共查询到19条相似文献，搜索用时 171 毫秒
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认知诊断模型是新一代心理测量理论——认知诊断理论的核心。它可分为潜在特质模型和潜在分类模型两大类。其中,潜在分类模型主要用于分析被试的作答过程从而探讨被试的潜在知识结构,克服了CCT和IRT的缺陷,开创了教育与心理测量领域新的里程碑。本文首先介绍作为该类模型基础的规则空间模型,然后集中探讨在此基础上发展起来的较新的潜在分类模型,最后对这类模型进行了评价和展望。 相似文献
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在探讨态度改变的理论发展的基础上介绍态度研究的新进展——联想和命题过程评价模型,该模型认为理解态度的评价判断应该根据其潜在的心理过程：联想过程和命题过程,二者分别对应内隐和外显态度,并相互影响。同时,该模型还对态度改变进行了新的诠释。因此,该模型对于今后的态度研究具有一定的理论意义,文章最后还指出了该模型的不足之处。 相似文献
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文章对国内外的应对研究模型及应对分类以及影响个体应对行为的进行因素进行了分析,并认为当前国内的应对研究仍处于对国外应对研究的重复验证研究的阶段,需要在理论和实践方面进行深入的研究. 相似文献
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通过介绍国外心理学界提出的几个主要的数学认知策略模型，即小值模型、网络干预模型、表搜索模型、联结分布模型等，以进一步深入了解认知策略的出现、消失和发展，最后对模型进行分析评价，并对未来研究进行了展望。 相似文献
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文章对国内外的应对研究模型及应对分类以及影响个体应对行为的进行因素进行了分析,并认为当前国内的应对研究仍处于对国外应对研究的重复验证研究的阶段,需要在理论和实践方面进行深入的研究. 相似文献
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国外自我概念结构与测量研究综述 总被引：1，自引：0，他引：1
赵必华 《集美大学学报(教育科学版》2008,9(1):51-54
自我概念一直是国外心理学研究的重要内容。对自我概念结构的探讨,经历了从单维模型向阶层模型的转变,与此对应,自我概念的测量也经历了从测量单维结构的第一代量表向测量多维阶层结构的第二代量表的转变。国外通过理论模型建构与实证测量验证互动来研究自我概念对于探究中国人自我概念具有方法论的启示。 相似文献
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对人脸检测所面临的问题进行探讨,分析有关人脸检测问题的研究方法,并对其进行分类和评价。从基于模板的方法、基于肤色模型的方法、基于统计理论的方法三方面进行了阐述。分析各种方法的优缺点,并提出了关于人脸检测问题的进一步研究方向。 相似文献
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Popular longitudinal models allow for prediction of growth trajectories in alternative ways. In latent class growth models (LCGMs), person-level covariates predict membership in discrete latent classes that each holistically define an entire trajectory of change (e.g., a high-stable class vs. late-onset class vs. moderate-desisting class). In random coefficient growth models (RCGMs, also known as latent curve models), however, person-level covariates separately predict continuously distributed latent growth factors (e.g., an intercept vs. slope factor). This article first explains how complex and nonlinear interactions between predictors and time are recovered in different ways via LCGM versus RCGM specifications. Then a simulation comparison illustrates that, aside from some modest efficiency differences, such predictor relationships can be recovered approximately equally well by either model—regardless of which model generated the data. Our results also provide an empirical rationale for integrating findings about prediction of individual change across LCGMs and RCGMs in practice. 相似文献
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This article examines the problem of specification error in 2 models for categorical latent variables; the latent class model and the latent Markov model. Specification error in the latent class model focuses on the impact of incorrectly specifying the number of latent classes of the categorical latent variable on measures of model adequacy as well as sample reallocation to latent classes. The results show that the clarity of remaining latent classes, as measured by the entropy statistic depends on the number of observations in the omitted latent class—but this statistic is not reliable. Specification error in the latent Markov model focuses on the transition probabilities when a longitudinal Guttman process is incorrectly specified. The findings show that specifying a longitudinal Guttman process that is not true in the population impacts other transition probabilities through the covariance matrix of the logit parameters used to calculate those probabilities. 相似文献
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The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that Step 1 of the 3-step approach shows adequate results in class enumeration, we suggest using an alternative approach: (a) decide the number of latent classes without predictors of latent classes, and (b) bring the latent class predictors into the model with the inclusion of hypothesized direct covariate effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students’ academic achievement outcome. Implications of the study are discussed. 相似文献
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赵雅丹 《华东师范大学学报(哲学社会科学版)》2012,(2):23-29,152,153
17世纪,中国和荷兰都出现了追求利润的海商集团,但是郑氏集团和荷兰东印度公司商业组织的产生形式、组织形式、决策模式均不尽相同。荷兰东印度公司是大规模经营、以国家政权为后盾运作的股份制垄断公司。而郑氏集团区别于中国传统的组织松散的、基于血缘、地缘结合的海商联盟,建立起一个复杂的商业—军事复合体,即依靠血缘、地缘为主、指令性结构的军事组织,集中管理的海陆五商和具备海关功能的海商管理部门。中西出现差异的原因在于形成垄断的方式与国家的关系不同,中国的海商集团立足于某一区域内的垄断和强大,而西方的海商集团则不计成本的全球扩张。 相似文献
