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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 educational 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 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. 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. Sörbom (Eds.), Structural equation modeling: Present and future — A Festschrift in honor of Karl Jöreskog (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 educational 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.

Gagné, 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). 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.

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.