Online Resources for:
Bowen, N. K. & Guo, S. (2011). Structural Equation Modeling. Oxford University Press.
Online resources accompanying book. Please cite appropriately.
List of Resource Readings
(with brief annotation)
Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage.
A general source on the nature of missing data and how to address them in analyses.
Bollen, K. A. (1989). Structural equations with latent variables. New York: John Wiley & Sons.
Bollen’s book is a seminal and comprehensive source on virtually all aspects of SEM. It’s
especially useful for those who are interested in derivations and proofs of key statistical
principles of SEM.
Byrne, B. M. (2010). Structural equation modeling with Amos: Basic concepts, applications, and
programming (2nd ed.). New York, NY: Taylor and Francis Group.
Byrne’s book provides instructions on using Amos as well as on SEM analyses in general.
She includes many examples on multiple group analyses.
Byrne, B. M. (2012). Structural equation modeling with Mplus: Basic concepts, applications,
and programming. New York, NY: Taylor and Francis Group.
A thorough guide to using Mplus for CFA, general structural, multiple group, and growth
models.
Byrne, B. M., Shavelson, R. J., & Muthén, B. (1989). Testing for the equivalence of factor
covariance and mean structures: The issue of partial measurement invariance.
Psychological Bulletin, 105(3), 456-466.
This article describes conceptual and statistical issues in studying the invariance of
measurement models across groups.
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Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the
behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
Cohen and Cohen is an essential source on basic and advanced topics of regression and
correlation and their relationship to each other.
Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data:
Questions and tips in the use of structural equation modeling. Journal of Abnormal
Psychology.
This article is a useful source on various modeling techniques for repeated measures data
with cross-lagged models.
DeVellis, R. F. (2003). Scale development: Theory and applications (2nd ed.). Thousand Oaks,
CA: Sage.
DeVellis provides a useful conceptual discussion of latent variables.
Fabrigar, L. R., Porter, R. D., & Norris, M. E. (2010). Some things you should know about
structural equation modeling but never thought to ask. Journal of Consumer Psychology,
20, 221-225.
These authors discuss issues of inferring causality from SEM analysis results.
Ferron, J. M., & Hess, M. R. (2007). Estimation in SEM: A concrete example. Journal of
Educational and Behavioral Statistics, 32(1), 110-120.
This article provides a concrete example of the matrix calculations used in ML estimation
of a simple model.
Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of
Psychology, 60, 549-576.
A general source on the nature of missing data and how to address them in analyses.
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Iacobucci, D. (2009). Everything you always wanted to know about SEM (structural equation
modeling) but were afraid to ask. Journal of Consumer Psychology, 19, 673-680.
This author provides a summary of SEM notation and its relationship to the matrices.
Kaplan, D. (2009). Structural equation modeling: Foundations and extensions (2nd ed.).
Thousand Oaks, CA: Sage Publications, Inc.
Kaplan’s book is a recent comprehensive source on SEM.
Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New
York: Guilford.
Kline’s presentation of SEM topics is user-friendly and practical. The third edition was
released in 2010.
Little, R. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York,
NY: Wiley & Sons.
This book is a source of information on missing data and multiple imputation.
Long, J. S. (1983). Confirmatory factor analysis. New York: Sage.
Long provides a technical, yet accessible, presentation that helps the reader gain a deep
understanding of CFA.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and
determination of sample size for covariance structure modeling. Psychological Methods,
1, 130-149.
This article is a must for those interested in gaining a deeper understanding of power
analysis using RMSEA.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., et al. (2008). Global
sensitivity analysis: The primer. New York: NY: John Wiley & Sons.
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As the name implies, this book discusses what sensitivity analysis is and how to conduct
sensitivity analyses.
Saunders, J. A., Morrow-Howell, N., Spitznagel, E., Dore, P., Proctor, E. K., & Pescarino, R.
(2006). Imputing missing data: A comparison of methods for social work researchers.
Social Work Research, 30(1), 19-31.
This article is a good source on multiple imputation that was written specifically for
social workers.
Schafer, J. L. (1997). Analysis of incomplete multivariate data. New York, NY: Chapman & Hall.
Schafer’s book is a respected source on the nature of missing data and how to address
them in analyses.
Rosenthal, J. A. (2001). Statistics and data interpretation for the helping professions. Belmont,
CA: Brooks/Cole Publishing Co.
Rosenthal’s book is a useful source on statistics for social workers.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Allyn
& Bacon.
This book is a comprehensive source on statistical procedures. It includes useful
information on diagnosing distributional problems and transforming variables, among
many other topics.
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