Visualising mental representations: A primer on noise-based reverse correlation in social psychology

With the introduction of the psychophysical method of reverse correlation, a holy grail of social psychology appears to be within reach – visualising mental representations. Reverse correlation is a data-driven method that yields visual proxies of mental representations, based on judgements of randomly varying stimuli. This review is a primer to an influential reverse correlation approach in which stimuli vary by applying random noise to the pixels of images. Our review suggests that the technique is an invaluable tool in the investigation of social perception (e.g., in the perception of race, gender and personality traits), with ample potential applications. However, it is unclear how these visual proxies are best interpreted. Building on advances in cognitive neuroscience, we suggest that these proxies are visual reflections of the internal representations that determine how social stimuli are perceived. In addition, we provide a tutorial on how to perform reverse correlation experiments using R.     

Multilevel modeling in industrial and personnel psychology

Many of the topics of interest in the social and behavior sciences are often hierarchical or multilevel in nature. These multiple levels (e.g., individual versus group) create problems for researchers related to the choice of measurement and analysis. Recent innovations in statistical analysis have made it possible to account for the hierarchical nature of observations. Therefore, in this article we begin with a review of multilevel analysis techniques and discuss advances that have been made in the social sciences using multilevel models. Next, we summarize contemporary research specific to the organizational psychology literature that uses multilevel analysis. Possible applications for industrial and personnel psychology are then discussed. Guidelines for determining if multilevel analysis is appropriate for a given applied research project are provided. We conclude with a summary and call for increased use of multilevel analysis in industrial and personnel psychology.     

Multilevel modeling in industrial and personnel psychology

Many of the topics of interest in the social and behavior sciences are often hierarchical or multilevel in nature. These multiple levels (e.g., individual versus group) create problems for researchers related to the choice of measurement and analysis. Recent innovations in statistical analysis have made it possible to account for the hierarchical nature of observations. Therefore, in this article we begin with a review of multilevel analysis techniques and discuss advances that have been made in the social sciences using multilevel models. Next, we summarize contemporary research specific to the organizational psychology literature that uses multilevel analysis. Possible applications for industrial and personnel psychology are then discussed. Guidelines for determining if multilevel analysis is appropriate for a given applied research project are provided. We conclude with a summary and call for increased use of multilevel analysis in industrial and personnel psychology.     

L’alexithymie aujourd’hui : essai d’articulation avec les conceptions contemporaines des émotions et de la personnalité

More than thirty years after his introduction and despite many criticisms, the alexithymia concept is giving rise to a growing body of research and interest. Today, it is thought to reflect a deficit in the cognitive processing and regulation of emotion. After a brief historical introduction, this paper proposes an exploration of the links between the alexithymia construct, the multicomponent emotion theories and the five-factor model of personality. Specifically, the potential associations between alexithymia and emotion regulation are examined, referring to recent studies in psychology of emotions, neurobiology, cognitive psychology, developmental psychology and psychology of personality.     

Capturing the interplay among within- and between-person processes using multilevel modeling techniques

Multilevel modeling is an excellent way to analyze nested or clustered data of the type commonly collected through investigations into the linkages between psychological functioning and relationship processes. This article describes two especially relevant applications of multilevel modeling. The first application, growth curve analysis, is already familiar to many researchers and involves modeling individuals’ change trajectories over time and relating the derived change parameters to person-level characteristics or phenomena. The purpose of this paper is to emphasize a second application, multilevel process analysis, which involves modeling within-subject characteristics other than change over a representation of time. Multilevel analysis of within-subject processes is particularly well-suited for hypotheses common to clinical psychology investigations, yet has received substantially less attention in the literature than its growth curve counterpart. Types of research questions and methodologies that can be addressed within the multilevel process analysis framework are described. Finally, aspects of multilevel process analysis are demonstrated with daily diary data collected from wives who reported on their marital happiness and depressed mood for 3 weeks.     

Contextualizing personality psychology

The issue of how to contextualize personality psychology constructively has been a longstanding dilemma. This special issue brings together personality, social, self, clinical, and cultural psychologists who have attempted to contextualize the self, personality, attachment, and cultural constructs in an integrative fashion, with a focus on broader situations, such as social roles. In this introductory essay, I describe the potential advantages of integrating traditional concepts in personality psychology with social roles and provide an overview of the contributions made to the special issue.     

