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Sample Size Tables for Correlation Analysis with Applications in Partial Correlation and Multiple Regression

James Algina, Stephen Olejnik

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    Summary
    This summary is machine-generated.

    This study provides tables to determine the appropriate sample size for correlation studies. These tables ensure desired accuracy and statistical power for analyzing correlation coefficients, aiding researchers in study design.

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    Area of Science:

    • Statistics
    • Biostatistics
    • Psychometrics

    Background:

    • Determining adequate sample size is crucial for the reliability of correlation studies.
    • Existing methods for sample size selection may not cover all desired accuracy levels or power considerations.

    Purpose of the Study:

    • To present tables for selecting sample size in correlation analyses.
    • To facilitate the choice of sample size for achieving specific accuracy intervals around population parameters.
    • To enable sample size selection for desired statistical power in hypothesis testing.

    Main Methods:

    • Development of tables for sample size selection based on desired precision (intervals around population parameter) with 95% probability.
    • Creation of tables for sample size determination to achieve target statistical power for a zero correlation hypothesis test (alpha = 0.05).
    • Discussion of applications in partial correlation and multiple regression, with accompanying SAS and SPSS programs for flexible sample size selection.

    Main Results:

    • Provided tables allow sample size selection for achieving accuracy intervals of ±.05, ±.10, ±.15, and ±.20.
    • Tables also support sample size selection for achieving specific statistical power levels in hypothesis testing.
    • Developed computer programs offer flexibility beyond the presented tables for various parameters and error rates.

    Conclusions:

    • The presented tables and accompanying software provide valuable tools for researchers to optimize sample size in correlation studies.
    • These resources enhance the rigor of study design by ensuring adequate statistical power and precision in estimating correlation coefficients.
    • Facilitates informed decisions regarding sample size for various correlation and regression analyses.