SPSS Portfolio Brittany Murray BUSA 2182 MWF 1:00pm-1:50pmbrittanylmurray.weebly.com/uploads/2/1/3/5/... · Brittany Murray BUSA 2182 MWF 01:00pm-1:50pm . Explanatory Paragraph for

SPSS Portfolio

Brittany Murray

BUSA 2182

MWF 1:00pm-1:50pm

Table Of Contents

I) SPSS Computer Lab Assignment # 1 – Frequency Distribution

a) Cover Page

b) Explanatory Paragraph

c) Appendix

II) SPSS Computer Lab Assignment # 2 – Stem-and-Leaf Plot

a) Cover Page


c) Appendix

III) SPSS Computer Lab Assignment # 3 – Multiple Regression

a) Cover Page


c) Appendix

IV) SPSS Computer Lab Assignment # 4 – Multiple Regression (Stepwise and Entry)

a) Cover Page

b) Explanatory Paragraph # 1 (Stepwise Regression)

c) Explanatory Paragraph # 2 (Correlation Matrix)

d) Conceptual Model (Figure 1)

e) Correlation Matrix (Table 1)

f) Regression Output Table (Table 2)

g) Appendix

Table of Contents (Continued)

V) SPSS Computer Lab Assignment # 5 – Multiple Regression (Entry)

a) Cover Page

b) Explanatory Paragraph # 1 (Regression)



e) Zero-Order Correlation Matrix (Table 1)

f) Descriptive Statistics (Table 2)

g) Regression Output Table (Table 3)

h) Appendix

VI) SPSS Computer Lab Assignment # 6 – One-Way ANOVA (Gender)

a) Cover Page


c) ANOVA Output Table (Table 1)

d) Appendix

VII) SPSS Computer Lab Assignment # 7 – One-Way ANOVA (League)

a) Cover Page


c) ANOVA Output Table (Table 1)

d) Appendix

Table of Contents (Continued)

VIII) SPSS Computer Lab Assignment # 8 – Regression Analysis

a) Cover Page

b) Explanatory Paragraph # 1 (Regression)



e) Correlation Matrix (Table 1)

f) Regression Output Table (Table 2)

g) Appendix

IX) SPSS Computer Lab Assignment # 9 – T-Test Analysis

a) Cover Page

b) Explanatory Paragraph # 1 (T-Tests)


d) Correlation Matrix (Table 1)

e) Appendix

X) SPSS Computer Lab Assignment # 10 – Chi-Square Test

a) Cover Page


c) Appendix

SPSS Computer Lab Assignment #1– Frequency Distribution

Brittany Murray

BUSA 2182

MWF 01:00pm-1:50pm

Explanatory Paragraph for Lab #1

A Frequency distribution was created using Group Status, Attachment, Situational

Involvement, Enduring Involvement, Identity Salience, Satisfaction, Attendance, Gender and

Salary. The skewness for Attachment, Situational Involvement, Enduring Involvement, Identity

Salience, Satisfaction, Attendance, and Salary are unacceptable. However, Gender is acceptable.

For the Kurtosis value the of Attachment is acceptable but the values of Situational

Involvement, Enduring Involvement, Identity Salience, Satisfaction, Attendance, and Salary are

unacceptable.

SPSS Computer Lab Assignment #2– Stem-and-Leaf Plot

Brittany Murray

BUSA 2182

MWF 1:00pm-1:50pm


A Stem-and-Leaf Plot Analysis was conducted using the following variables: Group Status, Attachment,

Situational Involvement, Enduring Involvement, Identity Salience, Satisfaction, and Salary. The skewness for

Attachment, Enduring Involvement, Identity Salience, Satisfaction, and Salary were positive for the Steam-and-

Leaf plot; whereas for Situational Involvement, its Stem-And-Leaf plot was negative.

