How to Use SPSS Software for Quantitative Research Class (10 Hours Total)

Upon completing the "How to Use SPSS Software for Quantitative Research" class, participants will achieve several key outcomes:

• Proficiency in SPSSs
• Effective Data Management
• Mastery of Descriptive Statistics
• Understanding of Inferential Statistics (including t-tests, ANOVA, chi-square tests, and Correlation and regression analysis)
• Interpretation and Reporting Skills
• Confidence in Quantitative Research

Module 1: Introduction to SPSS (2 Hours)

Overview of SPSS: Understanding the interface, features, and capabilities of SPSS for quantitative analysis.

Setting Up SPSS: Installing the software, navigating the interface, setting preferences.

Basic Operations: Importing data, understanding data types, entering data manually, and basic data manipulation (filter, sort, and compute variables).

Module 2: Data Management in SPSS (2 Hours)

Data Cleaning: Identifying and handling missing data, outliers, and errors.

Variable Transformation: Recoding variables, computing new variables, and categorizing continuous variables.

File Management: Merging data files, splitting files based on groups.

Module 3: Descriptive Statistics in SPSS (2 Hours)

Understanding Descriptive Statistics: Mean, median, mode, standard deviation, and variance.

Generating Descriptive Statistics: Using SPSS to explore data through frequencies, descriptives, explore, and cross-tabs functions.

Visualizing Data: Creating histograms, box plots, scatter plots, and bar charts.

Module 4: Basic Inferential Statistics in SPSS (2 Hours)

Introduction to Inferential Statistics: The concept of hypothesis testing.

T-tests and ANOVA: Conducting one-sample t-tests, independent and paired samples t-tests, and one-way ANOVA.

Chi-Square Tests: Running and interpreting chi-square tests for categorical data.

Activity: Perform a t-test and ANOVA on a given dataset, interpret the results.

Module 5: Advanced Analysis Techniques in SPSS (2 Hours)

Correlation and Regression Analysis: Understanding and applying correlation coefficients, simple linear regression, and multiple regression analysis.

Non-parametric Tests: When and how to use non-parametric tests in SPSS.

Output Interpretation: Reading SPSS output, understanding significance levels, and drawing conclusions.