semrefs

Jason Newsom's
Structural Equation Modeling Reference List
(Journal Articles and Chapters on Structural Equation Models)

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INTRODUCTORY CHAPTERS

Alwin, D.F., & Jackson, D.J. (1979). Measurement models for response errors in surveys: Issues and applications. Sociological Methodology, 1980. San Francisco, CA: Jossey-Bass.

Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation modeling in practice : A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.

Bentler, P. M., & Chou, C. (1987). Practical issues in structural modelling. Sociological Methods and Research, 16, 78-117.

Bentler, P.M., & Chou, C.-P. (1988). Practical issues in structural modeling. In J.S. Long (Ed.), Common problems/proper solutions (pp. 161-192). Beverly Hills, CA: Sage.

Glaser, D. (2002). Structural Equation Modeling Texts: A primer for the beginner. Journal of Clinical Child Psychology, 31(4), 573-578.

Diamantopoulos, A.(1994), 'Modeling with LISREL: A guide for the uninitiated', Journal of Marketing Management, 10, 105-136.

Kelm, L. (2000). Structural equation modeling. In L. G. Grimm & P. R. Yarnold (Eds.) Reading and understanding more multivariate statistics (pp.

227-260). Washington, DC: American Psychological Association.

MacCallum, R. (1986). Specification searches in covariance structure modeling. Psychological Bulletin, 100, 107-120.

Mueller, R. (1997). Structural equation modeling: Back to basics. Structural Equation Modeling, 4, 353-369.

Tanaka, J.S., Panter, A.T., Winborne, W.C., & Huba, G.J. (1990). Theory testing in personality and social psychology with structural equation models: A primer in 20 questions. In C. Hendrick, & M.S. Clark (Eds.), Review of personality and social psychology (Vol 11, pp. 217-241). Newbury Park, CA: Sage.

INTRODUCTORY TEXTS
(See the SEMbooks list also)

Arbuckle, James (1997). AMOS Users' Guide Version 3.6. Smallwaters Corporation. (1-56827-125-5)

Bollen, K.A. (1989). Structural equations with latent variables. New York: Wiley.

Hoyle, Rick (1995). Structural Equation Modeling: Concepts, Issues and Applications. Sage Publications (0-8039-5318-6).

Kline, R. B. (2004). Principles and practice of structural equation modeling (Second Edition). New York: Guilford Press. (1572306904)

Loehlin, John C. (1998). Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis. 3^rd ed. Mahwah, N.J.: Lawrence Erlbaum Associates.

Maruyama (1998).Basics of Structural Equation Modeling. Thousand Oaks: Sage.

Mueller, Ralph (1996). Basic Principles of Structural Equation Modeling: An introduction to LISREL and EQS, Springer Press (0-387-94516-4).

Schumacker, Randall & Lomax, Richard (1996). A Beginner's Guide to Structural Equation Modeling. Lawrence Erlbaum. (0-8058-1766-2).

HISTORY OF STRUCTURAL EQUATION MODELING

Aigner, D.J., Hsiao, C., Kapteyn, A., & Wansbeek, T. (1984). Latent variable models in econometrics. IN Z. Griliches & M.D. Intriligator (Eds.), Handbook of Econometrics (Vol. 2, pp. 1321-1393). Amsterdam: North-Holland.

Austin, J.T., & Wolfle, D. (1991). Theoretical and technical contributions to structural equation modeling: An updated annotated biliography. Structural Equation Modeling, 3, 105-175.

Bentler, P.M. (1980). Multivariate analysis with latent variables: Causal modeling. Annual Review of Psychology, 31, 419-456.

Bentler, P.M. (1986). Structural equation modeling and Psychometrika: An historical perspective on growth and achievements. Psychometrika, 31, 35-51.

Bielby, W.T., & Hauser, R.M. (1977). Structural equation models. Annual Review of Sociology, 3, 137-161.

Bollen, K.A. (1989). Structural equations with latent variables. New York: Wiley. [Chapter 1]

Epstein, R.J. (1987). A history of econometrics. Amsterdam: Elsevier.

Tremblay, P.F., & Gardner, R.C. (1996). On the growth of structural equation modeling in psychological journals. Structural Equation Modeling, 3, 93-104.

PATH ANALYSIS, PATH MODELS
(see also mediation, indirect effects)

Alwin, D. F., & Hauser, R. M. (1975). The decomposition of effects in path analysis. American Sociological Review, 40, 37-47.

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, D.C.: American Sociological Association.

DeShon, R.P. (1998). A cautionary note on measurement error corrections in structural equation models. Psychological Methods, 3, 412-423.

Everitt, B. S., and G. Dunn, G. (1991). Applied multivariate data analysis. London: Edward Arnold.

Duncan, O.D. (1966). Path analysis: Sociological examples. American Journal of Sociology, 72, 219-316.

Duncan, O.D. (1975). Introduction to structural equation models. New York: Academic Press.

Freedman, D.A. (1987). As others see us: A case study in path analysis. Journal of Educational Statistics, 12, 101-128.

Kelm, L. (2000). Path Analysis. In L. G. Grimm & P. R. Yarnold (Eds.) Reading and understanding multivariate statistics (pp. 65-97). Washington, DC: American Psychological Association.

Loehlin, John C. (1998). Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis. 3^rd ed. Mahwah, N.J.: Lawrence Erlbaum Associates.

Maassen, G. H., & Bakker, A. B. (2001). Suppressor variables in path models: Definitions and interpretations. Sociological Methods and Research, 30, 241-270.

McDonald, R.P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31, 239-270.

Holland, P. W. (1988). Causal inference, path analysis, and recursive structural equations models. Sociological Methodology, 18, 449-493.

Pedhazur, Elazer J. (1982). Multiple regression in behavioral research, 2nd edition. NY: Holt. Chapter 15 (pp. 577-635) covers path analysis. Widely used textbook.

Wolfe, L.M. (1999). Sewall Wright on the method of path coefficients: An annotated bibliography. Structural Equation Modeling, 6, 280-291.

Wright, S. (1934). The method of path coefficients. Annals of Mathematical Statistics, Vol. 5: 161-215.

CRITICAL REVIEWS, CRITIQUES, and GENERAL COMMENTARY

Baumrind, D. (1983). Specious causal attributions in the social sciences: The reformulated stepping-stone theory of heroin use as an exemplar. Journal of Personality and Social Psychology, 45, 1289-1298.

Berk, R.A. (1988). Causal inference for sociological data. In N.J. Smelser (Ed.), Handbook of Sociology. Newbury Park, CA: Sage.

Biddle, B. J., & Marlin, M. M. (1987). Causality, confirmation, credulity, and structural equation modeling. Child Development, 58, 4-17.

Breckler, S. J. (1990). Applications of covariance structure modeling in psychology: Cause for Concern? Psychological Bulletin, 107, 260-273.

Cliff, N. (1983). Some cautions concerning the application of causal modeling methods. Multivariate Behavioral Research, 18, 81-105.

Cohen, Cohen, Teresi, Marchi, and Velez (1990). Problems in the measurement of latent variables in structural equations casual models. Applied Psychological Measurement, vol 14,(2), 183-196.

Freedman, D.A. (1987). As others see us: A case study in path analysis. Journal of Educational Statistics, 12, 101-128.

MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin. 111, 490-504

MacCallum, R. C., Wegener, D. T., Uchino, B. N., & Fabrigar, L. R.(1993). The problem of equivalent models in applications of covariance structure analysis. Psychological Bulletin, 114, 185-199.

Mueller, R. (1997). Structural equation modeling: Back to basics. Structural Equation Modeling, 4, 353-369.

