Pairwise likelihood estimation and limited-information goodness-of-fit test statistics for binary factor analysis models under complex survey sampling.

This paper discusses estimation and limited-information goodness-of-fit test statistics in factor models for binary data using pairwise likelihood estimation and sampling weights. The paper extends the applicability of pairwise likelihood estimation for factor models with binary data to accommodate complex sampling designs. Additionally, it introduces two key limited-information test statistics: the Pearson chi-squared test and the Wald test.

Iron and manganese mobilisation due to dam height increase for a tropical reservoir in South East Asia.

The aim of this research was the analysis of the effect of a dam height raise on the water quality of a tropical reservoir used for drinking water purposes in South East Asia. Analyses of iron, manganese, pH and ammonia were performed over a 5-year period from daily water sampling at the reservoir. In addition, high-frequency monitoring data of nitrate, ammonium, pH and blue-green algae were obtained using a monitoring probe.

Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random.

Methods for the treatment of item non-response in attitudinal scales and in large-scale assessments under the pairwise likelihood (PL) estimation framework and under a missing at random (MAR) mechanism are proposed. Under a full information likelihood estimation framework and MAR, ignorability of the missing data mechanism does not lead to biased estimates. However, this is not the case for pseudo-likelihood approaches such as the PL.

Latent Class Analysis: Insights about design and analysis of schistosomiasis diagnostic studies.

Various global health initiatives are currently advocating the elimination of schistosomiasis within the next decade. Schistosomiasis is a highly debilitating tropical infectious disease with severe burden of morbidity and thus operational research accurately evaluating diagnostics that quantify the epidemic status for guiding effective strategies is essential. Latent class models (LCMs) have been generally considered in epidemiology and in particular in recent schistosomiasis diagnostic studies as a flexible tool for evaluating diagnostics because assessing the true infection status (via a gold standard) is not possible.