Bayesian inference for low-rank Ising networks

Estimating the structure of Ising networks is a notoriously difficult problem. We demonstrate that using a latent variable representation of the Ising network, we can employ a full-data-information approach to uncover the network structure. Thereby, only ignoring information encoded in the prior distribution (of the latent variables). The full-data-information approach avoids having to compute the partition function and is thus computationally feasible, even for networks with many nodes. We illustrate the full-data-information approach with the estimation of dense network

Psychometric Framework for Modeling Parental Involvement and Reading Literacy

Assessment, Testing and Evaluatio

Psychometric Framework for Modeling Parental Involvement and Reading Literacy

Assessment, Testing and Evaluatio

Variance Decomposition Using an IRT Measurement Model

Large scale research projects in behaviour genetics and genetic epidemiology are often based on questionnaire or interview data. Typically, a number of items is presented to a number of subjects, the subjects’ sum scores on the items are computed, and the variance of sum scores is decomposed into a number of variance components. This paper discusses several disadvantages of the approach of analysing sum scores, such as the attenuation of correlations amongst sum scores due to their unreliability. It is shown that the framework of Item Response Theory (IRT) offers a solution to most of these problems. We argue that an IRT approach in combination with Markov chain Monte Carlo (MCMC) estimation provides a flexible and efficient framework for modelling behavioural phenotypes. Next, we use data simulation to illustrate the potentially huge bias in estimating variance components on the basis of sum scores. We then apply the IRT approach with an analysis of attention problems in young adult twins where the variance decomposition model is extended with an IRT measurement model. We show that when estimating an IRT measurement model and a variance decomposition model simultaneously, the estimate for the heritability of attention problems increases from 40% (based on sum scores) to 73%

Application of multidimensional IRT models to longitudinal data

The application of multidimensional item response theory (IRT) models to longitudinal educational surveys where students are repeatedly measured is discussed and exemplified. A marginal maximum likelihood (MML) method to estimate the parameters of a multidimensional generalized partial credit model for repeated measures is presented. It is shown that model fit can be evaluated using Lagrange multiplier tests. Two tests are presented: the first aims at evaluation of the fit of the item response functions and the second at the constancy of the item location parameters over time points. The outcome of the latter test is compared with an analysis using scatter plots and linear regression. An analysis of data from a school effectiveness study in Flanders (Belgium) is presented as an example of the application of these methods. In the example, it is evaluated whether the concepts "academic self-concept," "well-being at school," and "attentiveness in the classroom" were constant during the secondary school period. \u

Practical methods for dealing with 'not applicable' item responses in the AMC Linear Disability Score project

Background:\ud Whenever questionnaires are used to collect data on constructs, such as functional status or health related quality of life, it is unlikely that all respondents will respond to all items. This paper examines ways of dealing with responses in a 'not applicable' category to items included in the AMC Linear Disability Score (ALDS) project item bank. \ud \ud Methods:\ud The data examined in this paper come from the responses of 392 respondents to 32 items and form part of the calibration sample for the ALDS item bank. The data are analysed using the one-parameter logistic item response theory model. The four practical strategies for dealing with this type of response are: cold deck imputation; hot deck imputation; treating the missing responses as if these items had never been offered to those individual patients; and using a model which takes account of the 'tendency to respond to items'. \ud \ud Results:\ud The item and respondent population parameter estimates were very similar for the strategies involving hot deck imputation; treating the missing responses as if these items had never been offered to those individual patients; and using a model which takes account of the 'tendency to respond to items'. The estimates obtained using the cold deck imputation method were substantially different. \ud \ud Conclusions:\ud The cold deck imputation method was not considered suitable for use in the ALDS item bank. The other three methods described can be usefully implemented in the ALDS item bank, depending on the purpose of the data analysis to be carried out. These three methods may be useful for other data sets examining similar constructs, when item response theory based methods are used

Working mechanism of a multidimensional computerized adaptive test for fatigue in rheumatoid arthritis

Background This paper demonstrates the mechanism of a multidimensional computerized adaptive test (CAT) to measure fatigue in patients with rheumatoid arthritis (RA). A CAT can be used to precisely measure patient-reported outcomes at an individual level as items are consequentially selected based on the patient’s previous answers. The item bank of the CAT Fatigue RA has been developed from the patients’ perspective and consists of 196 items pertaining to three fatigue dimensions: severity, impact and variability of fatigue. Methods The CAT Fatigue RA was completed by fifteen patients. To test the CAT’s working mechanism, we applied the flowchart-check-method. The adaptive item selection procedure for each patient was checked by the researchers. The estimated fatigue levels and the measurement precision per dimension were illustrated with the selected items, answers and flowcharts. Results The CAT Fatigue RA selected all items in a logical sequence and those items were selected which provided the most information about the patient’s individual fatigue. Flowcharts further illustrated that the CAT reached a satisfactory measurement precision, with less than 20 items, on the dimensions severity and impact and to somewhat lesser extent also for the dimension variability. Patients’ fatigue scores varied across the three dimensions; sometimes severity scored highest, other times impact or variability. The CAT’s ability to display different fatigue experiences can improve communication in daily clinical practice, guide interventions, and facilitate research into possible predictors of fatigue. Conclusions The results indicate that the CAT Fatigue RA measures precise and comprehensive. Once it is examined in more detail in a consecutive, elaborate validation study, the CAT will be available for implementation in daily clinical practice and for research purpose