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406
Account age
3 yrs
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New Jamovi module functionality to determine appropriate model fit cutoffs for confirmatory factor analysis now available in easyRasch2jmv v0.4.0. Install/update from Jamovi Library within the app.
#rstats #psychometrics #stats #openscience
1 views
I've made an R package for Bayesian Rasch analysis with brms models, easyRaschBayes (on CRAN), implementing simple functions to create figures and tables with model fit metrics, etc. Attaching some figures from conditional item infit, item-restscore with GK gamma, and the log-likelihood criterion.
📊 Post engagement
4
Avg engagement / post
1%
Engagement vs followers
Oct 2022
On Mastodon since
🔥 Top post: My simulation study on item misfit detection in Rasch models is · 11 likes + reposts
📊 Activity & format
Posting cadence
0.13 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly text
Recent: 9 text · 3 image · 0 video.
Follower / following
0.8×
Follows 514 back. A more reciprocal / networked account.
Recent posts
View on Mastodon ↗
New Jamovi module functionality to determine appropriate model fit cutoffs for confirmatory factor analysis now available in easyRasch2jmv v0.4.0. Install/update from Jamovi Library within the app.
#rstats #psychometrics #stats #openscience
So tired of the mindless use of mean/SD to describe data when it is obviously not very useful. Most recent example "patients had suffered from depression on average for 7.6 years (SD = 7.1)" Same paper reports BDI post treatment scores wit…
A recent tutorial paper on Bayesian #ItemResponseTheory with brms called for the creation of an R package make it easier for researchers to run this type of analysis. While my package is focused on #Rasch models, I hope it contributes towa…
Here is a comparison with frequentist and Bayesian output from the #psychometric Rasch analysis of PHQ-9 depression questionnaire data from NHANES: https://pgmj.github.io/rasch_bayes_comp.html
You can also get traceplots for item category probabilities and targeting (person-item Wright map), and conditional item characteristic curves.
I've made an R package for Bayesian Rasch analysis with brms models, easyRaschBayes (on CRAN), implementing simple functions to create figures and tables with model fit metrics, etc. Attaching some figures from conditional item infit, item…
I've made a Jamovi module for modern psychometric analysis using Rasch Measurement Theory, "easyRasch2jmv", that is available in Jamovi's library. The module implements simulation-based (dynamic) cutoff values for conditional item fit and …
I have made an R package for test equating using the Leunbach method. Thanks to the gracious sharing of DIGRAM source code by Svend Kreiner and some assistance of Claude Opus 4.5, the package `leunbachR` is now available on GitHub: https:/…
The first paper I've written entirely using #quartopub and the newish manuscript format.
New reproducible preprint on detection of item misfit in Rasch #psychometrics : https://pgmj.github.io/rasch_itemfit/
By use of simulation/bootstrapping we can leave rule-of-thumb critical values behind us and use adaptive cutoffs instead…
Rule of thumb cutoff values for various model fit metrics are ubiquitous in #psychometrics, despite evidence of the pitfalls with using them. I’ve added functions to my #rstats package to easily get correct simulation based cutoffs for som…
🐘 Community & instance
Home server
scicomm.xyz
Their home server on the fediverse — the instance a creator picks signals the community they belong to.
✅ Link-verified
Verified link
Proved ownership of a website linked on their profile — Mastodon's green-check verification, a real identity signal rather than a paid badge.
On Mastodon since
Oct 2022
Joined in the Twitter-exodus wave of late 2022 — part of the migration that made Mastodon a real destination.
💡 Facts
🗓️Joined Mastodon in 2022 — 3 years ago.
👁️Averages 4 views per post.
📤Posts about 0.1× per week.
🕵️ Fake follower check
Estimated75/100
Good
Credibility score
92%
Real
Real audience
Low
Fake-follower risk
High
Data confidence
- Est. 92% real, active audience · Low fake-follower risk.
- Engagement (~1.0% of followers engage each post) is around typical for Mastodon.
- Verified account.
- Established account (3+ years old).
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
PhD & lic. psychologist. Research specialist in psychometrics and statistics at Karolinska Institutet. R package for Rasch psychometrics: https://pgmj.github.io/easyRasch/ and easyRaschBayes on CRAN, and easyRasch2jmv for Jamovi.
#openscience, #psychometrics, #rstats, #photo
📸 Gallery
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
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Kayla “Iacovino with an i”
11%
jmorley
11%
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