General information on
making the MDM file, model specification, graphing options
and output files, which is intended as an introduction to
HLM, can be found here. The discussion that follows assumes
the use of the Windows graphical user interface of HLM in
performing the analysis. It is, however, also possible to
run HLM in batch/interactive mode.
To run an analysis in HLM,
four steps are required:
 The type of
model to be fitted must be decided on.
 An appropriate
MDM file must be created.
 The model is
specified and various statistical and output options specified.
 The model is
run, after which modelbased graphs can be obtained.
To fit models in HLM, 8
statistical applications are used: HLM2, which fits 2level
linear and nonlinear (HGLM) models; HLM3, which fits 3level
linear/nonlinear models; HLM4, which fits 4level linear/nonlinear
models; HMLM, which fits hierarchical multivariate 2level
linear models; HMLM2, which fits hierarchical multivariate
3level models; HCM2, which fits 2level crossedandnested
models, ; HCM3, which fits 3level crossedandnested models;
and HLMHCM, which fits linear models with crossedandnested
random effects. For more on the types of models, please see
the type of model to be fitted.
For specific examples of each step for the different modules,
please see below.
HLM analyses are based
on Multivariate Data Matrix (MDM) files. Making the MDM file
is the first step in analyzing data using HLM. Users may construct
the MDM directly from different types of input files including
SPSS, ASCII, SAS, SYSTAT and STATA, or indirectly from many
additional types of data file formats through the thirdparty
software module included in the HLM program.
For more detail, please
select one of the following:
Examples for HLM3 and HMLM2
were not included, as it would, to a very large extent, be
a duplication of the material for HLM2 and HMLM. SPSS input
was used as this is the most common approach used by HLM users.
Fully annotated examples
of a 2level linear (HLM2) model, a 2level crossedandnested
model (HCM2) and a hierarchical multivariate linear model
(HMLM) are available. A similar approach as in 2 was followed
for model specification, due to the close similarity between
2level (HLM2) and 3level (HLM3) models, and 2level multivariate
linear (HMLM) and 3level multivariate linear (HMLM2) models.
HLM also offers dataand
modelbased graphing options. Databased graphing options
include groupspecific scatter plots, line plots, and cubic
splines that can be color coded by values of predictor variables;
boxplots displayed for overall data and data grouped within
higherlevel units. For examples of the databased graphs,
please select one of the following links:
 Box and whisker plots, which
can be used to display univariate distributions of level1
variables for each level2 unit, with and without a level2
classification variable.
 Line plots, where, for example,
level1 repeated measures observations are joined by lines
to describe changes or developments over time during the
course of the research study.
 Scatter plots, which can
be used to explore bivariate relationships between level1
variables for individual or a group of level2 units, with
and without controlling level2 variables.
HLM provides graphing options
to display the relationships between the outcome and the predictor(s)
based on the final analytic results. The options allow us
to visually represent the results of the models for the whole
or a subset of population, and to graphically examine underlying
model assumptions as well. These options are available after
running an analysis  for databased graphics that can be
obtained prior to analysis, please see databased graphs options
discussed above.
Modelbased graphing options
available in HLM include graphing of groupspecific equations,
boxplots of level1 residuals for each group, plots of residuals
by predicted values for each group, posterior credibility
intervals for random coefficients. For threelevel models,
level1 trajectories are displayed in separate graphs or grouped
by level3 units. Graphs can be color coded by values of predictor
variables.
Five options are available:
In addition, HLM offers
the user the option to specify a variety of outcome variable
types and a choice of estimation method. As data analyzed
with HLM are frequently from complex surveys, the option to
include weights at the various levels of the model is also
offered. For more on these additional features of HLM please
see
