We present two post-stratification weighting methods to validate survey data collected using Amazon Mechanical Turk (AMT). Two surveys focused on appliance and consumer electronics devices were administered in the spring and summer of 2012 to each of approximately 3,000 U.S. households. Specifically, the surveys asked questions about residential refrigeration products, televisions (TVs) and set-top boxes (STBs). Filtered data were assigned weights using each of two weighting methods, termed “sequential” and “simultaneous,” by examining up to eight demographic variables (income, education, gender, race, Hispanic origin, number of occupants, ages of occupants, and geographic region) in comparison to reference U.S. demographic data from the 2009 Residential Energy Consumption Survey (RECS). Five key questions from the surveys (number of refrigerators, number of freezers, number of TVs, number of STBs and primary service provider) were evaluated with a set of statistical tests to determine whether either method improved the agreement of AMT with reference data, and if so, which method was better. The statistical tests used were: differences in proportions, distributions of proportions (using Pearson’s chi-squared test), and differences in average numbers of devices as functions of all demographic variables. The results indicated that both methods generally improved the agreement between AMT and reference data, sometimes greatly, but that the simultaneous method was usually superior to the sequential method. Some differences in sample populations were found between the AMT and reference data. Differences in the proportion of STBs reflected large changes in the STB market since the time our reference data was acquired in 2009. Differences in the proportions of some primary service providers suggested real sample bias, with the possible explanation that AMT user are more likely to subscribe to providers who also provide home internet service. Differences in other variables, while statistically significant in some cases, were nonetheless considered to be minor. Depending on the intended purpose of the data collected using AMT, these biases may or may not be important; to correct them, additional questions and/or further post-survey adjustments could be employed. In general, based on the analysis methods and the sample datasets used in this study, AMT surveys appeared to provide useful data on appliance and consumer electronics devices.