Latent Curve Models: A Structural Equation Perspective

Latent Curve Models: A Structural Equation Perspective

Volumn , Issue , 2006, Pages 1-293

  Bollen, Kenneth A ^a   Curran, Patrick J ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EQUIVALENCE CLASSES; TEACHING; TRAJECTORIES;

LATENT VARIABLE MODELS; MULTIPLE INDICATORS; PEDAGOGICAL TOOLS; SOCIAL AND BEHAVIORAL SCIENCE; STRUCTURAL EQUATION MODELS; STRUCTURAL EQUATIONS; STRUCTURAL MODELING; TRAJECTORY MODELING;

BEHAVIORAL RESEARCH;

EID: 84949810763     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/0471746096     Document Type: Book

References (194)

1. Aber, M. S., & McArdle, J. J. (1991). Latent growth curve approaches to modeling the development of competence. In M. Chandler & M. Chapman (Eds.), Criteria for Competence (pp. 231-258). Hillsdale, NJ: Lawrence Earlbaum Associates.
2. Adam, J. (1978). Sequential strategies and the separation of age, cohort, and timeof-measurement contributions to developmental data. Psychological Bulletin, 85, 1309-1316.
3. Agresti, A. (1990). Categorical Data Analysis. New York: Wiley.
4. Aiken, L. S., & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA: Sage.
5. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), Proceedings of the 2nd International Symposium on Information Theory (pp. 267-281). Budapest: Akademiai Kiado.
6. Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317-332.
7. Allison, P. D. (2002). Missing Data. Thousand Oaks, CA: Sage.
8. Amemiya, T., & Anderson, T. W. (1990). Asymptotic chi-square tests for a large class of factor analysis models. Annals of Statistics, 18, 1453-1463.
9. Anderson, J., & Gerbing, D. W. (1984). The effects of sampling error on convergence, improper solutions and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173.
10. Anderson, T. W. (1960). Some stochastic process models for intelligence test scores. In K. J. Arrow, S. Karlin, & P. Suppes (Eds.), Mathematical Methods in the Social Sciences (pp. 205-220). Stanford, CA: Stanford University Press.
11. Anderson, T. W., & Rubin, H. (1956). Statistical inference in factor analysis. Proceedings of the 3rd Berkeley Symposium for Mathematical Statistics Problems, 5, 111-150.
12. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced Structural EquationModeling (pp. 243-278). Hillsdale, NJ: Lawrence Erlbaum Associates.
13. Arbuckle, J. L. (1997). AMOS 3.6 User's Guide, 3.6 ed. Chicago: SmallWaters Corp.
14. Arminger, G., & Schoenberg, R. (1989). Pseudo maximum likelihood estimation and a test for misspecification in mean and covariance structure models. Psychometrika, 54, 409-425.
15. Arminger, G., & Stein, P. (1997). Finite mixtures of covariance structure models with regressors. Sociological Methods and Research, 26, 148-182.
16. Arminger, G., Stein, P., & Wittenberg, J. (1999). Mixtures of conditional mean- and covariance-structure models. Psychometrika, 64 (4), 475-494.
17. Baker, G. A. (1954). Factor analysis of relative growth. Growth, 18, 137-143.
18. Baltes, P. B. (1968). Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 11, 145-171.
19. Bartlett, M. S. (1937). The statistical conception of mental factors. British Journal of Psychology, 28, 97-104.
20. Bauer, D. J. (2003). Estimating multilevel linear models as structural equation models. Journal of Educational and Behavioral Statistics, 28, 135-167.
21. Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes. Psychological Methods, 8, 338-363.
22. Bauer, D. J., & Curran, P. J. (2004). The integration of continuous and discrete latent variable models: potential problems and promising opportunities. Psychological Methods, 9 (1), 3-29.
23. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychometrika, 107 (2), 238-246.
24. Biesanz, J. C., & Bollen, K. A. (2003). A common framework for factor score estimates. Manuscript under review.
25. Biesanz, J. C., Deeb-Sossa, N., Papadakis, A. M., Bollen, K. A., & Curran, P. J. (2004). The role of coding time in estimating and interpreting growth curve models. Psychological Methods, 9 (1), 30-52.
