Assessing food dietary intakes in Japanese-Brazilians using factor analysis

We describe the use of factor analysis for assessing food habits in Japanese-Brazilians. Dietary data from 1,283 participants of a cross-sectional study were used. Besides statistical criteria, we also used the conceptual meaning of identified profiles to obtain scores for dietary patterns (Japanese or Western profile). Paired Student t test, linear regression and Poisson models were used to verify the existence of relationship between these scores and generation, body mass index (BMI), waist circumference and presence of metabolic syndrome, respectively. First generation subjects had higher mean Japanese profile scores and lower Western profile scores than those of second generation. The Western dietary pattern was associated with BMI (p = 0.001), waist circumference (p = 0.023) and metabolic syndrome (p < 0.05). We concluded that these scores were able to discriminate subjects who maintained their traditional Japanese lifestyle or otherwise, and that the incorporation of a Western lifestyle is associated to high values of BMI, waist circumference and presence of metabolic syndrome.

An SPSS R-Menu for Ordinal Factor Analysis

Exploratory factor analysis is a widely used statistical technique in the social sciences. It attempts to identify underlying factors that explain the pattern of correlations within a set of observed variables. A statistical software package is needed to perform the calcula- tions. However, there are some limitations with popular statistical software packages, like SPSS. The R programming language is a free software package for statistical and graphical computing. It o ers many packages written by contributors from all over the world and programming resources that allow it to overcome the dialog limitations of SPSS. This paper o ers an SPSS dialog written in the R programming language with the help of some packages, so that researchers with little or no knowledge in programming, or those who are accustomed to making their calculations based on statistical dialogs, have more options when applying factor analysis to their data and hence can adopt a better approach when dealing with ordinal, Likert-type data.

Multilevel model of safety climate for furniture industries

BACKGROUND: Furniture companies can analyze their safety status using quantitative measures. However, the data needed are not always available and the number of accidents is under-reported. Safety climate scales may be an alternative. However, there are no validated Portuguese scales that account for the specific attributes of the furniture sector. OBJECTIVE: The current study aims to develop and validate an instrument that uses a multilevel structure to measure the safety climate of the Portuguese furniture industry. METHODS: The Safety Climate in Wood Industries (SCWI) model was developed and applied to the safety climate analysis using three different scales: organizational, group and individual. A multilevel exploratory factor analysis was performed to analyze the factorial structure. The studied companies’ safety conditions were also analyzed. RESULTS: Different factorial structures were found between and within levels. In general, the results show the presence of a group-level safety climate. The scores of safety climates are directly and positively related to companies’ safety conditions; the organizational scale is the one that best reflects the actual safety conditions. CONCLUSIONS: The SCWI instrument allows for the identification of different safety climates in groups that comprise the same furniture company and it seems to reflect those groups’ safety conditions. The study also demonstrates the need for a multilevel analysis of the studied instrument.

Factor analysis in the stock market: An application to statistical arbitrage

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Urinary tract infection in full-term newborn infants: risk factor analysis

OBJECTIVE: To analyze the correlation of risk factors to the occurrence of urinary tract infection in full-term newborn infants. PATIENTS AND METHODS: Retrospective study (1997) including full-term infants having a positive urine culture by bag specimen. Urine collection was based on: fever, weight loss > 10% of birth weight, nonspecific symptoms (feeding intolerance, failure to thrive, hypoactivity, debilitate suction, irritability), or renal and urinary tract malformations. In these cases, another urine culture by suprapubic bladder aspiration was collected to confirm the diagnosis. To compare and validate the risk factors in each group, the selected cases were divided into two groups: Group I - positive urine culture by bag specimen collection and negative urine culture by suprapubic aspiration, and Group II - positive urine culture by bag specimen collection and positive urine culture by suprapubic aspiration . RESULTS: Sixty one infants were studied, Group I, n = 42 (68.9%) and Group II, n = 19 (31.1%). The selected risk factors (associated infectious diseases, use of broad-spectrum antibiotics, renal and urinary tract malformations, mechanical ventilation, parenteral nutrition and intravascular catheter) were more frequent in Group II (p<0.05). Through relative risk analysis, risk factors were, in decreasing importance: parenteral nutrition, intravascular catheter, associated infectious diseases, use of broad-spectrum antibiotics, mechanical ventilation, and renal and urinary tract malformations. CONCLUSION: The results showed that parenteral nutrition, intravascular catheter, and associated infectious diseases contributed to increase the frequency of neonatal urinary tract infection, and in the presence of more than one risk factor, the occurrence of urinary tract infection rose up to 11 times.

