初中生考试成绩归因模式研究

在广泛调查初中生对考试成绩的归因和情感反应的基础上,根据Ｗｅｉｎｅｒ的动机和情绪的归因理论,提出了初中生对考试成绩归因的假设模式,然后运用协方差结构模型的统计方法对这个假设模型进行了检验．虽然最初模型经ｘ２拟合度检验被拒绝,但经设定探查产生的最终模型较合适地拟合了数据,并部分支持了Ｗｅｉｎｅｒ的归因理论．按照这个模型,努力归因一内源性维度对初中生的自信心、期望、成就动机产生积极的影响．     

Effect analysis and causation in linear structural equation models

This paper considers total and direct effects in linear structural equation models. Adopting a causal perspective that is implicit in much of the literature on the subject, the paper concludes that in many instances the effects do not admit the interpretations imparted in the literature. Drawing a distinction between concomitants and factors, the paper concludes that a concomitant has neither total nor direct effects on other variables. When a variable is a factor and one or more intervening variables are concomitants, the notion of a direct effect is not causally meaningful. Even when the notion of a direct effect is meaningful, the usual estimate of this quantity may be inappropriate. The total effect is usually interpreted as an equilibrium multiplier. In the case where there are simultaneity relations among the dependent variables in tghe model, the results in the literature for the total effects of dependent variables on other dependent variables are not equilibrium multipliers, and thus, the usual interpretation is incorrect. To remedy some of these deficiencies, a new effect, the total effect of a factorX on an outcomeY, holding a set of variablesF constant, is defined. When defined, the total and direct effects are a special case of this new effect, and the total effect of a dependent variable on a dependent variable is an equilibrium multiplier.For helpful comments, I am grateful to G. Arminger, K. Bollen, W. Faris, R. m. Hauser, T. Petersen, three anonymous Psychometrikas reviewers, and the Editor. For computational assistance, I am grateful to B. D. Kim.     

Confirmation of linear system theory prediction: Rate of change of Herrnstein's kappa as a function of response-force requirement

Four human subjects worked on all combinations of five variable-interval schedules and five reinforcer magnitudes (¢/reinforcer) in each of two phases of the experiment. In one phase the force requirement on the operandum was low (1 or 11 N) and in the other it was high (25 or 146 N). Estimates of Herrnstein's κ were obtained at each reinforcer magnitude. The results were: (1) response rate was more sensitive to changes in reinforcement rate at the high than at the low force requirement, (2) κ increased from the beginning to the end of the magnitude range for all subjects at both force requirements, (3) the reciprocal of κ was a linear function of the reciprocal of reinforcer magnitude for seven of the eight data sets, and (4) the rate of change of κ was greater at the high than at the low force requirement by an order of magnitude or more. The second and third findings confirm predictions made by linear system theory, and replicate the results of an earlier experiment (McDowell & Wood, 1984). The fourth finding confirms a further prediction of the theory and supports the theory's interpretation of conflicting data on the constancy of Herrnstein's κ.     

Reanalysis of the equation for simple action

De Villiers and Herrnstein (1976) have shown that the equation for simple action, derived from the matching law, predicts change in behavioral output for some 40 experiments in which the value of a single source of reinforcement has been varied. Using only the positive-reinforcement studies they cite that used five or more different reinforcement values, we found the high percentage of variance they report accommodated by this equation (94%) is predicated on instances of averaging rates of behavioral output before making a least-squares fit of the equation. In our reanalysis, which minimizes rate averaging, only 78% of the data variance is accommodated. This diminished data-variance accommodation can be improved by adding parameters that permit the equation's scaling constant to change as a function of reinforcement. Although these parameters permit acceptable levels of accommodation of data variance, they correspond to no obvious psychological processes. These findings support the view that the equation for simple action is an inadequate model for behavioral output.     

A Bayesian Approach to More Stable Estimates of Group-Level Effects in Contextual Studies

Multilevel analyses are often used to estimate the effects of group-level constructs. However, when using aggregated individual data (e.g., student ratings) to assess a group-level construct (e.g., classroom climate), the observed group mean might not provide a reliable measure of the unobserved latent group mean. In the present article, we propose a Bayesian approach that can be used to estimate a multilevel latent covariate model, which corrects for the unreliable assessment of the latent group mean when estimating the group-level effect. A simulation study was conducted to evaluate the choice of different priors for the group-level variance of the predictor variable and to compare the Bayesian approach with the maximum likelihood approach implemented in the software Mplus. Results showed that, under problematic conditions (i.e., small number of groups, predictor variable with a small ICC), the Bayesian approach produced more accurate estimates of the group-level effect than the maximum likelihood approach did.     

