SPSS Portfolio. Brittany Murray BUSA MWF 1:00pm-1:50pm

Transcription

1 SPSS Portfolio Brittany Murray BUSA 2182 MWF 1:00pm-1:50pm

2 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 b) Explanatory Paragraph c) Appendix III) SPSS Computer Lab Assignment # 3 Multiple Regression a) Cover Page b) Explanatory Paragraph 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

3 Table of Contents (Continued) V) SPSS Computer Lab Assignment # 5 Multiple Regression (Entry) a) Cover Page b) Explanatory Paragraph # 1 (Regression) c) Explanatory Paragraph # 2 (Correlation Matrix) d) Conceptual Model (Figure 1) 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 b) Explanatory Paragraph c) ANOVA Output Table (Table 1) d) Appendix VII) SPSS Computer Lab Assignment # 7 One-Way ANOVA (League) a) Cover Page b) Explanatory Paragraph c) ANOVA Output Table (Table 1) d) Appendix

4 Table of Contents (Continued) VIII) SPSS Computer Lab Assignment # 8 Regression Analysis a) Cover Page b) Explanatory Paragraph # 1 (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 IX) SPSS Computer Lab Assignment # 9 T-Test Analysis a) Cover Page b) Explanatory Paragraph # 1 (T-Tests) c) Explanatory Paragraph # 2 (Correlation Matrix) d) Correlation Matrix (Table 1) e) Appendix X) SPSS Computer Lab Assignment # 10 Chi-Square Test a) Cover Page b) Explanatory Paragraph c) Appendix

5 SPSS Computer Lab Assignment #1 Frequency Distribution Brittany Murray BUSA 2182 MWF 01:00pm-1:50pm

6 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.

7 SPSS Computer Lab Assignment #2 Stem-and-Leaf Plot Brittany Murray BUSA 2182 MWF 1:00pm-1:50pm

8 Explanatory Paragraph for Lab #2 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.

9 SPSS Computer Lab Assignment #3 Multiple Regression Brittany Murray BUSA 2182 MWF 01:00pm-1:50pm

10 Explanatory Paragraph for Lab #3 Ŷ= Enduring Involvement 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 ; the prediction equation was performing satisfactorily.

11 SPSS Computer Lab Assignment #4 Multiple Regression (Stepwise and Entry) Brittany Murray BUSA 2182 MWF 1:00pm-1:50pm

12 Explanatory Paragraph 1 for Lab #4 Ŷ= Attendance Attachment 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 ; the prediction equation was satisfactorily.

13 Explanatory Paragraph 2 for Lab #4 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.

14 Figure 1: A Conceptual Model of Attendance (Lab 4) Attendance Satisfaction Enduring Involvement Identity Salience Attachment Situational Involvement

15 Table 1: Means, Standard Deviations, and Zero-Order Correlations (Lab 4) Variables Means S.D Identity Salience Attendance ** Satisfaction **.880** Enduring Involvement **.803**.149** Attachment **.655*.602**.806** Situational Involvement ** -.472**.535**.618** 0.143** *Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

16 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) Attendance ** Attachment ** Satisfaction ** R-Squared.786 *Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

17 SPSS Computer Lab Assignment #5 Multiple Regression (Entry) Brittany Murray BUSA MWF 1:00pm-1:50pm

18 Explanatory Paragraph 1 for Lab #5 Ŷ= Identity Salience 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= , 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 ; the prediction equation was satisfactorily.

19 Explanatory Paragraph 2 for Lab #5 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.

20 Figure 1: A Conceptual Model of Attendance (Lab 5) Identity Salience Attachment Enduring Involvement Attendance Satisfaction Situational Involvement

21 Table 1: Means, Standard Deviations, and Zero-Order Correlations (Lab 5) Variables Means S.D Attendance Identity Salience ** Attachment **.784** Enduring Involvement * ** Satisfaction ** Situational Involvement * Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

22 Table-2: Descriptive Statistics (Lab 5) Variables Means S.D. Attendance Identity Salience Attachment Enduring Involvement Satisfaction Situational Involvement

23 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 Beta T-Value Tolerance P-Value (Constant) Identity Salience Attachment Enduring Involvement Satisfaction Situational Involvement * Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

24 SPSS Computer Lab Assignment #6--One-Way ANOVA (Gender) Brittany Murray BUSA MWF 1:00pm-1:50pm

25 Explanatory Paragraph for Lab #6 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.

26 Table-1: Results of One-Way ANOVA Testing Procedure for Gender, (n = 50) (Lab 6) Factor T-Value P-Value Friendly Employees * Competitive Prices ** Competent Employees * Wide Variety of Food Friendly Employees Rank * Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

27 SPSS Computer Lab Assignment #7--One-Way ANOVA (League) Brittany Murray BUSA MWF 1:00pm-1:50pm

28 Explanatory Paragraph for Lab #7 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.

29 Table-1: Results of One-Way ANOVA Testing Procedure for the Categorical Variables, (n = 30) (Lab 7) Factor T-Value P-Value Salary Wins ERA Stolen Size

30 SPSS Computer Lab Assignment #8 Regression Analysis Brittany Murray BUSA MWF 1:00pm-1:50pm

31 Explanatory Paragraph 1 for Lab #8 Ŷ= Education Female Age 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= , 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 and the Multi-collinearly does not appear due to the coefficient values of Education, Female, and Age being above average. R²

32 Explanatory Paragraph 2 for Lab #8 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.

33 Figure 1: Conceptual Model of Wages (Lab #8) Education South Female Wages Married Age

34 Table 1: Means, Standard Deviations, and Zero-Order Correlations, (n = 100). (Lab 8) * Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed) Variables Means S.D (Constant) Wages Education ** South Female Married Age

35 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 Beta T-Value Tolerance P-Value (Constant) Education South Female Married Age R-Squared.375 * Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

36 SPSS Computer Lab Assignment #9 Chi-Square Test Brittany Murray BUSA 2182 MWF 01:00pm-1:50pm

37 Explanatory Paragraph 1 for Lab #9 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.

38 Explanatory Paragraph 2 for Lab #9 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.

39 Table 1: Means, Standard Deviations, and Zero-Order Correlations (Lab 9) Variables Means S.D Age Experience ** Wages Married **.334**.248 *Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

40 SPSS Computer Lab Assignment #10 T-Test Analysis Brittany Murray BUSA 2182 MWF 01:00pm-1:50pm

41 Explanatory Paragraph for Lab #10 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.