Publications

Post-prints of all published papers can be found here

Underlined italics denote authors that were mentored students or post-docs when the work was completed

Accepted & In Press

1. McNeish, D. (in press). How do psychologists determine whether a measure is good? A quarter-century of scale validation with Hu & Bentler (1999). Annual Review of Psychology.

2. McNeish, D. (in press). Dynamic measurement invariance cutoffs for two-group fit index differences. Psychological Methods

3. McNeish, D. (in press). A practical guide to selecting (and blending) approaches for clustered data: Clustered errors, multilevel models, and fixed effect models. Psychological Methods 

4. McNeish, D. (in press). Dynamic fit index cutoffs for treating Likert items as continuous. Psychological Methods.

5. McNeish, D. (in press). Missing not at random intensive longitudinal data with dynamic structural equation models. Psychological Methods.

6. McNeish, D. & Savord, A. (in press). Exploring how many categories are needed to model ordinal intensive longitudinal data as continuous with dynamic structural equation models. Psychological Methods.  

7. Oesterle, S., McNeish, D., Guttmannova, K., Skinner, M., Kuklinski, M.R., & Hawkins, J.D. (in press). The interrelated association between young adults' legal and normative cannabis environments and their association with cannabis use. Journal of the Society for Social Work and Research.

8. Coffman, J.L., Westover, A.E., Grammer, J.K., McNeish, D., & Ornstein, P.A. (in press). Deliberate memory strategy development: The interplay of children’s self-regulated

learning abilities and teachers’ cognitive processing language. Journal of Applied Research in Memory and Cognition

2025

1.  McNeish, D. & Mackinnon, D.P. (2025). Intensive longitudinal mediation in Mplus. Psychological Methods, 30(2), 393-415.

2. McNeish, D. & Dumas, D.G. (2025). Reliability representativeness: How well does coefficient alpha summarize reliability across the score distribution? Behavior Research Methods, 57, 93.

3. McNeish, D. & Matta, T.H. (2025). SRMR for models with covariates. Psychometrika, 90 (1), 22-46.

4. McNeish, D. (2025). Less heuristic approximate local fit evaluation in structural equation models. Structural Equation Modeling, 32(4), 590-605.

5. Levy, R. & McNeish, D. (2025). Measurement and uncertainty preserving parametric modeling for continuous latent variables with discrete indicators and external variables. Journal of Educational and Behavioral Statistics, 50 (2), 239-271.

6. Plaitano, E.G., McNeish, D., Bartels, S., Bell, K., Daller, J., Garbinski, M., ... Marsch, L.A. (2025). Adherence to a digital therapeutic mediates the relationship between momentary self-regulation and health risk behaviors. Frontiers in Digital Health, 7, 1467772.

7. Liu, X. & McNeish, D. (2025). Optimal number of replications for obtaining stable dynamic fit index cutoffs. Educational and Psychological Measurement, 83(3), 539-564.

8. Ahn, L.H., Kivlighan, D.M., Hill, C.E., & McNeish, D. (2025). Dyadic working alliance, therapist insight skills, and client outcomes: Longitudinal mediation analyses. Psychotherapy, 62(2), 180-190.

2024

1. McNeish, D., Dumas, D., Dong, Y., & Duellberg, D. (2024). Promoting inclusive recruiting & selection into military training schools: Admission waivers versus retesting. Journal of Applied Psychology, 109 (3), 415-436.

2. McNeish, D. & Wolf, M.G. (2024). Direct discrepancy dynamic fit index cutoffs for arbitrary covariance structure models. Structural Equation Modeling, 31 (5), 835-862.  

3. McNeish, D. & Manapat, P.D. (2024). Dynamic fit index cutoffs for hierarchical and second-order factor models. Structural Equation Modeling, 31 (1), 27-47.

4. McNeish, D., Somers, J.A., & Savord, A. (2024). Dynamic structural equation models with binary and ordinal outcomes in Mplus. Behavior Research Methods, 56 (3), 1506-1532.

5. McNeish, D. (2024). Practical implications of sum scores being psychometrics’ greatest accomplishment. Psychometrika, 89 (4), 1148-1169.

2023

1. McNeish, D. (2023). Dynamic fit index cutoffs for categorical factor analysis with Likert-type, ordinal, or binary responses. American Psychologist, 79 (9), 1061-1075.

