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Objective: Evaluate the factor structure and stability of 4 dietary items (fruit, fruit juice, vegetables, and milk) from the School Physical Activity and Nutrition questionnaire--elementary school version. Methods: Secondary analysis of intervention data from third graders measured at pre-intervention, post-intervention (10 weeks), and 3-month follow-up. The researchers conducted structural equation modeling invariance analysis to test the stability of the factor structure of the 4 items. Results: Data from 1,147 students. Fit indices revealed good fit for a single factor remaining stable across time (χ²/degrees of freedom [DF] = 59.75/59, P = .45), gender (χ²/DF = 149.72/128, P = .09), and study groups (χ²/DF = 143.04/128, P = .17). Conclusions and Implications: A healthy food factor consisting of the 4 items can be used in future data analysis. This offers several advantages in analysis, including the use of latent change scores that are more powerful, more informative, and more easily interpreted than traditional approaches. [ABSTRACT FROM AUTHOR]

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]

Objective: To clarify the underlying relationship between nutrition self-efficacy and outcome expectations because the direction of the relationship (unidirectional vs bidirectional) is debated in the literature. Methods: Secondary data analysis of a 10-week, 10-lesson school-based nutrition education intervention among 3rd grade students (N  =  952). Nutrition self-efficacy (7 items) and nutrition outcome expectations (9 items) were measured through student self-report at intervention pre- (time 1) and post- (time 2) assessments. A series of two time point, multi-group cross-lagged bivariate change score models were used to determine the direction of the relationship. Results: A cross lag from nutrition self-efficacy at time 1 predicting changes in nutrition outcome expectations at time 2 significantly improved the fit of the model (Model 3), whereas a cross lag from nutrition outcome expectations at time 1 to changes in nutrition self-efficacy at time 2 only slightly improved the fit of the model (Model 2). Furthermore, adding both cross lags (Model 4) did not improve model fit compared to the model with only the self-efficacy cross lag (Model 3). Lastly, the nutrition outcome expectations cross lag did not significantly predict changes in nutrition self-efficacy in any of the models. Conclusions: Data suggest that there is a unidirectional relationship between nutrition self-efficacy and outcome expectations, in which self-efficacy predicts outcome expectations. Therefore, theory-based nutrition interventions may consider focusing more resources on changing self-efficacy because it may also lead to changes in outcome expectations as well. [ABSTRACT FROM AUTHOR]

The scientific literature consistently supports a negative relationship between adolescent depression and educational achievement, but we are certainly less sure on the causal determinants for this robust association. In this article we present multivariate data from a longitudinal cohort-sequential study of high school students in Hawai‘i (following McArdle, 2008; McArdle, Johnson, Hishinuma, Miyamoto, & Andrade, 2001). We first describe the full set of data on academic achievements and self-reported depression. We then carry out and present a progression of analyses in an effort to determine the accuracy, size, and direction of the dynamic relationships among depression and academic achievement, including gender and ethnic group differences. We apply 3 recently available forms of longitudinal data analysis: (a)Dealing with incomplete data—We apply these methods to cohort-sequential data with relatively large blocks of data that are incomplete for a variety of reasons (Little & Rubin, 1987; McArdle & Hamagami, 1992). (b)Ordinal measurement models(Muthén & Muthén, 2006)—We use a variety of statistical and psychometric measurement models, including ordinal measurement models, to help clarify the strongest patterns of influence. (c)Dynamic structural equation models(DSEMs; McArdle, 2008). We found the DSEM approach taken here was viable for a large amount of data, the assumption of an invariant metric over time was reasonable for ordinal estimates, and there were very few group differences in dynamic systems. We conclude that our dynamic evidence suggests that depression affects academic achievement, and not the other way around. We further discuss the methodological implications of the study. [ABSTRACT FROM AUTHOR]

