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PubMed1989

[Bone dynamic study--evaluation for factor analysis of hip joint].

Factor analysis was applied to dynamic study of Tc-99m MDP for the evaluation of hip joint disorders. Fifteen patients were examined; eight were normal, six were osteoarthritis in which one accompanied synovitis was included, and one was aseptic necrosis on the head of the femur. In normals, according to the Tc-99m MDP kinetics, three factor images and time-activity curves were obtained which were named as blood vessel, soft tissue, and bone factor images and curves. In the patient with osteoarthritis, increased accumulation of the hip joint was shown in bone factor image only. But in one patient, who took osteoarthritis with synovitis, marked accumulations of the Tc-99m MDP appeared not only on the bone factor image but also on the soft tissue. Operation revealed thickening synovial tissue around the hip joint, caused by inflammatory process. In follow-up studies of the patient with aseptic necrosis on the head of the left femur, excessive accumulations, which were seemed in his left hip joint on both bone and soft tissue factor images at first, were decreased respondently to the treatment of this lesion. In conclusion, the factor analysis was useful for differencial diagnosis of the hip joint disorders and observation of the clinical course of the hip joint disorders.

Adult↗
PubMed2004

Nicotine dependence criteria of the DIS and DSM-III-R: a factor analysis.

This paper reports a factor analysis of the symptoms of nicotine dependence that were determined in an assessment of 821 current cigarette-smoking research volunteers, according to the Diagnostic and Statistical Manual of Mental Disorders, 3rd edition, revised (DSM-III-R) of the American Psychiatric Association as well as an analysis of a subset who unsuccessfully attempted to quit (n=636). In the total sample, two factors with eigenvalues greater than 1 accounted for 62.7% of the variance. When the factor analysis was repeated with the subset of research volunteers who unsuccessfully attempted to quit, only one DSM-III-R nicotine dependence symptom loaded on the second factor. This finding suggests that the two-factor structure found in this and a previous factor analysis study of the nicotine dependence segment of the DSM-III-R may be an artifact of the skipout pattern of the DSM-III-R, which assumes that smokers who have not attempted to quit have not experienced withdrawal symptoms or used tobacco to avoid these symptoms. Goodness-of-fit measures suggested that the two-factor structure is a better fit than the one-factor structure for both the total population and the subset who unsuccessfully attempted to quit or cut down. Our sample of current smokers who had not attempted to quit (n=185) was too small to permit factor analyses. Further work with other large samples from the general population of current smokers who have unsuccessfully attempted to quit as well as those who have not attempted to quit will enhance our understanding of the factor structure of the nicotine dependence segment of the DSM-III-R and clarify the effect of the skipout pattern on its factor structure.

Adolescent↗
PubMed2004

Metabolic and inflammation variable clusters and prediction of type 2 diabetes: factor analysis using directly measured insulin sensitivity.

Factor analysis, a multivariate correlation technique, has been used to provide insight into the underlying structure of the metabolic syndrome. The majority of previous factor analyses, however, have used only surrogate measures of insulin sensitivity; very few have included nontraditional cardiovascular disease (CVD) risk factors such as plasminogen activator inhibitor (PAI)-1, fibrinogen, and C-reactive protein (CRP); and only a limited number have assessed the ability of factors to predict type 2 diabetes. The objective of this study was to investigate, using factor analysis, the clustering of metabolic and inflammation variables using data from 1,087 nondiabetic participants in the Insulin Resistance Atherosclerosis Study (IRAS) and to determine the association of these clusters with risk of type 2 diabetes at follow-up. This study includes information on directly measured insulin sensitivity (S(i)) from the frequently sampled intravenous glucose tolerance test among African-American, Hispanic, and non-Hispanic white subjects aged 40-69 years. Principal factor analysis of data from nondiabetic subjects at baseline (1992-1994) identified three factors, which explained 28.4, 7.4, and 6% of the total variance in the dataset, respectively. Based on factor loadings of >or= 0.40, these factors were interpreted as 1) a "metabolic" factor, with positive loadings of BMI, waist circumference, 2-h glucose, log triglyceride, and log PAI-1 and inverse loadings of log S(i) + 1 and HDL; 2) an "inflammation" factor, with positive loadings of BMI, waist circumference, fibrinogen, and log CRP and an inverse loading of log S(i) + 1; and 3) a "blood pressure" factor, with positive loadings of systolic and diastolic blood pressure. The results were similar within strata of ethnicity, and there were only subtle differences in sex-specific analyses. In a prospective analysis, each of the factors was a significant predictor of diabetes after a median follow-up period of 5.2 years, and each factor remained significant in a multivariate model that included all three factors, although this three-factor model was not significantly more predictive than models using either impaired glucose tolerance or conventional CVD risk factors. Factor analysis identified three underlying factors among a group of inflammation and metabolic syndrome variables, with insulin sensitivity loading on both the metabolic and inflammation variable clusters. Each factor significantly predicted diabetes in multivariate analysis. The findings support the emerging hypothesis that chronic subclinical inflammation is associated with insulin resistance and comprises a component of the metabolic syndrome.

