基于过程数据的问题解决能力测量及数据分析方法

问题解决能力是指在没有明显解决方法的情况下个体从事认知加工以理解和解决问题情境的能力。对问题解决能力的测量需要借助相对更复杂、更真实、具有可交互性的问题情境来诱导问题解决行为的呈现。使用虚拟测评抓取问题解决的过程数据并分析其中所蕴含的潜在信息是当前心理计量学中测量问题解决能力的新趋势。首先, 回顾问题解决能力测量方式的发展：从纸笔测验到虚拟测评。然后, 总结对比两类过程数据的分析方法：统计建模法和数据挖掘法。最后, 从非认知因素的影响、多模态数据的利用、问题解决能力发展的测量、其他高阶思维能力的测量和问题解决能力概念及结构的界定五个方面展望未来可能的研究方向。     

计算机动态测验中问题解决过程策略的分析：多水平混合IRT模型的拓展与应用

学生在完成计算机动态测验过程中, 会产生大量带有时间标记的过程性数据。本研究基于5个国家(地区) 3196名学生在PISA2012一道交通问题解决任务上的139990条数据, 将多水平混合IRT (MMixIRT)模型进行拓展, 用于探索问题解决过程策略的类别特点。结果表明, 该模型不仅可以基于行为序列对不同国家(地区)学生在解决问题时策略使用情况的典型特征进行分析, 还可以提供个体水平的能力估计值。拓展的MMixIRT模型可用于分析过程性数据的特征。     

四参数Logistic模型和传统模型对被试作答拟合能力的比较研究

针对测验中高能力被试答错容易试题的睡眠现象,可使用四参数Logistic模型分析数据。研究选取了来自心理测验和成就测验的实际数据,分别采用传统模型和四参数Logistic模型进行拟合,对不同模型的拟合指标及参数估计结果进行比较。结果表明,四参数Logistic模型能够提高拟合程度,增强估计结果的准确性,有效纠正高能力被试能力被低估的现象。建议在必要时使用四参数Logistic模型进行数据分析。     

有关创造力测量的一些思考

创造力测量是创造力研究的基础, 然而该领域研究成果却一直饱受质疑, 因此如何准确测量创造力是研究者亟需解决的问题。近几年, 创造力测量领域围绕一些研究热点, 在多方面取得显著进展。例如：发散思维测验独特性维度的计分问题, 或许可以通过主观计分法解决; 顿悟类测验可能表征个体创造力水平, 但效度仍有待于进一步确认; 创造力成就测验可能带来的共同方法变异问题, 需要通过合理应用测验规避; 同感评估技术或许会引起评定者效应; 研究者开始从语义网络角度测量创造力等等。未来该领域研究应当在：基本概念问题上达成共识; 从测验内容和施测过程优化测验质量; 采用混合测验的策略以及通过跨领域研究增进测量技术多样化等方面进行努力。     

数学能力发展水平不同的学生的创造性思维的比较研究

本研究结果表明,数学能力的强弱同创造性思维能力的高低基本上成正比。但在一般发散思维能力上,这种关系对数学能力强的学生来说,不是绝对的。一般发散思维能力和数学发散思维能力虽有关联,但却是两种不同性质的能力;在解决数学问题时,数学发散思维是不可缺少的重要成分,可作为鉴别学生数学能力强弱的一个重要标志;在一般创造性思维测验和数学创造性思维测验中,男女学生之间无显著性差异。     

IRT框架下追踪数据的测量不变性分析——————以4至5岁儿童认知能力测验为例

本研究以4岁~5岁儿童认知能力测验为例,在IRT框架下探讨了如何进行追踪数据的测量不变性分析。分析模型采用项目间多维项目反应理论模型(between-item MIRT model)和项目内（within-item MIRT model）多维two-tier model,被试为来自全国的882名48个月的儿童,工具为自编4岁~5岁儿童认知能力测验。经测验水平 分析和项目水平分析,结果表明：(1)本文对追踪数据的测量不变性分析方法合理有效; (2)该测验在两个时间点上满足部分测量不变性要求,测验的潜在结构稳定; (3)“方位题”的区分度和难度参数都发生变化,另有4题难度参数出现浮动; (4)儿童在4岁~5岁期间认知能力总体呈快速发展趋势,能力增长显著。     

效度概化：预测效度元分析的30年成果述评

效度概化是通过元分析技术对普遍化的预测效度的估计。元分析是研究效度概化问题的技术,是对具有“预测因子-效标”特征的相关数据进行定量综合的方法。效度概化促进了预测因子与效标之间关系的理论研究和应用研究,是应用心理学领域近30年（1977~2007）来最重要的进展之一。30年来的效度概化研究表明,认知能力测验、知识和技能测验、人格测验、结构化面试和评价中心技术等的预测效度具有鲁棒性、对应性和联合增值性     

行政职业能力测验阅读理解能力考查体系研究

本研究对行政职业能力测验中的阅读理解分测验考查何种能力要素进行了深入探讨.研究一首先通过文献法初步概括出行政职业能力测验阅读理解的能力考查体系,进而使用出声思维的方法对该考查体系进行验证和补充,最后通过专家调查论证方法对其进行最终确定.结果表明,行政职业能力测验阅读理解考查了6种能力,分别是提取信息能力、理解意义能力、理解细节能力、分析结构能力、概括能力及推断能力,其中提取信息能力和理解意义能力是基础.在此基础上,研究二使用该能力框架选取合适的行政职业能力测验阅读理解题目,组成一套阅读理解测验,并借助认知诊断方法,通过专家标定测验的Q矩阵,利用实测数据对研究一构建的能力考查体系的完备性进行了验证.     

勒希模式的项目难度参数与人的能力参数恒定性之研究

本文用小学生数学能力测验的数据对勒希模式中的项目难度参数和人的能力参数进行了研究,结果表明,当勒希模式与测验数据吻合时,该模式的项目难度参数值和人的能力参数值具有恒定的特性.由于这一特性,我们可以获得客观的项目难度值,充分地利用项目库.     

大学生认知方式与顿悟问题解决的关系

很多学者认为场认知方式是影响顿悟问题解决的重要因素之一。本研究采用《镶嵌图形测验》对随机抽取的51名大学生进行了认知方式调查,并将其分为场独立和场依存两组进行火柴棍算式问题实验,进一步探讨不同认知方式被试在解决顿悟问题时的个体差异。结果表明:相比场依存型解决者,场独立型解决者的顿悟问题解决能力更高,但性别差异不显著;在解决火柴棒顿悟问题时,被试更注重火柴棒算式中的数值而非符号。     

人工智能辅助下的心理健康新型测评

近年来, 人工智能技术的飞速发展及应用催生了“智能化心理健康测评”这一领域。智能化心理健康测评能够弥补传统方法的不足, 降低漏诊率并提高诊断效率, 这对于心理健康问题的普查及预警具有重大意义。目前, 智能化心理健康测评处于初步发展阶段, 研究者基于在线行为数据、便携式设备数据等开展主要以数据驱动为导向的探索研究, 旨在实现更高的预测准确率, 但是测评结果的可解释性等指标尚不够理想。未来的智能化心理健康测评需要强调心理学领域知识和经验的深度介入, 提高测评的针对性和精细化程度, 加强信效度检验, 这对于智能化心理健康测评工具的进一步发展和应用至关重要。     

无均值结构的潜变量交互效应模型的标准化估计

潜变量交互效应建模研究近年来有两项重要进展, 一是提出了潜变量交互效应模型的标准化估计及其计算公式; 二是发现无均值结构模型可以取代传统的有均值结构模型, 建模大为简化。但标准化估计是在传统的有均值结构模型中建立的, 在简化的模型中同样适用吗？本文在无均值结构模型的框架内, 给出了潜变量交互效应模型的标准化形式、计算公式和建模步骤, 并通过模拟研究比较了极大似然和广义最小二乘两种估计方法、配对乘积指标和全部乘积指标两种指标类型, 结果表明, 在计算交互效应的标准化估计时, 应当使用配对乘积指标建模, 并且首选极大似然估计。     

Performance on auditory and visual temporal information processing is related to psychometric intelligence

The present study investigated the relationship between psychometric intelligence and temporal resolution power (TRP) as simultaneously assessed by auditory and visual psychophysical timing tasks. In addition, three different theoretical models of the functional relationship between TRP and psychometric intelligence as assessed by means of the Adaptive Matrices Test (AMT) were developed. To test the validity of these models, structural equation modeling was applied. Empirical data supported a hierarchical model that assumed auditory and visual modality-specific temporal processing at a first level and amodal temporal processing at a second level. This second-order latent variable was substantially correlated with psychometric intelligence. Therefore, the relationship between psychometric intelligence and psychophysical timing performance can be explained best by a hierarchical model of temporal information processing.     

Information-theoretic latent distribution modeling: distinguishing discrete and continuous latent variable models

Distinguishing between discrete and continuous latent variable distributions has become increasingly important in numerous domains of behavioral science. Here, the authors explore an information-theoretic approach to latent distribution modeling, in which the ability of latent distribution models to represent statistical information in observed data is emphasized. The authors conclude that loss of statistical information with a decrease in the number of latent values provides an attractive basis for comparing discrete and continuous latent variable models. Theoretical considerations as well as the results of 2 Monte Carlo simulations indicate that information theory provides a sound basis for modeling latent distributions and distinguishing between discrete and continuous latent variable models in particular.     

新世纪20年国内心理统计方法研究回顾

新世纪头20年, 国内心理学11本专业期刊一共发表了213篇统计方法研究论文。研究范围主要包括以下10类(按论文篇数排序)：结构方程模型、测验信度、中介效应、效应量与检验力、纵向研究、调节效应、探索性因子分析、潜在类别模型、共同方法偏差和多层线性模型。对各类做了简单的回顾与梳理。结果发现, 国内心理统计方法研究的广度和深度都不断增加, 研究热点在相互融合中共同发展; 但综述类论文比例较大, 原创性研究论文比例有待提高, 研究力量也有待加强。     

Psychometric Modeling of response speed and accuracy with mixed and conditional regression

Human performance in cognitive testing and experimental psychology is expressed in terms of response speed and accuracy. Data analysis is often limited to either speed or accuracy, and/or to crude summary measures like mean response time (RT) or the percentage correct responses. This paper proposes the use of mixed regression for the psychometric modeling of response speed and accuracy in testing and experiments. Mixed logistic regression of response accuracy extends logistic item response theory modeling to multidimensional models with covariates and interactions. Mixed linear regression of response time extends mixed ANOVA to unbalanced designs with covariates and heterogeneity of variance. Related to mixed regression is conditional regression, which requires no normality assumption, but is limited to unidimensional models. Mixed and conditional methods are both applied to an experimental study of mental rotation. Univariate and bivariate analyzes show how within-subject correlation between response and RT can be distinguished from between-subject correlation, and how latent traits can be detected, given careful item design or content analysis. It is concluded that both response and RT must be recorded in cognitive testing, and that mixed regression is a versatile method for analyzing test data.I am grateful to Rogier Donders for putting his data at my disposal.     

结构方程模型中调节效应的标准化估计

回归分析和结构方程分析中,标准化估计对解释模型和比较效应大小有重要作用。对于调节效应模型（或交互效应模型）,通常的标准化估计没有意义。虽然显变量的调节效应模型标准化估计问题已经解决,但潜变量的调节效应模型标准化估计问题复杂得多。本文先介绍回归分析中显变量调节效应模型的标准化估计,然后提出了一种通过参数的原始估计和通常标准化估计来计算潜变量调节效应模型的“标准化”估计的方法,得到的“标准化”估计是尺度不变的,说明可以用“标准化”估计来解释和比较主效应和调节效应     

Latent Growth Modeling for Logistic Response Functions

Throughout much of the social and behavioral sciences, latent growth modeling (latent curve analysis) has become an important tool for understanding individuals' longitudinal change. Although nonlinear variations of latent growth models appear in the methodological and applied literature, a notable exclusion is the treatment of growth following logistic (sigmoidal; S-shape) response functions. Such trajectories are assumed in a variety of psychological and educational settings where learning occurs over time, and yet applications using the logistic model in growth modeling methodology have been sparse. The logistic function, in particular, may not be utilized as often because software options remain limited. In this article we show how a specialized version of the logistic function can be modeled using conventional structural equation modeling software. The specialization is a reparameterization of the logistic function whose new parameters correspond to scientifically interesting characteristics of the growth process. In addition to providing an example using simulated data, we show how this nonlinear functional form can be fit using transformed subject-level data obtained through a learning task from an air traffic controller simulation experiment. LISREL syntax for the empirical example is provided.     

Learning Orthographic Structure With Sequential Generative Neural Networks

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high‐order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high‐quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non‐connectionist probabilistic models (n‐grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain.     

Latent variables should remain as such: Evidence from a Monte Carlo study

Use of subject scores as manifest variables to assess the relationship between latent variables produces attenuated estimates. This has been demonstrated for raw scores from classical test theory (CTT) and factor scores derived from factor analysis. Conclusions on scores have not been sufficiently extended to item response theory (IRT) theta estimates, which are still recommended for estimation of relationships between latent variables. This is because IRT estimates appear to have preferable properties compared to CTT, while structural equation modeling (SEM) is often advised as an alternative to scores for estimation of the relationship between latent variables. The present research evaluates the consequences of using subject scores as manifest variables in regression models to test the relationship between latent variables. Raw scores and three methods for obtaining theta estimates were used and compared to latent variable SEM modeling. A Monte Carlo study was designed by manipulating sample size, number of items, type of test, and magnitude of the correlation between latent variables. Results show that, despite the advantage of IRT models in other areas, estimates of the relationship between latent variables are always more accurate when SEM models are used. Recommendations are offered for applied researchers.