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ANSWER SHEET TASK A It was hypothesised that workers who take less time to travel to work each day would generally be satisfied with their work life. In a random sample of 150 participants from a large Melbourne based company there was a moderate negative linear relationship between the travel time and satisfaction with work life., and Pearson’s r show that is relationship is significant, r = -.49, n =150, p < .001. The 95% confidence interval for Pearson’s correlation indicates that the strength of the relationship is between 𝜌 = -.60 and 𝜌 = -.36. In the sample satisfaction of work life increased on average for every 22 minutes less travelled. As expected, the less time spent travelling to work leads to more satisfaction of work life. APPENDIX A TASK B 1. Work satisfaction (r = 44, p < .001) and Years of tertiary education (r = 30, p < .001) 2. Number of years working for a company = 3.38 + .09(work satisfaction) -.09 (Years of tertiary education) -.32 (sex) 3. Number of years working for a company 3.38 + .09(40) -.09(4) -32(0) =3.38+.09-.32 = 3.38+ 3.6 -.36- .32= 6.3 4. When years of tertiary education and sex are statistically controlled for, each additional unit of work satisfaction, on average was 0.85 units higher. 5. The most important predictor in this regression is work satisfaction at .497 6. When all the predictors are considered, work satisfaction contributes significantly to the multiple regression (t = 4.33, p = < .001) 7. Multiple R is significant, F(3, 146) = 11.89, p = < .001 8. 19.6% of the variation in the number of years spent working for a company can be explained by this linear model. APPENDIX B TASK C Section 1 Error: Intercorrelations Table 1 is incomplete Why it is an error: There is no correlation figure between YearT.Edu and Age in the last column Change to: The correlation between YearT.Edu and Age is .44 Section 2 Error: People with more years of tertiary education tend to have higher annual incomes, as did younger workers. Why it is an error: The linear relationship of age is lower than years of tertiary education Change to: Pearson’r shows a positive linear relationship between age and income r = .426, n = 150, p = < .001 and the relationship is significant. Section 3 Error: The figures for F Why it is an error: It is incorrect because figures in the bracket reads F(3,149) which is the regression number and the total number. Change to: The correct F should be using the regression and the residual F( 3,146) Section 4 Error: Contrary to expectations, there is insufficient evidence to suggest that the sex of a person influences their income. Why it is an error: This is an error because sex has a negative linear relationship, and Pearson’s r show that this relationship is not significant. Change to: r = -.057, n = 150, p = .243 APPENDIX C TASK D A study was conducted to investigate the factors that effect work satisfaction for sample of office workers. The researchers hypothesised that people with higher incomes will be more satisfied with work. It was also hypothesised that full time workers will be more satisfied with their work than parttime works. They also hypothesised that people who have spent more time at their current job will be more satisfied with work. A multiple regression was performed on this data with work satisfaction as the dependent variable. The four predictors included in this model: (1) annual income, (2) number of years working at a company, (3) travel time, and (4) load(part-time/fulltime). The intercorrelations between the variables are provided in Table 1 and the regression statistics are provided in Table 2. Table 1 Intercorrelations among the variables Work satisfaction Annual income Annual income .91 *** Years at company .44*** .42 *** Travel time -.49 *** -.48 *** Travel time .42*** .07 Work load Years at company Load .09 .06 -.13 .09 .06 .48 .15 Note: * p < .05, ** p < .01, *** p < .001; N = 150 As can be seen from Table 1, only Load was not significantly correlated to the dependent variable Work satisfaction. There were however significant correlations between Work satisfaction and other predictors. People with higher incomes tend to have more work satisfaction. On the other hand, people who have spent more time at their current job will have more work satisfaction. Table 2 Results of the regression, with work satisfaction as the DV Variable Squared part correlations Standardised Partial Correlations regression coefficients Income .442 854 .842 *** Years at company .004 152 .069 Travel time -.005 -.168 -.081 Work load .006 .020 .008 R2 = .836 Note: * p < .05, ** p < .01, *** p < .001; N = 150 The four predictors together explain 83.6% of the variance in work satisfaction, and this is significant F(4,145) = 185.27, p < .001. When all the predictors are considered years at company is no longer significant. The correlation between years at company and work satisfaction can be explained in terms of income. People with more years at a company tend to have higher income, and people with higher income tend to have more work satisfaction. The most important predictor in this model was income. People with higher income tend to have more work satisfaction. APPENDIX D