scholarly journals Implications of sample size and acquired number of steps to investigate running biomechanics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anderson Souza Oliveira ◽  
Cristina Ioana Pirscoveanu
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Loading Rate ◽  
Sequential Estimation ◽  
Human Movement ◽  
Effect Sizes ◽  
Medium Effect ◽  
Dependent Manner ◽  
Sample Sizes ◽  
Running Speed

AbstractLow reproducibility and non-optimal sample sizes are current concerns in scientific research, especially within human movement studies. Therefore, this study aimed to examine the implications of different sample sizes and number of steps on data variability and statistical outcomes from kinematic and kinetics running biomechanical variables. Forty-four participants ran overground using their preferred technique (normal) and minimizing the contact sound volume (silent). Running speed, peak vertical, braking forces, and vertical average loading rate were extracted from > 40 steps/runner. Data stability was computed using a sequential estimation technique. Statistical outcomes (p values and effect sizes) from the comparison normal vs silent running were extracted from 100,000 random samples, using various combinations of sample size (from 10 to 40 runners) and number of steps (from 5 to 40 steps). The results showed that only 35% of the study sample could reach average stability using up to 10 steps across all biomechanical variables. The loading rate was consistently significantly lower during silent running compared to normal running, with large effect sizes across all combinations. However, variables presenting small or medium effect sizes (running speed and peak braking force), required > 20 runners to reach significant differences. Therefore, varying sample sizes and number of steps are shown to influence the normal vs silent running statistical outcomes in a variable-dependent manner. Based on our results, we recommend that studies involving analysis of traditional running biomechanical variables use a minimum of 25 participants and 25 steps from each participant to provide appropriate data stability and statistical power.

scholarly journals Methodological Reporting Behavior, Sample Sizes, and Statistical Power in Studies of Event- Related Potentials: Barriers to Reproducibility and Replicability

2019 ◽  
Author(s):  
Peter E Clayson ◽  
Kaylie Amanda Carbine ◽  
Scott Baldwin ◽  
Michael J. Larson
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Event Related Potentials ◽  
Reporting Guidelines ◽  
Medium Effect ◽  
Sample Sizes ◽  
Reporting Behavior ◽  
Average Sample Size ◽  
Related Potentials ◽  
Average Sample

Methodological reporting guidelines for studies of event-related potentials (ERPs) were updated in Psychophysiology in 2014. These guidelines facilitate the communication of key methodological parameters (e.g., preprocessing steps). Failing to report key parameters represents a barrier to replication efforts, and difficultly with replicability increases in the presence of small sample sizes and low statistical power. We assessed whether guidelines are followed and estimated the average sample size and power in recent research. Reporting behavior, sample sizes, and statistical designs were coded for 150 randomly-sampled articles from five high-impact journals that frequently publish ERP research from 2011 to 2017. An average of 63% of guidelines were reported, and reporting behavior was similar across journals, suggesting that gaps in reporting is a shortcoming of the field rather than any specific journal. Publication of the guidelines paper had no impact on reporting behavior, suggesting that editors and peer reviewers are not enforcing these recommendations. The average sample size per group was 21. Statistical power was conservatively estimated as .72-.98 for a large effect size, .35-.73 for a medium effect, and .10-.18 for a small effect. These findings indicate that failing to report key guidelines is ubiquitous and that ERP studies are primarily powered to detect large effects. Such low power and insufficient following of reporting guidelines represent substantial barriers to replication efforts. The methodological transparency and replicability of studies can be improved by the open sharing of processing code and experimental tasks and by a priori sample size calculations to ensure adequately powered studies.

scholarly journals Statistical Power in Content Analysis Designs: How Effect Size, Sample Size and Coding Accuracy Jointly Affect Hypothesis Testing ‐ A Monte Carlo Simulation Approach.

2021 ◽  
Vol 3 (1) ◽  
pp. 61-89
Author(s):  
Stefan Geiß
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Content Analysis ◽  
Sample Size ◽  
Effect Size ◽  
Statistical Power ◽  
Effect Sizes ◽  
Sample Sizes ◽  
Expected Effect ◽  
Sample Size Effect

