Some Behaviorial Science Measurement Concerns and Proposals

References

• Ahmavaara, Y. (1954). The mathematical theory of factorial invariance under selection. Psychometrika, 19, 27–38.
   Google Scholar
• Bai, J., & Wang, P. (2012). Identification and estimation of dynamic factor models. Munich Personal RePEc Archive. Retrieved from http:mpra.ub.uni-muenchen.de38434
   Google Scholar
• Bereiter, C. (1963). Some persisting dilemmas in the measurement of change. In C.W. Harris (Ed.), Problems in measuring change. Madison, WI: University of Wisconsin Press.
   Google Scholar
• Browne, M.W., & Nesselroade, J.R. (2005). Representing psychological processes with dynamic factor models: Some promising uses and extensions of ARMA time series models. In A. Maydeu–Olivares & J.J. McArdle (Eds.), Psychometrics: A festschrift to Roderick P. McDonald (pp. 415–452). Mahwah, NJ: Lawrence Erlbaum Associates.
   Google Scholar
• Carstensen, L. (1993). Motivation for social contact across the life span: A theory of socioemotional selectivity. In J.E. Jacobs (Ed.), Nebraska Symposium on Motivation (pp. 209–254). Lincoln: University of Nebraska Press.
   Google Scholar
• Cattell, R.B. (1944). “Parallel proportional profiles” and other principles for determining the choice of factors by rotation. Psychometrika, 9, 267–283.
   Google Scholar
• Cattell, R.B. (1952). The three basic factor–analytic research designs–Their interrelations and derivatives. Psychological Bulletin, 49, 499–520. doi: 10.1037/h0054245
   PubMed Web of Science ®Google Scholar
• Cattell, R.B. (1957). Personality and motivation structure and measurement. New York, NY: World Book.
   Google Scholar
• Cattell, R.B. (1963). The structuring of change by P–technique and incremental R–technique. In C.W. Harris (Ed.), Problems in measuring change (pp. 167–198). Madison, WI: University of Wisconsin Press.
   Google Scholar
• Cattell, R. B. (Ed.). (1966a). Handbook of multivariate experimental psychology. Chicago, IL: Rand McNally. doi: 10.1016/0005-7967(68)90057-0
   Google Scholar
• Cattell, R.B. (1966b). The data box: Its ordering of total resources in terms of possible relational systems. In R.B. Cattell (Ed.), Handbook of multivariate experimental psychology (1st ed., pp. 67–128). Chicago, IL:
   Google Scholar
• Cattell, R.B., Cattell, A. K.S., & Rhymer, R.M. (1947). P-technique demonstrated in determining psychophysical source traits in a normal individual. Psychometrika, 12, 267–288.
   PubMed Web of Science ®Google Scholar
• Cervone, D. (2005). Personality architecture: Within-person structures and processes. Annual Review of Psychology, 56, 423–452. doi: 10.1146/annurev.psych.56.091103.070133
   PubMed Web of Science ®Google Scholar
• Chen, Z., Godeka, G., & Yu, Y. (2010). An introduction to direction-of-arrival estimation. Norwood, MA: Artech House.
   Google Scholar
• Cronbach, L.J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 71–84. doi: 10.1037/h0043943
   Web of Science ®Google Scholar
• Cronbach, L.J., & Furby, L. (1970). How should we measure “change”–or should we?Psychological Bulletin, 74(1), 68–80. doi: 10.1037/h0029382
   Web of Science ®Google Scholar
• Durbin, J., & Koopman, S.J. (2012). Time series analysis by state space methods. New York, NY: Oxford University Press.
   Google Scholar
• Embretson, S.E. (1999). A cognitive design system approach to generating valid tests: Application to abstract reasoning. Psychological Methods, 3, 380–396.
   Web of Science ®Google Scholar
• Embretson, S.E., & Poggio, J. (2012). The impact of scaling and measurement methods on individual differences in growth. In B. Laursen, T.D. Little, & N.A. Card (Eds.), Handbook of developmental research methods (pp. 82–108). New York, NY: Guilford Press.
   Google Scholar
• Fleishman, E.A., & Fruchter, B. (1960). Factor structure and predictability of successive stages of learning Morse code. Journal of Applied Psychology, 44, 96–101. doi: 10.1037/h0046447
   Web of Science ®Google Scholar
• Gates, K.M., & Molenaar, P. C.M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63, 310–319.
   PubMed Web of Science ®Google Scholar
• Guilford, J.P. (1977). The invariance problem in factor analysis. Educational and Psychological Measurement, 37, 11–19.
