Nodewise Parameter Aggregation for Psychometric Networks.

Psychometric networks can be estimated using nodewise regression to estimate edge weights when the joint distribution is analytically difficult to derive or the estimation is too computationally intensive. The nodewise approach runs generalized linear models with each node as the outcome. Two regression coefficients are obtained for each link, which need to be aggregated to obtain the edge weight (i.

Estimated Factor Scores Are Not True Factor Scores.

In this tutorial, we clarify the distinction between estimated factor scores, which are weighted composites of observed variables, and true factor scores, which are unobservable values of the underlying latent variable. Using an analogy with linear regression, we show how predicted values in linear regression share the properties of the most common type of factor score estimates, regression factor scores, computed from single-indicator and multiple indicator latent variable models. Using simulated data from 1- and 2-factor models, we also show how the amount of measurement error affects the reliability of regression factor scores, and compare the performance of regression factor scores with that of unweighted sum scores.

The prevalence of direct replication articles in top-ranking psychology journals.

Despite lip service about replication being a cornerstone of science, replications have historically received little real estate in the published literature. Following psychology's recent replication crisis, we assessed the prevalence of one type of replication contribution: direct replication articles-articles where a direct or close replication of a previously published study is one of the main contributions of the article. This prevalence provides one indicator of how much the field values and incentivizes this type of self-correction.

The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items.

The deployment of statistical models-such as those used in item response theory-necessitates the use of indices that are informative about the degree to which a given model is appropriate for a specific data context. We introduce the InterModel Vigorish (IMV) as an index that can be used to quantify accuracy for models of dichotomous item responses based on the improvement across two sets of predictions (i.e.

A graph theory based similarity metric enables comparison of subpopulation psychometric networks.

Network psychometrics leverages pairwise Markov random fields to depict conditional dependencies among a set of psychological variables as undirected edge-weighted graphs. Researchers often intend to compare such psychometric networks across subpopulations, and recent methodological advances provide invariance tests of differences in subpopulation networks. What remains missing, though, is an analogue to an effect size measure that quantifies differences in psychometric networks.

Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain.

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.

Pay Attention to the Ignorable Missing Data Mechanisms! An Exploration of Their Impact on the Efficiency of Regression Coefficients.

The use of modern missing data techniques has become more prevalent with their increasing accessibility in statistical software. These techniques focus on handling data that are (MAR). Although all MAR mechanisms are routinely treated as the same, they are not equal.

Ubiquitous bias and false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties.

Studies of interaction effects are of great interest because they identify crucial interplay between predictors in explaining outcomes. Previous work has considered several potential sources of statistical bias and substantive misinterpretation in the study of interactions, but less attention has been devoted to the role of the outcome variable in such research. Here, we consider bias and false discovery associated with estimates of interaction parameters as a function of the distributional and metric properties of the outcome variable.

A causal theory of error scores.

In modern test theory, response variables are a function of a common latent variable that represents the measured attribute, and error variables that are unique to the response variables. While considerable thought goes into the interpretation of latent variables in these models (e.g.

What do participants think of our research practices? An examination of behavioural psychology participants' preferences.

What research practices should be considered acceptable? Historically, scientists have set the standards for what constitutes acceptable research practices. However, there is value in considering non-scientists' perspectives, including research participants'. 1873 participants from MTurk and university subject pools were surveyed after their participation in one of eight minimal-risk studies.

Comparing estimation methods for psychometric networks with ordinal data.

Ordinal data are extremely common in psychological research, with variables often assessed using Likert-type scales that take on only a few values. At the same time, researchers are increasingly fitting network models to ordinal item-level data. Yet very little work has evaluated how network estimation techniques perform when data are ordinal.

Treating random effects as observed versus latent predictors: The bias-variance tradeoff in small samples.

Random effects in longitudinal multilevel models represent individuals' deviations from population means and are indicators of individual differences. Researchers are often interested in examining how these random effects predict outcome variables that vary across individuals. This can be done via a two-step approach in which empirical Bayes (EB) estimates of the random effects are extracted and then treated as observed predictor variables in follow-up regression analyses.

Standardized measurement error: A universal metric of data quality for averaged event-related potentials.

Event-related potentials (ERPs) can be very noisy, and yet, there is no widely accepted metric of ERP data quality. Here, we propose a universal measure of data quality for ERP research-the standardized measurement error (SME)-which is a special case of the standard error of measurement. Whereas some existing metrics provide a generic quantification of the noise level, the SME quantifies the data quality (precision) for the specific amplitude or latency value being measured in a given study (e.

Eavesdropping on Missing Data: What Are University Students Doing When They Miss Experience Sampling Reports?

Participants in experience sampling method (ESM) studies are "beeped" several times per day to report on their momentary experiences-but participants do not always answer the beep. Knowing whether there are systematic predictors of missing a report is critical for understanding the extent to which missing data threatens the validity of inferences from ESM studies. Here, 228 university students completed up to four ESM reports per day while wearing the Electronically Activated Recorder (EAR)-an unobtrusive audio recording device-for a week.

Robust data and power in infant research: A case study of the effect of number of infants and number of trials in visual preference procedures.

As in many areas of science, infant research suffers from low power. The problem is further compounded in infant research because of the difficulty in recruiting and testing large numbers of infant participants. Researchers have been searching for a solution and, as illustrated by this special section, have been focused on getting the most out of infant data.

A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure.

We created a facet atlas that maps the interrelations between facet scales from 13 hierarchical personality inventories to provide a practically useful, transtheoretical description of lower-level personality traits. We generated this atlas by estimating a series of network models that visualize the correlations among 268 facet scales administered to the Eugene-Springfield Community Sample (Ns = 571-948). As expected, most facets contained a blend of content from multiple Big Five domains and were part of multiple Big Five networks.

Two-stage maximum likelihood approach for item-level missing data in regression.

Psychologists use scales comprised of multiple items to measure underlying constructs. Missing data on such scales often occur at the item level, whereas the model of interest to the researcher is at the composite (scale score) level. Existing analytic approaches cannot easily accommodate item-level missing data when models involve composites.

Testing Cold and Hot Cognitive Control as Moderators of a Network of Comorbid Psychopathology Symptoms in Adolescence.

Comorbidity is pervasive across psychopathological symptoms, diagnoses, and domains. Network analysis is a method for investigating symptom-level associations that underlie comorbidity, particularly through connecting diagnostic syndromes. We applied network analyses of comorbidity to data from a population-based sample of adolescents ( = 849).

On Penalty Parameter Selection for Estimating Network Models.

Network models are gaining popularity as a way to estimate direct effects among psychological variables and investigate the structure of constructs. A key feature of network estimation is determining which edges are likely to be non-zero. In psychology, this is commonly achieved through the graphical lasso regularization method that estimates a precision matrix of Gaussian variables using an -penalty to push small values to zero.

Latent Variable Models and Networks: Statistical Equivalence and Testability.

Networks are gaining popularity as an alternative to latent variable models for representing psychological constructs. Whereas latent variable approaches introduce unobserved common causes to explain the relations among observed variables, network approaches posit direct causal relations between observed variables. While these approaches lead to radically different understandings of the psychological constructs of interest, recent articles have established mathematical equivalences that hold between network models and latent variable models.

Worse than measurement error: Consequences of inappropriate latent variable measurement models.

Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model. We explore what happens when data that are not generated from a common factor model are nonetheless modeled as reflecting a common factor.

Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits.

Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modelling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap.

On Nonregularized Estimation of Psychological Networks.

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix.