Evaluation of Multi-parameter Test Statistics for Multiple Imputation

In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences. However, little is known about the performance of these tests, and virtually no research study has compared the Type 1 error rates and statistical power of these tests in scenarios that are typical of behavioral science data (e.g., small to moderate samples, etc.). This paper uses Monte Carlo simulation techniques to examine the performance of these multi-parameter test statistics for multiple imputation under a variety of realistic conditions. We provide a number of practical recommendations for substantive researchers based on the simulation results, and illustrate the calculation of these test statistics with an empirical example.     

Behavioral landscapes and earth mover’s distance: A new approach for studying individual differences in density distributions

Contemporary views of personality highlight intraindividual variability. We forward a general method for quantifying individual differences in behavioral tendencies based on Earth Mover’s Distance. Using data from 150 individuals who reported on their and others’ interpersonal behavior in 64,112 social interactions, we illustrate how this new approach can advance notions of personality as density distributions. Results provide independent confirmation and establish validity of existing representations of individual differences in interpersonal behavior, and identify new dimensions and profiles of personality and well-being. Benefits of the EMD method include freedom from assumptions about the shape and form of density distributions, generality of application to n-dimensional behavior captured in experience sampling studies, and natural integration of personality structure and dynamics.     

Introductory remarks by the issue editors: The multiple dimensions of the parent–baby story

Abstract

In their introductory remarks, the guest editors, themselves presenters at the conference “Parent–Infant Disturbance: Theory and Therapy,” introduce the philosophy and methodology underlying this interdisciplinary psychoanalytic conference and outline the multiple dimensions of the issue at hand, as they are addressed by the individual contributions to this volume. The paper concludes with a remembrance of and dedication to the victims of the Paris terrorist attack of November 13, 2015.     

互联网数据中的文化差异与文化变迁

互联网时代,人们在网络上留下了各种反映其心理过程与文化特征的信息。这些庞大多样的互联网数据为文化心理学研究提供了新视角。首先,当前文化心理学存在文化差异和文化变迁两种研究取向,互联网数据相较传统来源数据在这两种研究上均存在若干优势;其次,针对4种类型的互联网数据,文化心理学家利用文本分析、多媒体分析、社会网络分析和互联网使用行为分析的方法进行研究;再次,基于互联网数据及其分析方法,文化差异与变迁研究取得了丰硕的成果;最后,当前基于互联网数据的文化心理学研究存在效度、技术与理论局限,未来研究需通过合理抽样、检验新指标有效性、准因果分析、充分运用新技术、数据驱动等方法来提升方法效度、结果深度及理论多样性。     

Measuring the ability of transitive reasoning,using product and strategy information

Cognitive theories disagree about the processes and the number of abilities involved in transitive reasoning. This led to controversies about the influence of task characteristics on individuals' performance and the development of transitive reasoning. In this study, a computer test was constructed containing 16 transitive reasoning tasks having different characteristics with respect to presentation form, task format, and task content. Both product and strategy information were analyzed to measure the performance of 6- to 13-year-old children. Three methods (MSP, DETECT, and Improved DIMTEST) were used to determine the number of abilities involved and to test the assumptions imposed on the data by item response models. Nonparametric IRT models were used to construct a scale for transitive reasoning. Multiple regression was used to determine the influence of task characteristics on the difficulty level of the tasks. It was concluded that: (1) the qualitatively distinct abilities predicted by Piaget's theory could not be distinguished by means of different dimensions in the data structure; (2) transitive reasoning could be described by one ability, and some task characteristics influenced the difficulty of a task; and (3) strategy information provided a stronger scale than product information.     

Geometric representation of association between categories

Categories can be counted, rated, or ranked, but they cannot be measured. Likewise, persons or individuals can be counted, rated, or ranked, but they cannot be measured either. Nevertheless, psychology has realized early on that it can take an indirect road to measurement: What can be measured is the strength of association between categories in samples or populations, and what can be quantitatively compared are counts, ratings, or rankings made under different circumstances, or originating from different persons. The strong demand for quantitative analysis of categorical data has thus created a variety of statistical methods, with substantial contributions from psychometrics and sociometrics. What is the common basis of these methods dealing with categories? The basic element they share is that the sample space has a special geometry, in which categories (or persons) are point masses forming a simplex, while distributions of counts or profiles of ratings are centers of gravity, which are also point masses. Rankings form a discrete subset in the interior of the simplex, known as the permutation polytope, and paired comparisons form another subset on the edges of the simplex. Distances between point masses form the basic tool of analysis. The paper gives some history of major concepts, which naturally leads to a new concept: the shadow point. It is then shown how loglinear models, Luce and Rasch models, unfolding models, correspondence analysis and homogeneity analysis, forced classification and classification trees, as well as other models and methods, fit into this particular geometrical framework.     

Enhancing frequency recording by developmental disabilities treatment staff

We evaluated a staff training and management package for increasing accuracy of recording frequency of problem behavior in a residential care facility. A multiple baseline design across the first and second work shifts showed that 2 of 8 participants increased their accuracy following in-service training, and all 8 improved during a condition with supervisor presence and feedback. Improvements were maintained when feedback was removed and generalized to activity periods when neither supervisor presence nor feedback was provided. Other staff behavior was not adversely affected by the intervention package.     

Consumer brand choice: individual and group analyses of demand elasticity

Following the behavior-analytic tradition of analyzing individual behavior, the present research investigated demand elasticity of individual consumers purchasing supermarket products, and compared individual and group analyses of elasticity. Panel data from 80 UK consumers purchasing 9 product categories (i.e., baked beans, biscuits, breakfast cereals, butter, cheese, fruit juice, instant coffee, margarine and tea) during a 16-week period were used. Elasticity coefficients were calculated for individual consumers with data from all or only 1 product category (intra-consumer elasticities), and for each product category using all data points from all consumers (overall product elasticity) or 1 average data point per consumer (interconsumer elasticity). In addition to this, split-sample elasticity coefficients were obtained for each individual with data from all product categories purchased during weeks 1 to 8 and 9 to 16. The results suggest that: 1) demand elasticity coefficients calculated for individual consumers purchasing supermarket food products are compatible with predictions from economic theory and behavioral economics; 2) overall product elasticities, typically employed in marketing and econometric research, include effects of interconsumer and intraconsumer elasticities; 3) when comparing demand elasticities of different product categories, group and individual analyses yield similar trends; and 4) individual differences in demand elasticity are relatively consistent across time, but do not seem to be consistent across products. These results demonstrate the theoretical, methodological, and managerial relevance of investigating the behavior of individual consumers.     

The Short Mood and Feelings Questionnaire (SMFQ): A Unidimensional Item Response Theory and Categorical Data Factor Analysis of Self-Report Ratings from a Community Sample of 7-through 11-Year-Old Children

Item response theory (IRT) and categorical data factor analysis (CDFA) are complementary methods for the analysis of the psychometric properties of psychiatric measures that purport to measure latent constructs. These methods have been applied to relatively few child and adolescent measures. We provide the first combined IRT and CDFA analysis of a clinical measure (the Short Mood and Feelings Questionnaire—SMFQ) in a community sample of 7-through 11-year-old children. Both latent variable models supported the internal construct validity of a single underlying continuum of severity of depressive symptoms. SMFQ items discriminated well at the more severe end of the depressive latent trait. Item performance was not affected by age, although age correlated significantly with latent SMFQ scores suggesting that symptom severity increased within the age period of 7–11. These results extend existing psychometric studies of the SMFQ and confirm its scaling properties as a potential dimensional measure of symptom severity of childhood depression in community samples.