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Two simulation studies were conducted to investigate the effects of the practice of item parceling. In Study 1, unidimensional sets of normally and nonnormally distributed item-level data were categorized into 2-, 3-, and 4-item parcels. Analyses revealed that the use of item parcels resulted in better fitting solutions, as measured by the root mean squared error of approximation (RMSEA), comparative fit index (CFI), and chi-square test, when items had a unidimensional structure. Parceled solutions also resulted in less bias in estimates of structural parameters under these conditions than did solutions based on the individual items. In Study 2 the issue of whether the use of item parceling could mask a known multidimensional factor structure among a set of items was investigated. Results indicated that certain types of item parceling can obfuscate a multidimensional factor structure in such a way that acceptable values of fit indexes are found for a misspecified solution. In addition, parceling under these conditions was found to result in bias in the estimates of structural parameters. Although parceling can ameliorate the effects of coarsely categorized and nonnormally distributed item-level data when the items are unidimensional, the use of parceling with items that are multidimensional or for which the factor structure is unknown cannot be recommended.