Abstract This study uses Monte Carlo simulation techniques to estimate the minimum required levels of intercoder reliability in content analysis data for testing correlational hypotheses, depending on sample size, effect size and coder behavior under uncertainty. The ensuing procedure is analogous to power calculations for experimental designs. In most widespread sample size/effect size settings, the rule-of-thumb that chance-adjusted agreement should be ≥.80 or ≥.667 corresponds to the simulation results, resulting in acceptable α and β error rates. However, this simulation allows making precise power calculations that can consider the specifics of each study’s context, moving beyond one-size-fits-all recommendations. Studies with low sample sizes and/or low expected effect sizes may need coder agreement above .800 to test a hypothesis with sufficient statistical power. In studies with high sample sizes and/or high expected effect sizes, coder agreement below .667 may suffice. Such calculations can help in both evaluating and in designing studies. Particularly in pre-registered research, higher sample sizes may be used to compensate for low expected effect sizes and/or borderline coding reliability (e.g. when constructs are hard to measure). I supply equations, easy-to-use tables and R functions to facilitate use of this framework, along with example code as online appendix.

scholarly journals Effect Size and Power in fMRI Group Analysis

2018 ◽  
Author(s):  
Stephan Geuter ◽  
Guanghao Qi ◽  
Robert C. Welsh ◽  
Tor D. Wager ◽  
Martin A. Lindquist
Keyword(s):  
Sample Size ◽  
Group Analysis ◽  
Population Level ◽  
Small Sample ◽  
Effect Sizes ◽  
Medium Effect ◽  
Single Subject ◽  
Parameter Estimates ◽  
Sample Sizes ◽  
Human Connectome Project

AbstractMulti-subject functional magnetic resonance imaging (fMRI) analysis is often concerned with determining whether there exists a significant population-wide ‘activation’ in a comparison between two or more conditions. Typically this is assessed by testing the average value of a contrast of parameter estimates (COPE) against zero in a general linear model (GLM) analysis. In this work we investigate several aspects of this type of analysis. First, we study the effects of sample size on the sensitivity and reliability of the group analysis, allowing us to evaluate the ability of small sampled studies to effectively capture population-level effects of interest. Second, we assess the difference in sensitivity and reliability when using volumetric or surface based data. Third, we investigate potential biases in estimating effect sizes as a function of sample size. To perform this analysis we utilize the task-based fMRI data from the 500-subject release from the Human Connectome Project (HCP). We treat the complete collection of subjects (N = 491) as our population of interest, and perform a single-subject analysis on each subject in the population. We investigate the ability to recover population level effects using a subset of the population and standard analytical techniques. Our study shows that sample sizes of 40 are generally able to detect regions with high effect sizes (Cohen’s d > 0.8), while sample sizes closer to 80 are required to reliably recover regions with medium effect sizes (0.5 < d < 0.8). We find little difference in results when using volumetric or surface based data with respect to standard mass-univariate group analysis. Finally, we conclude that special care is needed when estimating effect sizes, particularly for small sample sizes.

An Examination of Effect Sizes and Statistical Power in Speech, Language, and Hearing Research

2020 ◽  
Vol 63 (5) ◽  
pp. 1572-1580
Author(s):  
Laura Gaeta ◽  
Christopher R. Brydges
Keyword(s):  
Effect Size ◽  
Statistical Power ◽  
Effect Sizes ◽  
Medium Effect ◽  
Speech Language Pathology ◽  
Sample Sizes ◽  
Hearing Research ◽  
Language Pathology ◽  
Meta Analyses ◽  
Published Research

Purpose The purpose was to examine and determine effect size distributions reported in published audiology and speech-language pathology research in order to provide researchers and clinicians with more relevant guidelines for the interpretation of potentially clinically meaningful findings. Method Cohen's d, Hedges' g, Pearson r, and sample sizes ( n = 1,387) were extracted from 32 meta-analyses in journals in speech-language pathology and audiology. Percentile ranks (25th, 50th, 75th) were calculated to determine estimates for small, medium, and large effect sizes, respectively. The median sample size was also used to explore statistical power for small, medium, and large effect sizes. Results For individual differences research, effect sizes of Pearson r = .24, .41, and .64 were found. For group differences, Cohen's d /Hedges' g = 0.25, 0.55, and 0.93. These values can be interpreted as small, medium, and large effect sizes in speech-language pathology and audiology. The majority of published research was inadequately powered to detect a medium effect size. Conclusions Effect size interpretations from published research in audiology and speech-language pathology were found to be underestimated based on Cohen's (1988, 1992) guidelines. Researchers in the field should consider using Pearson r = .25, .40, and .65 and Cohen's d /Hedges' g = 0.25, 0.55, and 0.95 as small, medium, and large effect sizes, respectively, and collect larger sample sizes to ensure that both significant and nonsignificant findings are robust and replicable.