   Web of Science ®Google Scholar
• Horn, J., McArdle, J.J., & Mason, R. (1983). When invariance is not invariant: A practical scientist’s view of the ethereal concept of factorial invarience. The Southern Psychologist, 1, 179–188.
   Google Scholar
• Hox, J.J., Leeuw, E. D.D., & Zijlmans, E. A.O. (2015). Measurement equivalence in mixed mode surveys. Frontiers in Psychology, 6, 1–11. doi: 10.3389/fpsyg
   PubMed Web of Science ®Google Scholar
• Jöreskog, K.G., & Sörbom, D. (1993). LISREL 8 user’s reference guide. Chicago, IL: Scientific Software International.
   Google Scholar
• Judd, C.M., & Kenny, D.A. (2010). Data analysis in social psychology: Recent and recurring issues. In S.T. Fiske, D.T. Gilbert, & G. Lindzey (Eds.), Handbook of social psychology (Vol. I, pp. 115–139). New York, NY:
   Google Scholar
• Kagan, J. (1971). Change and continuity in infancy. New York, NY: Wiley. doi: 10.1111/jcpp.12093
   Google Scholar
• Kelderman, H., & Molenaar, P. C.M. (2007). The effect of individual differences in factor loadings on the standard factor model. Multivariate Behavioral Research, 42, 435–456.
   Web of Science ®Google Scholar
• Keyser, C.J. (1956). The group concept. In J.R. Newman (Ed.), The world of mathematics (Vol. 3, pp. 1538–1557). New York, NY:
   Google Scholar
• Kluckhohn, C., & Murray, H.A. (1953). Personality formation: The determinants. In C. Kluckhohn, H.A. Murray, & D. Schneider (Eds.), Personality in nature, society and culture (pp. 53–70). New York, NY: Knopf.
   Google Scholar
• Lebo, M.A., & Nesselroade, J.R. (1978). Intraindividual differences dimensions of mood change during pregnancy identified in five P–technique factor analyses. Journal of Research in Personality, 12, 205–224.
   Web of Science ®Google Scholar
• Loehlin, J.C. (1998). Latent variable models: An introduction to factor, path, and structural analysis (4th ed.). Mahwah, NJ: Erlbaum. doi: 10.1080/10705519909540143
   Google Scholar
• Lommen, M. J.J., van de Schoot, R., & Engelhard, I.M. (2014). The experience of traumatic events disrupts the measurement invariance of a posttraumatic stress scale. Frontiers in Psychology, 5, 1–7. doi: 10.3389/fpsyg.2014.01304
   PubMed Web of Science ®Google Scholar
• Luce, R.D. (2000). Utility of gains and losses: Measurement-theoretical and experimental approaches. Hillsdale, NJ: Erlbaum. doi: 10.3389/fpsyg.2012.00173
   Google Scholar
• 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(1), 63–113.
   PubMed Web of Science ®Google Scholar
• Meredith, W. (1964a). Notes on factorial invariance. Psychometrika, 29, 177–185. doi: 10.1007/BF02289699
   Web of Science ®Google Scholar
• Meredith, W. (1964b). Rotation to achieve factorial invariance. Psychometrika, 29, 186–206. doi: 10.1007/BF02289700
   Web of Science ®Google Scholar
• Meredith, W. (1993). Measurement invariance, factor analysis and factor invariance. Psychometrika, 58, 525–543. doi: 10.1007/BF02294825
   Web of Science ®Google Scholar
• Millsap, R.E. (2011). Statistical approaches to measurement invariance. New York, NY: Routledge, Taylor & Francis Group.
   Google Scholar
• Molenaar, P. C.M. (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50(2), 181–202.
   Web of Science ®Google Scholar
• Molenaar, P. C.M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology – this time forever. Measurement: Interdisciplinary Research and Perspectives, 2, 201–218.
   Google Scholar
• Molenaar, P. C.M., Huizenga, H.M., & Nesselroade, J.R. (2003). The relationship between the structure of interindividual and intraindividual variability: A theoretical and empirical vindication of developmental systems theory. In U.M. Staudinger & U. Lindenberger (Eds.), Understanding human development: Dialogues with lifespan psychology (pp. 339–360). Norwell, MA: Kluwer.
