Some Behaviorial Science Measurement Concerns and Proposals

Multivariate Behavioral Research

Volumn 51, Issue 2-3, 2016, Pages 396-412

  Nesselroade, John R ^a   Molenaar, Peter C M ^b  

^a UNIVERSITY OF VIRGINIA   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

idiographic filter; invariance; latent variable; Measurement

Indexed keywords

ALGORITHM; BEHAVIORAL SCIENCE; COMPUTER SIMULATION; HUMAN; MULTIVARIATE ANALYSIS; PROCEDURES; REGRESSION ANALYSIS; STATISTICAL ANALYSIS;

ALGORITHMS; BEHAVIORAL SCIENCES; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; MULTIVARIATE ANALYSIS; REGRESSION ANALYSIS;

EID: 84973122116     PISSN: 00273171     EISSN: None     Source Type: Journal    
DOI: 10.1080/00273171.2015.1050481     Document Type: Article

References (62)

1. Y.Ahmavaara, (1954). The mathematical theory of factorial invariance under selection. Psychometrika, 19, 27–38.
2. J.Bai,, & P.Wang, (2012). Identification and estimation of dynamic factor models. Munich Personal RePEc Archive. Retrieved from http:mpra.ub.uni-muenchen.de38434
3. C.Bereiter, (1963). Some persisting dilemmas in the measurement of change. In C.W.Harris (Ed.), Problems in measuring change. Madison, WI: University of Wisconsin Press.
4. M.W.Browne,, & J.R.Nesselroade, (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.
5. L.Carstensen, (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.
6. R.B.Cattell, (1944). “Parallel proportional profiles” and other principles for determining the choice of factors by rotation. Psychometrika, 9, 267–283.
7. R.B.Cattell, (1952). The three basic factor–analytic research designs–Their interrelations and derivatives. Psychological Bulletin, 49, 499–520. doi: 10.1037/h0054245
8. R.B.Cattell, (1957). Personality and motivation structure and measurement. New York, NY: World Book.
9. R.B.Cattell, (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.
10. R.B.Cattell, R. B. (Ed.). (1966a). Handbook of multivariate experimental psychology. Chicago, IL: Rand McNally. doi: 10.1016/0005-7967(68)90057-0
11. R.B.Cattell, (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
12. R.B.Cattell,, A.K.S.Cattell,, & R.M.Rhymer, (1947). P-technique demonstrated in determining psychophysical source traits in a normal individual. Psychometrika, 12, 267–288.
13. D.Cervone, (2005). Personality architecture: Within-person structures and processes. Annual Review of Psychology, 56, 423–452. doi: 10.1146/annurev.psych.56.091103.070133
14. Z.Chen,, G.Godeka,, & Y.Yu, (2010). An introduction to direction-of-arrival estimation. Norwood, MA: Artech House.
15. L.J.Cronbach, (1957). The two disciplines of scientific psychology. American Psychologist, 12, 71–84. doi: 10.1037/h0043943
16. L.J.Cronbach,, & L.Furby, (1970). How should we measure “change”–or should we?Psychological Bulletin, 74(1), 68–80. doi: 10.1037/h0029382
17. J.Durbin,, & S.J.Koopman, (2012). Time series analysis by state space methods. New York, NY: Oxford University Press.
18. S.E.Embretson, (1999). A cognitive design system approach to generating valid tests: Application to abstract reasoning. Psychological Methods, 3, 380–396.
19. S.E.Embretson,, & J.Poggio, (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.
20. E.A.Fleishman,, & B.Fruchter, (1960). Factor structure and predictability of successive stages of learning Morse code. Journal of Applied Psychology, 44, 96–101. doi: 10.1037/h0046447
21. K.M.Gates,, & P.C.M.Molenaar, (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63, 310–319.
22. J.P.Guilford, (1977). The invariance problem in factor analysis. Educational and Psychological Measurement, 37, 11–19.
23. J.Horn,, J.J.McArdle,, & R.Mason, (1983). When invariance is not invariant: A practical scientist’s view of the ethereal concept of factorial invarience. The Southern Psychologist, 1, 179–188.
24. J.J.Hox,, E.D.D.Leeuw,, & E.A.O.Zijlmans, (2015). Measurement equivalence in mixed mode surveys. Frontiers in Psychology, 6, 1–11. doi: 10.3389/fpsyg
25. K.G.Jöreskog,, & D.Sörbom, (1993). LISREL 8 user’s reference guide. Chicago, IL: Scientific Software International.
26. C.M.Judd,, & D.A.Kenny, (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
27. J.Kagan, (1971). Change and continuity in infancy. New York, NY: Wiley. doi: 10.1111/jcpp.12093
28. H.Kelderman,, & P.C.M.Molenaar, (2007). The effect of individual differences in factor loadings on the standard factor model. Multivariate Behavioral Research, 42, 435–456.
