Revisiting the latent structure of eating disorders: Taxometric analyses with nonbehavioral indicators.

Researchers have debated whether clinical eating disorders are best conceptualized as one extreme along a continuum of degree or as qualitatively different from less severe eating disturbances. Taxometric analysis, a set of procedures that assess the structure of a given disorder by detecting whether a latent taxon exists among its indicators, has just begun to be applied to eating disorder research. Its application to this domain may clarify whether eating disorders best conform to a dimensional (i.e.. continuum) or taxonic model. The latent structure of eating disorders was examined in a sample of 532 college women by using 2 popular taxometric procedures (i.e., maximum covariance analysis and latent mode analysis) with 5 nonbehavioral indicators of eating disorder symptomatology. Results across procedures suggest that the data are more indicative of a dimensional solution, as no latent taxon was uncovered. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Taxometrics, polytomous constructs, and the comparison curve fit index: A Monte Carlo analysis.

The taxometric method effectively distinguishes between dimensional (1-class) and taxonic (2-class) latent structure, but there is virtually no information on how it responds to polytomous (3-class) latent structure. A Monte Carlo analysis showed that the mean comparison curve fit index (CCFI; Ruscio, Haslam, & Ruscio, 2006) obtained with 3 taxometric procedures—mean above minus below a cut (MAMBAC), maximum covariance (MAXCOV), and latent mode factor analysis (L-Mode)—accurately identified 1-class (dimensional) and 2-class (taxonic) samples and produced taxonic results when applied to 3-class (polytomous) samples. From these results it is concluded that using the simulated data curve approach and averaging across procedures is an effective way of distinguishing between dimensional (1-class) and categorical (2 or more classes) latent structure. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Estimating the nonlinear and interactive effects of latent variables.

Describes a procedure that enables researchers to estimate nonlinear and interactive effects of latent variables in structural equation models. Given that the latent variables are normally distributed, the parameters of such models can be estimated. To do this, products of the measured variables are used as indicators of latent product variables. Estimation must be done using a procedure that allows nonlinear constraints on parameters. The procedure is demonstrated in 3 examples. The 1st 2 examples use artificial data with known parameter values. These parameters are successfully recovered by the procedure. The final complex example uses national election survey data. (14 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Distributed and focused attention: Neuropsychological evidence for separate attentional mechanisms when counting and estimating.

Evidence is presented for 2 modes of attention operating in simultanagnosia. The authors examined visual enumeration in a patient, GK, who has severe impairments in serially scanning across a scene and is unable to count the numbers of items in visual displays. However, GK's ability to judge the relative magnitude of 2 displays was consistently above chance, even when overall luminosity did not vary with the number of items present. In addition, several variables had a differential impact on GK's counting and magnitude estimation. Magnitude estimation but not counting was facilitated by using elements that grouped more easily and by presenting the elements in regular configurations. In contrast, counting was facilitated by placing the elements in different colors while magnitude estimation was disrupted. Also GK's performance on magnitude estimation tasks was disrupted by asking him to count the elements present. The data suggest that GK can process visual stimuli in either a focused or distributed attention mode. When in a focused attention mode, performance is limited by poor serial scanning of attention due to an impaired explicit representation of visual space. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Moderators of the Relationship Between Implicit and Explicit Evaluation.

Automatic and controlled modes of evaluation sometimes provide conflicting reports of the quality of social objects. This article presents evidence for 4 moderators of the relationship between automatic (implicit) and controlled (explicit) evaluations. Implicit and explicit preferences were measured for a variety of object pairs using a large sample. The average correlation was r = .36, and 52 of the 57 object pairs showed a significant positive correlation. Results of multilevel modeling analyses suggested that (a) implicit and explicit preferences are related, (b) the relationship varies as a function of the objects assessed, and (c) at least 4 variables moderate the relationship: self-presentation, evaluative strength, dimensionality, and distinctiveness. The variables moderated implicit-explicit correspondence across individuals and accounted for much of the observed variation across content domains. The resulting model of the relationship between automatic and controlled evaluative processes is grounded in personal experience with the targets of evaluation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Variational learning in nonlinear gaussian belief networks

