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PhD Ed. Neuroscience: DOCM730- Quantitative Research Methods

Course Description

This course will prepare students to undertake quantitative research in education. Students will gain an understanding of the nature of quantitative research designs; major worldviews on and methods of doing quantitative research, including their assorted rationales, strengths, weaknesses, and applications. Students will also gain skills in in designing, conducting, analysing, interpreting, and reporting quantitative research. Specifically, this course, using a combination of formal lectures, in-class activities, practical sessions, and discussions about course readings, will provide a detailed introduction to three widely used quantitative research designs: correlational, experimental/quasi-experimental, and survey. Special attention will be paid to the underlying theory, assumptions, and statistical interpretations of these three commonly used quantitative research designs. Hands-on practical sessions, employing IBM SPSS Statistics, Excel or MATLAB, will enable students to implement these powerful and versatile quantitative tools and techniques in the context of specific educational research applications.

Articles

Haig, B. D. (2019.) The importance of scientific method for psychological science. Psychology, Crime and Law, 25(6), 527-541. https://doi.org/10.1080/1068316X.2018.1557181

Jacobson, M. J., Levin, J. A., & Kapur, M. (2019) Education as a Complex System: Conceptual and Methodological Implications. Educational Researcher, 48(2), 112-119. https://doi.org/10.3102%2F0013189X19826958

Hemphill, J. F. (2003) Interpreting the Magnitudes of Correlation Coefficients. American Psychologist, 58(1), 78-80. https://doi.org/10.1037/0003-066x.58.1.78

Peng, C. Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96(1), 3-14. https://doi.org/10.1080/00220670209598786

Niu, L. (2020) A review of the application of logistic regression in educational research: common issues, implications, and suggestions. Educational Review, 72(1), 41-67. https://doi.org/10.1080/00131911.2018.1483892

Bloom, H., Bell, A., & Reiman, K. (2020). Using Data from Randomized Trials to Assess the Likely Generalizability of Educational Treatment-Effect Estimates from Regression Discontinuity Designs. Journal of Research on Educational Effectiveness, 13:3, 488-517. https://doi.org/10.1080/19345747.2019.1634169

Lu, B. (2019). How can we evaluate the effectiveness of grammar schools in England? A regression discontinuity approach. British Educational Research Journal, 46(2), 339-363. https://doi.org/10.1002/berj.3581

Kelley, K., Clark, B., Brown, V., & Sitzia, J. (2003). Good practice in the conduct and reporting of survey research, International Journal for Quality in Health Care, 15(3): 261–266. https://doi.org/10.1093/intqhc/mzg031

Stedman, R. C., Connelly, N. A., Heberlein, T. A., Decker, D. J., & Allred, S. B. (2019). The End of the (Research) World As We Know It? Understanding and Coping with Declining Response Rates to Mail Surveys. Society and Natural Resources, 32(10), 1139-1154. https://doi.org/10.1080/08941920.2019.1587127

Hasson, F., Keeney, S., McKenna, H. (2000) Research guidelines for the Delphi survey technique. J Adv Nurs. 32(4), 1008-1015. https://doi.org/10.1046/j.1365-2648.2000.t01-1-01567.x

Eysenbach, G. (2004). Improving the Quality of Web Surveys: The Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J Med Internet Res, 6(3), e34. https://dx.doi.org/10.2196%2Fjmir.6.3.e34

Trespalacios, J. H., Perkins, R. A. (2016) Effects of Personalization and Invitation Email Length on Web-Based Survey Response Rates. TechTrends, 60, 330-335. https://doi.org/10.1080/1364557042000203107

Norris, J. M., Plonsky, L., Ross, S. J., & Schoonen, R. (2015). Guidelines for Reporting Quantitative Methods and Results in Primary Research. Language Learning, 65(2), 470-476. https://doi.org/10.1111/lang.12104

