In order to evaluate the suitability of these personality measures for inclusion in your company's planned...
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In order to evaluate the suitability of these personality measures for inclusion in your company's planned selection process, you need to evaluate how well they predict performance. Your boss has asked you to investigate this in more detail. Specifically: a. Before you begin your analysis in earnest, which of the two dependent variables, DV's (i.e, perfc& perf m), would you expect a stronger relationship, based solely on the distribution of the data (without knowing anything else about them)? That is, based on the dispersion of each DV, which do you expect to be able to better predict using personality variables Why? + Personality Trait Machiavellianism Psychopathy perf m perf c % Variance % Variance Intercept Slope Explained Intercept Slope Explained 4.0 0.0 0.0 3.1 -0.13 1.4 3.8 0.10 2.5 3.6 -0.41 14.1 b. Based on the table in part B, fill in the table below with what you would predict the value of perfc to be for an employee with the following scores on the personality variables. Personality Trait Machiavellianism Value (Observed) 1.5 3.5 4.8 Psychopathy 4.8 3.5 1.5 Predicted perfc i. Based on your results in part b, what do you notice about the trend for each of these variables? That is: 1. As Machiavellianism increases, perf_c (Increases / Decreases / Stays the same). This is because these two variables are (Positively / Negatively) related. 2. As psychopathy decreases, perf_c (Increases / Decreases / Stays the same). This is because these two variables are (Positively / Negatively) related. c. If you could only measure one of the two personality traits considered and wanted to predict perf c, which would you choose? Why? d. Based on the results of your analysis, what would you report back to your boss? What next-step would you suggest to more completely answer the focal question and why? In order to evaluate the suitability of these personality measures for inclusion in your company's planned selection process, you need to evaluate how well they predict performance. Your boss has asked you to investigate this in more detail. Specifically: a. Before you begin your analysis in earnest, which of the two dependent variables, DV's (i.e, perfc& perf m), would you expect a stronger relationship, based solely on the distribution of the data (without knowing anything else about them)? That is, based on the dispersion of each DV, which do you expect to be able to better predict using personality variables Why? + Personality Trait Machiavellianism Psychopathy perf m perf c % Variance % Variance Intercept Slope Explained Intercept Slope Explained 4.0 0.0 0.0 3.1 -0.13 1.4 3.8 0.10 2.5 3.6 -0.41 14.1 b. Based on the table in part B, fill in the table below with what you would predict the value of perfc to be for an employee with the following scores on the personality variables. Personality Trait Machiavellianism Value (Observed) 1.5 3.5 4.8 Psychopathy 4.8 3.5 1.5 Predicted perfc i. Based on your results in part b, what do you notice about the trend for each of these variables? That is: 1. As Machiavellianism increases, perf_c (Increases / Decreases / Stays the same). This is because these two variables are (Positively / Negatively) related. 2. As psychopathy decreases, perf_c (Increases / Decreases / Stays the same). This is because these two variables are (Positively / Negatively) related. c. If you could only measure one of the two personality traits considered and wanted to predict perf c, which would you choose? Why? d. Based on the results of your analysis, what would you report back to your boss? What next-step would you suggest to more completely answer the focal question and why?
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a Based solely on the dispersion of each DV we would expect a stronger relationship with perf c performance in creative tasks compared to perf m perfo... View the full answer
Related Book For
Financial Accounting Tools for Business Decision Making
ISBN: 978-1118024492
5th Canadian edition
Authors: Paul D. Kimmel, Jerry J. Weygandt, Donald E. Kieso, Barbara Trenholm, Wayne Irvine
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