International journal of population data science · 2026
Global Mind Project data in the United States: A comparison with national statistics.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Taylor Joseph, Sukhoi Oleksii, Newson Jennifer Jane, Thiagarajan Tara C
Аннотация
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.
HIGHLIGHTS: The Global Mind Project (GMP) uses quota-based dynamic online ad targeting (Q-DOAT) via Meta and Google Ads to recruit large-scale populations.Analysis of 114,721 US responses (2020-2024) showed GMP demographic trends aligned within 5-7% of national statistics from ACS, HPS, and ATP.Slight differences were observed, including 5-10% higher representation of single individuals, those with fewer close friends, and those seeking mental health treatment.GMP data demonstrate that online recruitment, combined with post-stratification, can produce data that aligns well with national demographic and social trends.The study supports the utility of GMP as a scalable, near real-time platform for population health monitoring.
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.
HIGHLIGHTS: The Global Mind Project (GMP) uses quota-based dynamic online ad targeting (Q-DOAT) via Meta and Google Ads to recruit large-scale populations.Analysis of 114,721 US responses (2020-2024) showed GMP demographic trends aligned within 5-7% of national statistics from ACS, HPS, and ATP.Slight differences were observed, including 5-10% higher representation of single individuals, those with fewer close friends, and those seeking mental health treatment.GMP data demonstrate that online recruitment, combined with post-stratification, can produce data that aligns well with national demographic and social trends.The study supports the utility of GMP as a scalable, near real-time platform for population health monitoring.