All posts by Mathilda du Toit

Announcing the release of PARSCALE 5

A new version of the PARSCALE program is immediately available.

New features

  1. The number of examinees in a dataset has been increased from 1 million to ten million.
  2. The maximum length of all file names has been changed from 128 to 256 characters. This “feature” is only useful if the DLL can handle syntax file with directory. Currently the UI break the full path into <drive>/<dir> and <filename>, change the current working directory to <drive>/<dir> and call the BLM.exe with <filename> (together with other parameters).
  3. The phases can be run in batch mode

Introducing New Features in HLM 8

The HLM8 program has a number of new statistical features.

Estimating HLM from incomplete data

In HLM 8, 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”.

Flexible Combinations of Fixed Intercepts and Random Coefficients

Another new feature of HLM 8 is that flexible combinations of Fixed Intercepts and Random Coefficients (FIRC) are now included in HLM2, HLM3, HLM4, HCM2, HCM3 and HLM2.

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. HLM 8 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).