当代大学生职业兴趣结构特征研究

通过实际调查对当代大学生的职业兴趣结构特征进行了分析研究。用主成分分析方法以及多维尺度方法探索出当代在学生的职业兴趣有三个潜在维度,可以分为18类。利用RANDALL和18RQNd96程序检验职业兴趣结构,构建了职业兴趣的球形模型。     

消费者购买决定与前额叶脑活动的不对称性——来自EEG的证据

摘要：目前营销科学界对消费者购买决策阶段的神经运作机制仍然缺乏准确的理解,本研究探索了大脑前额叶不对称性是否与消费者购买决策有关。采用田野实验方法,21名受试者在真实的在线购物中,分别对自己购物车里5件物品做出真实的购买决策,他们的EEG脑电图被完整记录了下来,共观察到104次购买决策过程。结果表明,在产品页面观看过程中,受试者的gamma频带前额叶不对称,与随后真实购买行为显著相关。alpha、beta频带额叶不对称与购买决策无关。结果还发现与商品的售价对beta和gamma不对称指数的调节效应,且调节效果正好相反。本研究发现了消费者在购买决策过程中一种独特的EEG成分——前额叶gamma波不对称指数（PAIγ）与肯定的购买意愿和实际购买行为相关性最强。     

资源两难问题的研究及其新进展

作为多人社会两难问题的基本范式之一,近年来资源两难问题引起了包括社会学家、经济学家和心理学家在内诸多领域研究者越来越多的关注。文章介绍了资源两难问题的定义、特点、研究意义并总结了以往的研究成果,着重介绍了Samuelson和Messick于1994年提出的资源两难决策模型。在此基础上进一步指出以往研究存在的一些不足,如忽视了对不确定性的研究等。文章最后指出了近年来有关资源两难问题的研究在心理学界的发展,并提出了对以后研究的一些建议。     

Working on Gender as a Gender-Nonconformist

No abstract available for this article.     

Different Effects of Cognitive Shifting and Intelligence on Creativity

The relationship between creativity and executive control has long been controversial. Some researchers view creative thinking as a defocused process with little executive control involvement, whereas others claim that executive control plays a vital role in creative thinking. In this study, we focused on one subcomponent of executive control, cognitive shifting, and examined its relationship with creativity by using latent variable analysis and structural equation modeling. We also analyzed whether this relation was mediated by intelligence. The results showed that: (a) cognitive shifting ability had a positive relationship with creativity, but only on the quantitative aspects (fluency and flexibility); (b) Intelligence had a positive relationship with both quantitative and qualitative aspects (originality) of creativity, and its effect on qualitative aspect was stronger than that on the quantitative aspect; (c) There was a mediating effect of intelligence on the relationship between creativity cognitive and shifting.     

Training analysis and training models

This article has the intention of giving impulses to a hopefully more creative and open-minded discussion of the function of training analysis and the kind of training model we seem to take for granted in the Scandinavian countries, the Eitingon model. I will consider some historical background data on this model as well as some historical data on the development in France which have created an alternative model of training. Especially in Sweden, there has been a stormy debate during the last years about the training analyst functions and some members have actually decided to leave our association because of this. I would like to try to shift focus from just a ‘yes-or-no-perspective’? to a broader question of our psychoanalytical society’s purpose and structure and how the training-model could sustain it.     

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.     

The Power to Detect and Predict Individual Differences in Intra-Individual Variability Using the Mixed-Effects Location-Scale Model

Our goal is to provide empirical scientists with practical tools and advice with which to test hypotheses related to individual differences in intra-individual variability using the mixed-effects location-scale model. To that end, we evaluate Type I error rates and power to detect and predict individual differences in intra-individual variability using this model and provide empirically-based guidelines for building scale models that include random and/or systematically-varying fixed effects. We also provide two power simulation programs that allow researchers to conduct a priori empirical power analyses. Our results aligned with statistical power theory, in that, greater power was observed for designs with more individuals, more repeated occasions, greater proportions of variance available to be explained, and larger effect sizes. In addition, our results indicated that Type I error rates were acceptable in situations when individual differences in intra-individual variability were not initially detectable as well as when the scale-model individual-level predictor explained all initially detectable individual differences in intra-individual variability. We conclude our paper by providing study design and model building advice for those interested in using the mixed-effects location-scale model in practice.     

Testing Process Factor Analysis Models Using the Parametric Bootstrap

Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.     

Misspecification in Latent Change Score Models: Consequences for Parameter Estimation,Model Evaluation,and Predicting Change

Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS. When constraints were incorrectly applied, several parameters, most notably the slope (i.e., constant change) factor mean and autoproportion coefficient, were severely and consistently biased, as were regression paths to the slope factor when external predictors of change were included. Standard fit indices indicated that the misspecified models fit well, partly because mean level trajectories over time were accurately captured. Loosening constraint improved the accuracy of parameter estimates, but estimates were more unstable, and models frequently failed to converge. Results suggest that potentially common sources of misspecification in LCS can produce distorted impressions of developmental processes, and that identifying and rectifying the situation is a challenge.