A generalized method of image analysis from an intercorrelation matrix which may be singular

Following the works of Guttman (1953) and Kaiser (1976), we show that the image and anti-image covariance matrices can be derived from a singular correlation matrix, by making use of the orthogonal projector and a weaker generalized inverse matrix.     

Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients

The method of finding the maximum likelihood estimates of the parameters in a multivariate normal model with some of the component variables observable only in polytomous form is developed. The main stratagem used is a reparameterization which converts the corresponding log likelihood function to an easily handled one. The maximum likelihood estimates are found by a Fletcher-Powell algorithm, and their standard error estimates are obtained from the information matrix. When the dimension of the random vector observable only in polytomous form is large, obtaining the maximum likelihood estimates is computationally rather labor expensive. Therefore, a more efficient method, the partition maximum likelihood method, is proposed. These estimation methods are demonstrated by real and simulated data, and are compared by means of a simulation study.     

The relationship between external variables and common factors

A theorem is presented which gives the range of possible correlations between a common factor and an external variable (i.e., a variable not included in the test battery factor analyzed). Analogous expressions for component (and regression component) theory are also derived. Some situations involving external correlations are then discussed which dramatize the theoretical differences between components and common factors.Support by the National Research Council of Canada (NRC Grant No. A4640) and the University of British Columbia (UBC Humanities and Social Sciences Grant 26-9718) is gratefully acknowledged.This paper is based in part on the author's Ph.D. dissertation. I am particularly grateful to my dissertation advisor, Dr. Peter H. Schönemann. Thanks also to the editor and the anonymous reviewers, who contributed many helpful comments and suggestions.     

Combined antecedent variables as motivating operations within functional analyses

Functional analysis test conditions typically manipulate a single antecedent variable and an associated consequence to better isolate response-reinforcer relations. In some instances no problem behavior is observed, perhaps representing a false-negative finding. The present study evaluated one approach to assess potentially false-negative findings within functional analyses. Participants were exposed to single-antecedent functional analysis test conditions and combined-antecedent test conditions within a multielement design. Both participants engaged in problem behavior primarily during the combined-antecedent test conditions, and treatments matched to the results were effective in reducing problem behavior. Findings are discussed in terms of clinical implications of combining antecedent variables to further examine potentially false-negative functional analysis results.     

A random forest approach to explore how situational variables affect perceived exertion of elite youth soccer players

The aim of this study was to explore how situational variables affect youth soccer players’ perceived exertion (RPE) after official matches. Thirty-five elite youth male players (14.33 ± 0.86 years; 173.49 ± 6.16 cm; 63.44 ± 5.98 kg) who belonged to two different teams of a professional club participated in this study. Data collection was conducted during two seasons (2016–2017, 2017–2018) and included 60 official matches (30 official matches per team). Ten minutes after each match players rated their RPE and using a modified Borg CR-10 scale. A Random Forest Regression was used to quantify the importance of match-related situational variables in RPE. Afterwards, a linear mixed model analysis was applied to identify the variability in RPE among the situational variables. The game-playing time, the player status (starter or substitute) and the player identity were the strongest predictors of RPE. Moreover, the match outcome and the final scoreline showed significant effects on both starter and substitute players but the main effect of the quality of the opponent was only identified in starter players (p < 0.05). These results allow practitioners to know how situational variables interact and modulate RPE after official matches and help them to prescribe and adapt the players’ training content and load before and after matches.     

The variability of client,therapist and clinic in psychotherapy outcomes: A three-level hierarchical model

This article investigates the percentage of variance in psychotherapy outcomes explained by the case-mix variables for individual cases, by the therapist (therapist effects), and what additional variance is explained by the clinic with which the therapist is affiliated. While there has been substantial recent research regarding therapist effects, very little has been published regarding clinic-level effects after controlling for therapist effects. The study utilised the largest sample reported to date, using data from 28 clinics with a minimum of 2,000 cases in a clinical range of severity of symptoms with pre-post change scores on an outcome questionnaire. Only cases treated by a therapist with at least 30 cases were included. These selection criteria resulted in a case count of 156,258 clients treated by 874 therapists located at 28 clinics. After controlling for differences in case mix using diagnosis and intake score (severity of symptoms), the resulting analyses indicated that 5.21% of the variance in treatment outcome was explained by the therapist, while another 1.13% of the variance was associated with the clinic. Findings are discussed with implications for practice and policy.     

