Here - Tilburg University

Author and presenter
Gorter, Rosalie; VUmc, Dept. of Epidemiology and Biostatistics, EMGO+
institute of health and care research
Authors
Rosalie Gorter; Martijn W. Heymans; Jos W.R. Twisk; VUmc, Dept. of
Epidemiology and Biostatistics, Emgo+ Institute of Health and Care Research.
Michiel R. de Boer, VU University, Faculty of Earth and Life Sciences, Institute for
Health Sciences, Dept.of Methodology and Applied Biostatistics.
Rien van der Leeden, Leiden University, Faculty of Social Sciences, Institute
Psychology, Methodology & Statistics.
Title
Comparing the performance of software packages in estimating the parameters
of multilevel IRT models for longitudinal data
Background
Many questionnaires used in patient research consist of items with a likert
answering scale. An example is the increasing utilization of quality of life
questionnaires in epidemiological and medical research. When the answers on
such questionnaire are used as outcome variable, usually a score is attached to
the answering categories and these scores are than added in order to obtain a
total score for the construct. A theoretically more appropriate way of analyzing
these data is by using an IRT model that estimates item and person parameters.
An adjacent category (ordinal) logit model can be used to estimate the
probability of a person to choose a specific category given his or her level of the
latent variable theta. In addition to using IRT specific software packages for such
analysis, the models can also be formulated as hierarchical models and analyzed
with general software packages. An important advantage of this reformulation is
that levels can be added for analyzing longitudinal or otherwise clustered data.
There are several different software packages for fitting ordinal logit models
which are capable of estimating the parameters for this type of longitudinal data.
However, these packages use different estimation methods which may lead to
different estimates depending on the combination of parameter specific
characteristics of the data such as sample size, item and person characteristics.