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Quantitative Research in Education with IBM SPSS

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Overview

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This course offers a practical guide to conducting quantitative research in education using IBM SPSS. It covers essential statistical techniques, from basic descriptive statistics to advanced methods like regression and multilevel modelling, tailored for educational research. This resource offers a practical and accessible guide to conducting quantitative research in education using IBM SPSS. It simplifies complex statistical concepts and applies them to real-world educational research scenarios. Designed for learners at all levels, it helps build confidence in data analysis and interpretation. This resource is ideal for students, researchers, and educators in the field of education who want to integrate statistical methods into their work. A basic understanding of research design and statistical concepts is recommended. No prior advanced statistical knowledge is required. This course provides a step-by-step guide to mastering quantitative research in education, using IBM SPSS as a tool. It covers everything from the basics of statistical analysis to advanced techniques such as regression models and multilevel modelling. This course is based on Doing Quantitative Research in Education with IBM SPSS Statistics, by Daniel Muijs. Copyright ©2022 by Sage Publications Limited. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Published by Sage Publications Limited, London. Used by arrangement with Sage Publications Limited.

Syllabus

  • Introduction to quantitative research
    • This module introduces learners to the principles of quantitative research, including its definition, methodology, and how it differs from qualitative approaches. It covers key concepts such as research paradigms, data collection, and the types of research questions best suited for quantitative methods. Learners will gain a foundational understanding of how to apply quantitative techniques in educational and social science research.
  • Experimental and quasi-experimental research
    • 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.
  • Designing non-experimental studies
    • 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.
  • Validity, reliability and generalisability
    • 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.
  • Introduction to IBM SPSS Statistics and the data set
    • 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.
  • Univariate statistics
    • 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.
  • Bivariate analysis: comparing two groups
    • 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.
  • Bivariate analysis: looking at the relationship between two variables
    • 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.
  • Multivariate analysis: using regression models to look at the relationship between several predictors and one dependent variable
    • 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.
  • Using analysis of variance to compare more than two groups
    • 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.
  • Developing scales and measures: item and factor analysis
    • 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.
  • One step beyond: introduction to multilevel modelling and structural equation modelling
    • 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.

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