Missing data are pervasive in psychological and educational assessments. Naive remedies, including listwise deletion and item-mean imputation, often degrade research validity and misinform subsequent decisions. Recent advances in artificial neural networks have demonstrated their efficacy in prediction-related tasks by using observed features to infer unknown values. Building on this potential, we propose the columnwise neural imputation (COLNI) algorithm to impute missing ordinal responses in psychometric data. Simulation studies demonstrated that, when benchmarked against conventional methods, COLNI more accurately recovered item means, inter-item correlations, and person and item parameters under the multidimensional graded response model. We further evaluated COLNI using data from the Short Dark Triad test, confirming its effectiveness in a multidimensional empirical setting. We conclude with implementation guidelines and avenues for refining and extending this artificial neural network-based imputation approach in future research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Differential item functioning (DIF) analysis is essential for evaluating measurement invariance in educational and psychological assessments. In cognitive diagnostic assessment, however, most existing methods require prespecified comparison subgroups and anchor items. When subgroup membership and anchor items are unavailable or mis-specified, DIF detection and parameter estimation may be biased. To overcome these limitations, this study puts forward a DIF detection method that incorporates an extended modelling framework and a two-stage estimation algorithm. The proposed modelling framework directly integrates DIF parameters into the measurement model and uses a structural model to characterize subgroup differences in attribute mastery distributions. A two-stage expectation maximization algorithm with an adaptive lasso penalty is developed to identify anchor items, classify respondents into latent subgroups and estimate model parameters. The performance of the proposed method was evaluated through a simulation study and an empirical data analysis. Simulation results indicated generally satisfactory DIF detection and parameter recovery, although subgroup-classification accuracy varied across conditions. When applied to the empirical data, the proposed method identified 10 of the 28 items as exhibiting DIF.

Engagement with natural surroundings is essential in fostering the psychological connection between individuals and the environment, which can significantly improve people's mental health. This study adopted a comprehensive meta-analytic approach to examine the relationships between nature connectedness and nature contact, and between nature connectedness and positive psychological outcomes, respectively. In addition, the moderating effects of several variables (i.e., gender, age, geographic area, data collection method, measurement instrument, nature contact timing, and type of nature contact) were examined. The meta-analysis synthesized findings from 70 cross-sectional studies published between 2013 and 2024, comprising 68 peer-reviewed journal articles and 2 theses. The included studies exhibited considerable variation in sample sizes, ranging from 102 to 4960 participants. A three-level meta-analytic model was employed to analyze the pooled data, revealing a positive correlation between nature connectedness and nature contact. Participants with higher levels of nature connectedness exhibited greater well-being, enhanced meaning in life, and increased life satisfaction. The type of measurement instrument significantly moderated the relationship between nature connectedness and nature contact, with studies employing standardized scales reporting significantly larger effect sizes than those using single-item or question-based measures. Geographic area emerged as a significant moderator in the connection between nature connectedness, well-being, and meaning in life. Studies conducted in Asia reported larger effect sizes compared to those conducted in other regions. These findings highlight the importance of nature connectedness and its associations with both nature contact and positive psychological outcomes, offering valuable practical implications.

Online calibration is a key technology for calibrating new items in computerized adaptive testing (CAT). As multidimensional polytomous data become popular, online calibration methods applicable to multidimensional CAT with polytomously scored items (P-MCAT) have been proposed. However, the existing methods are mainly based on marginal MLE with an expectation-maximization algorithm (MMLE/EM), making it difficult to accurately estimate parameters in high-dimensional scenarios without sufficient calibration sample size or suitable initial values. To conquer these challenges, a neural network (NN)-based online calibration framework was put forward. The new NN-based methods differ profoundly from the traditional ones in that the parameter estimates of new items are obtained by learning the patterns between input and output data instead of finding solutions to the log-marginal likelihood. Moreover, an alternative solution was proposed for traditional methods to obtain appropriate initial values. Simulation studies were conducted to compare the NN- and MMLE/EM-based methods under various conditions, and further explore the properties of the NN-based methods. Results showed that both the NN-based methods and the alternative solution found their strengths in recovering the item parameters of new items, while the MMLE/EM-based methods struggled to converge when more than three dimensions were involved in the test.

The Q-matrix is a crucial component of cognitive diagnostic theory and an important basis for the research and practical application of cognitive diagnosis. In practice, the Q-matrix is typically developed by domain experts and may contain some misspecifications, so it needs to be refined using Q-matrix validation methods. Based on signal detection theory, this paper puts forward a new Q-matrix validation method (i.e., β method) and then conducts a simulation study to compare the new method with existing methods. The results show that when the model is DINA (deterministic inputs, noisy ‘and’ gate), the β method outperforms the existing methods under all conditions; under the generalized DINA (G-DINA) model, the method still has the highest validation rate when the sample size is small, and the item quality is high or the rate of Q-matrix misspecification is ≥.4. Finally, a sub-dataset of the PISA 2000 reading assessment is analysed to evaluate the reliability of the β method.

