Screening for learning disabilities in Oman: confirmatory factor analysis of the Arabic version of the learning disabilities diagnostic inventory.

Students at risk for learning disabilities (LD) are overidentified in elementary schools in Oman due to the absence of adequate instruments which teachers can use in validating their observations. Teachers need valid instruments so that their judgment of students' behaviours can help in making academic and non-academic decision. The Learning Disabilities Diagnostic Inventory (LDDI) is widely used to examine manifestations of LD as an intrinsic processing disorder. This study aimed to verify the six-factor structure with confirmatory factor analysis, and test its measurement invariance across child gender. Teachers completed the Arabic version of the LDDI for an Omani sample of 1564 children aged 7 to 11. Overall, the six-factor model showed an acceptable fit after performing some post-hoc modifications that were justified on a theoretical ground. Results indicated metric invariance across gender. Zero-order correlations, however, were highly significant, which reflected that the Arabic version of the LDDI showed poor discriminant validity compared to the original LDDI. The Arabic version of the LDDI, however, can be a handy tool to screen for the LD manifestations and help in responding to teachers' academic concerns about students in Key stages 1 and 2 in Oman.

Normalization of mean squared differences to measure agreement for continuous data.

Agreement among observations on two variables for reliability or validation purposes is usually assessed by the evaluation of the mean squared differences (MSD). Many transformations of MSD have been proposed to interpret and make statistical inferences about the agreement between the two variables, including the concordance correlation coefficient (CCC) and the random marginal agreement coefficient (RMAC). This paper presents a normalization of MSD based on a reference range and uses it to derive CCC and RMAC (or ACC alternatively). The normalization of MSD enables the comparison between these two coefficients. The paper compares thoroughly the differences between these two coefficients and their properties at different agreement levels. Results show that ACC has promising properties over CCC. A Monte Carlo simulations as well as real data applications are performed. ACC for more than two variables are also derived.