scholarly journals Detecting Consensus Emergence in Organizational Multilevel Data: Power Simulations

2019 ◽  
pp. 109442811987395
Author(s):  
Jonas W. B. Lang ◽  
Paul D. Bliese ◽  
J. Malte Runge
Keyword(s):  
Sample Size ◽  
Multilevel Model ◽  
Statistical Power ◽  
Field Data ◽  
Multilevel Methods ◽  
Multilevel Data ◽  
Moderating Effects ◽  
Organizational Research ◽  
Future Studies ◽  
Power Simulation

Theories suggest that groups within organizations often develop shared values, beliefs, affect, behaviors, or agreed-on routines; however, researchers rarely study predictors of consensus emergence over time. Recently, a multilevel-methods approach for detecting and studying emergence in organizational field data has been described. This approach—the consensus emergence model—builds on an extended three-level multilevel model. Researchers planning future studies based on the consensus emergence model need to consider (a) sample size characteristics required to detect emergence effects with satisfactory statistical power and (b) how the distribution of the overall sample size across the levels of the multilevel model influences power. We systematically address both issues by conducting a power simulation for detecting main and moderating effects involving consensus emergence under a variety of typical research scenarios and provide an R-based tool that readers can use to estimate power. Our discussion focuses on the future use and development of multilevel methods for studying emergence in organizational research.

scholarly journals Sensitivity of discrete symmetry metrics: implications for metric choice

2021 ◽  
Author(s):  
Allen D Hill ◽  
Julie Nantel
Keyword(s):  
Statistical Power ◽  
Discrete Symmetry ◽  
Absolute Value ◽  
Future Studies ◽  
Group Condition ◽  
Power Simulation ◽  
Asymmetric Data ◽  
Increased Sensitivity ◽  
Low Sensitivity ◽  
Bilateral Difference

Gait asymmetry is present in several pathological populations, including those with Parkinson's disease, Huntington's disease, and stroke survivors. Previous studies suggest that commonly used discrete symmetry metrics, which compare single bilateral variables, may not be equally sensitive to underlying effects of asymmetry, and the use of a metric with low sensitivity could result in unnecessarily low statistical power. The purpose of this study was to provide a comprehensive assessment of the sensitivity of commonly used discrete symmetry metrics to better inform design of future studies. Monte Carlo simulations were used to estimate the statistical power of each symmetry metric at a range of asymmetry magnitudes, group/condition variabilities, and sample sizes. Power was estimated by repeated comparison of simulated symmetric and asymmetric data with a paired t-test, where the proportion of significant results is equivalent to the power. Simulation results confirmed that not all common discrete symmetry metrics are equally sensitive to reference effects of asymmetry. Multiple symmetry metrics exhibit equivalent sensitivities, but the most sensitive discrete symmetry metric in all cases is a bilateral difference (e.g. left - right). A ratio (e.g. left/right) has poor sensitivity when group/condition variability is not small, but a log-transformation produces increased sensitivity. Additionally, two metrics which included an absolute value in their definitions showed increased sensitivity when the absolute value was removed. Future studies should consider metric sensitivity when designing analyses to reduce the possibility of underpowered research.

scholarly journals Best (but oft forgotten) practices: sample size planning for powerful studies

2019 ◽  
Vol 110 (2) ◽  
pp. 280-295 ◽  
Author(s):  
Samantha F Anderson
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Special Consideration ◽  
Effective Sample Size ◽  
High Quality ◽  
Future Studies ◽  
Sample Size Planning ◽  
High Level

ABSTRACT Given recent concerns regarding replicability and trustworthiness in several areas of science, it is vital to encourage researchers to conduct statistically rigorous studies. Achieving a high level of statistical power is one particularly important domain in which researchers can improve the quality and reproducibility of their studies. Although several factors influence statistical power, appropriate sample size planning is often under the control of the researcher and can result in powerful studies. However, the process of conducting sample size planning to achieve a specified level of desired statistical power is often complex and the literature can be difficult to navigate. This article aims to provide an approachable overview of statistical power and sample size planning, with emphasis on why statistical power is important for high-quality science. Thorough examples relevant to nutrition researchers are included to illustrate the process of sample size planning. Special consideration is also given to issues that may arise when conducting sample size planning in practice. The overarching goal is to provide nutrition researchers with the tools and expertise needed to conduct effective sample size planning for future studies.

