\(\color{blue}{\text{Welcome}}\)
\(\color{maroon}{\text{Please
Note:}}\)
\(\color{maroon}{\text{This web page will
be moving by 01-May-2026}}\)
\(\color{red}{\text{To:
brooksg.net}}\)
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.

\(\color{green}{\text{Information}}\)
\(\color{green}{\text{Recent
Publications}}\)
2024
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
2023
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
2022
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
2021
- DIAZ, E. A., Brooks, G. P., & Johanson, G. A. (2021). Detecting
differential item functioning: Item response theory methods versus the
Mantel-Haenszel procedure. International Journal of Assessment Tools in
Education, 8(2), 376-393. https://dergipark.org.tr/en/download/article-file/1080666
2010-2021
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
Wordcloud for Scholarship

\(\color{green}{\text{Recent Conferences
& Apps}}\)
R Shiny App:Human-Friendly
Comparisons
- Brooks, G. P., & ADJANIN, N. (2025, July). Getting Something for
Nothing: Human-Friendly Scheffé Complex Comparisons. Poster presented at
the 2025 U.S. Conference on Teaching Statistics (USCOTS) sponsored by
the Consortium for the Advancement of Undergraduate Statistics
Education, Ames, Iowa. {https://www.causeweb.org/cause/uscots/uscots25/program/posters/poster34}
- Brooks, G. P., & ADJANIN, N. (2024, July). Using Human-Friendly
Scheffé Comparisons to Explore Group Differences in One-way ANOVA.
Presentation at the Athens Institute for Education and Research 18th
Annual International Conference on Statistics: Teaching, Theory &
Applications. 1-4 July 2024, Athens, Greece. {see the 2024 Program and
Abstract links at https://www.atiner.gr/statistics}
MWERA 2024
- ADJANIN, N., MENSAH, F., OTIENO, D. A., & Brooks, G. (2024,
October). Seeing shapes in clouds: Using R for qualitative
visualizations (yes, you read that right… R… Qualitative). Workshop
presented at the annual meeting of the Mid-Western Educational Research
Association, Cincinnati.
AERA 2024
- LIU, Y., OPPONG, F. A., ADJANIN, N., & Brooks, G. P. (2024,
April). The Homogeneity of Covariances Assumption in MANOVA:
Differential Impact of Heterogenous Variances and Covariances. Paper
presented (assigned to roundtable paper session with paper provided) at
the annual meeting of the American Educational Research Association,
Philadelphia.
- OPPONG, F. A., LIU, Y., ADJANIN, N., Johanson, G. A., & Brooks,
G. P. (2024, April). Sample Size Determination for (Planned) Post Hoc
Multiple Comparisons in One-Way ANOVA. Paper presented (assigned to
roundtable paper session with paper provided) at the annual meeting of
the American Educational Research Association, Philadelphia.
AERA 2023
- Brooks, G. P., & ADJANIN, N. (2023, April). Back to the Future:
Human-friendly Scheffé Contrasts, or, the Art of Multiple Comparisons.
Paper presented at the annual meeting of the American Educational
Research Association, Chicago {We chose in-person conference
presentation}.
\(\color{green}{\text{Copyright info for
Programs}}\)
Copyright
- All software is copyrighted by Gordon P Brooks (2001-2025)
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.
Documentation
- Most of the documents available with the programs (e.g., user
guides, instructor guides, lesson plans, student exercises) are in Adobe
Acrobat PDF format. If you need the Adobe Reader, you can download it
from their web site free.
\(\color{green}{\text{Newer Programs for
Download}}\)
FISH: Friendly Introductory Statistics Help

- FISH is a Windows program written in Delphi Pascal that performs
introductory descriptive and correlation analyses (last updated January
2022). It is intended to be used as a supplement to introductory
statistics courses. It is designed for use in basic statistics courses
where a conceptual understanding of statistics is desired without much
calculation by hand. It takes the user step-by-step through the
calculation of means, standard deviations, z-scores, and correlations.
Although some AUXILIARY MATERIALS may have been created for use with a
previous version of the program, they should still be very useful with
newer versions of the program; that is, changes to the program usually
are not dramatic enough to make earlier manuals obsolete. An article
about FISH was published in Teaching Statistics and FISH was most
recently presented at the annual meeting of the Joint Statistical
Meetings, August 2004, Toronto, Canada.
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}
Download
FISH.EXE
Download
FISH.ZIP
User’s
Guide by Holly Raffle
Instructor’s
Guide by Holly Raffle
Sample
Lesson 1 (univariate) by Marsha Lewis & Valerie Blom
Sample
Lesson 1 (z-scores) by Marsha Lewis
Sample
Lesson 1 (correlation) by Valerie Blom
MC2G: Monte Carlo Analyses for 1 or 2 Groups

