Missing data is a problem that many researchers face, particularly when using large surveys. Information is lost when analyzing a dataset with missing data, leading to less precise estimates. Multiple imputation (MI) using chained equations is a way to handle the missing value while using all available information given in the dataset to predict the missing values. In this study, we used data from the Survey of Midlife Development in the United States (MIDUS), a large national study of health and well-being that contains missing data. We created a complete dataset using MI. Following that we performed multiple regression analyses probing the relationships between sociodemographic and psychosocial factors and numbers of chronic conditions. Importantly, we compared the results from analyses using imputed data to those from the original dataset. We found that using multiple imputation substantially increased sample size from 3,204 to 7,108 participants and decreased standard errors by an average of 4.81%. This research supports the use of appropriate methods of multiple imputation to facilitate more accurate estimates of associations between disease risk factors and health outcomes in survey research.

Author Bio

Ashley Peterson is currently a senior majoring in Math and Applied Statistics. She plans to pursue a graduate degree in Data Science after graduation. This research was funded by the National Science Foundation (NSF) for a sophomore research experience. It created a community of sophomores who had similar courses and were all involved on different projects with statistics in various fields. Ashley worked with healthcare data with a professor in Human Development and Family Studies. Outside of school, her hobbies include watching movies, loving animals, playing basketball, and baking desserts.

Emily Martin is currently a senior majoring in Movements and Sports Sciences and planning on going to graduate school to become a physical therapist. This research was done through a grant given by the NSF that allowed sophomores to work with professors in different areas to help analyze data statistically. Emily worked with a professor in Human Development and Family Studies. Her hobbies include reading, playing softball, and working with children.