We view perceptual tasks such as vision and speech recognition as inference problems where the goal is to estimate the posterior distribution over latent variables (e.g., depth in stereo vision) given the sensory input. The recent flurry of research in independent component analysis exemplifies the importance of inferring the continuous-valued latent variables of input data. The latent variables found by this method are linearly related to the input, but perception requires nonlinear inferences such as classification and depth estimation. In this article, we present a unifying framework for stochastic neural networks with nonlinear latent variables. Nonlinear units are obtained by passing the outputs of linear gaussian units through various nonlinearities. We present a general variational method that maximizes a lower bound on the likelihood of a training set and give results on two visual feature extraction problems. We also show how the variational method can be used for pattern classification and compare the performance of these nonlinear networks with other methods on the problem of handwritten digit recognition.     

Differentiating categorical and dimensional data with taxometric analysis: Are two variables better than none?

Taxometric analyses have proven helpful for distinguishing categorical and dimensional data. Many taxometric procedures require at least 3 variables for analysis. What if a construct is defined by only 2 conceptually nonredundant characteristics or a data set contains only 2 empirically nonredundant variables? In Study 1, we performed extensive simulations to determine whether informative results can be obtained when only 2 variables are available for taxometric analysis. The mean above minus below a cut (MAMBAC) and maximum slope (MAXSLOPE) procedures, used with parallel analyses of comparison data, successfully differentiated categorical and dimensional structure. With just 2 variables, it seems especially important that indicators vary across as many distinct values as possible and that investigators obtain as large a sample as possible. Additional findings address questions about the most effective way to implement taxometric analyses. In Study 2, the potential utility of 2-variable taxometric analysis is illustrated using data on proactive and reactive childhood aggression, where the results provided strong support for dimensional structure. As long as high-quality data are available, it appears that one can have confidence in the results of taxometric analyses performed with only 2 variables. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Generalized full-information item bifactor analysis.

Full-information item bifactor analysis is an important statistical method in psychological and educational measurement. Current methods are limited to single-group analysis and inflexible in the types of item response models supported. We propose a flexible multiple-group item bifactor analysis framework that supports a variety of multidimensional item response theory models for an arbitrary mixing of dichotomous, ordinal, and nominal items. The extended item bifactor model also enables the estimation of latent variable means and variances when data from more than 1 group are present. Generalized user-defined parameter restrictions are permitted within or across groups. We derive an efficient full-information maximum marginal likelihood estimator. Our estimation method achieves substantial computational savings by extending Gibbons and Hedeker's (1992) bifactor dimension reduction method so that the optimization of the marginal log-likelihood requires only 2-dimensional integration regardless of the dimensionality of the latent variables. We use simulation studies to demonstrate the flexibility and accuracy of the proposed methods. We apply the model to study cross-country differences, including differential item functioning, using data from a large international education survey on mathematics literacy. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Sex Differences in the Behavioral Response to Spatial and Object Novelty in Adult C57BL/6 Mice.

The present studies examined sex differences in object localization and recognition in C57BL/6 mice. Experiment 1 measured responses to spatial novelty (object displacement) and object novelty (object substitution). Males strongly preferred displaced and substituted objects over unchanged objects, whereas females showed a preference in only 1 measure of object novelty. Experiment 2 further examined object recognition by presenting mice with 2 identical objects, followed 24 hr or 7 days later by testing with a familiar and a novel object. After 24 hr, males preferentially explored the novel object, whereas females exhibited no such preference. Neither sex displayed a preference for the novel object after 7 days. The data suggest that male mice are superior to females at localizing and recognizing objects. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Most Degrading Postbuckling Mode of Pin-Jointed Structures

This paper describes a numerical method which is used to determine the most unstable postbuckling mode capable of developing at the ultimate critical state of complex pin-jointed structures. In this method, the set of independent variables are the flexural shortenings of those members that are a subset of the critically loaded members in the lattice structure. This choice of independent variables greatly simplifies the analysis and promotes the evaluation of the most degrading mode under both equilibrium and collapse conditions. In this Technical Note, this method is used to evaluate these two modes for some rather simple structures. The deformed modes are plotted in each case and differences between the most degrading mode under equilibrium and collapse conditions are noted.     

