Cover -- Haf-title -- Title -- Copyright -- Dedication -- Contents -- Contributors -- Preface -- Section 1 Theory -- 1 Structural equation modeling: an introduction -- Abstract -- Introduction -- Definition and specification of a structural equation model -- Model identification -- Model estimation -- Model assessment and fit -- Model modification -- Multisample models -- The LISREL model and program -- Example of LISREL analysis -- References -- 2 Concepts of structural equation modeling in biological research -- Abstract -- Introduction -- Measurement error -- Latent variables -- Hypothesis testing -- Modeling complex systems -- Conclusions -- Appendix 2.1. The EQS program (Bentler, 1992) used to generate a random sample from a population defined by a model… -- References -- 3 Modeling a complex conceptual theory of population change in the Shiras moose: history and recasting as a structural… -- Abstract -- Introduction -- The Shiras moose population model -- Data -- Construction of the data simulation -- Data analysis -- Measurement model -- The full model -- Data1 -- Data2 -- Discussion -- Appendix 3.1. -- References -- 4 A short history of structural equation models -- Abstract -- Introduction -- Wright's invention of path analysis -- Path models in sociology -- Theoretical constructs and indicators in sociology -- Models and structures in econometric thought - the identification problem -- Estimation of parameters in econometric models -- Estimation by the full information maximum likelihood method (FIML) -- Other methods of estimation of econometrics models -- Model building and tests of significance -- Latent variables in econometrics -- Charles Spearman and the beginning of exploratory and confirmatory factor analysis -- Maximum likelihood estimation of factor loadings in exploratory factor analysis.

Maximum likelihood estimation of factor loadings in confirmatory factor analysis and SEM in general -- Likelihood ratio tests -- Identification problems in confirmatory factor analysis -- General comment on exploratory and confirmatory factor analysis -- The need for a synthesis -- The LISREL model: Keesling, Jöreskog, and Wiley -- Keesling's formulation -- Jöreskog and Wiley's formulations -- The extension of the LISREL model to include means -- Generalized least squares -- The extension of the LISREL model to include dichotomous and ordered variables -- Distribution free estimators -- Indices of fit -- Conclusion -- References -- 5 Guidelines for the implementation and publication of structural equation models -- Abstract -- Cause and correlation -- Latent variables -- The justification of the proposed model -- Specification of the model identification issues -- Sample size and number of indicators per latent variable -- The selection of the estimation method -- The case of a true model -- The case of a simplified model -- Evaluation of fit -- True models -- Misspecified models -- Cut-off criteria -- Indexes for what purpose? -- Considerations of power -- The interpretation of the results - the issue of equivalent models -- Model modification -- Existing guidelines -- References -- Section 2 Applications -- 6 Modeling intraindividual variability and change in bio-behavioral developmental processes -- Abstract -- Introduction -- Small-n research and modes of selection -- The study at hand -- Organizing analytical tools -- Factor analytical approaches -- Previous research -- Common and unique processes -- Describing intraindividual variability -- Our tool -- P-technique factor analysis -- Individual differences and nomothetic generalizations -- The dynamic nature of multi-occasion data.

Relationship between standard P-technique and dynamic P-technique -- Conditions of applicability -- Advantages of P-technique factor analysis -- Confirmatory applications -- Summary -- Empirical illustration of P-technique factor analysis -- Subjects and procedure -- Subjects -- Behavioral checklist -- Data collection -- Pre-analysis data treatment -- Model specifications -- Results -- Empirical tests of dynamic relations in the analyzed covariance matrices -- Standard P-technique factor patterns -- Interfactor correlations and factor similarity -- Discussion -- Coherent patterns of change -- Construct validity -- Conclusions -- Acknowledgments -- References -- 7 Examining the relationship between environmental variables and ordination axes using latent variables and structural… -- Abstract -- Introduction -- Structural equation modeling -- Historical perspective -- Basic terms and concepts -- Methods -- Results -- Regression and principal components -- Structural equation modeling -- Discussion -- Consideration of the SEM results -- Consideration of methodology -- Regression does not attempt to explain correlations among predictors -- PCA provides an empirical characterization of the correlations among predictors -- SEM incorporates hypotheses about underlying processes into the specification of the measurement model -- SEM has a number of additional strengths -- SEM is not without limitations -- The estimation of latent variables using SEM deserves further consideration by ecologists -- Acknowledgments -- References -- 8 From biological hypotheses to structural equation models: the imperfection of causal translation -- Abstract -- Introduction -- How do equivalent models arise? -- Using equivalent models in the design stage -- Equivalent models and hypothesis testing in SEM -- An alternative notation for equivalent models.