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The purpose of this ITEMS module is to provide an introduction to differential item functioning (DIF) analysis using mixture item response models. The mixture item response models for DIF analysis involve comparing item profiles across latent groups, instead of manifest groups. First, an overview of DIF analysis based on latent groups, called latent DIF analysis, is provided and its applications in the literature are surveyed. Then, the methodological issues pertaining to latent DIF analysis are described, including mixture item response models, parameter estimation, and latent DIF detection methods. Finally, recommended steps for latent DIF analysis are illustrated using empirical data. 相似文献
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Kristina R. Cassiday Youngmi Cho Jeffrey R. Harring 《Educational and psychological measurement》2021,81(4):668
Simulation studies involving mixture models inevitably aggregate parameter estimates and other output across numerous replications. A primary issue that arises in these methodological investigations is label switching. The current study compares several label switching corrections that are commonly used when dealing with mixture models. A growth mixture model is used in this simulation study, and the design crosses three manipulated variables—number of latent classes, latent class probabilities, and class separation, yielding a total of 18 conditions. Within each of these conditions, the accuracy of a priori identifiability constraints, a priori training of the algorithm, and four post hoc algorithms developed by Tueller et al.; Cho; Stephens; and Rodriguez and Walker are tested to determine their classification accuracy. Findings reveal that, of all a priori methods, training of the algorithm leads to the most accurate classification under all conditions. In a case where an a priori algorithm is not selected, Rodriguez and Walker’s algorithm is an excellent choice if interested specifically in aggregating class output without consideration as to whether the classes are accurately ordered. Using any of the post hoc algorithms tested yields improvement over baseline accuracy and is most effective under two-class models when class separation is high. This study found that if the class constraint algorithm was used a priori, it should be combined with a post hoc algorithm for accurate classification. 相似文献
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Yan Wang Eunsook Kim John M. Ferron Robert F. Dedrick Tony X. Tan Stephen Stark 《Educational and psychological measurement》2021,81(1):61
Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed. 相似文献
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This article examines the effects of clustering in latent class analysis. A comprehensive simulation study is conducted, which begins by specifying a true multilevel latent class model with varying within- and between-cluster sample sizes, varying latent class proportions, and varying intraclass correlations. These models are then estimated under the assumption of a single-level latent class model. The outcomes of interest are measures of bias in the Bayesian Information Criterion (BIC) and the entropy R 2 statistic relative to accounting for the multilevel structure of the data. The results indicate that the size of the intraclass correlation as well as between- and within-cluster sizes are the most prominent factors in determining the amount of bias in these outcome measures, with increasing intraclass correlations combined with small between-cluster sizes resulting in increased bias. Bias is particularly noticeable in the BIC. In addition, there is evidence that class separation interacts with the size of the intraclass correlations and cluster sizes in producing bias in these measures. 相似文献
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A latent variable modeling procedure for examining whether a studied population could be a mixture of 2 or more latent classes is discussed. The approach can be used to evaluate a single-class model vis-à-vis competing models of increasing complexity for a given set of observed variables without making any assumptions about their within-class interrelationships. The method is helpful in the initial stages of finite mixture analyses to assess whether models with 2 or more classes should be subsequently considered as opposed to a single-class model. The discussed procedure is illustrated with a numerical example. 相似文献