Sample size

Facets produces estimates for any data set in which there is overlapping randomness in the observations across elements. It cannot estimate measures when the data have a Guttman pattern or there is only one observation element. But even in these cases, anchoring and other analytical technique may make the element measures estimable.

 

Estimates which are likely to have some degree of stability across samples require at least 30 observations per element, and at least 10 observations per rating-scale category.

 

There is no maximum sample size apart from those imposed by computer constraints. Large sample sizes tend to make convergence slower and fit statistics overly sensitive.

 

Estimation technique

RSA and MFRM derive Newton-Raphson iteration equations for the estimation of Rasch measures using the JMLE approach. In Facets, this approach is not robust enough against spikey data. Consequently, since the basic functional shape of all the estimation equations is the logistic ogive, Facets estimates measures by means of iterative curve-fitting to that shape. The resulting measures also accord with the JMLE approach.