Identifying trajectories of adolescent smoking: an application of latent growth mixture modeling.

The goal of the current study was to identify discrete longitudinal patterns of change in adolescent smoking using latent growth mixture modeling. Five distinct longitudinal patterns were identified. A group of early rapid escalators was characterized by early escalation (at age 13) that rapidly increased to heavy smoking. A pattern characterized by occasional puffing up until age 15, at which time smoking escalated to moderate levels was also identified (late moderate escalators). Another group included adolescents who, after age 15, began to escalate slowly in their smoking to light (0.5 cigarettes per month) levels (late slow escalators). Finally, a group of stable light smokers (those who smoked 1-2 cigarettes per month) and a group of stable puffers (those who smoked only a few puffs per month) were also identified. The stable puffer group was the largest group and represented 25% of smokers.     

Dimensional, categorical, or dimensional-categories: testing the latent structure of anxiety sensitivity among adults using factor-mixture modeling

The present study tested multiple, competing latent structural models of anxiety sensitivity (AS), as measured by the Anxiety Sensitivity Index-3 (ASI-3; Taylor et al., 2007). Data were collected from 3 sites in North America (N = 634). Participants were predominantly university students (M = 21.3 years, SD = 5.4). ASI-3 data were evaluated using an integration of mixture modeling and confirmatory factor analysis—factor mixture modeling (FMM; Muthén, 2008). Results supported a 2-class 3-factor partially invariant model of AS. Specifically, the FMM analyses indicated that AS is a taxonic (two-class) variable, and that each categorical class has a unique multidimensional factor structure. Consistent with the specific point-prediction regarding the hypothesized parameters of the putative latent class variable, FMM indicated that the putatively “high-risk” subgroup of cases or latent form of AS composed approximately 12% of the studied sample whereas the putatively “normative” subgroup of cases or latent form of AS composed 88% of the sample. In addition, the AS Physical and Psychological Concerns subscales, but not the Social Concerns subscale, most strongly discriminated between the two latent classes. Finally, comparison of continuous levels of AS Physical and Psychological Concerns between FMM-derived AS latent classes and independent clinical samples of patients with anxiety disorders provided empirical support for the theorized taxonic-dimensional model of AS and anxiety psychopathology vulnerability. Findings are discussed in regard to the implications of this and related research into the nature of AS and anxiety psychopathology vulnerability.     

Refining the borderline personality disorder phenotype through finite mixture modeling: implications for classification

Borderline personality disorder (BPD) is characterized by considerable heterogeneity. Prior approaches to resolving heterogeneity in BPD pathology have used factor and cluster analytic as well as latent class analysis strategies. These prior studies have been atheoretical in nature, but provide an initial empirical corpus for further sub-typing efforts in BPD. A model-based taxonomy for BPD that is supported by evidence from an advanced statistical methodology would enhance investigations of BPD etiology, pathophysiology, and treatment. This study applied finite mixture modeling analysis, in a model-guided fashion, to selected dimensions of pathology within a group of well-characterized BPD patients to determine if latent groups are harbored within the disorder. Subjects with BPD (N = 90) were examined on a variety of model-relevant psychopathology dimensions. We applied finite mixture modeling to these dimensions. We then evaluated the validity of the obtained solution by reference to a variety of external measures not included in the initial mixture modeling. Three phenotypically distinct groups reside within the overall BPD category. Group-1 is characterized by low levels of antisocial, paranoid, and aggressive features. Group-2 is characterized by elevated paranoid features, whereas Group-3 is characterized by elevated antisocial and aggressive features. External correlates reveal a pattern of differences consistent with the validity of this proposed grouping structure. A theory-guided finite mixture modeling analysis supports a parsing of the BPD category into three subgroups. This proposed BPD taxonomy represents an approach to reducing heterogeneity observed among BPD patients and it may prove useful in studies seeking to understand etiologic and pathophysiologic factors as well as treatment response in BPD.     

