Table 1.

Glossary of Terms

Multilevel/hierarchical/clustered/nested data: Data that have some inherent group membership (eg, students within schools, patients with clinics) or hierarchical structure

Multilevel models/hierarchical linear models (HLMs): A type of statistical procedure belonging to the class of general linear models, adapted for analysis of clustered data

Analysis of variance (ANOVA): A statistical procedure used to compare means of a continuous outcome variable for more than 2 groups, classified by 1 or more categorical variables; for example, comparison of patient scores on a functional health survey for 4 non-overlapping diagnostic groups by 2 sex categories

Analysis of covariance (ANCOVA): An extension of ANOVA in which the means of a continuous outcome variable are compared across groups, as described above, adjusting for 1 or more continuous covariates; for example, comparison of patient scores on a functional health survey for 4 nonoverlapping diagnostic groups, adjusted for age

Fixed effects: A condition in which the levels of a factor include all levels of interest to the researcher (eg, sex: male or female)

Intraclass correlation coefficient (ICC): A measure that describes the extent to which individuals within the same group are more similar to each other than individuals in different groups

Random effects: A condition in which the levels of a factor represent a random sample of all possible levels (eg, clinics)

Linear regression analysis:Simple linear regression analysis assesses how a continuous outcome variable (or dependent variable) changes per unit change in a predictor variable (or independent variable). Multiple linear regression analysis assesses the relationship between 1 dependent variable and more than 1 independent variables.

Residual variance: The remaining variance in the outcome variable (dependent variable) after accounting for all predictors (independent variables) and random effects of interest