## Description

**Submit **a synthesis of statistical findings derived from multiple regression analysis that follows the Week 6 Assignment Template. Your synthesis must include the following:

- 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:
- A properly formatted research question
- A properly formatted H1
_{0}(null) and H1_{a}(alternate) hypothesis - A descriptive statistics narrative and properly formatted descriptive statistics table
- A properly formatted scatterplot graph
- A properly formatted inferential APA Results Section to include a properly formatted Normal Probability Plot (P-P) of the Regression Standardized Residual and the scatterplot of the standardized residuals
- 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

**Note:** You will cut and paste the appropriate SPSS output into the Appendix. The SPSS output is not in APA format, so you will need to type the information from the SPSS output to the appropriate sections of the APA table. **You must use the Week 6 Assignment Template to complete this Assignment.** Also, refer to the Week 6 Assignment Rubric for specific grading elements and criteria. Your Instructor will use this rubric to assess your work.

**To prepare for this Assignment, **review Lesson 16A and Lessons 31–35 in your Green and Salkind (2017) text, the Week 6 Assignment Exemplar and Week 6 Assignment Template documents, as well as the tutorial videos provided in this week’s Resources. Consider how a multiple regression analysis will allow you to answer your research questions effectively.

## Explanation & Answer

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1

DDBA 8307 Week 6 Assignment – Multiple Regression

Your Name

DDBA 8307-6

Your Instructor's Name

2

Multiple Linear Regression

Numerous studies have been dedicated to determining which factors contribute to

a heightened job performance. Such questions as whether salary, experience, or the

experience with the colleagues actually contribute to a heightened work performance,

will be attempted to be answered in this analysis. Fifty-one employees of a major

corporation were randomly chosen, while data on each of the variables was collected for

each individual. Job performance will be assessed using a five-point scale, administered

to each employee's immediate supervisor. The other variables, such as years of

experience, salary, and the number of friends among colleagues, will be self-reported in

survey format.

The use of a multiple linear regression is justified in this analysis, since all of the

variables are either ratio, interval, or ordinal (Liebscher, 2012). The independent variable

(performance rating) will be measured on the ordinal level. The dependent variables –

salary (in 1000's of (S)), experience (in years), and the number of friends in the

workplace are also ratio-level. The necessary assumptions have been tested using the

appropriate assessment measures. The scatterplots of the dependent variable against each

of the predictors indicated a roughly linear relationship. Normal probability plots were

created to confirm the normality of residuals. The correlations between the independent

variables were examined to test whether multicollinearity existed among the predictors.

The multicollinearity issue came up, and will be addressed in further research on this

topic. Finally, the residuals were plotted to test the homoscedascity assumption (Slinker

& Glantz, 2007).

3

Research Question

Do the years of experience, salary, and the number of friends at work significantly

influence employee performance rating?

Hypotheses

H0: The years of experience, salary, and the number of friends at work do not

significantly influence employee performance rating.

H1: The years of experience, salary, and the number of friends at work

significantly influence employee performance rating.

Results

Descriptive Statistics

A sample of 51 workers were surveyed, and their results analyzed. The

assumptions of outliers, normality, linear relationships, and evenly scattered and

independent residuals were assessed with no major violations were detected. Table 1

displays descriptive statistics for all variables.

Table 1. Descriptive Measures and Bootstrapping

Variable

M

SD

Bootstrapped 95% CI (M)

5.14

2.33

[4.45, 5.76]

61.61

17.22

[56.75, 66.24]

Number of Friends

2.41

1.46

[2.00, 2.80]

Performance Rating

2.73

1.56

[2.25, 3.14]

Years of Experience

Salary

Note: N = 51.

Descriptive Measures for All Variables

Variable

M

SD

Years of Experience

5.14

2.33

Salary

61.61

17.22

4

Number of Friends

2.41

1.46

Performance Rating

2.73

1.56

Tests of Assumptions

The appropriate assumptions for multiple linear regression (see paragraph 2) were

addressed. Bootstrapping, with use of 1,000 samples, was employed to help detect

violations.

Multicollinearity. Multicollinearity was assessed via testing the correlations

among the predictors. Although significant correlations between the variables were

found (Table 2), we decided to nevertheless include these variables in the analysis, as

they were qualitatively important to the assessment. Further analyses must be conducted

in order to account for the multicollinearity of these variables.

Table 2

Correlations (r) among The Independent Variables

Experience

Salary

Experience

1.00

.947

Number of

Friends

.841

Salary

.947

1.00

.867

Number of

Friends

Note. N = 51.

.841

.867

1.00

Variable

Outliers, normal distributions, linear relationships, even scatter and

independenc...