SPSS Output Summary
Research Question
Did the campout event of the company improve the level of job satisfaction among employees?
Hypotheses
The null hypothesis
H0: The level of job satisfaction between employees who attended and those who did not attend a weekend’s campout is not significantly different.
The null hypothesis assumes that campout event does not influence the level of job satisfaction among employees. This means that employees who attended and those who did not attend have the same level of job satisfaction.
The alternative hypothesis
H1: The level of job satisfaction between employees who attended and those who did not attend a weekend’s campout is significantly different.
In contrast to the null hypothesis, the alternative hypothesis presupposes that the campout even improves the level of job satisfaction among employees. According to the alternative hypothesis, there is a significant difference in the level of job satisfaction between employees who attended and those who did not attend a campout event.
Variables of the Study
The independent variable of the study is the attendance of the campout event (the employees who attended and employees who did not attend). Attendance is a discrete variable because an employee can either attend or fail to attend a campout event. Since attendance a qualitative variable, its scale of measurement is a nominal scale, which exists in two categories. Comparatively, the level of job satisfaction is a dependent variable, which exists as a continuous variable. The level of job satisfaction is a dependent variable because it varies according to the campout attendance. Given that the level of job satisfaction is a quantitative variable, it is measurable on an interval scale.
Descriptive Statistics
The employees who participated in the study are 40 (N=40). Among the 40 employees, 20 of them did attend the campout event while 20 of them did not attend. The frequency table below indicates the distribution of frequencies and a cumulative number of participants.
The descriptive table below shows descriptive statistics of the level of job satisfaction of employees who attended and those who did not attend the campout event. Among employees who attended the campout event, the minimum level of job satisfaction is 35, while the maximum level is 48, which has a range of 13. As a measure of central tendency and dispersion respectively, the mean is 42.39 and the standard deviation is 3.342 (M = 42.39, SD = 3.342). The variance of the level of job satisfaction is 11.168. Comparatively, employees who did not attend the campout event show a level of satisfaction that ranges from 18 to 36. The means the level of job satisfaction among employees who did not attend campout event is 26.50 with a standard deviation of 4.494 (M = 26.50, SD = 4.494).
The distribution depicted by the level of job satisfaction is normal in both the employees who attended and those who did not attend the campout event. The kurtosis and skewness of the level of job satisfaction among the employees who attended the campout event are 0.64 and 0.403 respectively. Moreover, the kurtosis and skewness of the level of job satisfaction among employees who did not attend the campout event are 0.100 and 0.135 respectively. Since these values are close to zero, it implies that the distribution of the level of job satisfaction among employees who attended and those who did not attend follows normal distribution as depicted by the following histograms.
Independent Samples T-test
The appropriate kind of t-test employed in the analysis of these data is an independent samples t-test. According to Bryman and Cramer (2005), data must meet six assumptions of the t-test for the analysis to provide valid results. The first assumption is that the dependent variable should exist as an interval scale and the second assumption is that the independent variable should consist of two independent categories. The third assumption is that there should be independence in observations, while the fourth assumption is that significant outliers must not be present. Moreover, the fifth assumption requires dependent variable should follow a normal distribution with homogeneity of variances as the sixth assumption (Weinberg, & Abramowitz, 2008). Therefore, both independent and dependent variables comply with all the assumptions of the independent samples t-test.
Results
The independent samples t-test is a two-tailed test because it aims at testing if there is a significant difference in the level of job satisfaction between employees who attended and those who did not attend the campout event.
Hypothesis Testing
The independent samples t-test shows that there is a significant difference in the level of job satisfaction between employees who attended and those who did not attend the campout event. From the t-test table, the two-tailed p-value under both the assumption of equal variances and the assumption of unequal variance is 0.000. The p-value of 0.000 is less than the significant level of 0.05 (p<0.05). When the p-value is less than the α level, one rejects the null hypothesis and accepts the alternative hypothesis (Jackson, 2012). In this case, the t-test then rejects the null hypothesis and accepts the alternative one, which states that the level of job satisfaction between employees who attended and those who did not attend the campout event is significantly different. Thus, from the descriptive statistics and t-test results, it means that the observed difference in the level of job satisfaction among employees is statistically significant.
Conclusion
A significant difference exists in the level of job satisfaction between employees who attended and those who did not attend the campout event. In the study, one can conclude that the campout event improved the level of job satisfaction among employees. Using these results, one should not conclude that campout event is solely responsible for the significant difference because other confounding variables exist. Since the findings have limited external validity, one cannot extrapolate the findings and apply them in all organizations because they are unique to the organization of the study.
References
Bryman, A., & Cramer, D. (2005). Quantitative data analysis with SPSS12 and13: A Guide for Social Scientists. New York: Routledge.
Jackson, S. L. (2012). Research methods and statistics: A Critical Thinking Approach (4th ed.). Belmont, CA: Wadsworth.
Weinberg, S., & Abramowitz, S. (2008). Statistics Using SPSS: An Integrative Approach. Cambridge: Cambridge University Press.