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Articles 1 - 19 of 19
Full-Text Articles in Social and Behavioral Sciences
Errors In A Program For Approximating Confidence Intervals, Andrew V. Frane
Errors In A Program For Approximating Confidence Intervals, Andrew V. Frane
Journal of Modern Applied Statistical Methods
An SPSS script previously presented in this journal contained nontrivial flaws. The script should not be used as written. A call is renewed for validation of new software.
Bias And Precision Of The Squared Canonical Correlation Coefficient Under Nonnormal Data Condition, Lesley F. Leach, Robin K. Henson
Bias And Precision Of The Squared Canonical Correlation Coefficient Under Nonnormal Data Condition, Lesley F. Leach, Robin K. Henson
Journal of Modern Applied Statistical Methods
Monte Carlo methods were employed to investigate the effect of nonnormality on the bias associated with the squared canonical correlation coefficient (Rc2). The majority of Rc2 estimates were found to be extremely biased, but the magnitude of bias was impacted little by the degree of nonnormality.
Constructing Confidence Intervals For Effect Sizes In Anova Designs, Li-Ting Chen, Chao-Ying Joanne Peng
Constructing Confidence Intervals For Effect Sizes In Anova Designs, Li-Ting Chen, Chao-Ying Joanne Peng
Journal of Modern Applied Statistical Methods
A confidence interval for effect sizes provides a range of plausible population effect sizes (ES) that are consistent with data. This article defines an ES as a standardized linear contrast of means. The noncentral method, Bonett’s method, and the bias-corrected and accelerated bootstrap method are illustrated for constructing the confidence interval for such an effect size. Results obtained from the three methods are discussed and interpretations of results are offered.
A Robust Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
A Robust Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
Journal of Modern Applied Statistical Methods
A robust Root Mean Square Standardized Effect Size (RMSSER) was developed to address the unsatisfactory performance of the Root Mean Square Standardized Effect Size. The coverage performances of the confidence intervals (CI) for RMSSER were investigated. The coverage probabilities of the non-central F distribution-based CI for RMSSER were adequate.
New Effect Size Rules Of Thumb, Shlomo S. Sawilowsky
New Effect Size Rules Of Thumb, Shlomo S. Sawilowsky
Journal of Modern Applied Statistical Methods
Recommendations to expand Cohen’s (1988) rules of thumb for interpreting effect sizes are given to include very small, very large, and huge effect sizes. The reasons for the expansion, and implications for designing Monte Carlo studies, are discussed.
Estimation Of The Standardized Mean Difference For Repeated Measures Designs, Lindsey J. Wolff Smith, S. Natasha Beretvas
Estimation Of The Standardized Mean Difference For Repeated Measures Designs, Lindsey J. Wolff Smith, S. Natasha Beretvas
Journal of Modern Applied Statistical Methods
This simulation study modified the repeated measures mean difference effect size, d=RM , for scenarios with unequal pre- and post-test score variances. Relative parameter and SE bias were calculated for dRM ≠ versus dRM = . Results consistently favored d≠RM over d=RM with worse positive parameter and negative SE bias identified for d=RM for increasingly heterogeneous variance conditions.
Estimating Explanatory Power In A Simple Regression Model Via Smoothers, Rand R. Wilcox
Estimating Explanatory Power In A Simple Regression Model Via Smoothers, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
Consider the regression model Y = γ(X) + ε , where γ(X) is some conditional measure of location associated with Y , given X. Let Υ̂ be some estimate of Y, given X, and let τ2 (Y) be some measure of variation. Explanatory power is η2 = τ2 (Υ̂) /τ2(Y) . When γ(X) = β0 + β1X and τ2(Y) is the variance of Y , η2 = ρ2 , …
Confidence Intervals For The Squared Multiple Semipartial Correlation Coefficient, James Algina, H. J. Keselman, Randall D. Penfield
Confidence Intervals For The Squared Multiple Semipartial Correlation Coefficient, James Algina, H. J. Keselman, Randall D. Penfield
Journal of Modern Applied Statistical Methods
The squared multiple semipartial correlation coefficient is the increase in the squared multiple correlation coefficient that occurs when two or more predictors are added to a multiple regression model. Coverage probability was investigated for two variations of each of three methods for setting confidence intervals for the population squared multiple semipartial correlation coefficient. Results indicated that the procedure that provides coverage probability in the [.925, .975] interval for a 95% confidence interval depends primarily on the number of added predictors. Guidelines for selecting a procedure are presented.