Adult↗
PubMed2006

[Exploratory and confirmatory factor analysis].

This article describes the utility of factor analysis in the context of developing questionnaires for clinical use in rehabilitation. The basic principles of both exploratory factor analysis and confirmatory factor analysis are presented in order to give the necessary knowledge for choosing between both concepts. Exploratory and confirmatory factor analyses complement one another, and there is a place for both in the process of questionnaire development. Finally, the potential use of factor analysis for scaling procedures as well as intercultural validity studies is outlined.

Data Interpretation, Statistical↗
PubMed2004

[Application of factor analysis in the study of risk factors on human parvovirus B19 infection during pregnancy].

OBJECTIVE: To explore the risk factors of human parvovirus B19 infection in pregnancy and to provide guidelines for its prevention and control strategy. METHODS: Four hundred and eighty-six cases of gravida serum were detected for parvovirus B19 DNA by nested-polymerase chain reaction assay. Factors associated with parvovirus B19 infection in pregnancy were investigated and analyzed, using multiple logistic regression and factor analysis. RESULTS: Multiple logistic regression analysis suggested that there were 16 agents associated with parvovirus B19 infection during pregnancy, which were dominated by 6 potential factors listed as follows: countryside and bad hygienic habit, mental factor, occupational exposure to hospital and environmental condition, health and illness, bad behavior and health education and blood type. CONCLUSION: The prevention strategy of parvovirus B19 infection in pregnancy should include reasonable allocation of public health resources between city and countryside, and to promote health education and occupational health during pregnancy.

Adult↗
PubMed2001

An evaluation of the NINCDS-ADRDA neuropsychological criteria for the assessment of Alzheimer's disease: a confirmatory factor analysis of single versus multi-factor models.

Neuropsychological test batteries are frequently used to assess the nature and severity of cognitive deficits among patients with early Alzheimer's Disease (AD) and related disorders. The NINCDS-ADRDA criteria are among the most widely used guidelines to diagnose dementia (McKhann et al.,1984). These criteria specify eight distinct areas of neuropsychological function that should be evaluated in patients with suspected cognitive impairment. Recent studies have suggested that neuropsychological deficits observed in AD may be explained by a single general factor related to memory deficits or to executive dysfunction. In contrast, the results of other investigations have indicated that multiple qualitatively different factors underlie cognitive abilities in AD. In the present study, we used confirmatory factor analysis to examine the structure of cognitive abilities in AD and to assess the extent to which single and multiple ability factors accurately represent neuropsychological test data obtained from patients with AD. Results indicated that the NINCDS-ADRDA model fit the data better than a single factor model. However, a more parsimonious model specifying memory, verbal abilities, visuospatial skills, executive function, and higher as well as lower functional activities of daily living fit the data better than the NINCDS-ADRDA model. These results have important theoretical and practical implications for diagnostic evaluation.

Activities of Daily Living↗
PubMed2001

Factor analysis of the interrelationships between clinical variables in horses with colic.

A prospective survey of horses with colic referred to the Large Animal Hospital at the Royal Veterinary and Agricultural University of Copenhagen, Denmark, was undertaken between August 1994 and December 1997. The interrelationships between 17 clinical variables were analysed using factor analysis. Factor analysis uncovers the structure of the variability in data and therefore detects multicollinearity. A total of 528 horses were admitted in the study period. Of these, 16 were excluded from the analysis as a result of miscellaneous conditions. Only 205 horses had observations for all 17 variables. Because no major change occurred in the main diagnostic categories, this population was considered as a representative subset. Factor analysis confirmed the clinical impression of correlation between variables, but the multicollinearity turned out not to be strong. Four factors were extracted, and these accounted for 51% of the total variance. The retained factors were interpreted by integrating previously reported clinical research. The first factor, which was interpreted as endotoxaemia, had high loadings on capillary refill time, mucous-membrane colour, degree of pain, heart rate, packed-cell volume and abdominal sounds. In the second factor, cecal decompression, admission month and gastric reflux had the predominant influence, and this factor was explained as cecal tympany. The third factor was simply interpreted as age because it had high loadings on gender, age and temperature. In the fourth factor, the interpretation was not straightforward, although breed had the greatest influence in the formation of this factor. Subsequently, the extracted factors were used in a logistic-regression analysis to determine their association with outcome (survival/death). The two factors interpreted as endotoxaemia and age were related to the outcome.