Multilevel model of safety climate for furniture industries

BACKGROUND: Furniture companies can analyze their safety status using quantitative measures. However, the data needed are not always available and the number of accidents is under-reported. Safety climate scales may be an alternative. However, there are no validated Portuguese scales that account for the specific attributes of the furniture sector. OBJECTIVE: The current study aims to develop and validate an instrument that uses a multilevel structure to measure the safety climate of the Portuguese furniture industry. METHODS: The Safety Climate in Wood Industries (SCWI) model was developed and applied to the safety climate analysis using three different scales: organizational, group and individual. A multilevel exploratory factor analysis was performed to analyze the factorial structure. The studied companies’ safety conditions were also analyzed. RESULTS: Different factorial structures were found between and within levels. In general, the results show the presence of a group-level safety climate. The scores of safety climates are directly and positively related to companies’ safety conditions; the organizational scale is the one that best reflects the actual safety conditions. CONCLUSIONS: The SCWI instrument allows for the identification of different safety climates in groups that comprise the same furniture company and it seems to reflect those groups’ safety conditions. The study also demonstrates the need for a multilevel analysis of the studied instrument.

Exploring the factor structure of neurocognitive measures in older individuals

Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the "best fit" model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate.

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.

Dimensionality of the Rosenberg self-esteem scale and its relationships with the three- and five-factor personality models

The authors investigated the dimensionality of the French version of the Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965) using confirmatory factor analysis. We tested models of 1 or 2 factors. Results suggest the RSES is a 1-dimensional scale with 3 highly correlated items. Comparison with the Revised NEO-Personality Inventory (NEO-PI-R; Costa, McCrae, & Rolland, 1998) demonstrated that Neuroticism correlated strongly and Extraversion and Conscientiousness moderately with the RSES. Depression accounted for 47% of the variance of the RSES. Other NEO-PI-R facets were also moderately related with self-esteem.

Long term outcome after mitral valve repair : a risk factor analysis

Objectif: La réparation de la valve mitrale constitue le traitement de choix pour restaurer ta fonction de celle-ci. Elle est actuellement reconnue pour garantir une bonne évolution à long terme. Dans le but de faciliter les décisions périopératoires, nous avons analysé nos patients afin de déterminer les facteurs de risque ayant affecté leur évolution. Méthodes: Nous avons étudié rétrospectivement 175 premiers patients consécutifs (âge moyen : 64 +/-10.4 ans ;113 hommes) qui ont subi une réparation primaire de la valve mitrale associée à toute autre intervention cardiaque entre 1986 et 1998. Les facteurs de risque influençant le taux de réopération et la survie à long terme ont été analysés de manière uni et multivariée. Résultats: La mortalité opératoire était de 3.4 % (6 décès, 0 -22 et jours post-opératoires). La mortalité tardive était de 9.1 % (16 décès, 3e-125e mois post-opératoires). Cinq patients ont dû être réopérés. L'analyse actuarielle selon Kaplan-Meier a montré une survie à 1 année de 96 +l-1 %, une survie à 5 ans de 88 +/- 3 % et une survie à 10 ans de 69 +/- 8 %. Après 1 année, la fraction de population sans réopération était de 99 %, elle était de 97 +/-2 % après 5 ans et de 88+/-6 % après 10 ans. L'analyse multivariée a montré qu' un stade NYHA III et IV résiduel ( p=0.001, RR 4.55, 95 % IC :1.85 -14.29), une mauvaise fraction d'éjection préopératoire(p=0.013, RR 1.09, 95 % IC 1.02 -1.18), ,une régurgitation mitrale d'origine fonctionnelle (p=0.018, RR 4.17, 95% IC 1.32-16.67) ainsi qu'une étiologie ischémique (p=0.049, RR 3.13, 95% IC 1.01-10.0) constituaient tous des prédicteurs indépendant de mortalité. Une régurgitation mitrale persistante au 7 e jour post-opératoire (p= 0.005, RR 4.55, 95 % IC :1.56 -20.0), un âge inférieur à 60 ans (p = 0.012, RR 8.7, 95 % IC 2.44 - 37.8) et l'absence d'anneau prothétique (p = 0.034, RR 4.76, 95 % IC 1.79-33.3) se sont tous révélés être des facteurs de risque indépendant de réopération. Conclusion: Les réparations mitrales sont accompagnées d'une excellente survie à long terme même si leur évolution peut être influencée négativement par de nombreux facteurs de risques periopératoires. Les risques de réopération sont plus élevés chez des patients jeunes présentant une régurgitation mitrale résiduelle et n'ayant pas bénéficié de la mise en place d'un anneau prothétique.

