family Income
-.160**
-.114*
-.196**
** Correlation is significant at the 0.01 level
* Correlation is significant at the 0.05 level
Job Satisfaction is rated inversely such that high scores indicate low satisfaction.
Is the relationship between job satisfaction, education, and income similar for men and women? What do you base your answer on?
The associations between Education, Age and Income with Job Satisfaction change slightly when factoring the variable of the participant's gender. For men, the relationship between Education and Job Satisfaction is not significant, while it is significant for women. The relationships between income and job satisfaction and age and job satisfaction remain significant for both genders, although the correlation is slightly stronger women between income and job satisfaction (-.114 for men vs. -.196 for women).
When examining the ANOVA with the addition of gender, income no longer explains any variance in job satisfaction for men, and level of education no longer explains any variance in job satisfaction for women. Overall, it appears that the relationships between these variables are somewhat similar between men and women, although there are slight differences, most keenly pointed out in the ANOVA results.
Correlations
Respondent's Sex
Age of Respondent
Highest Year of School Completed
Total Family Income
Job Satisfaction
Male
Age of Respondent
Pearson Correlation
1
-.240**
-.065
-.125**
Sig. (2-tailed)
.000
.103
.005
N
Highest Year of School Completed
Pearson Correlation
-.240**
1
.419**
-.042
Sig. (2-tailed)
.000
.000
.350
N
Total Family Income
Pearson Correlation
-.065
.419**
1
-.114*
Sig. (2-tailed)
.103
.000
.012
N
Job Satisfaction
Pearson Correlation
-.125**
-.042
-.114*
1
Sig. (2-tailed)
.005
.350
.012
N
Female
Age of Respondent
Pearson Correlation
1
-.275**
-.115**
-.123**
Sig. (2-tailed)
.000
.001
.002
N
Highest Year of School Completed
Pearson Correlation
-.275**
1
.459**
-.093*
Sig. (2-tailed)
.000
.000
.018
N
Total Family Income
Pearson Correlation
-.115**
.459**
1
-.196**
Sig. (2-tailed)
.001
.000
.000
N
Job Satisfaction
Pearson Correlation
-.123**
-.093*
-.196**
1
Sig. (2-tailed)
.002
.018
.000
N
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Correlations
Age of Respondent
Highest Year of School Completed
Total Family Income
Job Satisfaction
Age of Respondent
Pearson Correlation
1
-.259**
-.099**
-.124**
Sig. (2-tailed)
.000
.000
.000
N
Highest Year of School Completed
Pearson Correlation
-.259**
1
.437**
-.068*
Sig. (2-tailed)
.000
.000
.022
N
Total Family Income
Pearson Correlation
-.099**
.437**
1
-.160**
Sig. (2-tailed)
.000
.000
.000
N
Job Satisfaction
Pearson Correlation
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