A History of Effect Size Indices

Huberty, Carl J.

doi:10.1177/0013164402062002002

Cited by 199 publications

(141 citation statements)

References 48 publications

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“…281 The between-groups standardised effect is the situation that mirrors the specification of the target difference in a parallel groups trial, in which a difference between two groups is tested. In the simplest case (equal group sizes) it is calculated for an outcome, x, as:…”

Section: Variations On the Standardised Effect Sizementioning

confidence: 99%

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Assessing methods to specify the target difference for a randomised controlled trial: DELTA (Difference ELicitation in TriAls) review

Cook

Hislop

Adewuyi

et al. 2014

Health Technology Assessment

111

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Section: Variations On the Standardised Effect Sizementioning

confidence: 99%

“…281,304 For a continuous outcome, the standardised difference (typically expressed as Cohen's d 'effect size', i.e. mean difference divided by the SD) is overwhelmingly the most commonly used although other formulae exist (e.g.…”

Section: Standardised Effect Sizementioning

confidence: 99%

Assessing methods to specify the target difference for a randomised controlled trial: DELTA (Difference ELicitation in TriAls) review

Cook

Hislop

Adewuyi

et al. 2014

Health Technology Assessment

111

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“…Correspondingly, numerous practical guidelines and suggestions for selecting, calculating, and interpreting effect size indices for various types of statistical analyses have been provided in the literature, such as Alhija and Levy (2009), Breaugh (2003), Durlak (2009), Ferguson (2009, Fern and Monroe (1996), Grissom and Kim (2012), Huberty (2002), Kirk (1996), Kline (2004), Olejnik and Algina (2000), Richardson (1996), Rosenthal, Rosnow, and Rubin (2000), Rosnow and Rosenthal (2003), and Vacha-Haase and Thompson (2004). It has steadily become a general consensus in the methodological literature of behavior, education, management, and related disciplines that effect sizes accompanied by their corresponding confidence intervals are perhaps the best approach for conveying quantitative information in applied research.…”

mentioning

confidence: 99%

Confidence intervals and sample size calculations for the standardized mean difference effect size between two normal populations under heteroscedasticity

Shieh

2013

Behav Res

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The use of effect sizes and associated confidence intervals in all empirical research has been strongly emphasized by journal publication guidelines. To help advance theory and practice in the social sciences, this article describes an improved procedure for constructing confidence intervals of the standardized mean difference effect size between two independent normal populations with unknown and possibly unequal variances. The presented approach has advantages over the existing formula in both theoretical justification and computational simplicity. In addition, simulation results show that the suggested one-and two-sided confidence intervals are more accurate in achieving the nominal coverage probability. The proposed estimation method provides a feasible alternative to the most commonly used measure of Cohen's d and the corresponding interval procedure when the assumption of homogeneous variances is not tenable. To further improve the potential applicability of the suggested methodology, the sample size procedures for precise interval estimation of the standardized mean difference are also delineated. The desired precision of a confidence interval is assessed with respect to the control of expected width and to the assurance probability of interval width within a designated value. Supplementary computer programs are developed to aid in the usefulness and implementation of the introduced techniques. Kirk (1996), Kline (2004), Olejnik and Algina (2000), Richardson (1996), Rosenthal, Rosnow, and Rubin (2000), Rosenthal (2003), andVacha-Haase andThompson (2004). It has steadily become a general consensus in the methodological literature of behavior, education, management, and related disciplines that effect sizes accompanied by their corresponding confidence intervals are perhaps the best approach for conveying quantitative information in applied research.According to the general review of Ferguson (2009), effect sizes can be categorized into four general classes: (1) group difference, (2) strength of association, (3) corrected estimates, and (4) risk estimates. The group difference indices estimate the magnitude of difference between two or more groups, and Cohen's d (Cohen, 1969) is the most commonly used measure across virtually all disciplines of the social sciences. Specifically, Cohen's d is an estimate of the standardized mean difference that reflects the difference between two sample means divided by their pooled sample standard deviation under homoscedasticity. For the purpose of measuring the size of effect between two treatment groups with unequal variances, Cohen's d is no longer a proper estimator, because its standardizer, the Electronic supplementary material The online version of this article