Impact of School Psychological Environment Variables on Happiness of Korean Youths

Abstract: This study analyzed the structural relationships between school‐related psychological variables (teacher support, learning flow, friendship) and the happiness of Korean youths, and compared structural equation models for middle and high schools. Questionnaire data were collected from students in middle and high schools in 10 cities and provinces of Korea. The final data set from 870 students was analyzed with Amos 20.0. Goodness of fit indices for the hypothetical model were good, and all paths were significant. That is, a model incorporating indirect and direct impact of school‐related psychological environment variables, such as teacher support, learning flow, and friendship, on the happiness of Korean youths is valid. Second, structural equation models for middle and high schools demonstrated significant differences. Therefore, different types of assistance are needed to increase the happiness of students in middle schools and high schools.     

Toward resolution of a longstanding controversy in personality disorder diagnosis: Contrasting correlates of schizoid and avoidant traits

Although the construct validity and clinical utility of separate schizoid and avoidant personality disorder (PD) categories has been controversial since avoidant PD was first introduced in DSM-III, few studies have compared individuals with schizoid versus avoidant features on variables relevant to their contrasting personality dynamics. Those few investigations that exist have yielded inconclusive results. In this study a mixed-sex sample of nonclinical participants (N = 123) completed the International Personality Disorder Examination Screening Questionnaire (IPDE-SQ) and self-report measures of attachment style, defense style, empathy, internalized shame, need to belong, rejection sensitivity, and social anhedonia. High levels of social anhedonia were uniquely predictive of schizoid features; high levels of need to belong and internalized shame were uniquely predictive of avoidance. These findings support retaining the two PD categories in future versions of the DSM. Supplementary analyses revealed that among women—but not men—schizoid and avoidant traits were positively and significantly intercorrelated; it may be that women show more of a blended schizoid–avoidant profile, whereas men display the more prototypical categorical profile where either schizoid or avoidant features predominate.     

Virtual Levels and Role Models: N-Level Structural Equations Model of Reciprocal Ratings Data

A general latent variable modeling framework called n-Level Structural Equations Modeling (NL-SEM) for dependent data-structures is introduced. NL-SEM is applicable to a wide range of complex multilevel data-structures (e.g., cross-classified, switching membership, etc.). Reciprocal dyadic ratings obtained in round-robin design involve complex set of dependencies that cannot be modeled within Multilevel Modeling (MLM) or Structural Equations Modeling (SEM) frameworks. The Social Relations Model (SRM) for round robin data is used as an example to illustrate key aspects of the NL-SEM framework. NL-SEM introduces novel constructs such as ‘virtual levels’ that allows a natural specification of latent variable SRMs. An empirical application of an explanatory SRM for personality using xxM, a software package implementing NL-SEM is presented. Results show that person perceptions are an integral aspect of personality. Methodological implications of NL-SEM for the analyses of an emerging class of contextual- and relational-SEMs are discussed.     

Accounting for Missing Correlation Coefficients in Fixed-Effects MASEM

Meta-analytic structural equation modeling (MASEM) is increasingly applied to advance theories by synthesizing existing findings. MASEM essentially consists of two stages. In Stage 1, a pooled correlation matrix is estimated based on the reported correlation coefficients in the individual studies. In Stage 2, a structural model (such as a path model) is fitted to explain the pooled correlations. Frequently, the individual studies do not provide all the correlation coefficients between the research variables. In this study, we modify the currently optimal MASEM-method to deal with missing correlation coefficients, and compare its performance with existing methods. This study is the first to evaluate the performance of fixed-effects MASEM methods under different levels of missing correlation coefficients. We found that the often used univariate methods performed very poorly, while the multivariate methods performed well overall.     

Developmental models for children's temperament: alternatives to chronometric polynomial curves

Partridge and Lerner (2007), in a secondary analysis of the New York Longitudinal Study, employed a chronometric polynomial growth curve model to argue that the developmental course of difficult temperament follows a non‐linear trajectory over the first 5 years of life. The free curve slope intercept (FCSI) growth curve model of Meredith and Tisak (1990) is presented as a preferable conceptual alternative because it contains a number of currently popular statistical models, including repeated measures multivariate analysis of variance, factor mean, linear growth, linear factor analysis, and hierarchical linear models as special cases. As such, researchers can compare the fit of each of these models relative to the FCSI model, and, at times, to each other. The present paper conducts a re‐analysis of the data, and establishes that fit of the FCSI model is arguably better than other statistical alternatives. The FCSI model is also used as the basis for identifying subgroups of individuals with their qualitatively distinct growth patterns within a growth mixture modeling framework. Copyright © 2010 John Wiley & Sons, Ltd.