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    Multiple Linear Regression - Job Satisfaction

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    Multiple linear regression is a logical extension to the Pearson Product-Moment Correlation test. Researchers use multiple linear regression to examine the relationship between at least two predictor variables and a scale (numerical) dependent variable. Multiple linear regression is the most commonly used statistical test for quantitative DBA studies.
    Run a multiple linear regression using the Week 6 Data File for Multiple Linear Regression. You will use "job satisfaction" as the dependent variable.

    Submit a synthesis of statistical findings derived from multiple regression analysis:

    • An APA Results section for the multiple regression test [see an example in Lesson 34 of the Green and Salkind (2017) text].
    • Only the critical elements of your SPSS output:
    o Research question
    o H10 (null) and H1a (alternate) hypothesis
    o Descriptive statistics narrative and properly formatted descriptive statistics table
    o Scatterplot graph
    o Inferential APA Results Section to include a Normal Probability Plot (P-P) of the Regression Standardized Residual and the scatterplot of the standardized residuals
    o An Appendix including the SPSS output generated for descriptive and inferential statistics

    • An explanation of the differences and similarities of bivariate regression analysis and multiple regression analyses.

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    Multiple Linear Regression

    Linear regression is used to predict one response (dependent) variable on the basis of one predictor (independent) variable. In a multiple linear regression, there are more than one independent (continuous or categorical) variables or we can simply say, it is an extension of linear regression which takes many independent variables into the account to predict one dependent variable. Also by using multiple linear regression, the impact (contribution) of each independent variable can also be examined, while the effect of other independent variables are controlled.
    A statistical technique used for estimating the relationship among variables which have reason and result relation is known as regression analysis and a regression analysis where there is only one dependent variable and more than one independent variable is known as multivariate regression analysis (Gülden Kaya Uyanık and Neşe Güler, 2013). A simple linear regression with more than one explanatory variable is considered as multiple linear regression is considered (A. Petrie et al., 2002).
    Research Question

    Is there a statistically significant relationship between normative commitment, affective commitment, and continuous commitment, and job satisfaction?

    H0: There is not a statistically significant relationship between normative commitment, affective commitment, continuous commitment, and job satisfaction.
    H1: There is a statistically significant relationship between normative commitment, affective commitment, continuous commitment, and job satisfaction.

    In this subheading, I will present descriptive statistics, discuss testing of the assumptions, present inferential statistic results, and conclude with a concise summary.
    Descriptive Statistics
    A total of 100 employees participated in the study. The assumptions of outliers, multicollinearity, normality, linearity, homoscedasticity, and independence of residuals were evaluated with no significant violations noted. Table 1 depicts descriptive statistics for the study variables.
    Table 1
    Means and Standard Deviations for Quantitative Study ...