Multilevel modeling: current and future applications in personality research

Traditional statistical analyses can be compromised when data are collected from groups or multiple observations are collected from individuals. We present an introduction to multilevel models designed to address dependency in data. We review current use of multilevel modeling in 3 personality journals showing use concentrated in the 2 areas of experience sampling and longitudinal growth. Using an empirical example, we illustrate specification and interpretation of the results of series of models as predictor variables are introduced at Levels 1 and 2. Attention is given to possible trends and cycles in longitudinal data and to different forms of centering. We consider issues that may arise in estimation, model comparison, model evaluation, and data evaluation (outliers), highlighting similarities to and differences from standard regression approaches. Finally, we consider newer developments, including 3-level models, cross-classified models, nonstandard (limited) dependent variables, multilevel structural equation modeling, and nonlinear growth. Multilevel approaches both address traditional problems of dependency in data and provide personality researchers with the opportunity to ask new questions of their data.     

Applications of covariance structure modeling in psychology: cause for concern?

Methods of covariance structure modeling are frequently applied in psychological research. These methods merge the logic of confirmatory factor analysis, multiple regression, and path analysis within a single data analytic framework. Among the many applications are estimation of disattenuated correlation and regression coefficients, evaluation of multitrait-multimethod matrices, and assessment of hypothesized causal structures. Shortcomings of these methods are commonly acknowledged in the mathematical literature and in textbooks. Nevertheless, serious flaws remain in many published applications. For example, it is rarely noted that the fit of a favored model is identical for a potentially large number of equivalent models. A review of the personality and social psychology literature illustrates the nature of this and other problems in reported applications of covariance structure models.     

A Meta-View of Multivariate Statistical Inference Methods in European Psychology Journals

We investigated the extent and nature of multivariate statistical inferential procedures used in eight European psychology journals covering a range of content (i.e., clinical, social, health, personality, organizational, developmental, educational, and cognitive). Multivariate methods included those found in popular texts that focused on prediction, group difference, and advanced modeling: multiple regression, logistic regression, analysis of covariance, multivariate analysis of variance, factor or principal component analysis, structural equation modeling, multilevel modeling, and other methods. Results revealed that an average of 57% of the articles from these eight journals involved multivariate analyses with a third using multiple regression, 17% using structural modeling, and the remaining methods collectively comprising about 50% of the analyses. The most frequently occurring inferential procedures involved prediction weights, dichotomous p values, figures with data, and significance tests with very few articles involving confidence intervals, statistical mediation, longitudinal analyses, power analysis, or meta-analysis. Contributions, limitations and future directions are discussed.     

Evolutionary biology and personality psychology: Implications of genetic variability

Biological considerations raise an important set of issues for psychology: what behavioral attributes of the species are genetically based, what are the mechanisms by which genetic influences affect behavior, what are the evolutionary antecedents of genetically-based attributes, and what are their consequences? This article examines a subset of these issues by exploring some potential consequences of extant genetic variability for personality functioning, social interaction and the current genetic evolution of our species. Behavior genetics provides a methodology for discovering genetically-influenced behavioral variation. Five disparate areas (socialization, personality development, personality assessment, interactionism and assortative mating) are examined in which findings from behavior genetics can guide research and theory in personality psychology. Relationships between organismic and social parameters are emphasized. The final section combines these five areas by placing them within the broader context of theory-building in psychology.     

Notes for a Critical Personality Psychology: Making Room under the Critical Psychology Umbrella

Inspired by the liberation psychologist Martin‐Baró who provocatively defined personality as that of which individuals can be robbed in conditions of social injustice and research psychologists in training whose appreciation of the possibilities of personality psychology has been limited by the dominance of trait approaches, this paper claims that we need and can practice a critical personality psychology. Conceptual and methodological tools for such an enterprise are identified in two arenas of current research: the study of narratives and new forms of history in personality psychology. Within critical personality psychology, personality is understood to be an expression of (i) a multifaceted organization that includes individual, interpersonal, social, cultural, and political contexts; (ii) individual and social change; and (iii) the moral dimensions of human psychology. Notes on future directions draw on areas of inquiry within and outside personality psychology to insure a place under the critical psychology umbrella.     

Predicting Response to Intensive Multimodal Inpatient Treatment: A Comparison of Single- and Multiple-Class Growth Modeling Approaches

In a modest body of research, personality functioning assessed via performance-based instruments has been found to validly predict treatment outcome and, to some extent, differential response to treatment. However, state-of-the-science longitudinal and mixture modeling techniques, which are common in many areas of clinical psychology, have rarely been used. In this article, we compare multilevel growth curve modeling (MLM) and latent class growth modeling (LCGM) approaches with the same data set to illustrate the different research questions that can be addressed by each method. Global Assessment of Functioning (GAF) scores collected at 6 points during the course of a long-term multimodal inpatient treatment of 58 severely and persistently mentally ill adults were used to model the trajectory of treatment outcome. Pretreatment Rorschach-based markers of personality functioning and other markers of psychiatric severity were examined as covariates in each modeling approach. The results of both modeling approaches generally indicated that more psychologically impaired clients responded less favorably to treatment. The LCGM approach revealed 2 unique trajectories of improvement (a persistently low group and a higher starting, improving group). Personality functioning and baseline psychiatric variables significantly predicted group membership and the rate of change within the groups. A side-by-side examination of these 2 methods was found to be useful in predicting differential treatment response with personality functioning variables.     

