Reinforcer efficacy in a delayed matching-to-sample task.

Five domestic hens were exposed to a delayed matching-to-sample task. Conditions 1, 5, and 8 were variable-delay conditions in which five delays (0.25, 1, 2, 4, and 8 s) from the red or green sample to the presentation of the red and green comparison stimuli were presented a number of times during each session. In the fixed-delay condition (Condition 3), each delay was presented for 15 sessions under a Latin square design across birds. When improvements in accuracy across the variable-delay conditions are taken into account, the data were similar under both the variable and fixed delays. In Conditions 2, 4, 6, and 7 sample-reinforcer intervals were held at 8, 8, 4, and 2 s, respectively, while sample-choice intervals were varied within these during each session. With increasing sample-reinforcer interval, both initial discriminability (i.e., with sample-choice delay = 0) and rate of decrement in discriminability decreased. Although the former would be predicted if accuracy depends of the average sample-reinforcer interval, the latter would not. These data show that increasing the sample-choice interval had less effect on matching accuracy than increasing the sample-reinforcer interval did.     

An estimating equations approach for the LISCOMP model

Maximum likelihood estimation is computationally infeasible for latent variable models involving multivariate categorical responses, in particular for the LISCOMP model. A three-stage generalized least squares approach introduced by Muthén (1983, 1984) can experience problems of instability, bias, non-convergence, and non-positive definiteness of weight matrices in situations of low prevalence, small sample size and large numbers of observed indicator variables. We propose a quadratic estimating equations approach that only requires specification of the first two moments. By performing simultaneous estimation of parameters, this method does not encounter the problems mentioned above and experiences gains in efficiency. Methods are compared through a numerical study and an application to a study of life-events and neurotic illness.The authors would like to thank Bengt Muthén for many helpful discussions and Scott Henderson for generously providing the Canberra data set. This work was supported in part by grant number GM49909 of the National Institutes of Health.     

Clusteringn objects intok groups under optimal scaling of variables

We propose a method to reduce many categorical variables to one variable withk categories, or stated otherwise, to classifyn objects intok groups. Objects are measured on a set of nominal, ordinal or numerical variables or any mix of these, and they are represented asn points inp-dimensional Euclidean space. Starting from homogeneity analysis, also called multiple correspondence analysis, the essential feature of our approach is that these object points are restricted to lie at only one ofk locations. It follows that thesek locations must be equal to the centroids of all objects belonging to the same group, which corresponds to a sum of squared distances clustering criterion. The problem is not only to estimate the group allocation, but also to obtain an optimal transformation of the data matrix. An alternating least squares algorithm and an example are given.The authors thank Eveline Kroezen and Teije Euverman for their comments on a previous draft of this paper.     

执行功能可分离性及与年龄关系的潜变量分析

应用潜变量分析研究抑制优势反应、记忆刷新和注意转换三种执行功能的可分离性,以及与年龄的关系。被试为18~85岁的健康成年人142名,完成九项执行功能任务。结果显示：本研究设计的九项执行功能任务有较好的信度;验证性因子分析显示,九项任务与对应执行功能间的因子负荷较高（汇聚性效度好）,而不同执行功能间的相关较低（鉴别性效度好）;在此基础上建立了三种执行功能及其与年龄关系的潜变量分析模型。研究结果表明,执行系统不是单一的认知结构,可以分离为若干相对独立的执行加工过程。     

Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random

Methods for the treatment of item non-response in attitudinal scales and in large-scale assessments under the pairwise likelihood (PL) estimation framework and under a missing at random (MAR) mechanism are proposed. Under a full information likelihood estimation framework and MAR, ignorability of the missing data mechanism does not lead to biased estimates. However, this is not the case for pseudo-likelihood approaches such as the PL. We develop and study the performance of three strategies for incorporating missing values into confirmatory factor analysis under the PL framework, the complete-pairs (CP), the available-cases (AC) and the doubly robust (DR) approaches. The CP and AC require only a model for the observed data and standard errors are easy to compute. Doubly-robust versions of the PL estimation require a predictive model for the missing responses given the observed ones and are computationally more demanding than the AC and CP. A simulation study is used to compare the proposed methods. The proposed methods are employed to analyze the UK data on numeracy and literacy collected as part of the OECD Survey of Adult Skills.     

