Regression Analysis

Regression Analysis

TEST 1: Regression Analysis Benefits and Intrinsic

In this section of analysis the statistical analysis focuses on enlightening the influence of gender upon intrinsic job satisfaction. In this context, gender is an independent variable and intrinsic job satisfaction is dependent variable. There is a set of hypothesis constructed to validate the results obtained from statistical analysis of the data. These hypotheses are enlightened below:

Null Hypothesis: Gender does not have a significant influence on intrinsic job satisfaction.

Alternative Hypothesis: Gender has a significant influence on intrinsic job satisfaction.

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

.310

1

.310

.353

.555a

Residual

43.964

50

.879

Total

44.274

51

a. Predictors: (Constant), Gender

b. Dependent Variable: Intrinsic job satisfaction

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

4.742

.401

11.810

.000

Gender

.183

.309

.084

.594

.555

a. Dependent Variable: Intrinsic job satisfaction

The analysis depict that gender does not have a significant influence on intrinsic job satisfaction. This influence is enlightened by ANOVA table as the sig value revealed by the analysis is greater than desired significance level (0.555). Therefore, the analysis carried out using gender and intrinsic job satisfaction as study variables support the acceptance of null hypothesis.

TEST 2: Regression Analysis Benefits and Extrinsic

The analysis conducted in this section considers gender is an independent variable and extrinsic job satisfaction is dependent variable. In order to validate the results of analysis hypothesis are constructed to represent statistical values obtained through analysis (Eye, Schuster, 1998, 59-77). These hypotheses are enlightened below:

Null Hypothesis: Gender does not have a significant influence on extrinsic job satisfaction.

Alternative Hypothesis: Gender has a significant influence on extrinsic job satisfaction.

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

.060

1

.060

.370

.546a

Residual

8.159

50

.163

Total

8.219

51

a. Predictors: (Constant), Gender

b. Dependent Variable: Extrinsic job satisfaction

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

5.503

.173

31.818

.000

Gender

-.081

.133

-.086

-.608

.546

a. Dependent Variable: Extrinsic job satisfaction

According to the results obtained through regression analysis applied to the data, it can be interpreted that in this context acceptance of null hypothesis would be adequate as the sig value revealed by regression analysis is greater than the threshold (0.555). Therefore, the null hypothesis is accepted which states that gender does not have a significant influence on extrinsic job satisfaction.

TEST 3: Regression Analysis Benefits Overall job Satisfaction

This section of analysis is inclined to explore the influence of benefits offered by an organization to overall job satisfaction. In order to facilitate the analysis a set of hypothesis are constructed one which represents the influence and the other denies it. The constructed hypotheses are enlightened below:

Null Hypothesis: Benefits offered by the company does not have a significant influence on overall job satisfaction.

Alternative Hypothesis: Benefits offered by the company has a significant influence on overall job satisfaction.

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

2.238

1

2.238

10.688

.002a

Residual

10.469

50

.209

Total

12.707

51

a. ...