Objective: Deficits in emotion regulation (ER) skills are considered a putative maintaining factor for major depressive disorder (MDD) and hence a promising target in the treatment of MDD. However, to date, the association between the successful application of arguably adaptive ER skills and changes in depressive symptom severity (DSS) has yet to be investigated over the course of treatment. Thus, the primary aim of this study was to clarify reciprocal prospective associations between successful ER skills application and DSS over the course of inpatient cognitive behavioral therapy for MDD. Additionally, we explored whether such associations would differ across specific ER skills. Method: We assessed successful ER skills application and DSS 4 times during the first 3 weeks of treatment in 152 inpatients (62.5% women, average age 45.6 years) meeting criteria for MDD. We first tested whether successful skills application and depression were cross-sectionally associated by computing Pearson's correlations. Then, we utilized latent curve modeling to test whether changes in successful skills application were negatively associated with changes in DSS during treatment. Finally, we used latent change score models to clarify whether successful skills application would predict subsequent reduction of DSS. Results: Cross-sectionally, successful ER skills application was associated with lower levels of DSS at all assessment times, and an increase of successful skills application during treatment was associated with a decrease of DSS. Moreover, successful overall ER skills application predicted subsequent changes in DSS (but not vice versa). Finally, strength of associations between successful application and DSS differed across specific ER skills. Among a broad range of potentially adaptive skills, only the abilities to tolerate negative emotions and to actively modify undesired emotions were significantly associated with subsequent improvement in DSS. Conclusions: Systematically enhancing health-relevant ER skills with specific interventions may help reduce DSS in patients suffering from MDD. [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]

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]

John L. Horn (1928–2006) was a pioneer in multivariate thinking and the application of multivariate methods to research on intelligence and personality. His key works on individual differences in the methodological areas of factor analysis and the substantive areas of cognition are reviewed here. John was also our mentor, teacher, colleague, and friend. We overview John Horn's main contributions to the field of intelligence by highlighting 3 issues about his methods of factor analysis and 3 of his substantive debates about intelligence. We first focus on Horn's methodological demonstrations describing (a) the many uses of simulated random variables in exploratory factor analysis; (b) the exploratory uses of confirmatory factor analysis; and (c) the key differences between states, traits, and trait-changes. On a substantive basis, John believed that there were important individual differences among people in terms of cognition and personality. These sentiments led to his intellectual battles about (d) Spearman'sgtheory of a unitary intelligence, (e) Guilford's multifaceted model of intelligence, and (f) the Schaie and Baltes approach to defining the lack of decline of intelligence earlier in the life span. We conclude with a summary of John Horn's unique approaches to dealing with common issues. [ABSTRACT FROM AUTHOR]

This is an application of contemporary multilevel regression modeling to the prediction of academic performances of 1st-year college students. At a first level of analysis, the data come fromN> 16,000 students who were college freshman in 1994–1995 and who were also participants in high-level college athletics. At a second level of analysis, the student data were related to the different characteristics of theC= 267 colleges in Division I of the NCAA. The analyses presented here initially focus on the prediction of freshman GPA from a variety of high school academic variables. The models used are standard multilevel regression models, but we examine nonlinear prediction within these multilevel models, and additional outcome variables are considered. The multilevel results show that (a) high school grades are the best available predictors of freshman college grades, (b) the ACT and SAT test scores are the next best predictors available, (c) the number of high school core units taken does not add to this prediction but does predict credits attained, (d) college graduation rate has a second-level effect of a small negative outcome on the average grades, and (e) nonlinear models indicate stronger effects for students at higher levels of the academic variables. These results show that standard multilevel models are practically useful for standard validation studies. Some difficulties were found with more advanced uses and interpretations of these techniques, and these problems lead to suggestions for further research. [ABSTRACT FROM PUBLISHER]

In recent years the use of the latent curve model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and the availability of specialized literature. Extensions of the LCM, like the the latent change score model (LCSM), have also increased in popularity. At the same time, the R statistical language and environment, which is open source and runs on several operating systems, is becoming a leading software for applied statistics. We show how to estimate both the LCM and LCSM with the sem, lavaan, and OpenMx packages of the R software. We also illustrate how to read in, summarize, and plot data prior to analyses. Examples are provided on data previously illustrated by Ferrer, Hamagami, and McArdle (2004). The data and all scripts used here are available on the first author's Web site. [ABSTRACT FROM AUTHOR]

