D. Betsy McCoach
Expertise: latent variable modeling, multilevel modeling, instrument design, quantitative research methods, longitudinal modeling, gifted education
Areas of Expertise: Latent Variable Modeling, Instrument Design, Multilevel Modeling, Longitudinal Analysis, Quantitative Research Methodology, Gifted Education, Underachievement
Dr. D. Betsy McCoach is a professor of Research Methods, Measurement, and Evaluation at the University of Connecticut. She has extensive experience in latent variable and structural equation modeling, longitudinal data analysis, hierarchical linear modeling, instrument design, and factor analysis. Her substantive areas of research interest include gifted education and underachievement. Betsy has published over 100 peer reviewed journal articles, book chapters, and books, including Introduction to Modern Modeling Methods (2021) with Dakota Cintron, Multilevel Modeling of Educational Data with Ann O’Connell and Instrument Development in the Affective Domain (3rd edition), co-authored with Robert K. Gable. Betsy served as the founding co-editor for the Journal of Advanced Academics and co-editor of Gifted Child Quarterly. Betsy is the founder and conference chair of the Modern Modeling Methods conference, held at UCONN every May (pre-Covid!). Betsy has served as a Co-Principal Investigator and research methodologist on several federally funded research grants, including Project Early Vocabulary Intervention, the National Center for Research on Gifted Education, and Project NEEDs2, all funded by IES, and Science of Learning and Art of Communication (SLAC), funded by NSF. Betsy is past chair of several AERA SIGs, including the Structural Equation Modeling SIG, Multilevel Modeling SIG, Educational Statisticians SIG, and Research on Giftedness, Creativity, and Talent Development SIG. Betsy is a fellow of the American Psychological Association, Division 5. Betsy is accepting Masters and PhD student applications for the 2022-2023 academic year.
Funded Research (Selected):
Co-Principal Investigator, Factors Affecting Comprehension by Teens During Online Reading in Science: The FACTORS Project. Funding Source: IES ($600,000). PI: Don Leu
Co-Principal Investigator, Science of learning, from neurobiology to real-world application: a problem-based approach. Funding Source: NSF ($3 million) PI: James Magnuson.
Co-Principal Investigator, Project EVI: Early Vocabulary Intervention. U. S. Department of Education, Institute of Education Sciences, Reading & Writing Research – Special Education Research (Goal 3 – Efficacy). $4,097,835. (PI: Mike Coyne ).
Co-Principal Investigator, School Structure and Science Success: Organization and Leadership Influences on Student Success, funded by the National Science Foundation (PI- John Settlage, $2,700,000.)
Co-Principal Investigator, Project IVI: Intensifying Vocabulary Intervention for Kindergarten Students at Risk of Learning Disabilities. U. S. Department of Education, Institute of Education Sciences, Language and Vocabulary Development – Special Education Research (Goal 2 – Development). $884,306. (PI: Mike Coyne).
Co-Principal Investigator, National Research Center on the Gifted and Talented
Project Director/Principal Investigator. Project PAPER: Preparing Academicians in Psychometrics and Educational Research. (2012-2015). U. S. Department of Education, $399,000.
Selected Recent Publications/Presentations:
McCoach, D. B. & Cintron, D. W.* (2022). Introduction to Modern Modeling Methods. London: SAGE.
O’Connell, A.A. McCoach, D.B. , & Bell, B. A. (2022). Multilevel Modeling Methods with Introductory and Advanced Applications. (Eds.) Charlotte, NC: Information Age Publishing.
Peters, S., McBee, M., Matthews, M., & McCoach, D. B. (2013). Beyond gifted education: Designing and implementing advanced academic programs. Waco, TX: Prufrock Press.
O’Connell, A.A. & McCoach, D.B. (2008). Multilevel modeling of educational data. (Eds.) Charlotte, NC: Information Age Publishing.
Selected Journal Articles (see CV for full list)
Coyne, M. D., McCoach, D. B., Ware, S., Loftus-Rattan, S., Baker, D., Santoro, L., & Oldham, A. (in press). Supporting vocabulary development within a multi-tiered system of support: Evaluating the efficacy of supplementary kindergarten vocabulary intervention. Journal of Educational Psychology.
Dineen, J., Chafouleas, S., Briesch, A. M., McCoach, D. B., Newton, S. D., & Cintron, D. W. (online first). Exploring Approaches to Identifying and Supporting Students’ Social, Emotional, and Behavioral Needs in U.S. Public School Districts. American Educational Research Journal. https://doi.org/10.3102/00028312211000043
McCoach, D. B., Perez, J.,* & Reyna, K.* (in press). Methodologists: You need us more than we need you! Special issue of Research in the Schools entitled Methodologists: Who Needs ‘Em?
McCoach, D. B. (in press). Achieving Equity Within Public Education. Gifted Child Quarterly.
McCoach, D. B., Siegle, D., & Rubenstein, L. (2020). Pay attention to inattention: Exploring ADHD symptoms in a sample of underachieving gifted students. Gifted Child Quarterly, 64(2) 100–116. https://doi.org/10.1177/0016986219901320
Coyne, M. D., McCoach, D. B., Ware, S., Austin, C. R., Loftus-Rattan, S. M., & Baker, D. L., (2019). Racing Against the Vocabulary Gap: Matthew Effects in Early Vocabulary Instruction and Intervention. Exceptional Children, 85, 163-179.
McCoach, D. B., Rifenbark, G.*, Newton, S. D.*, Li, X.*, Kooken, J.*, Yomtov, D.*, Gambino, A.*, & Bellara, A. (2018). Does the package matter? A Comparison of Five Common Multilevel Modeling Software Packages. Journal of Educational and Behavioral Statistics, 43, 594-627.
Hamilton. R., McCoach, D. B., Tutwiler, M. S., Siegle, D., Callahan, C., Gubbins, E. J., & Broderson, A. (2018). Disentangling the roles of institutional and individual poverty in the identification of gifted students. Gifted Child Quarterly, 62, 6-24. http://journals.sagepub.com/doi/pdf/10.1177/0016986217738053
Kooken, J*., McCoach, D. B., & Chafouleas, S. M. (2018). The Impact and Interpretation of Residual Non-invariance in Growth Mixture Modeling. Journal of Experimental Education. https://doi.org/10.1080/10705511.2017.1374187
Yu, H. H.*, McCoach, D. B., Gottfried, A. W., & Gottfried, A. E. (2018). Stability of Intelligence from Infancy through Adolescence: An Autoregressive Latent Variable Model. Intelligence, 69, 8-15.
Flake, J.* & McCoach, D. B. (2017). An Investigation of the Alignment Method with polytomous indicators under conditions of partial measurement invariance. Structural Equation Modeling. Online First: http://dx.doi.org/10.1080/10705511.2017.1374187
McCoach, D. B., Yu, H. H., Gottfried, A. W., Gottfried, A. E. (2017). Developing Talents: A Longitudinal Examination of Intellectual Ability and Academic Achievement. High Ability Studies. http://dx.doi.org/10.1080/13598139.2017.1298996
McCoach, D. B., Newton, S. D., Siegle, D., Baslanti, U, & Picho, K. (2016). Is Having Low Motivation the Same as Not Having High Motivation? Comparing the CSAS-R and the SAAS-R. High Ability Studies. DOI: 10.1080/13598139.2015.1103209
McCoach, D. B. & Kenny, D. A. (2014). A Few Thoughts on the Similarities and the Differences Between Causal or Reflective Indicators of Latent Variables. Measurement: Interdisciplinary Perspectives, 12, 151-154. https://doi.org/10.1080/15366367.2014.981067
McCoach, D. B., Gubbins, E. J., Foreman, J., Rubenstein, L., & Rambo, K., (2014). Evaluating the Efficacy of Using Pre-differentiated and Enriched Mathematics Curricula for Grade 3 Students. Gifted Child Quarterly. https://doi.org/10.1177/0016986214547631
Kenny, D. A., *Kaniskan, B., & McCoach, D. B. (2014). How small is too small? The performance of RMSEA in models with small df. Sociological Research Methods. https://doi.org/10.1177/0049124114543236
*Rambo, K. & McCoach, D. B. (2014). Using summer growth patterns to assess the impact of schools on high achieving and gifted students’ reading skills. Journal of Educational Research. DOI: 10.1080/00220671.2013.850398
McCoach, D. B., Rambo, K., & Welsh, M. (2013). Assessing the growth of gifted students. Gifted Child Quarterly, 57, 56-67. https://doi.org/10.1177/0016986212463873
*Adelson, J. L., McCoach, D. B., & Gavin, M. K. (2012). Examining the effects of gifted programming in mathematics and reading using the ECLS-K. Gifted Child Quarterly, 56, 25-39. https://doi.org/10.1177/0016986211431487
McCoach, D. B., & Black, A. C. (2012). Introduction to estimation issues in multilevel modeling. In J. L. Lott and J. S. Antony (Eds.) Multilevel modeling techniques and applications in institutional research, 154. San Francisco, CA: Jossey Bass.