Inferring the structure of latent class

Present optimization techniques in latent class analysis apply the expectation maximization algorithm or the Newton-Raphson algorithm for optimizing the parameter values of a prespecified model. These techniques can be used to find maximum likelihood estimates of the parameters, given the specified structure of the model, which is defined by the number of classes and, possibly, fixation and equality constraints. The model structure is usually chosen on theoretical grounds. A large variety of structurally different latent class models can be compared using goodness-of-fit indices of the chi-square family, Akaike’s information criterion, the Bayesian information criterion, and various other statistics. However, finding the optimal structure for a given goodness-of-fit index often requires a lengthy search in which all kinds of model structures are tested. Moreover, solutions may depend on the choice of initial values for the parameters. This article presents a new method by which one can simultaneously infer the model structure from the data and optimize the parameter values. The method consists of a genetic algorithm in which any goodness-of-fit index can be used as a fitness criterion. In a number of test cases in which data sets from the literature were used, it is shown that this method provides models that fit equally well as or better than the models suggested in the original articles.     

Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration

Abstract

Recent advances have allowed for modeling mixture components within latent growth modeling using robust, skewed mixture distributions rather than normal distributions. This feature adds flexibility in handling non-normality in longitudinal data, through manifest or latent variables, by directly modeling skewed or heavy-tailed latent classes rather than assuming a mixture of normal distributions. The aim of this study was to assess through simulation the potential under- or over-extraction of latent classes in a growth mixture model when underlying data follow either normal, skewed-normal, or skewed-t distributions. In order to assess this, we implement skewed-t, skewed-normal, and conventional normal (i.e., not skewed) forms of the growth mixture model. The skewed-t and skewed-normal versions of this model have only recently been implemented, and relatively little is known about their performance. Model comparison, fit, and classification of correctly specified and mis-specified models were assessed through various indices. Findings suggest that the accuracy of model comparison and fit measures are dependent on the type of (mis)specification, as well as the amount of class separation between the latent classes. A secondary simulation exposed computation and accuracy difficulties under some skewed modeling contexts. Implications of findings, recommendations for applied researchers, and future directions are discussed; a motivating example is presented using education data.     

A latent structure for the simplex

A perfect Guttman scale generates a perfect Guttman simplex. This is capitalized on to get a transformation useful in a kind of non-linear factor analysis applicable to empirical correlations resembling but not necessarily conforming to a perfect simplex.A Gramian factorization—square, symmetric, possibly of low rank but with no negative characteristic roots—of empirical correlations is transformed into latent information about nonlinear regressions of tests on a single underlying dimension. Class intervals on that continuum are defined in terms of their standard score profiles on the tests. Each class interval exhibits local independence, so that individual members may vary but may not covary in their profiles.The method is flexible as to choice of diagonals, fitted rank, and exact shape of resulting non-linear regressions, so long as those regressions are essentially monotonic and have a proper progression of curvatures. Construction of the needed transformation for data of any size is outlined, and the problem of metric for the latent continuum is given several solutions.An empirical example is provided with full and low rank solutions involving different diagonal choices.This research was conducted under Grant Nonr(G)-00033-63 of the Office of Naval Research. Reproduction of this paper, in whole or in part, is permitted for any purpose of the United States government.     

The latent structure of occupations

Triads of occupations were given to 1300 high-school pupils of both sexes in order to find the affinities of the occupations and the values they used to arrive at the affinities. The values used to perceive occupations come from the subject; nevertheless they could be categorized into a scheme of 20 different values, with the residue category containing less than 1% of the answers. In accordance with previous research in this field, an almost universal structure of occupations was found. The following variables had no influence on the choice of occupational affinities: age, trend of study, SES, father's occupation, mother's occupation, vocational maturity, and preferred occupation. Only the pupils of one school were significantly different in affiliating the occupations in the triads. Furthermore, girls perceive the world of occupations more uniformly than boys.Underlying this universal structure of occupations there exists a latent structure, specific to each individual, which explains why the world of occupations is seen in the way it is. This latent structure of occupations is influenced by the individual's specific background.     

Understanding the latent structure of job performance ratings

This study quantified the effects of 5 factors postulated to influence performance ratings: the ratee's general level of performance, the ratee's performance on a specific dimension, the rater's idiosyncratic rating tendencies, the rater's organizational perspective, and random measurement error. Two large data sets, consisting of managers (n = 2,350 and n = 2,142) who received developmental ratings on 3 performance dimensions from 7 raters (2 bosses, 2 peers, 2 subordinates, and self) were used. Results indicated that idiosyncratic rater effects (62% and 53%) accounted for over half of the rating variance in both data sets. The combined effects of general and dimensional ratee performance (21% and 25%) were less than half the size of the idiosyncratic rater effects. Small perspective-related effects were found in boss and subordinate ratings but not in peer ratings. Average random error effects in the 2 data sets were 11% and 18%.     