Response styles pose great threats to psychological measurements. This research compares IRTree models and anchoring vignettes in addressing response styles and estimating the target traits. It also explores the potential of combining them at the item level and total-score level (ratios of extreme and middle responses to vignettes). Four models were evaluated: three multidimensional IRTree models with different levels of using vignette data and a nominal response model (NRM) addressing extreme and midpoint response styles with item-level vignette responses. Simulation results indicated that the IRTree model using item-level vignette responses outperformed others in estimating the target trait and response styles to different extents, with performance improving as the number of vignettes increased. Empirical findings further demonstrated that models using item-level vignette information yielded higher reliability and closely aligned target trait estimates. These results underscore the value of integrating anchoring vignettes with IRTree models to enhance estimation accuracy and control for response styles.

Depression is the second most common mental disorder among adolescents worldwide. From the perspectives of emotional security theory and interpersonal acceptance–rejection theory, parental rearing behaviors impact adolescents’ depressive symptoms. The current study aims to uncover the underlying relationship mechanisms between different parental rearing behaviors and depressive symptoms. A sample of 372 junior high school students participated in this study. The results indicated that parental rejection and overprotection were associated with psychological insecurity and depressive symptoms, and parental emotional warmth was positively related to core self-evaluations. Paternal rejection had a more substantial impact on the lower distribution of depressive symptoms scores in the 25th percentile, while maternal rejection was significantly related to depressive symptoms in the 50th and 75th percentiles. Adolescents’ depressive symptoms were reduced when they had more paternal and maternal emotional warmth. Indirect effects of parenting behavior on depressive symptoms through core self-evaluations and psychological insecurity were found. Adolescents with higher levels of parental emotional warmth reported lower levels of psychological insecurity, which was related to higher levels of core self-evaluations; moreover, core self-evaluations were negatively related to depressive symptoms.

Although career construction theory has been utilized to understand how to construct the career process, it remains unclear how the various factors involved in this process interact comprehensively. In this study, we introduced the career construction network to explore the career development of junior high school students ( N = 372) and senior high school students ( N = 516) and its influencing factors, and investigated the adaptive readiness, adaptability resources, adapting responses, adaptation results and environmental factors within the network. The results showed that hope and proactive personality occupied influential positions in the network of junior high school students; hope exhibited the highest values of strength and closeness in the senior high school student sample; parental career-related behaviors, as the environmental factors, were located in the peripheral clusters of the two networks. In conclusion, this study provides insights and targeted intervention suggestions for adolescent career training methods.

The Q-matrix is a crucial component of cognitive diagnostic theory and an important basis for the research and practical application of cognitive diagnosis. In practice, the Q-matrix is typically developed by domain experts and may contain some misspecifications, so it needs to be refined using Q-matrix validation methods. Based on signal detection theory, this paper puts forward a new Q-matrix validation method (i.e., β method) and then conducts a simulation study to compare the new method with existing methods. The results show that when the model is DINA (deterministic inputs, noisy 'and' gate), the β method outperforms the existing methods under all conditions; under the generalized DINA (G-DINA) model, the method still has the highest validation rate when the sample size is small, and the item quality is high or the rate of Q-matrix misspecification is ≥.4. Finally, a sub-dataset of the PISA 2000 reading assessment is analysed to evaluate the reliability of the β method.

Physical activity among university students significantly influences anxiety, yet the underlying mechanisms require further exploration. This study examines mental toughness as a mediator and social support as a moderator to elucidate these relationships.MethodsUsing a cross-sectional design, convenient sampling was employed to select 997 first to fourth-year students from Jishou University for a self-reported survey. Measures included assessments of physical activity, mental toughness, social support, anxiety, and basic demographic variables. Descriptive statistics, correlations, and a moderated mediation model were conducted.ResultsPhysical activity was significantly negatively correlated with anxiety and positively correlated with mental toughness. Mental toughness was significantly negatively correlated with anxiety, mediating the relationship between physical activity and anxiety to a certain extent. Social support moderated the latter part of the mediated model pathways.ConclusionPhysical activity negatively correlates with anxiety among university students. Mental toughness mediates the relationship between physical activity and anxiety, while social support moderates the latter stages of this mediated model (mental toughness → anxiety).

This paper presents a novel approach known as the cross estimation network (CEN) for fitting the datasets obtained from psychological or educational tests and estimating the parameters of item response theory (IRT) models. The CEN is comprised of two subnetworks: the person network (PN) and the item network (IN). The PN processes the response pattern of individual respondent and generates an estimate of the underlying ability, while the IN takes in the response pattern of individual item and outputs the estimates of the item parameters. Four simulation studies and an empirical study were comprehensively and rigorously conducted to investigate the performance of CEN on parameter estimation of the two-parameter logistic model under various testing scenarios. Results showed that CEN effectively fit the training data and produced accurate estimates of both person and item parameters. The trained PN and IN adhered to AI principles and acted as intelligent agents, delivering commendable evaluations for even unseen patterns of new respondents and items.

This study examines future work self as a mediator and future time perspective as a moderator in the relationship between career maturity and career adaptability, from both theoretical and empirical perspectives. A sample of 636 Chinese vocational high school students was investigated. The results indicated that career maturity was associated with a clearly envisioned future work self, which in turn was related to high career adaptability. The interaction effect of career maturity and future time perspective was significantly associated with career adaptability. These results offer valuable insights for career development among vocational high school students.

Online calibration is a key technology for item calibration in computerized adaptive testing (CAT) and has been widely used in various forms of CAT, including unidimensional CAT, multidimensional CAT (MCAT), CAT with polytomously scored items, and cognitive diagnostic CAT. However, as multidimensional and polytomous assessment data become more common, only a few published reports focus on online calibration in MCAT with polytomously scored items (P-MCAT). Therefore, standing on the shoulders of the existing online calibration methods/designs, this study proposes four new P-MCAT online calibration methods and two new P-MCAT online calibration designs and conducts two simulation studies to evaluate their performance under varying conditions (i.e., different calibration sample sizes and correlations between dimensions). Results show that all of the newly proposed methods can accurately recover item parameters, and the adaptive designs outperform the random design in most cases. In the end, this paper provides practical guidance based on simulation results.

解释性项目反应理论模型(Explanatory Item Response Theory Models, EIRTM)是指基于广义线性混合模型和非线性混合模型构建的项目反应理论(Item Response Theory, IRT)模型。EIRTM能在IRT模型的基础上直接加入预测变量, 从而解决各类测量问题。首先介绍EIRTM的相关概念和参数估计方法, 然后展示如何使用EIRTM处理题目位置效应、测验模式效应、题目功能差异、局部被试依赖和局部题目依赖, 接着提供实例对EIRTM的使用进行说明, 最后对EIRTM的不足之处和应用前景进行讨论。

测验模式效应（Test Mode Effect, TME）是指同一测验采用不同测验形式施测而产生的测验功能差异。TME的存在会对测验公平、选拔标准和测验等值等产生影响，因此对TME进行准确检测和合理解释具有重要意义。通过对TME的来源、检测（包括实验设计和检测方法）以及研究结果进行系统梳理，全面展示TME研究的方法论。对TME模型进行进一步解释、对TME研究中的测验形式进行拓展以及将TME的研究成果应用于我国的大规模教育测评项目，都是TME领域的未来重要发展方向。

Computerized classification testing (CCT) commonly chooses items maximizing information at the cut score, which yields the most information for decision-making. However, a corollary problem is that all examinees will be given the same set of items, resulting in high test overlap rate and unbalanced item bank usage, which threatens test security. Moreover, another pivotal issue for CCT is time control. Since both the extremely long response time (RT) and large RT variability across examinees intensify time-induced anxiety, it is crucial to reduce the number of examinees exceeding the time limitation and the differences between examinees' test-taking times. To satisfy these practical needs, this paper proposes the novel idea of stage adaptiveness to tailor the item selection process to the decision-making requirement in each step and generate fresh insight into the existing response time selection method. Results indicate that a balanced item usage as well as short and stable test times across examinees can be achieved via the new methods.

视图的首次响应时间和平均响应时间深刻影响着Web应用的开发效率和运行效率.文章设计了一种模板语言,通过开发翻译器分别生成动态语言和静态语言源码,实现视图在开发过程中解释动态语言执行,而发布时编译静态语言执行,并支持热部署.仿真实验结果显示:视图首次响应时间的均值减少86.47％、标准差减少51.77％.对148个视图的开发日志分析表明,文章方法能在保障软件运行效率的同时,提高软件的开发效率,具有广泛的应用前景.

2020年,各级教育、财政等部门和各级各类学校,大力推进精准资助,完善管理规范 [1]. 精准资助存在的挑战 学生资助工作是一项复杂、系统、长期的工作,在精准化过程中存在诸多挑战,具体如下: 1.信息来源单一,准确度不足.资助信息主要来源于学生提交的申报材料,可能存在有的困难学生由于心理因素不提交申报,以及还有学生在求学期间家庭突发重大事故没有及时申报的情况.这样一来,收集的信息可能存在缺失或与客观事实存在失真,对资助对象的识别提出了更高的要求 [2].

标准设定可用于对学生表现进行分类和解释甚至是跨国、跨省市区的比较,因此备受国内外大规模测评项目的青睐.针对我国基础教育质量监测标准设定实践中面临的挑战,提出应对策略并介绍3项关键测量技术:1)采用极大似然估计方法将安戈夫法设定的分界分数转换到项目反应理论能力量尺上;2)利用Rasch簇模型中原始分与能力值存在一一对应关系的性质将学科各子领域的分界分数合成学科整体层面的分界分数;3)通过采用项目反应理论计算真分数的方式获得呈现给评委的影响数据.

EDITORIAL article Front. Psychol., 19 April 2022Sec. Quantitative Psychology and Measurement https://doi.org/10.3389/fpsyg.2022.895399