Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Factor Analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8Linked to original sources

An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data.

Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent process determines each observed variable. The extent to which violations of this assumption undermine CFA estimation is not well-known. In this article, the authors empirically study this issue using a computer simulation study. The results suggest that estimation of polychoric correlations is robust to modest violations of underlying normality. Further, WLS performed adequately only at the largest sample size but led to substantial estimation difficulties with smaller samples. Finally, robust WLS performed well across all conditions.

Factor Analysis, Statistical↗

Exploratory factor analysis of borderline personality disorder criteria in hospitalized adolescents.

OBJECTIVE: The authors examined the factor structure of borderline personality disorder (BPD) in hospitalized adolescents and also sought to add to the theoretical and clinical understanding of any homogeneous components by determining whether they may be related to specific forms of Axis I pathology. METHOD: Subjects were 123 adolescent inpatients, who were reliably assessed with structured diagnostic interviews for Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition Axes I and II disorders. Exploratory factor analysis identified BPD components, and logistic regression analyses tested whether these components were predictive of specific Axis I disorders. RESULTS: Factor analysis revealed a 4-factor solution that accounted for 67.0% of the variance. Factor 1 ("suicidal threats or gestures" and "emptiness or boredom") predicted depressive disorders and alcohol use disorders. Factor 2 ("affective instability," "uncontrolled anger," and "identity disturbance") predicted anxiety disorders and oppositional defiant disorder. Factor 3 ("unstable relationships" and "abandonment fears") predicted only anxiety disorders. Factor 4 ("impulsiveness" and "identity disturbance") predicted conduct disorder and substance use disorders. CONCLUSIONS: Exploratory factor analysis of BPD criteria in adolescent inpatients revealed 4 BPD factors that appear to differ from those reported for similar studies of adults. The factors represent components of self-negation, irritability, poorly modulated relationships, and impulsivity--each of which is associated with characteristic Axis I pathology. These findings shed light on the nature of BPD in adolescents and may also have implications for treatment.

Adolescent↗

Morphological abstraction of thyroid tumor cell nuclei using morphometry with factor analysis.

Various morphonuclear studies by digital image analysis have successfully been applied to quantify the nuclear morphology, including chromatin distribution pattern, in cytology of various organs; however, the majority of past reports have not shown correlation between the quantitative data by digital image analysis and cytological findings in practical diagnosis. In this report, we present the usefulness of morphological abstraction to combine the objective data and subjective observation in cytological diagnosis. Randomly selected, 100 cells in each Papanicolaou-stained ABC smear samples of 39 benign and malignant thyroid tumor cases were studied. Gray-level image data provided seven parameters for nuclear size, four parameters for nuclear shape, and 16 parameters showing the nuclear chromatin patterns from high-dimensional texture analysis of using co-occurrence and run-length matrices. To statistically abstract nuclear morphology, factor analysis was used. Factor analysis classified morphological nuclear characters as abstraction parameter into five abstract parameters composed of nuclear size, shape, heterogeneity, and contrast and homogeneity of chromatin pattern. The nuclei of papillary carcinoma showed larger size, more irregular shape, and higher contrast of chromatin pattern than those of the benign group. The follicular carcinomas have larger nucleus in each cell and more monotonous chromatin pattern among cells in each case than those of the benign group. Morphological abstraction by morphometry with factor analysis may provide a practical approach to the detection of the underlying characteristics of nuclear morphology in aspiration biopsy cytology.

Cell Nucleus↗

[Factor analysis on the intelligence of patients with schizophrenia].

The authors made a factor analysis on the intelligence of patients with schizophrenia. 119 patients with schizophrenia were measured by Wechsler Adult Intelligence Scale-Revised China (WAIS-RC). The results revealed the average full intelligence quotient (IQ) of schizophrenia was 79.70, the verbal intelligence quotient(VIQ) 84.43, and the performance intelligence quotient(PIQ) 74.10. Multiple regression showed the regression coefficient of educational level for IQ was 8.6721. The regression coefficient of the course of disease for IQ was negative 0.1519. Factor analysis showed that the cumulative percent of three-factor solution of schizophrenia was 73.5. In conclusion, the IQ of most patients with schizophrenia was lower than norm, VIQ > PIQ(P = 0.0000). Educational level had greater influence on IQ. The longer the course of disease was, the worse the intelligence came to be. Factor analysis indicated that three-factor solution of schizophrenia was similar to that of norm, but each subtest value was lower than norm.

