School profiles of at-risk student concentration: Differential growth in oral reading fluency

The present study provides a data-driven approach to identifying groups of schools based on the concentration of at-risk students the school serves. The percentage of English language learners, minority students, and students eligible for free or reduced priced lunch were used as indicators in a latent profile analysis of 569 schools. The goal of the present study was to determine whether school-level average student reading performance varied as a function of the groups identified in the latent profile analysis. To do so, groups extracted by the latent profile analysis were used as school-level predictors of growth in oral reading fluency, which was modeled at the within-student level of a three-level hierarchical growth curve model. Oral reading fluency was measured at four points during the year in a large cross-sectional sample of first-, second-, and third-grade students. Results indicated that schools were able to be classified into four distinct groups based on their concentrations and types of at-risk students. Further, in all three grades, there were significant differences between the four identified groups observed in average reading fluency scores at the beginning of the year, the end of the year, and growth during the year indicating that groups based on school-concentration of at-risk students were significantly related to average student achievement in reading ability.     

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 Signal Detection Model for Multiple-Choice Exams

A model for multiple-choice exams is developed from a signal-detection perspective. A correct alternative in a multiple-choice exam can be viewed as being a signal embedded in noise (incorrect alternatives). Examinees are assumed to have perceptions of the plausibility of each alternative, and the decision process is to choose the most plausible alternative. It is also assumed that each examinee either knows or does not know each item. These assumptions together lead to a signal detection choice model for multiple-choice exams. The model can be viewed, statistically, as a mixture extension, with random mixing, of the traditional choice model, or similarly, as a grade-of-membership extension. A version of the model with extreme value distributions is developed, in which case the model simplifies to a mixture multinomial logit model with random mixing. The approach is shown to offer measures of item discrimination and difficulty, along with information about the relative plausibility of each of the alternatives. The model, parameters, and measures derived from the parameters are compared to those obtained with several commonly used item response theory models. An application of the model to an educational data set is presented.     

Recognition hypermnesia: How to get it

Although recall hypermnesia (enhanced recall) over time with repeated testing has by now become an established empirical fact, its recognition counterpart, recognition hypermnesia, has defied clear-cut laboratory confirmation. In four studies, which relied on the retrieval component of recognition memory, it was shown that recognition memory, indexed by d′, reliably improved over three successive recognition tests. The stimuli consisted of 140 cartoons, each comprising a picture and a verbal caption. Recognition memory was tested on transforms or part-forms (parts) of the original stimulus material (pictures only, verbal paraphrases of the pictures, the latent content of the cartoons, or the combination of paraphrases and latent contents). The strongest effects were obtained when the originally presented cartoons were tested on their latent (deep semantic) contents. Recognition hypermnesia for part-forms or transforms of earlier presented stimuli has potentially wide-ranging implications since real-world recognition—of faces, texts, visual scenes—usually involves recognising stimuli that are variants, not exact copies, of the originally encountered materials.     

Strategic grouping in the spatial span memory task

Spatial short-term memory performance was examined in relation to participants’ strategies. A total of 20 adult participants viewed and reproduced sequences of locations that varied in length (five, six, seven, or eight locations) and spatial separability (a manipulation of the configurations). In trial-by-trial self-reports, participants described five types of strategies. Chunking the spatial sequences into groups of three or four locations was the sole strategy associated with increased accuracy. Participants demonstrated considerable variability in the strategies that they selected, suggesting that cognitive resources are allocated to strategy selection, execution, and monitoring in the spatial span task. Spatially separable sequences were more accurately recalled than nonseparable sequences, independent of strategic grouping, suggesting two levels of grouping in the spatial span task.     

A Minister's Information-Handling

ABSTRACT

One of the roles which ministers perform is that of “caregiver.” Caregiving functions include such activities as visiting parishioners, hearing confessions, and counselling people. These functions are carried out within a legal context. In this paper, two aspects of the parish minister's information handling in respect to his/her role as caregiver are examined. Specific attention is given to the pastor's vulnerability, while carrying out pastoral duties, to civil lawsuits for malpractice and to her/his expectation of privilege against testifying in criminal proceedings. It is observed that in North America, considerable confusion exists over who actually owns and who can control the information a minister acquires and disseminates.     

The response dynamics of preferential choice

The ubiquity of psychological process models requires an increased degree of sophistication in the methods and metrics that we use to evaluate them. We contribute to this venture by capitalizing on recent work in cognitive science analyzing response dynamics, which shows that the bearing information processing dynamics have on intended action is also revealed in the motor system. This decidedly “embodied” view suggests that researchers are missing out on potential dependent variables with which to evaluate their models—those associated with the motor response that produces a choice. The current work develops a method for collecting and analyzing such data in the domain of decision making. We first validate this method using widely normed stimuli from the International Affective Picture System (Experiment 1), and demonstrate that curvature in response trajectories provides a metric of the competition between choice options. We next extend the method to risky decision making (Experiment 2) and develop predictions for three popular classes of process model. The data provided by response dynamics demonstrate that choices contrary to the maxim of risk seeking in losses and risk aversion in gains may be the product of at least one “online” preference reversal, and can thus begin to discriminate amongst the candidate models. Finally, we incorporate attentional data collected via eye-tracking (Experiment 3) to develop a formal computational model of joint information sampling and preference accumulation. In sum, we validate response dynamics for use in preferential choice tasks and demonstrate the unique conclusions afforded by response dynamics over and above traditional methods.     

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.     

In search of average growth: Describing within-year oral reading fluency growth across Grades 1–8

Measures of oral reading fluency (ORF) are perhaps the most often used assessment to monitor student progress as part of a response to intervention (RTI) model. Rates of growth in research and aim lines in practice are used to characterize student growth; in either case, growth is generally defined as linear, increasing at a constant rate. Recent research suggests ORF growth follows a nonlinear trajectory, but limitations related to the datasets used in such studies, composed of only three testing occasions, curtails their ability to examine the true functional form of ORF growth. The purpose of this study was to model within-year ORF growth using up to eight testing occasions for 1448 students in Grades 1 to 8 to assess (a) the average growth trajectory for within-year ORF growth, (b) whether students vary significantly in within-year ORF growth, and (c) the extent to which findings are consistent across grades. Results demonstrated that for Grades 1 to 7, a quadratic growth model fit better than either linear or cubic growth models, and for Grade 8, there was no substantial, stable growth. Findings suggest that the expectation for linear growth currently used in practice may be unrealistic.