Training analysis and training models

This article has the intention of giving impulses to a hopefully more creative and open-minded discussion of the function of training analysis and the kind of training model we seem to take for granted in the Scandinavian countries, the Eitingon model. I will consider some historical background data on this model as well as some historical data on the development in France which have created an alternative model of training. Especially in Sweden, there has been a stormy debate during the last years about the training analyst functions and some members have actually decided to leave our association because of this. I would like to try to shift focus from just a ‘yes-or-no-perspective’? to a broader question of our psychoanalytical society’s purpose and structure and how the training-model could sustain it.     

Cross-Sectional Analysis of Longitudinal Mediation Processes

Statistical mediation analysis can help to identify and explain the mechanisms behind psychological processes. Examining a set of variables for mediation effects is a ubiquitous process in the social sciences literature; however, despite evidence suggesting that cross-sectional data can misrepresent the mediation of longitudinal processes, cross-sectional analyses continue to be used in this manner. Alternative longitudinal mediation models, including those rooted in a structural equation modeling framework (cross-lagged panel, latent growth curve, and latent difference score models) are currently available and may provide a better representation of mediation processes for longitudinal data. The purpose of this paper is twofold: first, we provide a comparison of cross-sectional and longitudinal mediation models; second, we advocate using models to evaluate mediation effects that capture the temporal sequence of the process under study. Two separate empirical examples are presented to illustrate differences in the conclusions drawn from cross-sectional and longitudinal mediation analyses. Findings from these examples yielded substantial differences in interpretations between the cross-sectional and longitudinal mediation models considered here. Based on these observations, researchers should use caution when attempting to use cross-sectional data in place of longitudinal data for mediation analyses.     

Virtual Levels and Role Models: N-Level Structural Equations Model of Reciprocal Ratings Data

A general latent variable modeling framework called n-Level Structural Equations Modeling (NL-SEM) for dependent data-structures is introduced. NL-SEM is applicable to a wide range of complex multilevel data-structures (e.g., cross-classified, switching membership, etc.). Reciprocal dyadic ratings obtained in round-robin design involve complex set of dependencies that cannot be modeled within Multilevel Modeling (MLM) or Structural Equations Modeling (SEM) frameworks. The Social Relations Model (SRM) for round robin data is used as an example to illustrate key aspects of the NL-SEM framework. NL-SEM introduces novel constructs such as ‘virtual levels’ that allows a natural specification of latent variable SRMs. An empirical application of an explanatory SRM for personality using xxM, a software package implementing NL-SEM is presented. Results show that person perceptions are an integral aspect of personality. Methodological implications of NL-SEM for the analyses of an emerging class of contextual- and relational-SEMs are discussed.     

The Power to Detect and Predict Individual Differences in Intra-Individual Variability Using the Mixed-Effects Location-Scale Model

Our goal is to provide empirical scientists with practical tools and advice with which to test hypotheses related to individual differences in intra-individual variability using the mixed-effects location-scale model. To that end, we evaluate Type I error rates and power to detect and predict individual differences in intra-individual variability using this model and provide empirically-based guidelines for building scale models that include random and/or systematically-varying fixed effects. We also provide two power simulation programs that allow researchers to conduct a priori empirical power analyses. Our results aligned with statistical power theory, in that, greater power was observed for designs with more individuals, more repeated occasions, greater proportions of variance available to be explained, and larger effect sizes. In addition, our results indicated that Type I error rates were acceptable in situations when individual differences in intra-individual variability were not initially detectable as well as when the scale-model individual-level predictor explained all initially detectable individual differences in intra-individual variability. We conclude our paper by providing study design and model building advice for those interested in using the mixed-effects location-scale model in practice.     

Testing Process Factor Analysis Models Using the Parametric Bootstrap

Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.     

Misspecification in Latent Change Score Models: Consequences for Parameter Estimation,Model Evaluation,and Predicting Change

Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS. When constraints were incorrectly applied, several parameters, most notably the slope (i.e., constant change) factor mean and autoproportion coefficient, were severely and consistently biased, as were regression paths to the slope factor when external predictors of change were included. Standard fit indices indicated that the misspecified models fit well, partly because mean level trajectories over time were accurately captured. Loosening constraint improved the accuracy of parameter estimates, but estimates were more unstable, and models frequently failed to converge. Results suggest that potentially common sources of misspecification in LCS can produce distorted impressions of developmental processes, and that identifying and rectifying the situation is a challenge.     

Preference,resistance to change,and the cumulative decision model

According to behavioral momentum theory (Nevin & Grace, 2000a), preference in concurrent chains and resistance to change in multiple schedules are independent measures of a common construct representing reinforcement history. Here I review the original studies on preference and resistance to change in which reinforcement variables were manipulated parametrically, conducted by Nevin, Grace and colleagues between 1997 and 2002, as well as more recent research. The cumulative decision model proposed by Grace and colleagues for concurrent chains is shown to provide a good account of both preference and resistance to change, and is able to predict the increased sensitivity to reinforcer rate and magnitude observed with constant‐duration components. Residuals from fits of the cumulative decision model to preference and resistance to change data were positively correlated, supporting the prediction of behavioral momentum theory. Although some questions remain, the learning process assumed by the cumulative decision model, in which outcomes are compared against a criterion that represents the average outcome value in the current context, may provide a plausible model for the acquisition of differential resistance to change.     

父母心理控制和儿童消极情绪性对学前儿童问题行为的共同作用:“素质-压力”还是“差别易感性”

本研究以226名学前儿童和其父母为研究对象,从儿童气质-家庭环境交互作用的视角,采用父母报告的方法收集数据,考察了父母心理控制及儿童消极情绪性对儿童问题行为的共同作用。结果发现：在预测外化问题时,父亲和母亲心理控制的主效应均显著,同时父亲心理控制与儿童消极情绪性的交互作用也显著。显著性区域分析发现,高消极情绪性的学前儿童更易受高父亲心理控制的不利影响,也更易受低父亲心理控制的有利影响,该结果支持差别易感性模型;在预测内化问题时,父亲和母亲心理控制的主效应及其与儿童消极情绪性的交互作用均不显著,说明无论是对高消极情绪性还是低消极情绪性儿童,父母心理控制对其内化问题的影响相对较弱。     

政策执行偏差严重程度对公众地方政府 责任判断的影响:多重中介模型的检验

为探讨地方政府政策执行偏差严重程度对公众地方政府责任判断的影响机制。采用问卷法对678名社会公众进行调查,结果显示:(1)地方政府政策执行偏差情境下,公众地方政府愿望判断和公众地方政府信念判断在政策执行偏差严重程度和公众地方政府责任判断之间起多重中介作用。(2)公众地方政府信念判断在政策执行偏差严重程度和公众地方政府责任判断之间的中介效应量大于公众地方政府愿望判断的中介效应量。     

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.