Abstract
To build a robust, replicable, and generalizable family science we must ensure that our research includes samples that are large enough that we can test effects reliably and are diverse enough to speak broadly to families’ experiences. This can be challenging for family science researchers who focus on family processes because many of the features of high-quality family process research make the experience quite onerous for participants; often multiple family members must participate, and data is typically collected through intensive methods, such as video observation or daily diaries. These methodologies allow us to capture rich and detailed data about family processes, but can make it difficult to achieve a large and diverse sample. Fortunately, there are a number of promising methods already in use in family science, or currently being deployed in other related fields, that offer good prospects for family science researchers seeking to improve the samples used in their research by increasing sample size and/or diversity. This article highlights innovative methods that will be useful in overcoming some of the sampling challenges facing family science researchers, focusing on creative ways to use existing datasets, including secondary data analysis and integrative data analysis, and methods that can be deployed when collecting new data, including accessing alternative data sources such as digital trace data, collecting observational data remotely, methods for reaching underrepresented groups, and big-team science.