- MC2G is a Windows program written in Delphi Pascal that runs Monte
Carlo simulations for several single sample and two-sample tests:
Independent t, Dependent t, Single-sample t, Mann-Whitney-Wilcoxon,
Wilcoxon Signed Rank, Pearson Correlation, Spearman Rank-Order
Correlation (last updated 2018). By providing the total number of
rejections at the user-specified level of significance, MC2G performs
robustness analyses when means are equal (or correlations are zero) and
power analyses when the means are not equal (or correlations are
non-zero). The original purpose of the program was to assist in the
instruction of power analysis and violations of assumptions for
introductory educational statistics courses. However, with a little
effort the program can be used to answer actual Monte Carlo research
questions. MC2G was most recently presented at the annual meeting of the
Mid-Western Educational Research Association, October 2003, Columbus,
OH, and a program announcement and example lesson plans for MC2G was
published in 2003 in Understanding Statistics.
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.
Download
MC2G.EXE
Download
MC2G.ZIP
User’s
Guide
Sampling
Distribution Lesson Plan
Robustness
Lesson Plan by Khaleel Al-Harbi
Power
Lesson Plan by Abdulbaset Abdulla
Sample
Size Lesson Plan by Gibbs Kanyongo
MC4G: Monte Carlo Analyses for up to 4 Groups

- MC4G is a Windows program written in Delphi Pascal that runs Monte
Carlo simulations for One-Way ANOVA with 2, 3, or 4 levels (last updated
2018). By providing the total number of rejections at the user-specified
level of significance, MC4G performs robustness analyses when means are
equal and power analyses when the means are not equal. It can also be
used to estimate required sample sizes. Its initial purpose was to
illustrate a variety of Type I Error problems associated with ANOVA
(e.g., violations of assumptions, compared to multiple t tests,
probability of at least one Type I Error from multiple orthogonal
tests). MC4G was most recently presented at the annual meeting of the
American Psychological Society, May 2004, Chicago, IL, and an article
about MC4G is in press to be published in 2005 in Teaching of
Psychology.
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
Download
MC4G.EXE
Download
MC4G.ZIP
MC4G
Instructor’s Manual by Holly Raffle
Multiple
t-Test Type I Error Inflation Lesson Plan by Hua Gao
Violation
of Homoscedasticity Student Worksheet by Holly Raffle
Violation
of Homoscedasticity Lesson Plan by Gulsah Gocmen
Underappreciated
Factors that Effect Statistical Power by Gordon Brooks, George Johanson,
& Robert Barcikowski
Asymptotic
Alpha: Characterizing Type I Error Rates under Heterogeneity of
Variances by Gordon Brooks, Victor Heh, & Hua Fang
MCMR: Monte Carlo for Multiple Regression

- MCMR is a Windows program written in Delphi Pascal that performs
Monte Carlo analyses for Multiple Linear Regression with up to 6
predictors (last updated 2018). The user must specify means and standard
deviations as well as correlations, but can also import a single file
for analysis. MCMR was most recently presented at the annual meeting of
the American Educational Research Association, March 2008, New York, NY,
and an article about MCMR was in Multiple Linear Regression Viewpoints
(www.glmj.org/).
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.
Download
MCMR.EXE
Download
MCMR.ZIP
Instructor’s
Guide from AERA 2008
TAP: Test Analysis Program

- TAP is a Windows program written in Delphi Pascal that performs test
analyses and item analyses based on classical test theory (last updated
December 2018). TAP is a classical test and item analysis program. It
provides reports for examinee total scores, item statistics (e.g., item
difficulty, item discrimination, point-biserial), options analyses, and
other useful information. TAP also provides individual examinee reports
of total scores and item responses. Although any AUXILIARY MATERIALS may
have been created for use with a previous version of the program, they
should still be very useful with newer versions of the program; that is,
changes to the program usually are not dramatic enough to make earlier
manuals obsolete. TAP was most recently presented at the annual meeting
of the American Psychological Society, May 2004, Chicago, IL, and a
program announcement for TAP was published in 2003 in Applied
Psychological Measurement.
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.
Download
TAP.EXE
Download
TAP.ZIP
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
\(\color{green}{\text{Older Programs for
Download}}\)
BMA: Basic Matrix Algebra (for Statistics)

- BMA is a Windows program written in Delphi Pascal that performs
matrix manipulations (last updated 2004). It is designed for use in
advanced statistics courses where a conceptual understanding of matrix
algebra is desired without much calculation by hand. It takes the user
step-by-step through several matrix algebra functions, including
addition, multiplication, transposition, and inversion. BMA was
presented most recently at the annual meeting of the Mid-Western
Educational Research Association, October 2003, 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.
Download
BMA.EXE
Download
BMA.ZIP
User’s
Guide by Hua Fang
DGW: Data Generator for Windows

