人工语法学习中习得知识的分离:基于信号检测论和结构知识的视角

习得知识的意识觉知问题一直以来是内隐学习领域的研究热点和难点。以加工分离程序来分解击中率和虚报率、以结构知识中意识和无意识测量为视角,采用2（测验方式：SDTT,SKT）×2（学习程度：30试次,60试次）混合设计,试图去分离人工语法学习中习得的意识知识和无意识知识。结果发现：（1）学习程度影响到习得的无意识知识,但还未影响到习得的意识知识;（2）在分离人工语法学习的习得知识上,SDTT的敏感性高于SKT;（3）SKT夸大了元认知中的意识成分。     

预测视角下双因子模型与高阶因子模型的一般性模拟比较

高阶因子模型本质上是一种特殊的双因子模型, 应用中却常被当做双因子模型的竞争模型。已有研究以满足比例约束的双因子模型(此时等价于一个高阶因子模型)为真实测量模型产生模拟数据, 比较了用双因子模型和高阶因子模型作为测量模型的预测效果。本文使用不满足比例约束的双因子模型(此时不与任何高阶因子模型等价)为真实测量模型产生模拟数据进行比较, 所得结果与满足比例约束的双因子模型的结果有很大差别, 双因子模型结构系数的相对偏差较小、检验力较高, 但第Ⅰ类错误率略高。结论是, 在比例约束条件成立时可以使用高阶因子模型, 否则, 从统计角度看, 一般情况下使用双因子模型进行预测比较好。     

Structural Analysis of the Trauma Symptom Inventory–2

The Trauma Symptom Inventory (TSI; Briere, 1995), a measure of the psychological effects of trauma and other adverse events, was recently expanded and updated (TSI–2; Briere, 2011). This study evaluated 4 competing models of TSI–2 dimensionality and determined the predictive validity of the best fitting solution. Data were collected from 679 adults in the general population. Confirmatory factor analysis indicated that a 4-factor solution best fit the data. All 4 factors of the final model were significant predictors of exposure to trauma or some other very upsetting event, especially posttraumatic stress. Additional research is indicated to further probe the characteristics of the TSI–2, including its dimensionality in other groups.     

Exploring Factor Model Parameters across Continuous Variables with Local Structural Equation Models

Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23 years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.     

Rejoinder: Remaining Challenges in Investigating Grade-Retention Effectiveness

This rejoinder, in response to the commentaries of Steiner, Park, and Kim (this issue) and Reshetnyak, Cham, and Hughes (this issue), discusses remaining challenges in grade retention research. First, a same-age comparison assumes that the instruments used in different grades measure ability equally well. We discuss the importance of evaluating the properties of the scaling process to address whether this assumption has been met. Second, we discuss issues in the selection of covariates to be included in the weights. Third, we discuss the unconfoundedness assumption and the problem of remaining imbalance. Finally, we provide an empirical illustration showing that studying grade-retention effectiveness comes with multiple methodological decisions that are rooted in a bias–variance trade-off.     

Using Marginal Structural Modeling for Grade Retention Effects

Vandecandelaere, Vansteelandt, De Fraine, and Van Damme (this issue) described marginal structural modeling (MSM) and used it to estimate the effects of a time-varying intervention, retention (holding back) in school grades, on students' math achievement. This commentary supplements Vandecandelaere et al. (this issue) and discusses several topics in retention studies and MSM. First, we discuss the importance of equating time-varying confounders in retention studies. Second, we discuss same-grade and same-age comparisons in retention studies. Third, we discuss one important section in the authors' overview of MSM: why standard methods (e.g., ANCOVA, propensity score analysis) cannot properly adjust for time-varying confounders. Finally, using the grade retention analyses in Vandecandelaere et al. (this issue) as an example, we provide our insights on four aspects of MSM: (a) covariate selection, (b) estimation of weights, (c) evaluation of balance properties, and (d) missing data handling.     

家庭环境对中学生阅读能力的影响:阅读投入、阅读兴趣的中介作用

采用学生阅读能力测试卷和学生阅读状况调查问卷,对随机选取的559名中学生进行调查,并运用结构方程模型探讨了家庭环境影响阅读能力的内在机制。结果发现,家庭环境影响阅读投入的路径系数为0.18,阅读投入影响阅读兴趣的路径系数为0.51,阅读兴趣影响阅读投入的路径系数为0.80,阅读投入影响阅读能力的路径系数为0.46,各路径系数均达到显著水平(p0.05)。结果表明,(1)家庭环境通过阅读投入的完全中介作用影响阅读兴趣;阅读兴趣通过阅读投入的完全中介作用影响阅读成绩;(2)家庭环境对阅读能力的影响是通过阅读投入和阅读兴趣的循环互动作用实现的。     

Psychological ownership in relation to workplace trust and turnover intent

The study investigated the relationship between workplace trust, psychological ownership and turnover intent within a South African professional services organisation. A cross-sectional quantitative survey design was used to collect data from a purposive sample (n = 302) of skilled, highly skilled and professional employees in a professional services industry. The participants completed the Psychological Ownership Questionnaire, Workplace Trust Survey, and Turnover Intentions Scale. The results from structural equation modelling (SEM) suggest psychological ownership to be significantly related to workplace trust (positively) and turnover intent (negatively). In addition, the findings indicate psychological ownership to fully mediate the relationship between workplace trust and turnover intent. By implication, work environments that fostered workplace trust would increase the level of psychological ownership that employees’ experienced, which, in turn, would reduce employees’ intent to leave their workplace.