This module teaches learners how to locate, manage, and clean data for analysis, covering the use of secondary data, administrative data, and open data sources. It emphasizes strategies for accessing and evaluating data quality, and introduces practical steps for working with real-world datasets.

This module examines the fundamentals of measurement in social science research, including variable classification, cultural influences, and the challenges of interpreting data. It explores how variables are categorized, measured, and used to draw meaningful insights from social phenomena. Learners will gain the ability to critically assess measurement practices and their implications.

This module covers the analysis of categorical data using SPSS and Excel, focusing on univariate and bivariate techniques. Learners will gain skills in handling missing data, interpreting valid percentages, and presenting categorical variables effectively for accurate statistical reporting.

This module explores the concept of averages and their limitations in data analysis, focusing on how different measures of central tendency can be used to interpret social data. It covers the practical application of these measures, including how to calculate them using tools like SPSS and Excel. Learners will also analyze how outliers and income inequality affect the interpretation of average values.

This module introduces learners to various data visualization techniques, helping them understand how to effectively communicate data through charts, graphs, and maps. It covers different types of visual representations and their appropriate use cases, empowering learners to make informed choices in data storytelling.

This module explores how narratives influence the interpretation of social data, examines gender and sex differences through statistical analysis, and evaluates the accuracy of data measurement methods. Learners will gain insights into the stories behind numbers and how they shape societal understanding.