延迟学习判断研究的发展与展望

延迟学习判断是学习判断的一种形式,是指在材料学习完以后间隔一段时间才发生的学习判断。在与即时学习判断的对比研究中发现,延迟学习判断具有较高的相对准确性,这种现象被称为延迟学习判断效应。研究者进行了大量的研究并提出了多种理论来解释这种延迟学习判断效应。随着研究的不断深入,延迟学习判断的研究从研究指标、研究方法甚至是研究的理论基础都在不断更新。延迟学习判断的研究进展,包括主要理论和相关实验,以及最新研究成果将被介绍。最后,文章梳理了延迟JOL的研究进程,并指出了未来的研究方向。     

内隐联想测验“内隐性”的可控性研究

在许多社会科学研究中,社会称许性会对研究的效度产生不良影响。研究者希望能得到被试的真实态度和反应,从而极力避免这种社会称许性偏见。内隐测量工具的一个主要目的是让研究者能够获得未加任何粉饰的态度测量结果从而消除和避免自我表现和社会称许性。本文通过四个实验考察了内隐联想测验的可掩饰性、掩饰的持久性、掩饰的条件化和IAT经验的迁移性,结果显示：（1）被试可以通过一定的反应策略来掩饰其真实的内隐态度,从而使所测得的个体内隐态度和群体内隐态度失真,典型的反应策略是均衡相容和不相容任务的反应速度;（2）内隐联想测验的可掩饰性需以先前的IAT经验为基础,且IAT经验无需属于相同的测验范畴,对IAT原理的掌握将有助于被试成功地实施掩饰;（3）内隐联想测验的可掩饰性具有一定的时效性,先前的IAT经验越久远,则成功掩饰的可能性越低;总之,内隐联想测验并不能彻底避免社会称许性。     

Investigation of {111} stacking faults and nanotwins in epitaxial BaTiO3 thin films by high-resolution transmission electron microscopy

{111} stacking faults and nanotwins in epitaxial BaTiO₃ thin films on MgO substrates have been investigated by high-resolution transmission electron microscopy. In many cases, the stacking faults and nanotwins were found to be accompanied by partial dislocations. These partial dislocations can be classified as two different types, analogous to the situation in the fcc structure. One is of the Shockley type with the Burgers vector (a/3)<112>. The other is of the Frank type with the Burgers vector (a/3)<111>. The movements of both types of partial can lead to the {111} stacking faults and the {111} twins observed in these films.     

Stair-rod dislocations in perovskite films on LaAlO3 substrates

Two types of stair-rod dislocation formed at junctions of {111} stacking faults in SrTiO₃-SrRuO₃ two-layer films on LaAlO₃ substrates have been studied by high-resolution transmission electron microscopy. The first type, formed by the interaction of two stacking faults situated at the flat SrRuO₃-LaAlO₃ interface without any step, has a Burgers vector (a/3)<110>. The other type, formed by the interaction of two stacking faults, of which one starts from the SrRuO₃-LaAlO₃interface area involving an interface step, has a Burgers vector (a/3)<120>. The formation mechanism of the stair-rod dislocations and the effect of the step are discussed.     

Buying Life Experiences for the “Right” Reasons: A Validation of the Motivations for Experiential Buying Scale

Although numerous studies have demonstrated the hedonic benefits of spending money on life experiences instead of material possessions, there has been no attempt to determine how different motivations for experiential consumption relate to psychological need satisfaction and well-being. Across five studies (N = 931), guided by self-determination theory, we developed a reliable and valid measure of motivation for experiential consumption—the Motivation for Experiential Buying Scale—to test these relations. Those who spend money on life experience for autonomous reasons (e.g., “because they are an integral part of my life”) report more autonomy, competence, relatedness, flourishing, and vitality; however, those who spend money on life experiences for controlled (e.g., “for the recognition I’ll get from others”) or amotivated reasons (e.g., “I don’t really know”) reported less autonomy, competence, and relatedness. These results demonstrated that the benefits of experiential consumption depend on why one buys life experiences.     