Rogosa, D. (1987). Casual models do not support scientific conclusions: A comment in support of Freedman. Journal of Educational Statistics, 12, 185-195.

Williams, L. J., Bozdogan, H., & Aiman-Smith, L. (1996). Inference problems with equivalent models. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp.279-314). Mahwah, NJ: Erlbaum.

SAMPLE SIZE ISSUES

Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation modeling in practice : A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.

Barrett, P. T., & Kline, P. (1981). The observation to variable ratio in factor analysis. Personality Study and Group Behavior, 1, 23-33.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.

Bentler, P. M., & Chou, C. (1987). Practical issues in structural modelling. Sociological Methods and Research, 16, 78-117.

Bollen, K.A. (1990) Overall fit in covariance structure models: Two types of sample size effects. Psychological Bulletin, 107, 256-259.

Boomsma, A. (1982). The robustness of LISREL against small sample size in factor analysis models. In K. G. JF6reskog & H. Wold (Eds.), Systems under indirect observation, Part 1 (pp. 149-173). Amsterdam: North-Holland.

Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling, 6, 56-83.

Finch, J. F., West, S. G., & MacKinnon, D. P. (1997). Effects of sample size and nonnormality on the estimation of mediated effects in latent variable models. Structural Equation Modeling, 4(2), 87-107.

Gerbing, D.W., & Anderson, J.C. (1993). Monte Carlo evaluations of goodness-of-fit indices for structural equation modeling. In

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. Newbury Park, CA: Sage.

Guadagnoli, E., & Velicer, W. F. (1988). Relation of sample size to the stability of component patterns. Psychological Bulletin, 103(2),265-275.

Hu, L.-T., & Bentler, P. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural Equation Modeling. Concepts, Issues, and Applications (pp. 76-99). London: Sage.

Hu, L.-T., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.

Tanaka, J.S. (1987). "How big is big enough?": Sample size and goodness of fit in structural equation models with latent variables. Child Development, 58, 134-146.

Tanaka, J.S. (1993). Multifaceted conceptions of fit in structural equation models. In K.A. Bollen, & J.S. Long (eds.), Testing structural equation models. Newbury Park, CA: Sage.

CORRELATION VS. COVARIANCE MATRICES

Cudek, R. (1989). Analysis of correlation matrices using covariance structure models. Psychological Bulletin, 2, 317-327.

MISSING DATA, MISSING DATA IMPUTATION, MISSING DATA ESTIMATION, MULTIPLE IMPUTATION

Allison, P.D. (1987) Estimation of linear models with incomplete data. In C.C. Clogg [Ed.] Sociological Methodology (pp. 71-103). San Francisco: Jossey-Bass,

Arbuckle, J.L. (1996) Full information estimation in the presence of incomplete data. In G.A. Marcoulides and R.E. Schumacker [Eds.] Advanced structural equation modeling: Issues and Techniques. Mahwah, NJ: Lawrence Erlbaum Associates.

Duncan, T.E., Duncan, S.C. and Li, F. (1998) A comparison of model and multiple imputation-based approaches to longitudinal analyses with partial missingness. Structural Equation Modeling: A Multidisciplinary Journal, 5(1), 1-21.

Enders, C.K. (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling, 8, 128-141.

Enders, C.K. (2001). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6, 352-370.

Enders, C. K., Peugh, J.L. (2004). Using an EM covariance matrix to estimate structural equation models with missing data: Choosing an adjusted sample size to improve the accuracy of inferences. Structural Equation Modeling: A Multidisciplinary Journal, 11, 1-19.

Enders, C. K. (2005). A SAS macro for implementing the modified Bollen-Stine bootstrap for missing data: Implementing the bootstrap using existing structural equation modeling software. Structural Equation Modeling: A Multidisciplinary Journal, 12(4), 620-641.

Enders, C. K. (2006). Analyzing structural equation models with missing data. In G.R. Hancock & R.O. Mueller (Eds.), Sstructural equation modeling: A second course. Greenwich, CT: .Information Age.

Ford, B. L. (1983). An overview of hot-deck procedures. In W. G. Madow, I. Olkin, & D. B. Rubin (Eds.), Incomplete Data in Sample Survey. Volume II: Theory and Bibliographies (pp. 185-207). New York: Academic Press.

Gold, M.S., & Bentler, P.M. (2000). Treatments of missing data: A Monte Carlo comparison of RBHDI, iterative stochastic regression imputation, and expectation-maximization. Structural Equation Modeling, 7, 319-355.

Graham, J. W., Hofer, S.M., Donaldson, S.I., MacKinnon, D.P., & Schafer, J.L. (1997). Analysis with missing data in prevention research. In K. Bryant, M. Windle, & S. West (Eds.), The science of prevention: methodological advances from alcohol and substance abuse research. (pp. 325-366). Washington, D.C.: American Psychological Association.

Graham, J. W., Hofer, S. M., & MacKinnon, D. P. (1996). Maximizing the usefulness of data obtained with planned missing value patterns: An application of maximum likelihood procedures. Multivariate Behavioral Research, 31, 197-218.

Hedeker & Gibbons (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2, 64-78.

Jinn, J.H., & Sedransk, J. (1989). Effect on secondary data analysis of common imputation methods. Sociological Methodology. Washington, DC: American Sociological Association.

Jones, M. P. (1996). Indicator and Stratification Methods for Missing Explanatory Variables in Multiple Linear Regression. Journal of the American Statistical Association, 91, 222-230.

Kaplan, D. (1995). The impact of BIB-spiraling induced missing data patterns on goodness-of-fit tests in factor analysis. Journal of Educational and Behavioral Statistics, 20, 69-82.

Lepkowski, J. M., Landis, J. R., & Stehouwer, S. A. (1987). Strategies for the analyses of imputed data from a sample survey: The national medical care utilization and expenditure survey. Medical Care, 25, 705-716.

Little, R.J.A., & Rubin, d.B. (1989). The analysis of social science data with missing values, Sociological Methods and Research, 18, 292-326.

Little, RJA & Rubin, D (1987). Statistical analysis with missing data. Wiley.

Marsh, H.W. (1998). Pairwise deletion for missing data in structural equation models with missing data: Nonpositive definite matrices, parameter estimates, goodness of fit, and adjusted sample sizes. Structural Equation Modeling, 5, 22-36.

Muthen, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 51,431-462.

Roth, P. L. (1994). Missing data: A conceptual review from applied psychologists. Personnel Psychology, 47, 537-560.

Roth, P.L. (1994). Missing data: A conceptual review for applied psychologists. Personnel Psychology, 47, 537-560.

Roth, P. L., Switzer, F. S. , & Switzer, D. (1999). Missing data in multiple item scales: A monte carlo analysis of missing data techniques. Organizational Research Methods, 2(3), 211-232.

Rubin, D. (1987). Multiple imputation for nonresponse in surveys. Wiley.

Santos, R. (1981). Effects of imputation on regression coefficients. Proceedings of the Section on Survey research Methods, American Statistical Association, 1981, 1401. (Cited in Lepkowski et al, 1987).

Schafer, J (1997). Analysis of incomplete multivariate data. Chapman & Hall.

Schafer, J.L., & Graham, J.W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.

Wothke, W. (in press) Longitudinal and multi-group modeling with missing data. In T.D. Little, K.U. Schnabel, and J. Baumert [Eds.] Modeling longitudinal and multiple group data: Practical issues, applied approaches and specific examples. Mahwah, NJ: Lawrence Erlbaum Associates. (forthcoming Summer1999, also available at http://www.smallwaters.com/whitepapers)

LATENT CLASS ANALYSIS

Dillon, W.R., & Goldstein, M. (1984). Latent structure analysis (pp. 491-520). In Multivariate analysis: Methods and applications. New York: Wiley.

