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.     

Mapclus: A mathematical programming approach to fitting the adclus model

We present a new algorithm, MAPCLUS (MAthematicalProgrammingCLUStering), for fitting the Shepard-Arabie ADCLUS (forADditiveCLUStering) model. MAPCLUS utilizes an alternating least squares method combined with a mathematical programming optimization procedure based on a penalty function approach, to impose discrete (0,1) constraints on parameters defining cluster membership. This procedure is supplemented by several other numerical techniques (notably a heuristically based combinatorial optimization procedure) to provide an efficient general-purpose computer implemented algorithm for obtaining ADCLUS representations. MAPCLUS is illustrated with an application to one of the examples given by Shepard and Arabie using the older ADCLUS procedure. The MAPCLUS solution uses half as many clusters to achieve nearly the same level of goodness-of-fit. Finally, we consider an extension of the present approach to fitting a three-way generalization of the ADCLUS model, called INDCLUS (INdividualDifferencesCLUStering).We are indebted to Scott A. Boorman, W. K. Estes, J. A. Hartigan, Lawrence J. Hubert, Carol L. Krumhansl, Joseph B. Kruskal, Sandra Pruzansky, Roger N. Shepard, Edward J. Shoben, Sigfrid D. Soli, and Amos Tversky for helpful discussions of this work, as well as the anonymous referees for their suggestions and corrections on an earlier version of this paper. We are also grateful to Pamela Baker and Dan C. Knutson for technical assistance. The research reported here was supported in part by LEAA Grant 78-NI-AX-0142 and NSF Grants SOC76-24512 and SOC76-24394.     

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.     

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.     

A parametric procedure for ultrametric tree estimation from conditional rank order proximity data

The psychometric and classification literatures have illustrated the fact that a wide class of discrete or network models (e.g., hierarchical or ultrametric trees) for the analysis of ordinal proximity data are plagued by potential degenerate solutions if estimated using traditional nonmetric procedures (i.e., procedures which optimize a STRESS-based criteria of fit and whose solutions are invariant under a monotone transformation of the input data). This paper proposes a new parametric, maximum likelihood based procedure for estimating ultrametric trees for the analysis of conditional rank order proximity data. We present the technical aspects of the model and the estimation algorithm. Some preliminary Monte Carlo results are discussed. A consumer psychology application is provided examining the similarity of fifteen types of snack/breakfast items. Finally, some directions for future research are provided.     

Ordinal network representation: Representing proximities by graphs

Ordinal network representations are graph-theoretic representations of proximity data. They seek to provide parsimonious representations of the ordinal (nonmetric) information in observed proximity data by means of the minimum-path-length distance of a connected and weighted graph. In contrast to traditional tree-based graph-theoretic approaches, ordinal network representation is not limited to but includes the representation by trees. Asymmetry in the proximity data and violations of zero-minimality are allowed for. The paper explores fundamental representation and uniqueness results and discusses a method of constructing ordinal network representations. A simple strategy for handling the problem of errors in the data is described and illustrated.This work was supported by grant Fe 75/20-2 of the Deutsche Forschungsgemeinschaft. The author is indebted to Hubert Feger for many inspiring discussions.     

关于假设-验证和范畴化认知方式与学业不良的相关研究

该研究采用自行设计的能力问卷量表,结合教师问卷、个案访谈和调查等方法对80名小学四年级学生、94名初中一年级学生、85名高中一年级学生,共259名被试进行了假设一验证和范畴化认知方式与学业不良关系的研究。结果表明：各学习年限段学业不良学生在这两方面的得分均低于学业优秀者,且差异显著;各学习年限段学业不良学生间在这两方面的得分差异显著,呈随年龄增长而增长的趋势。