Back to the basics: Rethinking partial correlation network methodology

The Gaussian graphical model (GGM) is an increasingly popular technique used in psychology to characterize relationships among observed variables. These relationships are represented as elements in the precision matrix. Standardizing the precision matrix and reversing the sign yields corresponding partial correlations that imply pairwise dependencies in which the effects of all other variables have been controlled for. The graphical lasso (glasso) has emerged as the default estimation method, which uses ℓ₁-based regularization. The glasso was developed and optimized for high-dimensional settings where the number of variables (p) exceeds the number of observations (n), which is uncommon in psychological applications. Here we propose to go ‘back to the basics’, wherein the precision matrix is first estimated with non-regularized maximum likelihood and then Fisher Z transformed confidence intervals are used to determine non-zero relationships. We first show the exact correspondence between the confidence level and specificity, which is due to 1 minus specificity denoting the false positive rate (i.e., α). With simulations in low-dimensional settings (p ≪ n), we then demonstrate superior performance compared to the glasso for detecting the non-zero effects. Further, our results indicate that the glasso is inconsistent for the purpose of model selection and does not control the false discovery rate, whereas the proposed method converges on the true model and directly controls error rates. We end by discussing implications for estimating GGMs in psychology.     

Proximal and Distal Predictors of Homophobia: Framing the Multivariate Roots of Outgroup Rejection1

A proximal-distal theoretical framework was developed to examine the multivariate roots of homophobia. Using data from a sample of 288 undergraduates who had participated in a computer-assisted panel study over a three-year period, the effects on homophobia of distal and proximal family factors (e. g., parental education and family environment), distal and proximal individual factors (e. g., personality constructs and current beliefs), and proximal individual-situational factors (e. g., acquaintance with homosexuals) were tested in regression analyses. Both analyses employing sets of variables varying in proximity to the homophobic attitude and hierarchical regression employing variables contributing significantly in each set supported the proposed framework. All sets predicted significantly when regressed separately on homophobia; the sets containing the distal factors contributed most weakly, and the sets containing the proximal factors contributed most strongly. In hierarchical regression analysis, this pattern maintained such that the incremental increase in r² decreased with the addition of each more distal set, with the most distal factors failing to contribute significantly to the prediction. The importance of study findings for attitude change efforts is discussed.     

Direction of dependence in measurement error models

Methods to determine the direction of a regression line, that is, to determine the direction of dependence in reversible linear regression models (e.g., x→y vs. y→x), have experienced rapid development within the last decade. However, previous research largely rested on the assumption that the true predictor is measured without measurement error. The present paper extends the direction dependence principle to measurement error models. First, we discuss asymmetric representations of the reliability coefficient in terms of higher moments of variables and the attenuation of skewness and excess kurtosis due to measurement error. Second, we identify conditions where direction dependence decisions are biased due to measurement error and suggest method of moments (MOM) estimation as a remedy. Third, we address data situations in which the true outcome exhibits both regression and measurement error, and propose a sensitivity analysis approach to determining the robustness of direction dependence decisions against unreliably measured outcomes. Monte Carlo simulations were performed to assess the performance of MOM-based direction dependence measures and their robustness to violated measurement error assumptions (i.e., non-independence and non-normality). An empirical example from subjective well-being research is presented. The plausibility of model assumptions and links to modern causal inference methods for observational data are discussed.     

