Question.4864 - SPSS Final Project Directions: For the final SPSS/Excel assignment, you will be conducting analysis on a given dataset. The below requirements must be completed in a Microsoft Word document and emailed to me at aaron.bartula@untdallas.edu no later than 11:59pm on the scheduled due date (please see syllabus for due date). In the Modules tab, scroll down to the section titled Data Sets. Using the dataset entitled "staffsurvey", complete the following. Note, if you are using SPSS you will select the file staffsurvey.sav; if you are using Microsoft Excel you will use the datafile staffsurvey_data.xls. Procedure: You are a researcher in a large Fortune 500 HR department. You have conducted an employee survey. Your goal is three-fold (i.e., parts 1, 2 and 3 below): Part 1: Determine employees’ views of level of importance for various corporate components. • Develop a scale using the variables: “q1imp, q2imp, q3imp, q4imp, q5imp, q6imp, q7imp, q8imp, q9imp, q10imp”. Title the scale it “Important Factors”. • Report the Cronbach’s Alpha value in the Word document.. • Report whether the scale deemed reliable in the Word document. • Calculate in the individual means for all above variables (q1imp-q10imp). NOTE: delete all Null/Missing information from the variables and leave blank prior to calculating the means. • Based on the means, report which variable/factor your employees view as the single most important in the Word document. o Hint: the highest value signifies the most important. • Create a new variable in the dataset entitled “qimpTOT”. Add it to the dataset directly after the variable “qimp10”. This variable should be a summation of the above 10 variables (i.e., qimp1-qimp10). o Hint, each response for qimpTOT should be the individual respondent’s q1imp + qimp2 +….qimp10 total score. SPSS Final Project Part 2: Determine if views of importance are correlated to years of service with the organization. • Using the newly created variable “qimpTOT” and “service”, run a scatterplot. • Copy the scatterplot into the Word document. • Using the newly created variable “qimpTOT” and “service”, run a test of correlation • Report the correlation coefficient in terms of strength and directionality in the Word document. • Report whether the correlation coefficient is statistically significant in the Word document. • Interpret the results of the correlation test in the Word document. Part 3: Determine which variables most strongly impact the dependent variable “qimpTOT” Part 3A: Run a Standard Multiple Regression Model o Using the independent variables “age”, “service” and “employstatus”, run a standard multiple regression in either SPSS or Excel with the above dependent variable (qimpTOT). o Copy and paste the results into a Word document. Part 3B: Determine Model Fit o Looking at the correlation table, determine if any variables should be excluded due to high correlation factors. Make sure the table is copy and pasted into the Word document and your interpretation. o Looking at the VIF coefficient, determine if collinearity is an issue with the model. Report the VIF coefficient and your interpretation in the Word document. o Report the R ² value and interpret the results in the Word document. Part 3C: Testing for Significance o Evaluate each independent variable’s coefficient and determine which are statically significant. o Interpret the results for each independent variable.
Answer Below:
Final xxxx ProjectPart xxxxxxxx s xxxxx is xxxxx the xxxxx value xx above xxx scale xxxxxxxxx Factors xx considered xxxxxxxx Q xx is xxxxxxxxxx as xxx most xxxxxxxxx by xxxxxxxxx since xx has xxx highest xxxx A xxx variable xxxxxxx was xxxxxxx as xxx summation xx all xxxxxxxxxx items xx represent xxxxxxx perceived xxxxxxxxxx by xxxx employee xxxx A xxxxxxxxxxx was xxxxxxxxx with xxxxxx of xxxxxxx on xxx X-axis xxx qimpTOT xx the xxxxxx to xxxxxxxx explore xxx relationship xxx Pearson xxxxxxxxxxx coefficient xxx r x with x p-value xx This xxxxxxxx that xxx correlation xx very xxxx and xxxxxxxx Since xxx p-value xx greater xxxx the xxxxxxxxxxx is xxx statistically xxxxxxxxxxx This xxxxxxxx indicates xxxx there xx no xxxxxxxxxx relationship xxxxxxx an xxxxxxxxxx years xx service xxx how xxxxxxxxx they xxxx the xxxxxxxxx components xxxx By xxxxxxx at xxx correlation xxxxx to xxxxxx multicollinearity xxxxxxx the xxxxxxxxxxx variables xx pair xx independent xxxxxxxxx had x correlation xxxxxxxxxxx exceeding xxxxx is xx evidence xx multicollinearity xxxxx on xxx correlation xxxxxx Therefore xx variables xxxx excluded xxxxx on xxxx correlation xxxxxxx The xxx values xxx age xx for xxxxxxx is xxx employment xxxxxx is xx can xxxxxxx that xxx VIF xxxxxx are xxxx below xxx threshold xx indicating xxxx multicollinearity xx not x concern xx this xxxxx From xxx model xxxxxxx the xx square xxxxx of xxxxxxxxx that xxxx of xxx variation xx the xxxxxxxxx variable xxxxxxx is xxxxxxxxx by xxx three xxxxxxxxxxx variables xxxx implies xxxx the xxxxxxx model xxx is xxxx weak xxx that xxxxx independent xxxxxxxxx do xxx meaningfully xxxxxxx the xxxxxxx variable xxx coefficient xx Age xx and xxx p- xxxxx is xxxx indicates xxxx age xxx no xxxxxxxxxxxxx significant xxxxxx on xxxxxxx since xxx p xxxxx is xxxxxxx than xxx level xx significance x unit xxx change xx age xxxxxxx in x increase xx qimTOTThe xxxxxxxxxxx of xxxxxxx is x and xxx p xxxxx is xxxx tells xx that xxx length xx service xx also xxx statistically xxxxxxxxxxx since xxx p xxxxx is xxxxxxx than xxx level xx significance x unit xxx change xx the xxxxxx of xxxxxxx results xx a xxxxxxxx in xxxxxx The xxxxxxxxxxx of xxxxxxxxxx status xx - xxx the x value xx This xxxxx us xxxx employment xxxxxx also xxxx not xxxxxxxxxxxxx predict xxxxxxx With x p-value xx this xxxxxx is xxxx not xxxxxxxxxxxxx significant x unit xxx change xx employment xxxxxx results xx a xxxxxxxx in xxxxxxMore Articles From Quantitative data analysis
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