SPSS Computer Lab Assignment #3– Multiple Regression

Brittany Murray

BUSA 2182 MWF 01:00pm-1:50pm


Ŷ= 46.471 + .818 Enduring Involvement -.803 Satisfaction

A regression analysis was conducted with Situational Involvement as the endogenous variable and

Attachment, Attendance, Enduring Involvement, Identity Salience, and Satisfaction as the exogenous variables.

The regression model was statistically significant (F=55.848, p=.000). Enduring Involvement and Satisfaction

had significant factors however; Attachment, Attendance, and Identity Salience was not acceptable. The model

fit index, the coefficient of determination (R²), was 0.814; meaning 81.4 percent of the variation in Enduring

Involvement and Situation. The coefficient of correlation (r) indicated a strong relationship between the

predictors and Enduring Involvement (r= .902). The adjusted R², which considers the number of predictors and

the sample size, was 0.814, which indicated that extraneous predicator’s were included in the model. The

standard error of the estimate was 3.05388; the prediction equation was performing satisfactorily.

SPSS Computer Lab Assignment #4 – Multiple Regression (Stepwise and Entry)

Brittany Murray

BUSA 2182

MWF 1:00pm-1:50pm

Explanatory Paragraph 1 for Lab #4

Ŷ= .178+ .957 Attendance +.182 Attachment -.434 Satisfaction

A regression analysis was conducted with Identity Salience as the dependent and Attendance,

Satisfaction, Enduring Involvement, Attachment, and Situational Involvement as the independent variables.

Overall the statistically significant (F= 7.185, p= .009). Attendance, Attachment and Satisfactory were

significant predictors of Identity Salience. However, Enduring Involvement, and Situational Involvement were

not significant predicators of Identity Salience. The model fit index, the coefficient of determination (R²), was

(0.775); meaning 75.7 percent of the variation can be explained by Attendance, Attachment, and Satisfaction.

The coefficient of correlation (r) indicated a strong relationship between the predicators and Identity Salience

(r= .886). The adjusted R², which considers a number of predicators and the sample size, was .775, which

indicated extraneous predictors were not included in the model. The standard error of the estimate was 1.79673;

the prediction equation was satisfactorily.


A bivarte correlation analysis was conducted using Identity Salience as the dependent variable and Attendance,

Satisfaction, Enduring Involvement, Attachment, and Situational Involvement as the independent variables.

Satisfaction, Attachment, and Attendance were positively correlated with Identity Salience. However, Enduring

Involvement and Situational Involvement were negatively correlated to Identity Salience.

Figure 1: A Conceptual Model of Attendance (Lab 4)

Attendance

Satisfaction

Enduring Involvement

Attachment

Situational Involvement

Identity Salience

Table 1: Means, Standard Deviations, and Zero-Order Correlations (Lab 4)

Variables Means S.D. 1 2 3 4 5 6

Identity Salience 10.87 3.79

Attendance 10.27 3.13 .807**

Satisfaction 10.85 2.81 .645** .880**

Enduring Involvement 33.34 7.70 .493** .803** .149**

Attachment


30.64

54.57

9.41

6.81

.784**

-.153**

.655*

-.472**

.602**

.535**

.806**

.618**

0.143**

*Correlation is significant at the 0.05 level (2-tailed)

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

Table 2: Regression Analysis with Attendance, Attachment and Satisfactory as the Predicator Variables, (n= ) (Lab 4)

Independent Variables Beta T-Value Tolerance P-Value

(Constant) .629 .532

Attendance .767 5.645 .170 .000**

Attachment .180 2.342 .091 .023**

Satisfaction -.473 -2.582 .180 .541**

R-Squared .786



SPSS Computer Lab Assignment #5– Multiple Regression (Entry)

Brittany Murray

BUSA 2182-02

MWF 1:00pm-1:50pm


Ŷ= 3.461+.343 Identity Salience +.529 Satisfaction

A regression analysis was conducted with Attendance as the dependent and Identity

Salience, Attachment, Enduring Involvement, Satisfaction and Situational Involvement as

independent variables. Overall the statistically significant (F= 97.887, p= .000). Identity Salience

and Satisfaction were significant predictors of Attendance. However, Attachment, Enduring

Involvement, and Situational Involvement were not significant predicators of the significant

predicator. The model fit index, the coefficient of determination (R²), was (.884); meaning 88.4

percent of the variation can be explained by Identity Salience and Satisfaction. The coefficient of

correlation (r) indicated a strong relationship between the predicators and Identity Salience (r=

.940). The adjusted R², which considers a number of predicators and the sample size, was .884,

which indicated extraneous predictors were not included in the model. The standard error of the

estimate was 1.10507; the prediction equation was satisfactorily.