Adult↗

Statistical methodology: VIII. Using confirmatory factor analysis (CFA) in emergency medicine research.

How many underlying characteristics (or factors) does a set of survey questions measure? When subjects answer a set of self-report questions, is it more appropriate to analyze the questions individually, to pool responses to all of the questions to form one global score, or to combine subsets of related questions to define multiple underlying factors? Factor analysis is the statistical method of choice for answering such questions. When researchers have no idea beforehand about what factors may underlie a set of questions, they use exploratory factor analysis to infer the best explanatory model from observed data "after the fact." If, on the other hand, researchers have a hypothesis beforehand about the underlying factors, then they can use confirmatory factor analysis (CFA) to evaluate how well this model explains the observed data and to compare the model's goodness-of-fit with that of other competing models. This article describes the basic rules and building blocks of CFA: what it is, how it works, and how researchers can use it. The authors begin by placing CFA in the context of a common research application-namely, assessing quality of medical outcome using a patient satisfaction survey. They then explain, within this research context, how CFA is used to evaluate the explanatory power of a factor model and to decide which model or models best represent the data. The information that must be specified in the analysis to estimate a CFA model is highlighted, and the statistical assumptions and limitations of this analysis are noted. Analyzing the responses of 1,614 emergency medical patients to a commonly-used "patient satisfaction" questionnaire, the authors demonstrate how to: 1) compare competing factor-models to find the best-fitting model; 2) modify models to improve their goodness-of-fit; 3) test hypotheses about relationships among the underlying factors; 4) examine mean differences in "factor scores"; and 5) refine an existing instrument into a more streamlined form that has fewer questions and better conceptual and statistical precision than the original instrument. Finally, the role of CFA in developing new instruments is discussed.

Data Interpretation, Statistical↗

Exploratory factor analysis: its role in item analysis.

The special characteristics of items-low reliability, confounds by minor, unwanted covariance, and the likelihood of a general factor-and better understanding of factor analysis means that the default procedure of many statistical packages (Little Jiffy) is no longer adequate for exploratory item factor analysis. It produces too many factors and precludes a general factor even when that means the factors extracted are nonreplicable. More appropriate procedures that reduce these problems are presented, along with how to select the sample, sample size required, and how to select items for scales. Proposed scales can be evaluated by their correlations with the factors; a new procedure for doing so eliminates the biased values produced by correlating them with either total or factor scores. The role of exploratory factor analysis relative to cluster analysis and confirmatory factor analysis is noted.

Journal Article↗

Use of factor analysis in the evaluation of left to right cardiac shunts.

We have compared two methods of data processing for the quantitation of left-to-right cardiac shunts using first-pass radionuclide angiography. These two methods are used for curve generation in the deconvolution analysis. The standard method involves manual definition of regions of interest. A newer method--factor analysis--provides automatic curve generation and is therefore more operator-independent. Both techniques yield curves of the venous input, lung, and background. The venous input curve is deconvolved by the lung curve, and the resultant unit impulse response is fitted by the gamma variate method to quantitate the left-to-right shunt fraction. Both techniques--factor analysis and regions of interest (ROIs)--separated the shunt patients (n = 16) from the control subjects (n = 20) with a p less than 0.001. There was less interobserver variability with the curves obtained by factor analysis than with those obtained by regions of interest. The coefficient of correlation of factor analysis results with oximetry, r, was 0.90. High sensitivity and specificity, each 94%, was achieved with curves generated by factor analysis. Time vs. activity curves generated by ROIs can achieve high sensitivity and low specificity, or vice versa, depending on the cutoff level defined for separation of the left-to-right shunt patients from the control group.

