MFA researchers use different types of studies for different purposes. 

• National or international cohort studies that follow a large group of individuals over time to understand factors that lead to different outcomes. They are useful for studies that need a large amount of data, perhaps collected over many years, and to compare patterns in different populations.
  • Registry data: MFA researchers use registry data collected by the Danish government for decades on the entire population in Denmark and can be used for research under strict data security and privacy measures. These data cover health, sociodemographic and environmental data (for example, air pollution levels over time) which are useful for many different types of research questions. (Examples: PANDa, ARCH, MINERvA)
• Consortia studies that combine information from different individual research studies to obtain large numbers of participants. These studies are useful for research that needs large numbers of people, like how genes interact with environmental factors. (Example: ECHO, ASDEU)
• Case-Control studies that compare persons with an outcome (such as autism) to persons who do not. They are especially useful for studies that need a large amount of data, especially on factors that may be common in persons with a specific outcome but less common in people who do not. They are often used for more focused research questions that need a large amount of data to be collected over a relatively short time period. (Example: SEED)
• Family-based studies that collect information on family members to understand how factors like genetics or specific health conditions in the family could be linked to an outcome. In particular, in autism research there are a number of ‘baby sibling’ studies that follow the development of a younger sibling of an older child who has autism. These studies are very useful for understanding the factors that influence the course of development. (Example: EARLI)
• Health record studies use information from hospitals or registries and are useful for studies that need a large amount of data, especially to compare across different geographic areas or populations, and to get medical confirmation of conditions under study. (Example: Maternal Health in Pregnancy and Autism Risk study)
• Multi-generation studies to understand how family factors in an older generation could be linked to autism in a younger generation. MRF researchers have used multi-generation studies to look at how factors in grandparents and parents are linked to autism in the child. (Example: MINERVa)