Recursive Partitioning to Identify Potential Causes of Differential Item Functioning in Cross-National Data

Differential item functioning (DIF) assessment is key in score validation. When DIF is present scores may not accurately reflect the construct of interest for some groups of examinees, leading to incorrect conclusions from the scores. Given rising immigration, and the increased reliance of educational policymakers on cross-national assessments such as Programme for International Student Assessment, Trends in International Mathematics and Science Study, and Progress in International Reading Literacy Study (PIRLS), DIF with regard to native language is of particular interest in this context. However, given differences in language and cultures, assuming similar cross-national DIF may lead to mistaken assumptions about the impact of immigration status, and native language on test performance. The purpose of this study was to use model-based recursive partitioning (MBRP) to investigate uniform DIF in PIRLS items across European nations. Results demonstrated that DIF based on mother's language was present for several items on a PIRLS assessment, but that the patterns of DIF were not the same across all nations.     

Combining diversity and dispersion criteria for anticlustering: A bicriterion approach

Most partitioning methods used in psychological research seek to produce homogeneous groups (i.e., groups with low intra-group dissimilarity). However, there are also applications where the goal is to provide heterogeneous groups (i.e., groups with high intra-group dissimilarity). Examples of these anticlustering contexts include construction of stimulus sets, formation of student groups, assignment of employees to project work teams, and assembly of test forms from a bank of items. Unfortunately, most commercial software packages are not equipped to accommodate the objective criteria and constraints that commonly arise for anticlustering problems. Two important objective criteria for anticlustering based on information in a dissimilarity matrix are: a diversity measure based on within-cluster sums of dissimilarities; and a dispersion measure based on the within-cluster minimum dissimilarities. In many instances, it is possible to find a partition that provides a large improvement in one of these two criteria with little (or no) sacrifice in the other criterion. For this reason, it is of significant value to explore the trade-offs that arise between these two criteria. Accordingly, the key contribution of this paper is the formulation of a bicriterion optimization problem for anticlustering based on the diversity and dispersion criteria, along with heuristics to approximate the Pareto efficient set of partitions. A motivating example and computational study are provided within the framework of test assembly.     

Model selection for minimum‐diameter partitioning

The minimum‐diameter partitioning problem (MDPP) seeks to produce compact clusters, as measured by an overall goodness‐of‐fit measure known as the partition diameter, which represents the maximum dissimilarity between any two objects placed in the same cluster. Complete‐linkage hierarchical clustering is perhaps the best‐known heuristic method for the MDPP and has an extensive history of applications in psychological research. Unfortunately, this method has several inherent shortcomings that impede the model selection process, such as: (1) sensitivity to the input order of the objects, (2) failure to obtain a globally optimal minimum‐diameter partition when cutting the tree at K clusters, and (3) the propensity for a large number of alternative minimum‐diameter partitions for a given K. We propose that each of these problems can be addressed by applying an algorithm that finds all of the minimum‐diameter partitions for different values of K. Model selection is then facilitated by considering, for each value of K, the reduction in the partition diameter, the number of alternative optima, and the partition agreement among the alternative optima. Using five examples from the empirical literature, we show the practical value of the proposed process for facilitating model selection for the MDPP.     

Recursive Partitioning with Nonlinear Models of Change

In this article, we introduce nonlinear longitudinal recursive partitioning (nLRP) and the R package longRpart2 to carry out the analysis. This method implements recursive partitioning (also known as decision trees) in order to split data based on individual- (i.e., cluster) level covariates with the goal of predicting differences in nonlinear longitudinal trajectories. At each node, a user-specified linear or nonlinear mixed-effects model is estimated. This method is an extension of Abdolell et al.'s (2002) longitudinal recursive partitioning while permitting a nonlinear mixed-effects model in addition to a linear mixed-effects model in each node. We give an overview of recursive partitioning, nonlinear mixed-effects models for longitudinal data, describe nLRP, and illustrate its use with empirical data from the Early Childhood Longitudinal Study—Kindergarten Cohort.     

An Exactification of the Monoid of Primitive Recursive Functions

We study the monoid of primitive recursive functions and investigate a onestep construction of a kind of exact completion, which resembles that of the familiar category of modest sets, except that the partial equivalence relations which serve as objects are recursively enumerable. As usual, these constructions involve the splitting of symmetric idempotents.     

The analysis of multitrait-multimethod matrices via constrained components analysis

Multitrait-Multimethod (MTMM) matrices are often analyzed by means of confirmatory factor analysis (CFA). However, fitting MTMM models often leads to improper solutions, or non-convergence. In an attempt to overcome these problems, various alternative CFA models have been proposed, but with none of these the problem of finding improper solutions was solved completely. In the present paper, an approach is proposed where improper solutions are ruled out altogether and convergence is guaranteed. The approach is based on constrained variants of components analysis (CA). Besides the fact that these methods do not give improper solutions, they have the advantage that they provide component scores which can later on be used to relate the components to external variables. The new methods are illustrated by means of simulated data, as well as empirical data sets.This research has been made possible by a fellowship from the Royal Netherlands Academy of Arts and Sciences to the first author. The authors are obliged to three anonymous reviewers and an associate editor for constructive suggestions on the first version of this paper.     

Cut-Elimination and a Permutation-Free Sequent Calculus for Intuitionistic Logic

We describe a sequent calculus, based on work of Herbelin, of which the cut-free derivations are in 1-1 correspondence with the normal natural deduction proofs of intuitionistic logic. We present a simple proof of Herbelin's strong cut-elimination theorem for the calculus, using the recursive path ordering theorem of Dershowitz.     

Assessment center construct-related validity: Stepping beyond the MTMM matrix

Recent Monte Carlo research has illustrated that the traditional method for assessing the construct-related validity of assessment center (AC) post-exercise dimension ratings (PEDRs), an application of confirmatory factor analysis (CFA) to a multitrait-multimethod matrix, produces inconsistent results [Lance, C. E., Woehr, D. J., & Meade, A. W. (2007). Case study: A Monte Carlo investigation of assessment center construct validity models. Organizational Research Methods, 10, 430-448]. To avoid this shortcoming, a variance partitioning procedure was applied to the examination of the PEDRs of 193 individuals. Overall, results indicated that the person, dimension, and person by dimension interaction effects together accounted for approximately 32% of the total variance in AC ratings. However, despite no apparent exercise effect, the person by exercise interaction accounted for approximately 28% of the total variance. Although these results are drawn from a single AC, they nevertheless provide general support for the overall functioning of ACs and encourage continued application of variance partitioning approaches to AC research. Implications for AC design and research are discussed.     

Transition between alloy–element partitioned and non-partitioned growth of austenite from a ferrite and cementite mixture in a high-carbon low-alloy steel

The growth of austenite from a mixture of ferrite and cementite in low-alloy steels can be classified into three temperature ranges above the eutectoid temperature. In the first range, the austenite growth and cementite dissolution are controlled by alloy element diffusion from the beginning. In the second, they are controlled by carbon diffusion and switch to alloy element diffusion control at a later stage. In the third, the cementite dissolution, if the ferrite matrix has transformed to austenite upon heating, is controlled by carbon diffusion until completion. The transition temperatures between these ranges are evaluated in a quaternary alloy containing Mn and Cr by Thermo-calc and DICTRA simulation, and are in essential agreement with earlier experimental results. The proposed simple approach of calculating the transition temperatures may facilitate our understanding of austenitization kinetics and the design of heat treatment, for example, homogenization and soaking, of high-carbon steels.