Distributional moderation analysis: Unpacking moderation effects in intervention research.

Moderation and subgroup analyses are well-established statistical tools to evaluate whether intervention effects vary across subpopulations defined by participants' demographic and contextual factors. Moderation effects themselves, however, can be subject to heterogeneity and can manifest in various outcome parameters that go beyond group-specific averages (i.e.

Latent growth mixture models as latent variable multigroup factor models: Comment on McNeish et al. (2023).

McNeish et al. argue for the general use of covariance pattern growth mixture models because these models do not involve the assumption of random effects, demonstrate high rates of convergence, and are most likely to identify the correct number of latent subgroups. We argue that the covariance pattern growth mixture model is a single random intercept model.

The Individual as Moderator of Variable Relations - A Configural Approach.

Moderators are variables that change the relations among other variables. Moderators are variables that are substantive just as the variables whose relations are moderated. In the present article, we propose using individuals as moderators.

Log-Linear and Configural Analysis of Intra-Individual Time Series under Consideration of Serial Dependence.

Serial dependence often prevents researchers from obtaining unbiased parameter estimates. In this article, we propose taking serial dependence into account, and exploiting the information that comes with serial dependence. This can be done in the form of shifted variables that are included in addition to the original variables, when models are specified.

One step at a time: A statistical approach for distinguishing mediators, confounders, and colliders using direction dependence analysis.

In observational data, understanding the causal link when estimating the causal effect of an independent variable () on a dependent variable () often requires researchers to identify the role of a third variable in the → relationship. Mediation, confounding, and colliding are three key third-variable effects that yield different theoretical and methodological implications for drawing causal conclusions. Commonly used covariance-based statistical methods, such as linear regression and structural equation modeling, cannot distinguish these effects in practice, however.

Detecting heterogeneity in the causal direction of dependence: A model-based recursive partitioning approach.

Methods of causal discovery and direction of dependence to evaluate causal properties of variable relations have experienced rapid development. The majority of causal discovery methods, however, relies on the assumption of causal effect homogeneity, that is, the identified causal structure is expected to hold for the entire population. Because causal mechanisms can vary across subpopulations, we propose combining methods of model-based recursive partitioning and non-Gaussian causal discovery to identify such subpopulations.

Covariate selection in causal learning under non-Gaussianity.

Understanding causal mechanisms is a central goal in the behavioral, developmental, and social sciences. When estimating and probing causal effects using observational data, covariate adjustment is a crucial element to remove dependencies between focal predictors and the error term. Covariate selection, however, constitutes a challenging task because availability alone is not an adequate criterion to decide whether a covariate should be included in the statistical model.

Parsimonious item response theory modeling with the negative log-log link: The role of inflection point shift.

In item response theory (IRT) modeling, the magnitude of the lower and upper asymptote parameters determines the degree to which the inflection point shifts above or below P = 0.50. The current study examines the one-parameter negative log-log model (NLLM), which is characterized by a downward shift in the inflection point, among other distinctive psychometric properties.

Latent Variables Analysis in Person-Oriented Research.

In this article, we demonstrate that latent variable analysis can be of great use in person-oriented research. Starting with exploratory factor analysis of metric variables, we present an example of the problems that come with generalization of aggregate-level results to subpopulations. Oftentimes, results that are valid for populations do not represent subpopulations at all.

Advanced Categorical Data Analysis in Prevention Science.

A variety of health and social problems are routinely measured in the form of categorical outcome data (such as presence/absence of a problem behavior or stages of disease progression). Therefore, proper quantitative analysis of categorical data lies at the heart of the empirical work conducted in prevention science. Categorical data analysis constitutes a broad dynamic field of methods research and data analysts in prevention science can benefit from incorporating recent advances and developments in the statistical evaluation of categorical outcomes in their methodological repertoire.

Awareness and use of home remedies in Italy's alps: a population-based cross-sectional telephone survey.

Background: Belief in complementary and alternative medicine practices is related to reduced preparedness for vaccination. This study aimed to assess home remedy awareness and use in South Tyrol, where vaccination rates in the coronavirus pandemic were lowest in Italy and differed between German- and Italian-speaking inhabitants.

Methods: A population-based survey was conducted in 2014 and analyzed using descriptive statistics, multiple logistic regression, and latent class analysis.

Rural-Urban Disparities in Vaccine Hesitancy among Adults in South Tyrol, Italy.

Background: The demographic determinants of hesitancy in Coronavirus Disease-2019 (COVID-19) vaccination include rurality, particularly in low- and middle-income countries. In the second year of the pandemic, in South Tyrol, Italy, 15.6 percent of a representative adult sample reported hesitancy.