Collins, L. M. (1991). Measurement in longitudinal research. In L. M.Collins and J. L. Horn (Eds.), Best methods for the analysis of change (pp. 137-148). Washington, DC: American Psychological Association.

Goodman, L. A. (1974). The analysis of systems of qualitative variables when some of the variables are unobservable. Part I—A modified latent structure approach. American Journal of Sociology, 79, 1179-1259.

Langeheine, R. (1994). Latent variables Markov models. In A.von Eye and C. C. Clogg (Eds.), Latent variables analysis (pp. 373-395). Thousand Oaks, CA: Sage Publications.

Macready, G. B., & Dayton, C. M. (1994). Latent class models for longitudinal assessment of trait acquisition. In A. von Eye and C. C. Clogg (Eds.), Latent variables analysis (pp. 245-273). Thousand Oaks, CA: Sage Publications.

McCutcheon, A. L. (1994). Latent logit models with polytomous effects variables. In A. von Eye and C. C. Clogg (Eds.), Latent variables analysis (pp. 353-372). Thousand Oaks, CA: Sage Publications.

von Eye, A., & Clogg, C. C. (1995, Editors). Latent variables analysis. Thousand Oaks, CA: Sage Publications.

CROSS-LAGGED PANEL MODELS, LONGITUDINAL MODELS, LONGITUDINAL ANALYSIS GENERAL (see also latent growth curve models)

Allison, P. (1990). Change scores as dependent variables in regression analysis. Sociological Methodology, 20, 93-114.

Collins, L.M., & Horn, J.L. (1991). Best Methods for the Analysis of Change . Washington, D.C: American Psychological Association.

Collins, L.M., & Sayer, A.G. (2001). New methods for the analysis of change. Washington, D.C.: American Psychological Association. (ISBN: 1557987548)

Finkel, S.E. (1995). Causal analysis with panel data. Thousand Oaks, CA: Sage. (QASS #105).

Kessler, R.C., Greenberg, D.F. (1981). Linear panel analysis: Models quantitative change. New York: Academic Press.

Gottman J.M. (1995). The Analysis of Change (pp. 261-276). Mahwah, NJ: Lawrence Erlbaum).

Marsh, H. W. (1993). Stability of individual differences in multiwave panel studies: Comparison of simplex models and one-factor models. Journal of Educational Measurement, 30, 157-183.

Menard, S. (1991). Longitudinal research. Newbury Park, NJ: Sage. (QASS #76).

Nielsen, F., & Rosenfeld, R. (1981). Substantive Interpretation of Differential Equation Models. American Sociological Review 46:159-174.

Sivo, S.A, & Willson, V.L. (2000). Modeling causal error structures in longitudinal panel data: A Monte Carlo study. Structural Equation Modeling, 7(2), 174-205.

Willett, J.B. (1988).Questions and answers in the measurement of change. Review of Research in Education, 15, 345-422.

Williams, L. J., & Podsakoff, P. M. (1989). Longitudinal field methods for studying reciprocal relationships in organizational behavior research: Toward improved causal analysis. In B. M. Staw & L. L. Cummings (Eds.), Research in organizational behavior, vol. 11. Greenwich, CT: JAI Press.

SIMPLEX MODELS

Jones, M. B. (1959). Simplex Theory . Pensacola: U.S. Naval Aviation Medicine Monograph No. 3.

Jones, M. B. (1960). Molar Correctional Analysis. Pensacola, FL: U.S. Naval Aviation Medicine Monograph No. 4.

Jöreskog, K. G. (1970). Estimation and testing of simplex models. British Journal of Mathematical and Statistical Psychology, 23, 121-145.

Joreskog, K. G. (1979). Statistical models and methods for analysis of longitudinal data. In J. Magidson (ed.) Advances in Factor Analysis and Structural Equation Models. Cambridge, MA: Abt Books.

Marsh, H. W. (1993). Stability of individual differences in multiwave panel studies: Comparison of simplex models and one-factor models. Journal of Educational Measurement, 30, 157-183.

LATENT GROWTH CURVE MODELS (LGC)

Bock, R. D. (1991). Prediction of growth. In L. M. Collins & J. L. Horn(Eds.), Best Methods for the Analysis of Change . Washington, D.C: American Psychological Association.

Chan, D. (1998). The conceptualization and analysis of change over time: An integrative approach incorporating longitudinal mean and covariance structures analysis (LMACS) and multiple indicator latent growth modeling (MLGM). Organizational Research Methods, 1,421-483.

Duncan, S.C., & Duncan, T.E. (1994). Modeling in complete longitudinal substance use data using latent variable growth curve methodology. Multivariate Behavioral Research, 29, 313-338.

Duncan, S.C., & Duncan, T.E. (1996). A multivariate growth curve analysis of adolescent substance use. Structural Equation Modeling, 3, 323-347.

Duncan, T. E., Duncan, S. C., & Li, F. (1998). A comparison of model- and multiple imputation-based approaches to longitudinal analyses with partial missingness. Structural Equation Modeling, 5(1), 1-21.

Duncan, T.E., Duncan, S.C., & Hops, H. (1994). The effects of family cohesiveness and peer encouragement on the development of adolescent alcohol use: A cohort-sequential approach to the analysis of longitudinal data. Journal of Studies on Alcohol, 55, 588-599.

Duncan, T.E., Duncan, S.C., Alpert, A., Hops, H., Stoolmiller, M., & Muthen, B. (1997). Latent variable modeling of longitudinal and multilevel substance use data. Multivariate Behavioral Research, 32, 275-318.

Duncan, T.E., Duncan, S.C., Strycker, L.A., Li, F., & Alpert, A. (1999). An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, NJ: Erlbaum.

Hancock, G.R., Kuo, W.-L., & Lawrence, F.R. (2001). An illustration of second-order latent growth models. Structural Equation Modeling, 8, 470-489.

Hancock, G. R., & Lawrence, F. R. (2006). Using latent growth models to evaluate longitudinal change. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course. Greenwood, CT: Information Age Publishing, Inc.

Lawrence, F. R., & Hancock, G. R. (1998). Assessing change over time using latent growth modeling. Measurement and Evaluation in Counseling and Development, 30, 211-224.

Lawrence, F. R., & Hancock, G. R. (1998). Methods, plainly speaking. Assessing change over time using latent growth modeling. Measurement and Evaluation in Counseling and Development, 30, 211-224.

Marsh, H. W., & Grayson, D. (1994). Longitudinal stability of latent means and individual differences: A unified approach. Structural Equation Modeling, 1, 317-359.

McArdle, J.J. (1986). Dynamic but structural equation modeling of repeated measures data. In Nesselroade, J.R., and Cattel, R.B. (eds.), Handbook of Multivariate Experimental Psychology (2nd ed.). New York: Plenum Press.

McArdle, J.J. (1996). Current directions in structural factor analysis. Current Directions, 5, 11-18.

Meredith, W, & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107-122.

Muthen, B.O. (1997). Latent variable modeling of longitudinal and multilevel data. In A.E. Raftery (Ed.), Sociological methodology (pp.453-480). Washington, DC: Blackwell.

Muthen, 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, 371-402.

Muthen, B. O., & Khoo, S.-T. (1998). Longitudinal studies of achievement growth using latent variable modeling. Learning and Individual Differences, 10, 73-101.

