Sample size requirements for interval estimation of the strength of association effect sizes in multiple regression analysis.

@article{Shieh2013SampleSR,
  title={Sample size requirements for interval estimation of the strength of association effect sizes in multiple regression analysis.},
  author={Gwowen Shieh},
  journal={Psicothema},
  year={2013},
  volume={25 3},
  pages={
          402-7
        }
}
  • G. Shieh
  • Published 31 December 2013
  • Business
  • Psicothema
BACKGROUND Effect size reporting and interpreting practices have been extensively recommended in academic journals when analyzing primary outcomes of all empirical studies. Accordingly, the sample squared multiple correlation coefficient is the commonly reported strength of association index in practical applications of multiple linear regression. METHOD This paper examines the sample size procedures proposed by Bonett and Wright for precise interval estimation of the squared multiple… 

Tables from this paper

Sample size calculations for model validation in linear regression analysis
TLDR
The results show that the exact approach has a distinct advantage over the current method with greater accuracy and high robustness.
Sample size planning for multiple correlation: reply to Shieh (2013).
TLDR
A simple 2-step sample size formula for desired absolute precision is proposed and its accuracy is evaluated under the conditions proposed by Shieh and indicates that the Bonett-Wright sample sizes formula for relative prediction and the new 2- step sample size model for absolute precision are remarkably accurate.
Reference sample size for multiple regression in corn
The objective of this work was to determine the number of plants required to model corn grain yield (Y) as a function of ear length (X1) and ear diameter (X2), using the multiple regression model Y =
Regression Analysis of Cloud Computing Adoption for U.S. Hospitals
TLDR
The developed predictive models provided a clearer understanding among hospital IT executives and cloud service providers of cloud adoption drivers and helped to understand what factors influence cloud adoption in U.S. hospitals.

References

SHOWING 1-10 OF 43 REFERENCES
Sample size requirements for multiple regression interval estimation
Summary Sample size planning is one of the most important issues in the design of a study. Simple and accurate sample size formulas for a desired confidence interval width have been developed for
Sample Size Planning for the Squared Multiple Correlation Coefficient: Accuracy in Parameter Estimation via Narrow Confidence Intervals
  • Ken Kelley
  • Mathematics
    Multivariate behavioral research
  • 2008
Methods of sample size planning are developed from the accuracy in parameter approach in the multiple regression context in order to obtain a sufficiently narrow confidence interval for the
Sample Size Calculation for Estimating or Testing a Nonzero Squared Multiple Correlation Coefficient
TLDR
It is shown that available one- sided tests are uniformly most powerful, and the one-sided confidence intervals are uniformlymost accurate.
Confidence Intervals, Power Calculation, and Sample Size Estimation for the Squared Multiple Correlation Coefficient under the Fixed and Random Regression Models: A Computer Program and Useful Standard Tables
In this article, the authors introduce a computer package written for Mathematica, the purpose of which is to perform a number of difficult iterative functions with respect to the squared multiple
Estimating R 2 Shrinkage in Multiple Regression: A Comparison of Different Analytical Methods
Abstract The effectiveness of various analytical formulas for estimating R 2 shrinkage in multiple regression analysis was investigated. Two categories of formulas were identified: estimators of the
Methodological and computational considerations for multiple correlation analysis
TLDR
The statistical methods and available programs of multiple correlation analysis purport to enhance pedagogical presentation in academic curricula and practical application in psychological research and develop corresponding Excel worksheets that facilitate the implementation of various aspects of the suggested significance tests.
Improved Shrinkage Estimation of Squared Multiple Correlation Coefficient and Squared Cross-Validity Coefficient
The sample squared multiple correlation coefficient is widely used for describing the usefulness of a multiple linear regression model in many areas of science. In this article, the author considers
Exact Interval Estimation, Power Calculation, and Sample Size Determination in Normal Correlation Analysis
This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed
Methodology Review: Estimation of Population Validity and Cross-Validity, and the Use of Equal Weights in Prediction
In multiple regression, optimal linear weights are obtained using an ordinary least squares (OLS) procedure. However, these linear weighted combinations of predictors may not optimally predict the
Effect size estimates: current use, calculations, and interpretation.
TLDR
A straightforward guide to understanding, selecting, calculating, and interpreting effect sizes for many types of data and to methods for calculating effect size confidence intervals and power analysis is provided.
...
1
2
3
4
5
...