Towards an Interactive Acculturation Model: A Social Psychological Approach

The first part of this paper proposes a continuum of ideological premises that seeks to account for the broad range of immigrant integration policies adopted by Western democratic states. In the second part, a review of Social Psychological models of immigrant acculturation strategies demonstrates the need to explain more clearly the interactive nature of immigrant and host community relations. The Interactive Acculturation Model (IAM) presented next proposes that relational outcomes are the product of the acculturation orientations of both the host majority and immigrant groups as influenced by state integration policies. The model makes predictions regarding the acculturation combinations most likely to produce consensual, problematic, and conflictual relational outcomes between immigrants and members of the host community. Social psychological research is needed to test the validity of the IAM model empirically.     

Pastoral work,metacommunication and myth

The writer discusses two phenomena the pastoral counselor encounters in work with families and individuals: family survival myths and individual survival myths or fatal statements. Family survival myths are inefficient defense mechanisms, directed at the support of the parental relationship and the maintenance of the family balance. Fatal statements are expressions inherited from the family of origin. They function as myths and are intended to help one survive. From his perspective the pastor can, through a process of metacommunication, open the possibility to discuss the myth and so help people to free themselves from an unhealthy and life inhibiting bondage.     

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.

     

SPSS macros to compare any two fitted values from a regression model

In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests—particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.     

Teacher Acquisition of Functional Analysis Methods Using Pyramidal Training

Pyramidal training involves an experienced professional training a subset of individuals who, in turn, train additional individuals. Pyramidal training is effective for training a variety of behavior-analytic skills with direct-care staff, parents, and teachers. As teachers’ roles in behavioral assessment increase, pyramidal training may be useful to improve the procedural fidelity with which teachers conduct functional analyses. In the current study, previously trained teachers taught six other teachers (trainees) to conduct standard functional analysis conditions in a pyramidal training model. All trainees quickly acquired the skills necessary to conduct attention, escape, play, and tangible conditions during analog role-plays. Trainees also implemented functional analysis conditions with fidelity in their classrooms with students. Using a pyramidal training model may help to disseminate behavior-analytic procedures.     

Functional analysis and treatment of aggression maintained by preferred conversational topics

After an initial functional analysis of a participant's aggression showed unclear outcomes, we conducted preference and reinforcer assessments to identify preferred forms of attention that may maintain problem behavior. Next, we conducted an extended functional analysis that included a modified attention condition. Results showed that the participant's aggression was maintained by access to preferred conversational topics. A function-based intervention decreased aggression and increased an appropriate communicative response.     

Deconstructing the construct: A network perspective on psychological phenomena

In psychological measurement, two interpretations of measurement systems have been developed: the reflective interpretation, in which the measured attribute is conceptualized as the common cause of the observables, and the formative interpretation, in which the measured attribute is seen as the common effect of the observables. We advocate a third interpretation, in which attributes are conceptualized as systems of causally coupled (observable) variables. In such a view, a construct like ’depression’ is not seen as a latent variable that underlies symptoms like ’lack of sleep’ or ’fatigue’, and neither as a composite constructed out of these symptoms, but as a system of causal relations between the symptoms themselves (e.g., lack of sleep → fatigue, etc.). We discuss methodological strategies to investigate such systems as well as theoretical consequences that bear on the question in which sense such a construct could be interpreted as real.