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Pornphan Sureeyatanapas; Panitas Sureeyatanapas; Uthumporn Panitanarak; Jittima Kraisriwattana; Patchanan Sarootyanapat; Daniel O'Connell – Language Testing in Asia, 2024
Ensuring consistent and reliable scoring is paramount in education, especially in performance-based assessments. This study delves into the critical issue of marking consistency, focusing on speaking proficiency tests in English language learning, which often face greater reliability challenges. While existing literature has explored various…
Descriptors: Foreign Countries, Students, English Language Learners, Speech
Rudner, Lawrence M.; Schafer, William D. – 2001
This digest discusses sources of error in testing, several approaches to estimating reliability, and several ways to increase test reliability. Reliability has been defined in different ways by different authors, but the best way to look at reliability may be the extent to which measurements resulting from a test are characteristics of those being…
Descriptors: Educational Testing, Error of Measurement, Reliability, Scores

Feldt, Leonard S. – Applied Measurement in Education, 2002
Considers the situation in which content or administrative considerations limit the way in which a test can be partitioned to estimate the internal consistency reliability of the total test score. Demonstrates that a single-valued estimate of the total score reliability is possible only if an assumption is made about the comparative size of the…
Descriptors: Error of Measurement, Reliability, Scores, Test Construction

Livingston, Samuel A. – Journal of Educational Measurement, 1982
For tests used to make pass/fail decisions, the relevant standard error of measurement (SEM) is the SEM at the passing score. If the test is highly stratified, this SEM should be estimated by a split-halves approach. A formula and its derivation are provided. (Author)
Descriptors: Cutting Scores, Error of Measurement, Estimation (Mathematics), Mathematical Formulas

Cohen, Patricia – Evaluation and Program Planning: An International Journal, 1982
The various costs of Type I and Type II errors of inference from data are discussed. Six methods for minimizing each error type are presented, which may be employed even after data collection for Type I and which minimizes Type II errors by a study design and analytical means combination. (Author/CM)
Descriptors: Analysis of Variance, Data Analysis, Data Collection, Error of Measurement