Publications
Vector Psychometric Group, LLC
References in support of capabilities
Vector Psychometric Group’s research, consulting, and publishing experience cover a range of topics focused on advancing psychometric methods and practice. We believe psychometrically sound PRO development, evaluation, and validation requires an in depth understanding of standard and advanced item factor models (factor analysis, structural equation models, and item response theory), estimation methods used to obtain model parameters, tests of model assumptions and fit, understanding of how to model IRT-based scores, experiences writing results for a diverse audience, and the ability to collaborate with a wide range of researchers. While you can obtain a complete list of publications by request, we offer a few selected readings for each of the topics we believe are most important.
Standard and advanced item factor models:
Bollen, K.A., Bauer, D.J., Christ, S.L., & Edwards, M.C. (2010). Overview of structural equation models and recent extensions. In S. Kolenikov, D. Steinley, & L. Thombs (Eds.), Statistics in the social sciences: Current methodological developments (pp. 37-79). Hoboken, NJ: Wiley.
Cai, L. (2010c). A two-tier full-information item factor analysis model with applications. Psychometrika, 75, 581–612.
Cai, L., Yang, J. S. & Hansen, M. (2011). Generalized full-information item bifactor analysis. Psychological Methods, 16, 221–248.
Edwards, M.C., Flora, D.B., Thissen, D. (2012). Multi-stage computerized adaptive testing with uniform item exposure. Applied Measurement in Education, 25, 118-141.
Edwards, M. C., & Wirth, R. J., Houts, C. R., & Xi, N. (2013). Categorical data in the structural equation modeling framework. In R. H. Hoyle (Ed.), Handbook of structural equation modeling. New York: Guilford Press.
MacCallum, R.C., Edwards, M.C., & Cai, L. (2012). Hopes and cautions in implementing Bayesian structural equation modeling. Psychological Methods, 17, 340-345.
Thissen, D., Cai, L., & Bock, R. D. (2010). The nominal categories item response model. In M. L. Nering & R. Ostini (Eds.), Handbook of polytomous item response theory models: Development and applications (pp. 43–75). New York, NY: Taylor & Francis.
Estimation methods:
Cai, L. (2008). SEM of another flavor: Two new applications of the supplemented EM algorithm. British Journal of Mathematical and Statistical Psychology, 61, 309–329.
Cai, L. (2010a). High-dimensional exploratory item factor analysis by Metroplis-Hastings RobbinsMonro algorithm. Psychometrika, 75, 33–57.
Cai, L. (2010b). Metropolis-Hastings Robbins-Monro algorithm for confirmatory item factor analysis. Journal of Educational and Behavioral Statistics, 35, 307–335.
Edwards, M.C. (2010). A Markov chain Monte Carlo approach to confirmatory item factor analysis. Psychometrika, 75, 474-497.
Moustaki, I., & Cai, L. (in press). Estimation methods in latent variable models for categorical outcome variables. In P. Irwing, T. Booth & D. Hughes (Eds.), The Wiley-Blackwell Handbook of Psychometric Testing. West Sussex, UK: John Wiley & Sons, Ltd.
Paek, I., & Cai, L. (2014). A comparison of item parameter standard error estimation procedures for unidimensional and multidimensional IRT modeling. Educational and Psychological Measurement, 74, 58–76.
Wirth, R. J., & Edwards, M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12, 58 – 79.
Tests of model assumptions and fit:
Cai, L., & Hansen, M. (2013). Limited-information goodness-of-fit testing of hierarchical item factor models. British Journal of Mathematical and Statistical Psychology, 66, 245–276
