Learning letter names and sounds: Effects of instruction, letter type, and phonological processing skill

Preschool-age children (N = 58) were randomly assigned to receive instruction in letter names and sounds, letter sounds only, or numbers (control). Multilevel modeling was used to examine letter name and sound learning as a function of instructional condition and characteristics of both letters and children. Specifically, learning was examined in light of letter name structure, whether letter names included cues to their respective sounds, and children’s phonological processing skills. Consistent with past research, children receiving letter name and sound instruction were most likely to learn the sounds of letters whose names included cues to their sounds regardless of phonological processing skills. Only children with higher phonological skills showed a similar effect in the control condition. Practical implications are discussed.     

Distribution-free tests of stochastic dominance for small samples

One variable is said to “stochastically dominate” another if the probability of observations smaller than x is greater for one variable than the other, for all x. Inferring stochastic dominance from data samples is important for many applications of econometrics and experimental psychology, but little is known about the performance of existing inferential methods. Through simulation, we show that three of the most widely used inferential methods are inadequate for use in small samples of the size commonly encountered in many applications (up to 400 observations from each distribution). We develop two new inferential methods that perform very well in a limited, but practically important, case where the two variables are guaranteed not to be equal in distribution. We also show that extensions of these new methods, and an improved version of an existing method, perform quite well in the original, unlimited case.     

Beta-MPT: Multinomial processing tree models for addressing individual differences

Traditionally, multinomial processing tree (MPT) models are applied to groups of homogeneous participants, where all participants within a group are assumed to have identical MPT model parameter values. This assumption is unreasonable when MPT models are used for clinical assessment, and it often may be suspect for applications to ordinary psychological experiments. One method for dealing with parameter variability is to incorporate random effects assumptions into a model. This is achieved by assuming that participants’ parameters are drawn independently from some specified multivariate hyperdistribution. In this paper we explore the assumption that the hyperdistribution consists of independent beta distributions, one for each MPT model parameter. These beta-MPT models are ‘hierarchical models’, and their statistical inference is different from the usual approaches based on data aggregated over participants. The paper provides both classical (frequentist) and hierarchical Bayesian approaches to statistical inference for beta-MPT models. In simple cases the likelihood function can be obtained analytically; however, for more complex cases, Markov Chain Monte Carlo algorithms are constructed to assist both approaches to inference. Examples based on clinical assessment studies are provided to demonstrate the advantages of hierarchical MPT models over aggregate analysis in the presence of individual differences.     

A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.     

TEM observation of oxide scale formed on a Ti–Al–Zr alloy oxidized at 360°C in alkaline steam

Ti–Al–Zr alloy has been oxidized at 360°C in alkaline steam at a pressure of 10.3?±?0.7?MPa. Cross-sectional transmission electron microscopy (TEM) observations indicated that the oxide scale of Ti–Al–Zr alloy was composed of outer and inner subscales, in which the outer layer consists of anatase-TiO₂ and the inner layer a mixture of TiO and Ti₂O. The thickness of the Ti₂O, TiO and anatase-TiO₂ were approximately 50, 100 and 400?nm, respectively. These results were confirmed by X-ray energy dispersive spectrometry (EDS) measurements. The enhanced corrosion of titanium alloys in LiOH solution is attributed to a high hexagonal Ti₂O to tetragonal TiO₂ phase transformation rate induced by the substitution of Li⁺ for Ti⁴⁺ in the oxide layer.     

新生市民主动发展倾向对城市生活适应性的影响:社会支持的中介效应

为探究主动发展倾向和社会支持对新生市民城市生活适应性的作用机制,本研究构建了结构方程模型.采用两个自编量表和一个现成量表,运用两阶段抽样调查法,在作为统筹城乡发展试验区的重庆市范围内,对已经取得城镇户口的新生市民进行问卷调查,获得有效样本322人.对采集的数据运用统计分析软件SPSS17.0和AMOS18.0进行数据分析和模型检验.结果表明,城市化新生市民的主动发展倾向和社会支持均对新生市民的城市生活适应具有显著影响,其中,社会支持在主动发展倾向对城市生活适应影响中具有部分中介作用.     

Exploring Pathways From Television Viewing to Academic Achievement in School Age Children

The author's purpose in this study was to test 4 hypotheses that proposed different paths for the influences of children's television viewing on their academic achievement. Data were drawn from the 1997 Child Development Supplement (CDS) to the Panel Study of Income Dynamics (PSID). The population for this study included 1,203 children between the ages of 6 and 13 years from the CDS-PSID data set. The author used structural equation modeling to test pathways from children's television viewing to their academic achievement. The author assumed that children's television viewing hindered their academic achievement by reducing certain traits that related to academic achievement. Results showed that 3 hypothetical models fit the data—the time-displacement hypothesis, the mental effort-passivity hypothesis, and the attention-arousal hypothesis. A 4th hypothetical model, the learning-information hypothesis, which proposed that children's television viewing practices stimulate their academic achievement, was not supported. In sum, children who watched more television tended to spend less time doing homework, studying, and reading for leisure. In addition, their behaviors became more impulsive, which resulted in an eventual decrease in their academic achievement.     

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.     

Prior distributions for random choice structures

We study various axioms of discrete probabilistic choice, measuring how restrictive they are, both alone and in the presence of other axioms, given a specific class of prior distributions over a complete collection of finite choice probabilities. We do this by using Monte Carlo simulation to compute, for a range of prior distributions, probabilities that various simple and compound axioms hold. For example, the probability of the triangle inequality is usually many orders of magnitude higher than the probability of random utility. While neither the triangle inequality nor weak stochastic transitivity imply the other, the conditional probability that one holds given the other holds is greater than the marginal probability, for all priors in the class we consider. The reciprocal of the prior probability that an axiom holds is an upper bound on the Bayes factor in favor of a restricted model, in which the axiom holds, against an unrestricted model. The relatively high prior probability of the triangle inequality limits the degree of support that data from a single decision maker can provide in its favor. The much lower probability of random utility implies that the Bayes factor in favor of it can be much higher, for suitable data.     

Hierarchical models for relational event sequences

Interaction within small groups can often be represented as a sequence of events, each event involving a sender and a recipient. Recent methods for modeling network data in continuous time model the rate at which individuals interact conditioned on the previous history of events as well as actor covariates. We present a hierarchical extension for modeling multiple such sequences, facilitating inferences about event-level dynamics and their variation across sequences. The hierarchical approach allows one to share information across sequences in a principled manner—we illustrate the efficacy of such sharing through a set of prediction experiments. After discussing methods for adequacy checking and model selection for this class of models, the method is illustrated with an analysis of high school classroom dynamics from 297 sessions.