![]() ![]() ![]() Standardized contrasts of treatment effects and interval procedures are presented in Kline, Steiger and Fouladi, and Steiger in the context of ANOVA. , Zhang, Zhang and Heyse, and the references therein.Ĭonsiderable attention has also been drawn toward the development of interval estimation methods for standardized mean difference, see Odgaard and Fowler, Smithson, Steiger and Fouladi, Tian, Wu, Jiang and Wei, and Zou, among others. Comprehensive expositions and practical uses about standardized mean difference and related effect size measures are available in Grissom and Kim, Kline, Lin and Aloe, Takeshima et al. Unlike the unstandardized contrasts, the effect size reporting and interpretation practices suggest that the standardized effect sizes are useful when comparing results from multiple studies using measurement instruments whose raw units are not directly comparable. Accordingly, the intuitive formula Hedges’s g or commonly known as of Cohen’s d is an estimate of the population standardized mean difference and is defined as the difference between two sample means divided by their pooled sample standard deviation under homoscedasticity. The standardized mean difference between two independent populations is the most frequently used effect size measure across virtually all disciplines of scientific researches. ![]() The utility of effect sizes and confidence intervals has been strongly emphasized in several editorial guidelines and methodological implications. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting information files.įunding: This work was supported by a grant from the Ministry of Science and Technology (MOST 107-2410-H-009-025-MY2). Received: JAccepted: FebruPublished: February 24, 2023Ĭopyright: © 2023 Gwowen Shieh. PLoS ONE 18(2):Įditor: Alessandro Barbiero, Universita degli Studi di Milano, ITALY The proposed approaches and developed computer programs fully utilize covariate properties in interval estimation and provide accurate sample size determinations under the precision considerations of the expected interval width and the assurance probability of interval width.Ĭitation: Shieh G (2023) Assessing standardized contrast effects in ANCOVA: Confidence intervals, precision evaluations, and sample size requirements. ![]() Numerical investigations of the existing method reveal that the omission of covariate variables has a negative impact on sample size calculations for precise interval estimation, especially when there is disparity in influential covariate variables. Exact interval estimation approach has theoretical and empirical advantages in coverage probability and interval width over the approximate interval procedures. Sample size procedures are also presented to assure that the resulting confidence intervals yield informative estimation with adequate precision. This paper aims to describe and compare confidence interval estimation methods for the standardized contrasts of treatment effects in ANCOVA designs. Standardized effect sizes and confidence intervals are useful statistical assessments for comparing results across different studies when measurement units are not directly comparable. ![]()
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