1
Questions and Answers / Re: Traditional item analysis / covariance matrix not positive definite
« on: January 17, 2021, 10:59:05 PM »
of course - I have removed the attachments.
This section allows you to view all posts made by this member. Note that you can only see posts made in areas you currently have access to.
For plausible values (estimates=latent) and expected a-posteriori estimates (estimates=eap):
The file will contain one row for each case. Each row will contain (in order):
Sequence ID
PID (if PID is not specified in datafile or format than this is equal to the Sequence ID)
Plausible values. Note there will be np plausible values (default is 5) for each of nd dimensions. Dimensions cycle faster than plausible values, such that for nd = 2, and np = 3, the columns are in the order PV1_D1, PV1_D2, PV2_D1, PV2_D2, PV3_D1, PV3_D2.
the posterior mean (EAP), posterior standard deviation, and the reliability for the case, for each dimension. Note that these columns cycle faster than dimensions such that for nd = 2, and np = 3, the columns are in the order EAP_1, PosteriorSD_1, Reliability_1, EAP_2, PosteriorSD_2, Reliability_2.
The Data File: ex1.dat
The format: id 1-5 responses 12-23
No case weights
The regression model:
Grouping Variables:
The item model: item
Slopes are fixed
Cases in file: 1000 Cases in estimation: 1000
Final Deviance: 13274.87615
Akaike Information Criterion (AIC): 13300.87615
Akaike Information Criterion Corrected (AICc): 13300.56785
Bayesian Information Criterion (BIC): 13364.67697
Total number of estimated parameters: 13
The number of iterations: 45
Termination criteria: Max iterations=1000, Parameter Change= 0.00010
Deviance Change= 0.00010
Wilson, M., Zheng, X., & McGuire, L. (2012). Formulating latent growth using an explanatory item response model approach. Journal of Applied Measurement, 13(1), 1.
For maximum likelihood estimates and weighted likelihood estimates (estimates=mle or estimates=wle):
The file will contain one row for each case that provided a valid response to at least one of the items analysed (one item per dimension is required for multidimensional models). The row will contain the case number (the sequence number of the case in the data file being analysed), the raw score and maximum possible score on each dimension, followed by the maximum likelihood estimate and error variance for each dimension. The format is (i5, nd(2(f10.5, 1x)), nd(2(f10.5, 1x))). If the pfit option is set then an additional column is added containing the case fit statistics. The format is then (i5, nd(2(f10.5, 1x)), nd(2(f10.5, 1x)), f10.5)
codes 0,1,2,3;
score (0,1,2,3) (0,0,0.5,1) ( ) ( ) !items (1);
score (0,1) (0,1) ( ) ( ) !items (2,6,7,8,12,16,19,20,23,25,26,31,32,35,39,42,43,44,45,48);
score (0,1) (0,1) ( ) ( ) !items (4,17,18,34,38,46,49);
score (0,1,2) (0,0.5,1) ( ) ( ) !items (11,47);
score (0,1) (0,1) ( ) ( ) !items (10,21,24,33,37);
score (0,1,2) ( ) (0,0,1) ( ) !items (5);
score (0,1) ( ) (0,1) ( ) !items (3,5,9,14,27,36,40);
score (0,1) ( ) ( ) (0,1) !items (13,15,22,28,29,30,41);
score (0,1,2) ( ) ( ) (0,0,1) !items (30);