Exploring Genetic Counseling Communication Patterns: The Role of Teaching and Counseling Approaches

The educational and counseling models are often touted as the two primary professional approaches to genetic counseling practice. Yet, research has not been conducted to examine how these approaches are used in practice. In the present study, we conducted quantitative communication analyses of BRCA1 genetic counseling sessions. We measured communication variables that represent content (e.g., a biomedical focus) and process (e.g., passive listening) to explore whether genetic counselor approaches are consistent with prevailing professional models. The Roter Interaction Analysis System (RIAS) was used to code 167 pre-test genetic counseling sessions of members of a large kindred with an identified BRCA1 mutation. Three experienced genetic counselors conducted the sessions. Creating composite categories from the RIAS codes, we found the sessions to be largely educational in nature with the counselors and clients devoting the majority of their dialogue to providing biomedical information (62 and 40%, respectively). We used cluster analytic techniques, entering the composite communication variables and identified four patterns of session communication: Client-focused psychosocial, biomedical question and answer, counselor-driven psychosocial, and client-focused biomedical. Moreover, we found that the counselors had unique styles in which they combined the use of education and counseling approaches. We discuss the importance of understanding the variation in counselor communication to advance the field and expand prevailing assumptions.     

Future‐oriented emotions: conceptualization and behavioral effects

We distinguish between two kinds of future‐oriented emotions (anticipatory and anticipated) and investigate their behavioral effects. Anticipatory emotions are currently experienced due to the prospect of a future event (e.g., hope or fear). Anticipated emotions, on the other hand, are expected to be experienced in the future if certain events do or do not occur (e.g., anticipated joy or regret). We discuss the theoretical differences between the two types of future‐oriented emotions and examine their role in motivating goal‐directed behavior. The results of a longitudinal study (n = 472) and a separate control group analysis (n = 340) provide consistent support for the convergent and discriminant validity of positive/negative anticipatory and anticipated emotions and their independent influence on goal‐directed behavior. Copyright © 2007 John Wiley & Sons, Ltd.     

Psychometric network models from time-series and panel data

Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)—an undirected network model of partial correlations—between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.

     

Building the perfect curse word: A psycholinguistic investigation of the form and meaning of taboo words

Taboo words represent a potent subset of natural language. It has been hypothesized that “tabooness” reflects an emergent property of negative valence and high physiological arousal of word referents. Many taboo words (e.g., dick, shit) are indeed consistent with this claim. Nevertheless, American English is also rife with negatively valenced, highly arousing words the usage of which is not socially condemned (e.g., cancer, abortion, welfare). We evaluated prediction of tabooness of single words and novel taboo compound words from a combination of phonological, lexical, and semantic variables (e.g., semantic category, word length). For single words, physiological arousal and emotional valence strongly predicted tabooness with additional moderating contributions from form (phonology) and meaning (semantic category). In Experiment 2, raters judged plausibility for combinations of common nouns with taboo words to form novel taboo compounds (e.g., shitgibbon). A mixture of formal (e.g., ratio of stop consonants, length) and semantic variables (e.g., ± receptacle, ± profession) predicted the quality of novel taboo compounding. Together, these studies provide complementary evidence for interactions between word form and meaning and an algorithmic prediction of tabooness in American English. We discuss applications for models of taboo word representation.

     

Action-control beliefs and behaviors as predictors of change in adjustment across the transition to middle school

Abstract

We examined longitudinal changes in young adolescents’ (N=368) action-control beliefs, coping behaviors, and adjustment (i.e., positive and negative affect, depression, aggression) across the transition from elementary school to middle school. Results indicated greater inter-individual instability in adjustment during this transition than during the previous school year. Using ordinary least-squares (OLS) growth models to extract intra-individual change scores for each variable (i.e., slopes and intercepts), we conducted a series of stepwise regressions to determine which features of control beliefs and coping behaviors best predicted changes in adjustment across the transition to middle school. We found that negative coping behaviors (i.e., antisocial coping) consistently predicted negative changes in the adjustment variables (e.g., greater depression, more aggression), whereas positive beliefs and behaviors did not consistently predict changes in the adjustment variables.     

