The Study Of Effects In Manova

A particular strategy for investigating effects resulting from a MANOVA is proposed. The strategy involves multiple two-group multivariate analyses. The two groups result from considering multivariate pairwise group contrasts or multivariate complex group contrasts. Assuming a given two-group analysis yields real effects, the resultant single linear discriminant function (LDF) may be studied. A rationale based on a transformation of LDF weights, due to V. Y. Urbakh, is recommended for assessing variable relative contribution. The analysis strategy is described in detail and illustrated with real data sets.     

Genetic transmission of major affective disorders: quantitative models and linkage analyses

We comprehensively reviewed 2 types of studies aimed at specifying the mode of inheritance of major affective disorders: quantitative models and linkage analyses. Quantitative models attempt to represent the genetic mechanism responsible for the familial distribution of a disorder. Despite efforts to refine models by incorporating the bipolar-unipolar distinction or the sex effect, consistent support for a specific mode of transmission has not been found. Some mixed genetic models support single major locus inheritance, but transmission probabilities do not conform to Mendelian expectations. Linkage analysis is a more powerful technique used for testing the single gene hypothesis. Linkage results have also been inconsistent, showing moderate support for an X-linked variant of bipolar-related disorder and equivocal support for linkages to Chromosomes 6 and 11. However, relatively few genetic loci have been examined. Methodological factors, genetic heterogeneity, and phenotypic heterogeneity are discussed as potential explanations for inconsistent findings.     

Finding the "genuine" schizotype: a model and method for resolving heterogeneity in performance on laboratory measures in experimental psychopathology research

Heterogeneity in the performance of persons affected with schizophrenia or schizotypic psychopathology on various laboratory tasks has long been recognized, both for its consistency across tasks and studies and for the massive methodological and substantive challenges it poses for experimental psychopathology, genetic, and other investigations. Traditional multivariate techniques, such as factor analysis, discriminant function analysis, and cluster analysis, have all been deemed inadequate for resolving heterogeneity, because of one or another statistical limitation. Here, an objective statistical approach based on a formal statistical model that uses the ubiquitous and well-developed expectation-maximization (EM) algorithm (A. P. Dempster, N. M. Laird, & D. B. Rubin, 1977) is presented, which enables one effectively to partition a group of experimental subjects, in this case identified initially using the well-known Perceptual Aberration Scale (L. J. Chapman, J. P. Chapman, & M. L. Raulin, 1978), in a manner that reduces heterogeneity and allows for the separation of what are termed genuine and false-positive schizotypes. The validity of the parsing strategy was supported by reference to other laboratory indexes of relevance to schizophrenia and schizotypy that were not included in the initial EM-based analyses. The potential utility of this approach is discussed with reference to future schizophrenia and schizotypy research.     

Diagnosing Autism using ICD-10 criteria: A comparison of neural networks and standard multivariate procedures

In a sample of 976 consecutive cases derived from the recent world-wide Field Trial of Autism and other Pervasive Developmental Disorders, we tested the accuracy of the 15 ICD-10 criteria for the diagnosis of Autism, by comparing neural network models (NN) to more conventional multivariate competitors, namely, linear and quadratic discriminant function analyses and logistic regression. NNs were less accurate than competitors, both in terms of cross-validation results as well as in levels of shrinkage from training to test conditions. The clinical research implications of these results are discussed.     

Analyzing aphasia data in a multidimensional symptom space

The utility of single-case vs. group studies has been debated in neuropsychology for many years. The purpose of the present study is to illustrate an alternative approach to group studies of aphasia, in which the same symptom dimensions that are commonly used to assign patients to classical taxonomies (fluency, naming, repetition, and comprehension) are used as independent and continuous predictors in a multivariate design, without assigning patients to syndromes. One hundred twenty-six Italian-speaking patients with aphasia were first classified into seven classic aphasia categories, based on fluency, naming, auditory comprehension, and repetition scales. There were two goals: (1) compare group analyses based on aphasia types with multivariate analyses that sidestep classification and treat aphasic symptoms as continuous variables; (2) present correlation-based outlier analyses that can be used to identify individuals who occupy unusual positions in the multivariate "symptom space." In the service of the first goal, group performance on an external validation measure (the Token Test) was assessed in three steps: analyses of variance based on aphasia type, regressions using the same cut-offs for fluency, naming, comprehension and repetition as independent but dichotomous predictors, and regressions using the same subscales as continuous predictors (with no cut-offs). More variance in Token Test performance was accounted for when symptoms were treated as continuous predictors than with the other two methods, though use of independent but dichotomous predictors accounted for more variance than aphasia taxonomies. Thus, if we by-pass classical taxonomies and treat patients as points in a multidimensional symptom space, better predictions are obtained. Outlier analyses show that group results depend on heterogeneity among patients, which can be used as a search tool to identify potentially interesting dissociations. Hence this multivariate group approach is complementary to and compatible with single-case methods.     

