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McArdle, J. J., Hamagami, F., Chang, J. Y., & Hishinuma, E. S. (2014). Longitudinal Dynamic Analyses of Depression and Academic Achievement in the Hawaiian High Schools Health Survey Using Contemporary Latent Variable Change Models. * Structural Equation Modeling: A Multidisciplinary Journal.*

McArdle, J. J., Hofer, S. M. (2014). Fighting for Intelligence: A Brief Overview of the Academic Work of John L. Horn. *Multivariate Behavioral Research, 49 *(1), 1-16.

McArdle, J. J., Hamagami, F., Bautista, R., Onoye, J., Hishinuma, E. S., Prescott, C. A., Takeshita, J., Zonderman, A. B., & Johnson, R. C. (2013). Multilevel Factor Analyses of Family Data from the Hawai'i Family Study of Cognition.* Education and Psychological Measurement, 20 *(10), 1-51.

McArdle, J. J. (2011). What Life-Span Data Do We Really Need? Presented at Western Psychological Association Convention in Los Angeles, California.

McArdle, J. J. & Prindle J. J. Basic Issues in the Measurement of Change. (2011, *draft*).

McArdle, J. J. (2010). Some Ethical Issues in Factor Analysis. In A. T. Panter & S. K. Sterba (Eds.), *Handbook of Ethics in Quantitative Methodology* (pp. 313-339). New York: Taylor and Francis Group.

McArdle, J. J. (2010). What Life-Span Data Do We Really Need? In R. L. Lerner & W. F. Overton (Eds.), *The Handbook of Life-Span Development, Volume 1: Cognition, Biology, and Methods* (pp. 55-88). Hoboken, NJ: John Wiley & Sons, Inc.

McArdle, J. J. & Prescott, C. A. (2010). Contemporary Modeling of gene x environment effects in randomized multivariate longitudinal studies. *Perspectives on Psychological Science, 5* (5), 606-621.

McArdle, J. J., Grimm, K. J., Hamagami, F., Bowles, R. P., & Meredith, W. (2009). Modeling life-span growth curves of cognition using longitudinal data with multiple samples and changing scales of measurement. *Psychological Methods, 14* (2), 126-149.

McArdle, J. J., & Plassman, B. L. (2009). A biometric latent curve analysis of memory decline in older men of the NAS-NRC Twin Registry. *Behavior Genetics, 39*(5), 472-495.

McArdle, J. J. & Wang, L. (2008). Modeling age based turning points in longitudinal life-span growth curves in cognition. *Applied data analytic techniques for turning points research.* 105-127.

McArdle, J. J. (2008). Latent variable modeling of differences and changes with longitudinal data. *Annual Review of Psychology, 60* 577-605.

McArdle, J. J., & Prindle, J. J. (2008). A latent change score analysis of a randomized clinical trial in reasoning training. *Psychology and Aging, 23* (4), 702-719.

Technical Appendix for McArdle & Prindle, 2008.

McArdle, J. J., & Wang, L. (2008). Modeling age-based turning points in longitudinal life-span growth curves of cognition. In P. Cohen (Ed.), *Applied data analytic techniques for turning points research* (pp. 105-127). New York, NY: Routledge.

McArdle, J. J. (2007). Five steps in the structural factor analysis of longitudinal data. In R. Cudeck & R. C. MacCallum (Eds.), *Factor analysis at 100: Historical developments and future directions* (pp. 99-130), Mahwah, NJ: Lawrence Erlbaum Associates.

McArdle, J. J., Fisher, G. G., & Kadlec, K. M. (2007). Latent variable analyses of age trends of cognition in the Health and Retirement Study, 1992-2004. *Psychology and Aging, 22*(3), 525-545.

McArdle, J. J. (2006). Latent curve analyses of longitudinal twin data using a mixed-effects biometric approach. *Twin Reseach and Human Genetics, 9*(3), 343-359.

McArdle, J. J. (2005). The development of RAM rules for latent variable structural equation modeling. In A. Madeau & J.J. McArdle, (Eds.) *Contemporary Advances in Psychometrics* (pp. 225-273). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

McArdle, J. J. & Prescott, C.A. (2005). Mixed-effects variance components models for biometric family analyses. *Behavior Genetics, 35* (5), 631-652.

McArdle, J. J., Small, B. J., Bäckman, L. & Fratiglioni, L. (2005). Longitudinal models of growth and survival applied to the early detection of Alzheimer's Disease. *Journal of Geriatric Psychiatry and Neurology, 18,* 234-241.

McArdle, J. J. (2004). Review of the book *Structural equation modeling: applications in ecological and evolutionary biology*. *The Quarterly Review of Biology, 79*(3), 330.

McArdle, J. J., & Hamagami, F. (2004). Methods for dynamic change hypotheses. In K. van Montfort, J. Oud, & A. Satorra, (Eds.) *Recent developments on structural equation models* (pp. 295-335) The Netherlands: Kluwer Academic Publishers.

McArdle, J. J., Hamagami, F., Jones, K., Jolesz, F., Kikinis, R., Spiro III, A. & Albert, M. S. (2004) Structural modeling of dynamic changes in memory and brain structure using longitudinal data from the Normative Aging Study. *Journal of Gerontology: Psychological Sciences, 59B*(6), 294-304.

McArdle, J. J., & Hamagami, F. (2003). Structural equation models for evaluating dynamic concepts within longitudinal twin analyses. * Behavior Genetics, 33*(2), 137-159.

McArdle, J. J. & Nesselroade, J. R. (2003). Growth curve analysis in contemporary psychological research. In J. Schinka & W. Velicer, (Eds.) *Comprehensive Handbook of Psychology, Volume 2* (pp. 447-480). New York: Pergamon Press.

McArdle, J. J., Ferrer-Caja, E., Hamagami, F. & Woodcock, R. W. (2002). Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. *Developmental Psychology, 38*(1), 115-142.

McArdle, J. J. (2001). A latent difference score approach to longitudinal dynamic structure analysis. In R. Cudeck, S. du Toit & D. Sörbom, (Eds.) *Structural equation modeling: present and future. A festschrift in honor of Karl Jöreskog.* (pp. 1-40). Lincolnwood, IL: Scientific Software International, Inc.

McArdle, J. J. (2001). Growth curve analysis. N. J. Smelser & P. B. Baltes, (Eds.) *The international encyclopedia of the behavioral and social sciences* (pp. 6439-6445). Amsterdam: Elsevier Science Ltd.

McArdle, J. J., Johnson, R. C., Hishinuma, E. S., Miyamoto, R. S., & Andrade, N. N. (2001). Structural equation modeling of group differences in CES-D ratings of native Hawaiian and non-Hawaiian high school students. *Journal of Adolescent Research, 16*(2), 108-149.

McArdle, J. J., & Bell, R. Q. (2000). An introduction to latent growth models for developmental data analysis. In T. D. Little, K. U. Schnabel & J. Baumert. (Eds.) *Modeling longitudinal and multiple-group data: practical issues, applied approaches, and scientific examples.* (pp. 69-107). Mahwah, NJ: Erlbaum.

McArdle, J. J., & Ghisletta, P. (2000). The future of latent variable modeling with interactions and non-linearity: Review of the book *Interaction and nonlinear effects in structural equation modeling*. *Contemporary Psychology, 45*(1), 91-95.

McArdle, J. J., Hamagami, F., Meredith, W. & Bradway, K. P. (2000). Modeling the dynamic hypotheses of Gf-Gc theory using longitudinal life-span data. *Learning and Individual Differences, 12*, 53-79.

McArdle, J. J. & Woodcock, R.W. (2000). Longitudinal multilevel analyses of test-retest data on the growth and decline of cognitive abilities. Submitted to *Psychological Methods*.

McArdle, J. J. (1998). Contemporary statistical models for examining test bias. In J.J. McArdle & R.W. Woodcock (Eds.), *Human abilities in theory and practice.* (pp. 157-195). Mahwah, NJ: Erlbaum.

McArdle, J. J. (1998). Modeling longidutinal data by latent growth curve methods. In G. Marcoulides (Ed.), *Modern methods for business research.* (pp. 359-406). Mahwah, NJ: Erlbaum.

McArdle, J. J., Prescott, C. A., Hamagami, F., & Horn, J. L. (1998). A contemporary method for developmental-genetic analyses of age changes in intellectual abilities. *Developmental Neuropsychology, 14*(1) 69-114.

McArdle, J. J. & Woodcock, R. W. (1997). Expanding test-retest designs to include developmental time lag components. *Psychological Methods, 2*(4) 403-435.

McArdle, J. J. (1996). Current directions in structural factor analysis. *Current Directions in Psychological Science, 5*(1) 11-18.

McArdle, J. J. (1996). Review of the book *A workshop on contemporary behavioral genetics models and methods*. *Contemporary Psychology, 41*(9).

McArdle, J. J., & Hamagami, F. (1996). Multilevel models from a multiple group structural equation perspective. In G. A. Marcoulides & R. E. Schumaker (Eds.), *Advanced structural equpation modeling: Issues and techniques.* (pp. 89-124). Mahwah, NJ: Erlbaum.

McArdle, J. J., & Allison, D. B. (1995). Regression change models with incomplete repeated measures data in obesity research. In D. B. Allison (Ed.), *Obesity Treatment.* (pp. 53-63). New York, NY: Plenum Press.

McArdle, J. J. (1994). Appropriate questions about causal inference from "direction of causation" analyses. *Genetic Epidemiology, 11*, 477-482.

McArdle, J. J. (1994). Factor analysis. In R. J. Sternberg (Ed.), *Encyclopedia of Human Intelligence.* (pp. 422-430). New York, NY: Macmillan.

McArdle, J. J. (1994). Structural factor analysis experiments with incomplete data. *Multivariate Behavioral Research, 29*(4) 409-454.

