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.     

Integrating Cognitive Process and Descriptive Models of Attitudes and Preferences

Discrete choice experiments—selecting the best and/or worst from a set of options—are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well‐established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi‐attribute discrete choice data. The fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best–worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator‐based models of decision making used in response time research can also provide process‐level instantiations for random utility models.     

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.     

The effects of intrinsic and extrinsic sources of motivation on well-being depend on time of day: The moderating effects of workday accumulation

Using self-determination theory and research on temporal aspects of work (e.g., time of day), this study investigates dynamic effects of task-specific motivation on well-being throughout the workday. We argue that the effects of task-specific intrinsic and extrinsic motivations on well-being outcomes (psychological vitality, job satisfaction) depend on the time of day in which the task is encountered, referred to here as workday accumulation. Our results showed that time of day interacted with intrinsic sources of motivation to predict job satisfaction, such that the relation was strong and positive early in the day and weak and positive later in the day. Time of day did not moderate the relationship of intrinsic task motivation with vitality, which was consistently positive throughout the day. The effects of extrinsic reasons for task pursuit on satisfaction and vitality were moderated by time of day, with extrinsic motivation having positive effects early in the day, but negative effects later in the day.     

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.     

在亚健康状态研究中运用结构方程模型的合理性

亚健康状态是处于健康与疾病之间的一种“非病但非健康”的中间状态,主要表现为躯体、心理和社会适应等方面的不适,目前尚没有公认的概念和测量诊断工具。由于亚健康状态研究数据中含有潜变量、测量误差以及变量间关系需要确定等因素,不能使用传统的统计学方法进行处理。结构方程模型能够同时处理观测变量、潜变量、测量误差及变量间的关系,应用结构方程模型验证亚健康状况测量量表的结构效度和亚健康状态理论模型是非常合理的。     

Generating variable and random schedules of reinforcement using Microsoft Excel macros

Variable reinforcement schedules are used to arrange the availability of reinforcement following varying response ratios or intervals of time. Random reinforcement schedules are subtypes of variable reinforcement schedules that can be used to arrange the availability of reinforcement at a constant probability across number of responses or time. Generating schedule values for variable and random reinforcement schedules can be difficult. The present article describes the steps necessary to write macros in Microsoft Excel that will generate variable-ratio, variable-interval, variable-time, random-ratio, random-interval, and random-time reinforcement schedule values.     

REINFORCER ACCUMULATION IN A TOKEN-REINFORCEMENT CONTEXT WITH PIGEONS

Four pigeons were exposed to a token-reinforcement procedure with stimulus lights serving as tokens. Responses on one key (the token-production key) produced tokens that could be exchanged for food during an exchange period. Exchange periods could be produced by satisfying a ratio requirement on a second key (the exchange-production key). The exchange-production key was available any time after one token had been produced, permitting up to 12 tokens to accumulate prior to exchange. Token accumulation, measured in terms of both frequency (percent cycles with accumulation) and magnitude (mean number of tokens accumulated), decreased as the token-production ratio increased from 1 to 10 across conditions (with exchange-production ratio held constant), and increased as the exchange-production ratio increased from 1 to 250 across conditions (with token-production ratio held constant). When tokens were removed, accumulation decreased markedly compared to conditions with tokens and the same schedules. These data show that token accumulation is an orderly function of token-production and exchange-production schedules, and they are broadly consistent with a unit-price model based on local and global responses per reinforcer.