The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region no longer explained variation in height. Our results suggest that the phenotypic variance explained by region might represent ancestry effects on height.

Aesthetic chills, broadly defined as a somatic marker of peak emotional-hedonic responses, are experienced by individuals across a variety of human cultures. Yet individuals vary widely in the propensity of feeling them. These individual differences have been studied in relation to demographics, personality, and neurobiological and physiological factors, but no study to date has explored the genetic etiological sources of variation. To partition genetic and environmental sources of variation in the propensity of feeling aesthetic chills, we fitted a biometrical genetic model to data from 14,127 twins (from 8995 pairs), collected by the Netherlands Twin Register. Both genetic and unique environmental factors accounted for variance in aesthetic chills, with heritability estimated at 0.36 ([0.33, 0.39] 95% CI). We found females more prone than males to report feeling aesthetic chills. However, a test for genotype x sex interaction did not show evidence that heritability differs between sexes. We thus show that the propensity of feeling aesthetic chills is not shaped by nurture alone, but it also reflects underlying genetic propensities.

The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region did no longer explain variation in height. Our results suggest that the phenotypic variance explained by region actually represent ancestry effects on height.

Introduction Analysis of covariance (ANCOVA) remains a widely misunderstood approach for dealing with group differences on potential covariates (Miller & Chapman, 2001). This misunderstanding of the ANCOVA has a long history and its discussion is dispersed across fields and journals, making it difficult to obtain a systematic overview. Here we present a network method to organize the results of a literature search conducted by 44 Master's students as part of the 2016 University of Amsterdam course "Good Research Practices". The ANCOVA Pitfall Dora wants to assess whether, in her own university, men earn more than women. She has access to the salaries of a subset of researchers, and, as expected, men earn significantly more than women (p < .005). But wait! The men in her sample are also older than the women, and this confounds the results: perhaps the salary difference is due to age rather than gender. To address this confound and "control for" age, Dora includes age as a covariate in an ANCOVA. This procedure is tempting but statistically problematic. The ANCOVA is easier to interpret correctly when age influences salary but does not differ across the groups. As explained in Miller and Chapman (2001; but see chapter 10 in Judd, McClelland, & Ryan, 2011, and Field, 2013, pp. 484-486), when groups differ on a covariate (e.g., age), removing the variance associated with the covariate also removes the shared variance associated with the group (e.g., gender). As a result, the grouping variable loses some of its representativeness. This occurs mostly when groups are pre-existing and are not obtained by random assignment (Jamieson, 2004). As an example, assume one has access to the height of several mountain peaks in the Himalayas and the Pyrenees (Cohen & Cohen, 1983). One may test whether the mountain ranges differ in height and it may be tempting to include air pressure as a covariate; after all, air pressure differs across the

Aesthetic chills, broadly defined as a somatic marker of peak emotional-hedonic responses, are experienced by individuals across a variety of human cultures. Yet individuals vary widely in the propensity of feeling them. These individual differences have been studied in relation to demographics, personality, and neurobiological and physiological factors, but no study to date has explored the genetic etiological sources of variation. To partition genetic and environmental sources of variation in the propensity of feeling aesthetic chills, we fitted a biometrical genetic model to data from 14127 twins (from 8995 pairs), collected by the Netherlands Twin Register. Both genetic and unique environmental factors accounted for variance in aesthetic chills, with heritability estimated at .36 ([.33, .39] 95% CI). We found females more prone than males to report feeling aesthetic chills. However, a test for genotype x sex interaction did not show evidence that heritability differs between sexes. We thus show that the propensity of feeling aesthetic chills is not shaped by nurture alone, but it also reflects underlying genetic propensities.

Contextual reactivity refers to the degree in which personality states are affected by contextual cues. Research into contextual reactivity has mainly focused on repeated measurement designs. In this paper, we propose a cross-sectional approach to study contextual reactivity. We argue that contextual reactivity can be operationalized as different response processes which are characterized by different mean response times and different measurement properties. We propose a within-person mixture modeling approach that adopts this idea and which enables studying contextual reactivity in cross-sectional data. We applied the model to data from the Revised Temperament and Character Inventory. Results indicate that we can distinguish between two response specific latent states. We interpret these states as a high contextual reactive state and a low contextual reactive state. From the results it appears that the low contextual reactive state is generally associated with smaller response times and larger discrimination parameters, as compared to the high contextual reactivity state. The utility of this approach in personality research is discussed.

Theories of psychological constructs like personality, pathology, or cognition, give rise to models that implicitly or explicitly impose a causal structure on mental processes. As there are rarely feasible ways to experimentally isolate and/or individually manipulate psychological variables, evaluating these causal structures is challenging. Competing causal theories, like latent variable and network theories, of psychological constructs are often evaluated based on correlational data, which rarely provide satisfactory conclusions on their relative merits. We discuss three methods that can improve our collective ability to distinguish between models derived from competing theories of psychological constructs. These methods significantly expand the set of models that are empirically identified in structural equation models (SEM) by respectively implementing penalized instrumental variable regression, modeled heteroskedasticity and modeled non-normality. To ease their application in psychological sciences we reformulate all three methods into SEMs. Once formulated as a SEMs, the core identification strategies can readily be adapted to the context of models applied widely in psychological science like growth-, (intense) longitudinal-, multivariate- or measurement-models.

We present the Multi Co-moment Structural Equation Model (MCM-SEM), a novel approach to estimating the direction and magnitude of causal effects in the presence of confounding. In MCM-SEM, not only covariance structures but also co-skewness and co-kurtosis structures are leveraged. Co-skewness and co-kurtosis provide information on the joint non-normality. In large scale non-normally distributed data, we can use these higher-order co-moments to identify and estimate both bidirectional causal effects and latent confounding effects, which would not have been identified in regular SEM. We performed an extensive simulation study which showed that MCM-SEM correctly reveals the direction of causality in the presence of confounding. Subsequently, we applied the model empirically to data of (1) height and weight and to (2) education and income, and compared the results to those obtained through instrumental variable regression. In the empirical application, MCM-SEM yielded expected results for (1), but also highlighted some caveats when applied to (2). We provide an MCM-SEM R-package and recommendations for future use.