26. Bishop, Y. M. M., Fienberg, S. E., & Holland., P. W. (1975). Discrete Multivariate Analysis: Theory and Practice. Cambridge, MA: MIT Press.
27. Bollen, K. A. (1987). Total, direct, and indirect effects in structural equation models. In C. C. Clogg (Ed.), Sociological Methodology, 1987 (pp. 37-69). Washington, DC: American Sociological Association.
28. Bollen, K. A. (1989a). A new incremental fit index for general structural equation models. Sociological Methods and Research, 17, 303-316.
29. Bollen, K. A. (1989b). Structural Equations with Latent Variables. New York: Wiley.
30. Bollen, K. A. (1990). Overall fit in covariance structure models: two types of sample size effects. Psychological Bulletin, 107 (2), 256-259.
31. Bollen, K. A. (1998). Cross National Indicators of Liberal Democracy, 1950-1990 Ann Arbor, Michigan: Interuniversity Consortium for Political and Social Research Study 2532.
32. Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53, 605-634.
33. Bollen, K. A. & Curran, P. J. (2004). Autoregressive latent trajectory (ALT) models: a synthesis of two traditions. Sociological Methods and Research, 32, 336-383.
34. Bollen, K. A., & Arminger, G. (1991). Observational residuals in factor analysis and structural equation models. Sociological Methodology, 21, 235-262.
35. Bollen, K. A., & Long, J. S. (Eds.) (1993a). Testing Structural Equation Models. Newbury Park, CA: Sage.
36. Bollen K. A. & Long J. S. (1993b). Introduction. In K.A. Bollen and J.S. Long (Eds.), Testing Structural Equation Models. Newbury Park, CA: Sage.
37. Bollen, K. A., & Stine, R. (1990). Direct and indirect effects: classical and bootstrap estimates of variability. Sociological Methodology, 20, 115-140.
38. Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods and Research, 21, 205-229.
39. Boomsma, A. (1982). The robustness of LISREL against small sample sizes in factor analysis models. In K. G. Joreskog & H. Wold (Eds.), Systems Under Indirect Observation, Part I (pp. 149-173). Amsterdam: North-Holland.
40. Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37 (1), 62-83.
41. Browne, M. W. (1987). Robustness of statistical inference in factor analysis and related models. Biometrika, 74, 375-384.
42. Browne, M. W., & Du Toit, S. H. C. (1991). Models for learning data. In L. M. Collins and J. L. Horn (Eds.), Best Methods for the Analysis of Change (pp. 47-68). Washington, DC: American Psychological Association.
43. Browne, M. W. (1993). Structured latent curve models. In C. M. Cuadras & C. R. Rao (Eds.), Multivariate Analysis: Future Directions, Vol. 2 (pp. 171-197). New York: Elsevier Science.
44. Browne,M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing Structural Equation Models. Newbury Park, CA: Sage.
45. Browne, M. W., & du Toit, S. H. C. (1991). Models for learning data. In L. M. Collins & J. L. Horn (Eds.), Best Methods for the Analysis of Change (pp. 47-68). Washington, DC: American Psychological Association.
46. Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. Newbury Park, CA: Sage.
47. Cadigan, N. G. (1995). Local influence in structural equations models. Structural Equation Modeling, 2 (1), 13-30.
48. Carrig, M., Wirth, R. J., & Curran, P. J. (2004). A SAS macro for estimating and visualizing individual growth curves. Structural Equation Modeling, 11, 132-149.
49. Chen, F., Bollen, K. A., Paxton, P., Curran, P. J., & Kirby, J. B. (2001). Improper solutions in structural equation models. Sociological Methods and Research, 29 (4), 468-508.
50. Cohen, J. (1978). Partialled products are interactions; partialled powers are curve components. Psychological Bulletin, 85, 858-866.
51. Cohen, J., & Cohen, P. (1983). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates.
52. Costner, H. L., & Schoenberg, R. (1973). Diagnosing indicator ills in multiple indicator models. In A. S. Goldberger & O. D. Duncan (Eds.), Structural Equation Models in the Social Sciences (pp. 167-200). New York: Seminar Press.
53. Curran, P. J., Stice, E. & Chassin, L. (1997). The relation between adolescent and peer alcohol use: A longitudinal random coefficients model. Journal of Consulting and Clinical Psychology, 65, 130-140.
