Data Analysis in SPSS Quick Book

What is SPSS

SPSS stands for Statistical Package for Social Science.General Purpose of SPSS is to used as Statistical Software.

It 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)

Data Entry & Preparation in SPSS

Data entry has basically two components:

• Data Definition
• Data Manipulation and Variable Development

Data Definition is the purpose where user gives meanings to the numbers for ease of reading the output via defining type of data,range,etc.

Definition Involves

• Data Format
• Variable Name
• Value Labels
• Missing Values

Command: Data ^Z Data Definition

Data Manipulation: means recoding of data ,to give new values to old values (especially reversing negatively worded questions),to form nominal variable from continuous data,variable development and to form new variables combinations of old ones or functions of old ones.

Command: Transform Z   Recode/ Compute

Data Analysis in SPSS: Descriptive

• When :To describe each variable – What is the current level of the variable of interest?
• Commands:
  • Frequency:Means, Minimum, Maximum, Standard  Deviation, Quartiles, Standard Deviation
    • Command:Analyze Z   Frequencies /Descriptives
  • Frequencies for two or more nominal variables
    • Command:Analyze Z  Summarize Z  Crosstabulation
  • Means of variables by subgroups defined by one or more nominal variables
    • Command:Analyze Z  Compare Means Z  Means (Use of Levels)
• Parametric -Test of Differences
  • When:dependent continuous variable and we want to test differences across groups
  • Command:Analyze ^Z  Compare Means ^Z  Independent t-test/ Paired t-test/ one-way ANOVA
• Non-Parametric Test of Differences
  • When:dependent variable ordinal or normal assumption not met
  • Command:Analyze ^Z  Non-parametric ^Z  2 Independent/ 2 related samples/ k independent samples/ k related samples
• Parametric Two-Way ANOVA
  • When:continuous dependent variable and related groups
  • Command:Analyze ^Z  General Linear Model ^Z  Simple
• Bivariate Relationship
  • When: Covariation between two variables
• Correlation
  • When:both are continuous or ordinal
  • Command”Analyze ^Z  Correlate ^Z  Bivariate (with option for Spearman if both ordinal)
• Regression Analysis
  • When:To establish relationship between one continuous dependent variable and a number of continuous independent variables
  • Command:Analyze ^Z  Regression ^Z  Linear (Use Statistics, Save options)
  • Issues:Assumptions of Regression – normality; constant variance, independence of independent variables; independence of error terms,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
  • Interpretation:Goodness of Model: R², F-statistics, Adj. R², Standard error
  • Strength of Influence of Independent Variables: beta and standardized beta
• Reliability Analysis
  • When:Before forming composite index to a variable from a number of items
  • Command: Analyze ^Z  Scale ^Z  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
  • Measures of Reliability
• Factor Analysis
  • When:To reduce the number of variables to underlying dimensions
  • Command:Analyze ^Z  Data Reduction ^Z  Factor (Option: rotation, save factor scores)
  • Issues:Assumptions sufficient correlations between the variables (Bartlett test; anti-image, KMO test of sufficiency)
•  Discriminant Analysis
  • When:Dependent Variable is Nominal and the Purpose is to predict group membership on the basis of independent variables
  • Command:Analyze ^Z  Classify ^Z  Discriminant (Option: Classify by summary tables; Select – for holdout and analysis samples
  • Issues:Similar to Regression
  • Interpretation:Goodness of Analysis: Hits Ratio – compared to maximum chance, proportional chance and Press Q.
  • Univariate Results: To establish the discriminating variables