Rogosa, D. R. (1995). Myths and methods: "Myths about longitudinal research," plus supplemental questions. In J. M. Gottman, (Ed.), The analysis of change (pp. 3-66) Hillsdale, New Jersey: Lawrence Erlbaum Associates.

Raykov, T. (1997). Growth curve analysis of ability means and variances in measures of fluid intelligence of older adults. Structural Equation Modeling,4(4), 283-319.

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. Decade of behavior. (pp. 179-200). Washington, DC: American Psychological Association.

Steyer, R., Eid, M., & Schwenkmezger, P. (1997). Modeling true intraindividual change: True change as a latent variable. Methods of Psychological Research, 2(1).

Tisak, J, & Meredith, W. (1990). Descriptive and associative developmental models. In A. von Eye (Ed.), Statistical methods in developmental research (Vol. 2, pp. 387-406). San Diego, CA: Academic Press.

Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363-381.

Willett, J. B., & Sayer, A. G. (1996). Cross-domain analysis of change overtime: Combining growth modeling and covariance structure analysis. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced Structural Equation Modeling. Issues and Techniques (pp.125-157). Mahwah, NJ: Lawrence Erlbaum.

Willett, JB, Ayoub, CC, Robinson, D. (1991). using growth modelling to examine systematic differences in growth: An example of change in the functioning of families at risk of maladaptive parenting, child abuse or neglect. Journal of Consulting & Clinical Psychology, 59, 38-47.

LATENT GROWTH CURVE EXAMPLES

Curran, P.J., Harford, T., & Muthen, B.O. (1996). The relation between heavy alcohol use and bar patronage: A latent growth model. Journal of Studies on Alcohol, 57, 410-418.

Curran, P.J., Stice, E., & Chassin, L. (1997). The relation between adolescent alcohol use and peer alcohol use: A longitudinal random coefficients model. Journal of Consulting and Clinical Psychology,65, 130-140.

Duncan, T. E. & Duncan, S. C. (1994) Modeling developmental processes using latent growth structural equation methodology. Applied Psychological Measurement, 18(4), 343-354.

Stoolmiller, M. (1995). Using latent growth curve models to study developmental processes. In J. M. Gottman (Ed.), The analysis ofchange. Mahwah, NJ: Lawrence Erlbaum.

Wickrama, K. A. S., Lorenz, F. O., & Conger, R. D. (1997). Parental support and adolescent physical health status: A latent growth-curve analysis. Journal of Health and Social Behavior, 38, 149-163.

LATENT STATE-TRAIT MODELS, LATENT TRAIT-STATE MODELS

Dumenci, L, & Windle, M. (1996). A latent trait-state model of adolescent depression using the center for epidemiologic studies-depression scale. Multivariate Behavioral Research, 31, 313-330.

Dumenci, L, & Windle, M. (1998). A multitrait-multioccassion generalization of the latent trait-state model: Description and application. Structural Equation Modeling, 5, 391-410.

Kenny, D.A., & Zautra, A. (1995). The trait-state-error model for multiwave data. Journal of Consulting and Clinical Psychology, 63, 52-59.

Kenny, D.A., & Zautra, A. (2001). Trait-state models for longitudinal data. In L.M. Collins & A.G. Sayer (Eds.), New methods for the analysis of change. Decade of behavior (pp. 243-263). Washington, DC: American Psychological Association .

Steyer, R., Majcen, A.-M., Schwenkmezger, P. & Buchner, A. (1989). A latent state-trait anxiety model and its application to determine consistency and specificity coefficients. Anxiety Research, 1, 281-231.

Steyer, R., Ferring, D., & Schmitt, M. J. (1992). States and traits in psychological assessment. European Journal of Psychological Assessment, 8, 79-98.

POWER, POWER ANALYSIS, SAMPLE SIZE

sem power analysis software by Paul Dudgeon www.psych.unimelb.edu.au/people/staff/DudgeonP.html

Hancock, G. R. (2006). Power analysis in covariance structure models. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course. Greenwood, CT: Information Age Publishing, Inc.

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, 1-55.

Jackson, D. L. (2001). Sample size and number of parameter estimates in maximum likelihood confirmatory factor analysis: A Monte Carlo investigation. Structural Equation Modeling, 8(2), 205-223.

Kaplan, D. (1995). Statistical power in structural equation modeling. In R. Hoyle (Ed). Structural Equation Modeling: Concepts, Issues, and Applications. pp. 100-117. Thousand Oaks, CA: Sage.

Kaplan, D., & George, R. A study of the power associated with testing factor mean differences under violations of factorial invariance. Structural Equation Modeling: A Multidisciplinary Journal, 2, 101-118.

Kaplan, D., & Wenger, R. N. (1993). Asymptotic independence and separability in covariance structure models: Implications for specification error, power, and model modification. Multivariate Behavioral Research, 28, 483-498.

MacCallum, R. C., & Hong, S. (1997). Power analysis in covariance structure modeling using GFI and AGFI. Multivariate Behavioral Research, 32(2), 193-210.

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.

MacCallum, R.C., Widaman, K.F., Zhang, S., & Hong, S. (1999). ``Sample Size in Factor Analysis.'' Psychological Methods 4:84-99

MacCallum, R. C., Browne, M. W., & Cai, L. (2006). Testing differences between nested covariance structure models: Power analysis and null hypotheses. Psychological Methods, 11, 19-35.

Matsueda, R. L. and Bielby, W. T. (1986). Statistical power in covariance structure models. pp. 120-158 in Sociological Methodology 1986, edited by Nancy B. Tuma. Washington, DC: American Sociological Association.

Muthén, L.K., & Muthén, B.O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 4, 599-620.

Preacher, K. J., & Coffman, D. L. (2006, May). Computing power and minimum sample size for RMSEA [Computer software]. Available from http://www.quantpsy.org/.

Saris, W. E. and Stronkhorst, H. (1984). Causal modelling in nonexperimental research: An introduction to the LISREL approach. Amsterdam, The Netherlands: Sociometric Research Foundation.

Saris. W. E., Satorra, A., and Sorbom, D. (1987). The detection and correction of specification errors in structural equation models. pp. 105-129 in Sociological Methodology 1987, edited by Clifford Clogg. Washington, DC: American Sociological Association.

Sarris, 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.

Satorra, A. and Saris, W. E. (1985). The power of the likelihood ratio test in covariance structure analysis. Psychometrika 50: 83-90.

SOFTWARE

Kline, R. B. (1998). Software programs for structural equation modeling: Amos, EQS, and LISREL. Journal of Psychoeducational Assessment,16, 302-323.

ALPHA INFLATION, FAMILYWISE ERROR

Green, Samuel B; Thompson, Marilyn S; Babyak, Michael A. (1998). A Monte Carlo investigation of methods for controlling Type I errors with specification searches in structural equation modeling. Multivariate Behavioral Research, 33, 365-383.

Green, Samuel B; Babyak, Michael A. (1997). Control of Type I errors with multiple tests of constraints in structural equation modeling. Multivariate Behavioral Research, 32, 39-51.

EXAMPLES OF SECOND-ORDER FACTOR MODELS
(HIEARCHICAL FACTOR MODELS)

Bunting, B, Saris, W.E. and McCormack, J.A (1987). Second-order factor analysis of the reliability and validity of the 11 plus examination in Northern Ireland. The Economic and Social Review, 18, 137-147.

Catalano, R. F., Kosterman, R., Hawkins, J. D., Newcomb, M. D., & Abbott, R. D. (Spring, 1996). Modeling the etiology of adolescent substance use: A test of the social development model. Journal of Drug Issues, 26 429-455.

