Sample size determination ≈ Sample size determination
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Sample sizes for saturation in qualitative research: A systematic review of empirical tests Open
To review empirical studies that assess saturation in qualitative research in order to identify sample sizes for saturation, strategies used to assess saturation, and guidance we can draw from these studies.We conducted a systematic review…
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iNEXT: an R package for rarefaction and extrapolation of species diversity (<span>H</span>ill numbers) Open
Summary Hill numbers (or the effective number of species) have been increasingly used to quantify the species/taxonomic diversity of an assemblage. The sample‐size‐ and coverage‐based integrations of rarefaction (interpolation) and extrapo…
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Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption Open
The MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in sensitivity analyses.
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Evaluating Effect Size in Psychological Research: Sense and Nonsense Open
Effect sizes are underappreciated and often misinterpreted—the most common mistakes being to describe them in ways that are uninformative (e.g., using arbitrary standards) or misleading (e.g., squaring effect-size rs). We propose that effe…
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A simple method to assess and report thematic saturation in qualitative research Open
Data saturation is the most commonly employed concept for estimating sample sizes in qualitative research. Over the past 20 years, scholars using both empirical research and mathematical/statistical models have made significant contributio…
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Calculating the sample size required for developing a clinical prediction model Open
Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that…
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Characteristics of Qualitative Descriptive Studies: A Systematic Review Open
Qualitative description (QD) is a term that is widely used to describe qualitative studies of health care and nursing‐related phenomena. However, limited discussions regarding QD are found in the existing literature. In this systematic rev…
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Scaling accurate genetic variant discovery to tens of thousands of samples Open
Comprehensive disease gene discovery in both common and rare diseases will require the efficient and accurate detection of all classes of genetic variation across tens to hundreds of thousands of human samples. We describe here a novel ass…
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Sample size determination and power analysis using the G*Power software Open
Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of calculating sample size and power require broad statistical knowledge, there i…
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A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization Open
Mendelian randomization (MR) uses genetic data to probe questions of causality in epidemiological research, by invoking the Instrumental Variable (IV) assumptions. In recent years, it has become commonplace to attempt MR analyses by synthe…
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Bias due to participant overlap in two‐sample Mendelian randomization Open
Mendelian randomization analyses are often performed using summarized data. The causal estimate from a one‐sample analysis (in which data are taken from a single data source) with weak instrumental variables is biased in the direction of t…
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Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General Open
Adequate statistical power contributes to observing true relationships in a dataset. With a thoughtful power analysis, the adequate but not excessive sample could be detected. Therefore, this paper reviews the issue of what sample size and…
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Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015 Open
Being able to replicate scientific findings is crucial for scientific progress1-15. We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science between 2010 and 201516-36. The replica…
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Machine learning algorithm validation with a limited sample size Open
Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intri…
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Estimation of clinical trial success rates and related parameters Open
Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial …
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Non-normal data: Is ANOVA still a valid option? Open
The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions.
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A national experiment reveals where a growth mindset improves achievement Open
A global priority for the behavioural sciences is to develop cost-effective, scalable interventions that could improve the academic outcomes of adolescents at a population level, but no such interventions have so far been evaluated in a po…
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Evaluating replicability of laboratory experiments in economics Open
Another social science looks at itself Experimental economists have joined the reproducibility discussion by replicating selected published experiments from two top-tier journals in economics. Camerer et al. found that two-thirds of the 18…
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Recruiting the ABCD sample: Design considerations and procedures Open
The ABCD study is a new and ongoing project of very substantial size and scale involving 21 data acquisition sites. It aims to recruit 11,500 children and follow them for ten years with extensive assessments at multiple timepoints. To deli…
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Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies Open
Calculating the sample size in scientific studies is one of the critical issues as regards the scientific contribution of the study. The sample size critically affects the hypothesis and the study design, and there is no straightforward wa…
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Sample Size Justification Open
An important step when designing an empirical study is to justify the sample size that will be collected. The key aim of a sample size justification for such studies is to explain how the collected data is expected to provide valuable info…
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Power Analysis and Effect Size in Mixed Effects Models: A Tutorial Open
In psychology, attempts to replicate published findings are less successful than expected. For properly powered studies replication rate should be around 80%, whereas in practice less than 40% of the studies selected from different areas o…
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Trial Sequential Analysis in systematic reviews with meta-analysis Open
Trial Sequential Analysis represents analysis of meta-analytic data, with transparent assumptions, and better control of type I and type II errors than the traditional meta-analysis using naïve unadjusted confidence intervals.
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Sample Size for Survey Research: Review and Recommendations Open
Determining an appropriate sample size is vital in drawing realistic conclusions from research findings. Although there are several widely adopted rules of thumb to calculate sample size, researchers remain unclear about which one to consi…
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An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings Open
Background Mendelian randomization (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilizing genetic variants that are instru…
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Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Open
The Pearson product–moment correlation coefficient (rp) and the Spearman rank correlation coefficient (rs) are widely used in psychological research. We compare rp and rs on 3 criteria: variability, bias with respect to the population valu…
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Using the Student's t-test with extremely small sample sizes Open
Researchers occasionally have to work with an extremely small sample size, defined herein as N ≤ 5. Some methodologists have cautioned against using the t-test when the sample size is extremely small, whereas others have suggested that u…
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Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes Open
When designing a study to develop a new prediction model with binary or time‐to‐event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants ( n ) and outcome events ( E ) relative to the n…
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Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption Open
Background Two-sample summary-data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the in…
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How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables Open
Given that an effect size of d = .4 is a good first estimate of the smallest effect size of interest in psychological research, we already need over 50 participants for a simple comparison of two within-participants conditions if we want t…