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 #2Cover PageExplanatory ParagraphAppendix A
SPSS Computer Lab Assignment #3Cover 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
SPSS Computer Lab Assignment #10Cover PageExplanatory ParagraphAppendix A
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.
Computer Lab Assignment #2
Sydney Taylor
BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.
Explanatory Paragraph
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.
Computer Lab Assignment #3
Sydney Taylor
BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.
Explanatory Paragraph
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.
Computer Lab Assignment #4
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.
Explanatory Paragraph #2
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
Computer Lab Assignment #5
Sydney Taylor
BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.
Explanatory Paragraph #1
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.
Explanatory Paragraph #2
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
Table 1: Mean, Standard Deviation, and Zero-Order Correlations
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).
Independent Variables Beta T-value Tolerance P-value
(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
* Significant at the 0.05 level
Computer Lab Assignment #6
Sydney Taylor
BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.
Explanatory Paragraph
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
Computer Lab Assignment #7
Sydney Taylor
BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.
Explanatory Paragraph
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
Computer Lab Assignment #8
Sydney Taylor
BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.
Explanatory Paragraph #1
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.
Explanatory Paragraph #2
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
Table 1: Mean, Standard Deviation, and Zero-Order Correlations
Variables Mean Standard 1 2 3 4 5Deviation
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).
Independent Variables Beta T-value Tolerance P-value
(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
* Significant at the 0.05 level
Computer Lab Assignment #9
Sydney Taylor
BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.
Explanatory Paragraph #1
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.
Explanatory Paragraph #2
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.
Table 1: Mean, Standard Deviation, and Zero-Order Correlations
Variables Mean Standard 1 2 3 4 5Deviation
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.
Computer Lab Assignment #10
Sydney Taylor
BUSA 2182, MWF 1:00 p.m. - 1:50 p.m.
Explanatory Paragraph
A Chi-Square analysis was conducted using Ruworking as the row and Gender as the Column. The
results indicated no Gender difference was observed.