Multivariate modelling of responses to conditional items: New possibilities for latent class analysis

Questionnaire data may contain missing values because certain questions do not apply to all respondents. For instance, questions addressing particular attributes of a symptom, such as frequency, triggers or seasonality, are only applicable to those who have experienced the symptom, while for those who have not, responses to these items will be missing. This missing information does not fall into the category 'missing by design', rather the features of interest do not exist and cannot be measured regardless of survey design. Analysis of responses to such conditional items is therefore typically restricted to the subpopulation in which they apply. This article is concerned with joint multivariate modelling of responses to both unconditional and conditional items without restricting the analysis to this subpopulation. Such an approach is of interest when the distributions of both types of responses are thought to be determined by common parameters affecting the whole population. By integrating the conditional item structure into the model, inference can be based both on unconditional data from the entire population and on conditional data from subjects for whom they exist. This approach opens new possibilities for multivariate analysis of such data. We apply this approach to latent class modelling and provide an example using data on respiratory symptoms (wheeze and cough) in children. Conditional data structures such as that considered here are common in medical research settings and, although our focus is on latent class models, the approach can be applied to other multivariate models.

Do multiple true-false items beat the commonly used one-best-answers questions regarding to the Ottawa Criteria for Good Assessment? Results of a literature review

Background: Multiple True-False-Items (MTF-Items) might offer some advantages compared to one-best-answer-questions (TypeA) as they allow more than one correct answer and may better represent clinical decisions. However, in medical education assessment MTF-Items are seldom used. Summary of Work: With this literature review existing findings on MTF-items and on TypeA were compared along the Ottawa Criteria for Good Assessment, i.e. (1) reproducibility, (2) feasibility, (3) validity, (4) acceptance, (5) educational effect, (6) catalytic effects, and (7) equivalence. We conducted a literature research on ERIC and Google Scholar including papers from the years 1935 to 2014. We used the search terms “multiple true-false”, “true-false”, “true/false”, and “Kprim” combined with “exam”, “test”, and “assessment”. Summary of Results: We included 29 out of 33 studies. Four of them were carried out in the medical field Compared to TypeA, MTF-Items are associated with (1) higher reproducibility (2) lower feasibility (3) similar validity (4) higher acceptance (5) higher educational effect (6) no studies on catalytic effects or (7) equivalence. Discussion and Conclusions: While studies show overall good characteristics of MTF items according to the Ottawa criteria, this type of question seems to be rather seldom used. One reason might be the reported lower feasibility. Overall the literature base is still weak. Furthermore, only 14 % of literature is from the medical domain. Further studies to better understand the characteristics of MTF-Items in the medical domain are warranted. Take-home messages: Overall the literature base is weak and therefore further studies are needed. Existing studies show that: MTF-Items show higher reliability, acceptance and educational effect; MTF-Items are more difficult to produce