There is a great deal of interest in the analysis of Genotype × Environment interactions (G×E). There are some limitations in the typical models for the analysis of G×E, including well-known statistical problems in identifying interactions and unobserved heterogeneity of persons across groups. The impact of a treatment may depend on the level of an unobserved variable, and this variation may dampen the estimated impact of treatment. Some researchers have noted that genetic variation may sometimes account for unobserved, and hence unaccounted for, heterogeneity. The statistical power associated with the G×E design has been studied in many different ways, and most results show that the small effects expected require relatively large or nonrepresentative samples (i.e., extreme groups). In this article, we describe some alternative approaches, such as randomized designs with multiple measures, multiple groups, multiple occasions, and analyses, to identify latent (unobserved) classes of people. These approaches are illustrated with data from the Aging, Demographics, and Memory Study (part of the Health and Retirement Study) examining the relations among episodic memory (based on word recall), APOE4 genotype, and educational attainment (as a proxy for an environmental exposure). Randomized clinical trials (RCTs) and randomized field trials (RFTs) have multiple strengths in the estimation of causal influences, and we discuss how measured genotypes can be incorporated into these designs. Use of these contemporary modeling techniques often requires different kinds of data be collected and encourages the formation of parsimonious models with fewer overall parameters, allowing specific G×E hypotheses to be investigated with a reasonable statistical foundation. [ABSTRACT FROM PUBLISHER]

In this article we provide a review of recent advances in longitudinal models for multivariate change. We first claim the need for dynamic modeling approaches as a way to evaluate psychological theories. We then describe one such approach, latent change score (LCS) models, and illustrate their utility with a summary of research findings in various areas of psychological science. We then highlight the most prominent features of LCS models. We conclude the article with suggestions for future research on multivariate models of change that can enhance our understanding of psychological science. [ABSTRACT FROM AUTHOR]

Previous research has shown cognitive abilities to have different biometric patterns of age-changes. We examined the variation in episodic memory (word recall task) for over 6,000 twin pairs who were initially aged 59–75, and were subsequently re-assessed up to three more times over 12 years. In cross-sectional analyses, variation in the number of words recalled independent of age was explained largely by non-shared influences (65–72%), with clear additive genetic influences (12–32%), and marginal shared family influences (1–18%). The longitudinal phenotypic analysis of the word recall task showed systematic linear declines over age, but several nonlinear models with more dramatic changes at later ages, improved the overall fit. A two-part spline model for the longitudinal twin data with an optimal turning point at age 74 led to: (a) a separation of non-shared environmental influences and transient measurement error (~50%); (b) strong additive genetic components of this latent curve (~44% at age 60) with increases (over 50%) up to age 74, but with no additional genetic variation after age 74; (c) the smaller influences of shared family environment (~15% at age 74) were constant over all ages; (d) non-shared effects play an important role over most of the life-span but diminish after age 74. [ABSTRACT FROM AUTHOR]

This review considers a common question in data analysis: What is the most useful way to analyze longitudinal repeated measures data? We discuss some contemporary forms of structural equation models (SEMs) based on the inclusion of latent variables. The specific goals of this review are to clarify basic SEM definitions, consider relations to classical models, focus on testable features of the new models, and provide recent references to more complete presentations. A broader goal is to illustrate why so many researchers are enthusiastic about the SEM approach to data analysis. We first outline some classic problems in longitudinal data analysis, consider definitions of differences and changes, and raise issues about measurement errors. We then present several classic SEMs based on the inclusion of invariant common factors and explain why these are so important. This leads to newer SEMs based on latent change scores, and we explain why these are useful. [ABSTRACT FROM AUTHOR]