Age Factors↗
PubMed2001

[Resolution of overlapping mass spectra of azobiphenyl dyes by subwindow factor analysis technique].

Subwindow factor analysis(SWFA) has been proven to be an effective data processing technique. In this paper, it is applied to the resolution of overlapping GC-MS peaks. A two-component sample of azobiphenyl dyes (3,3'-dichlorobenzidine, 4,4'-methylene-bis-(2-chloroaniline)) was prepared and a data set from a mixture of azobiphenyl dyes recorded by GC-MS was obtained. Then the overlapping GC-MS data were resolved by SWFA. The results show that both mass spectrum and chromatogram of components can be resolved from multicomponent overlapping data. The resolved spectra were in good consistence with the mass spectra of standard samples. At the same time, overlapping GC-MS data were also resolved by WFA. In Comparing with WFA technique, SWFA is more convenient, and the speed of resolution could be increased.

3,3'-Dichlorobenzidine↗
PubMed1985

Factor analysis in left ventricular first-pass radionuclide angiography: value of the ventricular factor to measure ejection fraction.

Factor analysis theoretically generates the time-activity curves of the various physiological compartments, or factors, which are superimposed in a dynamic series of scintigraphic frames. An image of the spatial distribution of each of these factors is also displayed. We tested the ability of one of these, the ventricular factor, to measure the left ventricular ejection fraction (LVEF) in first-pass radionuclide angiography (FPRA). Forty-nine patients divided into three groups were studied. In a group of 32 patients, factor analysis was compared to a conventional scintigraphic method and to contrast angiography. The coefficient of correlation was similar for both techniques (r = 0.83). To test reproducibility, another group of 10 patients received two successive injections of Au-195m, three minutes apart. The reproducibility of LVEF was r = 0.78 with factor analysis and r = 0.81 with the conventional method. In a third group of seven patients, three successive injections of Au-195m were performed in the right and in the left anterior oblique projections. The reproducibility of LVEF was r = 0.71 with factor analysis. However LVEF was significantly lower in LAO than in RAO, 50 +/- 11% vs 58 +/- 17% respectively. It is concluded that factor analysis does not offer a more reliable means of calculating LVEF than a conventional method.

Factor Analysis, Statistical↗
PubMed1983

[Functional recovery following surgical removal of traumatic epidural hematoma--factor analysis].

UNLABELLED: Factors contributing to functional recovery following evacuation of epidural hematoma were analyzed in 53 subjects. Subjects were limited to the cases with "pure" epidural hematoma. Fifty-three cases were classified into 3 groups based on presence or absence, and duration of preaggravation period (PAP) following occurrence of head trauma (Fig. 1). Level of consciousness at operation and at PAP is summarised in Fig. 2. Major neurological signs at operation are summarized in Table 1. Gradings of functional status were divided to 6 (Table 2). Gradings of 53 subjects which were judged 1 month after removal of epidural hematoma are summarized in Fig. 3. RESULTS: 1) As to outcome in relation to duration of preaggravation period (PAP) and consciousness level at operation (Fig. 4): The patients whose PAP was shorter and whose consciousness level at operation was more severe, took outcome of lower (worse) gradings. 2) As to outcome in relation to interval from the end of PAP to operation and PAP (Fig. 5): The patients whose PAP are within 3 hours, took outcome of relatively good recovery only when epidural hematoma was removed within 5.5 hours after the end of PAP. 3) As to outcome in relation to interval from the end of PAP to operation and consciousness level at operation (Fig. 6): The patients whose consciousness level at operation was better than semicoma took good recovery when epidural hematoma was evacuated within 5.5 hours after the end of PAP. This was right even in the patients who presented with decerebrate posture.(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed2006

Use of factor analysis in Journal of Advanced Nursing: literature review.

