This study investigates the potential impacts of different socio-economic-demographic (henceforth, SED) factors in COVID-19-related stay-at-home-tendencies (henceforth, COVID-19-SAHTs) in the US. This requires a state-level investigation rather than a country-level since the US states exhibit large SED differences from one another. To this aim, the K-Means Cluster analysis and the panel autoregressive distributed lag models are applied. The main empirical finding indicates that different SED factors in different US states matter in COVID-19-SAHTs. Additionally, people in the states which have more equal income distribution, higher rate of basic literacy, and less population density stay at their homes more during the COVID-19 pandemic. These findings may provide some vital pre-information to the state policymakers about how much the people from different SED statuses will tend to comply with future COVID-19 state restrictions such as stay-at-home orders and others. Until the scientists create a proven vaccine for the coronavirus, states will most likely continue to issue some COVID-19 restrictions to reduce the spread of this pandemic