The authors analyzed longitudinal data from a cognitive training experiment—Advanced Cognitive Training for Independent and Vital Elderly—using several alternative contemporary statistical models to test dynamic hypotheses based on latent change scores. The analyses focused on pretest and posttest data for only the group who received Reasoning training compared with the No-Contact (control) group. The initial structural equation modeling (SEM) path model isolated several training effects and an important source of transfer of training, Near→Far, but this transfer was not increased due to training. The subsequent models, which accounted for pretest differences and latent changes, implied that only the Near measurements were influenced by training, and the change transfer was small, Introduction of common factors for both Near and Far measurements showed the factor patterns were unaffected by training or time and suggested training was a broader effect than in any single variable. The bivariate analysis of common factors did not appear to alter the previous results. Addition of demographic covariates and latent mixture analysis of the trained group led to further results. The uses of contemporary SEMs with experimental data are discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The main purpose of this research is to evaluate the performance of a Bayesian approach for estimating unknown change points using Monte Carlo simulations. The univariate and bivariate unknown change point mixed models were presented and the basic idea of the Bayesian approach for estimating the models was discussed. The performance of Bayesian estimation was evaluated using simulation studies of longitudinal data with different sample sizes, varying change point values, different levels of Level-1 variances, and univariate versus bivariate outcomes. The numerical results compared the performance of the Bayesian methods with the first-order Taylor expansion method and the adaptive Gaussian quadrature method implemented in SAS PROC NLMIXED. These simulation results showed that the first-order Taylor expansion method and the adaptive Gaussian quadrature method were sensitive to the initial values, making the results somewhat unreliable. In contrast, these simulation results showed that Bayesian estimation was not sensitive to the initial values and the fixed-effects and Level-1 variance parameters can be accurately estimated in all of the conditions. One concern was that the estimates of the Level-2 covariance parameters were found to be biased when the Level-1 variance was large in the bivariate model. However, and in general, the new Bayesian approach to the estimation of turning points in longitudinal data proved to be quite robust and practically useful. [ABSTRACT FROM AUTHOR]

The authors applied linear dynamic models to longitudinal data to examine the dynamics of reading and cognition from 1st to 12th grade. They used longitudinal data (N = 445) from the Connecticut Longitudinal Study (S. E. Shaywitz, B. A. Shaywitz, J. M. Fletcher, & M. D. Escobar, 1990) to map the dynamic interrelations of various scales of the Wechsler Intelligence Scales for Children-Revised (i.e., Full, Performance, and Verbal) and specific markers of the Woodcock-Johnson Psycho-Educational Battery-Revised reading cluster (i.e., Letter-Word ID, Decoding, and Comprehension). The results of these analyses indicate that (a) there is a positive dynamic relation between reading and cognition across the selected age range; (b) this dynamic relation is symbiotic, with positive influences in both directions; (c) the influence from cognition to reading is stronger when considering the Wechsler Intelligence Scales for Children Performance-Revised Performance scale and weaker with the Verbal scale; (d) when examining the different Reading subtests, the influences from cognition are more apparent for Letter- Word ID and Comprehension and are less perceptible for Decoding; and (e) the dynamics of reading and cognition appear to be of stronger magnitude during 1st to 3rd grade, less strong during 4th to 8th grade, and weaker from 9th to 12th grade. [ABSTRACT FROM AUTHOR]

Bivariate dual change score models were applied to longitudinal data from the Swedish Adoption/Twin Study of Aging to compare the dynamic predictions of 2-component theories of intelligence and the processing speed theory of cognitive aging. Data from up to 5 measurement occasions covering a 16-year period were available from 806 participants ranging in age from 50 to 88 years at the first measurement wave. Factors were generated to tap 4 general cognitive domains: verbal ability, spatial ability, memory, and processing speed. Model fitting indicated no dynamic relationship between verbal and spatial factors, providing no support for the hypothesis that age changes in fluid abilities drive age changes in crystallized abilities. The results suggest that, as predicted by the processing speed theory of cognitive aging, processing speed is a leading indicator of age changes in memory and spatial ability, but not verbal ability. [ABSTRACT FROM AUTHOR]

The present study was conducted to better describe age trends in cognition among older adults in the longitudinal Health and Retirement Study (HRS) from 1992 to 2004 (N > 17,000). The authors used contemporary latent variable models to organize this information in terms of both cross-sectional and longitudinal inferences about age and cognition. Common factor analysis results yielded evidence for at least 2 common factors, labeled Episodic Memory and Mental Status, largely separable from vocabulary. Latent path models with these common factors were based on demographic characteristics. Multilevel models of factorial invariance over age indicated that at least 2 common factors were needed. Latent curve models of episodic memory were based on age at testing and showed substantial age differences and age changes, including impacts due to retesting as well as several time-invariant and time-varying predictors. [ABSTRACT FROM AUTHOR]