54. Curran, P. J. (1997). Comparing three modern approaches to longitudinal data analysis: an examination of a single developmental sample. Presented at the symposium conducted at the meeting of the Society for Research on Child Development (Patrick J. Curran, Chair), April, Washington, DC.
55. Curran, P. J., & Bollen, K. A. (2001). The best of both worlds: combining autoregressive and latent curve models. In L. M. Collins & A. G. Sayer (Eds.), New Methods for the Analysis of Change (pp. 107-135). Washington, DC: American Psychological Association.
56. Curran, P. J., & Willoughby, M. T. (2003). Reconciling theoretical and statistical models of developmental processes. Development and Psychopathology, 15, 581-612.
57. Curran, P. J., Muthén, B. O., & Harford, T. C. (1998). The influence of changes in marital status on developmental trajectories of alcohol use in young adults. Journal of Studies on Alcohol, 59, 647-658.
58. Curran, P. J., Bauer, D. J., & Willoughby, M. T. (2004). Testing and probing main effects and interactions in latent curve analysis. Psychological Methods, 9, 220-237.
59. Curran, P. J., Bauer, D. J., & Willoughby, M. T. (in press). Testing and probing interactions in hierarchical linear growth models. In C. S. Bergeman & S. M. Boker (Eds.), Methodological Issues in Aging Research, Vol. 1. Mahwah, NJ: Lawrence Erlbaum Associates.
60. Davis, W. R. (1993). The FC1 rule of identification for confirmatory factor analysis: a general sufficient condition. Sociological Methods and Research, 21 (4), 403-437.
61. du Toit, S. H. C., & Cudeck, R. (2001). The analysis of nonlinear random coefficient regression models with LISREL using constraints. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural Equation Modeling: Present and Future (pp. 259-278). Lincolnwood, IL: Scientific Software.
62. Duncan, S. C., & Duncan, T. E. (1996). A multivariate latent growth curve analysis of adolescent substance use. Structural Equation Modeling, 3 (4), 323-347.
63. Enders, C. K. (2001). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6 (4), 352-370.
64. Flora, D. B. (2002). Evaluation of categorical variable methodology for confirmatory factor analysis with liker t-type data. Unpublished dissertation. University of North Carolina at Chapel Hill.
65. Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9, 466-491.
66. Gerbing, D. W., & Anderson, J. C. (1993). Monte Carlo evaluations of goodness-of-fit indices for structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing Structural Equation Models (pp. 40-65). Newbury Park, CA: Sage.
67. Glenn, N. D. (1976). Cohort analysts' futile quest: statistical attempts to separate age, period and cohort effects. American Sociological Review, 41 (5), 900-904.
68. Gold, M. S., Bentler, P. M., & Kim, K. H. (2003). A comparison of maximum-likelihood and asymptotically distribution-free methods of treating incomplete nonnormal data. Structural Equation Modeling, 10 (1), 47-79.
69. Gompertz, B. (1820). A sketch of an analysis and notation applicable to the estimation of the value of life contingencies. Philosophical Transactions of the Royal Society, 110, 214-294.
70. Gompertz, B. (1825). On the nature of the function expressive of the law of human mortality. Philosophical Transactions of the Royal Society, 115, 513-583.
71. Graham, J. W. (2003). Adding missing-data relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10, 80-100.
72. Griliches, Z. (1957). Hybrid corn: an exploration in the economics of technical change. Econometrica, 48, 501-522.
73. Gurr, T. R. (1990). Polity II: Political Structures and Regime Change, 1800-1986, Ann Arbor, Michigan. Interuniversity Consortium for Political and Social Research Study 9263.
74. Haitovsky, Y. (1968). Missing data in regression analysis. Journal of the Royal Statistical Society, Series B, 30, 67-82.
75. Harman, H. H. (1967). Modern Factor Analysis, 2nd ed. Chicago: University of Chicago Press.
76. Heckman, J. T. (1979). Sample selection bias as a specification error. Econometrica, 45 :153-161.
77. Heise, D. R. (1969). Separating reliability and stability in test-retest correlation. American Sociological Review, 34 (1), 93-101.
