Updated version available on ResearchGate (will be up on sportrxiv shortly) addressing typos, adding missing demographics and dropout reasons (my bad for missing them), and also adding reference lines to the figures for the theory predictions as in the figure shared above.
tinyurl.com/y39b3355
Trying something out here I'd be interested in peoples thoughts on... Marrying Meehl's distinction of the theoreticians/statisticians problem with Dubin's ontologically agnostic framing of theoretical hypotheses. An idea for explaining the difference between theoretical and statistical hypotheses.
Trying something out here I'd be interested in peoples thoughts on... Marrying Meehl's distinction of the theoreticians/statisticians problem with Dubin's ontologically agnostic framing of theoretical hypotheses. An idea for explaining the difference between theoretical and statistical hypotheses.
This study is, to our knowledge, one of the largest to compare the effects of low and high volume RT interventions upon hypertrophy in previously trained participants. We found statistical equivalence between both conditions and both main effects of time, and any interaction effects for condition by time, are likely small. More broadly, this study further corroborates the linear-log theory of adaptation, that the effects of RT in trained persons should be expected to be small, and that current studies in the field of RT are woefully underpowered to be able to detect their effects, let alone test between intervention comparisons.
Results were as expected really and we conclude...
6/6
Continuing on from our previous work we took a similar approach to our and again conducted a multi site large sample study powered to test equivalence of the two interventions based on our more conservative theory led predictions about both time and interaction effects.
5/6
So we decided to put to the test the "optimal" fractional volumes based on the results of these recent dose response meta-analyses compared with a far lower volume approach (9 vs 36 weekly sets).
4/6
Volume in resistance training has been a topic getting everyones panties in a twist for decades at this stage and with two recent state of the art meta-analyses on it in the past year (pubmed.ncbi.nlm.nih.gov/41343037/ & sportrxiv.org/index.php/se...), it's the topic du jour once again.
3/6
A test of higher and lower fractional volumes of resistance training upon arm and thigh muscle area: A multi-site randomised trial
2/6
📣 New preprint!
Our latest Project Discover conducted in collaboration with the fantastic team at Discover Strength with delivery led by David Gschneidner and Luke Carlson and of course with the inimitable James Fisher PhD.
1/6
Plus the hodgepodge of statistical faux pas that come with something that seems to have the intention of explore mechanisms and also individual responses.
Anytime I've tried to look more deeply at the Realist Evaluation cult (at my old institution we had a group and everyone thought they were sort of cult-like) I see good intentions and bad execution... Bhaskar, Pawson, and Tilley revered, and caricature criticisms of things like SRs and RCTs.
I stand by spreadsheets with manual input by humans as being one of the worst sins of mankind 😂
That feeling when the time investment to try to automate data wrangling and cleaning (even with some assistance from an LLM) isn't worth it relative to just doing the wrangling bit manually because the excel spreadsheet you've been given is so ghastly it hurts your soul 🙃
Haven't taken a look as just a quick q. whilst scrolling... but aid the model used to estimate the slope here explicitly model the sampling error on the original effect sizes? If not, then the slope will be attentuated naturally and it'll be easier to reject a null model on the identity.
The organisers are prepaing a conference proceedings paper summarising key contributions and so as I prepared this for them I figured I may as well stick into a standalone doc to preprint.
If anyone is interested, I have preprinted with SportRxiv a short summary of my talk delivered at the conference on Inter-Individual Variation in Resistance Training Responses (www.jyu.fi/en/events/in...).
Available here: sportrxiv.org/index.php/se...
jfc... working through a paper that did performed a quantitative synthesis (badly) at the moment attempting to re-extract data from the underlying studies and it's honestly a mess. I have no idea where the authors got some of their values from... methods are utterly unclear.
This is why I love {targets}... pipeline fully reproducible from raw data out to everything but no need to keep rerunning stuff as can just load downstream targets when needed (but if even the data file changes it knows to rerun everything downstream).
Anyone have access to this?
Multiplicative Treatment Effects in Randomized Pretest-Posttest Experimental Designs
psycnet.apa.org/buy/2019-312...
Data Organization in Spreadsheets Karl W. Broman & Kara H. Woo Pages 2-10 | Received 01 Jun 2017, Accepted author version posted online: 29 Sep 2017, Published online: 24 Apr 2018 1. Introduction 2. Be Consistent 3. Choose Good Names for Things 4. Write Dates as YYYY-MM-DD 5. No Empty Cells 6. Put Just One Thing in a Cell 7. Make it a Rectangle 8. Create a Data Dictionary 9. No Calculations in the Raw Data Files 10. Do Not Use Font Color or Highlighting as Data 11. Make Backups 12. Use Data Validation to Avoid Errors 13. Save the Data in Plain Text Files ABSTRACT Spreadsheets are widely used software tools for data entry, storage, analysis, and visualization. Focusing on the data entry and storage aspects, this article offers practical recommendations for organizing spreadsheet data to reduce errors and ease later analyses. The basic principles are: be consistent, write dates like YYYY-MM-DD, do not leave any cells empty, put just one thing in a cell, organize the data as a single rectangle (with subjects as rows and variables as columns, and with a single header row), create a data dictionary, do not include calculations in the raw data files, do not use font color or highlighting as data, choose good names for things, make backups, use data validation to avoid data entry errors, and save the data in plain text files.
Every day is a good day for sharing one of the most useful papers about research data ever written. PLEASE get your people to understand and follow this advice.
www.tandfonline.com/doi/full/10....
This is great timing... Boss literally asked the other if I had any recommended reading on shrinkage estimators after sharing some re-analysis of existing work using measurement error models. Just sent him this 😊
New blog just dropped!
This one is all about estimators—we cover James-Stein, classical test theory, empirical Bayes, penalized regression, and hierarchical models, showing how they all can be used to do a better job than sample stats alone 🤓
haines-lab.com/post/how-to-...
📣Hey all! We're looking for collaborating labs with DEXA access to collaborate with us here at MacroFactor on a funded longitudinal study of body composition measurement methods. If your lab has access and you may be interested in working with us then get in contact: james@macrofactorapp.com
Any good papers on the problems with meta-analysis when the outcome variable's data generating process is (zero-inflated) negative binomial and not well represented by mean/SD? Suggested solutions also appreciated #stats #rstats
Yup 🥲
English breakfast tea tastes like the texture of caramel.
