Mapping non-suicidal self-injury in adolescence: Development and confirmatory factor analysis of the impulse, self-harm and sucidal ideation questionnaire for adolescents (ISSIQ-A)

Non-suicidal self-injury (NSSI) is the deliberate, self-inflicted destruction of body tissue without suicidal intent and an important clinical phenomenon. Rates of NSSI appear to be disproportionately high in adolescents and young adults, and is a risk factor for suicidal ideation and behavior. The present study reports the psychometric properties of the Impulse, Self-harm and Suicide Ideation Questionnaire for Adolescents (ISSIQ-A), a measure designed to comprehensively assess the impulsivity, NSSI behaviors and suicide ideation. An additional module of this questionnaire assesses the functions of NSSI. Results of Confirmatory Factor Analysis (CFA) of the scale on 1722 youths showed items' suitability and confirmed a model of four different dimensions (Impulse, Self-harm, Risk-behavior and Suicide ideation) with good fit and validity. Further analysis showed that youth׳s engagement in self-harm may exert two different functions: to create or alleviate emotional states, and to influence social relationships. Our findings contribute to research and assessment on non-suicidal self-injury, suggesting that the ISSIQ-A is a valid and reliable measure to assess impulse, self-harm and suicidal thoughts, in adolescence.

Wound infection after excision and primary midline closure for pilonidal disease: risk factor analysis to improve patient selection.

BACKGROUND: Excision and primary midline closure for pilonidal disease (PD) is a simple procedure; however, it is frequently complicated by infection and prolonged healing. The aim of this study was to analyze risk factors for surgical site infection (SSI) in this context. METHODS: All consecutive patients undergoing excision and primary closure for PD from January 2002 through October 2008 were retrospectively assessed. The end points were SSI, as defined by the Center for Disease Control, and time to healing. Univariable and multivariable risk factor analyses were performed. RESULTS: One hundred thirty-one patients were included [97 men (74%), median age = 24 (range 15-66) years]. SSI occurred in 41 (31%) patients. Median time to healing was 20 days (range 12-76) in patients without SSI and 62 days (range 20-176) in patients with SSI (P < 0.0001). In univariable and multivariable analyses, smoking [OR = 2.6 (95% CI 1.02, 6.8), P = 0.046] and lack of antibiotic prophylaxis [OR = 5.6 (95% CI 2.5, 14.3), P = 0.001] were significant predictors for SSI. Adjusted for SSI, age over 25 was a significant predictor of prolonged healing. CONCLUSION: This study suggests that the rate of SSI after excision and primary closure of PD is higher in smokers and could be reduced by antibiotic prophylaxis. SSI significantly prolongs healing time, particularly in patients over 25 years.

Factor Analysis of a Large DSGE Model

We study the workings of the factor analysis of high-dimensional data using artificial series generated from a large, multi-sector dynamic stochastic general equilibrium (DSGE) model. The objective is to use the DSGE model as a laboratory that allow us to shed some light on the practical benefits and limitations of using factor analysis techniques on economic data. We explain in what sense the artificial data can be thought of having a factor structure, study the theoretical and finite sample properties of the principal components estimates of the factor space, investigate the substantive reason(s) for the good performance of di¤usion index forecasts, and assess the quality of the factor analysis of highly dissagregated data. In all our exercises, we explain the precise relationship between the factors and the basic macroeconomic shocks postulated by the model.

A Factor analysis of hidrochemical composition of Llobregat river basin

Hydrogeological research usually includes some statistical studies devised to elucidate mean background state, characterise relationships among different hydrochemical parameters, and show the influence of human activities. These goals are achieved either by means of a statistical approach or by mixing models between end-members. Compositional data analysis has proved to be effective with the first approach, but there is no commonly accepted solution to the end-member problem in a compositional framework. We present here a possible solution based on factor analysis of compositions illustrated with a case study. We find two factors on the compositional bi-plot fitting two non-centered orthogonal axes to the most representative variables. Each one of these axes defines a subcomposition, grouping those variables that lay nearest to it. With each subcomposition a log-contrast is computed and rewritten as an equilibrium equation. These two factors can be interpreted as the isometric log-ratio coordinates (ilr) of three hidden components, that can be plotted in a ternary diagram. These hidden components might be interpreted as end-members. We have analysed 14 molarities in 31 sampling stations all along the Llobregat River and its tributaries, with a monthly measure during two years. We have obtained a bi-plot with a 57% of explained total variance, from which we have extracted two factors: factor G, reflecting geological background enhanced by potash mining; and factor A, essentially controlled by urban and/or farming wastewater. Graphical representation of these two factors allows us to identify three extreme samples, corresponding to pristine waters, potash mining influence and urban sewage influence. To confirm this, we have available analysis of diffused and widespread point sources identified in the area: springs, potash mining lixiviates, sewage, and fertilisers. Each one of these sources shows a clear link with one of the extreme samples, except fertilisers due to the heterogeneity of their composition. This approach is a useful tool to distinguish end-members, and characterise them, an issue generally difficult to solve. It is worth note that the end-member composition cannot be fully estimated but only characterised through log-ratio relationships among components. Moreover, the influence of each endmember in a given sample must be evaluated in relative terms of the other samples. These limitations are intrinsic to the relative nature of compositional data

