Sydney Taylor's SPSS Portfolio

SPSS Portfolio

Sydney Taylor

BUSA 2182, MWF 1:00 p.m. – 1:50 p.m.

Table of Contents

SPSS Computer Lab Assignment #1Cover PageExplanatory ParagraphAppendix A



SPSS Computer Lab Assignment #4Cover PageExplanatory Paragraph #1 (stepwise regression)Explanatory Paragraph #2 (correlation matrix)Conceptual Model (Figure 1)Conceptual Matrix (Table 1)Regression Output Table (Table 2)Appendix

SPSS Computer Lab Assignment #5Cover PageExplanatory Paragraph #1 (Regression)Explanatory Paragraph #2 (Correlation Matrix) Figure 1 (Conceptual Model)Table 1 (Zero-Order Correlation Matrix)Table 2 (Descriptive Statistics)Table 3 (Regression Output)Appendix

SPSS Computer Lab Assignment #6Cover PageExplanatory ParagraphANOVA output table (Table 1)Appendix

SPSS Computer Lab Assignment #7Cover PageExplanatory ParagraphANOVA output table (Table 1)Appendix

SPSS Computer Lab Assignment #8Cover PageExplanatory Paragraph #1 (regression)Explanatory Paragraph #2 (correlation matrix)Conceptual Model (Figure 1)Correlation Matrix (Table 1)Regression Output Table (Table 2)Appendix

SPSS Computer Lab Assignment #9Cover PageExplanatory Paragraph #1 (T-tests)Explanatory Paragraph #2 (correlation matrix)Correlation Matrix (Table 1)Appendix


Computer Lab Assignment #1

Sydney Taylor

BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.

Explanatory Paragraph

A frequency distribution was conducted using group status, attachment, situational involvement,

enduring involvement, identity salience, satisfaction and attendance. The skewness coefficients situational

involvement, satisfaction, and attendance were acceptable. However, the skewness coefficients for

attachment, enduring involvement, and identity salience were unacceptable. The kurtosis coefficients for

attachment were acceptable. However, the kurtosis coefficient for situational involvement, enduring

involvement, identity salience, satisfaction, and attendance were not significant.


Sydney Taylor

BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.


A steam-and-leaf plot analysis was conducted using area, population, old, literacy, imports, exports,

and gdp. Stem-and-leaf plots for area, population, old, literacy, and imports were positively skewed.

However, gdp, were consistent with normal distribution. In addition, the steam-and-leaf plots for imports

and exports were inconsistent with normal distribution.


Sydney Taylor

BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.


Ŷ= -13.019 - .638 Attachment - .250 Attendance + .524 Situational Involvement

A multiple regression analysis was conducted with Enduring Involvement as the Endogenous

Variable and Attachment, Attendance, Situational Involvement, and Identity Salience as the Exogenous

Variable. Overall, the regression model was statistically significant (F = 161.190, p = 0.0001). Attachment,

Attendance, and Situational Involvement were significant predictors of Enduring Involvement. In addition,

Attachment, Attendance, and Situational Involvement were inversely related to the dependent measure,

Enduring Involvement. However, Identity Salience was not a significant predictor of Enduring Involvement.

The coefficient of correlation (r) indicated a moderate relationship between the predictors and Enduring

Involvement (r = .953). A model fit index, the coefficient of determination (R²), was .908, indicating that

90.8 percent of the variation in Enduring Involvement can be explained by Attachment, Attendance, and

Situational Involvement. The adjusted R², which considers the number of predictors and the sample size,

was .903, which indicated no extraneous predictors were included in the model. Because the Standard Error

of the estimate was 2.40222, the prediction equation was performing satisfactorily.


Sydney Taylor

BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.

Explanatory Paragraph #1

Ŷ= -14.491 + .394 Attendance + .905 Satisfaction +.787 Enduring Involvement + .462 Identity Salience

A Stepwise regression analysis was conducted with Attachment as the dependent variable and

Attendance, Satisfaction, Enduring Involvement, and Identity Salience as the independent measures.

Overall, the regression model was statistically significant (F = 173.777, p = 0.0001). Attendance,

Satisfaction, Enduring Involvement and Identity Salience were significant predictors of Attachment. In

addition, Attendance, Satisfaction, Enduring Involvement and Identity Salience were inversely related to the

dependent measure, Attachment. The coefficient of correlation (r) indicated a moderate relationship between

the predictors and Attachment (r = .956). A model fit index, the coefficient of determination (R²), was .914,

indicating that .914 percent of the variation in Attachment can be explained by Attendance, Satisfaction,

Enduring Involvement and Identity Salience. The adjusted R², which considers the number of predictors and

the sample size, was .909, which indicated no extraneous predictors were included in the model. Because

the standard error of the estimate was 2.83614, the prediction equation was performing satisfactorily. Multi-

Collinearity was present in the regression model because the tolerance coefficient for Attendance was below

the minimal threshold value.


