As low-stakes testing contexts increase, low test-taking effort may serve as a serious validity threat. One common solution to this problem is to identify noneffortful responses and treat them as missing during parameter estimation via the Effort-Moderated IRT (EM-IRT) model. Although this model has been shown to outperform traditional IRT models (e.g., 2PL) in parameter estimation under simulated conditions, prior research has failed to examine its performance under violations to the model’s assumptions. Therefore, the objective of this simulation study was to examine item and mean ability parameter recovery when violating the assumptions that noneffortful responding occurs randomly (assumption #1) and is unrelated to the underlying ability of examinees (assumption #2). Results demonstrated that, across conditions, the EM-IRT model provided robust item parameter estimates to violations of assumption #1. However, bias values greater than 0.20 SDs were observed for the EM-IRT model when violating assumption #2; nonetheless, these values were still lower than the 2PL model. In terms of mean ability estimates, model results indicated equal performance between the EM-IRT and 2PL models across conditions. Across both models, mean ability estimates were found to be biased by more than 0.25 SDs when violating assumption #2. However, our accompanying empirical study suggested that this biasing occurred under extreme conditions that may not be present in some operational settings. Overall, these results suggest that the EM-IRT model provides superior item and equal mean ability parameter estimates in the presence of model violations under realistic conditions when compared to the 2PL model.
Suboptimal effort is a major threat to valid score-based inferences. While the effects of such behavior have been frequently examined in the context of mean group comparisons, minimal research has considered its effects on individual score use (e.g., identifying students for remediation). Focusing on the latter context, this study addressed two related questions via simulation and applied analyses. First, we investigated how much including noneffortful responses in scoring using a three-parameter logistic (3PL) model affects person parameter recovery and classification accuracy for noneffortful responders. Second, we explored whether improvements in these individual-level inferences were observed when employing the Effort Moderated IRT (EM-IRT) model under conditions in which its assumptions were met and violated. Results demonstrated that including 10% noneffortful responses in scoring led to average bias in ability estimates and misclassification rates by as much as 0.15 SDs and 7% respectively. These results were mitigated when employing the EM-IRT model, particularly when model assumptions were met. However, once model assumptions were violated, the EM-IRT model’s performance deteriorated, though still outperforming the 3PL model. Thus, findings from this study show that: (a) including noneffortful responses when using individual scores can lead to potential unfounded inferences and potential score misuse; and (b) the negative impact that noneffortful responding has on person ability estimates and classification accuracy can be mitigated by employing the EM-IRT model, particularly when its assumptions are met.
Predictive analytics in education can offer a benefit as long as educators heed the differences between how the tools are used in industry and how they should be used differently in schooling. Perhaps most important, teachers already know a great deal about their students — far more than an investor knows about a stock or a baseball scout about an up-and-coming pitcher. In fact, teachers are a veritable treasure trove of data on student behaviors, attitudes, and aspirations, information not typically included in a statistical model. Teachers also have far more power to shape what happens to students, an influence driven in part by their opinions of each child. Using predictive analytics in education while ignoring these differences may lead not only to misidentifying students at risk of dropping out but also negatively influence how teachers view those students.
The objective of the present study was to investigate item-, examinee-, and country-level correlates of rapid guessing (RG) in the context of the 2018 PISA science assessment. Analyzing data from 267,148 examinees across 71 countries showed that over 50% of examinees engaged in RG on an average proportion of one in 10 items. Descriptive differences were noted between countries on the mean number of RG responses per examinee with discrepancies as large as 500%. Country-level differences in the odds of engaging in RG were associated with mean performance and regional membership. Furthermore, based on a two-level cross-classified hierarchical linear model, both item- and examinee-level correlates were found to moderate the likelihood of RG. Specifically, the inclusion of items with multimedia content was associated with a decrease in RG. A number of demographic and attitudinal examinee-level variables were also significant moderators, including sex, linguistic background, SES, and self-rated reading comprehension, motivation mastery, and fear of failure. The findings from this study imply that select subgroup comparisons within and across nations may be biased by differential test-taking effort. To mitigate RG in international assessments, future test developers may look to leverage technology-enhanced items.
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