Applied Psychological Measurement最新文献

Methods to identify carelessness in survey research can be valuable tools in reducing bias during survey development, validation, and use. Because carelessness may take multiple forms, researchers typically use multiple indices when identifying carelessness. In the current study, we extend the literature on careless response identification by examining the usefulness of three item-response theory-based person-fit indices for both random and overconsistent careless response identification: infit MSE outfit MSE, and the polytomous l_z statistic. We compared these statistics with traditional careless response indices using both empirical data and simulated data. The empirical data included 2,049 high school student surveys of teaching effectiveness from the Network for Educator Effectiveness. In the simulated data, we manipulated type of carelessness (random response or overconsistency) and percent of carelessness present (0%, 5%, 10%, 20%). Results suggest that infit and outfit MSE and the l_z statistic may provide complementary information to traditional indices such as LongString, Mahalanobis Distance, Validity Items, and Completion Time. Receiver operating characteristic curves suggested that the person-fit indices showed good sensitivity and specificity for classifying both over-consistent and under-consistent careless patterns, thus functioning in a bidirectional manner. Carelessness classifications based on low fit values correlated with carelessness classifications from LongString and completion time, and classifications based on high fit values correlated with classifications from Mahalanobis Distance. We consider implications for research and practice.

Sparse rating designs, where each examinee's performance is scored by a small proportion of raters, are prevalent in practical performance assessments. However, relatively little research has focused on the degree to which different analytic techniques alert researchers to rater effects in such designs. We used a simulation study to compare the information provided by two popular approaches: Generalizability theory (G theory) and Many-Facet Rasch (MFR) measurement. In previous comparisons, researchers used complete data that were not simulated-thus limiting their ability to manipulate characteristics such as rater effects, and to understand the impact of incomplete data on the results. Both approaches provided information about rating quality in sparse designs, but the MFR approach highlighted rater effects related to centrality and bias more readily than G theory.

Aberrant responding on tests and surveys has been shown to affect the psychometric properties of scales and the statistical analyses from the use of those scales in cumulative model contexts. This study extends prior research by comparing the effects of four types of aberrant responding on model fit in both cumulative and ideal point model contexts using graded partial credit (GPCM) and generalized graded unfolding (GGUM) models. When fitting models to data, model misfit can be both a function of misspecification and aberrant responding. Results demonstrate how varying levels of aberrant data can severely impact model fit for both cumulative and ideal point data. Specifically, longstring responses have a stronger impact on dimensionality for both ideal point and cumulative data, while random responding tends to have the most negative impact on data model fit according to information criteria (AIC, BIC). The results also indicate that ideal point data models such as GGUM may be able to fit cumulative data as well as the cumulative model itself (GPCM), whereas cumulative data models may not provide sufficient model fit for data simulated using an ideal point model.

Test speededness refers to a situation in which examinee performance is inadvertently affected by the time limit of the test. Because speededness has the potential to severely bias both person and item parameter estimates, it is crucial that speeded examinees are detected. In this article, we develop a change-point analysis (CPA) procedure for detecting test speededness. Our procedure distinguishes itself from existing CPA procedures by using information from both item scores and distractors. Using detailed simulations, we show that under most conditions, the new CPA procedure improves the detection of speeded examinees and produces more accurate change-point estimates. It therefore seems there is a considerable amount of information to be gained from the item distractors, which, quite notably are available in all multiple-choice data. A real data example is also provided.

Large-scale tests often contain mixed-format items, such as when multiple-choice (MC) items and constructed-response (CR) items are both contained in the same test. Although previous research has analyzed both types of items simultaneously, this may not always provide the best estimate of ability. In this paper, a two-step sequential Bayesian (SB) analytic method under the concept of empirical Bayes is explored for mixed item response models. This method integrates ability estimates from different item formats. Unlike the empirical Bayes method, the SB method estimates examinees' posterior ability parameters with individual-level sample-dependent prior distributions estimated from the MC items. Simulations were used to evaluate the accuracy of recovery of ability and item parameters over four factors: the type of the ability distribution, sample size, test length (number of items for each item type), and person/item parameter estimation method. The SB method was compared with a traditional concurrent Bayesian (CB) calibration method, EAPsum, that uses scaled scores for summed scores to estimate parameters from the MC and CR items simultaneously in one estimation step. From the simulation results, the SB method showed more accurate and reliable ability estimation than the CB method, especially when the sample size was small (150 and 500). Both methods presented similar recovery results for MC item parameters, but the CB method yielded a bit better recovery of the CR item parameters. The empirical example suggested that posterior ability estimated by the proposed SB method had higher reliability than the CB method.

Rater effects are commonly observed in rater-mediated assessments. By using item response theory (IRT) modeling, raters can be treated as independent factors that function as instruments for measuring ratees. Most rater effects are static and can be addressed appropriately within an IRT framework, and a few models have been developed for dynamic rater effects. Operational rating projects often require human raters to continuously and repeatedly score ratees over a certain period, imposing a burden on the cognitive processing abilities and attention spans of raters that stems from judgment fatigue and thus affects the rating quality observed during the rating period. As a result, ratees' scores may be influenced by the order in which they are graded by raters in a rating sequence, and the rating order effect should be considered in new IRT models. In this study, two types of many-faceted (MF)-IRT models are developed to account for such dynamic rater effects, which assume that rater severity can drift systematically or stochastically. The results obtained from two simulation studies indicate that the parameters of the newly developed models can be estimated satisfactorily using Bayesian estimation and that disregarding the rating order effect produces biased model structure and ratee proficiency parameter estimations. A creativity assessment is outlined to demonstrate the application of the new models and to investigate the consequences of failing to detect the possible rating order effect in a real rater-mediated evaluation.

To provide more insight into an individual's response process and cognitive process, this study proposed three mixed sequential item response models (MS-IRMs) for mixed-format items consisting of a mixture of a multiple-choice item and an open-ended item that emphasize a sequential response process and are scored sequentially. Relative to existing polytomous models such as the graded response model (GRM), generalized partial credit model (GPCM), or traditional sequential Rasch model (SRM), the proposed models employ an appropriate processing function for each task to improve conventional polytomous models. Simulation studies were carried out to investigate the performance of the proposed models, and the results indicated that all proposed models outperformed the SRM, GRM, and GPCM in terms of parameter recovery and model fit. An application illustration of the MS-IRMs in comparison with traditional models was demonstrated by using real data from TIMSS 2007.