Automatic Extraction of Property Norm‐Like Data From Large Text Corpora

Traditional methods for deriving property‐based representations of concepts from text have focused on either extracting only a subset of possible relation types, such as hyponymy/hypernymy (e.g., car is‐a vehicle ) or meronymy/metonymy (e.g., car has wheels ), or unspecified relations (e.g., car — petrol ). We propose a system for the challenging task of automatic, large‐scale acquisition of unconstrained, human‐like property norms from large text corpora, and discuss the theoretical implications of such a system. We employ syntactic, semantic, and encyclopedic information to guide our extraction, yielding concept‐relation‐feature triples (e.g., car be fast , car require petrol , car cause pollution ), which approximate property‐based conceptual representations. Our novel method extracts candidate triples from parsed corpora (Wikipedia and the British National Corpus) using syntactically and grammatically motivated rules, then reweights triples with a linear combination of their frequency and four statistical metrics. We assess our system output in three ways: lexical comparison with norms derived from human‐generated property norm data, direct evaluation by four human judges, and a semantic distance comparison with both WordNet similarity data and human‐judged concept similarity ratings. Our system offers a viable and performant method of plausible triple extraction: Our lexical comparison shows comparable performance to the current state‐of‐the‐art, while subsequent evaluations exhibit the human‐like character of our generated properties.     

Mexican American emerging adults' relationships with siblings and dimensions of familism values

We used a person‐oriented approach to describe the profiles of Mexican American emerging adults' relationships with siblings using affective/behavioral qualities (e.g., positivity) and relationship maintenance strategies (e.g., communication frequency) and to examine how profiles of sibling relationships were related to dimensions of Mexican American emerging adults' familism values. Using data from 186 Mexican American emerging adults (M_age = 21.56), we conducted latent profile analyses and identified three profiles of emerging adults' relationships with siblings: moderate (n = 106), positive/involved (n = 64), and negative/distant (n = 16). Our findings suggest that the majority of emerging adults in our study reported close relationships with siblings and dimensions of familism values were differentially associated with profiles of sibling relationship qualities.     

A reappraisal of the relationship between span memory and intelligence via “best evidence synthesis”

This paper examines the relationship between span memory [e.g., immediate memory, short-term memory (STM), simple span] and general ability (g) though a reanalysis of two data sets [Christal, R. E. (1959). Factor analytic study of visual memory. Psychological Monographs: General and Applied, 72 (13, Whole No. 466); Kelley, H.P. (1964). Memory abilities: A factor analysis. Psychometric Monographs, No. 11]. Because of their large sample sizes and the multiple measures used to identify each construct, the Christal and Kelley studies were examined within a “best evidence synthesis” framework. Modern structural equation modeling (SEM) techniques were used to examine the relationship between immediate memory and g. Results indicated that in both studies, the relationship between immediate memory and g was quite substantial (.71 and .83), and that this relationship was essentially reduced by half when the common content variance of the tests was accounted for (e.g., verbal, spatial, numerical). Results are discussed within the context of recent research examining the relationship between working memory (WM) and g.     

Excessive Worrying as a Central Feature of Anxiety during the First COVID-19 Lockdown-Phase in Belgium: Insights from a Network Approach

Since the WHO declared the COVID-19 pandemic on March 11, 2020, the novel coronavirus, SARS-CoV-2, has profoundly impacted public health and the economy worldwide. But there are not the only ones to be hit. The COVID-19 pandemic has also substantially altered mental health, with anxiety symptoms being one of the most frequently reported problems. Especially, the number of people reporting anxiety symptoms increased significantly during the first lockdown-phase compared to similar data collected before the pandemic. Yet, most of these studies relied on a unitary approach to anxiety, wherein its different constitutive features (i.e., symptoms) were tallied into one sum-score, thus ignoring any possibility of interactions between them. Therefore, in this study, we seek to map the associations between the core features of anxiety during the first weeks of the first Belgian COVID-19 lockdown-phase (n = 2,829). To do so, we implemented, in a preregistered fashion, two distinct computational network approaches: a Gaussian graphical model and a Bayesian network modelling approach to estimate a directed acyclic graph. Despite their varying assumptions, constraints, and computational methods to determine nodes (i.e., the variables) and edges (i.e., the relations between them), both approaches pointed to excessive worrying as a node playing an especially influential role in the network system of the anxiety features. Altogether, our findings offer novel data-driven clues for the ongoing field’s larger quest to examine, and eventually alleviate, the mental health consequences of the COVID-19 pandemic.     