Motions in hemoglobin studied by normal mode analysis and energy minimization: evidence for the existence of tertiary T-like, quaternary R-like intermediate structures

The normal mode analysis of human hemoglobin showed the presence in the deoxy T-state of one main preferential direction that brings the structure close to the R-state, with a low-energy variation, while in the oxy R-state there are several modes that point towards the T-state, but with higher energy variations and less contribution to the transition. The displacement along a combination of normal modes, followed by energy minimization, starting from the R-state, did not allow one to obtain a structure significantly different from that of R, showing that the fully oxygenated hemoglobin is trapped in a deep and narrow potential energy minimum. On the contrary, starting from the deoxy T-state, the displacement along a combination of normal modes, followed by energy minimization, yielded an intermediate structure, that we designate Tmin(d1), which is closer to R; the normal modes of Tmin(d1) indicated that the potential energy minimum in the vicinity of this structure is as narrow as that of R but less deep. The procedure of displacement along the modes, followed by energy minimization, was applied to Tmin(d1), yielding Tmin(d2); then the procedure was repeated, yielding the intermediate structures Tmin(d3) and Tmin(d4). The structures Tmin(d2), Tmin(d3) and Tmin(d4) are not significantly different from each other, indicating that they are trapped in a narrow, deep energy minimum. This procedure revealed the existence of at least two intermediate sets of structures between T and R: the first one, Tmin(d1), is different from the T and R structures, while the second set, Tmin(d2), Tmin(d3) and Tmin(d4), is quaternary R-like and tertiary T-like, where the contacts at the interfaces alpha1 beta1 and alpha1 beta2 are R-like, and the alpha and beta heme environments are still T-like.     

Structural-equation models of current drug use: Are appropriate models so simple(x)?

The simplex and common-factor models of drug use were compared using maximum-likelihood estimation of latent variable structural models in two samples: a sample of 226 high school students, using ratio-scale measures of current drug use, and a sample of 310 industrial workers and 811 college students, using ordinal-scale measures of current drug use. Latent variables of alcohol, marihuana, enhancer hard drugs, and dampener hard drugs were specified in a series of structural models. Contrary to previous findings with cumulative drug-use data, the common-factor model provided a more acceptable representation of the observed current-use data than did the simplex model in both samples. In addition, the similarity of results across both of these samples supports recent contentions by Huba and Bentler (1982) that quantitatively measured variables are not necessarily superior to qualitative, ordinal indicators in latent variable models of drug use. (49 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Measurement of psychological disorders using cognitive diagnosis models.

Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article presents the development of a new cognitive diagnosis model for use in psychological assessment--the DINO (deterministic input; noisy "or" gate) model--which, as an illustrative example, is applied to evaluate and diagnose pathological gamblers. As part of this example, a demonstration of the estimates obtained by cognitive diagnosis models is provided. Such estimates include the probability an individual meets each of a set of dichotomous Diagnostic and Statistical Manual of Mental Disorders (text revision [DSM-IV-TR]; American Psychiatric Association, 2000) criteria, resulting in an estimate of the probability an individual meets the DSM-IV-TR definition for being a pathological gambler. Furthermore, a demonstration of how the hypothesized underlying factors contributing to pathological gambling can be measured with the DINO model is presented, through use of a covariance structure model for the tetrachoric correlation matrix of the dichotomous latent variables representing DSM-IV-TR criteria. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Measurement invariance of the Gifted Rating Scales—School Form across five cultural groups.