Coverage Performance Of The Non-Central F-Based And Percentile Bootstrap Confidence Intervals For Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
Coverage Performance Of The Non-Central F-Based And Percentile Bootstrap Confidence Intervals For Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
Journal of Modern Applied Statistical Methods
The coverage performance of the confidence intervals (CIs) for the Root Mean Square Standardized Effect Size (RMSSE) was investigated in a balanced, one-way, fixed-effects, between-subjects ANOVA design. The noncentral F distribution-based and the percentile bootstrap CI construction methods were compared. The results indicated that the coverage probabilities of the CIs for RMSSE were not adequate.
Reliability And Statistical Power: How Measurement Fallibility Affects Power And Required Sample Sizes For Several Parametric And Nonparametric Statistics, Gibbs Y. Kanyongo, Gordon P. Brook, Lydia Kyei-Blankson, Gulsah Gocmen
Reliability And Statistical Power: How Measurement Fallibility Affects Power And Required Sample Sizes For Several Parametric And Nonparametric Statistics, Gibbs Y. Kanyongo, Gordon P. Brook, Lydia Kyei-Blankson, Gulsah Gocmen
Journal of Modern Applied Statistical Methods
The relationship between reliability and statistical power is considered, and tables that account for reduced reliability are presented. A series of Monte Carlo experiments were conducted to determine the effect of changes in reliability on parametric and nonparametric statistical methods, including the paired samples dependent t test, pooled-variance independent t test, one-way analysis of variance with three levels, Wilcoxon signed-rank test for paired samples, and Mann-Whitney-Wilcoxon test for independent groups. Power tables were created that illustrate the reduction in statistical power from decreased reliability for given sample sizes. Sample size tables were created to provide the approximate sample sizes required …
Confidence Intervals For An Effect Size When Variances Are Not Equal, James Algina, H. J. Keselman, Randall D. Penfield
Confidence Intervals For An Effect Size When Variances Are Not Equal, James Algina, H. J. Keselman, Randall D. Penfield
Journal of Modern Applied Statistical Methods
Confidence intervals must be robust in having nominal and actual probability coverage in close agreement. This article examined two ways of computing an effect size in a two-group problem: (a) the classic approach which divides the mean difference by a single standard deviation and (b) a variant of a method which replaces least squares values with robust trimmed means and a Winsorized variance. Confidence intervals were determined with theoretical and bootstrap critical values. Only the method that used robust estimators and a bootstrap critical value provided generally accurate probability coverage under conditions of nonnormality and variance heterogeneity in balanced as …
Jmasm19: A Spss Matrix For Determining Effect Sizes From Three Categories: R And Functions Of R, Differences Between Proportions, And Standardized Differences Between Means, David A. Walker
Journal of Modern Applied Statistical Methods
The program is intended to provide editors, manuscript reviewers, students, and researchers with an SPSS matrix to determine an array of effect sizes not reported or the correctness of those reported, such as rrelated indices, r-related squared indices, and measures of association, when the only data provided in the manuscript or article are the n, M, and SD (and sometimes proportions and t and F (1) values) for twogroup designs. This program can create an internal matrix table to assist researchers in determining the size of an effect for commonly utilized r-related, mean difference, and difference in proportions indices when …
Bias Affiliated With Two Variants Of Cohen’S D When Determining U1 As A Measure Of The Percent Of Non-Overlap, David A. Walker
Bias Affiliated With Two Variants Of Cohen’S D When Determining U1 As A Measure Of The Percent Of Non-Overlap, David A. Walker
Journal of Modern Applied Statistical Methods
Variants of Cohen’s d, in this instance dt and dadj, has the largest influence on U1 measures used with smaller sample sizes, specifically when n1 and n2 = 10. This study indicated that bias for variants of d, which influence U1 measures, tends to subside and become more manageable, in terms of precision of estimation, around 1% to 2% when n1 and n2 = 20. Thus, depending on the direction of the influence, both dt and dadj are likely to manage bias in the U1 measure quite well for smaller to …
A Comparison Of Equivalence Testing In Combination With Hypothesis Testing And Effect Sizes, Christopher J. Mecklin
A Comparison Of Equivalence Testing In Combination With Hypothesis Testing And Effect Sizes, Christopher J. Mecklin
Journal of Modern Applied Statistical Methods
Equivalence testing, an alternative to testing for statistical significance, is little used in educational research. Equivalence testing is useful in situations where the researcher wishes to show that two means are not significantly different. A simulation study assessed the relationships between effect size, sample size, statistical significance, and statistical equivalence.
