The greatest lower bound to the reliability of a test and the hypothesis of unidimensionality

By J. F. Berge, Gregor Socan. 2004

314 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/eb4817d163430027c8a0539fca5601f228064570

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In Statistics and Probability

Citations in the corpus

Coefficients Alpha, Beta, Omega, and the glb: Comments on Sijtsma (2009, methodology): “The data sets and the glb estimates are taken from the six examples in Sijtsma (2008) (S1-S2c), two examples in Bentler & Woodward (1980), (B&W 1 & 2) and the De Leeuw (1983) dataset analyzed by Ten Berge & Socan (2004).”
The Dutch Review Process for Evaluating the Quality of Psychological Tests: History, Procedure, and Results (2010, unknown): “…the reliability of a test (e.g., Novick & Lewis, 1967), and other methods such as Guttman’s lambda2 (Guttman, 1945) and the greatest lower bound ( glb ; Ten Berge & Soˇcan, 2004) give higher underestimates that come closer to the real reliability of the test (Sijtsma, 2009a, 2009b) 4 .”
Rethinking Internal Consistency in Cronbach's Alpha (2017, background): “Second, alpha is not a measure of unidimensionality (Ten Berge & Socan, 2004).”
The Hull Method for Selecting the Number of Common Factors (2011, methodology): “To ensure a nonnegative definite reduced correlation matrix, Minimum Rank Factor Analysis (Ten Berge & Kiers, 1991, Ten Berge & Socan, 2004) could be used.”
Using Logistic Approximations of Marginal Trace Lines to Develop Short Assessments (2013, methodology): “For this situation, a variant of the Explained Common Variance (ECV) statistic proved useful as an index of unidimensionality (Reise, Moore, & Haviland, 2010; ten Berge & Socan, 2004).”
When Are Multidimensional Data Unidimensional Enough for Structural Equation Modeling? An Evaluation of the DETECT Multidimensionality Index (2015, methodology): “Ten Berge and Sočan (2004), however, referred to unidimensionality as a mere hypothesis and stated, “the hypothesis is either beyond verification or it is false” and that “assessing how close is a given test to unidimensionality is far more interesting than testing whether or not the test is…”
Multidimensionality and Structural Coefficient Bias in Structural Equation Modeling (2013, methodology): “First, based on the population loading matrix, we computed the explained common variance (ECV; see Reise, Moore, & Haviland, 2010, in press; Ten Berge & Socan, 2004), which is the common variance explained by the general factor divided by the total common variance (see Equation 1, where Gen is the g”
Best Alternatives to Cronbach's Alpha Reliability in Realistic Conditions: Congeneric and Asymmetrical Measurements (2016, methodology): “For the GLB and GLBa coefficients, as the sample size increases the RMSE and the bias tend to diminish; however they maintain a positive bias for the condition of normality even with large sample sizes of 1000 (Shapiro and ten Berge, 2000; ten Berge and Sočan, 2004; Sijtsma, 2009).”
On the Use, the Misuse, and the Very Limited Usefulness of Cronbach’s Alpha (2008, methodology): “Thus, it follows that CX = CT (also, see Ten Berge & Sočan, 2004, (5), first part) and CE = 0.”