Beyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis.

Steiger, James H.

doi:10.1037/1082-989x.9.2.164

Cited by 489 publications

(425 citation statements)

References 30 publications

(55 reference statements)

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“…We report 95% confidence intervals for Cohen's d (Cohen, 1992) and 90% confidence intervals for partial eta-squared (Steiger, 2004) To summarize, the results were inconsistent with our expectations that lineups would appear fairest when consensus descriptions were used and least fair when comprehensive descriptions were used. Lineups appeared largest when paired with individual descriptions and smallest when paired with consensus descriptions.…”

Section: Mock-witness Task Methodologycontrasting

confidence: 73%

Evaluating lineup fairness: Variations across methods and measures.

Mansour¹,

Beaudry²,

Kalmet³

et al. 2017

Law and Human Behavior

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Triers of fact sometimes consider lineup fairness when determining the suggestiveness of an identification procedure. Likewise, researchers often consider lineup fairness when comparing results across studies. Despite their importance, lineup fairness measures have received scant empirical attention and researchers inconsistently conduct and report mock-witness tasks and lineup fairness measures. We conducted a large-scale, online experiment (N = 1,010) to examine how lineup fairness measures varied with mock-witness task methodologies as well as to explore the validity and reliability of the measures. In comparison to descriptions compiled from multiple witnesses, when individual descriptions were presented in the mock-witness task, lineup fairness measures indicated a higher number of plausible lineup members but more bias toward the suspect. Target-absent lineups were consistently estimated to be fairer than target-present lineups—which is problematic because it suggests that lineups containing innocent suspects are less likely to be challenged in court than lineups containing guilty suspects. Correlations within lineup size measures and within some lineup bias measures indicated convergent validity and the correlations across the lineup size and lineup bias measures demonstrated discriminant validity. The reliability of lineup fairness measures across different descriptions was low and reliability across different sets of mock witnesses was moderate to high, depending on the measure. Researchers reporting lineup fairness measures should specify the type of description presented, the amount of detail in the description, and whether the mock witnesses viewed target-present and/or -absent lineups.

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Section: Mock-witness Task Methodologycontrasting

confidence: 73%

Evaluating lineup fairness: Variations across methods and measures.

Mansour¹,

Beaudry²,

Kalmet³

et al. 2017

Law and Human Behavior

View full text Add to dashboard Cite

show abstract

“…By conducting a sensitivity power analysis with G*power software (version 3.1.6) [28], we found a minimum detectable effect (MDE) [29]. A 90 percent (1 -2α) confidence interval (CI) for η 2 p was also reported since the effect size is an upper-tailed (0 to 1) parameter [30]. p-Values < 0.05 were regarded as significant.…”

Section: Discussionmentioning

confidence: 95%

“…Like the η 2 p estimate, the corresponding CIs cannot be less than 0. Since the CI represents the precision with which we are able to report the effect size, employing a confidence coefficient of 90 percent is an appropriate way to verify the size of an effect [30]. It must be pointed out that some CIs covered small or medium parameter values despite a corresponding large η 2 p absolute value.…”

Section: Discussionmentioning

confidence: 99%

Efficacy of simple integrated group rehabilitation program for patients with knee osteoarthritis: Single-blind randomized controlled trial

et al. 2015

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Abstract-We investigated the role of an evidence-based integrated group rehabilitation program on the treatment of patients with knee osteoarthritis (KOA). This was a two-group, randomized controlled, 8 wk trial with 41 patients with moderate to very severe KOA. Patients were assigned to an intervention group (IG) or control group (CG). After both groups had received a self-management education program, IG participants underwent a rehabilitation program, including educational aspects about KOA followed by exercises. CG participants received only general health orientation about KOA during this period. The outcome measures were the Lequesne algofunctional index; 36-Item Short Form Health Survey (SF-36); and chair-stand, sit-and-reach, timed up-and-go, and 6-minute walk tests. Analysis of covariance revealed significant postintervention improvements of IG participants compared with CG participants (p < 0.05) on Lequesne total score and pain and function subdomains; SF-36 physical function, role physical, bodily pain, general health, vitality, and role emotional subdomains; and performance assessed by chair-stand, timed up-and-go, and 6-minute walk tests. Focusing on the primary outcome (Lequesne total score), the mean +/-standard deviation after 8 wk was 5.50 +/-2.98 for the IG and 7.87 +/-3. https://clinicaltrials.gov/ct2/show/ NCT01850862?term=NCT01850862&rank=1

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“…Applying the noncentral method for constructing the CI for a standardized linear contrast of means is discussed in the literature (Cumming & Finch, 2001;Kline, 2004;Steiger, 2004). Liu (2010) illustrated the geometric meaning of the noncentrality parameter for a linear contrast in a Euclidian space.…”

Section: Noncentral Methodsmentioning

confidence: 99%

Constructing Confidence Intervals for Effect Sizes in ANOVA Designs

Chen

Peng

2013

J. Mod. App. Stat. Meth.

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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.

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Beyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis.

Cited by 489 publications

References 30 publications

Evaluating lineup fairness: Variations across methods and measures.

Evaluating lineup fairness: Variations across methods and measures.

Efficacy of simple integrated group rehabilitation program for patients with knee osteoarthritis: Single-blind randomized controlled trial

Constructing Confidence Intervals for Effect Sizes in ANOVA Designs

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