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.

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.

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.

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.

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.

A Simple Configural Approach for Testing Person-Oriented Mediation Hypotheses.

Statistical methods to test hypotheses about direct and indirect effects from a person-oriented research perspective are scarce. For categorical variables, previously suggested approaches use configural frequency analysis (CFA) to detect extreme patterns (CFA Types/Antitypes) that are responsible for the observed direct and indirect effects. Existing methods rest on complex (log-linear) model comparison strategies and may perform poorly with respect to Type I error protection and statistical power.

Reciprocal relations in categorical variables.

The analysis of reciprocal relations in categorical variables poses methodological challenges. Effects that go in opposite causal directions must be integrated into the same model, and parameters must be interpretable. In this article, we propose taking an event-based perspective and present a new approach to the analysis of reciprocal relations in manifest categorical variables.

A hybrid approach to regime shift detection.

In this article, we propose a method for the analysis of regime shifts in frequency data. This method identifies those points in the development of a process for which deviations are most extreme. Based on a statistical model, functions are estimated that describe the process.

Advances in Statistical Methods for Causal Inference in Prevention Science: Introduction to the Special Section.

The board of the Society for Prevention Research noted recently that extant methods for the analysis of causality mechanisms in prevention may still be too rudimentary for detailed and sophisticated analysis of causality hypotheses. This Special Section aims to fill some of the current voids, in particular in the domain of statistical methods of the analysis of causal inference. In the first article, Bray et al.

Testing the Causal Direction of Mediation Effects in Randomized Intervention Studies.

In a recent update of the standards for evidence in research on prevention interventions, the Society of Prevention Research emphasizes the importance of evaluating and testing the causal mechanism through which an intervention is expected to have an effect on an outcome. Mediation analysis is commonly applied to study such causal processes. However, these analytic tools are limited in their potential to fully understand the role of theorized mediators.

Locating Event-Based Causal Effects: A Configural Perspective.

Statistical models for the analysis of hypotheses that are compatible with direction dependence were originally specified based on the linear model. In these models, relations among variables reflected directional or causal hypotheses. In a number of causal theories, however, effects are defined as resulting from causes that did versus did not occur.

Direction of effects in categorical variables: Looking inside the table.

In the variable-oriented domain, direction of dependence analysis of metric variables is defined in terms of changes that the independent (or causal) variable has on the univariate distribution of the dependent variable. In this article, we take a person-oriented perspective and extend this approach in two aspects, for categorical variables. First, instead of looking at univariate frequency distributions, direction dependence is defined in terms of special interactions.

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.

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.

Testing Event-Based Forms of Causality.

Three fundamental types of causal relations are those of necessity, sufficiency, and necessity and sufficiency. These types are defined in contexts of categorical variables or events. Using statement calculus or Boolean algebra, one can determine which patterns of events are in support of a particular form of causal relation.

Using Perceived Self-efficacy to Improve Fatigue and Fatigability In Postsurgical Lung Cancer Patients: A Pilot Randomized Controlled Trial.

Background: Fatigue remains a prevalent and debilitating symptom in persons with non-small cell lung cancer (NSCLC). Exercise has been shown to be effective in reducing fatigue, yet interventions are limited for postsurgical NSCLC patients. To date, while surgery is offered as a standard curative treatment for NSCLC, no formal guidelines exist for postsurgical rehabilitation.

Direction of Effects in Multiple Linear Regression Models.

Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e.

Infant adrenocortical reactivity and behavioral functioning: relation to early exposure to maternal intimate partner violence.

Prenatal stress negatively affects fetal development, which in turn may affect infant hypothalamic-pituitary-adrenal (HPA) axis regulation and behavioral functioning. We examined effects of exposure to a traumatic stressor in families [intimate partner violence (IPV)] on both infants' HPA axis reactivity to stress and their internalizing and externalizing behaviors. Infants (n = 182, 50% girls, x age = 11.

The influence of prenatal intimate partner violence exposure on hypothalamic-pituitary-adrenal axis reactivity and childhood internalizing and externalizing symptoms.

This prospective longitudinal study examines the long-term influence of intimate partner violence (IPV) exposure in utero. We hypothesized that (a) prenatal IPV increases risk for internalizing and externalizing problems as well as for a profile of dysregulated cortisol reactivity, and (b) patterns of cortisol hyper- and hyporeactivity are differentially associated with internalizing and externalizing problems. The participants were 119 10-year-old children.

Direction of effects in mediation analysis.

Data collected in the social sciences are rarely normally distributed. The linear regression methods that are usually employed to test mediation hypotheses consider moments no higher than second order. Recently discussed methods of direction dependence do consider higher moments.