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The C-test is a type of gap-filling test designed to efficiently measure second language proficiency. The typical C-test consists of several short paragraphs with the second half of every second word deleted. The words with deleted parts are considered as items nested within the corresponding paragraph. Given this testlet structure, it is commonly taken for granted that the C-test design may violate the local independence assumption. However, this assumption has not been fully investigated in the C-test research to date, including the evaluation of alternative psychometric models (i.e., unidimensional and multidimensional) to calibrate and score the C-test. This study addressed each of these issues using a large data set of responses to an English-language C-test. First, we examined the local item independence assumption via multidimensional item response theory (IRT) models, Yen's Q3, and Jackknife Slope Index. Second, we evaluated several IRT models to determine optimal approaches to scoring the C-test. The results support an interpretation of unidimensionality for the C-test items within a paragraph, with only minor evidence of local item dependence. Furthermore, the two-parameter logistic (2PL) IRT model was found to be the most appropriate model for calibrating and scoring the C-test. Implications for designing, scoring, and analyzing C-tests are discussed.

This report investigates the prediction of labor force status using observed variables, such as gender, age, and immigrant status, and more importantly, measured skill variables, including literacy proficiency and a categorical rating of educational attainment based on the 1994 International Adult Literacy Survey (IALS), the 2003 Adult Literacy and Life Skills Survey (ALL), and the 2011 Programme for the International Assessment of Adult Competencies (PIAAC) projects. We explored the regression relations in the past two decades for six trend countries and subnational regions that provide data for all assessments: the United States, Norway, the Netherlands, Italy, Canada's English-speaking region, and Canada's French-speaking region. Probit regression models with latent predictors were used in this cross-sectional study to investigate how those variables are structurally related to labor market outcomes. Results show the importance of literacy proficiency and education in determining individuals' labor force status across countries/regions, but with key differences among these countries/regions.

The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo simulation study is conducted in which data generated according to a two-class mixture Rasch model with both dichotomous and continuous covariates are fitted to several mixture Rasch models with misspecified covariates to examine the effects of covariate inclusion on model parameter estimation. In addition, both complete response data and incomplete response data with different types of missingness are considered in the present study in order to simulate practical assessment settings. Parameter estimation is carried out within a Bayesian framework vis-à-vis Markov chain Monte Carlo algorithms.

In large-scale process industries, one of the great challenges is to achieve optimum operation of systems with multi-time-scale property and partially unknown models. To this end, this article proposes a novel multi-rate layered operational optimal control (OOC) method, which employs lifting technique to unify the relatively fast dual-rate of basic loop layer and relatively slow single-rate of operational layer. Besides, by integrating model-based predictive control of basic loop layer with data-based actor-critic reinforcement learning (RL) of operational layer, it overcomes the difficulty of building the operational process dynamic model. The convergence of the proposed method is proved, and dense medium separation (DMS) process is taken as an application case to illustrate the effectiveness of our proposed method via a self-developed simulation platform. [ABSTRACT FROM AUTHOR]