McArdle, J. J., & Cattell, R. B. (1994). Structural equation models of factorial invariance in parallel proportional profiles and oblique confactor problems. *Multivariate Behavioral Research, 29*(I) 63-113.

McArdle, J. J., & Hamagami, F. (1994). Logit and multilevel logit modeling of college graduation for 1984-1985 freshman student-athletes. *Journal of the American Statistical Association, 89* (427) 1107-1123.

McArdle, J. J., Hamagami, F., & Hulick, P. (1994). Latent variable path models in alcohol use research. In R. Zucker, G. Boyd, & J. Howard (Eds.), *The development of alcohol problems: Exploring the biopsychosocial matrix of risk* (pp. 341-397). Rockville, MD: U.S. Department of Health and Human Services.

McArdle, J. J. & Nesselroade, J. R. (1994). Using multivariate data to structure developmental change. In S. H. Cohen & H. W. Reese (Eds), *Life span developmental psychology: Methodological contributions.* (pp. 223-267). Hillsdale, NJ: Erlbaum.

McArdle, J. J., & Hamagami, F. (1992). Modeling incomplete longitudinal and cross-sectional data using latent growth structural models. *Experimental Aging Research, 18*(3) 145-166.

McArdle, J. J. & Lehman, R. S. (1992). A functionalist view of factor analysis. In D. F. Owens & M. Wagner (Eds.), *Progress in modern psychology: The contributions of functionalism to modern psychology* (pp. 167-187). Hillsdale, NJ: Erlbaum.

McArdle, J. J. & Prescott, C. A. (1992). Age-based construct validation using structural equation modeling. *Experimental Aging Research, 18* (3) 87-115.

McArdle, J. J. (1991). Comments on "Latent variable models for studying differences and change." In L. M. Collins & J. L. Horn (Eds.), *Best methods for the analysis of change* (pp. 164-169). Washington, DC: American Psychological Association.

McArdle, J. J. (1991). Principles versus principals of structural factor analysis. *Multivariate Behavioral Research, 25*(1) 81-87.

McArdle, J. J. (1991). Structural models of developmental theory in psychology. *Annals of Theoretical Psychology, 7,* 139-160.

McArdle, J. J., & Cohen, S. A. (1991). Quantitative topics in research on aging. *Experimental Aging Research, 17*(1) 3-5.

McArdle, J. J., & Goldsmith, H. H. (1991). Alternative common factor models for multivariate biometric analyses. *Behavior Genetics, 20*(5) 569-608.

McArdle, J. J., & Hamagami, F. (1991). Modeling incomplete longitudinal and cross-sectional data using latent growth structural models. In L. M. Collins & J. L. Horn (Eds.), *Best methods for the analysis of change* (pp. 275-303). Washington, DC: American Psychological Association.

McArdle, J. J., Hamagami, F., Aggen, S., Thompson, W. & Paskus, T. (1991). A statistical analysis of college graduation for 1984 freshman student athletes. *NCAA Research Report, 91*(2) 1-31.

McArdle, J. J., Hamagami, F., Elias, M. F. & Robbins, M. A. (1991). Structural modeling of mixed longitudinal and cross-sectional data. *Experimental Aging Research, 17*(1) 29-52.

McArdle, J. J., Prescott, C. A., & Horn, J. L. (1991). A large scale age-based biometric analysis of intellectual abilities measured by the WAIS. Presented at the *Annual meeting of the behavioral genetic association.* Aussois, France.

McArdle, J. J. (1990). Latent variable growth models for research on aging. In J. E. Birren & K. W. Schaie (Eds.), *Handbook of the psychology of aging* (3rd ed.). (pp. 21-44). San Diego, CA: Academic Press.

McArdle, J. J., & Aber, M. S. (1990). Patterns of change within latent variable structural equation models. In A. von Eye (Ed.), *New statistical methods in developmental research* (pp. 151-224). New York, NY: Academic Press.

McArdle, J. J. & Woodcock, R. W. (1990). A repeated measures reliability analysis of the Woodcock-Johnson Test of psycho-educational abilities. Manuscript.

McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade & R. B. Cattell (Eds.),

McArdle, J. J. (1988). Multivariate behavioral research computer software section: Guidelines for submissions.

McArdle, J. J. (1987). Mixtures of behavioral genetics and development psychology: Review of the book

McArdle, J. J. & Epstein, D. (1987). Latent growth curves within developmental structural equation models.

McArdle, J. J. (1986). Latent variable growth within behavior genetic models.

McArdle, J. J. (1984). On the madness in his method: R. B. Cattell's contributions to structural equation modeling.

McArdle, J. J. & McDonald, R. P. (1984). Some algebraic properties of the Reticular Action Model for moment structures.

McArdle, J. J. (1981). Review of the book

McArdle, J.J. & Horn, J.L. (1981).

McArdle, J. J. (1980). Causal modeling applied to psychonomic systems simulation.