A nonstandard instrument used in the field frequently becomes out-of-calibration due to environmental noise, misuse, aging, etc. A substantial amount of loss may result if such nonstandsrd instrument is used to check product quality and performances. Traditional periodic calibration at the calibration center is not capable of detecting out-of-calibration status while the instrment is in use, and therefore, statistical method needs to be developed to check the status of a nonstandard instrument in the field. Developed in this research is a unified measurement assurance model in which statistical calibration at the calibration center and measurement assurance test in the field are combined. We developed statistical procedures to detect changes in precision and in coefficients of the calibration equation. For the latter, two procedures, one based upon t-distribution and the other upon Z-distribution, are developed depending upon the amount of information on the variance of measurement error. Further, computational experiments are conducted to evaluate how the power of test varies with respect to the parameters involved. Based upon the computational results we suggest procedures for designing effective measurement assurance tests. We expect that the present research may be used to provide a flexible and economical measurement assurance program as compared to the traditional one.