This article explores new statistical methodologies for using longitudinal data in the early prediction of Alzheimer's disease (AD). Specifically, the authors examine some new techniques that allow the joint or "shared" estimation of longitudinal components based on both duration (survival) and quantitative changes (growth curves). These new shared growth-survival parameter models may be used to characterize the declining functions that anticipate the onset of AD. The authors apply these models to data from the Kungsholmen Project, a longitudinal study of aging in Stockholm, Sweden. They examine age-based survival-frailty models for the onset of AD, latent growth-decline curve models for changes in cognition over age, and 3 alternative forms of models for the shared relationships of survival and early cognitive decline. The accuracy and reliability of this approach is considered for a better understanding of the developmental course of AD in these data, including the potential removal of biases due to subject selection. [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]

This is an application of new longitudinal structural equation modeling techniques to time-dependent associations of memory and brain structure measurements. There were 225 participants aged 30–80 years at baseline who were measured again after a 7-year interval on both the lateral ventricular size and Wechsler memory score. Multiple regression analyses show nonlinear associations with age but no relationships among longitudinal changes. Mixed-effects latent growth curve analyses and analyses based on latent difference scores indicate that longitudinal changes in both variables are reasonably well described by an exponential or dual change model. Bivariate dynamic structural equation modeling analyses indicate age-lagged changes operate in a coupled-over-time fashion, with the brain measure (lateral ventricular size) as a leading indicator in time of memory (Wechsler memory score) declines. [ABSTRACT FROM AUTHOR]

This study examined the dynamics of cognitive abilities and academic achievement from childhood to early adulthood. Predictions about time-dependent "coupling" relations between cognition and achievement based on R. B. Cattell's (1971, 1987) investment hypothesis were evaluated using linear dynamic models applied to longitudinal data (N = 672). Contrary to Cattell's hypothesis, a first set of findings indicated that fluid and crystallized abilities, as defined by the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R; R. W. Woodcock & M. B. Johnson, 1989-1990), were not dynamically coupled with each other over time. A second set of findings provided support for the original predictions and indicated that fluid ability was a leading indicator of changes in achievement measures (i.e., quantitative ability and general academic knowledge). The findings of this study suggest that the dynamics of cognitive abilities and academic achievement follow a more complex pattern than that specified by Cattell's investment hypothesis. [ABSTRACT FROM AUTHOR]

Using Bowen and Bok's data from 23 selective colleges, we fit multilevel logit models to test two hypotheses with implications for affirmative action and group differences in attainment of science, math, or engineering (SME) degrees. Hypothesis 1, that differences in precollege academic preparation will explain later SME graduation disparities, was fully supported with respect to the outcome gap between Whites and underrepresented minorities, partially supported for that between Asians and underrepresented minorities, and between men and women. Hypothesis 2, that college selectivity, after accounting for student characteristics, will be positively associated with SME persistence, was not supported. We demonstrate that the significance of the selectivity effect is overestimated when unilevel models are used. Admission officials are advised to carefully consider the relative academic preparedness of science-interested students, and such students choosing among colleges are advised to compare their academic qualifications to those of successful science students at each institution. [ABSTRACT FROM AUTHOR]

Structural equation models are presented as alternative models for examining longitudinal data. The models include (a) a cross-lagged regression model, (b) a factor model based on latent growth curves, and (c) a dynamic model based on latent difference scores. The illustrative data are on motivation and perceived competence of students during their first semester in high school. The 3 models yielded different results and such differences were discussed in terms of the conceptualization of change underlying each model. The last model was defended as the most reasonable for these data because it captured the dynamic interrelations between the examined constructs and, at the same time, identified potential growth in the variables. [ABSTRACT FROM AUTHOR]