Vaccine Hesitancy during the Coronavirus Pandemic in South Tyrol, Italy: Linguistic Correlates in a Representative Cross-Sectional Survey.

Background: German is a minority language in Italy and is spoken by the majority of the inhabitants of the Autonomous Province of Bolzano, South Tyrol. Linguistic group membership in South Tyrol is an established determinant of health information-seeking behavior. Because the COVID-19 incidence and vaccination coverage in the second year of the pandemic in Italy was the worst in South Tyrol, we investigated whether linguistic group membership is related to COVID-19 vaccine hesitancy.

Distributional causal effects: Beyond an "averagarian" view of intervention effects.

The usefulness of mean aggregates in the analysis of intervention effectiveness is a matter of considerable debate in the psychological, educational, and social sciences. In addition to studying "average treatment effects," the evaluation of "distributional treatment effects," (i.e.

Assessing Heterogeneity in Students' Visual Judgment: Model-Based Partitioning of Image Rankings.

Differences in the ability of students to judge images can be assessed by analyzing the individual preference order (ranking) of images. To gain insights into potential heterogeneity in judgement of visual abstraction among students, we combine Bradley-Terry preference modeling and model-based recursive partitioning. In an experiment a sample of 1,020 high-school students ranked five sets of images, three of which with respect to their level of visual abstraction.

Configural Analysis in Component Space.

Unless very large samples are available, the number of variables and variable categories that can be simultaneously used in categorical data analysis is small when models are estimated. In this article, an approach is proposed that can help remedy this problem. Specifically, it is proposed to perform, in a first step, principal component analysis or factor analysis.

Accounting for Heteroskedasticity Resulting from Between-Group Differences in Multilevel Models.

Homogeneity of variance (HOV) is a well-known but often untested assumption in the context of multilevel models (MLMs). However, depending on how large the violation is, how different group sizes are, and the variance pairing, standard errors can be over or underestimated even when using MLMs, resulting in questionable inferential tests. We evaluate several tests (e.

Latent structure and measurement invariance of the Hospital Anxiety and Depression Scale in cancer outpatients.

Background/objective: The aim of the present study was to compare competing psychometric models and analyze measurement invariance of the (HADS) in cancer outpatients.

Method: The sample included 3,260 cancer outpatients. Latent structure of the HADS was analyzed using confirmatory factor analysis (CFA) with robust maximum likelihood estimation (MLR).

Parsimonious asymmetric item response theory modeling with the complementary log-log link.

Traditional item response theory (IRT) models assume a symmetric error distribution and rely on symmetric (logit or probit) link functions to model the response probabilities. As an alternative, we investigated the one-parameter complementary log-log model (CLLM), which is founded on an asymmetric error distribution and results in an asymmetric item response function with important psychometric properties. In a series of simulation studies, we demonstrate that the CLLM (a) is estimable in small sample sizes, (b) facilitates item-weighted scoring, and (c) accounts for the effect of guessing, despite the presence of a single parameter.

Health information-seeking behavior associated with linguistic group membership: latent class analysis of a population-based cross-sectional survey in Italy, August to September 2014.

Background: Evidence suggests an increasing demand for culturally and linguistically responsive disease prevention programs and health interventions. It is important to understand how individuals seek health information to address the potential needs of the health care system.

Methods: Latent classes of health information-seeking behaviors in a linguistically mixed region of Italy were explored through a population-based telephone survey of ten health information sources.

Base Models for Configural Frequency Analysis - Data Generation Processes.

In Configural Frequency Analysis (CFA), model-data discrepancies are interpreted with reference to CFA base models. Thus far, CFA base models are defined as probability models that differ in the constraints they place on variable relations. In this article, it is proposed extending the scope of CFA base models.

Configural Analysis of Oscillating Progression.

Oscillating series of scores can be approximated with locally optimized smoothing functions. In this article, we describe how such series can be approximated with locally estimated (loess) smoothing, and how Configural Frequency Analysis (CFA) can be used to evaluate and interpret results. Loess functions are often hard to describe because they cannot be represented by just one function that has interpretable parameters.

Local Effects of Intervention: a Configural Analysis.

In standard statistical data analysis, the effects of intervention or prevention efforts are evaluated in terms of variable relations. Results from application of regression-type methods suggest whether, overall, intervention is successful. In this article, we propose using configural frequency analysis (CFA) either in tandem with regression-type methods or by itself.

Configural frequency trees.

Although variable-oriented analyses are dominant in developmental psychopathology, researchers have championed a person-oriented approach that focuses on the individual as a totality. This view has methodological implications and various person-oriented methods have been developed to test person-oriented hypotheses. Configural frequency analysis (CFA) has been identified as a prime method for a person-oriented analysis of categorical data.