Gerbing, D.W., Hamilton, J.G., & Freeman, E.B. (1994). A large-scale second-order structural equation model of the influence of management participation on organizational planning benefits. Journal of Management, 20, 859-885.

Goldman, M. S., Greenbaum, P. E., & Darkes, J. (1997). A confirmatory test of hierarchical expectancy structure and predictive power: Discriminant validation of the Alcohol Expectancy Questionnaire. Psychological Assessment, 9, 145-157.

Gustafson, J. E., & Balke, G. (1993). General and specific abilities as predictors of school achievement. Multivariate Behavioral Research, 28, 407-434.

Kaplan, D. & Elliott. P. R. (1997) A model-based approach to validating education indicators using multilevel structural equation modeling. Journal of Educational and Behavioral Statistics, 22, 323-348.

Kaplan, D., & Elliott, P. R. (1997) A didactic example of multilevel structural equation modeling applicable to the study of organizations. Structural Equation Modeling, 4, 1-24.

McGrew, K. S., Flanagan, D. P., Keith, T. Z., & Vanderwood, M. (1997). Beyond g: The impact of Gf-Gc specific cognitive abilities research on the future use and interpretation of intelligence test batteries in the schools. School Psychology Review, 26, 189-210.

Mulaik, S. A. & Quartetti, D. A. (1997). First order or higher order general factor? Structural Equation Modeling, 4, 193-211.

Rindskopf, D., & Rose, T. (1988). Some theory and applications of confirmatory second-order factor analysis. Multivariate Behavioral Research, 23, 51-67.

Russell, D., & Cutrona, C. E. (1991). Social support, stress, and depressive symptoms among the elderly: Test of a process model. Psychology and Aging, 6, 190-201.

COEFFICIENT ALPHA, RELIABILITY, CRONBACH’S ALPHA

Rasmussen, J. L. (1989). Analysis of Likert-scale data: A reinterpretation of Gregoire and Driver. Psychological Bulletin, 105, 167-170.

Bandalos, D. L., & Enders, C. K. (1996). The effects of nonnormality and number of response categories on reliability. Applied Measurement in Education, 9, 151-160.

Bollen, K., & Lennox, R. (1991). Conventional Wisdom on Measurement: A structural equation perspective. Psychological Bulletin, Vol. 110(2): 305-314.

Cortina, J.M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78, 98-104.

Enders, C. K., & Bandalos, D. L. (1999). The effects of heterogeneous item distributions on reliability. Applied Measurement in Education,12, 133-150.

Enders, C. K. (2001). The impact of nonnormality on full information maximum likelihood estimation for structural equation models with missing data. Psychological Methods, 6, 352-370.

Enders, C. K. (2003). Using the EM algorithm to estimate coefficient alpha for scales with item level missing data. Psychological Methods, 8, 322-337.

Enders, C. K. (2004). The impact of missing data on sample reliability estimates: Implications for reliability reporting practices. Educational and Psychological Measurement, 64, 419-436.

Green, S.B., Lissitz, R.W., & Mulaik, S.A. (1977). Limitations of coefficient alpha as an index of test unidimensionality. Educational and Psychological Measurement, 37, 827-837.

Greene, V.L., & Carmines, E.G. (1979). Assessing the reliability of linear composites. Sociological Methodology, 1980. San Francisco: Jossey-Bass.

Guttman, L. (1953). Reliability formulas that do not assume experimental independence. Psychometrika, 18, 225-239.

Jenkins, G. D., & Taber, T. D. (1977). Monte Carlo study of factors affecting three indices of composite scale reliability. Journal of Applied Psychology, 62, 392-398.

Joreskog, K.G. (1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36, 109-133.

Komaroff, E. (1996). Coefficient alpha under simultaneous violations of essential tau-equivalence and uncorrelated errors. (Doctoral dissertation, University of Miami, 1996). Dissertation Abstracts International, 57-05, 2013.

Komaroff, E. (1997). Effect of simultaneous violations of essential tau-equivalence and uncorrelated error on coefficient alpha. Applied Psychological Measurement, 21(4) (in press).

Levine (1994). Trues scores, error, reliability, and unit of analysis in environment and behavior research. Environment and Behavior, 26, 261-293.

Li, Heng; Rosenthal, Robert; Rubin, Donald B. Reliability of measurement in psychology: From Spearman-Brown to maximal reliability. Psychological Methods. Vol 1(1) 98-107, Mar 1996.

Matell, M. S., & Jacoby, J. (1972). Is there an optimal number of alternatives for Likert-scale items? Effects of testing time and scale properties. Journal of Applied Psychology, 56, 506-509.

Matell, M. S., & Jacoby, J. (1971). Is there an optimal number of alternatives for Likert scale items? I. Reliability and validity. Educational & Psychological Measurement, 31, 657-674.

Miller, M. B. (1995). Coefficient alpha: A basic introduction from the perspectives of classical test theory and structural equation modeling. Structural Equation Modeling, 2(3), 255-273.

Novick, M.R., & Lewis, C. (1967). Coefficient alpha and the reliability of composite measurements. Psychometrika, 32, 1-13.

Okleshen-Peters, C. & Enders, C. K. (2002). A primer for the estimation of structural equation models in the presence of missing data: Maximum likelihood algorithms. Journal of Targeting, Measurement, and Analysis for Marketing, 11, 81-95.

Preston, C. C., & Colman, A. M. (2000). Optimal number of response categories in rating scales: Reliability, validity, discriminating power, and respondent preferences. Acta Psychologica, 104, 1-15.

Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21, 173-184.

Raykov, T. (2004). Estimation of maximal reliability: A note on a covariance structure modelling approach. British Journal of Mathematical & Statistical Psychology, 57, 21-27.

Raykov, T. (2001). Estimation of congeneric scale reliability using covariance structure analysis with nonlinear constraints. British Journal of Mathematical & Statistical Psychology, 54, 315-323.

Reuterberg, S.E., & Gustafsson, J.E. (1992). Confirmatory factor analysis and reliability: Testing measurement model assumptions. Educational and Psychological Measurement, 52, 795-811.

Richards, et al. (1991.) Units of analysis and the psychometrics of environmental assessment scales. Environment and Behavior, 23, 423-437.

Rozeboom, W.W. (1966). Foundations of the theory of prediction. Homewood, IL: Dorsey.

Smith, D. A., & Davidson, L. A. (1986). Interfacing Indicators and Constructs in Criminological Research: A Note on the Comparability of Self-Report Violence Data for Race and Sex Groups. Criminology,24, 473-488.

Van Zyl, J. M., Neudecker, H., & Nel, D. G. (2000). On the distribution of the maximum likelihood estimator of Cronbach's Alpha. Psychometrika,65(3), 271--280.

Zimmerman, D.W., Zumbo, B.D., & Lalonde, C. (1993). Coefficient alpha as an estimate of test reliability under violation of two assumptions. Educational and Psychological Measurement, 53, 33-49.

LIKERT RESPONSE SCALES, SCALING, ORDINAL MEASUREMENT

Champney, H., & Marshall, H. (1939). Optimal refinement of the rating scale. Journal of Applied Psychology, 23, 323-331.

Cox, E. P. (1980). The optimal number of response alternatives for a scale: A review. Journal of Marketing Research, 17, 407-422.

Ghiselli, E. E. (1939). All or none versus graded response questionnaires. Journal of Applied Psychology, 23, 405-413.

Green, P. E., & Rao, V. R. (1970). Rating scales and information recovery - how many scales and response categories to use? Journal of Marketing, 34, 33-39.

Jenkins, G. D., & Taber, T. D. (1977). Monte Carlo study of factors affecting three indices of composite scale reliability. Journal of Applied Psychology, 62, 392-398.

