The problem of combining information to estimate standardized partial regression coefficients in a linear model is considered. A combined estimate obtained from the pooled correlation matrix is proposed, and its large sample distribution is obtained. This estimate can be generalized to address situations in which not every study measures every variable. The method can be used to synthesize estimates of coefficients that cannot be estimated in any single study because no study obtains all of the necessary correlations. It can also be generalized to handle random effects models in which correlation parameters vary across studies. Notation and a model for the results of a series of studies examining intercorrelations among a set of "p+1" variables are provided. An estimator of the pooled correlation matrix and its standard error are derived, and a set of procedures for determining whether a common population correlation matrix underlies the set of empirical results is provided. How the pooled correlation matrix can be used to estimate the standardized coefficients of a regression model for variables measured in a series of studies is shown. Data from a synthesis of relationships among school science variables are used to illustrate the procedures. (RLC)