An attribute gage result can be used as a continuous response, but first I would recommend that you use more than 3 platters. The recommended number of parts is 10 and the minimal number of deviations or ranges is 15 (=number of operators * number of parts), see MSA 4 p.104. If you only compare the results of 3 platters (=3 parts) for 2 testers (=2 "operators"), you only have 6 different deviations instead of at least 15. Chances are high that you won't get a good evaluation of your measurement system, because you miss some vital causes for variation or a relevant part of your process spread.
Back to your original question: The common statistical approach for go/no-go or pass/fail results is to analyze the probability of failure instead of the proportion itself (binary logistic model). Therefore the proportions have to be transformed before the MSA/variance component calculations can be made. There are two common transformation functions for proportions p_i (e.g. p_i = proportion of tester A, platter 2, run 1):
- y_i* = ln( p_i / 1-p_i ) with ln being the natural logarithm (logit transformation)
- y_i* = arc sine ( sqrt ( p_i ) )
But if this model / approach reflects the structure within your data well should be checked (e.g. using residual analysis, goodness of fit like R▓, R▓(adj), and so on) additionally to the usual variance component analysis which will provide percentages for measurment uncertainty (EV%, AV%, GRR%, etc.)