This web page describes faculty activity by Gordon Brooks within the Educational Research and Evaluation (EDRE) program, which is in the Department of Educational Studies and the Patton College of Education at Ohio University.
If you have difficulty accessing these materials for any reason,
please feel free to email me.
Brooks, G. P., ADJANIN, N., OPPONG, F., & LIU, Y. (2024). Human-friendly Scheffé comparisons, or the art of complex multiple comparisons. General Linear Model Journal, 48(1), 11-28. https://www.glmj.org/archives/GLMJ_2024v48n1.html (PDF)
DO, H., & Brooks, G. P. (2024). Parameter recovery for the four-parameter item response model: A comparison of marginal maximum likelihood and Markov Chain Monte Carlo approaches. Psychological Test and Assessment Modeling, 66(1), 116-143. https://www.psychologie-aktuell.com/journale/psychological-test-and-assessment-modeling/currently-available/inhaltlesen/2024-1.html
DO, H., Wurm-Schaar, M., & Brooks, G. (2024). Examining the factor structure of a subjective well-being measure in a medical student sample. Mid-Western Educational Researcher, 36(1), Article 3. https://scholarworks.bgsu.edu/mwer/vol36/iss1/3/
AKMESE, I., Foreman, T., & Brooks, G. (2024). Bereavement during and not during the pandemic in terms of complicated grief and social support. OMEGA - Journal of Death and Dying. Online First published Mar 19, 2024 https://doi.org/10.1177/00302228241240944
Brooks, G. P., SIKA-POKOO, J., ADJANIN, N., & Johanson, G. A. (2023). Cross-loadings in scale development: Monte Carlo study of structural item-total correlation analysis with small samples. General Linear Model Journal, 47(2), 1-15. https://www.glmj.org/archives/GLMJ_2023v47n2.html
ADJANIN, N., & Brooks, G. P. (2023). Witnessing the last tropical glaciers: Student use of virtual reality technology to learn about climate change and protecting endangered environments. TOJET: Turkish Online Journal of Educational Technology, 22(4), 248-257. https://www.tojet.net/results.asp?volume=22&issue=4&year=2023
ZHOU, Y., REN, X., & Brooks, G. (2023). Which effect size calculation is the best to estimate the population effect size in the Welch t test? Journal of Modern Applied Statistical Methods, 22(1). https://jmasm.com/index.php/jmasm/issue/view/43
Brooks, G. P., AN, Q., LI, Y., & Johanson, G. A. (2023). For post hoc’s sake: Determining sample size for Tukey multiple comparisons in 4-Group ANOVA. General Linear Model Journal, 47(1), 1-14. https://www.glmj.org/archives/GLMJ_2023v47n1.html
ASEMPAPA, R. S., & Brooks, G. P. (2022). Factor analysis and psychometric evaluation of the mathematical modeling attitude scale for teachers of mathematics. Journal of Mathematics Teacher Education, 25, 131-161. https://doi.org/10.1007/s10857-020-09482-0
RUENGVIRAYUDH, P., & Brooks, G. P. (2022). Sample size for parallel analysis and not-so-common criteria for dimensions in factor analysis: Modifying the eigenvalue > 1 Kaiser rule. General Linear Model Journal, 46(1), 20-34. https://www.glmj.org/archives/GLMJ_2022v46n1.html
AL-ABDULLATIF, F. A., AL-ABDULLATIF, M. A., & Brooks, G. (2019). MANOVA post hoc techniques used in published articles: A systematic review. General Linear Model Journal, 45(1), 4-11. https://www.glmj.org/archives/GLMJ_2019v45n1.html
DAVID, S. L., Hitchcock, J. H., Ragan, B., Brooks, G., & Starkey, C. (2018). Mixing interviews and Rasch modeling: Demonstrating a procedure used to develop an instrument that measures trust. Journal of Mixed Methods Research, 12(1), 75-94. https://journals.sagepub.com/doi/full/10.1177/1558689815624586
Brooks, G. P., DIAZ, E. A., & Johanson, G. A. (2017). A precision-based and adaptive approach to number of replications for Monte Carlo studies of robustness and power. General Linear Model Journal, 43(1), 31-49. https://www.glmj.org/archives/GLMJ_2017v43n1.html
LIAO, H., LI, Y., & Brooks, G. P. (2017). Outlier impact and accommodation on power. Journal of Modern Applied Statistical Methods, 17(1), 261-278. http://digitalcommons.wayne.edu/jmasm/vol16/iss1/15/
Brooks, G. P., & RUENGVIRAYUDH, P. (2016). Best-subset selection criteria for multiple linear regression. General Linear Model Journal, 42(2), 14-25. http://www.glmj.org/archives/GLMJ_2016v42n2.html
RUENGVIRAYUDH, P., & Brooks, G. P. (2016). Comparing stepwise regression models to the best-subsets models, or, the art of stepwise. General Linear Model Journal, 42(1), 1-14. http://www.glmj.org/archives/GLMJ_2016v42n1.html
LIAO, H., LI, Y., & Brooks, G. (2016). Outlier impact and accommodation methods: Multiple comparisons of Type I error rates. Journal of Modern Applied Statistical Methods, 15(1), 452-471, article 23. http://digitalcommons.wayne.edu/jmasm/vol15/iss1/23/
AN, Q., XU, D., & Brooks, G. P. (2013). Type I Error rates and power of multiple hypothesis testing procedures in factorial ANOVA. Multiple Linear Regression Viewpoints, 39(2), 1-16. http://www.glmj.org/archives/GLMJ_2014v39n2.html
Brooks, G. P., & Barcikowski, R. S. (2012). The PEAR method for sample sizes in multiple linear regression. Multiple Linear Regression Viewpoints, 38(2), 1-16. http://www.glmj.org/archives/GLMJ_2014v38n2.html
All rights not expressly granted here are reserved to the author of the software.