A bivariate correlation analysis was conducted using, Attachment, Attendance, Satisfaction,

Enduring Involvement, and Identity Salience. Attachment was significantly correlated with Attendance,

Satisfaction, Enduring Involvement, and Identity Salience.

Figure 1: A Conceptual Model of Attachment

Attendance

Satisfaction

Enduring Involvement

Identity Salience

Attachment

Table 1: Mean, Standard Deviation, and Zero-Order Correlations

Variables Mean Standard 1 2 3 4 5Deviation

1. Attachment 30.64 9.41

2. Attendance 10.27 3.13** .665**

3. Satisfaction 10.86 2.81** .602** .880**

4. Enduring Involvement 33.34 7.70** .806** .226 .149

5. Identity Salience 10.87 3.79** .784** .807** .645** .493**

** Correlation is significant at the 0.01 level (2-tailed).

Table 3: Regression Analysis with Attachment as the Endogenous Variable and Attendance, Satisfaction,

Enduring Involvement, and Identity Salience as the Exogenous Variable, (n=70).

Independent Variables Beta T-value Tolerance P-value

(Constant) -7.346 .0001*

Attendance .131 1.257 .121 .213

Satisfaction .271 3.448 .214 .001

Enduring Involvement .644 14.554 .672 .000

Identity Salience .186 2.487 .236 .015

* Significant at the 0.05 level


Sydney Taylor

BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.


Ŷ = 9.896 +.604 Attendance + .135 Attachment

A Hierarchical regression analysis was conducted with Satisfaction as the dependent variable, Attendance,

Attachment, Enduring Involvement, Identity Salience, and Situational Involvement as the independent

measures. Overall, the regression model was statistically significant (F = 65.603, p = 0.0001). Attendance

and Attachment were significant predictors of Satisfaction. In addition, aforementioned predictors were

positively related to the dependent measure, Satisfaction. Conversely, Enduring Involvement, Identity

Salience, and Situational Involvement were not significant predictors of Satisfaction. The coefficient of

correlation (r) indicated a strong relation between predictors and Satisfaction (r = .915). A model fit index,

the coefficient of determination (R²), was .837, indicating that 83.7 percent of the variation in Satisfaction

can be explained by Attendance and Attachment. The adjusted R², which considers the numbers of

predictors and the sample size, was .824, which indicated no extraneous predictors included in the model.

Because the standard error of the estimate was 1.181, the prediction equation was performing satisfactorily.

The Multi-Collinearity was present in the regression model because the tolerance coefficient for Enduring

Involvement, Identity Salience, and Situational Involvement was below the minimum threshold value.


A bivariate correlation analysis was conducted using, Satisfaction, Attendance, Attachment,

Enduring Involvement, Identity Salience, and Situational Involvement. Satisfaction was significantly

correlated with Attendance, Attachment, Enduring Involvement, Identity Salience, and Situational

Involvement.

Figure 1: A Conceptual Model of Satisfaction

Attendance

Attachment

Enduring Involvement

Identity Salience

Satisfaction

Situational Involvement


Variables Mean S.D 1 2 3 4 5 6

1. Satisfaction 10.86 2.81

2. Attendance 10.27 3.13 .880**

3. Attachment 30.64 9.41 .602** .665**

4. Enduring Involvement 33.34 7.70 .149 .226 .806**

5. Identity Salience 10.87 3.79 .645** .807** .784** .493**

6. Situational Involvement 54.57 6.81 -.535** -.472** .143 .618** -.153

** Correlation is significant at the 0.0001 level

Table 2: Descriptive Statistics for Satisfaction, Attendance, Attachment, Enduring Involvement, Identity Salience, and Situational Involvement as the Predictor Variables, (n=70).

Variables Mean Standard Deviation

Satisfaction 10.86 2.81

Attendance 10.27 3.13

Attachment 30.64 9.41

Enduring Involvement 33.34 7.70 .

Identity Salience 10.87 3.79

Situational Involvement 54.57 6.81

Table 3: Regression Analysis with Satisfaction as the Endogenous Variable and Attendance, Attachment,

Enduring Involvement, Identity Salience, and Situational Involvement as the Exogenous Variable, (n=70).


(Constant) 4.541 .0001*

Attendance .672 5.485 .170 .0001*

Attachment .451 2.724 .093 .008

Enduring Involvement -.054 -.322 .092 .749

Identity Salience -.268 -2.582 .236 .012

Situational Involvement -.291 -2.613 .206 .011



Sydney Taylor

BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.


