Publications by Mark Sanderson-cimino
Troubleshoot MIRT
Data based on ~1300 participants. Those with at least one TabCat score. Data was NOT transformed One factor EFA fscores_efa_one<-read.csv("Fscores_EFA_one_MIRT.csv") #look at scatterplots to see if correlated, want no correlation. # shows correlation between factors in upper triangle. Want uncorelated. ggpairs( data=fscores_efa_one, m...
251 sym Python (16579 sym/33 pcs) 4 img
Abridged Examiner Results
Project summary Create executive factor score using IRT (MIRT package) for combined TabCat and Examiner Variables: Tabcat: Running dots, dot coutning, flanker, set shifting, match UDS: Animal fluency, vegitable fluency, L words, F words Issues: We have more fluency data than Tabcat data (see desriptives). Should we restrit data in some way? Dat...
1229 sym 5 img
Examiner abridged
Project summary Create executive factor score using IRT (MIRT package) for combined TabCat and Examiner Variables: Tabcat: Running dots, dot coutning, flanker, set shifting, match UDS: Animal fluency, vegitable fluency, L words, F words Issues: We have more fluency data than Tabcat data (see desriptives). Should we restrit data in some way? Dat...
1388 sym 2 img
practice examiner
Outline Descriptives (raw data) Binning (regular + transformed) EFA models (regular + transformed) IRT models (regular + transformed) 1. Descriptives (raw data) ## vars n mean sd median trimmed mad min max range skew kurtosis se ## X1 1 289 15 5 15 15 4 0 27 27 0 0 0 ## vars n mean sd median trimme...
863 sym 18 img