structural equation models, latent growth models, latent variable experimental design and analysis, power analysis

Professor and Program Director, Quantitative Methodology: Measurement and Statistics
Director, Center for Integrated Latent Variable Research (CILVR)
Affiliated Professor, Center for Advanced Study of Language, 含羞草研究所

Gregory R. Hancock is Professor, Distinguished Scholar-Teacher, and Director of the Quantitative Methodology: Measurement and Statistics program in the Department of Human Development and Quantitative Methodology at the 含羞草研究所, College Park, and Director of the Center for Integrated Latent Variable Research (CILVR). His research interests include structural equation modeling and latent growth models, the use of latent variables in (quasi)experimental design, and power analysis. His research has appeared in such journals as PsychometrikaMultivariate Behavioral ResearchStructural Equation Modeling: A Multidisciplinary JournalPsychological Bulletin, Psychological MethodsBritish Journal of Mathematical and Statistical PsychologyJournal of Educational and Behavioral StatisticsEducational and Psychological MeasurementReview of Educational Research, and Communications in Statistics: Simulation and Computation. He also co-edited the volumes Structural Equation Modeling: A Second Course (2006; 2013), The Reviewer's Guide to Quantitative Methods in the Social Sciences (2010; 2019), Advances in Latent Variable Mixture Models (2008), and Advances in Longitudinal Methods in the Social and Behavioral Sciences (2012). He is past chair of the SEM special interest group of the American Educational Research Association (three terms), serves on the editorial board of a number of journals including Multivariate Behavioral Research,  Psychological Methods, and Structural Equation Modeling: A Multidisciplinary Journal, and has taught over 200 methodological workshops in the United States, Canada, and abroad. He is a Fellow of the American Educational Research Association, the American Psychological Association, and the Association for Psychological Science, and received the 2011 Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring by the American Psychological Association. 

Award for Excellence in Faculty Mentorship, 2023
College of Education 
University of Maryland, College Park

Distinguished Service Award, 2015
Structural Equation Modeling Special Interest Group
American Educational Research Association

Fellow, 2014 
American Psychological Association 

Fellow, 2014 
Association for Psychological Science

Member (elected), 2014- 
Society of Multivariate Experimental Psychology

Distinguished Scholar-Teacher, 2013-2014 
University of Maryland, College Park

Distinguished Graduate Alumnus, 2012 
College of Education 
University of Washington, Seattle

Award for Outstanding Scholarship, 2012
College of Education 
University of Maryland, College Park

Award for Outstanding Contributions, 2012
Educational Statisticians Special Interest Group
American Educational Research Association

Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring, 2011
American Psychological Association, Division 5

Graduate Faculty Mentor of the Year Award, 2011
Graduate School
含羞草研究所, College Park

Fellow, 2010
American Educational Research Association

Outstanding Teacher Award Recipient, 2004
College of Education 
University of Maryland, College Park

Outstanding Graduate Professor Award nominee, 2002
Graduate Student Government 
University of Maryland, College Park

Outstanding Graduate Mentor Award recipient, 1999
Graduate Student Government 
University of Maryland, College Park

BOOKS

Hancock, G. R., & Mueller, R. O. (Eds.). (2013). Structural equation modeling: A second course (2nd ed.). Charlotte, NC: Information Age Publishing, Inc.

Harring, J. R., & Hancock, G. R. (Eds.). (2012). Advances in longitudinal methods in the social and behavioral sciences. Charlotte, NC: Information Age Publishing, Inc.

Hancock, G. R., & Mueller, R. O. (Eds.). (2010). The reviewer's guide to quantitative methods in the social sciences. New York: Routledge.

Hancock, G. R., Stapleton, L. M., & Mueller, R. O. (Eds.). (2019). The reviewer's guide to quantitative methods in the social sciences (2nd ed.). New York: Routledge.

Hancock, G. R., & Samuelsen, K. M. (Eds.). (2008). Advances in latent variable mixture models. Charlotte, NC: Information Age Publishing, Inc.

Hancock, G. R., & Mueller, R. O. (Eds.). (2006). Structural equation modeling: A second course. Greenwich, CT: Information Age Publishing, Inc.

 

BOOK CHAPTERS / ENCYCLOPEDIA ENTRIES

Hancock, G. R. (in press). Convergence. In B. Frey (Ed.), The SAGE encyclopedia of 含羞草研究所al research, measurement, and evaluation. Thousand Okas, CA: SAGE Publications.

Mueller, R. O., & Hancock, G. R. (2015). Factor analysis and latent structure: Confirmatory factor analysis. In J. D. Wright (Ed.), International encyclopedia of the social and behavioral sciences (2nd ed.) (pp. 686-690). Oxford, England: Pergamon.

