Towards a Psychology of Food and Eating: From Motivation to Module to Model to Marker, Morality, Meaning, and Metaphor

modeling in developmenlal research, m Experimental Psychology in the ruiure, V. Sarris and A. Parducci,'Eds. (Krlbaum, Hillsdale, NJ, 1984); j.J. McArdle, Principals versus principles in factor analysis, Multivariate Behavioral Researth, 25(1), Sl-87 (1990). 2 For path analysis diagrams, see S. VVrighi, On "Path analysis in genetic epidemiology: A critique," American journal of Human Genetics. 35, 757-762 (1982); J.]. McArdle and S.M. Boker, RAMpath (Eribaum, Hilhdalc, N), 1990). 3. I'or classical factor analysis, see J R. Nesseiroade and R. B. Cattell, Handbook of Multivaruitr Experimental Psychotogy (Rand MciMally, New York, 1988); J.B, Carroll," Human Cognitive Abilities: A Survey of Pactor-Analytic Studies (Cambridge Universih' Press, New York, 1993), 4. For statistical testing in factor analysis, see D.N. Lawley and A.E. Maxwell, fuctor Anaiysif^ (! ,•= a Statistical Method (BulterwoTihs, London, 1971); M, Browne and R. Cudeck, Alternative ways of assessing model fit, m Testin)^ Structural Ltjuatioii Modeh, K. Bollen and S. Long, Eds. (Sage, Newbury Park, CA, 1993). 5. Eor power analysis in SFA, see A. Sattora and W.E. Saris, Power of the likelihood ratio test in covariance structure analysis, Psychometrika, 50, 83-90 (19«5); M. Sobel and G.W. Bohrnstedt, Use of null models in evaluating the fit of covariance structure models, in SociologKai Methodoiof^y 1985, N.B. Tuma, Ed, 0ossey-Bass, San Francisco, 1985). 6. For factor analysis of item-level data, see R. Mislevy, Recent developments in the factor analysis of categorical variables, journal of Educational Statistics, I I , 3-31 (1986); D.H. Parry and ]. ]. McArdle, An applied comparison of methods for least squares factor analysis of dichotomous variables. Applied Psychological Meiifiiretnoit, J5(l), 3>46 (1991) 7. For construct validity and SFA, see L.J. Cronbach and P. Meehl, Construct validity in psychological tests, Psycholo<,^ical Hulletui, 52, 2S1302 (1955); J.J. McArdle and C.A. Prescott, Agebased construct validation using structural equation modeling. Experimental Agini; Research, iS(3), 87-115 (1992). 8. Cronbach and Meehl, note 7, p. 291 9. t^anonical regression models can be written as a restricted structural equation model where we eliminate (a) the error variance D^ ^ 0 on the latent variable C and (b) the internal validity assumptions of uncorrelated residuals among the V" variables; see McArdle and Prescott, note 7. 10. For the debate on job performance, see M,J. Ree and J.A. Earles, Intelligence is the best predictor of job performance, Ciirrenl Directions in Psi/chakigical Science, 1, 8^^ -89 ( 1 9 9 2 ) ; R J . S t e r n bcrg and R.K. Wagner, The ^. '-ocentnc view of intelligence and job performance is wrong. Cur rent Directions in Psycholo^iictt! Science, 2, 1 4 (1993). 11. For the debate on personality factors, see B. Mershon and R. Gorsuch, Number of factors in the personality sphere: Does increase in factors increase predictability in real-life criteria? lournal of Personality und Social Psycholo^ii, 55, 67. > . h8(l (1988); L.R,' Goldberg, An" alternative "description of personalily": I he Big-Five factor structure, journal of t'enaiialittf and Social Psycholo'^y, 59, 1216-1229 (1991)). 12. For the debate on behavioral genetic models, see J.J. McArdle and Fi.H Goldsmith. Some alternative structural equation models for niultivariatt? biometric analyses, Bt'havior CJeiit'ticf^, 20, 569-608 (1990); M.C. Neale and i . R. Cardon, ,\^dliOiiolo^y for Genetic Studies of Twins and families (Kluwer Academic, Boston, 1992), 13. For current issues in SFA group selection effects, see J,L, Fiorn and J.J, McArdle, A practical and theoretical guide to measurement invariance in aging research, E. \pt'>imental Aging Re search, 18, 117-144 (1992); J.J. McArdle and' R.B. Cattell, Structural equation models ol factorial invariance in parallel proportional profiles and oblique confactor problems, Multit'ariate Bchai'ioral Research, 290), 63-113(1994). 14. For classical issues of group selection etfects, ,see W. Meredith, A method lor studying diflerences between groups, I'syehoiiu'trika. 3<J{1), "15-29 (1965); J.R Nesselroade^ Temporal selection and factor invariance in the . study of development and change, in l.ife-Sfim Devehpnient and Behavior, Vol. 5, P.B Dalles and O.G. Brim, Fds. (Academic Press, New York, 1983). 15. For SFA issues in longitudinal data analysis, see I J. McArdle and F Anderson, Lalenl variable growth models in research on aging, in The Handbook of the Psychology of Aging, J.E. Birren and K,W. Schaie, Eds. (Plenum Press, New York, 1990); J.J. McArdle and F. Hamagami, Modehng incomplete longitudinal and cross-seclional data using latent growth structural models. Experimental Aging Research. 18, 145-167 (1992) 16. For further issues about SFA missing-data models, see I'. D. Allison, Estimation of linear models with incomplete data, m Sociological Methodology, 1987, C.C. Clogg, Ed. (Jossey-Bass, San Francisco, 1987): J.J. McArdle, Structural factor analysis with incomplete data, Multivariate Behavioral'R.esearch, . -i, 409 454 (1994). 17. For critiques of TEA, see S.J. Gould, 77ji' Mi^mcasure of Man (W W. Norton, New York, 1981], R.J. Sternberg, Human intelligences The modo] IS the message. Science, 230, 1111-1118 (1985). 18. For critiques o! SFA, see N Cliff, Some cautions concerning the application of causal modeling methods, Multivariate Behavioral Research, 18, 115-126 (1983); P. Cohen, J. Cohen, J. Teresi, M. Marchi, and C.N. Vekv. , Problems in the measurement of latent variables in struclural equations causal models. Applied Psychological Mcasuwment, 14, 183-196(199(1) 19. R.B. Cattell, I'actof Analysis' An Introduction and Manual for Ihe Psychologist and Social Scientist (Harper & Brothers, New York, 1952), p. 315