#statstab #371 Safeguard Power as a Protection Against Imprecise Power Estimates

Thoughts: tl;dr - when replicating a study, use the lower end of the CI of the original study as your effect in a power analysis.

#poweranalysis #samplesize #replication

https://journals.sagepub.com/doi/10.1177/1745691614528519

#statstab #360 Bayes Factor Design Analysis {bfda}

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

#bfda #bayesian #bayesfactor #samplesize #power #errorrate

https://shinyapps.org/apps/BFDA/

New blog post : Your Study Is Too Small (If You Care About Practically Significant Effects)

#effectsize #precision #poweranalysis #research #Psychology #MCID #SESOI #samplesize

https://open.substack.com/pub/mzloteanu/p/your-study-is-too-small-if-you-care?r=3b457w&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

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

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

#power #QRPs #NHST #posthoc #samplesize #effectsize

https://www.tandfonline.com/doi/abs/10.1198/000313001300339897

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

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

#clinical #dichotomania #qrp #shiny #r #samplesize

https://vanzwet.shinyapps.io/info_loss/

#statstab #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!

#r #samplesize #precision #estimation #power #Confidenceintervals

https://library.virginia.edu/data/articles/understanding-precision-based-sample-size-calculations

#statstab #297 Sample sizes for saturation in qualitative research

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

#qualitative #research #sample #samplesize #saturation #methodology #guide #review

https://www.sciencedirect.com/science/article/pii/S0277953621008558

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

Apparently another popular session at #RMeF23

One of the classics that got me into this area is Ukoumunne's
https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.1330

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

#Rstats #StudyDesign

* eg., https://rdcu.be/dqi20

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

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

For #HRQL researchers:
https://rdcu.be/dnfO4

And many people teach this stuff, e.g.,
https://www.researchgate.net/publication/335813329_Estimating_sample_sizes_based_on_required_precision_for_surveys_and_epidemiological_research

#StudyDesign #NightshiftEditor
[edit: typo in hashtag]

Online #workshop:
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 #HealthSciences and #HRQL research a key problem when designing studies.

Maybe worth a read as well:
https://link.springer.com/article/10.3758/s13428-021-01546-0

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

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.

This looks fascinating:

“We 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.”

#qualitative #research #saturation #samplesize #academicchatter

https://www.sciencedirect.com/science/article/pii/S0277953621008558