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In the past, several assessment reports on writing repeatedly showed that elementary school students do not develop the essential writing skills to be successful in school. In this respect, prior research has pointed to the fact that cognitive and motivational challenges are at the root of the rather basic level of elementary students' writing performance. Additionally, previous research has revealed gender and achievement-level differences in elementary students' writing. In view of providing effective writing instruction for all students to overcome writing difficulties, the present study provides more in-depth insight into (a) how cognitive and motivational challenges mediate and correlate with students' writing performance and (b) whether and how these relations vary for boys and girls and for writers of different achievement levels. In the present study, 1,577 fifth- and sixth-grade students completed questionnaires regarding their writing self-efficacy, writing motivation, and writing strategies. In addition, half of the students completed two writing tests, respectively focusing on the informational or narrative text genre. Based on multiple group structural equation modeling (MG-SEM), we put forward two models: a MG-SEMmodel for boys and girls and a MG-SEMmodel for low, average, and high achievers. The results underline the importance of studying writing models for different groups of students in order to gain more refined insight into the complex interplay between motivational and cognitive challenges related to students' writing performance. ??????????????????,?????????????????????????????????????,??????????????????????????????,???????????????????????????????????????????????????,?????????? (a)??????????????????????,??????????????, (b) ???????????????????????,??????????????????????????????????????1577?5???6?????????????????????????????????,????????????????????????????????????????????(mg-sem)????,?????mg-sem??: ??????????????,????????????????,?????????????????????????????????????,???????????????????????????????????????? En el pasado, varios informes de evaluación de escritura repetidamente mostraban que los estudiantes de escuela primaria no desarrollan las habilidades de escritura esenciales para tener éxito en la escuela. En cuanto a esto, la investigación previa ha indicado el hecho que los desafíos cognoscitivos y de motivación son la causa del nivel bastante básico de la escritura de los estudiantes. Además, la investigación anterior ha revelado diferencias de género y en los niveles de logro en la escritura de estudiantes de escuela primaria. Para suministrar instrucción eficaz de escritura para vencer las dificultades de todos los estudiantes, el presente estudio provee un discernimiento más profundo en cuanto a (a) cómo los desafíos cognoscitivos y de motivación median y tienen correlación con la escritura de los estudiantes y (b) si es que estas relaciones varían, y cómo, entre muchachos y muchachas y para escritores de niveles diferentes de rendimiento. En este estudio, 1,577 estudiantes de quinto y sexto grado llenaron cuestionarios sobre su autoeficacia escritora, su motivación para escribir, y sus estrategias de escritura. Además, mitad de los estudiantes completaron dos pruebas de escritura, enfocados respectivamente en textos informativos o narrativos. Basado en el modelaje de ecuaciones estructurales para múltiples grupos (MG-SEM), propusimos dos modelos: un modelo MG-SEM para muchachos y muchachas y un modelo MG-SEM para cumplidores bajos, medianos y altos. Los resultados subrayan la importancia de estudiar modelos de escritura para diferentes grupos de estudiantes para poder obtener un discernimiento más profundo en cuanto a la interacción compleja entre los desafíos de motivación y cognoscitivos relacionados con la escritura de los estudiantes. ?? ??????? ????? ?????? ?? ?????? ??????? ???????? ?????? ??????? ?? ???? ??????? ?????????? ?? ?????? ?????? ??????? ???????? ??????? ?????? ?? ???????. ?? ??? ?????? ????? ?????? ??????? ??? ?? ???????? ???????? ?????????? ?? ?? ?????? ?????? ??????? ???? ?? ?? ???? ??????? ?????????? ??????. ???????? ??? ???? ???? ??????? ??????? ???????? ??? ??????? ???????? ?? ??????? ?????????? ??????. ?? ????? ????? ??????? ????? ??????? ????? ?????? ?????? ??? ?????? ???????? ???? ??? ??????? ???? ???? ???? ?? (?) ????? ???? ??????? ???????? ???????? ?????????? ?? ???? ??????? ?????? ? (?) ?? ??? ???? ??? ???????? ????? ??????? ?????? ???????? ????????? ?? ????? ??????? ???????. ?? ??????? ???????? ???? 577 1 ????? ?? ?????? ?????? ??????? ????????? ????? ???????? ??????? ?? ???????? ?????? ???????? ???????????? ???????. ????????? ??? ???? ???? ??? ?????? ???????? ???????? ?? ??????? ??? ????? ?????????? ?? ???????? ????. ???????? ??? “ ????? ???????? ???????? ???????? ???????? “ (MG-SEM)? ????? ???????: ????? MG-SEM ?????? ??????? ?????? MG-SEM ?????? ???????? ???????? ???????? ???????. ????? ??????? ????? ????? ????? ??????? ???????? ?????? ?? ?????? ?? ??? ?????? ??? ???? ???? ??? ?? ??????? ?????? ??? ???????? ????????? ????????? ???????? ????? ??????? ??? ??????. ????? ???? ??????????? ? ???????????? ????????????, ??? ??? ????????? ???????? ???????? ????????? ????? ???????? ?? ??????? ???????? ??????? ?????????? ????. ?????? ?????????? ???? ???????? ?????? ?????????? ???? ???????????? ??????????? ? ????????????? ????????, ? ????? ????????? ???????? ? ??????? ???????????? ? ?????. ??? ??????????? ????????? ?????????? ? ??????????? ???????????? ???????? ?????????? ???? ? ???? ???????? ????????????? ?????????? ????? ???????? ????????? (?) ?????????? ??????????? ? ????????????? ??????? ???????? ? ?? ???????? ? ??????; ? (?) ?????????? ?????????? ???? ? ?????????? ?????????? ? ????? ??????? ????????????. ? ?????? ???????????? 1577 ???????? ?????? ? ??????? ??????? ????????? ??????, ?????? ???????? ????? ?????????? ????, ????????? ? ?????? ? ??????????? ??? ?????? ?????????. ????? ????, ???????? ???????? ?????? ??? ?????: ?? ?????????????? ? ??????????? ????? ??????. ????????? ????????????? ? ??????? ????????????? ??????????? ????????? ??? ????? (MG-SEM), ?????? ??????????? ??? ??????: MG-SEM ??? ????????? ? ??????? ? MG-SEM ??? ?????, ?????? ? ?????? ?????????? ????????. ?????????? ???????????? ???????? ???????? ??????? ?????? ??? ?????? ????? ???????? – ?????? ??? ????? ???????? ????? ?????? ????????????? ? ??????? ?????????????? ????????????? ? ??????????? ???????, ????????? ? ?????????? ?????. Plusieurs rapports de recherché sur l'écriture ont montré par le passé à plusieurs reprises que des élèves de l'élémentaire ne développent pas les compétences d'écriture qui sont nécessaires pour réussir à l'école. De ce point de vue, les recherches antérieures ont montré que les défis cognitifs et motivationnels sont à la base du niveau assez basique des résultats des élèves d'école élémentaire en écriture. En outre, ces recherches ont mis en évidence des différences de genre et de niveau de réussite en écriture des élèves d'école élémentaire. Afin d'offrir un enseignement efficace de l'écriture à tous les élèves pour qu'ils surmontent leurs difficultés à écrire, la recherche présentée ici se propose d'approfondir (a) comment les défis cognitifs et motivationnels interviennent et sont liés aux résultats en écriture des élèves, (b) si et comment ces liens varient selon les garçons et les filles, et à différents niveaux d'écriture. Dans cette recherche, 1 577 élèves de 5 et 6eannée ont rempli des questionnaires concernant leur efficacité personnelle en écriture, leur motivation à écrire, et leurs stratégies d'écriture. En outre, les élèves ont répondu à deux tests d'écriture, une moitié centrée sur les textes de type informatif et l'autre sur les textes narratifs. En nous basant sur une modélisation d'équation structurale à groupes multiples (MG-SEM), nous avons mis en évidence deux modèles : un modèle MG-SEM pour les garçons et les filles, et un modèle MG-SEM pour les résultats faibles, moyens et bons. Les résultats soulignent qu'il est important de définir différents modèles d'écriture pour des élèves différents afin d'avoir une idée plus fine du jeu complexe des défis motivationnels et cognitifs et des différents niveaux de réussite des élèves.

