Failsafe
            <i>N</i>
            or File‐Drawer Number

Becker, Betsy Jane

doi:10.1002/0470870168.ch7

Cited by 156 publications

(121 citation statements)

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“…More recently, Aguinis et al (2011) also debunked the myth that the failsafe N analysis is an effective indicator of publication bias. Similar caveats apply to modifications (e.g., Orwin, 1983) of Rosenthal's original failsafe N technique (Becker, 2005;Higgins & Green, 2009). Unfortunately, despite this evidence, failsafe N techniques appear to be the predominantly used method to detect the potential presence of publication bias in the organizational sciences (see Table 1).…”

Section: Methods For Detecting and Assessing Publication Biasmentioning

confidence: 88%

“…Kepes et al 629 Traditional Methods for Detecting and Assessing Publication Bias Failsafe N. Originally introduced by Rosenthal (1979), the failsafe N technique attempts to estimate the number of missing effect sizes that would be needed to make a meta-analytic mean effect size estimate statistically insignificant. The technique has several critical limitations, which were discussed more than a decade ago (Becker, 1994(Becker, , 2005Evans, 1996). For instance, the failsafe N assumes that all missing effect sizes are zero, which is improbable.…”

Section: Methods For Detecting and Assessing Publication Biasmentioning

confidence: 99%

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Publication Bias in the Organizational Sciences

Kepes

Banks

McDaniel

et al. 2012

Organizational Research Methods

356

450

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Publication bias poses multiple threats to the accuracy of meta-analytically derived effect sizes and related statistics. Unfortunately, a review of the literature indicates that unlike meta-analytic reviews in medicine, research in the organizational sciences tends to pay little attention to this issue. In this article, the authors introduce advances in meta-analytic techniques from the medical and related sciences for a comprehensive assessment and evaluation of publication bias. The authors illustrate their use on a data set on employment interview validities. Using multiple methods, including contour-enhanced funnel plots, trim and fill, Egger’s test of the intercept, Begg and Mazumdar’s rank correlation, meta-regression, cumulative meta-analysis, and selection models, the authors find limited evidence of publication bias in the studied data.

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Section: Methods For Detecting and Assessing Publication Biasmentioning

confidence: 88%

Section: Methods For Detecting and Assessing Publication Biasmentioning

confidence: 99%

Publication Bias in the Organizational Sciences

Kepes

Banks

McDaniel

et al. 2012

Organizational Research Methods

356

450

View full text Add to dashboard Cite

show abstract

“…To assess the robustness of our results, we conducted a sensitivity analysis to determine how sensitive the combined estimate was to any 1 study by repeatedly calculating the overall ES with 1 study omitted per iteration and compared the results with the overall study effect. We analyzed the threat of possible publication bias to the validity of the obtained outcomes using the funnel plot, 30 failsafe N, 31 and the trim and fill. 32 The failsafe N method determines the number of additional "negative" studies (eg studies showing no difference between ASD and non-ASD groups) needed to reduce the overall test to nonsignificance.…”

Section: Resultsmentioning

confidence: 99%

Gastrointestinal Symptoms in Autism Spectrum Disorder: A Meta-analysis

et al. 2014

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BACKGROUND:In pediatric settings, parents often raise concerns about possible gastrointestinal (GI) symptoms in autism spectrum disorder (ASD), yet the specificity of these concerns are not well studied. OBJECTIVE:To conduct a meta-analysis of research investigating GI symptoms among children with ASD. METHODS:We searched Medline, PsycINFO, and PubMed databases in peer-reviewed journals. Analysis involved studies with a comparison group presenting quantitative data on GI symptoms using combinations of terms for ASD and GI indicators. The systematic search yielded 15 studies. We calculated effect sizes and 95% confidence intervals (CIs) using a random-effects model. RESULTS:Children with ASD experience significantly more general GI symptoms than comparison groups, with a standardized mean difference of 0.82 (0.24) and a corresponding odds ratio (OR) of 4.42 (95% CI, 1.90-10.28). Analysis also indicated higher rates of diarrhea (OR, 3.63; 95% CI, 1.82-7.23), constipation (OR, 3.86; 95% CI, 2.23-6.71), and abdominal pain (OR, 2.45; 95% CI, 1.19-5.07). CONCLUSIONS:Results indicate greater prevalence of GI symptoms among children with ASD compared with control children. Identified studies involved high methodological variability and lack of comprehensive data prohibited analysis of GI pathophysiologies (eg, gastroesophageal reflux) typically associated with organic etiologies, limiting conclusions about the underpinnings of the observed association. Future research must address critical questions about the causes and long-term impact of GI symptoms in ASD. Such analyses will require more systematic research and clinical activities, including improved diagnostic screening, standardized assessment, and exploration of potential moderators (eg, dietary restrictions).