工业与组织心理学中的集体效能感

集体效能感是指团体成员对于团体成功地完成特定任务或取得特定水平成就的能力的共同信念,其测量方法主要有自我效能感总和法、个体评估平均法和团体讨论法等三种。在影响效果方面,集体效能感可以有效地预测绩效以及其他一些工作态度和行为变量,但过高的集体效能感也会产生负面作用;在影响因素方面,团体规模和团体间竞争等团体内外部因素均能显著影响集体效能感,并且目标设定训练等干预措施也能显著提高集体效能感。文章最后指出未来的研究有必要加强对新型团队中的集体效能感、集体效能感的动态变化等五方面问题的探讨     

Mental Health Aspects of Arab-Israeli Adolescents From Polygamous Versus Monogamous Families

Abstract

The authors considered the mental health consequences of polygamy in a sample of 101 Arab Muslim adolescents (19 from polygamous and 82 from monogamous families) at Juarish (Ramla), Israel. The respondents completed the Self-Esteem Scale (SE; M. Rosenberg, 1979), the Brief Symptom Inventory (BSI; L. Derogatis & N. Melsavados, 1983; L. Derogatis & P. Spencer, 1982), and the McMaster Family Assessment Device (FAD; N. B. Epstein, M. N. Baldwin, & D. S. Bishop, 1983). The respondents from polygamous families had lower SE scores, statistically significant higher scores in 2 BSI dimensions, higher scores in all other BSI dimensions, and higher levels of self-reported family dysfunction. The respondents from polygamous families reported lower levels of socioeconomic status, academic achievement, and parental academic attainment. Those variables may have had a more direct impact on mental health than did parental marital status. The data also indicated that perceived family functioning was the best predictor of mental health.     

What Causes the Mean Bias of the Likelihood Ratio Statistic with Many Variables?

Abstract

Survey data often contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. However, conventional SEM methods are not crafted to handle data with a large number of variables (p). A large p can cause T_ml, the most widely used likelihood ratio statistic, to depart drastically from the assumed chi-square distribution even with normally distributed data and a relatively large sample size N. A key element affecting this behavior of T_ml is its mean bias. The focus of this article is to determine the cause of the bias. To this end, empirical means of T_ml via Monte Carlo simulation are used to obtain the empirical bias. The most effective predictors of the mean bias are subsequently identified and their predictive utility examined. The results are further used to predict type I errors of T_ml. The article also illustrates how to use the obtained results to determine the required sample size for T_ml to behave reasonably well. A real data example is presented to show the effect of the mean bias on model inference as well as how to correct the bias in practice.     

Relationships between personality traits and positive/negative sexual cognitions

In this study we examined the relationship between gender, age, religion, neuroticism, extraversion, openness to experience, and obsessiveness on one side and the frequency and content of sexual cognitions experienced as being positive/pleasant or negative/unpleasant on the other. The sample was made up of 1,500 individuals (42.8% men and 57.2% women) aged between 18 and 72 years. Results indicated that all the socio-demographic variables as well as the traits neuroticism, openness to experience, and obsessiveness were important in the prediction of the overall frequency of positive sexual cognitions. Age and neuroticism, openness to experience, and obsessiveness were found to be significant in the prediction of the overall frequency of negative sexual cognitions. When considering the content of sexual cognitions (i.e., intimate, exploratory, sadomasochistic, and impersonal), all the socio-demographic variables and the trait openness to experience were found to be significantly related to all types of positive sexual cognitions. Neither socio-demographic variables nor personality traits were significantly associated with the content of negative sexual cognitions. Overall, this study highlights the weakness of the predictive value of personality traits on sexual cognitions, especially those experienced as being negative. We discuss the usefulness of classic personality traits in predicting sexual aspects.