Deconstructing Arguments From The Case Against Hypothesis Testing, Shlomo S. Sawilowsky
Deconstructing Arguments From The Case Against Hypothesis Testing, Shlomo S. Sawilowsky
Journal of Modern Applied Statistical Methods
The main purpose of this article is to contest the propositions that (1) hypothesis tests should be abandoned in favor of confidence intervals, and (2) science has not benefited from hypothesis testing. The minor purpose is to propose (1) descriptive statistics, graphics, and effect sizes do not obviate the need for hypothesis testing, (2) significance testing (reporting p values and leaving it to the reader to determine significance) is subjective and outside the realm of the scientific method, and (3) Bayesian and qualitative methods should be used for Bayesian and qualitative research studies, respectively.
Jmasm9: Converting Kendall’S Tau For Correlational Or Meta-Analytic Analyses, David A. Walker
Jmasm9: Converting Kendall’S Tau For Correlational Or Meta-Analytic Analyses, David A. Walker
Journal of Modern Applied Statistical Methods
Expanding on past research, this study provides researchers with a detailed table for use in meta-analytic applications when engaged in assorted examinations of various r-related statistics, such as Kendall’s tau (τ) and Cohen’s d, that estimate the magnitude of experimental or observational effect. A program to convert from the lesser-used tau coefficient to other effect size indices when conducting correlational or meta-analytic analyses is presented.
Not All Effects Are Created Equal: A Rejoinder To Sawilowsky, J. Kyle Roberts, Robin K. Henson
Not All Effects Are Created Equal: A Rejoinder To Sawilowsky, J. Kyle Roberts, Robin K. Henson
Journal of Modern Applied Statistical Methods
In the continuing debate over the use and utility of effect sizes, more discussion often helps to both clarify and syncretize methodological views. Here, further defense is given of Roberts & Henson (2002) in terms of measuring bias in Cohen’s d, and a rejoinder to Sawilowsky (2003) is presented.
Trivials: The Birth, Sale, And Final Production Of Meta-Analysis, Shlomo S. Sawilowsky
Trivials: The Birth, Sale, And Final Production Of Meta-Analysis, Shlomo S. Sawilowsky
Journal of Modern Applied Statistical Methods
The structure of the first invited debate in JMASM is to present a target article (Sawilowsky, 2003), provide an opportunity for a response (Roberts & Henson, 2003), and to follow with independent comments from noted scholars in the field (Knapp, 2003; Levin & Robinson, 2003). In this rejoinder, I provide a correction and a clarification in an effort to bring some closure to the debate. The intension, however, is not to rehash previously made points, even where I disagree with the response of Roberts & Henson (2003).
The Trouble With Trivials (P > .05), Shlomo S. Sawilowsky, Jina S. Yoon
The Trouble With Trivials (P > .05), Shlomo S. Sawilowsky, Jina S. Yoon
Journal of Modern Applied Statistical Methods
Trivials are effect sizes associated with statistically non-significant results. Trivials are like Tribbles in the Star Trek television show. They are cute and loveable. They proliferate without limit. They probably growl at Bayesians. But they are troublesome. This brief report discusses the trouble with trivials.