Work place: College of Education, Sultan Qaboos University, Alhouz, Muscat, Sultanate of Oman
E-mail: mehrzi@squ.edu.om
Website: https://orcid.org/0000-0001-7672-1451
Research Interests:
Biography
Rashid Almehrizi, Rashid Almehrizi currently works at the Department of Psychology, Sultan Qaboos University from August 1994 - Present. Rashid does research in Quantitative Psychology and Psychometrics. He has different skills and expertise in Reliability, Normalization, Correlation Coefficient, Variability, Reliability Analysis, Reliability Theory, Statistical Analysis, Linear Regression, Descriptive Statistics, Data Analysis, Variance, Applied Statistics, Multivariate Statistics, Quantitative Modelling, Software Reliability, Statistical Modeling, Statistical Inference, Mathematical Statistics, Maximum Likelihood, Hypothesis Testing, Multivariate Analysis, R Statistical Package, Factor Analysis, Correlation Analysis, Frequency Distribution, Normal Distribution, Statistical Testing, Confidence Intervals, Computational Statistics.
By Mohammed Al Ajmi Siti Salina Mustakim Samsilah Roslan Rashid Almehrizi
DOI: https://doi.org/10.5815/ijeme.2024.06.04, Pub. Date: 8 Dec. 2024
Employing Computerized Adaptive Testing (CAT) to evaluate verbal ability symptoms proves advantageous over traditional tests by delivering heightened measurement precision and reducing the testing burden. The CAT-Verbal Ability, developed from a large sample of 2689 participants in Gulf countries, underwent meticulous item bank development, ensuring unidimensionality, local independence, and investigating differential item functioning (DIF). The CAT-Verbal Ability item bank has high content validity, is unidimensional, locally independent, and does not have DIF; these outstanding psychometric qualities were confirmed by CAT simulations that were based on real data. With just 14 items needed, CAT simulations showed a high degree of measurement accuracy (r=0.73). In addition to being a psychometrically sound instrument, the proposed CAT-Verbal Ability demonstrated acceptable marginal reliability, criterion-related validity, sensitivity, and specificity. This makes it an efficient assessment method that reduces testing burden while maintaining information integrity, and it also saves time.
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