Approximations to the distribution of the sample coefficient alpha under nonnormality.
スポンサーリンク
概要
- 論文の詳細を見る
Approximate distributions of the sample coefficient alpha under nonnormality as well as normality are derived by using the single- and two-term Edgeworth expansions up to the term of order 1/n. The case of the standardized coefficient alpha including the weights for the components of a test is also considered. From the numerical illustration with simulation using the normal and typical nonnormal distributions with different types/degrees of nonnormality, it is shown that the variances of the sample coefficient alpha under nonnormality can be grossly different from those under normality. The corresponding biases and skewnesses are shown to be negative under various conditions. The method of developing confidence intervals of the population coefficient alpha using the Cornish-Fisher expansion with sample cumulants is presented.
- 日本行動計量学会の論文
著者
関連論文
- Stratified coefficients of reliability and their sampling behavior under nonnormality
- Theory of Core-Level Spectroscopy in Actinide Systems : Chapter 4. Optical Properties : 4-3. Theory
- Calculation of Cu 2p Resonant Photoemission Spectra in CuO
- Theory of X-Ray Emission Spectra in La and Ce Compounds
- Theory of 3d Core Photoemission Spectra in Rare Earth Oxides Series
- Asymptotic expansions of the distribution of the estimator for the generalized partial correlation under nonnormality.
- Approximations to the distribution of the sample coefficient alpha under nonnormality.
- On the standard errors of rotated factor loadings with weights for observed variables.
- Asymptotic correlations between rotated solutions in factor analysis.
- Standard errors for the Harris-Kaiser Case II orthoblique solution.
- Theory of Multiplet Structure in High Energy Spectra of Rare-Earth Compounds(Abstracts of Doctral Dissertations)
- ASYMPTOTIC CUMULANTS OF FUNCTIONS OF MULTINOMIAL SAMPLE PROPORTIONS WITH ADJUSTMENT FOR EMPTY CELLS
- Asymptotic biases of least squares estimators in factor analysis and structural equation modeling under nonnormality and normality
- A log-bilinear model with latent variables.