Abstract.Theoretical researchers consider Structural Equation Modeling (SEM) to be the preferred method to study the relationships among latent variables. However, SEM has the disadvantage of requiring a large sample size, especially if the model is complex. Furthermore, since SEM estimates all parameters simultaneously, one misspecification in the model may influence the whole model. For these reasons, applied researchers often use a two-step Factor Score Regression (FSR) approach. In the first step, factor scores are calculated for the latent variables, which are used to perform a linear regression in the second step. However, this method results in incorrect regression coefficients. Croon (2002) developed a method that corrects for this bias. We combine this method of Croon (2002) with path analysis, resulting in Factor Score Path Analysis. This method results in correct path coefficients and has some advantages over SEM: it requires smaller sample sizes, can handle more complex models and the method is less sensitive to misspecifications, because of its stepwise nature. In conclusion, this method can be a suitable alternative for SEM, when one is dealing with a complex model and small sample sizes.

Abstract.Researchers in the social and behavioral sciences often have clear expectations about the order and/or the sign of the parameters in their statistical model. For example, a researcher might expect that regression coefficient β1is larger than regression coefficients β2and β3. To test such a constrained hypothesis special methods have been developed. However, the existing methods for structural equation models (SEM) are complex, computationally demanding, and a software routine is lacking. Therefore, in this paper we describe a general procedure for testing order/inequality constrained hypotheses in SEM using the Rpackage lavaan. We use the likelihood ratio (LR) statistic to test constrained hypotheses and the resulting plug-in pvalue is computed by either parametric or Bollen-Stine bootstrapping. Since the obtained plug-in pvalue can be biased, a double bootstrap approach is available. The procedure is illustrated by a real-life example about the psychosocial functioning in patients with facial burn wounds.