Eye movement dysfunction in first-degree relatives of patients with schizophrenia: A meta-analytic evaluation of candidate endophenotypes

Several forms of eye movement dysfunction (EMD) are regarded as promising candidate endophenotypes of schizophrenia. Discrepancies in individual study results have led to inconsistent conclusions regarding particular aspects of EMD in relatives of schizophrenia patients. To quantitatively evaluate and compare the candidacy of smooth pursuit, saccade and fixation deficits in first-degree biological relatives, we conducted a set of meta-analytic investigations. Among 18 measures of EMD, memory-guided saccade accuracy and error rate, global smooth pursuit dysfunction, intrusive saccades during fixation, antisaccade error rate and smooth pursuit closed-loop gain emerged as best differentiating relatives from controls (standardized mean differences ranged from .46 to .66), with no significant differences among these measures. Anticipatory saccades, but no other smooth pursuit component measures were also increased in relatives. Visually-guided reflexive saccades were largely normal. Moderator analyses examining design characteristics revealed few variables affecting the magnitude of the meta-analytically observed effects. Moderate effect sizes of relatives v. controls in selective aspects of EMD supports their endophenotype potential. Future work should focus on facilitating endophenotype utility through attention to heterogeneity of EMD performance, relationships among forms of EMD, and application in molecular genetics studies.     

Eye movement dysfunction in first-degree relatives of patients with schizophrenia: a meta-analytic evaluation of candidate endophenotypes

Several forms of eye movement dysfunction (EMD) are regarded as promising candidate endophenotypes of schizophrenia. Discrepancies in individual study results have led to inconsistent conclusions regarding particular aspects of EMD in relatives of schizophrenia patients. To quantitatively evaluate and compare the candidacy of smooth pursuit, saccade and fixation deficits in first-degree biological relatives, we conducted a set of meta-analytic investigations. Among 18 measures of EMD, memory-guided saccade accuracy and error rate, global smooth pursuit dysfunction, intrusive saccades during fixation, antisaccade error rate and smooth pursuit closed-loop gain emerged as best differentiating relatives from controls (standardized mean differences ranged from .46 to .66), with no significant differences among these measures. Anticipatory saccades, but no other smooth pursuit component measures were also increased in relatives. Visually-guided reflexive saccades were largely normal. Moderator analyses examining design characteristics revealed few variables affecting the magnitude of the meta-analytically observed effects. Moderate effect sizes of relatives v. controls in selective aspects of EMD supports their endophenotype potential. Future work should focus on facilitating endophenotype utility through attention to heterogeneity of EMD performance, relationships among forms of EMD, and application in molecular genetics studies.     

The latent structure of anxiety sensitivity--revisited

Anxiety sensitivity has been implicated as a risk factor for the development and maintenance of panic and other anxiety disorders. Although researchers have generally assumed that anxiety sensitivity is a dimensional, rather than categorical, variable, recent taxometric research has raised questions concerning the accuracy of this assumption. The present study examined the latent structure of anxiety sensitivity by applying four taxometric procedures (MAXEIG, MAXCOV, MAMBAC, and L-Mode) to data collected from two large nonclinical samples (n = 1,025 and n = 744) using two distinct measures of anxiety sensitivity (Anxiety Sensitivity Profile and Anxiety Sensitivity Index-Revised). In contrast to previous taxometric analyses of anxiety sensitivity, results of the present research provided convergent evidence for a latent anxiety sensitivity dimension. Several potential explanations for the discrepancy between these findings and those of previous research are discussed, as well as the implications of these findings for the conceptualization and measurement of anxiety sensitivity.     

The latent structure and coincidence of hypohedonia and schizotypy and their validity as indices of psychometric risk for schizophrenia

Recent exchanges on the place of hypohedonia within Meehl's theory of schizotaxia, schizotypy, and schizophrenia focus on evidence of the taxonicity of asociality, not hedonic capacity per se. Two hypotheses were contrasted, one that hypohedonia is a nonpathological individual difference variable independent of schizotypy, the other that schizotypy and hypohedonia tap independent taxonic processes that predict the emergence of positive and negative signs of schizophrenia, respectively. Undergraduates (n = 1,543) completed a multifaceted measure of schizotypy and hypohedonia. A consecutive subsample of these (n = 284) also completed a continuous performance test and the Symptom Checklist 90-Revised. Independent taxometric analyses (maximum covariance and maximum eigenvalue) indicated schizotypy was taxonic and hypohedonia was dimensional. Their coincidence was no greater than chance. Schizotypy was associated with impaired attention and significant psychological distress, whereas hypohedonia was not. Hypohedonia does not behave like a core component of schizotypy.     