Latent variable selection in multidimensional item response theory models using the expectation model selection algorithm

The aim of latent variable selection in multidimensional item response theory (MIRT) models is to identify latent traits probed by test items of a multidimensional test. In this paper the expectation model selection (EMS) algorithm proposed by Jiang et al. (2015) is applied to minimize the Bayesian information criterion (BIC) for latent variable selection in MIRT models with a known number of latent traits. Under mild assumptions, we prove the numerical convergence of the EMS algorithm for model selection by minimizing the BIC of observed data in the presence of missing data. For the identification of MIRT models, we assume that the variances of all latent traits are unity and each latent trait has an item that is only related to it. Under this identifiability assumption, the convergence of the EMS algorithm for latent variable selection in the multidimensional two-parameter logistic (M2PL) models can be verified. We give an efficient implementation of the EMS for the M2PL models. Simulation studies show that the EMS outperforms the EM-based L₁ regularization in terms of correctly selected latent variables and computation time. The EMS algorithm is applied to a real data set related to the Eysenck Personality Questionnaire.     

潜变量交互效应模型标准化估计中的检验问题

讨论了潜变量交互效应模型是否能直接用统计软件输出的原始估计的t值对模型的标准化估计进行检验的问题,详细介绍了标准化估计的t值计算及其难点,用Bootstrap方法算出标准化估计的标准误和相应的t值（记为t_bs）,并将其与原始估计的t值比较.结果发现,当原始估计t值超过3时,无论用t值还是用t_ bs检验,结果都是显著,即可以使用t值进行检验.而当t值不超过3时,与t_bs很接近,也可以用t值检验,但t值在临界值附近（例如1.5 ～2.5）时,最好还是使用Bootstrap法计算t_bs进行检验.     

Modelling pedestrian crossing behaviour in urban roads: A latent variable approach

As pedestrians are the most exposed and vulnerable road users to traffic accidents, urban planners frequently propose alternatives to improve their safety. However, some solutions, such as pedestrian bridges and crosswalks at signalized intersections, usually imply longer walking distances compared to the direct crossing alternative which, in its turn, involves a higher risk.In this article, a hybrid framework is proposed to analyse the pedestrians’ choice on how to cross an urban road where three crossing options are available: crossing directly, crossing by using a pedestrian bridge or using a crosswalk at a signalized intersection. The decision process is modelled as a discrete choice model incorporating latent variables to consider perceptions and psychological factors, using stated preference data coming from a survey applied in Bogotá, Colombia.Results show that the latent variables security/safety and attractiveness of each crossing alternative are relevant to understand the pedestrian crossing behaviour. These latent variables are strongly determined by socioeconomic characteristics of the individual (age, gender, level of study) and conditioned by the circumstances of the trip (main mode of transport, walking or not with children). It was found that a longer walking distance to a pedestrian bridge or a signalized crosswalk increases the probability of direct crossing, having a more relevant effect in the case of the pedestrian bridge.     

Differential Transfer Benefits of Increased Practice for Constant,Blocked, and Serial Practice Schedules

The effects of practice schedule and amount of practice on the development of the generalized motor program (GMP) and on parameter estimation were investigated. Participants (N = 108) practiced the same relative timing but different absolute durations of a multisegment timing task. Practice schedules (constant, blocked, or serial) were crossed with amounts of practice (low and high). Inclusion of a constant practice condition allowed the authors to investigate the variability of practice prediction. Participants practiced the same proportional durations in a serial or a blocked schedule, which enabled the authors to examine contextual interference. A constant practice schedule enhanced GMP performance when task parameters remained the same, but varied practice schedules were beneficial when task parameters changed. A serial as opposed to a blocked practice schedule was superior when the performance of a task governed by a different GMP was required. Increased practice led to a consolidated task representation that was unavailable for updating.