Tutorial ProactivityEmotional IntelligenceSJTCustomer Service 28922 36811 4675 55611 65612 75512 74512 82412 92312 27810 34710 36710 56712 65612 65612...

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Tutorial ProactivityEmotional IntelligenceSJTCustomer Service 28922 36811 4675 55611 65612 75512 74512 82412 92312 27810 34710 36710 56712 65612 65612 74512 74512 73411 73310 81218 88912 98911 95615 96716 96716 126711 124514 147412 3236 4226 5316 58914 691014 77815 46711 45610 74512 83412 92312 3125 3218 88910 77815 76714 66713 65612 55611 54510 4459 4459 3458 3347 2349 92312 91211 88816 77815 66713 66713 56712 47610 55611 4459 4459 3458 2346 1345 1234 11218 128105 78916 57813 56712 46711 45610 3569 3158 2457 2457 2346 4237 41214 57914 710912 76714 45610 4459 3458 3458 2246 2235 157810 46711 45610 3458 3136 31214 4459 3458 3347 28922 36811 4675 55611 65612 75512 74512 82412 92312 27810 34710 36710 56712 65612 65612 74512 74512 73411 73310 81218 88912 98911 95615 96716 96716 126711 124514 147412 3236 4226 5316 58914 691014 77815 46711 45610 74512 83412 92312 3125 3218 88910 77815 76714 66713 65612 55611 54510 4459 4459 3458 3347 2349 92312 91211 88816 77815 66713 66713 56712 47610 55611 4459 4459 3458 2346 1345 1234 11218 128105 78916 57813 56712 46711 45610 3569 3158 2457 2457 2346 4237 41214 57914 710912 76714 45610 4459 3458 3458 2246 2235 157810 46711 45610 3458 3136 31214 4459 3458 3347 18924 36811 4675 55611 65612 75512 74512 82412 92312 27810 34710 36710 56712 65612 65612 74512 74512 73411 73310 8129 88912 98911 95615 96716 96716 126711 124514 147412 3236 4226 5316 58914 691027 77815 46711 45610 74512 83412 92312 3125 5218 48910 77815 76714 66713 65612 55611 54510 4459 4459 3458 3347 2349 92312 91211 88816 77815 66713 66713 56712 47610 55611 4459 4459 3458 2346 1345 1234 51218 128105 78916 57813 56712 46711 45610 3569 3158 2457 2457 2346 4237 41214 57922 710912 76714 45610 4459 3458 3458 2246 2235 157810 46711 45610 3458 3136 31214 4459 3458 3347 Practice WorkSampleStructuredInterviewJobPerformance 2.293.173.53 2.203.613.07 5.522.853.21 1.872.081.58 1.002.821.56 6.213.623.92 2.002.861.79 6.643.553.75 2.293.071.54 7.214.331.87 3.423.362.77 5.262.883.64 7.063.002.49 3.752.262.81 5.842.603.85 3.732.872.41 5.142.663.03 3.992.892.54 4.782.913.28 3.853.442.07 2.513.752.54 3.131.771.00 6.563.593.96 6.415.003.40 4.022.392.80 7.412.383.38 5.723.301.95 5.983.254.12 6.493.835.00 2.672.164.29 6.771.913.98 2.883.733.08 8.572.183.57 6.122.142.66 6.132.763.41 2.752.852.78 1.003.222.94 3.682.793.83 4.552.633.78 3.263.282.11 4.804.374.29 4.573.883.21 4.293.292.87 7.261.003.96 3.782.902.79 6.263.584.52 8.023.904.22 6.233.373.41 7.973.382.93 4.883.323.60 5.572.992.89 6.753.193.78 5.992.083.41 9.262.833.76 5.643.524.29 6.083.143.51 4.352.531.75 5.733.643.15 7.212.844.12 8.203.354.40 3.563.642.25 4.972.684.35 4.942.512.14 5.883.632.94 6.343.703.61 4.013.602.70 6.942.813.35 3.853.412.82 3.823.424.86 7.263.233.33 3.532.491.52 6.734.203.24 6.973.753.45 3.863.772.89 4.063.131.68 7.233.733.43 2.523.523.34 5.792.794.39 4.343.342.50 5.693.132.78 6.242.263.32 4.133.152.77 6.073.204.40 5.562.882.06 6.853.214.90 7.132.403.10 9.603.253.87 5.452.822.35 3.792.603.96 1.003.192.35 6.522.643.03 5.522.302.13 8.043.783.31 6.182.902.79 3.502.582.31 4.303.883.20 3.582.903.27 1.003.613.09 4.563.402.27 7.653.514.07 2.753.112.40 6.373.263.49 8.062.703.13 6.283.164.17 6.512.173.34 2.963.762.01 6.853.943.45 4.122.013.21 4.523.583.16 7.343.463.99 4.164.042.59 7.033.904.04 2.811.782.16 3.132.041.00 5.453.204.38 PowerPoint Presentation Chapter 7 Excel Extension: Now You Try! Assessing the Criterion-Related Validity of Selection Procedures Bauer, Human Resource Management, First Edition. SAGE Publishing, 2020. Background In this Excel Extension tutorial, you will learn how to assess the criterion-related validity of selection procedures using correlation and regression analyses. For the purposes of this exercise, imagine that your HR team is revamping the selection procedures for the Sales Associate job. Your team has conducted a concurrent validation study in which Sales Associate job incumbents complete the following selection procedures: proactivity assessment, emotional intelligence inventory, and situational judgment test (SJT). With regard to the criterion (outcome) of interest, the job incumbents were also assessed in terms of their customer service job performance. Bauer, Human Resource Management, First Edition. SAGE Publishing, 2020. Open the Excel workbook titled “Chapter 7 - Excel Extension.xlsx”. Click on the sheet called Tutorial. You will use the data contained in this sheet to learn how to create a correlation matrix and a multiple linear regression model in Excel. Note that the sheet includes four