Subtypes of clinical presentations in malingerers of posttraumatic stress disorder: an MMPI-2 cluster analysis

This paper investigated subtypes of individuals trained and instructed to malinger Posttraumatic Stress Disorder (PTSD) through a cluster analysis of their Minnesota Multiphasic Personality Inventory-2 (MMPI-2) clinical and validity scales. Participants were 84 men and women college students at a community college in the southeastern United States. Two well fitting MMPI-2 cluster solutions were evaluated with discriminant analyses and multivariate analyses of variance (MANOVAs); a 2-cluster solution was deemed optimal. Significant between-cluster differences emerged in follow-up analyses on most of the content scales of the MMPI-2. Most demographic variables did not account for differences in cluster membership. Clusters differed in their reported clarity of the materials used to educate them about PTSD. Discriminant analyses yielded better correct classification rates than those from previous studies, when the more severely symptomatic cluster was compared with a sample of clinical combat-related PTSD veterans. Implications are considered in conducting future malingered PTSD investigations.     

Content analysis: a comparison of manual and computerized systems

Computerized content analysis is a technique for classifying texts. In personality research it has been used to make inferences about psychological states and traits of the speaker or writer. Whereas computerized techniques have been increasing in both sophistication and ease of use, questions remain regarding their validity compared to hand-scored scales. In this study, we compare a relatively simple computerized technique; a more sophisticated, context-sensitive computerized approach; and a context-sensitive, human-scored system. Seventy-one subjects, representing four diagnostic groups, provided free-speech samples which were scored according to all three methods. Data from each method were then submitted to discriminant and classification analyses. In comparing the classification performance of the three methods, we found that both computerized methods were more accurate than the human-scored method. The time and cost benefits of computerized systems are discussed.     

The Anxiety Depression Distress Inventory–27 (ADDI–27): New Evidence of Factor Structure,Item-Level Measurement Invariance,and Validity

Three studies examining the factor structure and psychometric properties of the Anxiety Depression Distress Inventory–27 (ADDI–27) extended the initial instrument development studies for this recently introduced inventory. The ADDI–27 is an empirically derived short form of the Mood and Anxiety Questionaire–90 (MASQ–90) comprising three scales: Positive Affect, Somatic Anxiety, and General Distress. The main objectives of Study 1 (N = 700) were to examine the factor structure of the ADDI–27 and its measurement invariance across gender at the item level. The objective of Study 2 (N = 538) was to examine evidence for the convergent and discriminant validity of scores on the ADDI–27. The objective of Study 3 (N = 240) was to assess further evidence for the nomological network and convergent and discriminant validity of the ADDI–27 scores. Results of exploratory structural equation modeling yielded strong support for a 3-factor model, with approximate fit indexes meeting or exceeding the conventional cutoffs. With p ≤ .001 as the criterion for detecting noninvariance, results of measurement invariance analysis suggested that all of the ADDI–27 items were invariant across gender. Results of multivariate validity analyses across 2 studies provided support for the convergent and discriminant validity of scores on the ADDI–27 scales.     

Analyzing the convergent and discriminant validity of states and traits: development and applications of multimethod latent state-trait models

The analysis of convergent and discriminant validity is an integral part of the construct validation process. Models for analyzing the convergent and discriminant validity have typically been developed for cross-sectional data. There exist, however, only a few approaches for longitudinal data that can be applied for analyzing the construct validity of fluctuating states. In this article, the authors show how models of latent state-trait theory can be combined with models of multitrait-multimethod analysis to develop a model that allows for analyzing convergent and discriminant validity in time: the multimethod latent state-trait model. The model allows for identifying different sources of variance (trait consistency, trait-method specificity, occasion-specific consistency, occasion-specific method specificity, and unreliability). It is applied to the repeated measurement of depression and anxiety in children, which was assessed by self and teacher reports (N = 375). The application shows that the proposed models fit the data well and allow a deeper understanding of method effects in clinical assessment.     