2. McNeish, D. & Wolf, M.G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods, 28(1), 61-88.

3. McNeish, D., Bauer, D.J., Dumas, D.G., Clements, D.H., Cohen, J.R., Lin, W., Sarama, J., & Sheridan, M.A. (2023). Modeling individual differences in the timing of change onset and offset. Psychological Methods, 28 (2), 401-421.

4. McNeish, D., Harring, J.R., & Bauer, D.J. (2023). Nonconvergence, covariance constraints, and class enumeration in growth mixture models. Psychological Methods, 28 (4), 962-992. 

5. McNeish, D. & Wolf, M.G. (2023). Dynamic fit index cutoffs for one-factor models. Behavior Research Methods, 55 (3), 1157-1174.

6. McNeish, D. (2023). Psychometric properties of sum scores and factor scores differ even when their correlation is 0.98: A response to Widaman and Revelle. Behavior Research Methods, 55 (8), 4269-4290.

7. McNeish, D., Peña, A., Vander Wyst, K.B., Ayers, S.L., Olson, M.L., & Shaibi, G.Q. (2023). Facilitating growth mixture model convergence in preventive interventions. Prevention Science, 24 (3), 505-516. 

8. McNeish, D. (2023). Generalizability of dynamic fit index, equivalence testing, and Hu & Bentler cutoffs for evaluating fit in factor analysis. Multivariate Behavioral Research, 58 (1), 195-219. 

9. McNeish, D., Harring, J.R., & Dumas, D. (2023). A multilevel structured latent curve model for disaggregating student and school contributions to learning. Statistical Methods and Applications, 32, 545-575.

10. Levy, R. & McNeish, D. (2023). Alternative perspectives on Bayesian inference and their implications for data analysis. Psychological Methods, 28 (3), 719-739.

11. Savord, A., McNeish, D., Iida, M., Quiroz, S., & Ha, T. (2023). Fitting the longitudinal actor-partner interdependence model as a dynamic structural equation model. Structural Equation Modeling, 30 (2), 296-314.

12. Wolf, M.G. & McNeish, D. (2023). dynamic: An R package for deriving dynamic fit index cutoffs for factor analysis. Multivariate Behavioral Research, 58 (1), 189-194.

13. Dumas, D., Dong, Y., & McNeish, D. (2023). How fair is my test?: A ratio coefficient to help represent consequential validity. European Journal of Psychological Assessment, 39 (6), 416-423. 

14. West, S.G., Wu, W., McNeish, D., & Savord, A. (2023). Model fit in structural equation modeling. In R.H. Hoyle (Ed.), Handbook of Structural Equation Modeling (2nd Ed.) Guilford Press, pp. 184-205.

15. Pandika, D., Guttmannova, K., Skinner, M.L., Sanchez-Rodriguez, M., McNeish, D., Morales, L.S., & Oeserle, S. (2023). Tobacco use patterns from adolescence to young adulthood among Latinx youth from rural communities. Journal of Adolescent Health, 73 (4), 761-768.

16. Perez, M., Winstone, L.K., Hernandez, J.C., Curci, S.G., McNeish, D., & Luecken, L.J. (2023). Association of BMI trajectories with cardiometabolic risk at age 7.5 years among low-income Mexican American children. Pediatric Research, 93 (5), 1233-1238.

17. English, D., Smith, J.C., Scott-Walker, L., Lopez, F.G., Morris, M., Reid, M., … McNeish, D. (2023). Feasibility, acceptability, and preliminary HIV Care and psychological health effects of THRIVE 365. JAIDS: Journal of Acquired Immunodeficiency Syndrome, 93 (1), 55-63.

2022

1. McNeish, D. & Bauer, D.J. (2022). Reducing incidence of nonpositive definite covariance matrices in mixed effect models. Multivariate Behavioral Research, 57 (2-3), 318-340.

2. McNeish, D. (2022). Limitations of the sum-and-alpha approach to measurement in behavioral research. Policy Insights from the Brain and Behavioral Sciences, 9 (2), 196-203.

3. McNeish, D., Dumas, D., Torre, D., & Rice, N. (2022). Modelling time to maximum competency in medical student progress tests.  Journal of the Royal Statistical Society, Series A, 185 (4), 2007-2034.