AIM: This paper reports a review analysing the use of factor analysis in papers in Journal of Advanced Nursing. BACKGROUND: Factor analysis is a multivariate statistical method for reducing large numbers of variables to fewer underlying dimensions. There are several methods of factor analysis with principal components analysis being the most commonly applied. Factor analysis has been used by researchers in nursing for many years but the standards for use and reporting are variable. METHOD: Papers using factor analysis in Journal of Advanced Nursing were retrieved from 1982 to the end of 2004. The search term 'factor analysis' was used in the CINAHL database and applied specifically to Journal of Advanced Nursing in December 2004. Retrieved papers were included in the review if they came from Journal of Advanced Nursing and used factor analysis as part of the method of the reported study. RESULTS: One hundred and twenty-four papers were retrieved as a result of the initial search criteria of which 116 were from Journal of Advanced Nursing. Screening of papers for the use of factor analysis left 100 papers for review. Principal components analysis was the most commonly used method of factor analysis; Eigenvalues greater than one was the most commonly applied criterion for selecting the number of factors followed by orthogonal rotation to achieve simple structure. The majority of papers did not report the whole factor solution and there were papers that did not specify anything beyond the fact that they carried out factor analysis. Confirmatory factor analysis was rarely used and exploratory methods other than principal components analysis were also rarely used. CONCLUSIONS: Factor analysis is quite commonly used in nursing research reported in Journal of Advanced Nursing. While some papers are exemplary there is room for improvement in the reporting of all aspects of factor analysis.

Bibliometrics↗
PubMed1992

Examination of fungi in domestic interiors by using factor analysis: correlations and associations with home factors.

Factor analysis was utilized to investigate correlations among airborne microorganisms collected with Andersen samplers from homes in Topeka, Kans., during the winter of 1987 to 1988. The factors derived were used to relate microbial concentrations with categorical, questionnaire-derived descriptions of housing conditions. This approach successfully identified groups of common aboveground decay fungi including Cladosporium, Alternaria, Epicoccum, and Aureobasidium spp. The common soil fungi Aspergillus and Penicillium spp. were also separated as a group. These previously known ecological groupings were confirmed with air sampling data by a quantitative evaluation technique. The aboveground decay fungi sampled indoors in winter were present at relatively high concentrations in homes with gas stoves for cooking, suggesting a possible association between these fungi and increased humidity from the combustion process. Elevated concentrations of the soil fungi were significantly (P = 0.05) associated with the dirt floor, crawl-space type of basement. Elevated concentrations of water-requiring fungi, such as Fusarium spp., were shown to be associated with water collection in domestic interiors. Also, elevated mean concentrations for the group of fungi including Cladosporium, Epicoccum, Aureobasidium, and yeast spp. were found to be associated (P = 0.03) with symptoms reported on a health questionnaire. This finding was consistent with our previous study of associations between respiratory health and airborne microorganisms by univariate logistic regression analysis.

Air Microbiology↗
PubMed1990

[Evaluation of factor analysis and other functional images in exercise gated blood-pool study].

Factor analysis, a new method of functional imaging, has been applied to cardiovascular nuclear medicine. Because of the difficulty of its interpretation, it has not been popular as a method for detecting abnormal wall motion. The purpose of this study was to evaluate the usefulness of factor analysis in exercise gated blood-pool study in patients with ischemic heart disease. In our factor analysis, left ventricular region of interest (LVROI) was extracted to exclude the surrounding radioactivities. The new method was compared with 1) the conventional factor analysis using whole region (whole ROI method), and with 2) the other functional images, i.e. stroke volume, ejection fraction and phase images. At first we tried 3-factor analysis of the LVROI method, which resulted in many uninterpretable factors. Whereas in 2-factor analysis no uninterpretable factors were extracted. In comparison with cine-mode display, the LVROI method with 2-factor analysis showed the best sensitivity (85%) and specificity (100%). In exercise gated blood-pool study, it became easier to detect abnormal wall motion by comparing the factor image at exercise with resting image. In conclusion, the 2-factor analysis using the LVROI method greatly improved the limitation of conventional factor analysis, and will be useful in detecting wall motion abnormality in patients with ischemic heart disease.

Adult↗
PubMed1990

[The study of allergic children under one-year old. The degree of influence on allergic factors to atopic dermatitis by multiple factor analysis].

We investigated the allergic factors of 94 allergic children; 69 atopic dermatitis, 21 wheegy children, 3 urticaria and 1 milk allergy child under one year by multiple factor analysis. The results were as follows; 1) Investigating by relative range ratio the 3 factors, RAST score to Dermatophagoides farinae and egg white, and family history of allergic disease, had strong influence to the existence of atopic dermatitis. 2) By multiple factor analysis type III, the 6 groups had tight relationship to atopic dermatitis group. (1) IgE RIST more than 61 IU/ml group. (2) Eosinophil counts more than 401/mm3 group. (3) RAST score to Dermatophagoides farinae more than 0.70 PRU/ml group. (4) RAST score to egg white more than 0.70 PRU/ml group.