fields/columns/variables: Proactivity (productivity assessment; 1 = low proactivity, 15 = high proactivity) Emotional Intelligence (emotional intelligence inventory; 1 = low emotional intelligence, 10 = high emotional intelligence) SJT (situational judgment test; 1 = low SJT, 10 = high SJT) Customer Service (customer service job performance; 1 = low performance, 30 = high performance) Note that there are 300 employees in this sample. Step 1 Bauer, Human Resource Management, First Edition. SAGE Publishing, 2020. If you haven’t already, be sure to add in the Analysis ToolPak feature in Excel, which requires the following steps. First, click on the File tab. Step 2 Bauer, Human Resource Management, First Edition. SAGE Publishing, 2020. Select Options. Step 3 Bauer, Human Resource Management, First Edition. SAGE Publishing, 2020. When the Excel Options window opens, select the Add-Ins tab on the left, and then select the Analysis ToolPak in the Excel Add-Ins list, which appears next Manage at the bottom of the window, and click Go…. A window will open, and simply check the box next to Analysis ToolPak. If you already have the Analysis ToolPak application activated, it will appear in the Active Application Add-ins list, as shown in the image. Click OK. Step 4 Bauer, Human Resource Management, First Edition. SAGE Publishing, 2020. Click on the Data tab. Click on the Data Analysis button. Step 5 Bauer, Human Resource Management, First Edition. SAGE Publishing, 2020. As a first step in the criterion-related validation process, let’s create a correlation matrix that contains the intercorrelations between the three selection procedure scores and the customer service job performance scores. To do so, in the Data Analysis window, select Correlation. Click OK. Step 6 Bauer, Human Resource Management, First Edition. SAGE Publishing, 2020. When the Correlation window opens, in the field next to Input Range: enter the range of cells that contains all four variables and associated values, including the variable labels. Check the box next to Labels in First Row. Click the bubble next to New Worksheet Ply. If you would like, next to New Worksheet Ply:, type in what you would like to name the new sheet. Here, “Correlations” is entered in the field. Click OK. Step 7 5 1 2 3 4 Bauer, Human Resource Management, First Edition. SAGE Publishing, 2020. The sheet that we named “Correlations” should open automatically, and this sheet contains the correlation matrix we requested. The following correlation coefficients in the matrix represent the validity coefficients, which all happen to be statistically significant: Proactivity – Customer Service (.391) Emotional Intelligence – Customer Service (.426) SJT – Customer Service (.417) Note that the validity coefficients are all of about the same magnitude. We can describe the magnitude of a correlation (validity) coefficient as follows:< .10 (negligible) .10 ̶̶ .29 (small) .30 ̶ .49 (medium) .50 ̶ 1.00 (large) these three validity coefficients can be considered medium in magnitude. step 8 bauer, human resource management, first edition. sage publishing, 2020. now, take note of the correlations between the selection procedure variables. they are as follows: proactivity – emotional intelligence (.318) proactivity – sjt (.237) sjt – emotional intelligence (.932) the correlations between proactivity and emotional intelligence and between proactivity and sjt are small-to-medium in magnitude, which indicates that there is not much redundancy (shared variance) between them. the correlation between sjt and emotional intelligence, however, is concerning, as the correlation is nearly 1.00, which would indicate 100% redundancy (100% shared variance). as a rule of thumb, variables that share a correlation of .85 or higher can be considered nearly indistinguishable. this very high correlation indicates that we should pick the least resource-intensive and costly selection procedure of the two. for the time being, let’s assume the sjt procedure is considerably less costly and results in virtually no disparate (adverse) impact. thus, the next step is to determine how to weight proactivity and sjt using regression. step 9 bauer, human resource management, first edition. sage publishing, 2020. now we’re ready to run a multiple linear regression analysis to determine the respective regression weights for the proactivity and sjt in relation to customer service when included in the same model. excel can be a bit fussy when it comes to how the variables are ordered when running a regression analysis. specifically, the predictor variables need to be in adjacent columns. as such, let’s move the emotional intelligence column to the leftmost column, as we won’t need it for the time being. first, select