In multiple regression analysis, where resulting predictive equation effectiveness is subject to shrinkage, it is especially important to evaluate result replicability. Double cross-validation is an empirical method by which an estimate of invariance or stability can be obtained from research data. A procedure for double cross-validation is discussed, using heuristic and actual research data to illustrate a non-generalizable outcome and a generalizable outcome. The procedure involves the use of two samples or subsamples to produce two pairs of regression equations from which respective shrinkages can be determined. The more closely the shrinkage estimates approach zero, the greater the degree of stability across the subsamples and the more confidence the researcher can vest in the replicability of the results. The first example uses a heuristic data set of 20 (2 subsets of 11 and 9 subjects, respectively) with 5 predictors to illustrate that weights must be compared empirically rather than subjectively. In the second example, data are drawn from a study of the life satisfaction of 200 nursing home residents. Seven tables present study data. An appendix contains the Statistical Analysis System program used to analyze the data. (SLD)