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

Muhamad Jantan & T. Ramayah School of Management, Universiti Sains Malaysia

Data Analysis

Using SPSS


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)

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Data Entry & Preparation

Data entry New or Recalled (SPSS or non-SPSS)

Data Definition

Data Manipulation and Variable Development

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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

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Data Manipulation

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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


Data Analysis - Descriptive

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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


Data Analysis - Descriptive

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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)


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

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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

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Parametric Two-Way ANOVA

When continuous dependent variable and related

groups

Command Analyze General Linear Model Simple Note: Fixed Factor Effect

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Bivariate Relationship

When Covariation between two variables

Correlation: When both are continuous or ordinal

CommandAnalyze Correlate Bivariate (with option

for Spearman if both ordinal)

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Regression AnalysisWhen To establish relationship between one

continuous dependent variable and a number of continuous independent variables

CommandAnalyze Regression Linear (Use Statistics,

Save options)

Issues: Assumptions of Regression - normality;

constant variance, independence of independent variables; independence of error terms

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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

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Regression Analysis

Interpretation Goodness of Model: R2, F-statistics, Adj. R2,

Standard error Strength of Influence of Independent

Variables: beta and standardized beta

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Discriminant AnalysisWhen Dependent Variable is Nominal and the

Purpose is to predict group membership on the basis of independent variables

CommandAnalyze Classify Discriminant (Option:

Classify by summary tables; Select - for holdout and analysis samples

Issues Similar to Regression

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Discriminant AnalysisInterpretation Goodness of Analysis: Hits Ratio - compared

to maximum chance, proportional chance and Press Q.

Univariate Results: To establish the discriminating variables

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Factor AnalysisWhen To reduce the number of variables to

underlying dimensions

CommandAnalyze Data Reduction Factor (Option:

rotation, save factor scores)

Issues Assumptions sufficient correlations between

the variables (Bartlett test; anti-image, KMO test of sufficiency)

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Reliability AnalysisWhen Before forming composite index to a

variable from a number of items

CommandAnalyze 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

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