Objective: A new method named full information principal component analysis (simplified as full information analysis) for constructing multivariate reference ranges is presented

by which the high frequency of false positive is reduced when several laboratory test results are interpreted by comparing each single test result with corresponding reference intervals. The requests of multivariate Gaussian distribution and /or independence of tests by general multivariate analysis is lowered. Methods: Reference range of m one-dimensional TZ

which is transformed from n m-dimensional data by the new method

is constructed using parametric or non-parametric method

and comparing between the results of single variable

4 other multivariable approaches and the new method when applying to the children’ s general physical examination data is also presented. Results: The new method has the highest efficiency in reducing false frequency(13.2%)

the lowest difference between the actual and the expected positive probability(0.02%)

a suitable false positive frequency by which the hidden abnormal data can be accurately found

and the discriminating frequency is in accordance with the actual rule. Conclusion: No demands on the distribution and the maximum tolerance to the correlation of variables，no information missed and easily applied are characterized by this method and the comparing also showed that it is superior to the others. So the new method is worth applying.