29. C.J.Keyser, (1956). The group concept. In J.R.Newman (Ed.), The world of mathematics (Vol. 3, pp. 1538–1557). New York, NY
30. C.Kluckhohn,, & H.A.Murray, (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.
31. M.A.Lebo,, & J.R.Nesselroade, (1978). Intraindividual differences dimensions of mood change during pregnancy identified in five P–technique factor analyses. Journal of Research in Personality, 12, 205–224.
32. J.C.Loehlin, (1998). Latent variable models: An introduction to factor, path, and structural analysis (4th ed.). Mahwah, NJ: Erlbaum. doi: 10.1080/10705519909540143
33. M.J.J.Lommen,, R.van de Schoot,, & I.M.Engelhard, (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
34. R.D.Luce, (2000). Utility of gains and losses: Measurement-theoretical and experimental approaches. Hillsdale, NJ: Erlbaum. doi: 10.3389/fpsyg.2012.00173
35. J.J.McArdle,, & R.B.Cattell, (1994). Structural equation models of factorial invariance in parallel proportional profiles and oblique confactor problems. Multivariate Behavioral Research, 29(1), 63–113.
36. W.Meredith, (1964a). Notes on factorial invariance. Psychometrika, 29, 177–185. doi: 10.1007/BF02289699
37. W.Meredith, (1964b). Rotation to achieve factorial invariance. Psychometrika, 29, 186–206. doi: 10.1007/BF02289700
38. W.Meredith, (1993). Measurement invariance, factor analysis and factor invariance. Psychometrika, 58, 525–543. doi: 10.1007/BF02294825
39. R.E.Millsap, (2011). Statistical approaches to measurement invariance. New York, NY: Routledge, Taylor & Francis Group.
40. P.C.M.Molenaar, (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50(2), 181–202.
41. P.C.M.Molenaar, (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.
42. P.C.M.Molenaar,, H.M.Huizenga,, & J.R.Nesselroade, (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.
43. P.C.M.Molenaar,, & J.R.Nesselroade, (2012). Merging the idiographic filter with dynamic factor analysis to model process. Applied Developmental Science, 16, 210–219. doi: 10.1080/10888691.2012
44. J.R.Nesselroade, (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.
45. J.R.Nesselroade, (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.
46. J.R.Nesselroade, (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.
47. J.R.Nesselroade,, & C.R.Estabrook, (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.
48. J.R.Nesselroade,, & D.H.Ford, (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
49. J.R.Nesselroade,, D.Gerstorf,, S.A.Hardy,, & N.Ram, (2007). Idiographic filters for psychological constructs. Measurement: Interdisciplinary Research and Perspectives, 5, 217–235.
50. J.R.Nesselroade,, J.J.McArdle,, S.H.Aggen,, & J.M.Meyers, (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.
51. J.R.Nesselroade,, & P.C.M.Molenaar, (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.
52. F.Rijmen,, P.de Boek,, & K.U.Leuven, (2002). The random weights linear logistic test model. Applied Psychological Measurement, 26(3), 271–285. doi: 10.1177/0146621602026003003
53. O.C.Robinson, (2011). The idiographic / nomothetic dichotomy: Tracing historical origins of contemporary confusions. History & Philosophy of Psychology, 13, 32–39.
54. J.Schmidt,, & J.Leiman, (1957). The development of hierarchical factor solutions. Psychometrika, 22(1), 53–61. doi: 10.1007/bf02289209
55. B.F.Skinner, (1953). Science and human behavior. New York, NY: Macmillan. doi: 10.1002/sce.37303805120
56. G.Stemmler, (1992). Differential psychophysiology: Persons in situations. Berlin, Germany: Springer-Verlag.
57. S.S.Stevens, (1958). Problems and methods of psychophysics. Psychological Bulletin, 54, 177–196. doi: 10.1037/h0044883
58. L.L.Thurstone, (1947). Multiple factor analysis. Chicago, IL: University of Chicago Press. doi: 10.1002/1097
59. L.L.Thurstone, (1957). Multiple factor analysis (fifth impression). Chicago, IL: University of Chicago Press.
60. R.W.Woodcock,, & M.B.Johnson, (1990). WJ-R tests of cognitive ability. Allen, TX: DLM Teaching Resources.
61. M.Zevon,, & A.Tellegen, (1982). The structure of mood change: Idiographic/nomothetic analysis. Journal of Personality and Social Pyschology, 43, 111–122.
62. Z.Zhang,, M.W.Browne,, & J.R.Nesselroade, (2011). Higher-order factor invariance and idiographic mapping of constructs to observables. Applied Developmental Science, 15, 186–200. doi: 10.1080/10888691.2011.618099