78. Hipp, J. R., & Bauer, D. J. (in press). Local solutions in the estimation of growth mixture models. Psychological Methods.
79. Hipp, J. R., Bauer, D. J., Curran, P. J., & Bollen, K. A. (2004). Crimes of opportunity or crimes of emotion: testing two explanations of seasonal change in crime. Social Forces, 82 (4), 1333-1372.
80. Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6 (1), 1-55.
81. Humphreys, L. G. (1960). Investigations of the simplex. Psychometrika, 25, 313-323.
82. Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1994). Latent class structural equation models for marketing research. Unpublished paper.
83. Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1997). Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16 (1), 39-59.
84. Johnson, P. O., & Neyman, J. (1936). Tests of certain linear hypotheses and their applications to some educational problems. Statistical Research Memoirs, 1, 57-93.
85. Johnson, J. (1984). Econometric Methods. New York: McGraw-Hill.
86. Jones, B. L., Nagin, D. S., & Roeder, K. (2001). A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods and Research, 29, 374-393.
87. Jones, M. P. (1996). Indicator and stratification methods for missing explanatory variables in multiple linear regression. Journal of the American Statistical Association, 91 (433), 222-230.
88. Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34 (2), 183-202.
89. Jöreskog, K. G. (1970). A general method for analysis of covariance structures. Biometrika, 57, 239-251.
90. Jöreskog, K. G. (1979). Statistical models and methods for analysis of longitudinal data. In K. G. Jöreskog & D. Sörbom (Eds.), Advances in Factor Analysis and Structural Equation Models. Cambridge, MA: Abt Books.
91. Jöreskog, K. G. (1994). On the estimation of polychoric correlations and their asymptotic covariance-matrix. Psychometrika, 59 (3), 381-389.
92. Jöreskog, K. G. (2001a). Analysis of Ordinal Variables, Note 2, Cross-Sectional Data. pp. 1-26. www.ssicentral.com/lisrel/corner.htm.
93. Jöreskog, K. G. (2001b). Analysis of Ordinal Variables, Note 3, Longitudinal Data. pp. 1-26. www.ssicentral.com/lisrel/corner.htm.
94. Jöreskog, K. G., & Sörbom, D. (1981). LISREL V: Analysis of Linear Structural Relationships by Maximum Likelihood. Chicago: National Educational Resources.
95. Jöreskog, K. G., Sörbom, D., du Toit, S., & du Toit, M. (1999). LISREL 8: New Statistical Features. Chicago: Scientific Software.
96. Kenny, D. A., & Zautra, A. (1995). The trait-state-error model for multiwave data. Journal of Consulting and Clinical Psychology, 63, 52-59.
97. Lawley, D. N. (1940). The estimation of factor loadings by the method of maximum likelihood. Proceedings of the Royal Society of Edinburgh, 60, 64-82.
98. Lazarsfeld, P. F., & Henry, N. W. (1968). Latent Structure Analysis. Boston: Houghton Mifflin.
99. Lee, S.-Y., Poon, W. Y., & Bentler, P. M. (1990). Full maximum likelihood analysis of structural equation models with polytomous variables. Statistics and Probability Letters, 9, 91-97.
100. Li, F., Duncan, T. E., Duncan, S. C., & Acock, A. (2001). Latent growth modeling of longitudinal data: a finite growth mixture modeling approach. Structural Equation Modeling, 8 (4), 493-530.
101. Li, K.-H., Meng, X.-L., Raghunathan, T. E., & Rubin, D. B. (1991). Significance levels from repeated P-values with multiple-inputed data. Statistica Sinica, 1, 65-92.
102. Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83, 1198-1202.
103. Little, R. (1992). Regression with missing X's: A review. Journal of the American Statistical Association, 87, 1227-1237.
104. Little, R. J. A., & Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd ed. Hoboken, NJ: Wiley.
105. Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. Newbury Park, CA: Sage.
106. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1 (2), 130-149.
107. MacCallum, R. C., Kim, C., Malarkey, W. B., & Kiecolt-Glaser, J. K. (1997). Studying multivariate change using multilevel models and latent curve models. Multivariate Behavioral Research, 32 (3), 215-253.
108. Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57, 519-530.