Robust Factor Analysis for Compositional Data

Factor analysis as frequent technique for multivariate data inspection is widely used also for compositional data analysis. The usual way is to use a centered logratio (clr) transformation to obtain the random vector y of dimension D. The factor model is then y = Λf + e (1) with the factors f of dimension k < D, the error term e, and the loadings matrix Λ. Using the usual model assumptions (see, e.g., Basilevsky, 1994), the factor analysis model (1) can be written as Cov(y) = ΛΛT + ψ (2) where ψ = Cov(e) has a diagonal form. The diagonal elements of ψ as well as the loadings matrix Λ are estimated from an estimation of Cov(y). Given observed clr transformed data Y as realizations of the random vector y. Outliers or deviations from the idealized model assumptions of factor analysis can severely effect the parameter estimation. As a way out, robust estimation of the covariance matrix of Y will lead to robust estimates of Λ and ψ in (2), see Pison et al. (2003). Well known robust covariance estimators with good statistical properties, like the MCD or the S-estimators (see, e.g. Maronna et al., 2006), rely on a full-rank data matrix Y which is not the case for clr transformed data (see, e.g., Aitchison, 1986). The isometric logratio (ilr) transformation (Egozcue et al., 2003) solves this singularity problem. The data matrix Y is transformed to a matrix Z by using an orthonormal basis of lower dimension. Using the ilr transformed data, a robust covariance matrix C(Z) can be estimated. The result can be back-transformed to the clr space by C(Y ) = V C(Z)V T where the matrix V with orthonormal columns comes from the relation between the clr and the ilr transformation. Now the parameters in the model (2) can be estimated (Basilevsky, 1994) and the results have a direct interpretation since the links to the original variables are still preserved. The above procedure will be applied to data from geochemistry. Our special interest is on comparing the results with those of Reimann et al. (2002) for the Kola project data

Separation of performance-approach and performance-avoidance achievement goals: A broader analysis

In the literature on achievement goals, performance-approach goals (striving to do better than others) and performance-avoidance goals (striving to avoid doing worse than others) tend to exhibit a moderate to high correlation, raising questions about whether the 2 goals represent distinct constructs. In the current article, we sought to examine the separability of these 2 goals using a broad factor-analytic approach that attended to issues that have been overlooked or underexamined in prior research. Five studies provided strong evidence for the separation of these 2 goal constructs: Separation was observed not only with exploratory factor analysis across different age groups and countries (Studies 1a and 1b) but also with change analysis (Study 2), ipsative factor analysis (Study 3), within-person analysis (Study 4), and behavioral genetics analysis (Study 5). We conclude by discussing the implications of the present research for the achievement goal literature, as well as the psychological literature in general.

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles

Background: The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. New method: We propose a complete pipeline for the cluster analysis of ERP data. To increase the signalto-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA)to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). Results: After validating the pipeline on simulated data, we tested it on data from two experiments – a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership.

Exploratory semiparametric analysis of two-dimensional diffusions in finance

We examine bivariate extensions of Aït-Sahalia’s approach to the estimation of univariate diffusions. Our message is that extending his idea to a bivariate setting is not straightforward. In higher dimensions, as opposed to the univariate case, the elements of the Itô and Fokker-Planck representations do not coincide; and, even imposing sensible assumptions on the marginal drifts and volatilities is not sufficient to obtain direct generalisations. We develop exploratory estimation and testing procedures, by parametrizing the drifts of both component processes and setting restrictions on the terms of either the Itô or the Fokker-Planck covariance matrices. This may lead to highly nonlinear ordinary differential equations, where the definition of boundary conditions is crucial. For the methods developed, the Fokker-Planck representation seems more tractable than the Itô’s. Questions for further research include the design of regularity conditions on the time series dependence in the data, the kernels actually used and the bandwidths, to obtain asymptotic properties for the estimators proposed. A particular case seems promising: “causal bivariate models” in which only one of the diffusions contributes to the volatility of the other. Hedging strategies which estimate separately the univariate diffusions at stake may thus be improved.