the column containing emotional intelligence, and cut the cells by holding ctrl+x (or command+x on a mac). step 10 bauer, human resource management, first edition. sage publishing, 2020. second right click on the header of the leftmost column (a). third, select insert cut cells. step 11 bauer, human resource management, first edition. sage publishing, 2020. now the variables are in an order that will be amenable to multiple linear regression analysis in excel. step 12 bauer, human resource management, first edition. sage publishing, 2020. click on the data tab. click on the data analysis button. step 13 bauer, human resource management, first edition. sage publishing, 2020. to specify our multiple linear regression model, in the data analysis window, select regression. click ok. step 14 .10="" (negligible)="" .10="" ̶̶="" .29="" (small)="" .30="" ̶="" .49="" (medium)="" .50="" ̶="" 1.00="" (large)="" these="" three="" validity="" coefficients="" can="" be="" considered="" medium="" in="" magnitude.="" step="" 8="" bauer,="" human="" resource="" management,="" first="" edition.="" sage="" publishing,="" 2020.="" now,="" take="" note="" of="" the="" correlations="" between="" the="" selection="" procedure="" variables.="" they="" are="" as="" follows:="" proactivity="" –="" emotional="" intelligence="" (.318)="" proactivity="" –="" sjt="" (.237)="" sjt="" –="" emotional="" intelligence="" (.932)="" the="" correlations="" between="" proactivity="" and="" emotional="" intelligence="" and="" between="" proactivity="" and="" sjt="" are="" small-to-medium="" in="" magnitude,="" which="" indicates="" that="" there="" is="" not="" much="" redundancy="" (shared="" variance)="" between="" them.="" the="" correlation="" between="" sjt="" and="" emotional="" intelligence,="" however,="" is="" concerning,="" as="" the="" correlation="" is="" nearly="" 1.00,="" which="" would="" indicate="" 100%="" redundancy="" (100%="" shared="" variance).="" as="" a="" rule="" of="" thumb,="" variables="" that="" share="" a="" correlation="" of="" .85="" or="" higher="" can="" be="" considered="" nearly="" indistinguishable.="" this="" very="" high="" correlation="" indicates="" that="" we="" should="" pick="" the="" least="" resource-intensive="" and="" costly="" selection="" procedure="" of="" the="" two.="" for="" the="" time="" being,="" let’s="" assume="" the="" sjt="" procedure="" is="" considerably="" less="" costly="" and="" results="" in="" virtually="" no="" disparate="" (adverse)="" impact.="" thus,="" the="" next="" step="" is="" to="" determine="" how="" to="" weight="" proactivity="" and="" sjt="" using="" regression.="" step="" 9="" bauer,="" human="" resource="" management,="" first="" edition.="" sage="" publishing,="" 2020.="" now="" we’re="" ready="" to="" run="" a="" multiple="" linear="" regression="" analysis="" to="" determine="" the="" respective="" regression="" weights="" for="" the="" proactivity="" and="" sjt="" in="" relation="" to="" customer="" service="" when="" included="" in="" the="" same="" model.="" excel="" can="" be="" a="" bit="" fussy="" when="" it="" comes="" to="" how="" the="" variables="" are="" ordered="" when="" running="" a="" regression="" analysis.="" specifically,="" the="" predictor="" variables="" need="" to="" be="" in="" adjacent="" columns.="" as="" such,="" let’s="" move="" the="" emotional="" intelligence="" column="" to="" the="" leftmost="" column,="" as="" we="" won’t="" need="" it="" for="" the="" time="" being.="" first,="" select="" the="" column="" containing="" emotional="" intelligence,="" and="" cut="" the="" cells="" by="" holding="" ctrl+x="" (or="" command+x="" on="" a="" mac).="" step="" 10="" bauer,="" human="" resource="" management,="" first="" edition.="" sage="" publishing,="" 2020.="" second="" right="" click="" on="" the="" header="" of="" the="" leftmost="" column="" (a).="" third,="" select="" insert="" cut="" cells.="" step="" 11="" bauer,="" human="" resource="" management,="" first="" edition.="" sage="" publishing,="" 2020.="" now="" the="" variables="" are="" in="" an="" order="" that="" will="" be="" amenable="" to="" multiple="" linear="" regression="" analysis="" in="" excel.="" step="" 12="" bauer,="" human="" resource="" management,="" first="" edition.="" sage="" publishing,="" 2020.="" click="" on="" the="" data="" tab.="" click="" on="" the="" data="" analysis="" button.="" step="" 13="" bauer,="" human="" resource="" management,="" first="" edition.="" sage="" publishing,="" 2020.="" to="" specify="" our="" multiple="" linear="" regression="" model,="" in="" the="" data="" analysis="" window,="" select="" regression.="" click="" ok.="" step="">
Oct 23, 2021
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