1.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Data Analysis Using SPSS

2.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia What is SPSS?  General Purpose Statistical Software  Consists of three components Data Window - data entry and database (.sav) Output Window - all output from any SPSS session (.lst) Syntax Window - commands lines (.sps) SPSS-2

3.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Data Entry & Preparation  Data entry New or Recalled (SPSS or non-SPSS)  Data Definition  Data Manipulation and Variable Development SPSS-3

4.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Data Definition  Purpose: Give meanings to the numbers for ease of reading the output  Involves  Data Format  Variable Name  Value Labels  Missing Values Command: Data  Data Definition SPSS-4

5.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Data Manipulation SPSS-5 Recoding To give new values to old values (especially reversing negatively worded questions) To form nominal variable from continuous data Variable Development To form new variables combinations of old ones or functions of old ones Command: Transform  Recode/ Compute

6.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Data Analysis - Descriptive SPSS-6 Purpose: To describe each variable - What is the current level of the variable of interest? Command Frequency Means, Minimum, Maximum, Standard Deviation, Quartiles, Standard Deviation Analyze  Frequencies /Descriptives

7.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Data Analysis - Descriptive SPSS-7 Frequencies for two or more nominal variables Analyze  Summarize  Crosstabulation Means of variables by subgroups defined by one or more nominal variables Analyze  Compare Means  Means (Use of Levels)

8.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Parametric Test of Differences When  dependent continuous variable and we want to test differences across groups Command  Analyze  Compare Means  Independent t- test/ Paired t-test/ one-way ANOVA SPSS- 8

9.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Non-Parametric Test of Differences When  dependent variable ordinal or normal assumption not met Command  Analyze  Non-parametric  2 Independent/ 2 related samples/ k independent samples/ k related samples SPSS- 9

10.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Parametric Two-Way ANOVA When  continuous dependent variable and related groups Command  Analyze  General Linear Model  Simple  Note: Fixed Factor Effect SPSS- 10

11.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Bivariate Relationship When  Covariation between two variables Correlation:  When both are continuous or ordinal Command Analyze  Correlate  Bivariate (with option for Spearman if both ordinal) SPSS- 11

12.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Regression Analysis When  To establish relationship between one continuous dependent variable and a number of continuous independent variables Command Analyze  Regression  Linear (Use Statistics, Save options) Issues:  Assumptions of Regression - normality; constant variance, independence of independent variables; independence of error terms SPSS- 14

13.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Regression Analysis Issues (cont.)  Outliers and Leverage Values  Choice of Selection Method of Independent Variables - Enter, Backward, Forward, Stepwise  Dummy Independent Variables Options  Residual Analysis; Influence Statistics, Collinearity Diagnostics, Normality Plots SPSS- 15

14.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Regression Analysis Interpretation  Goodness of Model: R 2, F-statistics, Adj. R 2, Standard error  Strength of Influence of Independent Variables: beta and standardized beta SPSS- 16

15.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Discriminant Analysis When  Dependent Variable is Nominal and the Purpose is to predict group membership on the basis of independent variables Command Analyze  Classify  Discriminant (Option: Classify by summary tables; Select - for holdout and analysis samples Issues  Similar to Regression SPSS- 17

16.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Discriminant Analysis Interpretation  Goodness of Analysis: Hits Ratio - compared to maximum chance, proportional chance and Press Q.  Univariate Results: To establish the discriminating variables SPSS- 18

17.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Factor Analysis When  To reduce the number of variables to underlying dimensions Command Analyze  Data Reduction  Factor (Option: rotation, save factor scores) Issues  Assumptions sufficient correlations between the variables (Bartlett test; anti-image, KMO test of sufficiency) SPSS- 13

18.  Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia Reliability Analysis When  Before forming composite index to a variable from a number of items Command Analyze  Scale  Reliability Analysis (with option for Descriptives item, scale, scale if item deleted) Interpretation  alpha value greater than 0.7 is good; more than 0.5 is acceptable; delete some items if necessary SPSS- 12