Existing evaluating models for conflicting data sources usually take nothing but accuracy and precision into account, ignoring different impacts to the quality of data sources caused by false data values and empty values. In this paper, false descriptions provided by data sources were defined as initiative errors, while empty values were defined as passive errors. A new quality evaluating model was constructed, in which accuracy and precision were respectively substituted by sensitivity and specificity. Multiple descriptions from different sources were merged and a notion of inclusion relation as well as a calculating model for inclusion degrees was proposed as pretreatments to deal with multi-value problems. An evaluating algorithm TFDQ for conflicting data source quality based on the calculating model was put forward. Experiments on the universal data set Books-Authors show that the result from TFDQ is closer to the reality comparing to the classic Vote and TruthFinder algorithms.