The use of advanced quantitative methods within mixed methods research has been investigated in a limited capacity. In particular, hierarchical linear models are a popular approach to account for multilevel data, such as students within schools, but its use and value as the quantitative strand in a mixed methods study remains unknown. This article examines the role of hierarchical linear modeling in mixed methods research with emphasis on design choice, priority, and rationales. The results from this systematic methodological review suggest that hierarchical linear modeling does not overshadow the contributions of the qualitative strand. Our study contributes to the field of mixed methods research by offering recommendations for the use of hierarchical linear modeling as the quantitative strand in mixed methods studies.
- hierarchical linear modeling
- mixed methods research
- multilevel modeling
- nested data
ASJC Scopus subject areas
- Social Sciences (miscellaneous)
- Statistics, Probability and Uncertainty