   Google Scholar
• Molenaar, P. C.M., & Nesselroade, J.R. (2012). Merging the idiographic filter with dynamic factor analysis to model process. Applied Developmental Science, 16, 210–219. doi: 10.1080/10888691.2012
   Web of Science ®Google Scholar
• Nesselroade, J.R. (1970). Application of multivariate strategies to problems of measuring and structuring long–term change. In L.R. Goulet & P.B. Baltes (Eds.), Life-span developmental psychology: Research and theory (pp. 193–207). New York, NY: Academic Press.
   Google Scholar
• Nesselroade, J.R. (2007). Factoring at the individual level: Some matters for the second century of factor analysis. In R. Cudeck & R. MacCallum (Eds.), 100 years of factor analysis (pp. 249–264). Mahwah, NJ: Erlbaum.
   Google Scholar
• Nesselroade, J.R. (2010). On an emerging third discipline of scientific psychology. In K.M. Newell & P. C.M. Molenaar (Eds.), Individual pathways of change: Statistical models for analyzing learning and development (pp. 209–218). Washington, DC: American Psychological Association.
   Google Scholar
• Nesselroade, J.R., & Estabrook, C.R. (2008). Factor invariance, measurement, and studying development over the lifespan. In H. Bosworth & C.K. Hertzog (Eds.), Aging and cognition: Research methodologies and empirical advances. Washington, DC: American Psychological Association.
   Google Scholar
• Nesselroade, J.R., & Ford, D.H. (1985). P–technique comes of age: Multivariate, replicated, single–subject designs for research on older adults. Research on Aging, 7, 46–80. doi: 10.1177/0164027585007001003
   PubMed Web of Science ®Google Scholar
• Nesselroade, J.R., Gerstorf, D., Hardy, S.A., & Ram, N. (2007). Idiographic filters for psychological constructs. Measurement: Interdisciplinary Research and Perspectives, 5, 217–235.
   Google Scholar
• Nesselroade, J.R., McArdle, J.J., Aggen, S.H., & Meyers, J.M. (2002). Alternative dynamic factor models for multivariate time–series analyses. In D.M. Moskowitz & S.L. Hershberger (Eds.), Modeling intraindividual variability with repeated measures data: Advances and techniques (pp. 235–265). Mahwah, NJ: Erlbaum.
   Google Scholar
• Nesselroade, J.R., & Molenaar, P. C.M. (2010). Emphasizing intraindividual variability in the study of development over the lifespan. In W.F. Overton (Ed.), Cognition, biology, and methods across the lifespan: Vol. 1, Handbook of life-span development (pp. 30–54). New York, NY: Wiley.
   Google Scholar
• Rijmen, F., de Boek, P., & Leuven, K.U. (2002). The random weights linear logistic test model. Applied Psychological Measurement, 26(3), 271–285. doi: 10.1177/0146621602026003003
   Web of Science ®Google Scholar
• Robinson, O.C. (2011). The idiographic / nomothetic dichotomy: Tracing historical origins of contemporary confusions. History & Philosophy of Psychology, 13, 32–39.
   Google Scholar
• Schmidt, J., & Leiman, J. (1957). The development of hierarchical factor solutions. Psychometrika, 22(1), 53–61. doi: 10.1007/bf02289209
   Web of Science ®Google Scholar
• Skinner, B.F. (1953). Science and human behavior. New York, NY: Macmillan. doi: 10.1002/sce.37303805120
   Google Scholar
• Stemmler, G. (1992). Differential psychophysiology: Persons in situations. Berlin, Germany: Springer-Verlag.
   Google Scholar
• Stevens, S.S. (1958). Problems and methods of psychophysics. Psychological Bulletin, 54, 177–196. doi: 10.1037/h0044883
   Web of Science ®Google Scholar
• Thurstone, L.L. (1947). Multiple factor analysis. Chicago, IL: University of Chicago Press. doi: 10.1002/1097
   Google Scholar
• Thurstone, L.L. (1957). Multiple factor analysis (fifth impression). Chicago, IL: University of Chicago Press.
   Google Scholar
• Woodcock, R.W., & Johnson, M.B. (1990). WJ-R tests of cognitive ability. Allen, TX: DLM Teaching Resources.
   Google Scholar
• Zevon, M., & Tellegen, A. (1982). The structure of mood change: Idiographic/nomothetic analysis. Journal of Personality and Social Pyschology, 43, 111–122.
   Web of Science ®Google Scholar
• Zhang, Z., Browne, M.W., & Nesselroade, J.R. (2011). Higher-order factor invariance and idiographic mapping of constructs to observables. Applied Developmental Science, 15, 186–200. doi: 10.1080/10888691.2011.618099
   Web of Science ®Google Scholar