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The goal of the present study was to address the analytical complexity of incorporating responses and response times through applying the Jeon and De Boeck mixture item response theory model in Mplus 8.7. Using both simulated and real data, we attempt to identify subgroups of responders that are rapid guessers or engage knowledge retrieval strategies. When applying the mixture model to a measure of contextual error in linguistics results pointed to the presence of a knowledge retrieval strategy. That is, a participant either knows the content (morphology, grammar rules) and can identify the error, or lacks the requisite knowledge and cannot benefit from spending more time on an item. In contrast, as item difficulty progressed, the high-ability group utilized the additional time to make informed guesses. The methodology is illustrated using annotated code in Mplus 8.7.

Boys in the Kingdom of Saudi Arabia consistently and significantly underperform compared to girls across different grades and subjects, forming one of the largest gender gaps in student achievement in the world. Saudi Arabia offers a unique setting in which boys and girls attend separate schools on a universal basis starting from grade 1. This means that boys and girls are educated only by male and female teachers, respectively, in effect inhabiting parallel education systems. In this context, this study examines the factors that are associated with student achievement in mathematics and science in grades 4 and 8 and the extent to which these associations are different for boys and girls, in an effort to gain insights into boys' underachievement in mathematics and science in Saudi Arabia. The paper employs data from two recent large-scale assessments of education: Trends in International Mathematics and Science Study (TIMSS) 2019 and Saudi Arabia's National Assessment of Learning Outcomes (NALO) 2018. A series of hierarchical two-level linear regression models showed that in grade 4, school climate was more strongly associated with boys' compared with girls' achievement in both mathematics and science, with boys attending schools of poorer school climate having a considerably lower performance compared with girls attending such schools. The findings also indicated that although greater literacy and numeracy readiness was linked with higher science achievement among boys and girls, grade 4 boys tended to benefit more from this readiness than girls. In addition, the results show that student absenteeism in grade 4 is particularly strongly associated with decreases in mathematics achievement among boys. In grade 8, significant interactions between gender and the extent to which students feel confident in science, the degree of schools' emphasis on academic success, and teachers' age are observed. The paper concludes by discussing some of the implications of these findings for educators and policy makers in Saudi Arabia.

The purpose of the present article was to illustrate, using an example from a national assessment, the value from analyzing the behavior of distractors in measures that engage the multiple-choice format. A secondary purpose of the present article was to illustrate four remedial actions that can potentially improve the measurement of the construct(s) under study. Participants were 2,248 individuals who took a national examination of chemistry. The behavior of the distractors was analyzed by modeling their behavior within the Rasch model. Potentially informative distractors were (a) further modeled using the partial credit model, (b) split onto separate items and retested for model fit and parsimony, (c) combined to form a "super" item or testlet, and (d) reexamined after deleting low-ability individuals who likely guessed on those informative, albeit erroneous, distractors. Results indicated that all but the item split strategies were associated with better model fit compared with the original model. The best fitted model, however, involved modeling and crediting informative distractors via the partial credit model or eliminating the responses of low-ability individuals who likely guessed on informative distractors. The implications, advantages, and disadvantages of modeling informative distractors for measurement purposes are discussed.

The purpose of the present study was to extend the model of measurement invariance by simultaneously estimating invariance across multiple populations in the dichotomous instrument case using multi-group confirmatory factor analytic and multiple indicator multiple causes (MIMIC) methodologies. Using the Arabic version of the General Aptitude Test (GAT), invariance was tested at the configural, metric, and scalar levels. Results indicated that the hybrid model that incorporates both the multi-group case and the MIMIC model provide a viable alternative to the measurement of invariance between variables when they interact. Metric and scalar invariance were supported for all of GAT's subscales with the exception of Word Meaning for which lack of invariance was likely caused by model misspecification. Subtle effects were observed in favor of public school testees, but they did not exceed significance levels.

The purpose of the present studies was to evaluate and predict academic cheating with regard to a national examination in a Middle East country. In Study 1, 4,024 students took part and potential cheaters were classified as those having discrepant scores in multiple administrations that exceeded 1SDin absolute terms. A latent class mixture analysis suggested two pathways for potential cheating: (a) The first path involved students—most male—who changed city or region of examination during test taking, and (b) the second path described students—most male—who did not change city, region, or center of administration. Study 2 profiled cheaters using a sample of examinees who were actually caught cheating. Participants were 545 students, 253 of whom were caught cheating between 2002 and 2012. Both samples were selected from a pool of 319,219 testees using random sampling procedures. Results indicated that a 4-class solution best fitted the data as in Study 1. Furthermore, a predictive model was tested with an independent cross-validation sample of 112 examinees (56 cheaters, 56 noncheaters). Results indicated that the model classified correctly 78.57 of the new cheating cases (sensitivity) and 94.64% of noncheaters (specificity). [ABSTRACT FROM AUTHOR]