For more than 30 years, the Interpersonal Reactivity Index (IRI) has been used to measure the multidimensional aspects of empathy. But the 28-item, 4-factor model of Davis (1980) is currently contested because of methodological issues and for theoretical reasons. Confirmatory (CFA) and exploratory factor analyses (EFA) were applied in two French-speaking Belgian student samples (1,244 participants in the first and 729 in the second study) to test this model and to propose a shortened version. A non-optimal fit was found with respect to the CFI value (Study 1). By splitting the student group into two random subsamples, EFA and then CFA were used to propose a 15-item, 4-factor model with good fit indices. A CFA on the second student group (Study 2) replicated this model. Results are discussed considering the influence of social desirability response bias, an absence of strong invariance across sex and the usefulness of self-report scales to measure empathy.

AbstractBackground: Alexithymia is a disturbance in regulating affective states. Clinical observations suggest that alexithymic patients relate to others in a specific way. This paper explores whether specific relationship or transference patterns are typical of alexithymia. Sampling and Methods: Relationship patterns were assessed by means of the Core Conflictual Relationship Theme method, standard categories version. This method examines transference patterns and was applied to clinical interview data collected from a sample (n = 31) of mental health outpatients. Alexithymia was assessed by means of a score on the 20-item Toronto Alexithymia Scale corrected for the degree of depression (measured by the Beck Depression Inventory-II). Data were analysed by means of the leaps and bounds regression algorithm for selecting optimal subsets of indicators and by bootstrapping to determine 95% confidence intervals. Results: First, we observed that alexithymia can be meaningfully explained by typical wishes, typical subjective perceptions of how others respond and one’s own typical responses to others. This result indicates that the more marked a patient’s alexithymic traits are, the more probable it is that specific transference themes come to the fore. Second, a set of three core indicators of trait alexithymia was mapped: little concern about being good to others, a strong perception of others as cooperative, and weak levels of reacting to others and to conflict by means of somatic symptoms. Conclusion: Alexithymia is related to a double interpersonal indifference: not much is expected from others, nor is there a personal urge to fulfill the expectations of others. Moreover, in alexithymia somatic symptoms proved to be non-reactive to interpersonal situations. Implications for diagnosis and treatment are highlighted. Limitations of our study are that alexithymia was only assessed with a self-report measure and that conclusions are based only upon data from a heterogeneous mental health sample.Copyright © 2007 S. Karger AG, Basel

Abstract: In the study reported in this paper, we investigated the categorization of well-known and novel food items in the categories fruits and vegetables. Predictions based on Nosofsky’s (1984, 1986) generalized context model (GCM), on a multiplicative-similarity prototype model, and on an instantiation model as applied in Storms, De Boeck, and Ruts (2001) were compared. Despite suggestions in the literature that prototype models predict categorization from large categories better than exemplar models do, our results showed that the exemplar-based GCM yielded clearly better predictions than did a (multiplicative-similarity) prototype model.

Many currently popular models of categorization are either strictly parametric (e.g., prototype models, decision bound models) or strictly nonparametric (e.g., exemplar models) (F. G. Ashby & L. A. Alfonso-Reese, 1995, Journal of Mathematical Psychology, 39, 216–233). In this article, a family of semiparametric classifiers is investigated where categories are represented by a finite mixture distribution. The advantage of these mixture models of categorization is that they contain several parametric models and nonparametric models as a special case. Specifically, it is shown that both decision bound models (F. G. Ashby & W. T. Maddox, 1992, Journal of Experimental Psychology: Human Perception and Performance, 16, 598–612; 1993, Journal of Mathematical Psychology, 37, 372–400) and the generalized context model (R. M. Nosofsky, 1986, Journal of Experimental Psychology: General, 115, 39–57) can be interpreted as two extreme cases of a common mixture model. Furthermore, many other (semiparametric) models of categorization can be derived from the same generic mixture framework. In this article, several examples are discussed and a parameter estimation procedure for fitting these models is outlined. To illustrate the approach, several specific models are fitted to a data set collected by S. C. McKinley and R. M. Nosofsky (1995, Journal of Experimental Psychology: Human Perception and Performance, 21, 128–148). The results suggest that semi-parametric models are a promising alternative for future model development. [Copyright &y& Elsevier]

The present paper argues that category learning is both a data-driven and a knowledge-driven process. This is described in a generic model that distinguishes between categorical knowledge, conceptual knowledge, and implicit cognitive theories. The model assumes that each of these knowledge aspects may affect the process of category learning by affecting the way similarities between objects are perceived. This central assumption of the model is tested in two experiments. The first experiment shows that the presence or absence of prior categorical and conceptual knowledge affects the psychological stimulus space by changing the saliency of the stimulus dimensions. The second experiment uses these weights to predict the distribution of errors over the stimuli and the number of trials to criterion in category learning by other participants under the same knowledge conditions. We conclude that prior categorical and conceptual knowledge affect category learning by mediation of similarity perception, and discuss the implications of these results.