4. Blake, A. J., McNeish, D., & Chassin, L. (2022). Heterogeneity in effects of parent–child separation on young–adult substance use disorder. Journal of Family Psychology, 36(2), 159–169.

5. Somers, J.A., Luecken, L.J., McNeish, D., Lemery-Chalfant, K., & Spinrad, T.L. (in press). Second-by-second infant and mother emotion regulation and coregulation processes. Development and Psychopathology, 34 (5), 1887-1900.

6. Roberts, G. J., Dumas, D. G., McNeish, D., & Cote, B. (2022). Understanding the dynamics of reading intervention dosage response: A nonlinear meta-analysis. Review of Educational Research, 92(2), 209-248.

7. Aitken, A.A., Graham, S., & McNeish, D. (2022). The effects of choice vs preference on writing and the mediating role of perceived competence. Journal of Educational Psychology, 114 (8), 1844-1865. 

8. Cole, V.T., Hussong, A.M., McNeish, D., Ennett, S.T., Rothenberg, W.A., Gottfredson, N.C., & Faris, R.W. (2022). The role of social position within peer groups in distress-motivated smoking among adolescents. Journal of Studies on Alcohol and Drugs, 83, 420-429.

9. Wolf, M.G. & McNeish, D. (2022). dynamic: DFI cutoffs for latent variables models (version 1.1.0). [Software]. Available from CRAN, https://cran.r-project.org/web/packages/dynamic    

2021

1. McNeish, D. & Dumas, D. (2021). A seasonal dynamic measurement model for summer learning loss. Journal of the Royal Statistical Society, Series A, 184, 616-642.

2. McNeish, D. & Harring, J.R. (2021). Improving convergence in growth mixture models without covariance structure constraints. Statistical Methods in Medical Research, 30, 994-1012.

3. McNeish, D. (2021). Specifying location-scale models for heterogeneous variances as multilevel SEMs. Organizational Research Methods, 24, 630-653. 

4. McNeish, D., Mackinnon, D.P., Marsch, L.A., & Poldrack, R.A. (2021). Measurement in intensive longitudinal data. Structural Equation Modeling, 28, 807-822. 

5. Silverman, R.D., McNeish, D., Ritchey, K.D., & Speece, D.L. (2021). Early screening for decoding and language-related reading difficulties in 1st and 3rd grade. Assessment for Effective Intervention, 46, 99-109.

2020

1. McNeish, D. & Wolf, M.G. (2020). Thinking twice about sum scores. Behavior Research Methods, 52, 2287-2305. 

2. McNeish, D. & Hamaker, E.L. (2020). A primer on two-level dynamic structural equation modeling for intensive longitudinal data in Mplus. Psychological Methods, 25, 610-635. 

3. McNeish, D. & Harring, J.R. (2020). Covariance pattern mixture models: Eliminating random effects to improve convergence and performance. Behavior Research Methods, 52, 947-979.

4. McNeish, D., Dumas, D.G., & Grimm, K.J. (2020). Estimating new quantities from longitudinal test scores to improve forecasts of future performance. Multivariate Behavioral Research, 55, 894-909.

5. McNeish, D. (2020). Should we use F-tests for model fit instead of chi-square in over-identified structural equation models?. Organizational Research Methods, 23, 487-510.

6. McNeish, D. & Matta, T.H. (2020). Flexible treatment of time-varying covariates with time unstructured data. Structural Equation Modeling, 27, 298-317.

7. McNeish, D. (2020). Relaxing the proportionality assumption in latent basis models for nonlinear growth. Structural Equation Modeling, 27, 817-824

8. Smid, S.C., McNeish, D., Miočević, M., & van de Schoot, A.G.J. (2020). Bayesian versus frequentist estimation for structural equation models in small sample contexts: A systematic review. Structural Equation Modeling, 27, 131-161. 

9. Dumas, D.G., McNeish, D., & Greene, J.A. (2020). Dynamic measurement: A theoretical-psychometric paradigm for modern educational psychology. Educational Psychologist, 55, 88-105.

10. Peña, A., McNeish, D., Ayers, S.L., Olson, M.L., Vander Wyst, K.B., Williams, A.N., & Shaibi, G.Q. (2020). Response heterogeneity to lifestyle intervention among Latino adolescents. Pediatric Diabetes, 21, 1430-1436.