A bivarte correlation analysis was conducted using Attendance as the dependent variable and

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

independent variables. Identity Salience and Satisfaction were positively correlated with

Attendance. However, However, Attachment, Enduring Involvement, and Situational

Involvement were not were correlated to Attendance.

Figure 1: A Conceptual Model of Attendance (Lab 5)

Identity Salience

Attachment


Satisfaction


Attendance


Variables Means S.D. 1 2 3 4 5 6

Attendance 10.27 3.12

Identity Salience 10.87 3.78 .807**

Attachment 30.64 9.41 .665** .784**

Enduring Involvement 33.34 7.70 .226* .493 .806** .149 .618

Satisfaction


10.85

57.54

2.81

6.81

.880**

-.472

.644

-.153

.602

.143

.149

.618

-5.35

-.535

* Correlation is significant at the 0.05 level (2-tailed)


Table-2: Descriptive Statistics (Lab 5)

Variables Means S.D.

Attendance 10.27 3.12

Identity Salience 10.87 3.78

Attachment 30.64 9.41

Enduring Involvement 33.34 7.70

Satisfaction 10.85 2.81

Situational Involvement 57.54 6.81

Table 3: Regression Analysis with Attendance as the Criterion Variable. Attachment, Enduring Involvement, Satisfaction, and Identity

Salience were the Predictor Variables, (n=70) (Lab 5)



Independent Variables

(Constant)

Identity Salience

Attachment


Satisfaction


Beta

.807

.152

-.103

.476

-.112

T-Value

4.43

11.28

5.46

-.733

5.48

-1.15

Tolerance

1.000

.314

.092

.240

.190

P-Value

.000

.138

.000

.466

.000

.253

SPSS Computer Lab Assignment #6--One-Way ANOVA (Gender)

Brittany Murray

BUSA 2182-02

MWF 1:00pm-1:50pm


A One-Way ANOVA test was conducted using Wages as the factor variable and Education, Female,

Married, and Age as dependent variable. The Omnibus F-Test for Wide Variety of Food, and Friendly

Employees Rank are not significant. However, the Omnibus F-Test was in favor of Friendly Employees,

Competitive Prices and Competent Employees. The Contrast tests showed that Females reported significantly

higher scores in the areas of Friendly Employees and Competitive Prices. But Males reported slightly higher

scores in the area of Wide Variety of Food and Employees Rank.

Table-1: Results of One-Way ANOVA Testing Procedure for Gender, (n = 50) (Lab 6)

Factor T-Value P-Value

Friendly Employees -4.67 .000*

Competitive Prices 2.58 .006**

Competent Employees -5.92 .000*

Wide Variety of Food -.067 .947

Friendly Employees Rank -1.02 .311



SPSS Computer Lab Assignment #7--One-Way ANOVA (League)

Brittany Murray

BUSA 2182-02

MWF 1:00pm-1:50pm


A One-Way ANOVA Analysis was conducted using Salary, Wins, ERA, Stolen and Size

as dependent factors and League as the factor variable. None of the Predicator Values were

statically significant, however, Wins has the highest significant value with Salary falling slightly

below it leaving ERA with the lowest value. As stated earlier, the Omnibus F-Test also reports

there are no statically significant values. But when observing the Contrast Analysis, the National

League Baseball has higher scores in the areas of ERA and Stolen compared to the American

League Baseball has higher scores in the areas of Salary, Wins, and Size.

