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Quantitative methods and modelling

Presenter: Andrew Martin

Discussant: Paul Ginns

Time: 4:30-6:30pm, 29 October 2009 (refreshments at 4:30pm for 5:00pm start)

Venue: Room 351, Education Building (A35), The University of Sydney

Quantitative Modelling of Correlational Data in Educational Research, by Andrew J. Martin

This presentation examines quantitative modelling of correlational data in educational research, with particular focus on and examples from educational psychology. Traversing cross-sectional and longitudinal methodologies, the presentation looks at the various ways correlational techniques have been applied to answer important questions of relevance to educators, psychologists, policy makers, and researchers. Having scoped the yields of correlational data and research, the presentation then addresses some of the short-comings and limitations in correlational modelling that need to be addressed to fully progress educational research. Alternative quantitative (eg. experimental methods) and qualitative approaches are then introduced, with particular attention given to the questions these methods can answer that correlational methods cannot. Taken together, it is evident that complementing correlational modelling with other rigorous quantitative and qualitative techniques enables researchers to generate a more thorough understanding of key educational issues than any one of these techniques alone.

Experimentation in Educational Research, by Paul Ginns

Quantitative modelling using correlational data supports testing of complex educational theories, but causal theoretical claims can be made on the basis of correlational relations only under quite specific circumstances. This response will discuss the role of experimental methodologies in education, and how such methodologies might complement both quantitative and qualitative alternatives. Beginning with a review of the threats to validity of results of different experimental designs, it will outline the role that experiments may play in understanding causal relations. The potential range of the methodology in addressing educational design and policy questions will be considered, along with alternatives such as quasi-experiments and propensity score matching.

About the presenters

Associate Professor Andrew Martin is International Senior Research Fellow in the Faculty of Education and Social Work at the University of Sydney. Andrew is internationally recognised for his research in achievement motivation and advanced and cutting-edge quantitative methods in education and psychology. Andrew has written over 150 peer reviewed journal articles, chapters, and papers in published conference proceedings, written 3 books for parents and teachers (published in 5 languages), compiled 12 commissioned government reports, written over 45 articles/reprints for professional and general readerships (eg. teachers, psychologists, counsellors, parents), has won 8 Australian Research Council grants and more than 15 government and non-government research tenders. Andrew is Associate Editor of the Journal of Educational Psychology and the British Journal of Educational Psychology and is on Editorial Boards of 3 international journals.

Dr Paul Ginns is Lecturer in Educational Psychology at the Faculty of Education and Social Work, University of Sydney. His research has 2 broad foci: improving the student experience through institutionally-aligned, student-focussed teaching evaluation systems, and applying principles of cognitive science to instructional design. He uses a wide variety of research designs (e.g. experimental and survey-based research) and analytic methods (e.g. General Linear Models for experimental and non-experimental designs, exploratory and confirmatory factor analysis, structural modelling, meta-analysis). His work has been published in a number of local and international peer-reviewed journals. Paul is currently writing a book on teaching evaluation (with Professor David Kember).

Session archive

  • Session recording MP3 (NA).
  • Presentation slides PDF (TBA MB)

References & recommended readings

  • TBA