Adult↗

[Factor analysis of attributive determinants for life expectancy and infant mortality rate with recipient country data in consideration of socioeconomic environment].

OBJECTIVE: From a financial viewpoint, it is important that international medical care and cooperation in recipient countries by NGOs or governments of donor countries, and health promotion carried out by recipient countries' governments, are carried out efficiently. In this paper, we performed analysis of factors that determine life expectancy and infant mortality rates in recipient countries, in consideration of the socioeconomic environment. METHODS: We used data from World Development Indicators 2002 and 2003, published by the World Bank, and Human Development Indicators 2002, published by the United Nations Development Programme. We analyzed 68 countries classified as middle and low income countries by the World Bank, because complete data for these countries were available. We used life expectancy and infant mortality rates as health indicators and did multiple regression analysis; with these indicators as dependent variables, and with socioeconomic environmental data as independent variables. Furthermore, we undertook multiple regression analysis after carrying out group divisions of the countries according to the numbers of refugees, birthrates, and expenditures on armaments. RESULTS: We ascertained the following points. 1) For improving health of people in recipient countries, it is important to secure safe drinking water, improve literacy, and increase income and the possibility of access to basic medicines. 2) For countries where there are a lot of refugees, it is important to increase the measles vaccine inoculation rate. 3) In countries where there are few refugees, life expectancy will be prolonged by as much as three years if the measles vaccine inoculation rate increases by just 10%. 4) In countries with a high armaments expenditure rate in proportion to GNI, it is important to secure access to sanitary toilet facilities. 5) Life expectancy in countries tends to shorten if life expectancy in their neighboring countries is short. 6) The rate of public health expenditures in proportion to GDP has no affect on health. 7) If the literacy rate rises 10%, life expectancy will be prolonged by about 1.2 years and the infant mortality rate will decrease about 6%. CONCLUSION: Though improvement of the socioeconomic environment is more effective for improving life expectancy and infant mortality rates in recipient countries than medical conditions, the effectiveness differs according to the number of refugees, the birthrate and expenditures on armaments.

Developing Countries↗

Using factor analysis to identify neuromuscular synergies during treadmill walking.

Neuroscientists are often interested in grouping variables to facilitate understanding of a particular phenomenon. Factor analysis is a powerful statistical technique that groups variables into conceptually meaningful clusters, but remains underutilized by neuroscience researchers presumably due to its complicated concepts and procedures. This paper illustrates an application of factor analysis to identify coordinated patterns of whole-body muscle activation during treadmill walking. Ten male subjects walked on a treadmill (6.4 km/h) for 20 s during which surface electromyographic (EMG) activity was obtained from the left side sternocleidomastoid, neck extensors, erector spinae, and right side biceps femoris, rectus femoris, tibialis anterior, and medial gastrocnemius. Factor analysis revealed 65% of the variance of seven muscles sampled aligned with two orthogonal factors, labeled 'transition control' and 'loading'. These two factors describe coordinated patterns of muscular activity across body segments that would not be evident by evaluating individual muscle patterns. The results show that factor analysis can be effectively used to explore relationships among muscle patterns across all body segments to increase understanding of the complex coordination necessary for smooth and efficient locomotion. We encourage neuroscientists to consider using factor analysis to identify coordinated patterns of neuromuscular activation that would be obscured using more traditional EMG analyses.

Adult↗

Factor analysis of risk for coronary heart disease: an independent replication.

Eleven risk factors for coronary heart disease were measured on 187 patients with and 187 patients without this disease. Next, factor analysis was performed to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed risk factors. The factor analysis disclosed four factors that explained almost 55% of the variance: a dyslipidaemic and haemostatic complex, a pure hypercholesterolaemia, the metabolic syndrome X, and positive family and medical histories. The four extracted patterns of risk factors did not replicate previous factor analyses of coronary heart disease risk factors, in which the family history of coronary disease on one side and a generalised lack of self-control on the other were suggested. They, however, generated some understanding regarding coronary heart disease risk factors' interactions. Also, results of the present study do encourage efforts to explore new methods designated to bring about more basic forms of personality reorganisation in the realm of self-control, which could in turn have a positive impact on risk factors profile, health status and mortality.

