This sets the convergence criteria for the iterative joint JMLE (unconditional UCON) maximum likelihood estimation procedure. Both criteria (of size and change) must be satisfied for iteration to cease automatically. Select "Finish Iterating" from Files pull-down menu to override automatic operation. You may be able to speed up convergence by using the "Estimation" pull-down menu, and requesting Bigger changes.

 

The four criteria are:

i) the maximum size of the marginal score-point residual (i.e., difference between observed and expected "total" raw score after omission of extreme scores) for any element. The standard convergence value is 0.5 score points, half the smallest observable difference between raw scores. A value of 0 means this criterion is ignored.

 

ii) the maximum size of the largest logit change in any estimated measure for an element during the previous iteration (regardless of Umean=) . The standard convergence value is .01 logits, the smallest useful or printable difference. A value of 0 means this criterion is ignored.

 

iii) the maximum size of the largest marginal score point residual (i.e., difference between observed and expected "total" raw score) for any category. The standard convergence value is 0. A value of 0 means this criterion is ignored.

 

iv) the maximum size of the largest logit change in any estimated measure for a step calibration (Rasch-Andrich threshold) during the previous. The standard convergence values is 0. A value of 0 means this criterion is ignored.

 

If a criteria is not specified, then its value is not changed.

 

Example 1: In some situations, a pass-fail or other "high stakes" decision may hinge upon a difference of hundredths of a logit between a person measure and a criterion calibration. For the final, decisive analysis, set the convergence criteria very tightly, e.g.,

Iterations=0 ; unlimited number of iterations

Convergence=.01, .0001 ; exaggerated accuracy: .01 score points and .0001 logits

Be prepared to let your computer run a long time!

 

Example 2: You want convergence to occur when no marginal score point residual (e.g., difference between any element's observed and expected raw score) is greater than 1.0 score points, and the default logit change is left at its default value:

Convergence = 1.0 ; 1.0 score points and .01 logits (the default). Category defaults are unchanged at 0 and so are ignored.

 

Example 3: You want to apply the convergence criterion applied in Facets 3.38.

Convergence = 0.5, .01, 0.5, 0 ; category and item residuals apply

 

Example 4: You want to match the convergence criterion applied in Facets 3.22, the last DOS version.

Convergence = .01, .001 ; run apparently tighter convergence criteria