P1-188: Enrichment strategies for secondary prevention trials: The effect of restrictive inclusion criteria on statistical power and sample size

2008 ◽  
Vol 4 ◽  
pp. T263-T264
Author(s):  
Steven D. Edland ◽  
Linda K. McEvoy ◽  
Dominic Holland ◽  
John C. Roddey ◽  
Christine Fennema-Notestine ◽  
...  
Keyword(s):  
Secondary Prevention ◽  
Sample Size ◽  
Statistical Power ◽  
Inclusion Criteria ◽  
Prevention Trials

Statistical Power Analysis can Improve Fisheries Research and Management

1990 ◽  
Vol 47 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Randall M. Peterman
Keyword(s):  
Sample Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Small Sample Size ◽  
Industrial Effluent ◽  
Small Sample ◽  
Detectable Effect ◽  
Type Ii ◽  
Sampling Variability ◽  
Statistical Power Analysis

Ninety-eight percent of recently surveyed papers in fisheries and aquatic sciences that did not reject some null hypothesis (H0) failed to report β, the probability of making a type II error (not rejecting H0 when it should have been), or statistical power (1 – β). However, 52% of those papers drew conclusions as if H0 were true. A false H0 could have been missed because of a low-power experiment, caused by small sample size or large sampling variability. Costs of type II errors can be large (for example, for cases that fail to detect harmful effects of some industrial effluent or a significant effect of fishing on stock depletion). Past statistical power analyses show that abundance estimation techniques usually have high β and that only large effects are detectable. I review relationships among β, power, detectable effect size, sample size, and sampling variability. I show how statistical power analysis can help interpret past results and improve designs of future experiments, impact assessments, and management regulations. I make recommendations for researchers and decision makers, including routine application of power analysis, more cautious management, and reversal of the burden of proof to put it on industry, not management agencies.

scholarly journals Statistical Primer for Athletic Trainers: Understanding the Role of Statistical Power in Comparative Athletic Training Research

2018 ◽  
Vol 53 (7) ◽  
pp. 716-719
Author(s):  
Monica R. Lininger ◽  
Bryan L. Riemann
Keyword(s):  
Sample Size ◽  
Athletic Training ◽  
Treatment Effect ◽  
Statistical Power ◽  
Athletic Trainers ◽  
A Priori ◽  
Significant Treatment ◽  
Training Research ◽  
Α Level

Objective: To describe the concept of statistical power as related to comparative interventions and how various factors, including sample size, affect statistical power.Background: Having a sufficiently sized sample for a study is necessary for an investigation to demonstrate that an effective treatment is statistically superior. Many researchers fail to conduct and report a priori sample-size estimates, which then makes it difficult to interpret nonsignificant results and causes the clinician to question the planning of the research design.Description: Statistical power is the probability of statistically detecting a treatment effect when one truly exists. The α level, a measure of differences between groups, the variability of the data, and the sample size all affect statistical power.Recommendations: Authors should conduct and provide the results of a priori sample-size estimations in the literature. This will assist clinicians in determining whether the lack of a statistically significant treatment effect is due to an underpowered study or to a treatment's actually having no effect.

scholarly journals Occurrence of hybrid geese in Sweden—a conservation problem?

Ornis Svecica ◽  
2007 ◽  
Vol 17 (3–4) ◽  
pp. 154-186 ◽  
Author(s):  
Hakon Kampe-Persson ◽  
Henrik Lerner
Keyword(s):  
Mate Choice ◽  
Field Data ◽  
Basic Data ◽  
Positive Trend ◽  
Parent Species ◽  
Future Studies ◽  
Species Numbers ◽  
Conservation Problem ◽  
Bean Goose ◽  
Increasing Trend

This report provides basic data about hybrid geese and mixed pairs in Sweden; combinations of species, numbers, trends and origins, which can serve as a framework for future studies. Data published in national, regional and local magazines and reports as well as unpublished observations through August 2007 have been analysed. Sightings in this report were based on the observers’ suggestion of parent species. No less than 17 species were involved in the hybrid geese sighted in Sweden. Some of the combinations of species involved the red-listed species Lesser White-fronted Goose, the nominate race of Taiga Bean Goose and Red-breasted Goose. The first combinations of species appeared in Sweden already 1918–1930s, but since the last half a century, the number of hybrid geese in Sweden shows a positive trend. Several explanations to this increasing trend is proposed but not further analysed. Among all the several theories proposed for hybridisation in geese, field data from Swedish goose haunts support at least two; the ”Best-Option-Hypothesis” and ”Interspecific mate choice following false imprinting”.

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