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“…In order to select which characteristics of seedling quality should be combined to be used in the multivariate studies and make the FDF more informative, six selection criteria were applied: Akaike information criterion (AIC), Mallows Cp criterion (CHARNET et al, 2008) associated with the MANAVA F-tests, whose estimators were taken from Huberty (2002) and are described below in equations 1 to 4:…”

Section: Methodsmentioning

confidence: 99%

Alternative for the evaluation of coffee seedlings using Fisher's discriminant analysis

Campos

Morais

Paixão

2016

Revista Ciência Agronômica

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-One of the applications of Fisher's linear discriminant function (FDF) is its use in transforming multivariate data into a new univariate variable. This then makes possible a new option for the variance analysis of multivariate data, in addition to the multivariate analysis of variance (MANOVA). The aim of this work was to select groups of seven characteristics of quality in coffee seedlings using six criteria for selection, to use the FDF to transform such groupings of characteristics into a new variable, and then to compare interpretation of the results obtained from the univariate and multivariate analyses of variance of the characteristics and this new variable, with a view to its use in evaluating coffee seedlings. A randomised block design was used to assess the effect of organic fertiliser on the formation of seedlings in coffee cv. Catuaí Vermelho IAC-44, evaluating the following characteristics: seedling height, diameter, root length, dry weight of shoots and roots, leaf area, number of leaves and total dry weight. According to the selection criteria used, different subsets of the selected characteristics are possible. The use of the FDF is shown to be viable in discriminating between treatments. Univariate analysis of the new variable obtained with the FDF and multivariate analysis (MANOVA) was able to detect differences between the treatments, however, it is simpler to apply FDF methodology. RESUMO -Uma das aplicações da função discriminante linear de Fisher (FDF) é a sua utilização, para a transformação de dados multivariados, em nova variável univariada, de maneira a possibilitar uma nova opção de análise de variância dos dados multivariados além da análise de variância multivariada (MANAVA). O objetivo deste trabalho é selecionar agrupamentos de sete características de qualidade de mudas de cafeeiro, por meio de seis critérios de seleção, utilizar a FDF para transformar tais agrupamentos das características em uma nova variável e posteriormente comparar a interpretação dos resultados obtidos com as análises de variância univariada e multivariada das características mensuradas e essa nova variável, com vista em seu uso na avaliação de mudas de cafeeiro. O delineamento utilizado foi em blocos casualizados para avaliar o efeito de adubações orgânicas na formação de mudas de cafeeiro cv. Catuaí Vermelho IAC-44, avaliando as características altura de mudas, diâmetro, comprimento radicular, peso seco da parte aérea e da raiz, área foliar, número de folhas e peso seco total. De acordo com o critério de seleção usado, podem-se ter diferentes subconjuntos de características selecionados. A utilização da FDF se mostra viável na discriminação de tratamentos. A análise univariada da nova variável obtida pela FDF e a análise multivariada (MANAVA) foram capazes de detectar diferenças entre tratamentos, contudo, a aplicação da metodologia da FDF é mais simples.Palavras-chave: Seleção de variável. Análise multivariada. Transformação de dados. Análise de variância. Coffea arabica.

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A History of Effect Size Indices

Cited by 199 publications

References 48 publications

Assessing methods to specify the target difference for a randomised controlled trial: DELTA (Difference ELicitation in TriAls) review

Assessing methods to specify the target difference for a randomised controlled trial: DELTA (Difference ELicitation in TriAls) review

Confidence intervals and sample size calculations for the standardized mean difference effect size between two normal populations under heteroscedasticity

Alternative for the evaluation of coffee seedlings using Fisher's discriminant analysis

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