The growing use of synthetic population, which is a disaggregate representation of the population of an area similar to the real population currently or in the future, has motivated the analysis of its sensitivity in the population generation procedure. New methods in PopGen have enhanced the generation of synthetic populations whereby both household-level and person-level characteristics of interest can be matched in a computationally efficient manner. In the process of set up, population synthesis procedures need sample records for households and persons to match the marginal totals with a specific set of control variables for both the household and person levels, or only the household level, for a specific geographic resolution. In this study, an approach has been taken to analyze the sensitivity by changing and varying this number of controls, with and without taking person controls. The implementation of alternative constraints has been applied on a sample of three hundred block groups in Maricopa County, Arizona. The two datasets that have been used in this study are Census 2000 and a combination of Census 2000 and ACS 2005-2009 dataset. The variation in results for two different rounding methods: arithmetic and bucket rounding have been examined. Finally, the combined sample prepared from the available Census 2000 and ACS 2005-2009 dataset was used to investigate how the results differ when flexibility for drawing households is greater. Study shows that fewer constraints both in household and person levels match the aggregate total population more accurately but could not match distributions of individual attributes. A greater number of attributes both in household and person levels need to be controlled. Where number of controls is higher, using bucket rounding improves the accuracy of the results in both aggregate and disaggregates level. Using combined sample gives the software more flexibility as well as a rich seed matrix to draw households which generates more accurate synthetic population. Therefore, combined sample is another potential option to improve the accuracy in matching both aggregate and disaggregate level household and person distributions.