A comparison of mean phase difference and generalized least squares for analyzing single-case data

The present study focuses on single-case data analysis and specifically on two procedures for quantifying differences between baseline and treatment measurements The first technique tested is based on generalized least squares regression analysis and is compared to a proposed non-regression technique, which allows obtaining similar information. The comparison is carried out in the context of generated data representing a variety of patterns (i.e., independent measurements, different serial dependence underlying processes, constant or phase-specific autocorrelation and data variability, different types of trend, and slope and level change). The results suggest that the two techniques perform adequately for a wide range of conditions and researchers can use both of them with certain guarantees. The regression-based procedure offers more efficient estimates, whereas the proposed non-regression procedure is more sensitive to intervention effects. Considering current and previous findings, some tentative recommendations are offered to applied researchers in order to help choosing among the plurality of single-case data analysis techniques.

Design and evaluation of a panel os single-nucleotide polymorphisms in microRNA genomic regions for association studies in human disease

MicroRNAs (miRNA) are recognized posttranscriptional gene repressors involved in the control of almost every biological process. Allelic variants in these regions may be an important source of phenotypic diversity and contribute to disease susceptibility. We analyzed the genomic organization of 325 human miRNAs (release 7.1, miRBase) to construct a panel of 768 single-nucleotide polymorphisms (SNPs) covering approximately 1 Mb of genomic DNA, including 131 isolated miRNAs (40%) and 194 miRNAs arranged in 48 miRNA clusters, as well as their 5-kb flanking regions. Of these miRNAs, 37% were inside known protein-coding genes, which were significantly associated with biological functions regarding neurological, psychological or nutritional disorders. SNP coverage analysis revealed a lower SNP density in miRNAs compared with the average of the genome, with only 24 SNPs located in the 325 miRNAs studied. Further genotyping of 340 unrelated Spanish individuals showed that more than half of the SNPs in miRNAs were either rare or monomorphic, in agreement with the reported selective constraint on human miRNAs. A comparison of the minor allele frequencies between Spanish and HapMap population samples confirmed the applicability of this SNP panel to the study of complex disorders among the Spanish population, and revealed two miRNA regions, hsa-mir-26a-2 in the CTDSP2 gene and hsa-mir-128-1 in the R3HDM1 gene, showing geographical allelic frequency variation among the four HapMap populations, probably because of differences in natural selection. The designed miRNA SNP panel could help to identify still hidden links between miRNAs and human disease.

The determinants of the prevalence of single mothers: A cross-country analysis

This paper examines the effect of public assistance, labor market and marriage marketconditions on the prevalence of single mother families across countries and over time. Amultinomial logit derived from a random utility approach is estimated using individualleveldata for 14 countries. I find evidence that increases in the level of public support are significantly and positively associated with a higher incidence of both never marriedand divorced mothers. The results also suggest that single mothers are more prevalentwhen female wages are lower. Higher male earnings and employment opportunities in awoman s marriage market appear to lead to fewer never married mothers, but more divorced mothers. Higher child support or alimony payments are associated with a higher prevalence of divorced mothers.

Raman scattering and photoluminescence analysis of silicon on insulator structures obtained by single and multiple oxygen implants

An analysis of silicon on insulator structures obtained by single and multiple implants by means of Raman scattering and photoluminescence spectroscopy is reported. The Raman spectra obtained with different excitation powers and wavelengths indicate the presence of a tensile strain in the top silicon layer of the structures. The comparison between the spectra measured in both kinds of samples points out the existence in the multiple implant material of a lower strain for a penetration depth about 300 nm and a higher strain for higher penetration depths. These results have been correlated with transmission electron microscopy observations, which have allowed to associate the higher strain to the presence of SiO2 precipitates in the top silicon layer, close to the buried oxide. The found lower strain is in agreement with the better quality expected for this material, which is corroborated by the photoluminescence data.

Factors affecting visual analysis in single-case designs

Visual inspection remains the most frequently applied method for detecting treatment effects in single-case designs. The advantages and limitations of visual inference are here discussed in relation to other procedures for assessing intervention effectiveness. The first part of the paper reviews previous research on visual analysis, paying special attention to the validation of visual analysts" decisions, inter-judge agreement, and false alarm and omission rates. The most relevant factors affecting visual inspection (i.e., effect size, autocorrelation, data variability, and analysts" expertise) are highlighted and incorporated into an empirical simulation study with the aim of providing further evidence about the reliability of visual analysis. Our results concur with previous studies that have reported the relationship between serial dependence and increased Type I rates. Participants with greater experience appeared to be more conservative and used more consistent criteria when assessing graphed data. Nonetheless, the decisions made by both professionals and students did not match sufficiently the simulated data features, and we also found low intra-judge agreement, thus suggesting that visual inspection should be complemented by other methods when assessing treatment effectiveness.

DnaSP v5: A software for comprehensive analysis of DNA polymorphism data

DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser.

Reconciling phonological neighborhood effects in speech production through single trial analysis

A crucial step for understanding how lexical knowledge is represented is to describe the relative similarity of lexical items, and how it influences language processing. Previous studies of the effects of form similarity on word production have reported conflicting results, notably within and across languages. The aim of the present study was to clarify this empirical issue to provide specific constraints for theoretical models of language production. We investigated the role of phonological neighborhood density in a large-scale picture naming experiment using fine-grained statistical models. The results showed that increasing phonological neighborhood density has a detrimental effect on naming latencies, and re-analyses of independently obtained data sets provide supplementary evidence for this effect. Finally, we reviewed a large body of evidence concerning phonological neighborhood density effects in word production, and discussed the occurrence of facilitatory and inhibitory effects in accuracy measures. The overall pattern shows that phonological neighborhood generates two opposite forces, one facilitatory and one inhibitory. In cases where speech production is disrupted (e.g. certain aphasic symptoms), the facilitatory component may emerge, but inhibitory processes dominate in efficient naming by healthy speakers. These findings are difficult to accommodate in terms of monitoring processes, but can be explained within interactive activation accounts combining phonological facilitation and lexical competition.

Inversion polymorphism in two Serbian natural populations of Drosophila subobscura: Analysis of long-term changes

To study whether inversions (or arrangements) by themselves or karyotypes are the main global warming adaptive target of natural selection, two Drosophila subobscura Serbian populations (Apatin and Petnica) were re analyzed using different statistical approaches. Both populations were sampled in an approximately 15 years period: Apatin in 1994 and 2008 + 2009 and Petnica in 1995 and 2010. For all chromosomes, the four collections studied were in Hardy-Weinberg equilibrium. Thus, it seems that inversions (or arrangements) combined at random to constitute populations" karyotypes. However, there were differences in karyotypic fre quencies along the years, although they were significant only for Apatin population. It is possible to conclude that inversions (or arrangements) are likely the target of natural selection, because they presented long term changes, but combine at random to generate the corresponding karyotypic combinations. As a consequence, the frequencies of karyotypes also change along time.

The single principle of compositional data analysis, continuing fallacies, confusionsand misunderstandings and some suggested remedies

In any discipline, where uncertainty and variability are present, it is important to haveprinciples which are accepted as inviolate and which should therefore drive statisticalmodelling, statistical analysis of data and any inferences from such an analysis.Despite the fact that two such principles have existed over the last two decades andfrom these a sensible, meaningful methodology has been developed for the statisticalanalysis of compositional data, the application of inappropriate and/or meaninglessmethods persists in many areas of application. This paper identifies at least tencommon fallacies and confusions in compositional data analysis with illustrativeexamples and provides readers with necessary, and hopefully sufficient, arguments topersuade the culprits why and how they should amend their ways