A One-Way Anova Test was conducted using (X10) - Usage as the Factor Variable and X6 - Fast Service,

X8- Recommend to friend, X9 - Satisfaction Level, X11 - Distance Driven, and X12 - Friendly Employees

Rank as the Dependent Variables. The Omnibus F-test for X8- Recommend to friend, X9 - Satisfaction

Level, and X11 - Distance Driven were significant. However, the Omnibus F-test for X6 - Fast Service and

X12 - Friendly Employees Rank was not significant. The contrast analysis revealed that Heavy Users has

higher, X8- Recommend to friend, X9 - Satisfaction Level, and X11 - Distance Driven scores compared to

their Light User counterparts. However, Light Users have higher X6 - Fast Service and X12 Friendly

Employees Rank.

Table 1: Results of One-Way ANOVA Testing of Usage, (n=50)

Factors F-value P-value

Factor

X6 - Fast Service 3.844 0.56

X8- Recommend to friend 36.595 .0001**

X9 - Satisfaction Level 122.279 .0001**

X11 - Distance Driven 94.255 .0001**

X12 - Friendly Employees 2.164 .148

** Significant at the 0.05 level


Sydney Taylor

BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.


A One-Way ANOVA Test was conducted using Surface as the Factor Variable and Salary, Average,

Stolen, and Wins as the Dependent Variable. The Omnibus F-Test was not significant for Salary, Average,

Stolen, and Wins. The contrast analysis reported that the One Surface reported higher number of Stolen and

Wins compared to the Zero Surface counterparts. In addition, Zero Surface reported higher Salary and

Average compared to the One Surface counterparts.

Table 1: Results of One-Way ANOVA Testing of Surface, (n=30)

Factors F-value P-value

Factor

Surface

Salary 3.714 .064

Average .071 .794

Stolen .092 .763

Wins .013 .910

** Significant at the 0.05 level


Sydney Taylor

BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.


Ŷ = -26557.728 + 3140.973 Education + 435.174 Age

A regression analysis was conducted with Wages as the Dependent Variable and Education, South,

Union and Age as the Independent Variables. Overall, the regression model was statistically significant (F =

8.122, p = .0001). A model fit index, the coefficient of determination (R²), was .255, indicating that 25.5

percent of the variation in Wages can be explained by Education, South, Union and Age. The coefficient of

correlation (r) indicated a strong relationship between the dependent variable and Wages (r = .505). In

addition the Independent Variable South and Union were not significant predictors of Wages. The adjusted

R² which considers the numbers of predictors and the sample size was .223, which indicated the Dependent

Variable were included in the model. Because the standard error of estimate was 14934.165, the prediction

equation was performing satisfactorily. The Multi-collinearity for South and Union was below the minimum

threshold value.


A bivariate correlation analysis was conducted using, Wages, Education, South, Union, and Age.

Wages was significantly correlated with Education, Union, and Age. However, South was negatively

correlated with Wages.

Figure 1: A Conceptual Model of Wages

Education

South

Union

Age

Wages



1. Wages 30833.46 16947.097

2. Education 12.73 2.792 .408**

3. South .33 .473 -.081 -.276**

4. Union .18 .386 .052 .083 -.107

5. Age 39.11 12.572 .167 -.253* -.079 .273**

** Correlation is significant at the 0.01 level

*Correlation is significant at the 0.05 level

Table 3: Regression Analysis with Education, South, Union, and Age as the Independent Variable, (n=100).


(Constant) -2.493 .014

Education .517 5.324 .830 .0001

South .080 .851 .898 .397

Union -.070 -.751 .899 .454

Age .323 3.326 .833 .001



Sydney Taylor

BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.


An independent sample T-test was conducted using Age, Education, Experience, and Wages as the

Test Variables. The Grouping Variables was Union. The Omnibus F-test for Age and Experience were

significant. However, Education and Wages were not significant.


A bivariate correlation analysis was conducted using Age, Education, Experience, and Wages as the

test variables and Union as the grouping variable. Age, Education, Experience, and Wages were positively

correlated with Union.



1. Union .18 .386

2. Age 39.11 12.572 .273**

3. Education 12.73 2.792 .083 -.253**

4. Experience 20.38 13.550 .236** .980** -.440**

5. Wages 30833.46 16947.097 .052 .167 .408** .071

** Correlation is significant at the 0.01 level.

*Correlation is significant at the 0.05 level.


Sydney Taylor

BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.


A Chi-Square analysis was conducted using Ruworking as the row and Gender as the Column. The

results indicated no Gender difference was observed.

Sydney Taylor's SPSS Portfolio

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