109. Mardia, K. V. (1974). Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhya, series B, 36, 115-128.
110. Matsueda, R. L., & Bielby, W. T. (1986). Statistical power in covariance structure models. In N. B. Tuma (Ed.), Sociological Methodology, Vol. 16 (pp. 120-158). Washington, DC: American Sociological Association.
111. 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, 2nd ed. (pp. 561-614). New York: Plenum Press.
112. McArdle, J. J. (1989). Structural modeling experiments using multiple growth functions. In R. Kanfer, P. Ackerman, & R. Cudeck (Eds.), Abilities, Motivation, and Methodology: The Minnesota Symposium on Learning and Individual Differences (pp. 71-117). HIllsdale, NJ: Lawrence Erlbaum Associates.
113. McArdle, J. J. (2001). A latent difference score approach to longitudinal dynamic structural analyses. In R. Cudeck, S. du Toit, & D. Sorbom (Eds.), Structural Equation Modeling: Present and Future (pp. 342-380). Lincolnwood, IL: Scientific Software.
114. McArdle, J. J., & Hamagami, F. (2001). Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. In L. M. Collins & A. G. Sayer (Eds.), New Methods for the Analysis of Change (pp. 139-175). Washington, DC: American Psychological Association.
115. McDonald, R. P., & Marsh, H. W. (1990). Choosing a multivariate model: noncentrality and goodness of fit. Psychological Bulletin, 107 (2), 247-255.
116. McDonald, R. P., & Mulaik, S. A. (1979). Determinacy of common factors: a nontechnical review. Psychological Bulletin, 86 (2), 297-306.
117. McLachlan, G., & Basford, K. E. (1988). Mixture Models: Inference and Applications to Clustering. New York: Marcel Dekker.
118. Mehta, P. D., & West, S. G. (2000). Putting the individual back into individual growth curves. Psychological Methods, 5 (1), 23-43.
119. Mehta, P. D., Neale, M. C., & Flay, B. R. (2004). Squeezing interval change from ordinal panel data: latent growth curves with ordinal outcomes. Psychological Methods, 9 (3), 301-333.
120. Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525-543.
121. Meredith, W., & Tisak, J. (1984). On "Tuckerizing" curves. Presented at the annual meeting of the Psychometric Society, Santa Barbara, CA.
122. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55 (1), 107-122.
123. Muthén, B. O. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New Developments and Techniques in Structural EquationModeling (pp. 1-33). Mahwah, NJ: Lawrence Erlbaum Associates.
124. Muthén, B. O., & Curran, P. J. (1997). General longitudinal modeling of individual differences in experimental designs: a latent variable framework for analysis and power estimation. Psychological Methods, 2 (4), 371-402.
125. Muthén, B. O.,&Jöreskog, K. G. (1983). Selectivity problems in quasi-experimental studies. Evaluation Review, 7 (2), 139-174.
126. Muthén, L. K. & Muthén, B. O. (2000). Mplus User's Guide. Second Edition. Los Angeles, CA: Muthén & Muthén.
127. Muthén, B. O., & Muthén, L. K. (2001). MPlus: Statistical Analysis with Latent Variables User's Guide, 2nd ed. Los Angeles: Stat Model.
128. Muthén, B. O., & Satorra, A. (1995). Complex sample data in structural equation modeling. Sociological Methodology, 25, 267-316.
129. Muthén, B. O., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.
130. Muthén, B. O., du Toit, S. H. C., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. Unpublished paper.
131. Nagin, D. S. (1999). Analyzing developmental trajectories: a semiparametric, group-based approach. Psychological Methods, 4 (2), 139-157.
132. Nagin, D. S. (2005). Group-Based Models of Development. Cambridge, MA: Harvard University Press.
133. Nagin, D., & Tremblay, R. E. (2001). Parental and early childhood predictors of persistent physical aggression in boys from kindergarten to high school. Archives of General Psychiatry, 58, 389-394.
134. Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2002). Mx: Statistical Modeling, 6th ed. Richmond, VA: Virginia Commonwealth University, Department of Psychiatry.
135. Olsson, U. (1979). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44 (4), 443-460.
136. Olsson, U., Drasgow, F., & Dorans, N. J. (1982). The polyserial correlation coefficient. Psychometrika, 47, 337-347.