This study examined measurement invariance of the Gifted Rating Scales—School Form (GRS–S) across the United States, Puerto Rico, China, South Korea, and Turkey, using multigroup confirmatory factor analysis. A total of 1,817 students were rated by 287 teachers using either translated versions of GRS–S or the original English GRS–S. Results indicate a similar factor structure for the GRS–S across the five locations; six factors with each of the 72 items equivalently loaded to the same latent variable across groups. The metric invariance test and the factor variance and covariance invariance tests reveal that the patterns of factor loadings and the factor variances and covariances are invariant across the five groups. Moreover, the scalar invariance test indicates that item means are equivalent across the groups. These results suggest that the GRS–S has intercultural utility and can be similarly interpreted. Implications and limitations of the present research for gifted identification are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Does attention-deficit/hyperactivity disorder have a dimensional latent structure? A taxometric analysis.

An understanding of the latent structure of attention-deficit/hyperactivity disorder (ADHD) is essential for developing causal models of this disorder. Although some researchers have presumed that ADHD is dimensional and others have assumed that it is taxonic, there has been relatively little research directly examining the latent structure of ADHD. The authors conducted a set of taxometric analyses using data from the NICHD Study of Early Child Care and Youth Development (ns between 667 and 1,078). The results revealed a dimensional latent structure across a variety of different analyses and sets of indicators for inattention, hyperactivity/impulsivity, and ADHD. Furthermore, analyses of correlations with associated features indicated that dimensional models demonstrated stronger validity coefficients with these criterion measures than dichotomous models. These findings jibe with recent research on the genetic basis of ADHD and with contemporary models of ADHD. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

The measurement of consistency and occasion specificity with latent class models: A new model and its application to the measurement of affect.

After a short overview of different latent variable models for analyzing interindividual differences in intraindividual change, a latent class variability model is introduced. The model makes it possible to estimate the consistency of interindividual differences across naturally occurring situations. In addition, the contribution of latent trait variables and occasion-specific latent variables in predicting manifest responses can be analyzed, and the precision of predicting states by traits and occasion-specific effects can be assessed. The model is illustrated by an application to the measurement of positive affects. Finally, the model and its application are discussed with respect to conditional trait concepts in personality psychology and other potential areas of application. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Four-year cross-lagged associations between physical and mental health in the medical outcomes study.

This article provides an application of structural equation modeling to the evaluation of cross-lagged panel models. Self-reports of physical and mental health at 3 different time points spanning a 4-yr interval were analyzed to illustrate the cross-lagged analysis methodology. Data were collected from a sample of 856 patients with hypertension, diabetes, heart disease, or depression (or any combination of these) participating in the Medical Outcomes Study. Cross-lagged analyses of physical and mental health constructs revealed substantial stability effects across time. A structural model with standard effects revealed positive effects of physical health on mental health but negative (suppression) effects of mental health on physical health. The effects of mental health on physical health became nonsignificant when the model was revised by adding nonstandard effects (direct effects of measured variable residuals on latent variables). Recommendations for structural equation modeling of cross-lagged panel data are provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The latent structure of dietary restraint, body dissatisfaction, and drive for thinness: A series of taxometric analyses.

Although the latent structure of various eating disorders has been explored in previous studies, no published studies have examined the latent structure of theoretically relevant variables that have been shown to cut across eating disorder diagnoses. The current study examined 3 such variables (dietary restraint, body dissatisfaction, and drive for thinness) among undergraduate women using the taxometric method. The 5 items from the Eating Disorders Examination Questionnaire's Dietary Restraint subscale were used as dietary restraint indicators, whereas items from the Eating Disorders Inventory Body Dissatisfaction and Drive for Thinness subscales were used as indicators of body dissatisfaction and drive for thinness, respectively. As hypothesized, MAXCOV (maximum covariance) and MAMBAC (mean above minus below a cut) analyses suggested that all 3 variables are dimensional; therefore, individuals with high levels of reported dietary restraint, body dissatisfaction, and drive for thinness appear to differ in degree, but not in kind, from those with lower levels. Implications for prevention, assessment, classification, and treatment are discussed. (PsycINFO Database Record (c) 2011 APA, all rights reserved)