Seed dispersal in St Lucie's Cherry -- An interspecific model of gas exchange in leaves -- Conclusions -- Acknowledgements -- References -- 9 Analyzing dynamic systems: a comparison of structural equation modeling and system dynamics modeling -- Abstract -- Introduction -- System dynamics -- Definitions of equivalence -- Implied covariance -- Model selection -- Method -- Results -- Discussion -- Conclusion -- References -- 10 Estimating analysis of variance models as structural equation models -- Abstract -- Introduction -- The LISREL model -- Estimating an ANOVA model as SEM through the use of dummy-coding -- ANOVA as SEM -- An example -- Model 1 -- Model 2 -- A different parameterization for the ANOVA model -- Repeated measures ANOVA as SEM -- A one-way repeated measures ANOVA -- A two-way repeated measures ANOVA -- An example -- Estimating a random coefficients model as SEM -- Using SEM to estimate the linear curve model -- An example -- Conclusion -- Appendix 10.1 -- References -- 11 Comparing groups using structural equations -- Abstract -- Introduction -- Illustration of multigroup analysis -- Example data -- An overview of multigroup analysis -- Illustration of a multigroup analysis -- Applications of multigroup analysis -- Appendix 11.1. Data and program commands used in analyses -- References -- 12 Modeling means in latent variable models of natural selection -- Abstract -- Introduction -- A LISREL formulation of the means model -- A simulated study of phenotypic selection -- Data analysis -- The measurement model and tests of assumptions -- The means model -- Discussion -- Appendix 12.1. EQS data simulation program -- EQS simulation for pre-selection group data -- EQS simulation for post-selection group data -- References -- 13 Modeling manifest variables in longitudinal designs - a two-stage approach -- Abstract -- Introduction.

Development of growth curve methodology -- Polynomial extraction -- Approximation of time series using orthogonal polynomials -- Data example -- Using polynomial parameters in structural equations modeling -- Data example using manifest growth curve modeling -- "Tuckerized" growth curves -- Discussion -- References -- Section 3 Computing -- 14 A comparison of the SEM software packages Amos, EQS, and LISREL -- Abstract -- Introduction -- Structural equation modeling -- A comparison of Amos, EQS, and LISREL -- Criteria and results of comparison -- System requirements (Table 14.1) -- Documentation -- Documentation for Amos -- Documentation for EQS -- Documentation for LISREL -- Data management and entry -- Data management in Amos -- Data management in EQS -- Data management in LISREL -- Data input in Amos -- Data input in EQS -- Data input in LISREL -- Modeling options -- Modeling using Amos -- Modeling using EQS -- Modeling using LISREL -- Output options -- Ease of use -- A data example: Iris or the struggle for admissibility -- A comparison of features of Amos, EQS, and LISREL -- Programming features -- Amos 3.6 -- EQS 5.7 -- LISREL 8.20 -- Admissibility -- Figures -- Amos 3.6 -- EQS 5.7 -- LISREL 8.20 -- Time flies -- Appendix 14.1. EQS program file for Fisher's Iris data -- Appendix 14.2. LISREL command code for Fisher's Iris data -- Appendix 14.3. Amos command code for Fisher's Iris data -- References -- Index.