137. Pearl, R. (1924). Studies in Human Biology. Baltimore, MD: Williams & Wilkins.
138. Pearl, R. (1925). The Biology of Population Growth. New York: Alfred A. Knopf.
139. Pothoff, R. F. (1964). On the Johnson-Neyman technique and some extensions thereof. Psychometrika, 29, 241-256.
140. Quetelet, L. A. J. (1835). Sur l'homme et le développement de ses facultés ou essai de physique sociale. In E. Boyd, B. S. Savara, & J. F. Schilke (Eds.) (1980), Origins of the Study of Human Growth (pp. 317-332). Portland, OR: University of Oregon Health Sciences Center Foundation.
141. Quiroga, A.M. (1992). Studies of the polychoric correlation and other correlation measures for ordinal variables. Unpublished doctoral dissertation, University of Uppsala.
142. Raftery, A. E. (1993). Bayesian model selection in structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing Structural Equation Models (pp. 163-180). Newbury Park, CA: Sage.
143. Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111-163.
144. Rao, C. R. (1958). Some statistical methods for comparison of growth curves. Biometrika, 51, 83-90.
145. Raudenbush, S. W. (2001). Toward a coherent framework for comparing trajectories of individual change. In L. M. Collins & A. G. Sayer (Eds.), New Methods for the Analysis of Change (pp. 35-64). Washington, DC: American Psychological Association.
146. Reed, L. J. (1921). On the correlation between any two functions and its application to the general case of spurious correlation. Journal of the Washington Academy of Sciences, 11, 449-455.
147. Reed, L. J., & Love, A. G. (1932). Biometric studies on U.S. army officers: somatological norms, correlations, and changes with age. Human Biology, 4, 509-524.
148. Reed, L. J., & Pearl, R. (1927). On the summation of logistic curves. Journal of the Royal Statistical Society, 90, 729-746.
149. Robertson, T. B. (1908). On the normal rate of growth of an individual, and its biochemical significance. Archiv für Entwicklungsmechanik der Organismen, 25, 581-614.
150. Rogosa, D. R. (1980). Comparing nonparallel regression lines. Psychological Bulletin, 88, 307-321.
151. Rogosa, D. R. (1981). On the relationship between the Johnson-Neyman region of significance and statistical tests of parallel within group regressions. Educational and Psychological Measurement, 41, 73-84.
152. Rogosa, D. R., & Saner, H. M. (1995). Longitudinal data analysis examples with random coefficent models. Journal of Educational and Behavioral Statistics, 20, 149-170.
153. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63 (3), 581-592.
154. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
155. Saris, W. E., & Satorra, A. (1993). Power evaluations in structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing Structural Equation Models (pp. 181-204). Newbury Park, CA: Sage.
156. Saris, W. E. & Satorra, A. (1993). Power evaluations in structural equation models. In K.A. Bollen and J.S. Long (Eds.), Testing Structural Equation Models. Newbury Park, CA: Sage.
157. SAS Institute (2000). SAS/STAT User's Guide, 2nd ed. Vol. Version 8. Cary, NC: SAS.
158. Satorra, A. (1990). Robustness issues in structural equation modeling: a review of recent developments. Quality and Quantity, 24, 367-386.
159. Satorra, A. (1992). Asymptotic robust inferences in the analysis of mean and covariance structures. Sociological Methodology, 22, 249-278.
160. Satorra, A., & Bentler, P. M. (1988). Scaling corrections for chi-square statistics in covariance structure analysis. Proceedings of the Business and Economic Statistics Section of the American Statistical Association, pp. 308-313.
161. Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent Variables Analysis: Applications for Developmental Research (pp. 399-419). Newbury Park, CA: Sage.
162. Satorra, A., & Saris, W. E. (1985). Power of the likelihood ratio test in covariance structure analysis. Psychometrika, 50, 83-90.
163. Sayer, A. G., & Cumsille, P. E. (2001). Second-order latent growth models. In L. M. Collins & A. G. Sayer (Eds.), New Methods for the Analysis of Change (pp. 179-200). Washington, DC: American Psychological Association.
164. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. New York: Chapman & Hall.
165. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
166. Schwarz, G. (1978). Estimating the dimensions of a model. Annals of Statistics, 6, 461-464.
167. Shapiro, A. (1987). Robustness properties of the MDF analysis of moment structures. South African Statistical Journal, 21, 39-62.
168. Skrondal, A. and Rabe-Hesketh, S. (2004). Generalized Latest VariableModeling: Multilevel, Longitudinal and Structural Equation Models. Boca Raton, FL: chapman & Hall/CRC.
169. Snijders, T. A. B., & Bosker, R. (1999). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Newbury Park, CA: Sage.
170. Steiger, J. H. (1979). Factor indeterminacy in the 1930's and the 1970's: some interesting parallels. Psychometrika, 44, 157-167.
171. Steiger, J. H. (1989). EzPATH: A Supplementary Module for SYSTAT and SYGRAPH (computer program manual). Evanston, IL: Systat, Inc.
172. Steiger, J. H., & Lind, J. C. (1980). Statistically based tests for the number of common factors. Presented at the Psychometric Society, Iowa City, IA.
173. Steiger, J. H., Shapiro, A., & Browne, M. W. (1985). On the multivariate asymptotic distribution of sequential chi-square statistics. Psychometrika, 50, 253-264.
174. Thomson, G. H. (1939). The Factorial Analysis of Human Ability. Boston: Houghton Mifflin.
175. Tucker, L. R. (1958). Determination of parameters of a functional relation by factor analysis. Psychometrika, 23, 19-23.
176. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1-10.
177. U.S. Department of Justice (1995). Uniform Crime Reports. Washington, DC: U.S. Government Printing Office.
178. U.S. Department of Justice (Ed.) (2000). Uniform Crime Reporting Program Data: [United States]; 1975-1997 [Offenses Known and Clearances by Arrest, 1990] (computer file). Ann Arbor, MI: Inter-university Consortium for Political and Social Research (producer and distributor).
179. Vanhanen, T. (1990). The Process of Democratization: A Comparative Study of 147 States, 1980-88. New York: Crane Russak.
180. Verhulst, P. F. (1845). Recherches mathématiques sur la loi d'accroissement de la population. Nouveaux Mémoires de l'Académie Royale des Sciences et Belles-Lettres de Bruxelles, 18, 1-38.
181. Verhulst, P. F. (1847). Deuxième mémoire sur la loi d'accroissement de la population. Mémoires de l'Académie Royale des Sciences et Belles-Lettres de Bruxelles, 20, 1-32.
182. Vonesh, E. F., & Chinchilli, V. M. (1997). Linear and Nonlinear Models for the Analysis of Repeated Measurements. New York: Marcel Dekker.
183. Werts, C. E., Jöreskog, K. G., & Linn, R. L. (1971). Comment on the estimation of measurement error in panel data. American Sociological Review, 36, 110-112.
184. White, H. (1980). A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica, 48, 817-838.
185. Wiley, D. E., & Wiley, J. A. (1970). The estimation of measurement error in panel data. American Sociological Review, 35, 112-117.
186. Wilkinson, L., & The Talk Force on Statistical Inference. (1999). Statistical methods in psychology journals: guidelines and explanations. American Psychologist, 54, 594-604.
187. Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116 (2), 363-381.
188. Winship, C., & Mare, R. D. (1992). Models for sample selection bias. Annual Review of Sociology, 18, 327-350.
189. Wishart, J. (1938). Growth-rate determinations in nutrition studies with the bacon pig, and their analysis. Biometrika, 30 (1-2), 16-28.
190. Wothke, W. (1993). Nonpositive definite matrices in structural modeling. In K. A. Bollen & J. S. Long (Eds.), Testing Structural Equation Models (pp. 256-293). Newbury Park, CA: Sage.
191. Yuan, K.-H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology, 30, 165-200.
192. Yung, Y.-F. (1994). Finite mixtures in confirmatory factor-analytic models. Unpublished Ph.D. dissertation, UCLA, Los Angeles.
193. Yung, Y.-F. (1997). Finite mixtures in confirmatory factor-analysis models. Psychometrika, 62 (3), 297-330.
194. Zeger, S. L., & Harlow, S. D. (1987). Mathematical models from laws of growth to tolls for biological analysis: fifty years of growth. Growth, 51, 1-21.