Johnson, D.R., & Creech, J.C. (1983). Ordinal measures in multiple indicator models: A simulation study of categorization error. American Sociological Review, 48, 398-407.

Lissitz, R. W., & Green, S. B. (1975). Effect of the number of scale points on reliability: A monte carlo approach. Journal of Applied Psychology, 60, 10-13.

Matell, M. S., & Jacoby, J. (1971). Is there an optimal number of alternatives for Likert scale items? Study I: Reliability and validity. Educational and Psychological Measurement, 31, 657-674.

Matell, M. S., & Jacoby, J. (1972). Is there an optimal number of alternatives for Likert-scale items? Effects of testing time and scale properties. Journal of Applied Psychology, 56, 506-509.

Michell, J. (1986). Measurement scales and statistics: A clash of paragdigms. Psychological Bulletin, 100, 398-407.

Michell, J. (1990). An introduction to the logic of psychological measurement. Hillsdale, NJ: Lawrence Erlbaum Associates.

Narens, L., & Luce, R. D. (1986). Measurement: The theory of numerical assignments. Psychological Bulletin, 99, 166-180.

Ramsay, J. O. (1973). The effect of number of categories in rating scales on precision of estimation of scale values. Psychometrika, 38,513-532.

Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 667-680.

Suppes, P., & Zinnes, J. L. (1963). Basic measurement theory. In R. D. Luce & R. Bush & E. Galanter (Eds.), Handbook of mathematical psychology (Vol. 1, pp. 3-76). New York: Wiley.

Symonds, P. M. (1924). On the loss of reliability in rating due to coarseness of the scale. Journal of Experimental Psychology, 456-461.

Trout, J. D. (1999). Measurement. In W. H. Newton-Smith (Ed.), A companion to the philosophy of science. Oxford: Blackwell.

ADF, AGLS, and WLS ESTIMATION METHODS

Browne, M.W. (1984). Asymptotic distribution free methods in analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62-83.

McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). London: Chapman Hall.

Muthen, B. (1993). Goodness of fit with categorical and other nonnormal variables. In K.A. Bollen & J.S. Long (Eds.), Testing structural equation models (pp. 205-234). Newbury Park, CA: Sage.

Muthen, B., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189.

Olsson, U.H., Foss, T., Troye, S. V., & Roy D. Howell (2000). The Performance of ML, GLS and WLS Estimation in Structural Equation Modeling Under Conditions of Misspecification and Nonnormality. Structural Equation Modeling, 7 (4), 557-595.

Sugawara, Hazuki M. and Robert C. MacCallum (1993), "Effect of Estimation Method on Incremental Fit Indexes for Covariance Structure Models," Applied Psychological Measurement, 17, 365-77.

Yuan, K.-H., & Bentler, P. M. (1997). Mean and covariance structure analysis: Theoretical and practical improvements. Journal of the American Statistical Association, 92, 767-774.

NONNORMALITY, DISTRIBUTIONAL ASSUMPTIONS, CATEGORICAL DATA, ORDINAL, MULTIVARIATE KURTOSIS

Babakus, E., Ferguson, C.E., Jr., and Joreskog, K.G. (1987). The sensitivity of confirmatory maximum likelihood factor analysis to violations of measurement scale and distributional assumptions. Journal of Marketing Research, 24, 222-28.

Bandalos, D. L., & Enders, C. K. (1996). The effects of nonnormality and number of response categories on reliability. Applied Measurement in Education, 9, 151-160.

Curran, P. J., West, S. G, & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16-29.

D'Agostino, R. B., Belanger, A., & D'Agostino, R. B. (1990). A suggestion for using powerful and informative tests of normality. American Statistician, 44, 316-321.

DeCarlo, L. T. (1997), On the meaning and use of kurtosis. Psychological Methods, 2, 292-307.

Enders, C. K. (2001). The impact of nonnormality on full information maximum likelihood estimation for structural equation models with missing data. Psychological Methods, 6, 352-370.

Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation method, and model specification on structural equation modeling fit indexes. Structural Equation Modeling, 6, 56-83.

Finch, J.F., West, S.G., & MacKinnon, D. (1997). Effects of sample size and nonnormality on the estimation of mediated effects in latent variables models. Structural Equation Modeling, 4, 87-107.

Fouladi, R.T. (2000) Performance of modified test statistics in covariance and correlation structure analysis under conditions of multivariate nonnormality. Structural Equation Modeling, 7(3), 356-410.

Hoogland & Boomsma (1998). Robustness studies in Covariance Structure Modeling: An overview and a meta-analysis. Sociological Methods and Research, 26, 329-3

Hosking, J. R. M. (1997). Regional frequency analysis. An approach base on L-Moments. Cambridge, UK: Cambridge University Press.

Hu, L-T., & Bentler, P.M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424-453. Nevitt, J., & Hancock, G.R. (2000). Improving the root mean square error of approximation for nonnormal conditions in structural equation modeling. Journal of Experimental Education, 68(3), 251-268.

Muthén, B., & Kaplan, D. (1985). A comparison of methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189.

Muthén, B., & Kaplan, D. (1992). A comparison of some methodologies for the factor analysis of non-normal Likert variables: A note on the size of the model. British Journal of Mathematical and Statistical Psychology, 45, 19-30.

Olsson, U. H, Troye, S. V., and Howell, R. D. (1999). Theoretic fit and empirical fit: The performance of maximum likelihood versus generalized least squares estimation in structural equation models. Multivariate Behavioral Research, 34, 31-58.

Satorra, A., & Bentler, P.M. (1988). Scaling corrections for chi-square statistics in covariance structure analysis. 1988 Proceedings of the Business and Economic Statistics Section of the American Statistical Association, 308-313.

Satorra, A., & Bentler, P.M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye and C.C. Clogg (eds.), Latent Variable Analysis: Applications to Developmental Research (pp. 399-419). Newbury Park: Sage.

Stuart, A., & Ord, J. K. (1987). Kendall's advanced theory of statistics (Vol. 1). London: Charles Griffin and Co.

West, S. G., Finch, J.F, & Curran, P.J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R.H. Hoyle (Ed), (1995). Structural equation modeling: Concepts, issues, and applications. (pp. 56-75). Thousand Oaks, CA: Sage Publications.

Xie, Yu (1989) Structural equation models for ordinal variables, Sociological Methods & Research, 17, 325-352.

Yuan, K.-H., & Bentler, P. M. (1997). Improving parameter tests in covariance structure analysis. Computational Statistics & Data Analysis, 26, 177-198.

Yuan, K.-H., & Bentler, P. M. (1997). Mean and covariance structure analysis: Theoretical and practical improvements. Journal of the American Statistical Association, 92, 767-774.

Yuan, K-H., & Bentler, P. M. (1998). Normal theory based test statistics in structural equation modelling. British Journal of Mathematical and Statistical Psychology, 51, 289-309.

ASSUMPTIONS, MULTICOLLINEARITY, NONCONSTANT VARIANCE, HETEROSCEDASTICITY, OUTLIERS

Rensvold, R. B., & Cheung, G. W. (1999). Identification of influential cases in structural equation models using the jackknife method. Organizational Research Methods, 2(3), 293-308.

POLYCHORIC CORRELATIONS, TETRACHORIC CORRELATIONS, POLYSERIAL CORRELATIONS, and NONNORMALITY

Babakus, Ferguson and Joreskog (1987), The Sensitivity of Confirmatory Maximum Likelihood Factor Analysis to Violations of Measurement Scale and Distributional Assumptions, J. of Marketing Research, 24, 222-228.

