Looking for homogeneous groups of respondents and nonrespondents using latent class analysis.

Concerns about decreasing response rates have led to many nonresponse studies. Unfortunately, usually only a limited amount of data is available on nonrespondents. Linking administrative data makes information available on all the sampled units including nonrespondents. Statistics Netherlands has constructed a database in which several administrative records and several surveys are connected. In this explorative study we use this dataset to look for homogenous groups of respondents and nonrespondents in survey research using latent class cluster analysis. Latent class techniques can provide insight into the problem of selective nonresponse and currently under-represented societal groups. We identify four different latent classes in a recent Dutch survey and replicate the findings for the same survey conducted 4 years later. Two of the types of sampled units have above-average response rates and the other two have below-average response rates. We also evaluate various latent class models with other response outcomes to gain insight into the contact and cooperation process.

Minor physical anomalies in autism: a meta-analysis.

Autism is a complex neurodevelopmental disorder in which the interactions of genetic, epigenetic and environmental influences play a causal role. Despite the compelling evidence for a strong heritability, the etiology and molecular mechanisms underlying autism remain unclear. High phenotypic variability and genetic heterogeneity confounds the identification of susceptibility genes. The lack of robust indicators to tackle this complexity in autism has led researchers to seek for novel diagnostic tools to create homogenous subgroups. Several studies have indicated that patients with autism have higher rates of minor physical anomalies (MPAs) and that MPAs may serve as a diagnostic tool; however, the results have been inconsistent. Using the cumulative data from seven studies on MPAs in autism, this meta-analysis seeks to examine whether the aggregate data provide evidence of a large mean effect size and statistical significance for MPAs in autism. It covers the studies using multiple research methods till June 2007. The current results from seven studies suggested a significant association of MPAs in autism with a robust pooled effect size (d=0.84), and thereby provide the strongest evidence to date about the close association between MPAs and autism. Our results emphasize the importance of MPAs in the identification of heterogeneity in autism and suggest that the success of future autism genetics research will be exploited by the use of MPAs. Implications for the design of future studies on MPAs in autism are discussed and suggestions for further investigation of these important markers are proposed. Clarifying this relation might improve understanding of risk factors and molecular mechanisms in autism.