08_lab.docx - Regression Mediation Moderation Liang Shen...

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Regression, Mediation, Moderation Liang Shen 2019-06-01 Title : The influence of cognitive and affective based job satisfaction measures on the relationship between satisfaction and organizational citizenship behavior Abstract : One of the most widely believed maxims of management is that a happy worker is a productive worker. However, most research on the nature of the relationship between job satisfaction and job performance has not yielded convincing evidence that such a relationship exists to the degree most managers believe. One reason for this might lie in the way in which job performance is measured. Numerous studies have been published that showed that using Organizational Citizenship Behavior to supplant more traditional measures of job performance has resulted in a more robust relationship between job satisfaction and job performance. Yet, recent work has suggested that the relationship between job satisfaction and citizenship may be more complex than originally reported. This study investigated whether the relationship between job satisfaction and citizenship could depend upon the nature of the job satisfaction measure used. Specifically, it was hypothesized that job satisfaction measures which reflect a cognitive basis would be more strongly related to OCB than measures of job satisfaction, which reflect an affective basis. Results from data collected in two midwestern companies show support for the relative importance of cognition based satisfaction over affect based satisfaction. Implications for research on the causes of citizenship are discussed. Dataset: - Dependent variable (Y): OCB - Organizational citizenship behavior measure - Independent variables (X) - Affective - job satisfaction measures that measure emotion - Cognitive - job satisfaction measures that measure cognitions (thinking) - Years - years on the job - Type_work - type of employee measured (secretary, assistant, manager, boss) Data Screening: Assume the data is accurate with no missing values. You will want to screen the dataset using all the predictor variables to predict the outcome in a simultaneous multiple regression (all the variables at once). This analysis will let you screen for outliers and assumptions across all subsequent analyses/steps. Be sure to factor type_work.
library (haven) data= read_sav ( "C:/Users/steve/oneDrive/Desktop/HU/2019 SPRING ANLY 500/08_data.sav" ) data $ type_work= factor (data $ type_work) library (ppcor) ## Loading required package: MASS pcor (data[, - 5 ], method= "pearson" ) ## $estimate ## OCB cognitive affective years ## OCB 1.00000000 0.4648580 0.2797822 0.03707946 ## cognitive 0.46485801 1.0000000 0.2723065 -0.22154691 ## affective 0.27978221 0.2723065 1.0000000 0.27881169 ## years 0.03707946 -0.2215469 0.2788117 1.00000000 ## ## $p.value ## OCB cognitive affective years ## OCB 0.000000e+00 5.029931e-05 0.01898965 0.76055810 ## cognitive 5.029931e-05 0.000000e+00 0.02257576 0.06530028 ## affective 1.898965e-02 2.257576e-02 0.00000000 0.01942579 ## years 7.605581e-01 6.530028e-02 0.01942579 0.00000000 ## ## $statistic ## OCB cognitive affective years ## OCB 0.0000000 4.329547 2.403115 0.3059755 ## cognitive 4.3295474 0.000000 2.333686 -1.8734791 ## affective 2.4031152 2.333686 0.000000 2.3940752 ## years 0.3059755 -1.873479 2.394075 0.0000000 ## ## $n ## [1] 72 ## ## $gp ## [1] 2 ## ## $method ## [1] "pearson" Outliers a. Leverage: i. What is your leverage cut off score?

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