Bentler (1990), Comparative Fit Indices in Structural Models, Psychological Bulletin, 107, 238-246.

Brown. R.L. (1989). Using Covariance Modeling for Estimating Reliability on Scales with Ordered Polytomous Variables, Educational and Psychological Measurement, 49, 385-398.

Fornell and Larcker (1981), Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, J. of Marketing Research, 18, 39-50.

Muthen, B., & Hofacker, C. (1988). Testing the assumptions underlying tetrachoric correlations. Psychometrika, 53, 563-578.

Ollson (1979), On the Robustness of Factor Analysis Against Crude Classification of the Observations, Multivariate Behavioral Research, 14, 485-500.

Rigdon, E.E. and Ferguson, C.E., Jr. (1991), The Performance of the Polychoric Correlation Coefficient and Selected Fitting Functions in Confirmatory Factor Analysis with Ordinal Data, Journal of Marketing Research, 28, 491-497.

MTMM (MULTI-TRAIT MULTI-METHOD MATRIX)

Bagozzi, R. P., & Yi, Y. (1990). Assessing method variance in multitrait-multimethod matrices: The case of self-reported affect and perceptions at work. Journal of Applied Psychology, 75, 547-560.

Bagozzi, R. P., & Yi, Y. (1991). Multitrait-multimethod matrices in consumer research. Journal of Consumer Research, 17, 426-439.

Bagozzi, R. P., & Yi, Y. (1992). Testing hypotheses about methods, traits, and commonalties in the direct-product model. Applied Psychological Measurement, 16, 373-380.

Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991) Assessing construct validity in organizational research. Administrative Science Quarterly, 36, 421-458. Byrne, B & Goffin (1993). Modeling MTMM data from additive and multiplicative covariance structures: An audit of construct validity concordance. Multivariate Behavioral Research, 28, 67-96.

Bollen, K.A. & Joreskog, K.G. (1985). Uniqueness does not imply identification. Sociological Methods and Research, 14, 155-163.

Bollen, K.A., & Paxton, P. (1998). Detection and determinants of bias in subjective measures. American Sociological Review, 63, 465-478.

Cook, W.L. (1994). A structural equation model of dyadic relationships within the family system. Journal of Consulting and Clinical Psychology, 62, 500-509.

Cook, W. L. & Goldstein, M. J. (1993). Multiple perspectives on family relationships: A latent variables model. Child Development, 64,1377-1388.

Coovert, M. D., & Craiger, J. P., Teachout, M. S. (1997). Effectiveness of the direct product versus confirmatory factor model for reflecting the structure of multimethod-multirater job performance data. Journal of Applied Psychology, 82, 271-280.

Kenny, D. A., & Kashy, D. A. (1992). Analysis of the multitrait-multimethod matrix by confirmatory factor analysis. Psychological Bulletin, 112, 165-172.

Marsh, H. W. & Bailey, M. (1991). Confirmatory factor analysis of multitrait-multimethod data: A comparison of alternative models. Applied Psychological Measurement, 15, 47-70.

Marsh, H. W. (1989). Confirmatory factor analysis of multitrait-multimethod data: Many problems and a few solutions. Applied Psychological Measurement, 13, 335-361.

Marsh, H. W. (1993). Multitrait-multimethod analyses: Inferring each trait/method combination with multiple indicators. Applied Measurement in Education, 6, 49-81.

Marsh, H. W., & Bailey, M. (1991). Confirmatory factor analysis of multitrait-multimethod data: A comparison of alternative models. Applied Psychological Measurement, 15, 47-70.

Marsh, H. W., & Grayson, D. (1995). Latent-variable models of multitrait-multimethod data. In R. H. Hoyle (Ed.), Structural equation modeling: Issues and applications (pp. 177-198). Newbury, CA,. Sage.

Marsh, H. W., & Hocevar, D. (1988). A new, more powerful approach to multitrait-multimethod analyses: Application of second order confirmatory factor analysis. Journal of Applied Psychology, 73, 107-117.

Marsh, H. W., Byrne, B. M., & Craven, R. (1992). Overcoming problems in confirmatory factor analyses of MTMM data: The correlated uniqueness model and factorial invariance. Multivariate Behavioral Research, 27, 489-507.

Millsap, R.E. (1992) Sufficient conditions for rotational uniqueness in the additive MTMM model. British Journal of Mathematical and Statistical Psychology, 45, 125-138.

Scullen, S. E. (1999). Using confirmatory factor analysis of correlated uniqueness to estimate method variance in multitrait-multimethod matrices. Organizational Research Methods, 2(3), 275-292.

Widaman, K. F. (1985). Hierarchically nested covariance structure models for multitrait-multimethod data. Applied Psychological Measurement, 9, 1-26.

Wothke, W. (1996). Models for multitrait-multimethod matrix analysis. In G. A. Marcoulides & R. E. Schumacher (Eds.) Advanced Structural Equation Modelling. Mahwah, NJ: Erlbaum.

VALIDITY

Cronbach, L. J. (1971). Test validity. In R. L. Thorndike (Ed.)Educational Measurement (2nd ed., pp. 443-507) Washington, D.C.: American Council on Education.

Goodman, N. (1979). Fact fiction and forecast (4th ed.). Cambridge, MA: Harvard University press.

Hubley, A. M., & Zumbo, B. D. (1996). A dialectic on validity: Where we have been and where we are going. The Journal of General Psychology, 123,207-215.

Hubley, A.M. & Zumbo, B.D. (1996). A dialectic on validity: Where we have been and where we are going. The Journal of General Psychology, 123, 207-215.

Loevinger, J. (1967). Objective tests as instruments of psychological theory. In D. N. Jackson & S. Messick (Eds.) Problems in Human Assessment (pp. 78-123). New York: Krieger.

Messick, S. (1989). Validity. In R. L. Linn (Ed.) Educational measurement(3rd ed., pp. 13-103). New York: American Council on Education.

STANDARDIZED SOLUTIONS

Chou, C. P., & Bentler, P. M. (1993). Invariant standardized estimated parameter change for model modification in covariance structure analysis. Multivariate Behavioral Research, 28, 97-110.

Raykov, T., & Marcoulides, G.A. (2000). A method for comparing completely standardized solutions in multiple groups. Structural Equation Modeling, 7, 292-308.

Kim, J. O., & Ferree, G. D., Jr. (1981). Standardization in causal analysis. Sociological Methods and Research, 10, 187-210.

Kim, J. O., & Mueller, C. W. (1976). Standardized and unstandardized coefficients in causal analysis: An expository note. Sociological Methods and Research, 4, 428-438.

SAMPLE WEIGHTING, COMPLEX SURVEY DESIGNS, SAMPLING ADJUSTMENTS

Kaplan, D., & Ferguson, A.J. (1999). On the utilization of sample weights in latent variable models. Structural Equation Modeling, 6, 305-321.

Muthen, B., & Satorra, A. (1995). Complex sample data in structural equation modeling. Sociological methodology, 25, 267-316.

REGRESSION DIAGNOSTICS, SEM DIAGNOSTICS, OUTLIERS, MULTICOLLINEARITY

Belsley, D.A., Kuh, e., & Welsch, R.E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. New York: John Wiley.

Bollen, K.A. and G. Arminger. 1991. Observational Residuals in Factor Analysis and Structural Equation Models (pp. 235-62). In P.M. Marsden (Ed.), Sociological Methodology 1991. Oxford: Basil-Blackwell.

