Factor structure of the Mood and Anxiety Symptom Questionnaire does not generalize to an anxious/depressed sample

Objective: The tripartite model of anxiety and depression has been proposed as a representation of the structure of anxiety and depression symptoms. The Mood and Anxiety Symptom Questionnaire (MASQ) has been put forwards as a valid measure of the tripartite model of anxiety and depression symptoms. This research set out to examine the factor structure of anxiety and depression symptoms in a clinical sample to assess the MASQ's validity for use in this population. MethodsThe present study uses confirmatory factor analytic methods to examine the psychometric properties of the MASQ in 470 outpatients with anxiety and mood disorder. Results: The results showed that none of the previously reported two-factor, three-factor or five-factor models adequately fit the data, irrespective of whether items or subscales were used as the unit of analysis. Conclusions: It was concluded that the factor structure of the MASQ in a mixed anxiety/depression clinical sample does not support a structure consistent with the tripartite model. This suggests that researchers using the MASQ with anxious/depressed individuals should be mindful of the instrument's psychometric limitations.

Determining the effective dimensionality of the genetic variance-covariance matrix

Determining the dimensionality of G provides an important perspective on the genetic basis of a multivariate suite of traits. Since the introduction of Fisher's geometric model, the number of genetically independent traits underlying a set of functionally related phenotypic traits has been recognized as an important factor influencing the response to selection. Here, we show how the effective dimensionality of G can be established, using a method for the determination of the dimensionality of the effect space from a multivariate general linear model introduced by AMEMIYA (1985). We compare this approach with two other available methods, factor-analytic modeling and bootstrapping, using a half-sib experiment that estimated G for eight cuticular hydrocarbons of Drosophila serrata. In our example, eight pheromone traits were shown to be adequately represented by only two underlying genetic dimensions by Amemiya's approach and factor-analytic modeling of the covariance structure at the sire level. In, contrast, bootstrapping identified four dimensions with significant genetic variance. A simulation study indicated that while the performance of Amemiya's method was more sensitive to power constraints, it performed as well or better than factor-analytic modeling in correctly identifying the original genetic dimensions at moderate to high levels of heritability. The bootstrap approach consistently overestimated the number of dimensions in all cases and performed less well than Amemiya's method at subspace recovery.

Cluster analysis of high-dimensional data: A case study

Normal mixture models are often used to cluster continuous data. However, conventional approaches for fitting these models will have problems in producing nonsingular estimates of the component-covariance matrices when the dimension of the observations is large relative to the number of observations. In this case, methods such as principal components analysis (PCA) and the mixture of factor analyzers model can be adopted to avoid these estimation problems. We examine these approaches applied to the Cabernet wine data set of Ashenfelter (1999), considering the clustering of both the wines and the judges, and comparing our results with another analysis. The mixture of factor analyzers model proves particularly effective in clustering the wines, accurately classifying many of the wines by location.

Phobic anxiety in 11 nations: Part I: Dimensional constancy of the five-factor model

The Fear Survey Schedule-III (FSS-III) was administered to a total of 5491 students in Australia, East Germany, Great Britain, Greece, Guatemala, Hungary, Italy, Japan, Spain, Sweden, and Venezuela, and submitted to the multiple group method of confirmatory analysis (MGM) in order to determine the cross-national dimensional constancy of the five-factor model of self-assessed fears originally established in Dutch, British, and Canadian samples. The model comprises fears of bodily injury-illness-death, agoraphobic fears, social fears, fears of sexual and aggressive scenes, and harmless animals fears. Close correspondence between the factors was demonstrated across national samples. In each country, the corresponding scales were internally consistent, were intercorrelated at magnitudes comparable to those yielded in the original samples, and yielded (in 93% of the total number of 55 comparisons) sex differences in line with the usual finding (higher scores for females). In each country, the relatively largest sex differences were obtained on harmless animals fears. The organization of self-assessed fears is sufficiently similar across nations to warrant the use of the same weight matrix (scoring key) for the FSS-III in the different countries and to make cross-national comparisons feasible. This opens the way to further studies that attempt to predict (on an a priori basis) cross-national variations in fear levels with dimensions of national cultures. (C) 2002 Elsevier Science Ltd. All rights reserved.