Hancock, G. R., & French, B. F. (2013). Power analysis in covariance structure models. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed.) (pp. 117-159). Charlotte, NC: Information Age Publishing, Inc.

Hancock, G. R., Harring, J. R., & Lawrence, F. R. (2013). Using latent growth models to evaluate longitudinal change. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed.) (pp. 309- 341). Charlotte, NC: Information Age Publishing, Inc.

Preacher, K. J., & Hancock, G. R. (2012). On interpretable reparameterizations of linear and nonlinear latent growth curve models. In J. R. Harring & G. R. Hancock (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 25-58). Charlotte, NC: Information Age Publishing, Inc.

Hancock, G. R., & Liu, M. (2012). Bootstrapping standard errors and data-model fit statistics. In R. Hoyle (Ed.), Handbook of structural equation modeling (pp. 296-306). New York: Guilford Press.

Mueller, R. O., & Hancock, G. R. (2010). Structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer's guide to quantitative methods in the social sciences (pp. 371-383). New York: Routledge.

Hancock, G. R., Stapleton, L. M., & Arnold-Berkovits, I. (2009). The tenuousness of invariance tests within multisample covariance and mean structure models. In T. Teo & M. S. Khine (Eds.), Structural equation modeling: Concepts and applications in 含羞草研究所al research (pp. 137-174). Rotterdam, Netherlands: Sense Publishers.

Mueller, R. O., & Hancock, G. R. (2008). Best practices in structural equation modeling. In J. W. Osborne (Ed.), Best practices in quantitative methods. Thousand Oaks, CA: Sage Publications, Inc.

Hancock, G. R. (2006). Power analysis in covariance structure models. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 69-115). Greenwood, CT: Information Age Publishing, Inc.

Hancock, G. R., & Lawrence, F. R. (2006). Using latent growth models to evaluate longitudinal change. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 171-196). Greenwood, CT: Information Age Publishing, Inc.

Hancock, G. R. (2004). Experimental, quasi-experimental, and nonexperimental design and analysis with latent variables. In D. Kaplan (Ed.), The SAGE handbook of quantitative methodology for the social sciences (pp. 317-334) Thousand Oaks, CA: SAGE Publications.

Hancock, G. R. (2004). Errors (Type I and II). In W. E. Craighead & C. B. Nemeroff (Eds.), The concise Corsini encyclopedia of psychology and behavioral science (3rd ed.). New York: John Wiley & Sons, Inc.

Hancock, G. R., & Mueller, R. O. (2003). Path Analysis. In M. Lewis-Beck, A. Bryman, & T. F. Liao (Eds.), SAGE encyclopedia of social science research methods. Thousand Oaks, CA: SAGE Publications.

Hancock, G. R. (2001). Errors (Type I and II). In W. E. Craighead & C. B. Nemeroff (Eds.), Encyclopedia of psychology and neuroscience. New York: John Wiley & Sons, Inc.

Hancock, G. R., & Mueller, R. O. (2001). Rethinking construct reliability within latent variable systems. In R. Cudeck, S. du Toit, & D. S枚rbom (Eds.), Structural equation modeling: Present and future 鈥 A Festschrift in honor of Karl J枚reskog (pp. 195-216). Lincolnwood, IL: Scientific Software International, Inc.

Mueller, R. O., & Hancock, G. R. (2001). Factor analysis and latent structure: Confirmatory factor analysis. In N. J. Smelser & P. B. Baltes (Eds.), International encyclopedia of the social and behavioral sciences. Oxford, England: Pergamon.

 

METHODOLOGICAL ARTICLES

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

Liu, M., Harbaugh, A. G., Harring, J. R., Hancock, G. R. (in press). The effect of extreme response and non-extreme response styles on testing measurement invariance. Frontiers in Psychology (Quantitative Psychology and Measurement section).

Harring, J. R., McNeish, D. M., & Hancock, G. R. (in press). Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification. Psychological Methods.

Kang, Y., & Hancock, G. R. (in press). The effect of scale referent on tests of mean structure parameters. Journal of Experimental Education.

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

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

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

Rhemtulla, M., & Hancock, G. R. (2016). Planned missing data designs in 含羞草研究所al psychology research. Educational Psychologist, 51, 305-316.

Stapleton, L. M., Yang, J. S., & Hancock, G. R. (2016). Construct meaning in multilevel settings. Journal of Educational and Behavioral Statistics, 41, 481-520.

Hancock, G. R., & Schoonen, R. (2015). Structural equation modeling: Possibilities for language learning researchers. Language Learning, 65, 158-182.

