Conor V. Dolan

Age-related change in executive function: developmental trends and a latent variable analysis.

This study examined the developmental trajectories of three frequently postulated executive function (EF) components, Working Memory, Shifting, and Inhibition of responses, and their relation to performance on standard, but complex, neuropsychological EF tasks, the Wisconsin Card Sorting Task (WCST), and the Tower of London (ToL). Participants in four age groups (7-, 11-, 15-, and 21-year olds) carried out nine basic experimental tasks (three tasks for each EF), the WCST, and the ToL. Analyses were done in two steps: (1) analyses of (co)variance to examine developmental trends in individual EF tasks while correcting for basic processing speed, (2) confirmatory factor analysis to extract latent variables from the nine basic EF tasks, and to explain variance in the performance on WCST and ToL, using these latent variables. Analyses of (co)variance revealed a continuation of EF development into adolescence. Confirmatory factor analysis yielded two common factors: Working Memory and Shifting. However, the variables assumed to tap Inhibition proved unrelated. At a latent level, again correcting for basic processing speed, the development of Shifting was seen to continue into adolescence, while Working Memory continued to develop into young-adulthood. Regression analyses revealed that Working Memory contributed most strongly to WCST performance in all age groups. These results suggest that EF component processes develop at different rates, and that it is important to recognize both the unity and diversity of EF component processes in studying the development of EF.

A dynamical model of general intelligence: The positive manifold of intelligence by mutualism

Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new explanation of the positive manifold based on a dynamical model is proposed, in which reciprocal causation or mutualism plays a central role. It is shown that the positive manifold emerges purely by positive beneficial interactions between cognitive processes during development. A single underlying g factor plays no role in the model. The model offers explanations of important findings in intelligence research, such as the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the increase in heritability of g, and the Jensen effect, and is consistent with current explanations of the Flynn effect.

Severity Indices of Personality Problems (SIPP-118) : Development, Factor Structure, Reliability, and Validity

This article describes a series of studies involving 2,730 participants on the development and validity testing of the Severity Indices of Personality Problems (SIPP), a self-report questionnaire covering important core components of (mal)adaptive personality functioning. Results show that the 16 facets constituted homogeneous item clusters (i.e., unidimensional and internally consistent parcels) that fit well into 5 clinically interpretable, higher order domains: self-control, identity integration, relational capacities, social concordance, and responsibility. These domains appeared to have good concurrent validity across various populations, good convergent validity in terms of associations with interview ratings of the severity of personality pathology, and good discriminant validity in terms of associations with trait-based personality disorder dimensions. Furthermore, results suggest that the domain scores are stable over a time interval of 14-21 days in a student sample but are sensitive to change over a 2-year follow-up interval in a treated patient population. Taken together, the final instrument, the SIPP-118, provides a set of 5 reliable, valid, and efficient indices of the core components of (mal)adaptive personality functioning.

A third source of developmental differences

An illustrative list is presented of human and animal studies which each point to the existence of a third source, in addition to genetic and environmental factors, underlying phenotypic differences in development. It is argued that this third source may consist of nonlinear epigenetic processes that can create variability at all phenotypical-somatic and behavioral-levels. In a quantitative genetic analysis with human subjects, these processes are confounded with within-family environmental influences. A preliminary model to quantify these influences is introduced.

On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

Investigating sources of within- and between-group differences and measurement invariance (MI) across groups is fundamental to any meaningful group comparison based on observed test scores. It is shown that by placing certain restrictions on the multigroup confirmatory factor model, it is possible to investigate the hypothesis that within- and between-group differences are due to the same factors. Moreover, the modeling approach clarifies that absence of measurement bias implies common sources of within- and between-group variation. It is shown how the influence of background variables can be incorporated in the model. The advantages of the modeling approach as compared with other commonly used methods for group comparisons is discussed and illustrated by means of an analysis of empirical data.

TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies.

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATESs false positive rate is correct, and that TATESs statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.

