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Due to its flexibility, the multiple-indicator, multiple-causes (MIMIC) model has become an increasingly popular method for the detection of differential item functioning (DIF). In this article, we propose the mediated MIMIC model method to uncover the underlying mechanism of DIF. This method extends the usual MIMIC model by including one variable or multiple variables that may completely or partially mediate the DIF effect. If complete mediation effect is found, the DIF effect is fully accounted for. Through our simulation study, we find that the mediated MIMIC model is very successful in detecting the mediation effect that completely or partially accounts for DIF, while keeping the Type I error rate well controlled for both balanced and unbalanced sample sizes between focal and reference groups. Because it is successful in detecting such mediation effects, the mediated MIMIC model may help explain DIF and give guidance in the revision of a DIF item. [ABSTRACT FROM AUTHOR]

Within the framework of item response theory (IRT), there are two recent lines of work on the estimation of classification accuracy (CA) rate. One approach estimates CA when decisions are made based on total sum scores, the other based on latent trait estimates. The former is referred to as the Lee approach, and the latter, the Rudner approach, each after its representative contributor. In this article, the two approaches are delineated in the same framework to highlight their similarities and differences. In addition, a simulation study manipulating IRT model, sample size, test length, and cut score location was conducted. The study investigated the empirical CA that can be achieved using either the total scores or the latent trait estimates. It also evaluated the performances of the two approaches in estimating their respective empirical CAs. Results on the empirical CA suggest that when the model fits, classifications made with the latent trait estimate shall be equally or more accurate than classifications made with total score. The magnitude of difference was governed by divergence from the one-parameter logistic (1PL) model. Both Lee and Rudner approaches provided good estimates of their respective empirical CAs for every condition that was simulated. Practical implications of the simulation results are discussed. [ABSTRACT FROM PUBLISHER]

Home health care (HHC) is growing rapidly and yet health and safety conditions of HHC clinicians are poorly understood. Study of this workforce presents unique challenges because it is decentralised, often part-time and mobile. As part of a larger project on sharps injuries and blood exposures in HHC, this paper addresses the challenges of recruiting a large cohort of HHC nurses and aides and describes novel cross-sectional survey methodology. Recruitment was conducted with cooperation from eight HHC agencies and two labour unions. Intensive personal contacts and a financial incentive ($25) were employed. Some groups of HHC clinicians could be contacted only by mail, while others were contacted during a promotional 'mini-fair' at their agency. A total of 1772, 18-page health and safety surveys were distributed and 1225 usable surveys were collected. This 69% overall response rate is better than that in many recent health surveys. Survey returns were highest (67DS91%) where promotional events were held. The mailing-only strategy generated lower response rates (53DS55%), despite the same financial incentive. Despite the challenges of reaching out to the decentralised HHC workforce, adequate response to a detailed health survey is possible, using appropriate techniques and with the close cooperation of employers and labour unions. [ABSTRACT FROM AUTHOR]

The author offers information on the R programming language's classification accuracy and consistency under Item Response Theory package (cacIRT). The author says that the package computes classification accuracy (CA) and classification consistency (CC). The author explains that the two different approaches of W. Lee and L.M. Rudner are based on item response theory (IRT).