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In this study, we reanalyzed the classic Hawai'i Family Study of Cognition (HFSC) data using contemporary multilevel modeling techniques. We used the HFSC baseline data ("N" = 6,579) and reexamined the factorial structure of 16 cognitive variables using confirmatory (restricted) measurement models in an explicit sequence. These models were initially fitted using multilevel confirmatory factor analysis techniques and the invariant six-factor models with two higher order factors fit fairly well (e[subscript a < 0.08) to the total, between- and within-family data. More crucially, a model requiring metric factorial invariance proved to be a reasonable fit to the between and within matrices, and allowed the ratio of the between-family variation to total family variation (eta-squared) to be calculated separately for each common factor (?[superscript 2]: Gc = 0.27, Gf = 0.22, Gm = 0.15, Gs = 0.04, Gv = 0.30, and SP = 0.16). Higher order factors were fitted using multilevel structural equation modeling techniques and these suggested a reasonable two-factor solution with unequal family impacts. These results suggest that (a) A "G only model" does not fit the data very well, and there are many sources of individual differences in cognitive abilities; (b) the sources of the individual differences in cognition can be measured the same way between and within families; and (c) even after the unique test components are removed, "cognitive differences are larger within families than between families". We consider other general multivariate family models, and we raise questions about family influences.

In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree structures that separate a data set recursively into subsets with significantly different parameter estimates in a SEM. SEM Trees provide means for finding covariates and covariate interactions that predict differences in structural parameters in observed as well as in latent space and facilitate theory-guided exploration of empirical data. We describe the methodology, discuss theoretical and practical implications, and demonstrate applications to a factor model and a linear growth curve model. (Contains 6 figures.)

Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model and then a linear growth curve model. A third example is also provided to demonstrate how to iteratively run WinBUGS inside SAS for Monte Carlo simulation studies. The SAS codes used in this study are easily extended to accommodate many other models with only slight modification. This interface can be of practical benefit in many aspects of Bayesian methods because it allows the SAS users to benefit from the implementation of Bayesian estimation and it also allows the WinBUGS user to benefit from the data processing routines available in SAS. (Contains 3 figures and 3 tables.)

A new method is proposed that extends the use of regularization in both lasso and ridge regression to structural equation models. The method is termed regularized structural equation modeling (RegSEM). RegSEM penalizes specific parameters in structural equation models, with the goal of creating easier to understand and simpler models. Although regularization has gained wide adoption in regression, very little has transferred to models with latent variables. By adding penalties to specific parameters in a structural equation model, researchers have a high level of flexibility in reducing model complexity, overcoming poor fitting models, and the creation of models that are more likely to generalize to new samples. The proposed method was evaluated through a simulation study, two illustrative examples involving a measurement model, and one empirical example involving the structural part of the model to demonstrate RegSEM’s utility. [ABSTRACT FROM PUBLISHER]

Objectives. The aim of this research was to test the invariance of the cognitive variables in the Health and Retirement Study/Asset Health Dynamics Among the Oldest Old studies (HRS/AHEAD) across ethnicity, gender, and time. Method. Analyses were conducted using a selected subsample of the HRS/AHEAD data set. The cognitive performance tests measuring episodic memory and mental status were used, and invariance of a two-factor structure was tested using confirmatory factor analyses and multilevel modeling for longitudinal data. Results. Results provided some support for “strict” factorial invariance of the episodic memory and mental status measures across ethnicity and gender. Further support of weak (“metric”) measurement invariance was found across time. Discussion. Results of the research further our understanding of invariance of the HRS/AHEAD cognitive ability measures. Further implications are discussed. [ABSTRACT FROM AUTHOR]