Gray's model of personality and aggregate level factor analysis

Previous research shows that correlations tend to increase in magnitude when individuals are aggregated across groups. This suggests that uncorrelated constellations of personality variables (such as the primary scales of Extraversion and Neuroticism) may display much higher correlations in aggregate factor analysis. We hypothesize and report that individual level factor analysis can be explained in terms of Giant Three (or Big Five) descriptions of personality, whereas aggregate level factor analysis can be explained in terms of Gray's physiological based model. Although alternative interpretations exist, aggregate level factor analysis may correctly identify the basis of an individual's personality as a result of better reliability of measures due to aggregation. We discuss the implications of this form of analysis in terms of construct validity, personality theory, and its applicability in general. Copyright (C) 2003 John Wiley Sons, Ltd.

A genetic two-factor model of the covariation among a subset of Multidimensional Aptitude Battery and Wechsler Adult Intelligence Scale - Revised subtests

The phenotypic and genetic factor structure of performance on five Multidimensional Aptitude Battery (MAB) subtests and one Wechsler Adult Intelligence Scale-Revised (WAIS-R) subtest was explored in 390 adolescent twin pairs (184 monozygotic [MZ]; 206 dizygotic (DZ)). The temporal stability of these measures was derived from a subsample of 49 twin pairs, with test-retest correlations ranging from .67 to .85. A phenotypic factor model, in which performance and verbal factors were correlated, provided a good fit to the data. Genetic modeling was based on the phenotypic factor structure, but also took into account the additive genetic (A), common environmental (C), and unique environmental (E) parameters derived from a fully saturated ACE model. The best fitting model was characterized by a genetic correlated two-factor structure with specific effects, a general common environmental factor, and overlapping unique environmental effects. Results are compared to multivariate genetic models reported in children and adults, with the most notable difference being the growing importance of common genes influencing diverse abilities in adolescence. (C) 2003 Elsevier Inc. All rights reserved.

Size and book to market effects and the Fama French three factor asset pricing model: Evidence from the Australian stockmarket

The present study adds to the sparse published Australian literature on the size effect, the book to market (BM) effect and the ability of the Fama French three factor model to account for these effects and to improve on the asset pricing ability of the Capital Asset Pricing Model (CAPM). The present study extends the 1981–1991 period examined by Halliwell, Heaney and Sawicki (1999) a further 10 years to 2000 and addresses several limitations and findings of that research. In contrast to Halliwell, Heaney and Sawicki the current study finds the three factor model provides significantly improved explanatory power over the CAPM, and evidence that the BM factor plays a role in asset pricing.

Increased potency of a novel complement factor 5a receptor antagonist in a rat model of inflammatory bowel disease

We have previously shown that complement factor 5a(C5a) plays a role in the pathogenesis of 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis in rats by using the selective, orally active C5a antagonist AcF-[OP(D-Cha) WR]. This study tested the efficacy and potency of a new C5a antagonist, hydrocinnamate (HC)-[OP(D-Cha) WR], which has limited intestinal lumenal metabolism, in this model of colitis. Analogs of AcF-[OP(D-Cha) WR] were examined for their susceptibility to alimentary metabolism in the rat using intestinal mucosal washings. One metabolically stable analog, HC-[OP(D-Cha)WR], was then evaluated pharmacokinetically and investigated at a range of doses (0.03 - 10 mg/kg/ day p.o.) in the 8-day rat TNBS- colitis model, against the comparator drug AcF-[OP(D-Cha) WR]. Using various amino acid substitutions, it was determined that the AcF moiety of AcF-[OP(D-Cha) WR] was responsible for the metabolic instability of the compound in intestinal mucosal washings. The analog HC-[OP( D-Cha) WR], equiactive in vitro to AcF-[OP(D-Cha) WR], was resistant to intestinal metabolism, but it displayed similar oral bioavailability to AcF-[OP(D-Cha) WR]. However, in the rat TNBS- colitis model, HC-[OP(D-Cha) WR] was effective at reducing mortality, colon edema, colon macroscopic scores, and increasing food consumption and body weights, at 10- to 30- fold lower oral doses than AcF-[OP( D-Cha) WR]. These studies suggest that resistance to intestinal metabolism by HC-[OP(D-Cha) WR] may result in increased local concentrations of the drug in the colon, thus affording efficacy with markedly lower oral doses than AcF-[OP(D-Cha) WR] against TNBS-colitis. This large increase in potency and high efficacy of this compound makes it a potential candidate for clinical development against intestinal diseases such as inflammatory bowel disease.

Discovering user access pattern based on probabilistic latent factor model

There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster.