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Question 6 An organisational psychologist investigated how employees' satisfaction with their jobs may be predicted by four variables: (i) salary, (ii) contact time with
Question 6 An organisational psychologist investigated how employees' satisfaction with their jobs may be predicted by four variables: (i) salary, (ii) contact time with colleagues, (iii) overtime work duration, and (iv) amount of supervision received. A sample of employees were recruited and their salaries ('salary'), numbers of hours in contact with colleagues ('colleagues'), number of hours of overtime work ('overtime'), numbers of supervisory hours received ('supervision'), and job satisfaction ('satisfaction) were measured individually. The measured scores can all be taken as interval/ratio variables. Please read through the appendix (in the file "PSYC2060B_final_quiz_appendix.pdf" on Moodle) and choose the set of JAMOVI outputs that corresponds to the appropriate data analysis for addressing the research question of this study. a. Which set of JAMOVI outputs corresponds to the data analysis for answering the research question? b. When considered together, do the four predictor variables significantly predict employee satisfaction as the outcome variable? Report the relevant statistical values (the APA format is not necessary) for answering this question. What is the proportion of variance in employee satisfaction that is not explained by the four predictor variables together? c. Report (the APA format is not necessary) all the numerical parameters in the relationship between the predictor and outcome variables in the analysis of part b. Interpret the unstandardized parameter that reflects the unique contribution of overtime work hours for predicting job satisfaction in the present analysis. d. Identify the predictor variable with the largest relative unique contribution (among all the independent variables used) in predicting employee satisfaction in the present analysis, and interpret the standardized parameter of this predictor variable that reflects its unique contribution in the estimated relationship.
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