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In this paper, we investigate question quality among questions posted in Yahoo! Answers to assess what factors contribute to the goodness of a question and determine if we can flag poor quality questions. Using human assessments of whether a question is good or bad and extracted textual features from the questions, we built an SVM classifier that performed with relatively good classification accuracy for both good and bad questions. We then enhanced the performance of this classifier by using additional human assessments of question type as well as additional question features to first separate questions by type and then classify them. This two-step classifier improved the performance of the original classifier in identifying Type II errors and suggests that our model presents a novel approach for identifying bad questions with implications for query revision and routing.