Preacher, K. J., & Hancock, G. R. (2015). Meaningful aspects of change as novel random coefficients: A general method for reparameterizing longitudinal models. Psychological Methods, 20, 84-101.

Mao, X., Harring, J. R., & Hancock, G. R. (2015). A note on the specification of error structures in latent interaction models. Educational and Psychological Measurement, 75, 5-21.

Liu, M., & Hancock, G. R. (2014). Unrestricted mixture models for class identification in growth mixture modeling. Educational and Psychological Measurement, 74, 557-584.

Kohli, N., Harring, J. R., & Hancock, G. R. (2013). Piecewise linear-linear latent growth mixture models with unknown knots. Educational and Psychological Measurement, 73, 935-955.

Hancock, G. R., Mao, X., & Kher, H. (2013). On latent growth models for composites and their constituents. Multivariate Behavioral Research, 48, 619-638.

Fan, W., & Hancock, G. R. (2012). Robust means modeling: An alternative to hypothesis testing of independent means under variance heterogeneity and nonnormality. Journal of Educational and Behavioral Statistics, 37, 137- 156.

Hancock, G. R., & Mueller, R. O. (2011). The reliability paradox in assessing structural relations within covariance structure models. Educational and Psychological Measurement, 71, 306-324.

Liu, M., Hancock, G. R., & Harring, J. R. (2011). Using finite mixture modeling to deal with systematic measurement error: A case study. Journal of Modern Applied Statistical Methods, 10, 249-261.

Levy, R., & Hancock, G. R. (2011). An extended model comparison framework for covariance and mean structure models, accommodating multiple groups and latent mixtures. Sociological Methods and Research, 40, 256-278.

Koran, J., & Hancock, G. R. (2010). Using fixed thresholds with grouped data in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 17, 590-604.

Choi, J., Harring, J. R., & Hancock, G. R. (2009). Latent growth modeling for logistic response functions. Multivariate Behavioral Research, 44, 620-645.

Mann, H. M., Rutstein, D. W., & Hancock, G. R. (2009). The potential for differential findings among invariance testing strategies for multisample measured variable path models. Educational and Psychological Measurement, 69, 603-612.

Choi, J., Fan, W., & Hancock, G. R. (2009). A note on confidence intervals for two-group latent mean effect size measures. Multivariate Behavioral Research, 44, 396-406.

Hancock, G. R. (2009). Diagnostic classification modeling: Opportunity for identity. Measurement: Interdisciplinary Research and Perspectives, 7, 62- 64.

Hancock, G. R., & Buehl, M. M. (2008). Second-order latent growth models with shifting indicators. Journal of Modern Applied Statistical Methods, 7, 39-55.

Hancock, G. R. (2007). Models for illuminating things otherwise unseen: Co-editor's introduction. Contemporary Educational Psychology, 32, 4-7.

Levy, R., & Hancock, G. R. (2007). A framework of statistical tests for comparing mean and covariance structure models. Multivariate Behavioral Research, 42, 33-66.

Hancock, G. R., & Choi, J. (2006). A vernacular for linear latent growth models. Structural Equation Modeling: A Multidisciplinary Journal, 13, 352-377.

Fan, W., & Hancock, G. R. (2006). Impact of post hoc measurement model over- specification on structural parameter integrity. Educational and Psychological Measurement, 66, 748-764.

Gagn茅, P. E., & Hancock, G. R. (2006). Measurement model quality, sample size, and solution propriety in confirmatory factor models. Multivariate Behavioral Research, 41, 65-83.

Raykov, T., & Hancock, G. R. (2005). Examining change in maximal reliability for multiple-component measuring instruments. British Journal of Mathematical and Statistical Psychology, 58, 65-82.

Nevitt, J., & Hancock, G. R. (2004). Evaluating small sample approaches for model test statistics in structural equation modeling. Multivariate Behavioral Research, 39, 439-478.

Hancock, G. R. (2003). Fortune cookies, measurement error, and experimental design. Journal of Modern Applied Statistical Methods, 2, 293-305.

Hancock, G. R. (2001). Effect size, power, and sample size determination for structured means modeling and MIMIC approaches to between-groups hypothesis testing of means on a single latent construct. Psychometrika, 66, 373-388.

Hancock, G. R., & Freeman, M. J. (2001). Power and sample size for the RMSEA test of not close fit in structural equation modeling. Educational and Psychological Measurement, 61, 741-758.

Nevitt, J., & Hancock, G. R. (2001). Performance of bootstrapping approaches to model test statistics and parameter standard error estimation in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 8, 353-377.

