#samplesize

Dr Mircea Zloteanu 🌼🐝mzloteanu
2025-06-06

#360 Bayes Factor Design Analysis {bfda}

Thoughts: Sample size planning is confusing at first with Bayesian. But BFDA is the quick answer.

shinyapps.org/apps/BFDA/

Dr Mircea Zloteanu 🌼🐝mzloteanu
2025-06-01
Dr Mircea Zloteanu 🌼🐝mzloteanu
2025-05-28

#353 The Abuse of Power; The Pervasive Fallacy of Power Calculations for Data Analysis

Thoughts: An seminal paper on "post hoc" power calculations.

tandfonline.com/doi/abs/10.119

Dr Mircea Zloteanu 🌼🐝mzloteanu
2025-04-17

#324 Information loss due to dichotomization of the outcome of clinical trials. Also it costs more! πŸ’°

Thoughts: Killing the variance in your outcome measure is *not* data transformation.

vanzwet.shinyapps.io/info_loss/

Dr Mircea Zloteanu 🌼🐝mzloteanu
2025-04-01

#312 {presize} pkg: Understanding Precision-Based Sample Size Calculations

Thoughts: Do you care about effect sizes? Then precision-based planning is for you. Expect higher Ns!

library.virginia.edu/data/arti

Dr Mircea Zloteanu 🌼🐝mzloteanu
2025-03-11

#297 Sample sizes for saturation in qualitative research

Thoughts: A complicated (and contentious) topic for quals research.

sciencedirect.com/science/arti

2024-05-29

Application of JNDs to meta-science. Very sensible!

"For example, in clinical settings researchers may specify this smallest effect size of interest as the smallest difference in a health condition that patients themselves notice...this practice has been used at least since the advent of psychophysics in the second half of the 19th century"

#metascience #psychology #neuroscience #effectsize #psychophysics #samplesize

nature.com/articles/s41562-024

katch wreckkatchwreck
2023-11-30

`This review holds two main aims. The first aim is to explain the importance of sample size and its relationship to effect size (ES) and statistical significance. The second aim is to assist researchers planning to perform sample size estimations by suggesting and elucidating available alternative software, guidelines and references that will serve different scientific purposes.`

ncbi.nlm.nih.gov/pmc/articles/

Jan R. Boehnkejrboehnke
2023-11-05

I'll be offering an introduction to methods to determine -s for clustered / nested studies.

Apparently another popular session at πŸ™‡

One of the classics that got me into this area is Ukoumunne's
onlinelibrary.wiley.com/doi/ab

I was always interested in how to straddle the overlap between observational studies* and in this area.

* eg., rdcu.be/dqi20

NCRM advert for the "How to" session on:
How to use simulation methods for power analyses and sample size calculations in cluster-randomised designs

Wednesday, 08.11.2023, UK-4pm
2023-10-26

R to @Plants_EFSA: Second and last day: participants are working in groups to complete the exercise on surveillance optimisation by stats tool
#SampleSize
#ConfidenceLevel
#MethodSensitivity
#PestTrapping
#VisualInspection
#PlantHealth

πŸ¦πŸ”—: nitter.cz/Plants_EFSA/status/1

[2023-10-26 14:17 UTC]

Jan R. Boehnkejrboehnke
2023-09-29

One of the most problematic areas in the submission we get and papers I review are the sections on justifications.

See for example @lakens' excellent paper on the topic:
psyarxiv.com/9d3yf/
It now comes with a process guide in a , which is an excellent support:
shiny.ieis.tue.nl/sample_size_

For researchers:
rdcu.be/dnfO4

And many people teach this stuff, e.g., ☺️
researchgate.net/publication/3


[edit: typo in hashtag]

2023-08-17

My student at is running a personalty study and needs a huge number of participants! Please consider taking 5-10mins to complete this survey! mmu.eu.qualtrics.com/jfe/form/ #psychology #personality #samplesize #data

Jan R. Boehnkejrboehnke
2023-04-19

Online :
Simulation-based power analyses in (generalized) linear mixed models
17.05.2023, 10-12h CEST

The workshop will cover basics of power analysis, linear mixed models, and why the combination of both requires a simulation-based approach.