Stereotype Threat and Group Differences in Test Performance: A Question of Measurement Invariance

Studies into the effects of stereotype threat (ST) on test performance have shed new light on race and sex differences in achievement and intelligence test scores. In this article, the authors relate ST theory to the psychometric concept of measurement invariance and show that ST effects may be viewed as a source of measurement bias. As such, ST effects are detectable by means of multi-group confirmatory factor analysis. This enables research into the generalizability of ST effects to real-life or high-stakes testing. The modeling approach is described in detail and applied to 3 experiments in which the amount of ST for minorities and women was manipulated. Results indicate that ST results in measurement bias of intelligence and mathematics tests.

On the likelihood ratio test in structural equation modeling when parameters are subject to boundary constraints.

The authors show how the use of inequality constraints on parameters in structural equation models may affect the distribution of the likelihood ratio test. Inequality constraints are implicitly used in the testing of commonly applied structural equation models, such as the common factor model, the autoregressive model, and the latent growth curve model, although this is not commonly acknowledged. Such constraints are the result of the null hypothesis in which the parameter value or values are placed on the boundary of the parameter space. For instance, this occurs in testing whether the variance of a growth parameter is significantly different from 0. It is shown that in these cases, the asymptotic distribution of the chi-square difference cannot be treated as that of a central chi-square-distributed random variable with degrees of freedom equal to the number of constraints. The correct distribution for testing 1 or a few parameters at a time is inferred for the 3 structural equation models mentioned above. Subsequently, the authors describe and illustrate the steps that one should take to obtain this distribution. An important message is that using the correct distribution may lead to appreciably greater statistical power.

The Effectiveness of Online Cognitive Behavioral Treatment in Routine Clinical Practice

Context Randomized controlled trails have identified online cognitive behavioral therapy as an efficacious intervention in the management of common mental health disorders. Objective To assess the effectiveness of online CBT for different mental disorders in routine clinical practice. Design An uncontrolled before-after study, with measurements at baseline, posttest, 6-week follow-up, and 1-year follow-up. Participants & Setting 1500 adult patients (female: 67%; mean age: 40 years) with a GP referral for psychotherapy were treated at a Dutch online mental health clinic for symptoms of depression (n = 413), panic disorder (n = 139), posttraumatic stress (n = 478), or burnout (n = 470). Interventions Manualized, web-based, therapist-assisted CBT, of which the efficacy was previously demonstrated in a series of controlled trials. Standardized duration of treatment varied from 5 weeks (online CBT for Posttraumatic stress) to 16 weeks (online CBT for Depression). Main Outcome Measures Validated self-report questionnaires of specific and general psychopathology, including the Beck Depression Inventory, the Impact of Event Scale, the Panic Disorder Severity Scale-Self Report, the Oldenburg Burnout Inventory, and the Depression Anxiety Stress Scales. Results Treatment adherence was 71% (n = 1071). Study attrition was 21% at posttest, 33% at 6-week FU and 65% at 1-year FU. Mixed-model repeated measures regression identified large short-term reductions in all measures of primary symptoms (d = 1.9±0.2 to d = 1.2±0.2; P<.001), which sustained up to one year after treatment. At posttest, rates of reliable improvement and recovery were 71% and 52% in the completer sample (full sample: 55%/40%). Patient satisfaction was high. Conclusions Results suggest that online therapist-assisted CBT may be as effective in routine practice as it is in clinical trials. Although pre-treatment withdrawal and long-term outcomes require further study, results warrant continued implementation of online CBT.

Phenotypic complexity, measurement bias, and poor phenotypic resolution contribute to the missing heritability problem in genetic association studies.

Background The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this ‘missing heritability’ have been mainly genetic, such as genetic heterogeneity and complex (epi-)genetic mechanisms. Methodology We used comprehensive simulation studies to show that three phenotypic measurement issues also provide viable explanations of the missing heritability: phenotypic complexity, measurement bias, and phenotypic resolution. We identify the circumstances in which the use of phenotypic sum-scores and the presence of measurement bias lower the power to detect genetic variants. In addition, we show how the differential resolution of psychometric instruments (i.e., whether the instrument includes items that resolve individual differences in the normal range or in the clinical range of a phenotype) affects the power to detect genetic variants. Conclusion We conclude that careful phenotypic data modelling can improve the genetic signal, and thus the statistical power to identify genetic variants by 20–99%.