Alpha Values as a Function of Sample Size, Effect Size, and Power: Accuracy over Inference

2013 ◽  
Vol 112 (3) ◽  
pp. 835-844 ◽  
Author(s):  
M. T. Bradley ◽  
A. Brand
Keyword(s):  
Size Effect ◽  
Sample Size ◽  
Effect Size ◽  
Effect Sizes ◽  
Medium Effect ◽  
Sample Sizes ◽  
Adequate Number ◽  
Alpha Level ◽  
Sample Size Effect ◽  
Psychological Studies

Tables of alpha values as a function of sample size, effect size, and desired power were presented. The tables indicated expected alphas for small, medium, and large effect sizes given a variety of sample sizes. It was evident that sample sizes for most psychological studies are adequate for large effect sizes defined at .8. The typical alpha level of .05 and desired power of 90% can be achieved with 70 participants in two groups. It was perhaps doubtful if these ideal levels of alpha and power have generally been achieved for medium effect sizes in actual research, since 170 participants would be required. Small effect sizes have rarely been tested with an adequate number of participants or power. Implications were discussed.

scholarly journals An N-Pact Factor for Clinical Psychological Research

2018 ◽  
Author(s):  
Kathleen Wade Reardon ◽  
Avante J Smack ◽  
Kathrin Herzhoff ◽  
Jennifer L Tackett
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Empirical Studies ◽  
Psychological Research ◽  
Medium Effect ◽  
Medium Effect Size ◽  
One Step ◽  
Hard To Reach Populations ◽  
Self Examination ◽  
Adequate Sample Size

Although an emphasis on adequate sample size and statistical power has a long history in clinical psychological science (Cohen, 1992), increased attention to the replicability of scientific findings has again turned attention to the importance of statistical power (Bakker, van Dijk, &amp; Wicherts, 2012). These recent efforts have not yet circled back to modern clinical psychological research, despite the continued importance of sample size and power in producing a credible body of evidence. As one step in this process of scientific self-examination, the present study estimated an N-pact Factor (the statistical power of published empirical studies to detect typical effect sizes; Fraley &amp; Vazire, 2014) in two leading clinical journals (the Journal of Abnormal Psychology; JAP, and the Journal of Consulting and Clinical Psychology; JCCP) for the years 2000, 2005, 2010, and 2015. Study sample size, as one proxy for statistical power, is a useful focus because it allows direct comparisons with other subfields and may highlight some of the core methodological differences between clinical and other areas (e.g., hard-to-reach populations, greater emphasis on correlational designs). We found that, across all years examined, the average median sample size in clinical research is 179 participants (175 for JAP and 182 for JCCP). The power to detect a small-medium effect size of .20 is just below 80% for both journals. Although the clinical N-pact factor was higher than that estimated for social psychology, the statistical power in clinical journals is still limited to detect many effects of interest to clinical psychologists, with little evidence of improvement in sample sizes over time.

scholarly journals Comparison of statistical methods for analysis of small sample sizes for detecting the differences in efficacy between treatments for knee osteoarthritis

2020 ◽  
Author(s):  
Chia-Lung Shih ◽  
Te-Yu Hung
Keyword(s):  
Knee Osteoarthritis ◽  
Sample Size ◽  
Statistical Methods ◽  
Statistical Power ◽  
Permutation Test ◽  
Small Sample Size ◽  
Small Sample ◽  
T Test ◽  
Sample Sizes ◽  
Knee Oa

Abstract Background A small sample size (n < 30 for each treatment group) is usually enrolled to investigate the differences in efficacy between treatments for knee osteoarthritis (OA). The objective of this study was to use simulation for comparing the power of four statistical methods for analysis of small sample size for detecting the differences in efficacy between two treatments for knee OA. Methods A total of 10,000 replicates of 5 sample sizes (n=10, 15, 20, 25, and 30 for each group) were generated based on the previous reported measures of treatment efficacy. Four statistical methods were used to compare the differences in efficacy between treatments, including the two-sample t-test (t-test), the Mann-Whitney U-test (M-W test), the Kolmogorov-Smirnov test (K-S test), and the permutation test (perm-test). Results The bias of simulated parameter means showed a decreased trend with sample size but the CV% of simulated parameter means varied with sample sizes for all parameters. For the largest sample size (n=30), the CV% could achieve a small level (<20%) for almost all parameters but the bias could not. Among the non-parametric tests for analysis of small sample size, the perm-test had the highest statistical power, and its false positive rate was not affected by sample size. However, the power of the perm-test could not achieve a high value (80%) even using the largest sample size (n=30). Conclusion The perm-test is suggested for analysis of small sample size to compare the differences in efficacy between two treatments for knee OA.

