On a 2 × 2 factorial design where the use of randomized response is one of the factors

This paper discusses a survey where some respondents were asked sensitive questions directly and others were asked the same questions using randomized response. The use of randomized response was a factor in a 2 × 2 factorial design and dice were used to perform the randomization. First, the paper shows that the perturbation due to the dice can be described using the concept of misclassification and known conditional misclassification probabilities. Second, the paper formulates the likelihood for loglinear models and shows that latent class software can be used to analyse the data. An example including a power analysis is discussed.     

Longitudinal Meta-analysis

The goal of meta-analysis is to integrate the research results of a number of studies on a specific topic. Characteristic for meta-analysis is that in general only the summary statistics of the studies are used and not the original data. When the published research results to be integrated are longitudinal, multilevel analysis can be used for the meta-analysis. We will demonstrate this with an example of longitudinal data on the mental development of infants. We distinguish four levels in the data. The highest level (4) is the publication, in which the results of one or more studies are published. The third level consists of the separate studies. At this level we have knowledge about the degree of prematurity of the group of infants in the specific study. The second level are the repeated measures. We have data about the test age, the mental development, the corresponding standard deviations, and the sample sizes. The lowest level is needed for the specification of the meta-analysis model. Both the way in which the multilevel model has to be specified (the Mln-program is used) as the results will be presented and interpreted.