Atrial fibrillation and minimally invasive coronary artery bypass grafting: risk factor analysis.

Atrial fibrillation (AF) is a frequent arrhythmia after conventional coronary artery bypass grafting. With the advent of minimally invasive technique for left internal mammary artery-left anterior descending coronary artery (LIMA-LAD) grafting, we analyzed the incidence and the risk factors of postoperative AF in this patient population. This prospective study involves all patients undergoing isolated LIMA-LAD grafting with minimally invasive technique between January 1994 and June 2000. Twenty-four possible risk factors for postoperative AF were entered into univariate and multivariate logistic regression analyses. Postoperative AF occurred in 21 of the 90 patients (23.3%) analyzed. Double- or triple-vessel disease was present in 12/90 patients (13.3%). On univariate analysis, right coronary artery disease (p <0.01), age (p = 0.01), and diabetes (p = 0.04) were found to be risk factors for AF. On multivariate analysis, right coronary artery disease was identified as the sole significant risk factor (p = 0.02). In this patient population, the incidence of AF after minimally invasive coronary artery bypass is in the range of that reported for conventional coronary artery bypass grafting. Right coronary artery disease was found to be an independent predictor, and this may be related to the fact that in this patient population the diseased right coronary artery was not revascularized at the time of the surgical procedure. For the same reason, this risk factor may find a broader application to noncardiac thoracic surgery.

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 istheny = 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 analysismodel (1) can be written asCov(y) = ΛΛT + ψ (2)where ψ = Cov(e) has a diagonal form. The diagonal elements of ψ as well as theloadings matrix Λ are estimated from an estimation of Cov(y).Given observed clr transformed data Y as realizations of the random vectory. Outliers or deviations from the idealized model assumptions of factor analysiscan severely effect the parameter estimation. As a way out, robust estimation ofthe covariance matrix of Y will lead to robust estimates of Λ and ψ in (2), seePison et al. (2003). Well known robust covariance estimators with good statisticalproperties, like the MCD or the S-estimators (see, e.g. Maronna et al., 2006), relyon 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 thissingularity problem. The data matrix Y is transformed to a matrix Z by usingan orthonormal basis of lower dimension. Using the ilr transformed data, a robustcovariance matrix C(Z) can be estimated. The result can be back-transformed tothe clr space byC(Y ) = V C(Z)V Twhere the matrix V with orthonormal columns comes from the relation betweenthe clr and the ilr transformation. Now the parameters in the model (2) can beestimated (Basilevsky, 1994) and the results have a direct interpretation since thelinks to the original variables are still preserved.The above procedure will be applied to data from geochemistry. Our specialinterest is on comparing the results with those of Reimann et al. (2002) for the Kolaproject data

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 modelsbetween 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 thatlay 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 hiddencomponents, 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 totalvariance, 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. Graphicalrepresentation 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 analysisof 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, exceptfertilisers 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 areintrinsic to the relative nature of compositional data

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling

The interpretation of the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all crossloadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores.