Hierarchical linear modeling is a particular technique in regression analysis that has been developed to take the hierarchical structure of the data into consideration. Hierarchical linear modeling allows the analyst to clearly examine the various effects, like the effects on student outcomes of policy relevant variables, such as the size of the class or the implementation of a particular development.
Hierarchical linear modeling can be explained with the help of the above example in two steps. In the first step of conducting hierarchical linear modeling, separate analyses are conducted for every school taken under consideration with the help of the student level data. For example, the test scores of the students in some particular subjects could be regressed with the help of Hierarchical linear modeling on the basis of the set of the student level predictor variables, like the student’s socio economic status and a binary variable indicating the gender of the student.
In the second step of Hierarchical linear modeling, the regression parameters obtained from the first step of the analyses become the outcome variables of interest.
The statistical and computing techniques on which Hierarchical linear modeling is based incorporates into a single model during the regression analyses explained in both the steps.
The underlying idea of Hierarchical linear modeling is that there involves separate analyses for each unit in a Hierarchical linear modeling structure. There also exists certain complex Hierarchical linear models in Hierarchical linear modeling. In such types of models in Hierarchical linear modeling, the statistical analyses specified at each level are not only linear regression, but also certain higher level regression. In other words, Hierarchical linear modeling can also be called multi level modeling.
In comparison to the classical type of regression, Hierarchical linear modeling is an improvement. Hierarchical linear modeling can be used for the purpose of prediction. Hierarchical linear modeling can be used for the purpose of data reduction. Additionally, Hierarchical linear modeling can be helpful for drawing out the causal inference.
The Hierarchical data structure in Hierarchical linear modeling has basically two types of categories according to Bryk and Raudenbush (1992). The first category of the data structure in Hierarchical linear modeling includes repeated measure types of data and the second category in Hierarchical linear modeling includes the meta-analytic type of data. The meta-analytic type of data in Hierarchical linear modeling is that type of data that deals with the large number of existing studies.
Hierarchical linear modeling becomes an issue or a problem when there is increased homogeneity within the hierarchical data structures. In other words, in hierarchical linear modeling, there is the problem of independence of observations. Hierarchical linear modeling violates this assumption of independence of the observations.
Hierarchical linear modeling also becomes a problem in the case of cross level data. A way of stabilizing this type of problem is to cumulate the data structures.
Hierarchical linear modeling is a powerful tool that can be used by the user or the researcher to achieve more appropriate analyses of monitoring data. Hierarchical linear modeling cannot compensate the deficiencies in the quality of data being collected by the researcher, which is similar to other types of analytical tools.
Hierarchical Linear Modeling Resources
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Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publications.
Singer, J. (1998). Using SAS Proc Mixed to fit multilevel models, hierarchical models, and individual growth curves. Journal of Educational and Behavioral Statistics, 24(4), 323-355.
Sullivan, L. M., Dukes, K. A., & Losina, E. (2004). Tutorial in biostatistics: An introduction to hierarchical linear modelling. In R. B. D’Agostino (Ed.), Tutorials in biostatistics. Volume 2: Statistical modeling of complex medical data (pp. 35-126). Hoboken, NJ: John Wiley & Sons.
Wendorf, C. A. (2002). Comparisons of structural equation modeling and hierarchical linear modeling approaches to couples’ data. Structural Equation Modeling, 9(1), 126-140.
Wong, G. Y., & Mason, W. M. (1985). The hierarchical logistic regression model for multilevel analysis. Journal of the American Statistical Association, 80, 513-524.
