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Open AccessJournal ArticleDOI

Power Analysis and Effect Size in Mixed Effects Models: A Tutorial

Marc Brysbaert, +1 more
- Vol. 1, Iss: 1, pp 9-9
TLDR
It is recommended that a properly powered reaction time experiment with repeated measures has at least 1,600 word observations per condition, considerably more than current practice, and it is shown that researchers must include the number of observations in meta-analyses.
Abstract
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 of psychology can be replicated. Researchers in cognitive psychology are hindered in estimating the power of their studies, because the designs they use present a sample of stimulus materials to a sample of participants, a situation not covered by most power formulas. To remedy the situation, we review the literature related to the topic and introduce recent software packages, which we apply to the data of two masked priming studies with high power. We checked how we could estimate the power of each study and how much they could be reduced to remain powerful enough. On the basis of this analysis, we recommend that a properly powered reaction time experiment with repeated measures has at least 1,600 word observations per condition (e.g., 40 participants, 40 stimuli). This is considerably more than current practice. We also show that researchers must include the number of observations in meta-analyses because the effect sizes currently reported depend on the number of stimuli presented to the participants. Our analyses can easily be applied to new datasets gathered.

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Journal ArticleDOI

Analyzing linguistic data: a practical introduction to statistics using R

TL;DR: The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models to enable researchers and students alike to reproduce the analyses and learn by doing.
Journal ArticleDOI

How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables.

TL;DR: In this article, the authors describe reference numbers needed for the designs most often used by psychologists, including single-variable between-groups and repeated-measures designs with two and three levels, two-factor designs involving two repeated measures and one repeated measure, and split-plot design.
Posted ContentDOI

Sample Size Justification

Daniel Lakens
TL;DR: In this paper, six approaches are discussed to justify the sample size in a quantitative empirical study: collecting data from (an) almost) the entire population, choosing a sample size based on resource constraints, performing an a-priori power analysis, planning for a desired accuracy, using heuristics, or explicitly acknowledging the absence of a justification.
Journal ArticleDOI

A practical primer to power analysis for simple experimental designs

TL;DR: In this paper, the focus is on applications of power analysis for experimental designs often encountered in psychology, starting from simple two-group independent and paired groups and moving to one-way analysis of variance, factorial designs, contrast analysis, trend analysis, regression analysis, analysis of covariance, and mediation analysis.
References
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Journal ArticleDOI

Fitting Linear Mixed-Effects Models Using lme4

TL;DR: In this article, a model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms, and the formula and data together determine a numerical representation of the model from which the profiled deviance or the profeatured REML criterion can be evaluated as a function of some of model parameters.
Journal ArticleDOI

G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

TL;DR: G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested.
Journal ArticleDOI

Mixed-effects modeling with crossed random effects for subjects and items

TL;DR: In this article, the authors provide an introduction to mixed-effects models for the analysis of repeated measurement data with subjects and items as crossed random effects, and a worked-out example of how to use recent software for mixed effects modeling is provided.
Journal ArticleDOI

Power failure: why small sample size undermines the reliability of neuroscience

TL;DR: It is shown that the average statistical power of studies in the neurosciences is very low, and the consequences include overestimates of effect size and low reproducibility of results.
Journal ArticleDOI

Estimating the reproducibility of psychological science

Alexander A. Aarts, +290 more
- 28 Aug 2015 - 
TL;DR: A large-scale assessment suggests that experimental reproducibility in psychology leaves a lot to be desired, and correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
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