General

News 

We are pleased to announce that after several years of development, SSI is offering a brand new HLM software licensing, delivery, and support model. We call it SSI Live™. HLM is the first program to transition to the new model. SSI Live™ is a subscription, similar to the popular rental licenses that we used to market, but with many more features.With your renewable SSI Live™ subscription, you are entitled to many more benefits in comparison to previous licensing models.  Click here to view a side-by-side comparison chart of features.

The following subscriptions are available:

 

Standard subscriptions for 3, 6 or 12 months:

This subscription is a standard non-commercial, academic, and educational copy of HLM. While your renewable subscription is active, key benefits include: 

  • download and simultaneously install state-of-the-art HLM program on two (2) computers  
  • additional installs can be purchased and added at any time at discount
  • free access to all upgrades or updates 
  • free software support and technical support 
  • additional discounts on subscriptions of other SSI or VPG programs 

 

Basic subscriptions for 3 or 12 months:

This subscription is a basic non-commercial, academic, and educational copy of HLM. While your renewable subscription is active, you are entitled to the following benefits: 

  • download and install state-of-the-art HLM program on one computer  
  • free access to all upgrades or updates 

 

Trial version:

  • A fully-functioning 14-day trial subscription is available for a potential user’s evaluation purposes.  
  • For educators who plan to use HLM in instruction, your students may be eligible access the full version of HLM for free during your course or workshop. 

 

To acquire a subscription or to find out more about SSI Live™, please visit http://ssilive.com/. For help on registering, see our Quickstart guide. For more information on adding additional users to a license, see our FAQ on this topic

Current users with perpetual licenses will be able to continue to use the program according to the terms of the end user license agreement. Over time, this transition will impact on the technical support we offer our users who hold older perpetual licenses. 

 

HLM8 users:

Please refer to an email recently sent from This email address is being protected from spambots. You need JavaScript enabled to view it. sharing more details on how to activate your subscription account. 

 

HLM6 users:

As you are aware, the final release of HLM6 occurred in 2009, which is more than a decade ago.  The current release of HLM is on Version 8. Software products built on older operating systems and third-party components become increasingly more difficult to maintain and support, and as a consequence, also less secure.  Due to such concerns, we will phase out support for HLM6 at the end of this calendar year (12/31/2020).  Because you have a perpetual license, you certainly may continue to use the program according to the terms of the end user license agreement beyond this year.  However, as of January 1, 2021, our HLM Support Desk will not respond to HLM6 inquiries. 

 

Overview

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 and with level-3 units organized into another set of units at a fourth level. Examples of this abound in areas such as education (students at level 1, teachers at level 2, schools at level 3, and school districts at level 4) 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.

HLM fits 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.

HLM 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).

Due to increased interest in multivariate outcome models, such as repeated measurement data, contributions by Jennrich & Schluchter (1986), and Goldstein (1995) led to the inclusion of multivariate models in most of the available hierarchical linear modeling programs. These models allow the researcher to study cases where the variance at the lowest level of the hierarchy can assume a variety of forms/structures. The approach also provides the researcher with the opportunity to fit latent variable models (Raudenbush & Bryk, 2002), with the first level of the hierarchy representing associations between fallible, observed data and latent, "true" data. An application that has received attention in this regard recently is the analysis of item response models, where an individuals "ability" or "latent trait" is based on the probability of a given response as a function of characteristics of items presented to an individual.

In HLM7, unprecedented flexibility in the modeling of multilevel and longitudinal data was introduced with the inclusion of three new procedures that handle binary, count, ordinal and multinomial (nominal) response variables as well as continuous response variables for normal-theory hierarchical linear models. HLM7 introduced four-level nested models for cross-sectional and longitudinal models and four-way cross-classified and nested mixture models. Hierarchical models with dependent random effects (spatial design) were added. Another new feature was new flexibility in estimating hierarchical generalized linear models through the use of Adaptive Gauss-Hermite Quadrature (AGH) and high-order Laplace approximations to maximum likelihood. The AGH approach has been shown to work very well when cluster sizes are small and variance components are large. The high-order Laplace approach requires somewhat larger cluster sizes but allows an arbitrarily large number of random effects (important when cluster sizes are large).

In HLM8, the ability to estimate an HLM from incomplete data was added. This is a completely automated approach that generates and analyses multiply imputed data sets from incomplete data. The model is fully multivariate and enables the analyst to strengthen imputation through auxiliary variables. This means that the user specifies the HLM; the program automatically searches the data to discover which variables have missing values and then estimates a multivariate hierarchical linear model (”imputation model”) in which all variables having missed values are regressed on all variables having complete data. The program then uses the resulting parameter estimates to generate M imputed data sets, each of which is then analysed in turn. Results are combined using the “Rubin rules”.

Another new feature of HLM8 is that flexible combinations of Fixed Intercepts and Random Coefficients (FIRC) are now included in HLM2, HLM3, HLM4, HCM2, and HCM3. A concern that can arise in multilevel causal studies is that random effects may be correlated with treatment assignment. For example, suppose that treatments are assigned non-randomly to students who are nested within schools. Estimating a two-level model with random school intercepts will generate bias if the random intercepts are correlated with treatment effects. The conventional strategy is to specify a fixed effects model for schools. However, this approach assumes homogeneous treatment effects, possibly leading to biased estimates of the average treatment effect, incorrect standard errors, and inappropriate interpretation. HLM8 allows the analyst to combine fixed intercepts with random coefficients in models that address these problems and to facilitate a richer summary including an estimate of the variation of treatment effects and empirical Bayes estimates of unit-specific treatment effects. This approach was proposed in Bloom, Raudenbush, Weiss and Porter (2017).

 
 

Compatibility 

HLM8 is compatible with Windows 10. It has been tested on Windows 10 and no problems were reported.

  

HLM8 is compatible with Windows 7. It has been tested on Windows 7 and no problems were reported.