Bootstrap approach to inference and power analysis based on three test statistics for covariance structure models.

By Ke-Hai Yuan, K. Hayashi. 2003

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Citations in the corpus

Deep Reinforcement Learning that Matters (2017, methodology): “Power Analysis Another method to determine if the sample size must be increased is bootstrap power analysis (Tuff ´ ery 2011; Yuan and Hayashi 2003).”
Current Methodological Considerations in Exploratory and Confirmatory Factor Analysis (2011, methodology): “Other authors have applied the Satorra–Saris method using bootstrapping (e.g., Yuan & Hayashi, 2003).”
The perceived value of the rural tourism stay and its effect on rural tourist behaviour (2012, methodology): “Since the Chi-square test of multivariate normality of the variables included in the proposed model was significant, it was appropriate to undertake the estimation by using the maximum likelihood method combined with the bootstrap method (Yuan & Hayashi, 2003 ).”
On the Likelihood Ratio Test for the Number of Factors in Exploratory Factor Analysis (2007, background): “If we were to do this, we would have to be sure that we meet the other conditions mentioned earlier (see also Yuan & Hayashi, 2003).”
Normal theory likelihood ratio statistic for mean and covariance structure analysis under alternative hypotheses (2007, methodology): “For nonnormally distributed data, one may use the bootstrap to approximate the distribution of TLR before a better characterization is obtained (see [17]).”
Perceived Labor Productivity in Small Firms—The Effects of High–Performance Work Systems and Group Culture through Employee Retention (2012, methodology): “To test the power of the overall model, we use a Satorra–Bentler rescaled c2 test, assessing the level of power using a bootstrap-based inference method that uses the covariance structure (Yuan & Hayashi, 2003).”
Sample size planning for statistical power and accuracy in parameter estimation. (2008, background): “Muthén & Muthén (2002), Mooijaart (2003), and Yuan & Hayashi (2003) all extend Satorra & Saris (1985) by developing computational approaches to sample size planning, such that data are generated given a specific set of parameters from a specified model in 548 Maxwell · Kelley · Rausch A n n u .”
The Effect of Skewness and Kurtosis on Mean and Covariance Structure Analysis (2005, methodology): “The behavior of the LR statistic with misspecified models was studied in Shapiro (1983); Satorra and Saris (1985); Steiger, Shapiro, and Browne (1985); Satorra (1989); Yuan and Hayashi (2003); Yuan (2005); and Yuan and Bentler (forthcoming) Yuan, Hayashi and Bentler (2005).”
Comparison of Approaches to Constructing Confidence Intervals for Mediating Effects Using Structural Equation Models (2007, methodology): “Another alternative is to use bootstrap methods that do not assume normality of the data (e.g., Bollen & Stine, 1992; Yuan & Hayashi, 2003; Yung & Bentler, 1996).”
The Relation Among Fit Indexes, Power, and Sample Size in Structural Equation Modeling (2005, methodology): “Recognizing this limitation, Mooijaart (2003) and Yuan and Hayashi (2003) generalized the Satorra–Saris method by utilizing a bootstrap approach.”