Model fitting in (<Emphasis Type="Italic">n</Emphasis>+1) dimensions

Conventionally, fitting a mathematical model to empirically derived data is achieved by varying model parameters to minimize the deviations between expected and observed values in the dependent dimension. However, when functions to be fit are multivalued (e.g., an ellipse), conventional model fitting procedures fail. A novel (n+1)-dimensional [(n+1)-D] model fitting procedure is presented which can solve such problems by transforming then-D model and data into (n+1)-D space and then minimizing deviations in the constructed dimension. While the (n+1)-D procedure provides model fits identical to those obtained with conventional methods for single-valued functions, it also extends parameter estimation to multivalued functions.     

A Strengths-Based Consultancy Approach in Elite Sport: Exploring Super-Strengths

There is a lack of literature documenting strengths-based approaches in sport psychology. This study explored how a super-strengths approach has been implemented by sport psychologists (n = 7) and coaches (n = 8), with UK elite athletes. Findings were categorized into 3 general dimensions: defining super-strengths, identification methods, and phases of development. Super-strengths were defined as a strategy for performance, utilizing a potential world's-best resource to gain a competitive edge. Identification methods were subjective (e.g., asking/observing athletes) and objective (e.g., performance analysis). Participants emphasized 3 development phases: preparation, adaptation and monitoring. Findings offer considerations for implementing a strengths-based approach and future research.     

Explanatory,Multilevel Person-Fit Analysis of Response Consistency on the Spielberger State-Trait Anxiety Inventory

Self-report measures are vulnerable to concentration and motivation problems, leading to responses that may be inconsistent with the respondent's latent trait value. We investigated response consistency in a sample (N = 860) of cardiac patients with an implantable cardioverter defibrillator and their partners who completed the Spielberger State-Trait Anxiety Inventory on five measurement occasions. For each occasion and for both the state and trait subscales, we used the l ^p _z person-fit statistic to assess response consistency. We used multilevel analysis to model the between-person and within-person differences in the repeated observations of response consistency using time-dependent (e.g., mood states) and time-invariant explanatory variables (e.g., demographic characteristics). Respondents with lower education, undergoing psychological treatment, and with more post-traumatic stress disorder symptoms tended to respond less consistently. The percentages of explained variance in response consistency were small. Hence, we conclude that the results give insight into the causes of response inconsistency but that the identified explanatory variables are of limited practical value for identifying respondents at risk of producing invalid test results. We discuss explanations for the small percentage of explained variance and suggest alternative methods for studying causes of response inconsistency.     

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.     

Deconstructing trait anxiety: a network perspective

Background and objectives: For decades, the dominant paradigm in trait anxiety research has regarded the construct as signifying the underlying cause of the thoughts, feelings, and behaviors that supposedly reflect its presence. Recently, a network theory of personality has appeared. According to this perspective, trait anxiety is a formative construct emerging from interactions among its constitutive features (e.g., thought, feelings, behaviors); it is not a latent cause of these features.

Design: In this study, we characterized trait anxiety as a network system of interacting elements.

Methods: To do so, we estimated a graphical gaussian model via the computation of a regularized partial correlation network in an unselected sample (N?=?611). We also implemented modularity-based community detection analysis to test whether the features of trait anxiety cohere as a single network system.

Results: We find that trait anxiety can indeed be conceptualized as a single, coherent network system of interacting elements.

Conclusions: This radically new approach to visualizing trait anxiety may offer an especially informative view of the interplay between its constitutive features. As prior research has implicated trait anxiety as a risk factor for the development of anxiety-related psychopathology, our findings also set the scene for novel research directions.