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“…63,85 The failsafe N or filedrawer number estimates the number of studies that would need to be included in the meta-analysis to change the overall results. 55,82 The Duval and Tweedie trim-and-fill method assumes that the most undesirable studies are missing. 86 An asymmetric appearance of many missing studies suggest publication or small-sample bias.…”

Section: Publication Biasmentioning

confidence: 99%

“…Publication bias was assessed using 3 techniques, the funnel plot, 81 failsafe N, 82 and the Duval and Tweedie trimand-fill method. 83,84 In the absence of publication bias, the distribution of effect sizes in the funnel plot are symmetrical and take on an inverted funnel shape.…”

Section: Publication Biasmentioning

confidence: 99%

A Meta-analysis of Interventions That Target Children's Screen Time for Reduction

Maniccia¹,

Davison²,

et al. 2011

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BACKGROUND: Screen time, especially television viewing, is associated with risk of overweight and obesity in children. Although several interventions have been developed to reduce children's screen time, no systematic review of these interventions exists to date.OBJECTIVE: This is a systematic review and meta-analysis of interventions targeting a reduction in children's screen time. METHODS:Effect sizes and associated 95% confidence intervals (CIs) were calculated by using a random-effects model. Heterogeneity tests, moderator analyses, assessment of bias, and sensitivity analyses were conducted. Reliability was assessed with Cohen's . RESULTS:The systematic search identified 3002 documents; 33 were eligible for inclusion, and 29 were included in analyses. Most reported preintervention and postintervention data and were published in peerreviewed journals. Although heterogeneity was present, no moderators were identified. Overall Hedges g (Ϫ0.144 [95% CI: Ϫ0.217 to Ϫ0.072]) and standard mean difference (SMD) (Ϫ0.148 [95% CI: Ϫ0.224 to Ϫ0.071]) indicated that interventions were linked with small but statistically significant reductions in screen time in children. The results were robust; the failsafe N was large, and the funnel plot and trim-and-fill methods identified few missing studies. CONCLUSIONS:Results show that interventions to reduce children's screen time have a small but statistically significant effect. As the evidence base expands, and the number of screen-time interventions increases, future research can expand on these findings by examining the clinical relevance and sustainability of effects, conducting a more thorough analysis of effect modifiers, and identifying critical components of effective interventions. Pediatrics 2011;128:e193-e210 Dr Maniccia contributed to the study conception and design; data collection, analysis, and interpretation; and manuscript writing. Dr Davison contributed to the study design and manuscript writing and revision. Dr Marshall contributed to the study design, statistical analysis design, and manuscript revision. Drs Manganello and Dennison contributed to the study design and manuscript revision. Although the American Academy of Pediatrics recommends no more than 2 hours/day of screen time (watching television or videos/DVDs, playing video or computer games, and using a computer for purposes other than school work) for children aged 2 years old and older, 1,2 47% of children aged 2 to 15 years spend 2 or more hours/day using screen media. 3 A recent study 4 found that approximately two-thirds of a national sample of 3-year-old children watched ϳ3 hours/day of television and were exposed to more than 5 additional hours of indirect (ie, in the background) television daily. Contrary to the American Academy of Pediatrics recommendations, 33% of children aged 6 years and younger 5 and 71% of children aged 8 to 18 years 6 have televisions in their bedrooms, a characteristic associated with increased viewing. [5][6][7] Time spent watching television is associated w...

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Failsafe N or File‐Drawer Number

Cited by 156 publications

References 0 publications

Publication Bias in the Organizational Sciences

Publication Bias in the Organizational Sciences

Gastrointestinal Symptoms in Autism Spectrum Disorder: A Meta-analysis

A Meta-analysis of Interventions That Target Children's Screen Time for Reduction

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