Understanding planning ability measured by the Tower of London: an evaluation of its internal structure by latent variable modeling

The Tower of London (TOL) is a widely used instrument for assessing planning ability. Inhibition and (spatial) working memory are assumed to contribute to performance on the TOL, but findings about the relationship between these cognitive processes are often inconsistent. Moreover, the influence of specific properties of TOL problems on cognitive processes and difficulty level is often not taken into account. Furthermore, it may be expected that several planning strategies can be distinguished that cannot be extracted from the total score. In this study, a factor analysis and a latent class regression analysis were performed to address these issues. The results showed that 4 strategy groups that differed with respect to preplanning time could be distinguished. The effect of problem properties also differed for the 4 groups. Additional analyses showed that the groups differed on average planning performance but that there were no significant differences between inhibition and spatial working memory performance. Finally, it seemed that multiple factors influence performance on the TOL, the most important ones being the score measurements, the problem properties, and strategy use.     

The latent structure of decision styles

Responding to calls for theoretical development in research on decision styles, this article focuses on their structural relationships. A two-component model of decision styles is proposed. One component, pertaining to dual process theories of thinking, is concerned with the processes people use to make decisions, and the other is concerned with processes used to regulate decisions (e.g. whether they should be made immediately or delayed). The results of a first study (N = 629) using items from a comprehensive decision style measure (the Decision Styles Questionnaire), support the two-component model. The results of a second study (N = 305), based on an alternative set of style measures, provide support for an extended version of the model. Implications of the theory and research findings for future work on decision styles are discussed.     

A taxometric investigation of the latent structure of worry

Researchers have described 2 types of worriers, normal and pathological, who differ in the frequency, intensity, and controllability of their worry experiences. Although normal and pathological worry are generally treated as separate though related phenomena, no study has tested for separateness against the alternative hypothesis that all worry exists along a single dimension. In the present study, worry ratings of 1,588 college students were submitted to taxometric procedures designed to evaluate latent structure. Results provided evidence for the dimensionality of worry. These findings suggest that generalized anxiety disorder (GAD), whose central feature is worry, may also be quantitatively rather than qualitatively different from normal functioning. The authors argue that a focus on normal and pathological extremes has constrained the study of worry phenomena and that dimensional conceptualization of worry may significantly enhance understanding of both worry and GAD.     

On the estimation of parameters in latent structure analysis

In this note, we describe the iterative procedure introduced earlier by Goodman to calculate the maximum likelihood estimates of the parameters in latent structure analysis, and we provide here a simple and direct proof of the fact that the parameter estimates obtained with the iterative procedure cannot lie outside the allowed interval. Formann recently stated that Goodman's algorithm can yield parameter estimates that lie outside the allowed interval, and we prove in the present note that Formann's contention is incorrect.This research was supported in part by Research Contract No. NSF SOC 76-80389 from the Division of the Social Sciences of the National Science Foundation. The author is indebted to C. C. Clogg for helpful comments and for the numerical results reported here (see, e.g., Table 1).     

Strength and resistance to interference in practiced recognition: memory retrieval abilities investigated through latent structure modeling.

Participants (N = 811) practiced paired-associate recognition with and without an interference manipulation and then practiced a pattern recognition skill in which patterns discriminated had features in common. Structure models of the covariances in task reaction times indicated two factors or abilities. The first was a baseline factor, hypothesized to include the ability to strengthen traces and other abilities common to all tasks. The second was a resistance-to-interference factor, or the ability to quickly retrieve associations with elements in common with nonretrieved associations. Further modeling on a subset of the sample (n = 434) showed the baseline factor to reflect a memory strength ability independent of other confounding abilities (e.g., motor, reading abilities). Both memory abilities are discussed broadly with respect to cognitive skill acquisition, controlled versus automatic processing, and activation.