Please note that all my software is constantly under development. I do my best to verify the code and the algorithms; but unfortunately, I can test the software only so much by myself (or even with help from a few colleagues and students). Please contact me if you find bugs or errors and I will do my best to fix them quickly. I also welcome any suggestions you might have to improve the programs (or even new programming ideas). Of course, this also means that if you find something you like, you should check back occasionally to see if there is a new upgrade available.
Brooks, G. P., & RAFFLE, H. (2005). FISH: A new computer program for friendly introductory statistics help. Teaching Statistics: An International Journal for Teachers, 27, 81-88. doi: 10.1111/j.1467-9639.2005.00221.x
Brooks, G. P., RAFFLE, H., LEWIS,M., & BLOM, V. (2005). A computer program for friendly introductory statistics help. 2004 Proceedings of the American Statistical Association, Section on Statistical Education [CD-ROM]. Alexandria, VA: American Statistical Association. {Poster presented at the August, 2004, annual Joint Statistical Meeting, Toronto, Canada}
Brooks, G. P., Abdulla, A., Al-Harbi, K., Kanyongo, G., Kyei-Blankson, L., & Gocmen, G. (2002, April). Teaching introductory statistics with the help of Monte Carlo methods. Paper presented at the meeting of the American Educational Research Association, New Orleans, LA.
Brooks, G. P. (2003). Using Monte Carlo methods to teach statistics: The MC2G computer program. Understanding Statistics, 2, 137-150.
Brooks, G. P., & Raffle, H. (2004, May). Using Monte Carlo software for ANOVA to teach abstract statistical concepts. Paper presented as a Teaching Institute poster at the meeting of the American Psychological Society, Chicago, IL.
Raffle, H., & Brooks, G. P. (2005). Using Monte Carlo software to teach abstract statistical concepts: A case study. Teaching of Psychology, 32, 193-195. doi: 10.1207/s15328023top3203_12
Multiple t-Test Type I Error Inflation Lesson Plan by Hua Gao
Violation of Homoscedasticity Student Worksheet by Holly Raffle
Brooks, G. P. (2008, March). A Monte Carlo program for multiple linear regression. Paper accepted for presentation at the 2008 meeting of the American Educational Research Association, New York, NY.
Brooks, G. P. (2008). A Monte Carlo program for multiple linear regression. Multiple Linear Regression Viewpoints, 34(2), 15-43.
Brooks, G. P., Johanson, G. A., Lewis, M., & Kyei-Blankson, L. (2003, April). Using the Test Analysis Program in introductory measurement courses. Paper discussion presented at the meeting of the American Educational Research Association, Chicago, IL.
Brooks, G. P., & Johanson, G. A. (2003). Test Analysis Program. Applied Psychological Measurement, 27, 305-306.
User’s Guide by Marsha Lewis (written for TAP4 but mostly still valid)
Instructor’s Guide by George Johanson (written for TAP4 but mostly still valid)
Download TAP10K.EXE 2014 Version with N < 9999
Download TAP10K.ZIP 2014 Version with N < 9999
Download TAP6.EXE 2005 Version
Download TAP6.ZIP 2005 Version
Brooks, G. P., Raffle, H., Fang, H., & Heh, V. (2003, October). Teaching statistics with the help of three new computer programs. Workshop presented at the meeting of the Mid-Western Educational Research Association, Columbus, OH.
Brooks, G. P., Raffle, H., Fang, H., & Heh, V. (2003, October). Teaching statistics with the help of three new computer programs. Workshop presented at the meeting of the Mid-Western Educational Research Association, Columbus, OH.
Brooks, G. P., Abdulla, A., Al-Harbi, K., Kanyongo, G., Kyei-Blankson, L., & Gocmen, G. (2002, April). Teaching introductory statistics with the help of Monte Carlo methods. Paper presented at the meeting of the American Educational Research Association, New Orleans, LA.
MC3G has essentially been replaced by MC4G (see below), but it does do a few things differently.
Brooks, G. P. (2002). MNDG: Multivariate Normal Data Generator. Applied Psychological Measurement, 26, 353-354.
The software is Freeware. The user is licensed to make an unlimited number of exact copies of the software, to give these exact copies to any other person for their personal use, and to distribute the software in its unmodified form only via disk or local area network. If these methods of distribution are unavailable, any person wanting to use the software should be directed either to contact the author or to visit the author’s internet web site (the URL is provided below and may be posted on any web site).
If you find the software useful, if you copy it for others, if you find problems or bugs in the software, if you post a link to the author’s web site, or if you use the software for teaching, educational, or consulting purposes, you are requested to inform the author by email: brooksg@ohio.edu.
Great effort has been made to ensure the accuracy of the software, the algorithms and subroutines used, and the results produced by the software, both on screen and printed. However, no warranty is expressed or implied concerning the function or fitness of the software, subroutines, or results provided by the software. That is, the software is provided on an “as is” basis without warranty of any kind. The author shall have neither liability nor responsibility to any person or entity with respect to any liability, loss, or damage directly or indirectly arising from the use of or inability to use the software or the results of the analyses provided by the software, even if the author has been advised of the possibility of such damages or claims. In no event shall any liability exceed the license fee paid to the author of the software. In the event of invalidity of any provision of this license, the user agrees that such invalidity shall not affect the validity of the remaining portions of this license.
Link to Barcikowski & Brooks StatPro textbook files (click here)
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