Module 1: Introduction to SPSS

• Overview of SPSS Software
  • What is SPSS?
  • Key features and applications
  • Difference between SPSS versions (SPSS Statistics vs SPSS Amos)
• Installation and Setup
  • Installing SPSS on your computer
  • Navigating the SPSS interface (Data View, Variable View, Output Viewer)
• Creating and Opening Data Files
  • Creating a new dataset in SPSS
  • Importing data (Excel, CSV, databases)
  • Exporting data and results

Module 2: Understanding the Data Structure in SPSS

• Data Types and Variables
  • Types of data (Nominal, Ordinal, Interval, Ratio)
  • Defining variables (name, type, label, values)
• Variable View and Data View
  • Understanding the difference between Variable View and Data View
  • Entering and editing data in SPSS
• Managing and Organizing Data
  • Sorting and filtering data
  • Using the “Select Cases” and “Weight Cases” features
  • Handling missing data and outliers

Module 3: Data Cleaning and Transformation

• Identifying and Handling Missing Data
  • Identifying missing data patterns
  • Handling missing data (e.g., imputation, listwise deletion)
• Recoding Variables
  • Recoding variables (e.g., from continuous to categorical)
  • Creating dummy variables
• Computing New Variables
  • Using the Compute function to create new variables (e.g., sum, mean)
  • Transforming variables (e.g., log transformations, standardization)

Module 4: Descriptive Statistics

• Measures of Central Tendency
  • Calculating mean, median, mode
• Measures of Dispersion
  • Calculating range, variance, standard deviation
• Frequency Distributions
  • Creating frequency tables
  • Analyzing categorical data
• Graphical Representations
  • Creating bar charts, histograms, boxplots
  • Generating pie charts, scatterplots, and line graphs

Module 5: Inferential Statistics

• Introduction to Hypothesis Testing
  • Null and alternative hypotheses
  • Type I and Type II errors
  • p-values and significance levels (α = 0.05)
• One-Sample t-test
  • Performing a one-sample t-test
  • Interpreting results (t-value, p-value)
• Independent Samples t-test
  • Conducting an independent t-test
  • Assumptions of the t-test
• Paired Samples t-test
  • Performing a paired t-test
  • Understanding paired data and differences

Module 6: Analyzing Categorical Data

• Chi-Square Test of Independence
  • Conducting a chi-square test
  • Understanding chi-square distribution
  • Interpreting chi-square test results
• Crosstabulation and Contingency Tables
  • Creating and interpreting crosstabs
  • Using chi-square for association between categorical variables

Module 7: Correlation and Regression Analysis

• Pearson Correlation
  • Conducting a Pearson correlation analysis
  • Interpreting correlation coefficients (r-value, significance)
• Spearman’s Rank Correlation
  • When to use Spearman correlation
  • Conducting and interpreting results
• Simple Linear Regression
  • Running a simple linear regression model
  • Interpreting regression coefficients (slope, intercept)
  • Assessing model fit (R², F-statistic)
• Multiple Linear Regression
  • Running a multiple regression analysis
  • Interpreting coefficients for multiple predictors
  • Checking assumptions (multicollinearity, normality)

Module 8: Analysis of Variance (ANOVA)

• One-Way ANOVA
  • Conducting a one-way ANOVA test
  • Assumptions of ANOVA
  • Post-hoc tests (Tukey’s, Bonferroni)
• Repeated Measures ANOVA
  • Understanding repeated measures design
  • Running a repeated measures ANOVA
  • Interpreting results
• Two-Way ANOVA
  • Conducting a two-way ANOVA (with interaction terms)
  • Main effects and interaction effects

Module 9: Factor Analysis and Principal Component Analysis (PCA)

• Introduction to Factor Analysis
  • When to use factor analysis
  • Conducting an exploratory factor analysis (EFA)
  • Interpreting factor loadings
• Principal Component Analysis (PCA)
  • Conducting PCA for dimensionality reduction
  • Interpreting principal components and eigenvalues

Module 10: Non-Parametric Tests

• Introduction to Non-Parametric Tests
  • Understanding the need for non-parametric tests
  • Differences between parametric and non-parametric tests
• Mann-Whitney U Test
  • Performing and interpreting the Mann-Whitney U test
• Kruskal-Wallis H Test
  • Conducting Kruskal-Wallis for multiple groups
• Wilcoxon Signed-Rank Test
  • Performing the Wilcoxon test for paired data

Module 11: Advanced Statistical Techniques

• Logistic Regression
  • Running logistic regression for binary outcomes
  • Interpreting odds ratios and model coefficients
• Survival Analysis
  • Introduction to survival analysis (Cox regression)
  • Analyzing time-to-event data
• Multivariate Analysis of Variance (MANOVA)
  • Conducting MANOVA for multiple dependent variables
  • Interpreting results and assumptions

Module 12: Reporting and Visualizing Results

• Generating Output in SPSS
  • Interpreting SPSS output tables (Descriptive stats, ANOVA, Regression)
  • Exporting SPSS output (Excel, PDF, Word)
• Creating Charts and Graphs
  • Using SPSS Chart Builder to create professional graphs
  • Customizing chart properties (labels, legends, axis)
• Reporting Findings
  • Writing statistical results for reports
  • Best practices for presenting SPSS analysis results