STATISTICAL PROGRAMS SPSS

Undergraduate and graduate students conducting research involving quantitative data.

Early-career researchers and professionals who need to manage, analyze, and interpret data.

Social science, health, education, or business researchers seeking practical skills in statistical software.

Academics and professionals looking to improve proficiency in SPSS for research purposes.

• Understand and navigate the SPSS environment

  • Familiarize themselves with the SPSS interface and tools.

  • Create, open, save, and manage data files efficiently.

• Manage and prepare data for analysis

  • Define variables and cases appropriately for research needs.

  • Set variable types, labels, values, measures, and formatting correctly.

  • Read and import text files, merge data files, and handle missing values.

  • Sort, filter, weight, and split data files for analysis.

• Transform variables effectively

  • Compute new variables and recode existing ones.

  • Categorize variables and replace missing data systematically.

  • Perform counting and data manipulation to prepare datasets for analysis.

• Conduct descriptive and inferential analyses

  • Generate frequencies, descriptive statistics, and exploratory analyses.

  • Create contingency tables and perform chi-square tests.

  • Visualize data using graphs and interpret results accurately.

• Apply data analysis to research

  • Prepare datasets and analyses that support evidence-based conclusions.

  • Integrate SPSS outputs into research reports, presentations, and publications.

Study Plan

1. DATA COLLECTION FOR RESEARCH: INTRODUCTION TO SPSS

2. SPSS WORKING ENVIRONMENT

3. HANDLING DATA FILES

• Variables and cases

• Creating a data file

• Variable type

• Width and decimals

• Label

• Values

• Columns and alignment

• Measure

• Saving and reading data

4. READING TEXT FILES

5. MERGING FILES

• Merging variables

• Case merging

6. OTHER OPERATIONS WITH FILES

• Sorting cases

• Case filtering

• Case weighting

• File splitting

7. VARIABLE TRANSFORMATION

• Computing values

• Recoding variables

• Categorizing variables

• Replacing missing values

• Counting values

8. DESCRIPTIVE DATA ANALYSIS

• Frequencies procedure

• Descriptives procedure

• Explore procedure

9. CONTINGENCY TABLES

• The chi-square test

• Graphs in SPSS