Kivunja, C., Kuyini, A. B., (2017) Understanding and Applying Research Paradigms in Educational Contexts. International Journal of Higher Education, 6(5), 26-41. https://doi.org/10.5430/ijhe.v6n5p26

Kzdin, A. E. (2019) Single-case experimental designs. Evaluating interventions in research and clinical practice. Behaviour Research and Therapy, 117, 3-17. https://doi.org/10.1016/j.brat.2018.11.015

Ioannidis, J. P. A. (2005) Why Most Published Research Findings Are False. PLoS Med, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124

Simmons, J. P., Nelson, L. D., Simonsohn, U. (2011) False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 22(11), 1359-1366. https://doi.org/10.1177%2F0956797611417632

Gorard, S. (2015) Rethinking ‘quantitative’ methods and the development of new researchers. Review of Education, 3(1), 72-96. https://doi.org/10.1002/rev3.3041

Friston, K., (2005) Models of brain function in neuroimaging. Annual Review of Psychology. 56, 57-87. https://doi.org/10.1146/annurev.psych.56.091103.070311

Books

Mat Roni S., Merga M.K., & Morris J.E. (2020). Getting Started: What, Where, Why. In Conducting Quantitative Research in Education. Springer. https://doi.org/10.1007/978-981-13-9132-3_2

McMillan, J., Mohn, R. S., & Hammack, M. V. (2013). Quantitative research designs in educational research. In L. Meyer (Ed.), Oxford bibliographies in education. Oxford University Press. https://doi.org/10.1093/OBO/9780199756810-0113

Martinez, W.L., Martinez, A. R. (2016) Computational Statistics Handbook with MATLAB (3rd ed.). Chapman & Hall/CRC. https://www.routledge.com/Computational-Statistics-Handbook-with-MATLAB/Martinez-Martinez/p/book/9781466592735

Gallo, J. (2019). A Quick and Easy Guide in Using SPSS for Linear Regression Analysis. Independently published. https://www.amazon.com/Quick-Guide-Linear-Regression-Analysis-ebook/dp/B07VYLGTNT

Kalton, G. (2020) Introduction to Survey Sampling (Quantitative Applications in the Social Sciences) (2nd Ed.) SAGE Publications, Inc.

Verma, J. P. (2019). Research Design in Psychology. Statistics and Research Methods in Psychology with Excel. Springer. https://www.springer.com/gp/book/9789811334283

Tintle, N., Chance, B. L., Cobb, G. W., Rossman, A. J., Roy, S., Swanson, T., & VanderStoep, J. (2018) Introduction to Statistical Investigations. Wiley. https://professor.wiley.com/CGI-BIN/LANSAWEB?PROCFUN+PROF1+PRFFN15

Reinhart, A. (2015) Statistics Done Wrong: The Woefully Complete Guide. No Starch Press. https://nostarch.com/statsdonewrong

Bandalos, D. L., & Boehm, M. R. (2008). Four common misconceptions in exploratory factor analysis. In C. E. Lance & R. J. Vandenberg, (Eds.) Statistical and methodological myths and urban legends: Where, pray tell, did they get this idea? (pp. 63-88). Routledge. https://psycnet.apa.org/record/2008-09949-003 Introduction

McInnes, J. (2017). An introduction to secondary data analysis with IBM SPSS Statistics. Los Angeles, CA: Sage

Tessler, M., Palmer, M., Farah, T. E., & Ibrahim, B. (2019). The Evaluation and Application Of Survey Research In The Arab World. Routledge. https://www.taylorfrancis.com/books/9780429310676

Vogt, W. P. (2007). Quantitative research methods for professionals. Boston, MA: Pearson.

Wright, D. B. (2020) Understanding Statistics: An Introduction for the Social Sciences. SAGE Publications Ltd

Cook, T., & Wong, V. (2008). Better quasi-experimental practice. In P. Alasuutari, L. Bickman, & J. Brannen (Eds.), The SAGE handbook of social research methods. (pp. 134-166).