Aged↗

Human error analysis of commercial aviation accidents: application of the Human Factors Analysis and Classification system (HFACS).

BACKGROUND: The Human Factors Analysis and Classification System (HFACS) is a general human error framework originally developed and tested within the U.S. military as a tool for investigating and analyzing the human causes of aviation accidents. Based on Reason's (1990) model of latent and active failures, HFACS addresses human error at all levels of the system, including the condition of aircrew and organizational factors. The purpose of the present study was to assess the utility of the HFACS framework as an error analysis and classification tool outside the military. METHODS: The HFACS framework was used to analyze human error data associated with aircrew-related commercial aviation accidents that occurred between January 1990 and December 1996 using database records maintained by the NTSB and the FAA. RESULTS: Investigators were able to reliably accommodate all the human causal factors associated with the commercial aviation accidents examined in this study using the HFACS system. In addition, the classification of data using HFACS highlighted several critical safety issues in need of intervention research. CONCLUSION: These results demonstrate that the HFACS framework can be a viable tool for use within the civil aviation arena. However, additional research is needed to examine its applicability to areas outside the flight deck, such as aircraft maintenance and air traffic control domains.

Accidents, Aviation↗

Behavioral similarities and differences among alcohol-preferring and -nonpreferring rats: confirmation by factor analysis and extension to additional groups.

Thirteen behavioral variables from six tasks were measured in alcohol-preferring (AA, FH, and P) and -nonpreferring (ANA, FRL, and NP) rat lines/strains and subjected to Factor Analysis. Four Independent factors accounted for > 90% of the variance. Defecation in the open field and ultrasonic vocalizations after an air puff were negatively correlated with alcohol intake and preference, whereas the increase in daily fluid intake in the presence of saccharin was positively correlated. Other factors could be labeled Activity, Emotionality, and immobility Factors, and each was independent of the Alcohol Factor. When an additional alcohol-preferring rat line (HAD) and two additional nonpreferring groups (LAD and ACI) were tested, they were found to differ on most behaviors that were associated with alcohol intake and preference in the Factor Analysis; vocalizations and saccharin-induced increase in fluid intake, but not defection. A new Factor Analysis was then performed incorporating these three new groups and including five new behavioral measures. The following measures had high loadings on the Alcohol Factor: alcohol intake under choice conditions; alcohol preference; forced alcohol intake; alcohol acceptance (forced alcohol intake/basal water intake x 100); ultrasonic vocalization; saccharin intake; saccharin-induced increase in daily fluid intake; defecation in the open field test; and immobility in a modified forced swim test. These findings indicate that there are indeed certain behavioral characteristics that are common among alcohol-preferring rat lines/strains, but there are also substantial group differences on other behavioral measures. For those behavioral measures reflecting emotionality (defecation and ultrasonic vocalization) that loaded highly on the Alcohol Factor, the alcohol-preferring rats had lower scores.

Alcohol Drinking↗

Factor analysis of treatment response in posttraumatic stress disorder.

Factor analysis is applied to the symptoms of posttraumatic stress disorder (PTSD) to reduce the multidimensional symptom space to 2 dimensions; 1 dimension strongly correlated with depressive symptoms and 1 independent dimension weakly correlated with depressive symptoms. These factors are used to assess whether the effectiveness of an antidepressant medication is due to its antidepressant effect. The treatment is shown to be effective in both dimensions. The factor analysis sheds light on the symptom structure of PTSD, supports PTSD as a distinct psychiatric disorder, and supports the current diagnostic criteria.

Adult↗

[Application of factor analysis in development and validation of a new questionnaire for quality of life].