Latent growth models were applied to data from the Swedish Adoption/Twin Study of Aging to discover if the rate of change in cognitive performance increased from middle age to later adulthood. The sample included 590 participants aged 44 to 88 years at first measurement. Data were gathered at 2 follow-up occasions at intervals of 3 years. Cognitive ability was assessed through 11 tests that tapped crystallized, fluid, memory, and spatial abilities and perceptual speed. Results indicated stability for measures of crystallized ability, linear age changes for many cognitive abilities, and a significant acceleration in linear decline after age 65 for measures with a large speed component. Gender differences were found only in mean level, not in rate of decline. [ABSTRACT FROM AUTHOR]

A great deal of prior research using structural equation models has focused on longitudinal analyses and biometric analyses. Some of this research has even considered the simultaneous analysis of both kinds of analytic problems. The key benefits of these kinds of analyses come from the estimation of novel parameters, such as the heritability of changes. This paper discusses some recent extensions of longitudinal multivariate models that can be informative within biometric designs. In the methods section we review a previous latent growth structural equation analysis of the New York Twin (NYT) longitudinal data (from McArdle et al., 1998). In the models section we recast this growth model in terms of latent difference scores, add several new dynamic components, including coupling parameters, and consider biometric components and examine model stability. In the results section we present new univariate and bivariate dynamic estimates and tests of various dynamic hypotheses for the NYT data, and we consider a few ways to interpret the age-related biometric components of these models. In the discussion we consider our limitations and present suggestions for future dynamic-genetic research. [ABSTRACT FROM AUTHOR]

Examines the theory of fluid and crystallized intelligence in assessing growth and decline of cognitive abilities with age. General predictions of human intellectual abilities; Correlation between age changes and cognitive variables; Definition of fluid reasoning.

Focuses on the application of latent growth curves for analyzing the development of height. Frequency of height measurement; Significance of the integration of growth curve and structural equation model in estimating the curves spanning the entire range of sexes; Ability of the different model to discern the growth of the abuse and the control sample.

This report uses different forms of latent variable structural equation models (SEMs) of group differences in self-reported Center for Epidemiological Studies Depression (CES-D) inventory results among a sample of Hawaiian and non-Hawaiian high school students. The models examined are organized progressively by increasing complexity, including mean differences models in the ANOVA tradition, mean differences combined with path analysis models, extensions to confirmatory factor analysis models, and common factor differences in multiple group models. Within each type of analysis, SEMs are defined and fitted to real CES-D data, and broad issues of group differences and invariance are considered. The substantive results indicate a high degree of invariance between Hawaiian and non-Hawaiian students within the same high schools on self-ratings of depression from the CES-D scale. These analyses illustrate theoretical and practical issues about common features and relationships among standard analysis of variance approaches and contemporary structural equation methodology. [ABSTRACT FROM AUTHOR]

This research uses longitudinal data from the Wechsler Adult Intelligence Scale (WAIS) and linear structural equation models (e.g., LISREL) in an evaluation of the structural, kinematic, and dynamic hypotheses of the "theory of fluid and crystallized intelligence." In a first set of analyses we use linear dynamic models in a formal evaluation of the growth and declines of abilities through latent growth and linear dynamic models. Our first results indicate separate trends over age for different intellectual abilities including broad knowledge, spatial reasoning, perceptual speed, and immediate memory. In a second set of analyses we extend these multivariate dynamic structural equation models to explore the age-based leading and lagging indicators. These results indicate a complex system of relationships, with memory losses as an important leading indicator. In a third set of analyses we use confirmatory techniques to test specific aging hypotheses. These results indicate support for both the "general memory loss" hypothesis and the "general slowing" hypothesis, provide some support for the" investment theory" at the adult level, and also suggest a single "general" factor does not describe the complexity of cognitive aging. These result synthesize prior WAIS studies and provide methods for further research on the dynamics of the growth and decline of intellectual abilities across the adult life-span. (C) 2001 Elsevier Science Inc. All rights reserved. [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 research on the use of the Center for Epidemiologic Studies-Depression (CES-D) Scale scores to determine major depression and dysthymia in ethnic minority adolescents. Predictive validity of CES-D; Differences in CES-D characteristics; Statistical data analyses.