Age Factors↗
PubMed1999

Independent factor analysis.

We introduce the independent factor analysis (IFA) method for recovering independent hidden sources from their observed mixtures. IFA generalizes and unifies ordinary factor analysis (FA), principal component analysis (PCA), and independent component analysis (ICA), and can handle not only square noiseless mixing but also the general case where the number of mixtures differs from the number of sources and the data are noisy. IFA is a two-step procedure. In the first step, the source densities, mixing matrix, and noise covariance are estimated from the observed data by maximum likelihood. For this purpose we present an expectation-maximization (EM) algorithm, which performs unsupervised learning of an associated probabilistic model of the mixing situation. Each source in our model is described by a mixture of gaussians; thus, all the probabilistic calculations can be performed analytically. In the second step, the sources are reconstructed from the observed data by an optimal nonlinear estimator. A variational approximation of this algorithm is derived for cases with a large number of sources, where the exact algorithm becomes intractable. Our IFA algorithm reduces to the one for ordinary FA when the sources become gaussian, and to an EM algorithm for PCA in the zero-noise limit. We derive an additional EM algorithm specifically for noiseless IFA. This algorithm is shown to be superior to ICA since it can learn arbitrary source densities from the data. Beyond blind separation, IFA can be used for modeling multidimensional data by a highly constrained mixture of gaussians and as a tool for nonlinear signal encoding.

Algorithms↗
PubMedDate not supplied

Determination of the number and waveshapes of event related potential components using comparative factor analysis.

This paper applies new factor analysis methods to determine empirically the number and waveshapes of ERP components. It addresses several well-known problems in previous applications of factor analysis or principal components analysis (PCA) to ERP data, such as the assumption of simple structure by varimax rotation, low noise levels required by PCA, response latency variation, and background EEG (correlated noise). ERPs are modeled as consisting of nonstationary (response) components, stationary (random-phased EEG) processes, and independent noise. The analysis indicated that there was one main response component for the datasets analyzed, and that the EEG processes accounted for a major portion of the variance. Response components corresponded to the first and second derivatives of the dataset mean. This finding is due to response latency variation. Once the number and waveshapes of ERP components have been determined, source localization methods can be applied with much greater accuracy.

Adult↗
PubMed1999

Sputum analysis, bronchial hyperresponsiveness, and airway function in asthma: results of a factor analysis.

BACKGROUND: Recent studies have shown weak associations among FEV1, bronchial hyperresponsiveness (BHR), sputum eosinophils, and sputum eosinophil cationic protein (ECP), suggesting that they are nonoverlapping quantities. The statistical method of factor analysis enables reduction of many parameters that characterize the disease to a few independent factors, with each factor grouping associated parameters. OBJECTIVE: The purpose of this study was to demonstrate, by using factor analysis, that reversible airway obstruction, BHR, and eosinophilic inflammation of the bronchial tree, as assessed by cytologic and biochemical analysis of sputum, may be considered separate dimensions that characterize chronic bronchial asthma. METHODS: Ninety-nine clinically stable patients with a previous diagnosis of asthma underwent spirometry, sputum induction, and histamine inhalation tests. RESULTS: Most patients were nonobstructed (FEV1, 91% +/- 20%); a low level of bronchial reversibility (FEV1 increase after beta2 -agonist, 7.8% +/- 9.2%) and BHR (histamine PC20 FEV1 geometric mean, 0.98 mg/mL) were found. Sputum eosinophil differential count (12.4% +/- 17.7%) and sputum ECP (1305 +/- 3072 microg/mL) were in the normal range of our laboratory in 38 and 22 patients, respectively. Factor analysis selected 3 different factors, explaining 74.8% of variability. Measurements of airway function and age loaded on factor I, PC20 FEV1 and beta2 -response loaded on factor II, and sputum ECP and eosinophils loaded on factor III. Additional post hoc factor analyses provided similar results when the sample was divided into 2 subgroups by randomization, presence of airway obstruction, degree of BHR, percentage of sputum eosinophils, or concentration of sputum ECP. CONCLUSIONS: We conclude that airway function, baseline BHR, and airway inflammation may be considered separate dimensions in the description of chronic asthma. Such evidence supports the utility of routine measurement of all these dimensions.

Adolescent↗