Publications & Presentations by John J. McArdle

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2010s

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.

2000s

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.

1990s

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.

1980s

McArdle, J. J. & Hamagami, F. (1989). Review of the book Structural equation modeling with LISREL: Essential and advances. Applied Psychological Measurement, 13(1), 107-112.

McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade & R. B. Cattell (Eds.), The handbook of multivariate experimental psychology, vol. 2. (pp. 561-614). New York, NY: Plenum Press.

McArdle, J. J. (1988). Multivariate behavioral research computer software section: Guidelines for submissions. Multivariate Behavioral Resarch, 23, 121-123.

McArdle, J. J. (1987). Mixtures of behavioral genetics and development psychology: Review of the book Development, genetics, and psychology. Contemporary Psychology, 32(5), 433-435.

McArdle, J. J. & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58(1), 110-133.

McArdle, J. J. (1986). Latent variable growth within behavior genetic models. Behavior Genetics, 16(1), 163-200.

McArdle, J. J. (1984). On the madness in his method: R. B. Cattell's contributions to structural equation modeling. Multivariate Behavioral Research, 19, 245-267.

McArdle, J. J. & McDonald, R. P. (1984). Some algebraic properties of the Reticular Action Model for moment structures. British Journal of Mathematical and Statistical Psychology, 37, 234-251.

McArdle, J. J. (1981). Review of the book Correlation and causality. Applied Psychological Measurement, 5, 275-280.

McArdle, J.J. & Horn, J.L. (1981). Structural equation models of Gf-Gc intelligence theory. Presented at the American Psychological Association Annual Meeting, August 25, 1981, Los Angeles, CA.

McArdle, J. J. (1980). Causal modeling applied to psychonomic systems simulation. Behavior Research Methods and Instrumentation, 12(2), 193-209.