Global Mind Project data in the United States: A comparison with national statistics

Background The rapid growth of internet and mobile technologies has opened up new, low-cost methods for large-scale population surveys. The Global Mind Project (GMP) is one such survey that uses quota-based online strategies that dynamically target respondents by age, sex, and location. However, how well this method aligns with national population statistics remains unclear. Objective To evaluate how well GMP data collected through online recruitment aligns demographically with United States (US) benchmarks from traditional probability-based surveys, including the American Community Survey (ACS), Household Pulse Survey (HPS), and American Trends Panel (ATP). Methods We analysed 114,721 GMP responses collected in the US between 2020 and 2024. Participants were recruited via Facebook and Google AdSense using broad interest-based keywords and stratified demographic targeting. GMP data were time- and question-matched with ACS, HPS, and ATP data to compare trends in educational attainment, marital status, mental health treatment, and number of close friends. Results Demographic patterns in GMP data typically aligned with national statistics within a 5–7% margin. Educational attainment by age was similar to ACS data, except among 65+, where GMP consistently showed a 5% and 10% higher rate of High School and Bachelor’s completion, respectively. GMP and ACS matched near-perfectly for Divorced and Widowed marital status by age while ‘Not married’ in the GMP was 6-10% higher compared to ‘Never married’ individuals in the ACS and, conversely, lower in the Married group. GMP aggregate mental health treatment estimates were within ±1% of HPS values for three of the four years studied, although age-specific differences ranged from 5–8%. Compared to ATP, those reporting two or fewer friends were 15% higher in the GMP. These differences reflect differences in sampling methodology but also imperfect matches of categories and differing non-response bias arising from mode of survey. Conclusions GMP data demonstrate that with dynamic targeting and quota-based sampling, online recruitment methods can produce data that align well with traditional national surveys. This data, therefore, offers real-time, inclusive and cost-efficient population-level monitoring of mental health and social trends, with potential for use in public health research and policy.