POWPAL: A Program for Estimating Effect Sizes, Statistical Power, and Sample Sizes

1995 ◽  
Vol 55 (5) ◽  
pp. 773-776 ◽  
Author(s):  
Bernard S. Gorman ◽  
Louis H. Primavera ◽  
David B. Allison
Keyword(s):  
Statistical Power ◽  
Effect Sizes ◽  
Sample Sizes

Statistical power of negative randomized clinical trials (RCTs) presented at the American Society of Clinical Oncology (ASCO) Annual Meetings

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 6516-6516
Author(s):  
P. Bedard ◽  
M. K. Krzyzanowska ◽  
M. Pintilie ◽  
I. F. Tannock
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Annual Meeting ◽  
Primary Endpoint ◽  
Effect Size ◽  
Statistical Power ◽  
Randomized Clinical Trials ◽  
Medium Effect ◽  
Post Hoc ◽  
Adequate Sample Size

6516 Background: Underpowered randomized clinical trials (RCTs) may expose participants to risks and burdens of research without scientific merit. We investigated the prevalence of underpowered RCTs presented at ASCO annual meetings. Methods: We surveyed all two-arm parallel phase III RCTs presented at the ASCO annual meeting from 1995–2003 where differences for the primary endpoint were non-statistically significant. Post hoc calculations were performed using a power of 80% and a=0.05 (two-sided) to determine the sample size required to detect a small, medium, and large effect size between the two groups. For studies reporting a proportion or time to event as a primary endpoint, effect size was expressed as an odds ratio (OR) or hazard ratio (HR) respectively, with a small effect size defined as OR/HR=1.3, medium effect size OR/HR=1.5, and large effect OR/HR=2.0. Logistic regression was used to identify factors associated with lack of statistical power. Results: Of 423 negative RCTs for which post hoc sample size calculations could be performed, 45 (10.6%), 138 (32.6%), and 333 (78.7%) had adequate sample size to detect small, medium, and large effect sizes respectively. Only 35 negative RCTs (7.1%) reported a reason for inadequate sample size. In a multivariable model, studies presented at plenary or oral sessions (p<0.0001) and multicenter studies supported by a co-operative group were more likely to have adequate sample size (p<0.0001). Conclusion: Two-thirds of negative RCTs presented at the ASCO annual meeting do not have an adequate sample to detect a medium-sized treatment effect. Most underpowered negative RCTs do not report a sample size calculation or reasons for inadequate patient accrual. No significant financial relationships to disclose.

scholarly journals Multiple stressor null models frequently fail to detect most interactions due to low statistical power

2021 ◽  
Author(s):  
Benjamin J Burgess ◽  
Michelle C Jackson ◽  
David J Murrell
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Multiple Stressors ◽  
Statistical Significance ◽  
Null Model ◽  
Null Models ◽  
Data Uncertainty ◽  
Sample Sizes ◽  
Multiple Stressor ◽  
Null Expectation

1. Most ecosystems are subject to co-occurring, anthropogenically driven changes and understanding how these multiple stressors interact is a pressing concern. Stressor interactions are typically studied using null models, with the additive and multiplicative null expectation being those most widely applied. Such approaches classify interactions as being synergistic, antagonistic, reversal, or indistinguishable from the null expectation. Despite their wide-spread use, there has been no thorough analysis of these null models, nor a systematic test of the robustness of their results to sample size or sampling error in the estimates of the responses to stressors. 2. We use data simulated from food web models where the true stressor interactions are known, and analytical results based on the null model equations to uncover how (i) sample size, (ii) variation in biological responses to the stressors and (iii) statistical significance, affect the ability to detect non-null interactions. 3. Our analyses lead to three main results. Firstly, it is clear the additive and multiplicative null models are not directly comparable, and over one third of all simulated interactions had classifications that were model dependent. Secondly, both null models have weak power to correctly classify interactions at commonly implemented sample sizes (i.e., ≤6 replicates), unless data uncertainty is unrealistically low. This means all but the most extreme interactions are indistinguishable from the null model expectation. Thirdly, we show that increasing sample size increases the power to detect the true interactions but only very slowly. However, the biggest gains come from increasing replicates from 3 up to 25 and we provide an R function for users to determine sample sizes required to detect a critical effect size of biological interest for the additive model. 4. Our results will aid researchers in the design of their experiments and the subsequent interpretation of results. We find no clear statistical advantage of using one null model over the other and argue null model choice should be based on biological relevance rather than statistical properties. However, there is a pressing need to increase experiment sample sizes otherwise many biologically important synergistic and antagonistic stressor interactions will continue to be missed.

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