Screening Brazilian automotive gasoline quality through quantification of saturated hydrocarbons and anhydrous ethanol by gas chromatography and exploratory data analysis

In this paper a set of Brazilian commercial gasoline representative samples from São Paulo State, selected by HCA, plus six samples obtained directly from refineries were analysed by a high-sensitive gas chromatographic (GC) method ASTM D6733. The levels of saturated hydrocarbons and anhydrous ethanol obtained by GC were correlated with the quality obtained from Brazilian Government Petroleum, Natural Gas and Biofuels Agency (ANP) specifications through exploratory analysis (HCA and PCA). This correlation showed that the GC method, together with HCA and PCA, could be employed as a screening technique to determine compliance with the prescribed legal standards of Brazilian gasoline.

Relationship between diet and anticoagulant response to warfarin - A factor analysis

Background Diet composition is one of the factors that may contribute to intraindividual variability in the anticoagulant response to warfarin. Aim of the study To determine the associations between food pattern and anticoagulant response to warfarin in a group of Brazilian patients with vascular disease. Methods Recent and usual food intakes were assessed in 115 patients receiving warfarin; and corresponding plasma phylloquinone (vitamin K-1), serum triglyceride concentrations, prothrombin time (PT), and International Normalized Ratio (INR) were determined. A factor analysis was used to examine the association of specific foods and biochemical variables with anticoagulant data. Results Mean age was 59 +/- 15 years. Inadequate anticoagulation, defined as values of INR 2 or 3, was found in 48% of the patients. Soybean oil and kidney beans were the primary food sources of phylloquinone intake. Factor analysis yielded four separate factors, explaining 56.4% of the total variance in the data set. The factor analysis revealed that intakes of kidney beans and soybean oil, 24-h recall of phylloquinone intake, PT and INR loaded significantly on factor 1. Triglycerides, PT, INR, plasma phylloquinone, and duration of anticoagulation therapy loaded on factor 3. Conclusion Fluctuations in phylloquinone intake, particularly from kidney beans, and plasma phylloquinone concentrations were associated with variation in measures of anticoagulation (PT and INR) in a Brazilian group of patients with vascular disease.

Metabolic profiling by ultra-performance liquid chromatography-mass spectrometry and parallel factor analysis for the determination of disease biomarkers in Eucalyptus

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Confirmatory factor analysis of different versions of the Body Shape Questionnaire applied to Brazilian university students

This study aimed at evaluating the validity, reliability, and factorial invariance of the complete (34-item) and shortened (8-item and 16-item) versions of the Body Shape Questionnaire (BSQ) when applied to Brazilian university students. A total of 739 female students with a mean age of 20.44 (standard deviation = 2.45) years participated. Confirmatory factor analysis was conducted to verify the degree to which the one-factor structure satisfies the proposal for the BSQ's expected structure. Two items of the 34-item version were excluded because they had factor weights (lambda)< 40. All models had adequate convergent validity (average variance extracted =.43-.58; composite reliability=.85-.97) and internal consistency (alpha =.85-.97). The 8-item B version was considered the best shortened BSQ version (Akaike information criterion = 84.07, Bayes information criterion = 157.75, Browne-Cudeck criterion= 84.46), with strong invariance for independent samples (Delta chi(2)lambda(7)= 5.06, Delta chi(2)Cov(8)= 5.11, Delta chi(2)Res(16) = 19.30). (C) 2014 Elsevier Ltd. All rights reserved.

The Use of Factor Analysis in the Development of Hand Sizes for Glove Design

Factor analysis was used to develop a more detailed description of the human hand to be used in the creation of glove sizes; currently gloves sizes are small, medium, and large. The created glove sizes provide glove designers with the ability to create a glove design that can provide fit to the majority of hand variations in both the male and female populations. The research used the American National Survey (ANSUR) data that was collected in 1988. This data contains eighty-six length, width, height, and circumference measurements of the human hand for one thousand male subjects and thirteen hundred female subjects. Eliminating redundant measurements reduced the data to forty-six essential measurements. Factor analysis grouped the variables to form three factors. The factors were used to generate hand sizes by using percentiles along each factor axis. Two different sizing systems were created. The first system contains 125 sizes for male and female. The second system contains 7 sizes for males and 14 sizes for females. The sizing systems were compared to another hand sizing system that was created using the ANSUR database indicating that the systems created using factor analysis provide better fit.