Table-1: Results of One-Way ANOVA Testing Procedure for the Categorical Variables, (n = 30) (Lab 7)

Factor T-Value P-Value

Salary -.400 .692

Wins -.284 .779

ERA 1.15 .260

Stolen 1.12 .271

Size -1.11 .275

SPSS Computer Lab Assignment #8– Regression Analysis

Brittany Murray

BUSA 2182-02

MWF 1:00pm-1:50pm


Ŷ= -17058.77 + 2801.52 Education -11031.273 Female +337.015Age

A Regression Analysis was conducted with Wages as the dependent variable and Education, South,

Family, Married, and Age as independent variables. The Regression Model was statically significant (F=

11.288, p= .000) based off of the Omnibus F-Test. The Coefficient of Determination (R²), .375 based off of the

model fit index. This means 37.5 percent of the Variation in Wages can be explained by Education, Female, and

Age. The Coefficient of Correlation (r) is .613 in which is a positive relationship between the predicators and

wages. However, the negative predicator to Wages is Female whereas the positive ones are Education and age.

The Adjusted R² is .342 and indicates that there are no Extraneous Predictors included in the model. The

Standard Error of Estimate is 13747.707 and the Multi-collinearly does not appear due to the coefficient values

of Education, Female, and Age being above average. R²


The bivariate Correlation Analysis conducted consisted of Wages as the dependent variable and

Education, South, Female, Married, and Age as independent variables. Education, Female, and Age correlate

with Wages however, South and Married unacceptable.

Figure 1: Conceptual Model of Wages (Lab #8)

Female

South

Education

Married

Wages

Age

Table 1: Means, Standard Deviations, and Zero-Order Correlations, (n = 100). (Lab 8)



Variables Means S.D. 1 2 3 4 5 6 7

(Constant)

Wages

Education

30833.46

12.73

16947.097

2.79

1.000

408**

1.000

South .33 .473 -.081 -2.76 1.000

Female .47 .502 .356 -.082 .021

Married .67 .473 .248 .024 -.005 -.021

Age 39.11 12.57 .167 -.253 -.079 .037 .365 1.000

Table 2: Regression Analysis with Wages as the Dependent Variable and Education, South, Female, Married, Union and Age as the

Independent Variables, (n=100) (Lab 8)



Independent Variables

(Constant)

Education

South

Female

Married

Age

R-Squared

Beta

.462

.073

-.327

.140

.250

.375

T-Value

-1.73

5.14

.852

-3.98

1.57

.008

Tolerance

.827

.896

.992

.848

.774

P-Value

.086

.000

.397

.000

.118

.008

SPSS Computer Lab Assignment #9– Chi-Square Test

Brittany Murray

BUSA 2182

MWF 01:00pm-1:50pm


For the Independent Samples T-Test, Age, Experience, and Wages were used as test variables while

Married was used as the grouping variable. Experience and Age were statically significantly at the value of .01

percent. Wages has a significant value of .013.


A Bivariate Correlation Analysis was conducted using Age, Experience, and Wages as test variables and

Married grouping variable. Age and Education are positively and significantly correlated with the value of .01

percent. However, Age is unacceptable compared to wages whereas Education is acceptable. Wage is not

correlated with Married and significantly related at the .013 value.


Variables Means S.D. 1 2 3 4

Age 39.11 12.572

Experience 20.38 13.550 .980**

Wages 30833.46 16947.097 .167 .071

Married .67 .473 .365** .334** .248



SPSS Computer Lab Assignment #10– T-Test Analysis

Brittany Murray

BUSA 2182

MWF 01:00pm-1:50pm


A Chi-Square Analysis was conducted using Gender as the Row and Ruworking as the column. Person

Chi-Square indicated that the variables were Non-Significant and no Gender Differences were observed.

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SPSS Portfolio Brittany Murray BUSA 2182 MWF 1:00pm-1:50pmbrittanylmurray.weebly.com/uploads/2/1/3/5/... · Brittany Murray BUSA 2182 MWF 01:00pm-1:50pm . Explanatory Paragraph for