Efficacy estimates from parasite count data that include zero counts

Count Data in Psychological Applied Research

As some authors have noticed in fields other than psychology, level of measurement and distributional characteristics of count data are commonly not taken into account, so that they are analysed as normally distributed continuous variables, and therefore some general linear model is applied. In this work, we review a random sample of 457 articles published in the last four years in journals with the highest impact factor in the Journal Citation Reports (JCR Social Sciences Edition) of the Institute for Scientific Information. The goals are to know how often count variables appear in psychological applied research and which data analyses are used when dealing with count response variables. Examination showed that there is a notable presence of count variables, especially in some topics in psychology and a remarkable misapplication of the general linear model. One deals with causes and consequences of this approach to data analysis and suggests the use of specific models to analyse count data.

Evaluating faecal egg count reduction using a specifically designed package "eggCounts" in R and a user friendly web interface

The seemingly straightforward task of analysing faecal egg counts resulting from laboratory procedures such as the McMaster technique has, in reality, a number of complexities. These include Poisson errors in the counting technique which result from eggs being randomly distributed in well mixed faecal samples. In addition, counts between animals in a single experimental or observational group are nearly always over-dispersed. We describe the R package "eggCounts" that we have developed that incorporates both sampling error and over-dispersion between animals to calculate the true egg counts in samples of faeces, the probability distribution of the true counts and summary statistics such as the 95% uncertainty intervals. Based on a hierarchical Bayesian framework, the software will also rigorously estimate the percentage reduction of faecal egg counts and the 95% uncertainty intervals of data generated by a faecal egg count reduction test. We have also developed a user friendly web interface that can be used by those with limited knowledge of the R statistical computing environment. We illustrate the package with three simulated data sets of faecal egg count reduction experiments.

Technical variability and required sample size of helminth egg isolation procedures: revisited

Mes [Vet Parasitol (2003) 115:311-320] recently reported a quantitative study of repeated measurements of nematode egg counts in faecal samples from dairy cattle, in order to compare the faecal egg counts resulting from two different laboratory techniques, the widely used McMaster method and a newer salt-sugar flotation (SSF) method. He concluded that the SSF technique requires much smaller sample sizes, and is also potentially simpler to carry out, making it the method of choice. Here I re-analyse these data to show that if the comparison is done on the most appropriate measurement scale (lognormal), and the large difference in multiplication factors is taken into account, then there is little to choose between the McMaster and SSF techniques as far as the required sample size is concerned. In particular, the treatment of the data as normally rather than lognormally distributed leads to incorrect statistical tests, power analyses and confidence intervals.