In this study, we reanalyzed the classic Hawai’i Family Study of Cognition (HFSC) data using contemporary multilevel modeling techniques. We used the HFSC baseline data (N = 6,579) and reexamined the factorial structure of 16 cognitive variables using confirmatory (restricted) measurement models in an explicit sequence. These models were initially fitted using multilevel confirmatory factor analysis techniques and the invariant six-factor models with two higher order factors fit fairly well (εa < 0.08) to the total, between- and within-family data. More crucially, a model requiring metric factorial invariance proved to be a reasonable fit to the between and within matrices, and allowed the ratio of the between-family variation to total family variation (eta-squared) to be calculated separately for each common factor (η2: Gc = .27, Gf = .22, Gm = .15, Gs = .04, Gv = .30, and SP = .16). Higher order factors were fitted using multilevel structural equation modeling techniques and these suggested a reasonable two-factor solution with unequal family impacts. These results suggest that (a) A “G only model” does not fit the data very well, and there are many sources of individual differences in cognitive abilities; (b) the sources of the individual differences in cognition can be measured the same way between and within families; and (c) even after the unique test components are removed, cognitive differences are larger within families than between families. We consider other general multivariate family models, and we raise questions about family influences. [ABSTRACT FROM PUBLISHER]

Using cognitive data from the Health and Retirement Study and Asset Health Dynamics Among the Oldest Old studies that were collected between 1992 and 2004, McArdle and colleagues (2007) found that a two-factor model (episodic memory and mental status) fit better than a one-factor model. The question that was addressed in the present study was whether these results would replicate in newer cohorts of data, collected between 2006 and 2010. We also tested age, education, and gender as predictors of the identified factors. Results confirm that a two-factor structure fits better than the single-factor model in the newer cohorts. Differential predictors were also observed. [ABSTRACT FROM AUTHOR]

Recent substantive research on biometric analyses of twin and family data has used both a biometric path analysis model (PAM) and a biometric variance components model (VCM). Methodological research on these same topics have suggested benefits of using linear structural equation model algorithms (SEMA) as well as mixed effect multilevel algorithms (MEMA). To better understand the potential similarities and differences among these approaches we first highlight the algebraic equivalence between the standard biometric PAM and the corresponding biometric VCM models for family data. Second, we demonstrate how several SEMA programs based on either the PAM or VCM approach produce equivalent estimates for all phenotypic and biometric parameters. Third, we show how the biometric VCM approach (but not the PAM approach) can be easily programmed using current MEMA programs (e.g., SAS PROC MIXED). We then expand the scope of these different approaches to include measured covariates, observed variable interactions and multiple relatives within each family. MEMA software is compared to SEMA software for programming complex models, including the flexibility of data input, treatment of missing data, inclusion of covariates, and ease of accommodating varying numbers of observations (per family or individual). [ABSTRACT FROM AUTHOR]

With few exceptions. the dynamics underlying the mood structures of individuals with Parkinson's Disease have consistently been overlooked. Based on 12 participants' daily self-reports over 72 days, we identified 10 participants whose covariance matrices for positive and negative affect were similar enough to warrant pooling. Dynamic factor models that included factor autoregression and cross-regressions were fitted to the pooled, lagged covariance matrix representing approximately 700 occasions of measurement. Although results from the pooled data indicated that both positive and negative affect had a strong lag-1 autoregressive impact on current positive and negative affect, most individuals showed stronger autoregressive effects for positive than negative affect when examined individually. There was also a weak cross-regression effect of positive affect on negative affect, but the reverse was not true. Through model lining, we demonstrated that failure to incorporate lagged relations among factors could lead to an overestimation of concurrent correlations among latent factors. Implications of the findings in relation to the orthogonality of positive and negative affect are discussed. [ABSTRACT FROM AUTHOR]

The key idea in any factor analysis is that a large number of observed behaviors are a direct result of a smaller number of unobserved, or latent, sources. The purpose of this article is to give a brief overview of some new directions in the techniques and uses of factor analysis. Factor analysis has been used by psychologists for most of this century. Most researchers have used techniques now called exploratory or traditional factor analysis. However, during the past decade, there has been an increasing use of what is now termed confirmatory or structural factor analysis.

Presents some benefits and limitations of structural equation models for multivariate experiments with incomplete data. Kinds of incomplete data; Consideration of the cost-benefit analysis; Emphasis of the interbattery measurement analysis.