This module explores the fundamentals of experimental and quasi-experimental research designs, including hypothesis formulation, group assignment, and the strengths and limitations of experimental methods in educational settings. Learners will gain an understanding of how to design and evaluate research studies that aim to establish cause-and-effect relationships.

This module covers essential non-experimental research methods such as surveys, observational studies, and analysis of existing data sets. Learners will gain an understanding of how to design, implement, and evaluate these methods in educational research contexts.

This module explores essential concepts in quantitative research, including validity, reliability, and generalisability. Learners will gain an understanding of how to ensure accurate measurement, consistent results, and the broader applicability of research findings. It also touches on alternative perspectives such as effect size indices in statistical analysis.

This module introduces learners to IBM SPSS Statistics as a powerful tool for educational research, covering its key features, comparison with other software, and its role in data analysis and interpretation.

This module introduces key statistical concepts including frequency distributions, levels of measurement, and measures of central tendency and spread. Learners will develop skills in analyzing and interpreting univariate data using SPSS, enhancing their ability to summarize and describe data effectively.

This module covers bivariate analysis techniques to examine relationships between two variables, with a focus on statistical significance and effect size. Learners will gain hands-on experience with cross-tabulations, t-tests, and effect size measures using SPSS. The content helps build skills in interpreting educational data and making evidence-based conclusions.

This module explores statistical techniques for analyzing relationships between two variables, with a focus on Pearson's r and Spearman's rho. Learners will gain skills in calculating and interpreting correlation coefficients, as well as understanding their limitations and appropriate use cases.

This module introduces learners to multivariate analysis using regression models, covering topics such as interpreting diagnostics, handling different types of variables, and applying logistic and ordinal regression techniques. It equips students with the skills to analyze complex data relationships and build predictive models.

This module introduces the concept of Analysis of Variance (ANOVA) for comparing the means of more than two groups. It covers how to perform ANOVA in SPSS, interpret effect sizes like eta squared, and understand the extension of ANOVA to multivariate analysis (MANOVA). Learners will develop practical skills in statistical analysis for educational research.

This module guides learners through the process of validating survey items using factor analysis and Cronbach's alpha. It covers techniques for assessing internal consistency and constructing reliable scales for data analysis. Learners will gain practical skills in statistical validation and scale development.

This module introduces advanced statistical techniques such as multilevel modelling and structural equation modelling (SEM), explaining how they extend traditional regression analysis to handle complex data structures and relationships. It covers practical examples and common misconceptions, helping learners understand when and how to apply these methods in real-world research.