Hancock, G. R., Kuo, W., & Lawrence, F. R. (2001). An illustration of second- order latent growth models. Structural Equation Modeling: A Multidisciplinary Journal, 8, 470-489.

Berkovits, I., Hancock, G. R., & Nevitt, J. (2000). Bootstrap resampling approaches for repeated measure designs: Relative robustness to sphericity and normality violations. Educational and Psychological Measurement, 60, 877-892.

Hancock, G. R., Lawrence, F. R., & Nevitt, J. (2000). Type I error and power of latent mean methods and MANOVA in factorially invariant and noninvariant latent variable systems. Structural Equation Modeling: A Multidisciplinary Journal, 7, 534-556.

Klockars, A. J., & Hancock, G. R. (2000). Scheff茅鈥檚 more powerful F-protected post hoc procedure. Journal of Educational and Behavioral Statistics, 25, 13-19.

Nevitt, J., & Hancock, G. R. (2000). Improving the Root Mean Square Error of Approximation for nonnormal conditions in structural equation modeling. Journal of Experimental Education, 68, 251-268.

Hancock, G. R., & Nevitt, J. (1999). Bootstrapping and identification of exogenous latent variables within structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 6, 394-399.

Lawrence, F. R., & Hancock, G. R. (1999). Conditions affecting integrity of a factor solution under varying degrees of overextraction. Educational and Psychological Measurement, 59, 549-579.

Hancock, G. R. (1999). A sequential Scheff茅-type respecification procedure for controlling Type I error in exploratory structural equation model modification. Structural Equation Modeling: A Multidisciplinary Journal, 6, 158-168.

Nevitt, J., & Hancock, G. R. (1999). NNORMULT and PWRCOEFF: A set of GAUSS programs for simulating multivariate nonnormal data. Applied Psychological Measurement, 23, 54.

Klockars, A. J., & Hancock, G. R. (1998). A more powerful post hoc multiple comparison procedure in analysis of variance. Journal of Educational and Behavioral Statistics, 23, 279-289.

Lawrence, F. R., & Hancock, G. R. (1998). Assessing change over time using latent growth modeling. Measurement and Evaluation in Counseling and Development, 30, 211-224.

Hancock, G. R., & Klockars, A. J. (1997). Finite Intersection Tests: A paradigm for optimizing simultaneous and sequential inference. Journal of Educational and Behavioral Statistics, 22, 291-307.

Hancock, G. R. (1997). Structural equation modeling methods of hypothesis testing of latent variable means. Measurement and Evaluation in Counseling and Development, 30, 91-105.

Hancock, G. R. (1997). Correlation/validity coefficients disattenuated for score reliability: A structural equation modeling approach. Educational and Psychological Measurement, 57, 606-614.

Hancock, G. R., & Klockars, A. J. (1996). The quest for alpha: Developments in multiple comparison procedures in the quarter century since Games (1971). Review of Educational Research, 66, 269-306.

Klockars, A. J., & Hancock, G. R. (1996). Power of a stagewise intersection protected multiple comparison procedure. Communications in Statistics: Simulation and Computation, 25, 953-960.

Hancock, G. R., Butler, M. S., & Fischman, M. G. (1995). On the problem of two-dimensional error scores: Methods and analyses of accuracy, bias, and consistency. Journal of Motor Behavior, 27, 241-250.

Klockars, A. J., Hancock, G. R., & McAweeney, M. J. (1995). Power of unweighted and weighted versions of simultaneous and sequential multiple comparison procedures. Psychological Bulletin, 118, 300-307.

Hancock, G. R. (1994). Cognitive complexity and the comparability of multiple- choice and constructed-response test formats. Journal of Experimental Education, 62, 143-157.

Klockars, A. J., & Hancock, G. R. (1994). Per experiment error rates: The hidden costs of several multiple comparison procedures. Educational and Psychological Measurement, 54, 292-298.

Hancock, G. R., Thiede, K. W., Sax, G., & Michael, W. B. (1993). Reliability of comparably written two-option multiple-choice and true-false test items. Educational and Psychological Measurement, 53, 651-660.

Klockars, A. J., & Hancock, G. R. (1993). Manipulations of evaluative rating scales to increase validity. Psychological Reports, 73, 1059-1066.

Klockars, A. J., & Hancock, G. R. (1992). Power of recent multiple comparison procedures as applied to a complete set of planned orthogonal contrasts. Psychological Bulletin, 111, 505-510.

Hancock, G. R., & Klockars, A. J. (1991). The effect of scale manipulations on validity: Targetting frequency rating scales for specific performance levels. Applied Ergonomics, 22, 147-154.