This module explores the fundamental differences between categorical and real numbers in social science, focusing on their classification, application, and significance in data analysis. Learners will gain an understanding of how numbers are used to represent and interpret social phenomena accurately.

This module focuses on techniques for analyzing single variables in datasets, including summarizing categorical and numerical data, understanding distributions, and using measures of central tendency. Learners will gain skills in preparing data and interpreting statistical summaries effectively.

This module explores how to analyze relationships between categorical variables using cross-tabulations, odds ratios, and frequency analysis. Learners will gain the skills to interpret real-world data and uncover meaningful patterns. The focus is on applying these techniques to practical examples and datasets.

This module explores methods for comparing real numbers across categories, focusing on analyzing differences, standardizing results, and interpreting effect sizes. Learners will gain skills in assessing real-world data and understanding the practical implications of statistical differences. The module also introduces tools like the number needed to treat (NNT) for evaluating interventions.

This module explores the fundamentals of significance tests, including chi-squared and t-tests, and how to interpret p-values. It addresses the limitations of statistical significance in research and provides guidance on how to report results without relying solely on p-values. Learners will gain critical thinking skills for evaluating statistical evidence in real-world scenarios.

This module explores the limitations of significance tests and introduces confidence intervals as more informative alternatives for statistical analysis. Learners will gain an understanding of how statistical methods can mislead if not interpreted correctly and how to apply more reliable approaches to data evaluation.

This module explores the critical role of judgment in evaluating the trustworthiness, validity, and generalizability of research findings. Learners will develop skills in assessing the strength of evidence and interpreting analysis results through practical examples and exercises. It emphasizes how to apply the warrant principle to make informed judgments in research analysis.

This module explores the fundamentals of research design, including how to define cases, select populations, and ensure research validity. Learners will gain an understanding of how design elements contribute to the trustworthiness and clarity of research. The module also covers practical exercises to evaluate and improve research quality.

This module explores the fundamentals of sampling and population in research, covering techniques to select representative samples, the importance of sample size, and strategies to ensure validity and reliability in research findings.

This module explores the concept of randomness in statistics, including how it is defined, how random samples are generated, and the challenges involved in ensuring true randomness. Learners will gain insight into the implications of randomness for social science research and data analysis.

This module explores the causes and consequences of missing data in research, equipping learners with strategies to identify, report, and manage missing data effectively. It covers how to analyze its impact on study results and emphasizes best practices for minimizing bias and ensuring transparency in data reporting.

This module covers advanced strategies for managing missing data, including practical techniques for identifying, analyzing, and handling missing values in datasets. Learners will gain an understanding of complex methods like multiple imputation and how to apply them to maintain data integrity and accuracy.

This module explores the causes and implications of measurement errors in social science research. It covers how errors affect data reliability, validity, and interpretation, and provides strategies for assessing and mitigating their impact.

This module explores the fundamentals of correlation analysis, focusing on Pearson's R, its applications in multivariate data, and the interpretation of real-world examples. Learners will gain an understanding of how to evaluate correlations, recognize their limitations, and apply them in practical scenarios.

This module focuses on the principles of simple linear regression, including how to identify variables, interpret relationships, and apply regression models to make predictions. Learners will gain practical skills in analyzing data and understanding the foundations of statistical modeling.

This module introduces learners to multiple linear regression, including how to model complex relationships using interactions and dummy variables. It covers the practical application of regression techniques with real-world datasets and emphasizes the importance of selecting relevant variables for accurate predictions.

This module explores the key assumptions and limitations of multiple linear regression, including issues like case independence, hierarchical data structures, and multicollinearity. Learners will gain the knowledge to critically assess regression models and apply them appropriately in real-world scenarios.

This module covers the fundamentals of binary logistic regression, including how to interpret log odds, apply the model to real-world scenarios, and adjust variables to improve predictive accuracy. Learners will explore how to use logistic regression for categorical outcomes and understand the impact of different predictor variables. The module also includes practical exercises to reinforce these concepts.

This module covers essential techniques for data reduction, including factor analysis and reliability coefficients. Learners will gain an understanding of how to simplify complex datasets and evaluate questionnaire validity, while also recognizing the limitations of these methods in real-world scenarios.

This module equips learners with the skills to present numeric results effectively through clear writing, tables, and visualizations. It covers strategies for simplifying complex data and communicating findings in a way that is accessible to diverse audiences. Learners will gain practical knowledge on improving readability and clarity in research reports.