11. Hussong, A.M., Ennett, S.T., McNeish, D., Cole, V., Gottfredson, N., Rothenberg, W.A., & Farris, R.J. (2020). Social network isolation mediates associations between risky symptoms and substance use in the high school transition. Development and Psychopathology, 32, 615-630.

12. Somers, J.A., Kerr, M.L., McNeish, D., Smiley, P.A., Buttitta, K.V., Rasmussen, H.F., & Borelli, J.L. (2020). Quantitatively representing the dynamics of daily emotion: attachment-based differences in mothers’ emotion. Journal of Family Psychology, 34, 480-489.

13. Hox, J. J., & McNeish, D. (2020). Small samples in multilevel modeling. In R. Van de Schoot & M. Miočević (Eds.), Small sample size solutions: A guide for applied researchers and practitioners, Routledge, pp. 215-225.

14. Bauer, D.J., McNeish, D., Baldwin, S.A., & Curran, P.J. (2020). Analyzing nested data: Multilevel modeling and alternative approaches. In A. Wright & M. Hallquist (Eds.), Handbook of research methods in clinical psychology,  Cambridge University Press, pp. 426-443.

15. Matta, T.H. & McNeish, D. (2020). glvmfit: Methods to assess generalized latent variable model fit (version 0.0.0). [Software]. Available from https://cran.r-project.org/web/packages/glvmfit

16. Wolf, M. G. & McNeish, D. (2020). Dynamic Model Fit (version 1.1.0.). [Software]. Available from www.dynamicfit.app

2019

1. McNeish, D. (2019). Poisson multilevel models with small samples. Multivariate Behavioral Research, 54, 444-455.

2. McNeish, D. & Kelley, K. (2019). Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations. Psychological Methods, 24, 20-35. 

3. McNeish, D. & Dumas, D.G. (2019). Scoring repeated standardized tests to estimate capacity, not just current ability. Policy Insights from the Brain and Behavioral Sciences, 6, 218-224. [invited paper]. 

4. McNeish, D. (2019). Two-level dynamic structural equation models with small samples. Structural Equation Modeling, 26, 948-966.

5. McNeish, D. (2019). Effect partitioning in cross-sectionally clustered data without multilevel models. Multivariate Behavioral Research, 54, 906-925.

6. Dumas, D., McNeish, D., Schreiber-Gregory, D., Durning, S.J., & Torre, D.M. (2019). Dynamic measurement in health professions education: Rationale, application, and possibilities. Academic Medicine, 94, 1323-1398. 

7. Dumas, D., McNeish, D., Sarama, J., & Clements, D. (2019). Pre-school mathematics intervention can significantly improve student learning trajectories through elementary school. AERA Open, 5(4), 1-15. 

8. Silverman, R.D., Artzi, L., McNeish, D., Hartranft, A., Martin-Beltran, M., & Peercy, M. (2019).  The relationship between media type and vocabulary learning in a cross age peer-learning program for linguistically diverse elementary school students. Contemporary Educational Psychology, 56, 106-116.

9. Wentzel, K., Tomback, R., Williams, A., & McNeish, D. (2019). Perceptions of competence, control, and belongingness over the transition to high school: A mixed-method study. Contemporary Educational Psychology, 56, 55-66. 

10. McNeish, D., Lane, S., & Curran, P.J., (2019). Monte Carlo simulation studies. In G.R. Hancock, R.O. Mueller, & L.M. Stapleton (Eds.), The reviewer’s guide to quantitative methods in the social sciences, pp. 269-276. 

2018

1. McNeish, D. (2018). Thanks coefficient alpha, we'll take it from here. Psychological Methods, 23, 412-433.

2. McNeish, D., An, J., & Hancock, G.R. (2018). Illustrating the problematic relation between measurement quality and fit index cut-offs. Journal of Personality Assessment, 100, 43-52.

3. McNeish, D. & Hancock, G.R. (2018). The effect of measurement quality on targeted structural model fit indices: A comment on Lance, Beck, Fan, and Carter (2016). Psychological Methods, 23, 184-190.

4. McNeish, D. (2018). Applying Kaplan-Meier to item response data. Journal of Experimental Education, 86, 308-324.

5. McNeish, D. & Matta, T. (2018). Differentiating between mixed effects and latent curve approaches to growth modeling. Behavior Research Methods, 50, 1398-1414.

6. McNeish, D. & Dumas, D.G. (2018). Calculating conditional reliability for dynamic measurement model capacity estimates. Journal of Educational Measurement, 55, 614-634. 

7. McNeish, D. (2018). Growth models with small samples and missing data. Journal of Experimental Education, 86, 690-701.

8. Dumas, D.G. & McNeish, D. (2018). Increasing the consequential validity of reading assessment using dynamic measurement modeling. Educational Researcher, 47, 612-614.

9. Wentzel, K., Muenks, K.M., McNeish, D., & Russell, S. (2018). Emotional support, social goals, and classroom behavior: A multi-level multi-site study. Journal of Educational Psychology, 110, 611-627.

10. Hussong, A.M., Ennett, S.T., McNeish, D., Rothenburg, W.A., Cole, V., Gottfredson, N.C., & Faris, R.W. (2018). Teen social networks and depression-substance use associations: Developmental and demographic variation. Journal of Studies on Alcohol and Drug Use, 79, 770-780.

2017

1. McNeish, D. (2017). Challenging conventional wisdom for multivariate statistical models with small samples. Review of Educational Research, 87, 1117-1151.

2. McNeish, D. (2017). Small sample methods for multilevel modeling: A colloquial elucidation of REML and the Kenward-Roger correction. Multivariate Behavioral Research, 52, 661-670.

3. McNeish, D. (2017). Multilevel mediation with few clusters: A cautionary note on the multilevel structural equation modeling framework. Structural Equation Modeling, 24, 609-625.

4. McNeish, D. & Wentzel, K.R. (2017). Accommodating small sample sizes in three level models when the third level is incidental. Multivariate Behavioral Research, 52, 200-215. 

5. McNeish, D. & Dumas, D. (2017). Non-linear growth models as measurement models: A second-order growth curve model for measuring potential. Multivariate Behavioral Research, 52, 61-85.

6. McNeish, D. (2017). Exploratory factor analysis with small samples and missing data. Journal of Personality Assessment, 99, 637-652.

7. McNeish, D. & Harring, J.R. (2017). Correcting model fit criteria for small sample latent growth models with incomplete data. Educational and Psychological Measurement, 77, 990-1018.

8. McNeish, D. & Harring, J.R. (2017).  Model misspecification in growth mixture models. Journal of Classification, 34, 223-248.

9. McNeish, D. (2017). Missing data methods for arbitrary missingness with small samples. Journal of Applied Statistics, 44, 24-39. 

10. McNeish, D., Stapleton, L.M., & Silverman, R.D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22, 114-140.

11. McNeish, D. & Harring, J.R. (2017). Clustered data with small sample sizes: Comparing the performance of model-based and design-based approaches. Communications in Statistics: Simulation and Computation, 46, 855-869.

12. McNeish, D. (2017). Fitting residual error structures for growth models in SAS PROC MCMC. Educational and Psychological Measurement, 77, 587-612.

13. Harring, J.R., McNeish, D., & Hancock, G.R. (2017). Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification. Psychological Methods, 22, 616-631.

14. Hancock, G.R. & McNeish, D. (2017). More powerful tests of simple interaction contrasts for the two way factorial design. Journal of Experimental Education, 85, 24-35.

15. Dumas, D. & McNeish, D. (2017). Dynamic measurement modeling: Using nonlinear growth models to estimate student learning capacity. Educational Researcher, 46, 284-292.

16.  Wentzel, K.R., Muenks, K., McNeish, D., & Russell, S.L. (2017) Peer and teacher supports in relation to motivation and engagement: A multi-level study. Contemporary Educational Psychology, 49, 32-45.

17. Elzakkers, I.F.F.M., Danner, U.N., Sternheim, L.C., McNeish, D., Hoek, H.W., & Elburg, A.A. (2017). Mental capacity to consent to treatment and the association with outcome – a longitudinal study in anorexia nervosa patients. British Journal of Psychiatry Open, 3, 147-153.