Content Analysis: A Comparison of Manual and Computerized Systems

Computerized content analysis is a technique for classifying texts. in personality research it has beer used to make inferences about psychological states and traits of the speaker or writer. Whereas computerized techniques have been increasing in both sophistication and ease of use, questions remain regarding their validity compared to hand-scored scales, in this study, we compare a relatively simple computerized technique; a more sophisticated, context-sensitive computerized approach; and a context-sensitive, human-scored system. Seventy-one subjects, representing four diagnostic groups, provided free-speech samples which were scored according to all three methods. Data from each method were then submitted to discriminant and classification analyses. In comparing the classification performance of the three methods, we found that both computerized methods were more accurate than the human-scored method. The time and cost benefits of computerized systems are discussed.     

The moderator effect that wasn't there: statistical problems in ambivalence research

Ambivalence researchers often collapse separate measures of positivity and negativity into a single numerical index of ambivalence and refer to it as objective, operative, or potential ambivalence. The authors argue that this univariate approach to ambivalence models undermines the validity of subsequent statistical analyses because it confounds the effects of the index and its components. To remedy this situation, they demonstrate how the assumptions underlying the indices derived from the conflicting reactions model and similarity-intensity model can be tested using a multivariate approach to ambivalence models. On the basis of computer simulations and reanalyses of published moderator effects, the authors show that the frequently reported moderating influence of ambivalence on attitude effects may be a statistical artifact resulting from unmodeled correlations of positivity and negativity with attitude and the dependent variable. On the basis of extensive power analyses, they conclude that it may be extremely difficult to detect moderator effects of ambivalence in observational data. Therefore, they encourage ambivalence researchers to take an experimental approach to study design and a multivariate approach to data analysis.     

A depressive symptom scale for the California Psychological Inventory: construct validation of the CPI-D

To facilitate life span research on depressive symptomatology, a depressive symptom scale for the California Psychological Inventory (CPI) is needed. The authors constructed such a scale (the CPI-D) and compared its psychometric properties with 2 widely used self-report depression scales: the Beck Depression Inventory and the Center for Epidemiological Studies Depression Scale. Construct validity of the CPI-D was examined in 3 studies. Study 1 established content validity, classifying CPI-D items into Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition depressive symptoms. Study 2 used 3 large samples to gather evidence for reliability and validity: correlational analyses demonstrated alpha reliability and convergent and discriminant validity; factor analysis provided evidence for discriminant validity with anxiety; and regression analyses demonstrated comparative validity with existing standard PI scales. Study 3 used clinician ratings of depression and anxiety as criteria for external validity.     

Psychopathy in Bulgaria: The cross-cultural generalizability of the Hare Psychopathy Checklist

The generalizability of the psychopathy construct to Eastern European cultures has not been well-studied, and no prior studies have evaluated psychopathy in non-offender samples from this population. The current validation study examines the factor structure, internal consistency, and external validity of the Bulgarian translation of the Hare Psychopathy Checklist: Screening Version. Two hundred sixty-two Bulgarian adults from the general community were assessed, of which 185 had a history of substance dependence. Confirmatory factor analysis indicated good fit for the two-, three-, and four-factor models of psychopathy. Zero-order and partial correlation analyses were conducted between the two factors of psychopathy and criterion measures of antisocial behavior, internalizing and externalizing psychopathology, personality traits, addictive disorders and demographic characteristics. Relationships to external variables provided evidence for the convergent and discriminant validity of the psychopathy construct in a Bulgarian community sample.     

A model-based approach to multivariate principal component regression: Selecting principal components and estimating standard errors for unstandardized regression coefficients

Principal component regression (PCR) is a popular technique in data analysis and machine learning. However, the technique has two limitations. First, the principal components (PCs) with the largest variances may not be relevant to the outcome variables. Second, the lack of standard error estimates for the unstandardized regression coefficients makes it hard to interpret the results. To address these two limitations, we propose a model-based approach that includes two mean and covariance structure models defined for multivariate PCR. By estimating the defined models, we can obtain inferential information that will allow us to test the explanatory power of individual PCs and compute the standard error estimates for the unstandardized regression coefficients. A real example is used to illustrate our approach, and simulation studies under normality and nonnormality conditions are presented to validate the standard error estimates for the unstandardized regression coefficients. Finally, future research topics are discussed.