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131.
Haruhiko Ogasawara 《The Japanese psychological research》2001,43(2):72-82
A method of estimating item response theory (IRT) equating coefficients by the common-examinee design with the assumption of the two-parameter logistic model is provided. The method uses the marginal maximum likelihood estimation, in which individual ability parameters in a common-examinee group are numerically integrated out. The abilities of the common examinees are assumed to follow a normal distribution but with an unknown mean and standard deviation on one of the two tests to be equated. The distribution parameters are jointly estimated with the equating coefficients. Further, the asymptotic standard errors of the estimates of the equating coefficients and the parameters for the ability distribution are given. Numerical examples are provided to show the accuracy of the method. 相似文献
132.
In this paper we discuss the use of a recent dimension reduction technique called Locally Linear Embedding, introduced by
Roweis and Saul, for performing an exploratory latent structure analysis. The coordinate variables from the locally linear
embedding describing the manifold on which the data reside serve as the latent variable scores. We propose the use of semiparametric
penalized spline methods for reconstruction of the manifold equations that approximate the data space. We also discuss a crossvalidation
strategy that can guide in selecting an appropriate number of latent variables. Synthetic as well as real data sets are used
to illustrate the proposed approach. A nonlinear latent structure representation of a data set also serves as a data visualization
tool. 相似文献
133.
It is often assumed that the space we perceive is Euclidean, although this idea has been challenged by many authors. Here we show that if spatial cues are combined as described by Maximum Likelihood Estimation, Bayesian, or equivalent models, as appears to be the case, then Euclidean geometry cannot describe our perceptual experience. Rather, our perceptual spatial structure would be better described as belonging to an arbitrarily curved Riemannian space. 相似文献
134.
认知元反应理论--IRT直接应用于多值记分题 总被引:1,自引:0,他引:1
0-1记分测验的项目反应理论已经得到广泛的研究和应用.但是,许多测验都含有多值记分题,所以需要将IRT推广到此类情况.从认知理论的观点看,每个0-1记分题(项目)和多值记分题的每个测试点都可同样地看成一个由若干知识点构成的集合,称之为认知元;根据认知元之间存在的关系可以确定各受测者对各试题作出特定答案的概率,从而不需要引用任何其它假设就可将IRT的方法直接应用于含多值记分题的测验.本文应用这一理论分析了某些测验样本,结果表明是可行的. 相似文献
135.
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137.
Many studies of bribery acknowledge the important role of bribe-givers, but their true motives remain unclear. We propose that the likelihood of bribery depends on the willingness of an organization to affiliate with local parties or to be successful in a host country, or to have power over local parties. We further argue that different opportunities, either pervasive or arbitrary, facilitate different types of motives that affect the likelihood of bribery. In addition, we investigate the effect of perceived fairness on the likelihood of bribery. We employ a 3 (motives: affiliation vs. achievement vs. power)?×?2 (opportunities: pervasiveness vs. arbitrariness)?×?2 (perceived fairness: high vs. low) factorial design in experimental settings among Executive MBA students in southern Taiwan. Our findings indicate that, when companies perceive a higher level of distributive fairness, high-achieving organizations are more likely to offer a bribe when the condition is pervasive. When they have a powerful motive, arbitrariness engenders a higher likelihood of bribery. When they perceive less distributive fairness, there are no significant differences between motive and opportunity. 相似文献
138.
Philippa E. Pattison Garry L. Robins Tom A.B. Snijders Peng Wang 《Journal of mathematical psychology》2013,57(6):284-296
A complete survey of a network in a large population may be prohibitively difficult and costly. So it is important to estimate models for networks using data from various network sampling designs, such as link-tracing designs. We focus here on snowball sampling designs, designs in which the members of an initial sample of network members are asked to nominate their network partners, their network partners are then traced and asked to nominate their network partners, and so on. We assume an exponential random graph model (ERGM) of a particular parametric form and outline a conditional maximum likelihood estimation procedure for obtaining estimates of ERGM parameters. This procedure is intended to complement the likelihood approach developed by Handcock and Gile (2010) by providing a practical means of estimation when the size of the complete network is unknown and/or the complete network is very large. We report the outcome of a simulation study with a known model designed to assess the impact of initial sample size, population size, and number of sampling waves on properties of the estimates. We conclude with a discussion of the potential applications and further developments of the approach. 相似文献
139.
Gina L. Mazza Craig K. Enders Linda S. Ruehlman 《Multivariate behavioral research》2013,48(5):504-519
Often when participants have missing scores on one or more of the items comprising a scale, researchers compute prorated scale scores by averaging the available items. Methodologists have cautioned that proration may make strict assumptions about the mean and covariance structures of the items comprising the scale (Schafer &; Graham, 2002; Graham, 2009; Enders, 2010). We investigated proration empirically and found that it resulted in bias even under a missing completely at random (MCAR) mechanism. To encourage researchers to forgo proration, we describe a full information maximum likelihood (FIML) approach to item-level missing data handling that mitigates the loss in power due to missing scale scores and utilizes the available item-level data without altering the substantive analysis. Specifically, we propose treating the scale score as missing whenever one or more of the items are missing and incorporating items as auxiliary variables. Our simulations suggest that item-level missing data handling drastically increases power relative to scale-level missing data handling. These results have important practical implications, especially when recruiting more participants is prohibitively difficult or expensive. Finally, we illustrate the proposed method with data from an online chronic pain management program. 相似文献
140.
Gyeongcheol Cho Marko Sarstedt Heungsun Hwang 《The British journal of mathematical and statistical psychology》2022,75(2):220-251
Structural equation modelling (SEM) has evolved into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical SEM approaches should be used for estimating models whose construct representations correspond to what they assume. However, SEM approaches have often been evaluated and compared only under population factor models, providing misleading conclusions about their relative performance. This is partly because population component models and their relationships have not been clearly formulated. Also, it is of fundamental importance to examine how robust SEM approaches can be to potential misrepresentation of constructs because researchers may often lack clear theories to determine whether a factor or component is more representative of a given construct. Addressing these issues, this study begins by clarifying several population component models and their relationships and then provides a comprehensive evaluation of four SEM approaches – the maximum likelihood approach and factor score regression for factor-based SEM as well as generalized structured component analysis (GSCA) and partial least squares path modelling (PLSPM) for component-based SEM – under various experimental conditions. We confirm that the factor-based SEM approaches should be preferred for estimating factor models, whereas the component-based SEM approaches should be chosen for component models. Importantly, the component-based approaches are generally more robust to construct misrepresentation than the factor-based ones. Of the component-based approaches, GSCA should be chosen over PLSPM, regardless of whether or not constructs are misrepresented. 相似文献