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Learning Objectives

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Now · 1. SPSS Basics

Learning Objectives

Use SPSS for data management and analysis
Perform descriptive analysis, factor analysis, clustering
Interpret SPSS output
SPSS installed
Statistical concepts from previous weeks

1. SPSS Basics

Data View: Spreadsheet with rows = cases (respondents), columns = variables (questions) Variable View: Define variable properties: Name, Type, Width, Decimals, Label (question text), Values (response option labels), Missing (missing values) SPSS Syntax: Command-based analysis that can be saved and rerun. Useful for reproducibility, complex analyses, automation. Menu actions generate syntax.

2. Common SPSS Procedures

Descriptives: Analyze > Descriptive Statistics > Frequencies (categorical), Descriptives (continuous), Explore (normality checks) Compare Means: Analyze > Compare Means > Independent-Samples T Test, Paired-Samples T Test, One-Way ANOVA Factor Analysis: Analyze > Dimension Reduction > Factor. Select variables, specify extraction method (PCA), rotation (Varimax), check KMO and Bartlett's test. Cluster Analysis: Analyze > Classify > K-Means Cluster or Hierarchical Cluster. Select variables, specify clusters, interpret cluster centers. Reliability Analysis: Analyze > Scale > Reliability Analysis. Check Cronbach's alpha (> 0.7 acceptable).

3. Interpreting SPSS Output

Significance (Sig.): If p < 0.05, reject H0 (result is statistically significant) Cronbach's Alpha: > 0.7 acceptable, > 0.8 good, > 0.9 excellent KMO: > 0.6 adequate for factor analysis, > 0.8 excellent Bartlett's Test of Sphericity: p < 0.05 = correlations are adequate for factor analysis Communalities: Variance of each variable explained by retained factors. > 0.5 is good.

Q1: What is Cronbach's alpha and what values are acceptable?

Measures internal consistency reliability of a scale. Alpha > 0.7 is acceptable for research, > 0.8 is good, > 0.9 is excellent. Low alpha suggests items don't measure the same construct. Q2: What KMO value indicates factor analysis is appropriate?

KMO > 0.6: adequate. KMO > 0.8: excellent. KMO < 0.5: not appropriate for factor analysis. KMO measures sampling adequacy - whether correlations can be explained by underlying factors. Q3: How do you create a frequency table and bar chart in SPSS?

Analyze > Descriptive Statistics > Frequencies. Select variables. Check "Display frequency tables". For charts, click Charts > Bar Chart. Interpreting: check valid percent (excludes missing). Q4: What is the purpose of SPSS syntax?

Syntax records all analysis steps as text commands. Advantages: reproducible (rerun with one click), modifiable (change one parameter), batchable (run many analyses), shareable with colleagues. Q5: How do you recode variables in SPSS?

Transform > Recode into Same Variables (modify existing) or Recode into Different Variables (keep original). Useful for grouping ages, reverse-coding Likert items, or combining categories. Join Discord PreviousSegmentation, Targeting & Positioning (STP)NextReporting & Presenting Research