*Black, A. C., Harel, O., & McCoach, D. B. (2011). Missing data techniques for multilevel data: implications of model misspecification. Journal of Applied Statistics, DOI: 10.1080/02664763.2010.529882
McCoach, D. B., & Colbert, R. D. (2010). Factors underlying the Collective Teacher Efficacy Scale and their mediating role in the effect of socio-economic status on academic achievement at the school level. Measurement and Evaluation in Counseling and Development, 43, 31-47. https://doi.org/10.1177/0748175610362368
McCoach, D. B., Goldstein, J., Behuniak, P., Reis, S. M., Black, A. C., Rambo, K., & Sullivan, E. (2010). Examining the unexpected: Outlier analyses of factors affecting student achievement. Journal of Advanced Academics, 21, 426-268.
McCoach, D. B. & Kaniskan, B. (2010). Using time varying covariates in multilevel growth models. Frontiers in Quantitative Psychology and Measurement, 1:17. DOI: 10.3389/fpsyg.2010.00017
McCoach, D. B. (2010). Dealing with dependence (Part II): A gentle introduction to Hierarchical Linear Modeling. Gifted Child Quarterly, 54, 252-256. https://doi.org/10.1177/0016986210373475
McCoach, D. B. & Adelson, J. (2010). Dealing with dependence (Part I): Understanding the effects of Clustered Data. Gifted Child Quarterly, 54, 152-155. DOI: 10.1177/0016986210363076
Selected Book Chapters (see CV for full list)
McCoach, D. B. & Bell, B. A. (in press). Individual Growth Curve Models for Longitudinal Data. In A.A. O’Connell, D. B. McCoach, & B. A. Bell (Eds.) Multilevel Modeling Methods with Introductory and Advanced Applications. Information Age Press.
McCoach, D. B., Newton, S. D., & Gambino, A.* (in press). Evaluating the fit and adequacy of multilevel models. In A.A. O’Connell, D. B. McCoach, & B. A. Bell (Eds.) Multilevel Modeling Methods with Introductory and Advanced Applications. Information Age Press.
McCoach, D. B., Dineen, J. N., Chafouleas, S. M., & Briesh, A. (2020). Reproducibility in the era of Big Data: Lessons for developing robust data management and data analysis procedures. In C. A. Hill, P. P. Biemer, T. D. Buskirk, L. Japec, A. Kirchner, S. Kolenikov, & L. E. Lyberg (Eds.) Big Data Meets Survey Science: A Collection of Innovative Methods. John Wiley and Sons.
McCoach, D. B. (2018). Multilevel modeling. In G. R. Hancock & R. O. Mueller (Eds.) The reviewer’s guide to quantitative methods in the social sciences (Revised). New York: Routledge.
McCoach, D. B., & Rambo, K. (2018). Issues in the analysis of change. In C. Secolsky (Ed.) Handbook of measurement, assessment, and evaluation in higher education (Second edition).
McCoach, D. B. & Flake, J. (2017). Motivation of gifted students. In APA Handbook of Gifted Education and Talent Development. Washington, D.C.: APA.
McCoach, D. B. & Newton, S. D. (2017). Confirmatory Factor Analysis. In BERA-SAGE Handbook of Research Methods in Education.
O’Connell, A. A., Yeomans‐Maldonado, G., & McCoach, D. B. (2016.) Residual Diagnostics and Model Assessment in a Multilevel Framework: Recommendations toward Best Practice. In J. Harring, L. Stapleton, & T. Beretvas (Eds.) Advances in Multilevel Modeling for Educational Research. Charlotte, NC: Information Age.
McCoach, D. B. & Yu, H. H. (2016). Using individual growth curve models to understand reading fluency development. The Fluency Construct. New York. Springer.
|CV||D. Betsy McCoach|
|Mailing Address||249 Glenbrook Road, Unit 3064|
|Office Location||Gentry 339|
|Office Hours||By appointment|
|Courses||EPSY 6619: Advances in Latent Variable Modeling (Fall 2021)|
|Link||Modern Modeling Methods Conference|