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Gregory R. Hancock, Program Director

1230D Benjamin Building
Measurement, Statistics & Evaluation
University of Maryland
College
Park, MD 20742-1115
ghancock@umd.edu
tel  301.405.3621
fax 301.314.9245

  

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Personal information

Positions

Professor and Program Director
    Measurement, Statistics and Evaluation
    Department of Human Development and Quantitative Methodology

    College of Education, University of Maryland

Director
                         
    Center for Integrated Latent Variable Research (CILVR)
    University of Maryland

Affiliated Professor       
    Center for Advanced Study of Language
    University of Maryland

                      

Interim Associate Dean for Research and Graduate Education, 2007-2008        
    College of Education
    University of Maryland

 

                            
Education                        

Doctor of Philosophy      
    Statistics, Measurement, and Research Design
    Department of Educational Psychology

    College of Education, University of Washington
    August, 1991                                            

Master of Education
     
    Statistics, Measurement, and Research Design
    Department of Educational Psychology

    College of Education, University of Washington
    June, 1989                                            

Teaching Certif. (4th-12th)
         
    Mathematics and Chemistry
    University of Washington
    July, 1987                                            

Bachelors of Science
        
    Mathematics; Chemistry
    University of Washington
    June, 1986                                            

 

 

Awards and recognition

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

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
University of Maryland, 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

 

 

SELECTED SCHOLARLY WORK

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., & 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.

 

Chapters

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 educational 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 (pp. 488-508).  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 CourseGreenwood, 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 CourseGreenwood, 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 SciencesThousand 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, and T. F. Liao (Eds.), Sage Encyclopedia of Social Science Research MethodsThousand 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 NeuroscienceNew 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. Sörbom (Eds.), Structural Equation Modeling: Present and Future — A Festschrift in honor of Karl Jöreskog. 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 (pp. 5239-5244).  Oxford, England: Pergamon.

 

Methodological articles

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

Liu, M., & Hancock, G. R. (in press). Unrestricted mixture models for class identification in growth mixture modeling. Educational and Psychological Measurement.

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.

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.

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.

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

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

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

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

Gagné, P. E., & Hancock, G. R.  (2006).  Measurement model quality, sample size, and solution propriety in confirmatory factor modelsMultivariate 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).  Scheffé’s 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 Scheffé-type respecification procedure for controlling Type I error in exploratory structural equation model modification.  Structural Equation Modeling: A Multidisciplinary Journal, 6, 158-168.

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 a: 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.  (1992).  Power of recent multiple comparison procedures as applied to a complete set of planned orthogonal contrasts.  Psychological Bulletin, 111, 505-510.

 


Last Modified: 10 February 2014