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```ANSWER SHEET
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
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
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
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
Years at company
.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
.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
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