Stopping rules for multi-category computerized classification testing

Computerized classification testing (CCT) aims to classify persons into one of two or more possible categories to make decisions such as mastery/non-mastery or meet most/meet all/exceed. A defining feature of CCT is its stopping criterion: the test terminates when there is enough confidence to make a decision. There is abundant research on CCT with a single cut-off, and two common stopping criteria are the sequential probability ratio test (SPRT) statistic and the generalized likelihood ratio statistic (GLR). However, there is a relative scarcity of research extending the SPRT to the multi-hypothesis case for when there is more than one cut-off. In this paper, we propose a new multi-category GLR (mGLR) statistic as well as a stochastically curtailed version of the CCT with three or more categories. A simulation study was conducted to show that the mGLR statistic outperformed the existing stopping rules by generating shorter average test length without sacrificing classification accuracy. Results also revealed that the stochastically curtailed mGLR successfully increased test efficiency in certain testing conditions.     

Recommandations de l’ITC pour la traduction et l’adaptation de tests (seconde édition)

The second edition of the ITC guidelines for translating and adapting tests was prepared between 2005 and 2015 to improve upon the first edition, and to respond to advances in testing technology and practices. The 18 guidelines are organized into six categories to facilitate their use: pre-condition (3), test development (5), confirmation (4), administration (2), scoring and interpretation (2), and documentation (2). For each guideline, an explanation is provided along with suggestions for practice. A checklist is provided to improve the implementation of the guidelines.     

输入方式对学语前聋生语篇理解的影响：文本和测题类型的调节效应

为了考察信息输入方式、文本类型和测题类型对聋生语篇理解的影响,研究通过录像呈现,以口语、手语、书面语三种输入方式向学语前极重度七、八、九年级聋生呈现叙述性和描述性两种文本类型的语篇信息,并要求其完成理解测试。结果显示：输入方式对聋生语篇理解的效率存在影响,书面语成绩最高且速度最快,依次为手语、口语,文本类型和测题类型对该影响有调节作用。结果表明输入方式是聋生语篇理解的主要影响因素,文本和测题类型是调节因素。     

Generating interior search directions for multiobjective linear programming

A new multiobjective linear programming (MOLP) algorithm is presented. The algorithm uses a variant of Karmarkar's interior-point algorithm known as the affine-scaling primal algorithm. Using this single-objective algorithm, interior search directions are generated and used to provide an approximation to the gradient of the (implicitly known) utility function. The approximation is guided by assessing locally relevant preference information for the various interior directions through interaction with a decision maker (DM). The resulting algorithm is an interactive approach that makes its progress towards the solution through the interior of the constraints polytope.     

Using Lexicographic Parametric Programming for Searching a Non-dominated Set in Multiple-Objective Linear Programming

In this paper we propose an approach which makes it possible to search non-dominated and only non-dominated solutions in multiple-objective linear programming. The approach is based on the use of a reference direction and lexicographic parametric programming. The requisite theory is developed in the paper. The use of the approach is illustrated with a numerical example.     

Defensive reactions of “wild-type” and “Domesticated” wild rats to approach and contact by a threat stimulus

Selective breeding of wild rats over many generations on the basis of low or high defensive threat and attack to human approach and contact has produced highly polarized “domesticated” and “wild-type” animals. Because the selection procedure selectively involves these two defense patterns, and these clearly differ in the two groups, it is of interest to determine if other, nonselected, defensive behaviors to threat stimuli also change. “Domesticated” and “wild-type” rats of the thirty-fifth generation were run in a fear defense test battery (F/DTB) to systematically evaluate defensive behaviors to a variety of present threat stimuli. “Domesticated” rats showed reduced avoidance and slower flight speed to an approaching experimenter, reduced jump/startle response to handelap and dorsal contact, less vocalization and boxing to vibrissae stimulation or to an anesthetized conspecific, and reduced defensiveness to an attempted pickup by the experimenter. These results indicate that selective bi-directional breeding for defensive threat and attack to human approach and contact produces group differences in a variety of defensive behaviors, and in defensiveness to stimuli other than those on which the selection was based. © 1994 Wiley-Liss, Inc.     

Testing two variances for superiority/non-inferiority and equivalence: Using the exhaustion algorithm for sample size allocation with cost

The equality of two group variances is frequently tested in experiments. However, criticisms of null hypothesis statistical testing on means have recently arisen and there is interest in other types of statistical tests of hypotheses, such as superiority/non-inferiority and equivalence. Although these tests have become more common in psychology and social sciences, the corresponding sample size estimation for these tests is rarely discussed, especially when the sampling unit costs are unequal or group sizes are unequal for two groups. Thus, for finding optimal sample size, the present study derived an initial allocation by approximating the percentiles of an F distribution with the percentiles of the standard normal distribution and used the exhaustion algorithm to select the best combination of group sizes, thereby ensuring the resulting power reaches the designated level and is maximal with a minimal total cost. In this manner, optimization of sample size planning is achieved. The proposed sample size determination has a wide range of applications and is efficient in terms of Type I errors and statistical power in simulations. Finally, an illustrative example from a report by the Health Survey for England, 1995–1997, is presented using hypertension data. For ease of application, four R Shiny apps are provided and benchmarks for setting equivalence margins are suggested.     

The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects

In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem.