Course Outline

Introduction

Getting Started with SPSS

  • Introduction to SPSS interface and functionalities
  • Importing and exporting data files
  • Basic data entry and management

Obtaining, Editing, and Saving Statistical Output

  • Generating statistical reports
  • Customizing output tables and charts
  • Saving and exporting analysis results

Manipulating Data

  • Data transformation techniques
  • Re-coding variables and computing new ones
  • Managing missing data

Descriptive Statistics Procedures

  • Calculating measures of central tendency and variability
  • Frequency distributions and cross-tabulations
  • Visualizing data with charts and graphs

Evaluating Score Distribution Assumptions

  • Normality tests and graphical assessments
  • Assessing skewness and kurtosis
  • Checking for outliers

t-Tests

  • Independent samples t-test
  • Paired samples t-test
  • Interpreting t-test results

Univariate Group Differences: ANOVA and ANCOVA

  • One-way ANOVA and post-hoc comparisons
  • Factorial ANOVA for multiple variables
  • Introduction to ANCOVA and its applications

Multivariate Group Differences: MANOVA

  • Understanding MANOVA concepts
  • Running MANOVA tests in SPSS
  • Interpreting MANOVA output

Nonparametric Procedures for Analyzing Frequency Data

  • Chi-square tests of independence
  • Mann-Whitney U test and Wilcoxon signed-rank test
  • Kruskal-Wallis H test for non-parametric ANOVA

Correlations

  • Pearson correlation coefficient
  • Spearman rank correlation
  • Partial and point-biserial correlation

Regression with Quantitative Variables

  • Simple linear regression analysis
  • Multiple regression models
  • Interpreting regression coefficients and diagnostics

Regression with Categorical Variables

  • Dummy variable coding for categorical data
  • Logistic regression analysis
  • Interpreting odds ratios and logistic model fit

Principal Components Analysis and Factor Analysis

  • Exploratory factor analysis (EFA)
  • Principal components analysis (PCA) techniques
  • Factor rotation methods and interpretation of results

Summary and Next Steps

Requirements

  • Basic understanding of mathematical concepts
  • No prior experience with SPSS required
  • Familiarity with basic statistics is beneficial but not mandatory

Audience

  • Data analysts
  • Researchers
  • Business professionals working with statistical data
 21 Hours

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