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Short Description: HLM is a specialized statistical software package used for Hierarchical Linear Modeling.
Long Description: HLM was initially designed for analysis of the statistical modeling of two- and three-level data structures. It should be used in conjunction with the text Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and data analysis methods (2nd Edition). Thousand Oaks, CA: Sage). The HLM program has been tailored so that the basic program structure, input specification, and output of results closely coordinate with this textbook. Those who are interested in a full discussion of the details of parameter estimation and hypothesis testing may refer to the book.
The following description is from the HLM developer: ssicentral.com/hlm.
In social research and other fields, research data often have a hierarchical structure. That is, the individual subjects of study may be classified or arranged in groups which themselves have qualities that influence the study. In this case, the individuals can be seen as level-1 units of study, and the groups into which they are arranged are level-2 units. This may be extended further, with level-2 units organized into yet another set of units at a third level. Examples of this abound in areas such as education (students at level 1, schools at level 2, and school districts at level 3) and sociology (individuals at level 1, neighborhoods at level 2). It is clear that the analysis of such data requires specialized software. Hierarchical linear and nonlinear models (also called multilevel models) have been developed to allow for the study of relationships at any level in a single analysis, while not ignoring the variability associated with each level of the hierarchy.
The HLM program can fit models to outcome variables that generate a linear model with explanatory variables that account for variations at each level, utilizing variables specified at each level. HLM not only estimates model coefficients at each level, but it also predicts the random effects associated with each sampling unit at every level. While commonly used in education research due to the prevalence of hierarchical structures in data from this field, it is suitable for use with data from any research field that have a hierarchical structure. This includes longitudinal analysis, in which an individual's repeated measurements can be nested within the individuals being studied.
In addition, although the examples above implies that members of this hierarchy at any of the levels are nested exclusively within a member at a higher level, HLM can also provide for a situation where membership is not necessarily "nested", but "crossed", as is the case when a student may have been a member of various classrooms during the duration of a study period.
The HLM program allows for continuous, count, ordinal, and nominal outcome variables and assumes a functional relationship between the expectation of the outcome and a linear combination of a set of explanatory variables. This relationship is defined by a suitable link function, for example, the identity link (continuous outcomes) or logit link (binary outcomes).
Category: Quantitative Data Analysis
Programming Language: C++
Version: 7.0 Version Date: July 2013
Where Installed: HLM Student version is available through BC's Application server (Citrix).
Technical support: Please contact Rani Dalgin ( 617-552-1743 or email@example.com)
Free Student licenses with no technical support available from SSI and limitations on the model size may be downloaded from the ssicentral website:
A free 15 day trial version of HLM may also be downloaded from this website
Number of Licenses: Individually purchased.
Type of License: Individual licenses
Vendor/Developer: Scientific Software International, Inc
Vendor Website: www.ssicentral.com
Documentation: Documentation, and other information, is available at: ssicentral.com/hlm/resources.html.