Describes statistical research on the academic performance of student athletes in college-sports programs. Review of related literature; Characteristics of subjects in study; Student and college-level variables; Assumptions of the model.

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.

The article presents the research on classification and regression trees algorithms.

An abstract related to a biometric latent curve analysis of visual memory development using data from the Colorado Adoption Project (CAP) is presented.

The article discusses a study conducted to evaluate the utility and accuracy of regularized Structural Equation Modeling (RegSEM) as a confirmatory factor analysis model.

The article presents an obituary for John Leonard Horn, a psychologist, who died on August 18, 2006 at 77 in Los Angeles, California.

Although finite mixture models have received considerable attention, particularly in the social and behavioral sciences, an alternative method for creating homogeneous groups, structural equation model trees (Brandmaier, von Oertzen, McArdle, & Lindenberger, 2013), is a recent development that has received much less application and consideration. It is our aim to compare and contrast these methods for uncovering sample heterogeneity. We illustrate the use of these methods with longitudinal reading achievement data collected as part of the Early Childhood Longitudinal Study–Kindergarten Cohort. We present the use of structural equation model trees as an alternative framework that does not assume the classes are latent and uses observed covariates to derive their structure. We consider these methods as complementary and discuss their respective strengths and limitations for creating homogeneous groups. [ABSTRACT FROM PUBLISHER]

Change over time often takes on a nonlinear form. Furthermore, change patterns can be characterized by heterogeneity due to unobserved subpopulations. Nonlinear mixed-effects mixture models provide one way of addressing both of these issues. This study attempts to extend these models to accommodate time-unstructured data. We develop methods to fit these models in both the structural equation modeling framework as well as the Bayesian framework and evaluate their performance. Simulations show that the success of these methods is driven by the separation between latent classes. When classes are well separated, a sample of 200 is sufficient. Otherwise, a sample of 1,000 or more is required before parameters can be accurately recovered. Ignoring individually varying measurement occasions can also lead to substantial bias, particularly in the random-effects parameters. Finally, we demonstrate the application of these techniques to a data set involving the development of reading ability in children. [ABSTRACT FROM PUBLISHER]

Presents an obituary for psychologist Richard Quintin Bell who died on December 19, 2001.

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]

This research focuses on the problem of model selection between the latent change score (LCS) model and the autoregressive cross-lagged (ARCL) model when the goal is to infer the longitudinal relationship between variables. We conducted a large-scale simulation study to (a) investigate the conditions under which these models return statistically (and substantively) different results concerning the presence of bivariate longitudinal relationships, and (b) ascertain the relative performance of an array of model selection procedures when such different results arise. The simulation results show that the primary sources of differences in parameter estimates across models are model parameters related to the slope factor scores in the LCS model (specifically, the correlation between the intercept factor and the slope factor scores) as well as the size of the data (specifically, the number of time points and sample size). Among several model selection procedures, correct selection rates were higher when using model fit indexes (i.e., comparative fit index, root mean square error of approximation) than when using a likelihood ratio test or any of several information criteria (i.e., Akaike’s information criterion, Bayesian information criterion, consistent AIC, and sample-size-adjusted BIC). [ABSTRACT FROM AUTHOR]

The present paper focuses on the relationship between latent change score (LCS) and autoregressive cross-lagged (ARCL) factor models in longitudinal designs. These models originated from different theoretical traditions for different analytic purposes, yet they share similar mathematical forms. In this paper, we elucidate the mathematical relationship between these models and show that the LCS model is reduced to the ARCL model when fixed effects are assumed in the slope factor scores. Additionally, we provide an applied example using height and weight data from a gerontological study. Throughout the example, we emphasize caution in choosing which model (ARCL or LCS) to apply due to the risk of obtaining misleading results concerning the presence and direction of causal precedence between two variables. We suggest approaching model specification not only by comparing estimates and fit indices between the LCS and ARCL models (as well as other models) but also by giving appropriate weight to substantive and theoretical considerations, such as assessing the justifiability of the assumption of random effects in the slope factor scores. [ABSTRACT FROM AUTHOR]