Mixture Factor Analysis for Approximating a Nonnormally Distributed Continuous Latent Factor With Continuous and Dichotomous Observed Variables

Mixture factor analysis is examined as a means of flexibly estimating nonnormally distributed continuous latent factors in the presence of both continuous and dichotomous observed variables. A simulation study compares mixture factor analysis with normal maximum likelihood (ML) latent factor modeling. Different results emerge for continuous versus dichotomous outcomes. For dichotomous outcomes, normal ML path estimates have bias that worsens as latent factor skew/kurtosis increases and does not diminish as sample size increases, whereas the mixture factor analysis model produces nearly unbiased estimators as sample sizes increase (500 and greater) and offers near nominal coverage probability. For continuous outcome variables, both methods produce factor loading estimates with minimal bias regardless of latent factor skew, but the mixture factor analysis is more efficient. The method is demonstrated using data motivated by a study on youth with cystic fibrosis examining predictors of treatment adherence. In summary, mixture factor analysis provides improvements over normal ML estimation in the presence of skewed/kurtotic latent factors, but due to variability in the estimator relating the latent factor to dichotomous outcomes and computational issues, the improvements were only fully realized, in this study, at larger sample sizes (500 and greater).     

Relative Leader–Member Exchange Within Team Contexts: How and when Social Comparison Impacts Individual Effectiveness

A multilevel model was developed to examine how and when a focal individual's leader–member exchange (LMX) relative to the LMXs of coworkers within the team (relative LMX, or RLMX) influences individual in‐role performance, organizational citizenship behavior (OCB), and job satisfaction. Results, based on a sample of 275 leader–member dyads within 35 teams of a beverage company, largely supported the hypotheses. Specifically, using multilevel polynomial regression analyses, the results showed that self‐efficacy partially mediated the relationship between RLMX and in‐role performance and job satisfaction, and fully mediated the relationship between RLMX and OCB. Furthermore, the results demonstrated that team identification attenuated RLMX's direct effect on self‐efficacy, and indirect effects on in‐role performance and OCB and team supportive behavior attenuated RLMX's direct effect on self‐efficacy and indirect effect on in‐role performance.     

A Comparison of Imputation Strategies for Ordinal Missing Data on Likert Scale Variables

This article compares a variety of imputation strategies for ordinal missing data on Likert scale variables (number of categories = 2, 3, 5, or 7) in recovering reliability coefficients, mean scale scores, and regression coefficients of predicting one scale score from another. The examined strategies include imputing using normal data models with naïve rounding/without rounding, using latent variable models, and using categorical data models such as discriminant analysis and binary logistic regression (for dichotomous data only), multinomial and proportional odds logistic regression (for polytomous data only). The result suggests that both the normal model approach without rounding and the latent variable model approach perform well for either dichotomous or polytomous data regardless of sample size, missing data proportion, and asymmetry of item distributions. The discriminant analysis approach also performs well for dichotomous data. Naïvely rounding normal imputations or using logistic regression models to impute ordinal data are not recommended as they can potentially lead to substantial bias in all or some of the parameters.     

Psychological contracts and innovative behavior: a moderated path analysis of work engagement and job resources

This study explored the relationships between psychological contract types and innovative behavior. We focused on the mediating effect of work engagement and the moderating effect of job resources (organizational and social resources). Participants were 267 dyads of research and development engineers and their supervisors from 30 high‐tech companies. Moderated path analysis was used to test the hypotheses. Findings showed that: (a) work engagement fully mediated the negative relationship between transactional contracts and innovative behavior; in addition, it also fully mediated the positive relationship between relational contracts and innovative behavior; and (b) job resources attenuated the former and strengthened the latter mediating effects. These findings contribute to understanding how, why, and when psychological contract types lead to innovative behavior via work engagement.