18. Silverman, R.D., Kim, Y., Hartranft, A.M., Nunn, S.J., & McNeish, D. (2017). Effects of a multimedia enhanced reading buddies program in kindergarten and fourth grade. Journal of Educational Research, 110, 391-404.

19. Silverman, R.D., Martin-Beltran, M., Peercy, M.M., Hartranft, A.M., McNeish, D., Artzi, L., & Nunn, S.G. (2017).  Effects of a Cross-Age Peer Learning Program on the Vocabulary and Comprehension of ELs and Non-ELs in Elementary School.  Elementary School Journal, 117, 485-512.

2016

1. McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling, 23, 750-773. 

2. McNeish, D. & Stapleton, L.M. (2016). Modeling clustered data with very few clusters. Multivariate Behavioral Research. 51, 495-518.

3. McNeish, D. (2016). Estimation methods for mixed logistic models with small sample sizes. Multivariate Behavioral Research, 51, 790-804.

4. McNeish, D. & Stapleton, L.M. (2016). The effect of small sample size on two level model estimates: A review and illustration. Educational Psychology Review, 28, 295- 314. 

5. McNeish, D. (2016). Using data-dependent priors to mitigate small sample size bias in latent growth models: A discussion and illustration using Mplus. Journal of Educational and Behavioral Statistics. 41, 27-56.

6. Stapleton, L.M., McNeish, D., & Yang, J.S. (2016). Multi-level and single-level models for measured and latent variables when data are clustered. Educational Psychologist, 51, 317-330.

7. Kang, Y., McNeish, D., & Hancock, G.R. (2016). The role of measurement quality on practical guidelines for assessing measurement and structural invariance. Educational and Psychological Measurement. 76, 533-561.

8. Harring, J. R., McNeish, D., & Zhu, X. (2016). On the adequacy of SEM model fit criteria to detect cohort effects in accelerated longitudinal designs. Technical report. University of Maryland, College Park.

9. Peters, G.J. & McNeish, D. (2016). scaleStructure: scaleStructure (version 0.5-2) [Software]. Available from  https://cran.r-project.org/web/packages/userfriendlyscience

10. McNeish, D., Radunzel, J., & Sanchez, E. I. (2016). Adjusted differences in ACT® scores by race/ethnicity.  ACT Data Byte, 2016-7.

11. McNeish, D., Radunzel, J., & Sanchez, E. I. (2016). Adjusted differences in ACT® scores by parental education level. ACT Data Byte, 2016-6.

12. McNeish, D., Radunzel, J., & Sanchez, E. I. (2016). Adjusted differences in ACT® scores by family income. ACT Data Byte, 2016-5.

13. McNeish, D., Radunzel, J., & Sanchez, E. I. (2016). Relating student and school characteristics to performance on the ACT®. ACT Data Byte, 2016-4.

2015 & Earlier

1. McNeish, D., & Dumas, D. (2015). A second-order model for understanding potential [ABSTRACT]. Multivariate Behavioral Research, 50, 727. 

2. McNeish, D. (2015). Using Lasso for predictor selection and to assuage overfitting: A method long overlooked in behavioral sciences. Multivariate Behavioral Research, 50, 474-481.

3. McNeish, D., Radunzel, J., & Sanchez, E.I. (October, 2015). A multidimensional perspective of college readiness: Relating student and school characteristics to performance on the ACT. ACT Research Report Series, RR2015-6. 

4. McNeish, D. (2014). Modeling sparsely clustered data: Design-based, model-based, and single-level methods. Psychological Methods, 19, 552-563.

5. McNeish, D. (2014).  Analyzing clustered data with OLS regression: The effect of a hierarchical data structure. Multiple Linear Regression Viewpoints, 40, 11-16.

6. Stemler, S.E., Elliott, J.G., McNeish, D., Grigorenko, E.L.,& Sternberg, R.J. (2012). Examining the construct and cross-cultural validity of the Teaching Excellence Rating Scale (TERS). The International Journal of Educational and Psychological Assessment, 9, 121-138. 

Papers Under Review

Under Revision

1. Miller, A.H., Espinas, D.R., McNeish, D., & Barnes, M.A. (under review). Dosage response in intensive math interventions for early elementary students with or at-risk for mathematics difficulties. 

Under First Review

1. McNeish, D. (under review). A primer on intensive longitudinal psychometrics. Behavior Research Methods.