论自我同一性概念的整合

自我同一性是西方心理学一个重要的概念,但至今没有一个普遍接受的定义。通过对自我同一性概念内涵不一得归因分析,指出了整合自我同一性概念应关注的几个范畴,进而提出自我同一性是一个与自我、人格的发展有密切关系的多层次、多维度的心理学概念。本质上,它是指人格发展的连续性、成熟性和统合感,它包含三个层面的内涵：(1)最基本的层面,即ego-idernity;(2)个人同一性;(3)社会同一性。     

密集追踪数据分析：模型及其应用

在心理学、教育学和临床医学等领域, 越来越多的研究者开始关注个体内部的行为、心理、临床效果等随时间而产生的动态变化, 重视针对个体的差异化建模。密集追踪是一种在短时间内对个体进行多个时间节点密集追踪测量的方法, 更适合用于研究个体内部心理过程等的动态变化及其作用机制。近年来, 密集追踪成为心理学研究的一大热点, 但许多密集追踪的研究分析仍停留在较为传统的方法。方法学领域已涌现出较多用于密集追踪数据分析的模型方法, 较为主流的模型包括以动态结构方程模型(Dynamic Structural Equation Model, DSEM)为代表的自上而下的建模方法, 以及以组迭代多模型估计(Group Iterative Multiple Model Estimation, GIMME)为代表的自下而上的建模方法。二者均可以方便地对密集追踪数据中的自回归及交叉滞后效应进行建模。     

Toward a Social Psychology of Aging

The articles in this special issue highlight how social psychology can further the understanding of aging. One goal of this special issue is to generate more interest in aging as an area of study for social psychologists. A second goal is to challenge readers to think about how their research interconnects with issues in aging. A third goal is to demonstrate how social psychological processes have direct applications to real-world issues that face people as they age. This introduction to the special issue provides additional examples of how social psychology can contribute to a better understanding of aging.     

Multilevel modelling: Beyond the basic applications

Over the last 30 years statistical algorithms have been developed to analyse datasets that have a hierarchical/multilevel structure. Particularly within developmental and educational psychology these techniques have become common where the sample has an obvious hierarchical structure, like pupils nested within a classroom. We describe two areas beyond the basic applications of multilevel modelling that are important to psychology: modelling the covariance structure in longitudinal designs and using generalized linear multilevel modelling as an alternative to methods from signal detection theory (SDT). Detailed code for all analyses is described using packages for the freeware R.     

Gender and psychopathy: an overview of important issues and introduction to the special issue

One of the most important concepts to ever emerge in forensic psychology and law is psychopathy. It would be difficult to exaggerate the profound effect the construct has had on research and practice in correctional psychology, psychiatry, and criminology. Much less pronounced has been an interest in understanding the potential relevance and practical implications that this personality disorder might have for providing insights into antisocial behaviors and crimes committed by girls and women. In this paper we provide an overview of some of the pressing issues confronting clinicians and researchers and provide an introduction to this special issue dedicated to gender and psychopathy.     

Thinking and Modeling at Multiple Levels: The Potential Contribution of Multilevel Modeling to Communication Theory and Research

In this introduction to the special issue on applications of multilevel modeling (MLM) to communication research, we provide a conceptual overview of the benefits of MLM—the ability to simultaneously analyze data collected at multiple levels, the ease with which it can be used to assess trends and change over time, and its incorporation of the nested structure of data in the estimation process. We highlight ways in which MLM can be used to further theory and research in communication. In addition, we comment on the applications of MLM highlighted in this special issue and echo past calls for more multilevel theorizing and analysis in the field of communication.     

Multilevel Model Prediction

Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response outcomes—and provides a unified procedure for predicting them. These predictors are best linear unbiased and are commonly known via the acronym BLUP; they are optimal in the sense of minimizing mean square error and are Bayesian under a diffuse prior. For parameter estimation purposes, a multilevel model can be written as a linear mixed-effects model. In this way, parameters of the many equations can be estimated simultaneously and hence efficiently. For prediction purposes, we show that it is more convenient to retain the multiple equation feature of multilevel models. In this way, the efficient BLUPs are easy to compute and retain their intuitively appealing recursive form. We also derive explicit equations for standard errors of these different types of predictors. Prediction in multilevel modeling is important in a wide range of applications. To demonstrate the applicability of our results, this paper discusses prediction in the context of a study of school effectiveness. This research was supported by a grant from the Graduate School at the University of Wisconsin at Madision and the National Science Foundation, Grant number SES-0436274. We are grateful to Norman Webb at Wisconsin Center for Education Research for making available the data used in the reported application.