Bollen, K.A., & Jackman, R.W. (1990). Regression diagnostics: An expository treatment of outliers and influential cases. In J. Fox, J.S. Long (Eds.), Modern methods of data analysis. Newbury Park, CA: Sage.

Cook, R.D., & Weisberg, S. (1994). An introduction to regression graphics. New York: Wiley.

Fox, J. (1991). Regression diagnostics. Newbury Park, CA: Sage.

Kaplan, D. (1995). Estimator conditioning diagnostics for covariance structure models. Sociological Methods and Research, 23, 200-229.

Neter, J., Kutner, M.H., Nachtsheim, C.J., & Wasserman, W. (1996). Applied linear regression models (3^rd Ed.). Chicago, IL: Irwin.

Rensvold, R. B., & Cheung, G. W. (1999). Identification of influential cases in structural equation models using the jackknife method. Organizational Research Methods, 2(3), 293-308.

Yuan, Ke-Hai; Bentler, Peter M. (2001). Effect of outliers on estimators and tests in covariance structure analysis. British Journal of Mathematical & Statistical Psychology, 54(1), 161-175.

REVERSED ITEMS, SCALING, ACQUIESCENCE, RESPONSE BIAS, SOCIAL DESIRABILITY, METHODS FACTORS

Andrich, D. (1988) The application of an unfolding model of the PIRT type to the measurement of attitude. Applied Psychological Measurement, 12, 33-51.

Goldsmith, R. E. (1987). Two Studies of Yeasaying, Psychological Reports, 60, 239-244.

J. J. Ray (1983). Reviving the Problem of Acquiescent Response Bias. Journal of Social Psychology, 121, 81-96.

J. J. Ray (1985). Acquiescence and response skewness in scale constructional paradox," Person. Individual Differences, 6, 655-656.

Marsh, H. W. (1996). Positive and negative global Self-Esteem: A substantively meaningful distinction or artifactors? Journal of Personality and Social Psychology, 70, 810-819.

Mitchell, J. (1994). Measuring dimensions of belief by unidimensional unfolding. Journal of Mathematical Psychology, 38, 244-273

Russo, J. (1994). Thurstone's scaling model applied to assessment of self-reported depression severity. Psychological Assessment, 6, 159-171.

Spector (1976). Choosing response categories for summated rating scales. Journal of Applied Psychology, 61, 374-375.

Spector (1980). Ratings of equal and unequal response choice intervals. Journal of Social Psychology,112, 115-119.

Spector, P.E., Van Katwyk, P.T., Brannick, M.T., & Chen, P.Y. (1997). When two factors don't reflect two constructs: How item characteristics can produce artificial factors. Journal of Management, 23, 659-677.

Tomas, J.M., & Olivers, A. (1999). Rosenberg's self-esteem scale: Two factors or method effects. Structural Equation Modeling, 6, 84-98.

van Schuur, H. and Kiers, H. (1994). Why factor analysis is often the incorrect model for analyzing bipolar concepts, and what model to use instead. Applied Psychological Measurement, 18, 2, 97-110.

ITEM PARCELING, ITEM PARCELS

Bandalos, D.L. (2002). The effects of item parceling on goodness-of-fit and parameter estimate bias in structural equation modeling. Structural Equation Modeling, 9, 78-102.

Bogozzi, R. P., & Heatherton, T. F. (1994). A general approach to representing multifaceted personality constructs: Application to state self-esteem. Structural Equation Modeling, 1, 35-67.

Hall, R. J., Snell, A. F., & Foust, M. S. (1999). Item parceling strategies in SEM: Investigating the subtle effects of unmodeled secondary constructs. Organizational Research Methods 2(3), 233-256.

Kishton, J. M; Widaman, K. F. (1994). Unidimensional versus domain representative parceling of questionnaire items: An empirical example. Educational & Psychological Measurement, 54, 757-765.

Marsh HW. Hau KT. Balla JR. Grayson D. (1998). Is more ever too much: The number of indicators per factor in confirmatory factor analysis. Multivariate Behavioral Research, 33, 181-220.

Russell, D. W., Kahn, J. H., Spoth, R., & Altmaier, E. M. (1998). Analyzing data from experimental studies: A latent variable structural equation modeling approach. Journal of Counseling Psychology, 45, 18-29.

META-ANALYSIS

Erez, A., Bloom, M.C., and Wells, M.T. (1996). Using random rather than fixed effects models in meta-analysis: Implications for situational specificity and validity generalization. Personnel Psychology 49:275-306.

Knight, G. P; Fabes, R. A; Higgins, D. A. (1996). Concerns about drawing causal inferences from meta-analyses: An example in the study of gender differences in aggression. Psychological Bulletin, 119, 410-421.

Rice, N. and Leyland, A. (1996). Multilevel models: Applications to health data. Journal of Health Services Research and Policy1(3):154-164.\

Shadish, W. R. (1996). Meta-analysis and the exploration of causal mediating processes: A primer of examples, methods, and issues. Psychological Methods, 1, 47-65.

Viswesvaran, C., & Ones, D.S. (1995). Theory testing: Combining psychometric meta-analysis and structural equation modeling. Personnel Psychology, 48, 865-883.

EXAMPLES OF META-ANALYSIS with SEM

Becker, B. J. (1992). Models of science achievement: Forces affecting male and female performance in school science. In T. D. Cook, H.M. Cooper, D. S. Cordray, H. Hartmann, L. V. Hedges, R. J. Light, T. A. Louis, & F. Mosteller (Eds.), Meta-analysis for explanation: A casebook(pp. 209-281). New York: Russell Sage Foundation.

Brown, S. P., & Peterson, R. A. (1993). Antecedents and consequences of salesperson job satisfaction: Meta-analysis and assessment f causal effects. Journal of Marketing Research, 30, 63-77.

Harris, M. J., & Rosenthal, R. (1985). Mediation of interpersonal expectancy effects: 31 meta-analyses. Psychological Bulletin, 97, 363-386.

Hom, P. W., Caranikas-Walker, F., Prussia, G. E., & Griffeth, R. W.(1992). A meta-analytical structural equations analysis of a model of employee turnover. Journal of Applied Psychology, 77, 890-909.

Peters, L. H., Hartke, D. D., & Pohlmann, J. T. (1985). Fiedler's contingency theory of leadership: An application of the meta-analysis procedures of Schmidt and Hunter. Psychological Bulletin, 103, 223-234.

Premack, S. L., & Hunter, J. E. (1988). Individual unionization decisions. Psychological Bulletin, 103, 223-234.

Rooney & Murray (1996). A meta-analysis of smoking prevention programs after adjustment for errors in the unit of analysis. Health Education Quarterly,23(10, 48-64.

Schmidt, F. L., Hunter, J. E., & Outerbridge, A. N. (1986). The impact of job experience and ability on job knowledge, work sample performance, and supervisory ratings of job performance. Journal of Applied Psychology, 71, 432-439.

IRT, FULL-INFORMATION ML FACTOR ANALYSIS, DIF

Bock, R.D., Gibbons, R., and Muraki, E. Full-Information Item Factor Analysis. Applied Psychological Measurement 12(3):261-280, 1988.

Bock, RD (1989). Multilevel Analysis of Educational Data. San Diego: Academic Press.

Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110, 305-314.

Christensen, H., Jorm, A. F., Mackinnon, A. J., Korten, A. E., Jacomb, P. A., Henderson, A. S., & Rodgers, B. (1999). Age differences in depression and anxiety symptoms: a structural equation modeling analysis of data from a general population sample. Psychological Medicine, 29(2), 325-39.

Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7, 309-319.

Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, N.J.: Lawrence Erlbaum Associates.

Floyd, F. J., & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7, 286-299.