Confidence Intervals for Weighted Composite Scores Under the Compound Binomial Error Model

Kyung Yong Kim; Won-Chan Lee

doi:10.1111/jedm.12168

Four bootstrap confidence intervals for the binomial-error model

Psychometrika ◽

10.1007/bf02294417 ◽

1992 ◽

Vol 57 (4) ◽

pp. 499-520

Author(s):

Miao-Hsiang Lin ◽

Chao A. Hsiung

Keyword(s):

Confidence Intervals ◽

Error Model ◽

Bootstrap Confidence Intervals

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Increasing the reliability of ipsative interpretations in neuropsychology: A comparison of reliable components analysis and other factor analytic methods

Journal of the International Neuropsychological Society ◽

10.1017/s1355617704104049 ◽

2004 ◽

Vol 10 (4) ◽

pp. 578-589 ◽

Cited By ~ 6

Author(s):

THOMAS W. FRAZIER ◽

ERIC A. YOUNGSTROM ◽

GORDON J. CHELUNE ◽

RICHARD I. NAUGLE ◽

TARA T. LINEWEAVER

Keyword(s):

Confidence Intervals ◽

Neuropsychological Assessment ◽

Difference Scores ◽

Neuropsychological Measures ◽

Individual Test ◽

Analytic Methods ◽

Factor Analytic ◽

Components Analysis ◽

Composite Scores ◽

Improve Reliability

Ipsative approaches to neuropsychological assessment typically involve interpreting difference scores between individual test scores. The utility of these methods is limited by the reliability of neuropsychological difference scores and the number of comparisons between scores. The present study evaluated the utility of difference scores using factor analytic methods, including reliable components analysis (RCA), equally weighted composites and individual neuropsychological measures. Data from 1,364 individuals referred for neuropsychological assessment were factor analyzed and the resulting solutions were used to compute composite scores. Reliabilities and confidence intervals were derived for each method. Results indicated that RCA outperformed other factor analytic methods, but produced a slightly different factor structure. Difference scores derived using orthogonal solutions were slightly more reliable than oblique methods, and both were more reliable than those from equally weighted composites and individual measures. Confidence intervals for difference scores were considerably smaller for factor methods relative to those for individual test comparisons, due to the greater reliability of factor based difference scores and the smaller number of comparisons required. These findings suggest that difference scores derived from orthogonal factor solutions, particularly RCA solutions, may improve reliability for clinical assessment purposes. (JINS, 2004,10, 578–589.)

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Understanding the Impacts Induced by Cut-Off Thresholds and Likelihood Measures on Confidence Interval When Applying GLUE Approach

10.21203/rs.3.rs-389101/v1 ◽

2021 ◽

Author(s):

Zhenya Li ◽

Tao Yang ◽

Na Zhang ◽

Yandong Zhang ◽

Jiahu Wang ◽

...

Keyword(s):

Confidence Interval ◽

Confidence Intervals ◽

Yellow River ◽

Source Region ◽

Yellow River Basin ◽

Multiple Attribute Decision Making ◽

Error Model ◽

Error Models ◽

Multiple Attribute ◽

Likelihood Measures

Abstract Generalized likelihood uncertainty estimate (GLUE) approach are heavily affected by the choices of cut-off thresholds and likelihood measures. This work attempts to study the potential mechanisms behind the impacts induced by cut-off thresholds and likelihood measures on confidence interval obtained by GLUE. A theoretical analysis on typical likelihood measures reveals that the error model of likelihood measure has essential impacts on the sampling processes of GLUE. Likelihood measures based on a same error model are mathematically transferrable, leading to an identical population of acceptable parameter sets. A case study is conducted by applying GLUE to uncertainty analysis on daily flows simulated by HBV model for the source region of the Yellow River basin. Seven interval indicators are adopted to describe the geometric features of confidence intervals, which are integrated into a comprehensive score for an overall assessment by multiple attribute decision making (MADM) framework. Results indicate that 1) With an increase of cut-off threshold, confidence interval widens in low-level flow sections, moves upward in recession phases of medium-level flow sections whereas narrows in high-level flow sections. Trade-off mechanism amongst widening, moving and narrowing trends is a potential reason behind the variations of interval indicators with cut-off threshold. 2) Much higher similarities in confidence intervals can be detected for likelihood measures based on a same error model than those based on different error models; 3) increasing cut-off threshold highlights the impacts induced by the error models of likelihood measures, whereas weakens the impacts induced by the formulas of likelihood measures.

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