- DGW is a Windows program written in Delphi Pascal that generates
both Univariate and Multivariate data (last updated 2018). The program
assists in the process by providing simple choices for several research
scenarios (e.g., 3-group analysis, regression analysis). DGW was most
recently presented at the annual meeting of the Mid-Western Educational
Research Association, October 2003, 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.
Download
DGW.EXE
Download
DGW.ZIP
User’s
Guide by Victor Heh
HMF: Harmonic Mean Finder

- HMF is a little Windows program written in Delphi Pascal that
calculates the Harmonic Mean in a variety of ways, particularly useful
for Power and Sample Size analyses (last updated 2004). It will
calculate (a) the harmonic mean, (b) the sample size needed for group 2
for a given group 1 sample size and desired harmonic mean, (c) several
combinations of sample sizes that result in the given harmonic mean, and
(d) the harmonic mean for 3-6 groups.
MC3G: Monte Carlo Analyses for 3 Groups

- MC3G is a Windows program written in Delphi Pascal that runs Monte
Carlo simulations for 3-level ANOVA (last updated 2003). By providing
the total number of rejections at the user-specified level of
significance, MC3G performs robustness analyses when means are equal and
power analyses when the means are not equal. The original purpose of the
program was to assist in the instruction of power analysis and
violations of assumptions for introductory educational statistics
courses. However, with a little effort the program can be used to answer
actual Monte Carlo research questions. MC3G was most recently presented
at the annual meeting of the American Educational Research Association,
April 2002, New Orleans, LA.
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.
Download
MC3G.EXE
Download
MC3G.ZIP
Robustness
Lesson Plan by Gulsah Gocmen
Sample
Size Lesson Plan by Lydia Kyei-Blankson
MCCM: Monte Carlo Correlation Matrices

- MCCM is a Windows program written in Delphi Pascal that performs
Monte Carlo analyses for several bivariate correlations simultaneously,
particularly useful to show the effect on Type I error (or power) when
testing several bivariate correlations all at alpha of .05 (last updated
2018). That is, it will generate data to illustrate the effect on Type I
error of running multiple bivariate correlations simultaneously. The
user can specify item means and standard deviations as well as
correlations.
MNDG: Multivariate Normal Data Generator

- MNDG is a Windows program written in Delphi Pascal that generates
Multivariate Normal data (last updated 2018). A program announcement for
MNDG was published in 2002 in Applied Psychological Measurement.
MUD: Messy Ugly Data Generator

- MUD is a Windows program written in Delphi Pascal that generates
data to mimic survey/questionnaire data (last updated 2018). The user
can specify item means and standard deviations as well as inter-item
correlations. The user can specify an embedded multiple-item scale with
separate inter-item correlations (the total scale score is used in
calculating correlations for the questionnaire variables).
\(\color{green}{\text{User Agreement for
Programs}}\)
User Agreement
- Carefully read the following User Agreement (License, Terms of Use,
Restrictions, and Disclaimer of Warranty). Use of the software provided
by the author constitutes acceptance of these terms and conditions of
use. If you do not agree to the terms of this agreement, do not use the
software.
License
- ALL software (computer programs described above and listed below) is
copyrighted software (copyrighted by the author, Gordon P. Brooks) and
is NOT in the public domain. The user is granted license, not ownership,
to use the software on any computer, subject to the restrictions
described in the User Agreement and Disclaimer.
Terms of Use
- All software is copyrighted by Gordon P Brooks (2001-2025)
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.
Restrictions
- The software may be used and copied for personal use subject to the
following license restrictions:
- the software shall be copied and supplied in its original,
unmodified form;
- the software shall not be sold or used for profit, nor may any
amount or fee be charged for use, rental, lease, or distribution of the
software;
- the software shall not be included or bundled with any other goods
or services;
- the software may not be decompiled, disassembled, or otherwise
modified;
- Any such unauthorized use without expressed, written permission
granted by the author shall result in immediate and automatic
termination of this license.
Disclaimer of Warranty
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.
Anti-virus
- Please note that some Anti-Virus programs and sometimes Windows
itself will recommend that you do not download these programs or block
you from downloading or running them. You can usually bypass this block
by clicking more information and then choosing to keep anyway or run
anyway. They just don’t seem to like relatively new programs that don’t
have many users. I use both Norton Anti-Virus and Windows Defender on
different computers I use to compile these programs. Neither program
(nor Kaspersky nor Sophos) has ever complained while I compile the
programs.
\(\color{blue}{\text{Farewell}}\)
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