In my experience, this is for many areas of and research a key problem when designing studies.

Maybe worth a read as well:
link.springer.com/article/10.3

Simulation-based power analyses in (generalized) linear mixed models

Abstract

The statistical power of a research design is closely linked to the reliability and replicability of empirical findings. Accounting for power while planning a study is therefore crucial and often a requirement for submissions in scientific journals. However, this can quickly become highly difficult in practice – especially for more complex, but very popular analysis procedures like linear mixed models (LMMs). In this workshop, we will briefly cover the basics of power analysis, linear mixed models, and why the combination of both requires a simulation-based approach. We will then focus on the R-package mixedpower and how to use it to estimate power in LMMs. The general aim of this workshop will be to help researchers build intuitions about simulation-based power analyses, and to empower them to set up highly powered research designs when they plan to use mixed-effect models to analyse the resultant data. A prerequisite for this workshop is a basic knowledge of R. Although we will briefly cover the basics of LMMs, familiarity with LMMs and the R-package lme4 is strongly recommended.
2023-03-25

My latest post - OC Curve and Reliability/Confidence Sample Sizes:

#SampleSize #Statistics #Reliability #Confidence #Minitab

I have had a lot of feedback on one of my earlier posts on OC curves and how one can use it to generate a reliability/confidence statement based on sample size, n and rejects, c. This post is mostly geared towards giving an overview of using OC curves to generate reliability/confidence values and using Minitab to do the same.

harishsnotebook.wordpress.com/

2023-02-28

Can anyone point me to a paper/post explaining why you should not base your sample size calculation on the effect found in the literature or a pre-test? Pretty sure I read it once but cannot find it anymore. Maybe I saw it in @lakens's course? #poweranalysis #samplesize

IMPACTT MicrobiomeIMPACTT@qoto.org
2023-02-27

Find all the reviews from our Microbial IMPACTT review series with Mucosal Immunology in one convenient place: mucosalimmunology.org/microbia

#review #immunology #microbiome #mucosalimmunology #germfree #IBD #wildmicrobiome #microbiota #metabolomics #power #samplesize #metabolites

A dark blue background with immune cells. At the top of the image are the IMPACTT and Mucosal Immunology logos. Below is the title "Microbial IMPACTT Review Series", beneath which are images of five figures from five different reviews with the citation of each figure below.
Fern Woodsson 🌿fernwoodsson@mastodon.online
2023-02-13

I’m a #SampleSize of precisely one, but I’ve been taking #Flonase for a year to deal with seasonal allergies and I haven’t had so much as a runny nose in that time.

Claes de Vreese β˜‘οΈclaesdevreese
2023-01-31

This looks fascinating:

β€œWe reviewed empirically-based studies of sample sizes for saturation in qualitative research.

We confirmed qualitative studies can reach saturation at relatively small sample sizes.

Results show 9–17 interviews or 4–8 focus group discussions reached saturation.”

sciencedirect.com/science/arti

NoΓ©mi K. SchuurmanNKSchuurman@mastodon.online
2022-12-05

(Interested in) Working with the random-intercept Cross-lagged Panel Model and unsure what sample size to use?

Jeroen Mulder fixed it for you:
Mulder, J.D., (2022). Power analysis for the random intercept cross-lagged panel model using the powRICLPM R-package. Structural Equation Modeling: A Multidisciplinary Journal.

jeroendmulder.github.io/powRIC

Other questions about the RI-CLPM?
jeroendmulder.github.io/RI-CLP

#SEM #longitudinal #PanelData #RICLPM #CLPM #Dynamics #power #SampleSize

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