Data Analysis with SPSS For Research

Data Analysis with SPSS for Research

1. T-Tests

T-tests are statistical tools used to compare means between two groups or conditions. This module will cover:

• One-sample T-test: Comparing the mean of a sample to a reference value.
• Independent samples T-test: Comparing the means of two different groups.
• Paired samples T-test: Comparing means in situations where the same subjects are measured at two different times.
• Assumptions and conditions: Normality, homogeneity of variances, and sample size.
• Interpreting SPSS output: Analysis of output tables and statistical significance.

2. One-Way ANOVA: Between-Subjects and Repeated Measures

ANOVA (Analysis of Variance) is a technique used to compare more than two groups or conditions. This section will cover:

• One-way between-subjects ANOVA:
  • Comparing independent groups with a single categorical variable.
  • Assumptions (normality, homogeneity of variance, independence).
  • Interpretation of results and post hoc tests (Tukey, Bonferroni).
• Repeated measures ANOVA:
  • Comparing data obtained from the same subjects under different conditions or time points.
  • Sphericity assumption and Mauchly’s test.
  • Corrections (Greenhouse-Geisser, Huynh-Feldt).
  • Post hoc analysis and trend plots.

3. Factorial ANOVA: Between-Subjects, Within-Subjects, and Mixed Designs

Factorial ANOVA allows the simultaneous analysis of multiple factors. This section will cover:

• Between-subjects factorial ANOVA:
  • Comparing multiple factors across different groups.
  • Analysis of main effects and interactions.
• Within-subjects factorial ANOVA:
  • Comparing multiple factors within the same group under different conditions.
  • Interpretation of main effects and interactions.
• Mixed-design ANOVA:
  • Combining between-subjects and within-subjects factors.
  • Applications in longitudinal and experimental studies.
  • Interpretation of SPSS output and adjustments for assumption violations.

4. Multivariate Analysis: MANOVA

MANOVA (Multivariate Analysis of Variance) is an extension of ANOVA that allows analyzing multiple dependent variables simultaneously. This section will cover:

• Basic concepts and differences from ANOVA.
• MANOVA assumptions: Multivariate normality, homogeneity of variance-covariance matrices.
• Interpretation of key statistics: Wilks’ Lambda, Pillai’s Trace, Hotelling’s Trace, and Roy’s Largest Root.
• Applications in research: Use in studies with multiple outcome measures.
• Practical example in SPSS: Data setup, execution, and interpretation of output tables.