OBJECTIVE: To explore the application of factor analysis in creating a new health-related questionnaire for evaluating quality of life, the Chinese for Quality of Life Instrument (ChQOL). METHODS: Two hundred and seventy-three subjects selected from two provinces of North China (Ningxia) and South China (Guangdong) were investigated by ChQOL, and the preliminary version was completed with factor analysis. RESULTS: Fifty items were developed using factor analysis. The final structure model of ChQOL was confirmed through the screening and verification of factor structural model on the 78 items of ChQOL, a new questionnaire with more rational structure was confirmed. CONCLUSION: Factor analysis is an effective method in developing new questionnaire.

Adolescent↗

Factor analysis of cluster-specific gene expression levels from cDNA microarrays.

The ever-increasing use of cDNA microarrays in medical research will require the development of new algorithms designed specifically for desktop analysis of potentially large genetic data sets. This paper describes the CLUSFAVOR algorithm (CLUSter and Factor Analysis Using Varimax Orthogonal Rotation) for performing cluster and factor analysis of gene expression data obtained from cDNA microarrays. A unique feature of the CLUSFAVOR algorithm is that a user can perform cluster analysis, view dendograms, and run factor analysis on cluster-specific genes selected interactively within a single executable program. CLUSFAVOR also performs a varimax orthogonal rotation on the extracted factors to increase parsimony in the loadings, revealing unique expression profiles for genes and expressed sequence tags (ESTs) for which pathway and function information is unknown. Microarray data used by the program must be stored and input from a disk file. Optional output contains matrices for the input data, standardized data, distance matrices, factor loadings, eigenvalues, eigenvectors, and percentage of total variation for genes within a cluster. Color cluster image displays containing gene expression levels, dendograms for arrays and genes, and factor loadings are displayed for the entire group of genes and arrays analyzed as well as selected genes within a cluster. Cluster images are also exported to JPG and linked to HTML files for viewing.

Algorithms↗

Factor analysis, causal indicators and quality of life.

Exploratory factor analysis (EFA) remains one of the standard and most widely used methods for demonstrating construct validity of new instruments. However, the model for EFA makes assumptions which may not be applicable to all quality of life (QOL) instruments, and as a consequence the results from EFA may be misleading. In particular, EFA assumes that the underlying construct of QOL (and any postulated subscales or 'factors') may be regarded as being reflected by the items in those factors or subscales. QOL instruments, however, frequently contain items such as diseases, symptoms or treatment side effects, which are 'causal indicators'. These items may cause reduction in QOL for those patients experiencing them, but the reverse relationship need not apply: not all patients with a poor QOL need be experiencing the same set of symptoms. Thus a high level of a symptom item may imply that a patient's QOL is likely to be poor, but a poor level of QOL need not imply that the patient probably suffers from that symptom. This is the reverse of the common EFA model, in which it is implicitly assumed that changes in QOL and any subscales 'cause' or are likely to be reflected by corresponding changes in all their constituent items; thus the items in EFA are called 'effect indicators.' Furthermore, disease-related clusters of symptoms, or treatment-induced side-effects, may result in different studies finding different sets of items being highly correlated; for example, a study involving lung cancer patients receiving surgery and chemotherapy might find one set of highly correlated symptoms, whilst prostate cancer patients receiving hormone therapy would have a very different symptom correlation structure. Since EFA is based upon analyzing the correlation matrix and assuming all items to be effect indicators, it will extract factors representing consequences of the disease or treatment. These factors are likely to vary between different patient subgroups, according to the mode of treatment or the disease type and stage. Such factors contain little information about the relationship between the items and any underlying QOL constructs. Factor analysis is largely irrelevant as a method of scale validation for those QOL instruments that contain causal indicators, and should only be used with items which are effect indicators.

Causality↗

Factor analysis of three standardized tests of memory in a clinical population.

OBJECTIVES: The aim of this study was to determine the factor structure of three standardized memory tests: Wechsler Memory Scale-Revised (WMS-R), Warrington Recognition Memory Test (WRMT), Doors and People Test (D&P). We investigated whether these different standardized tests of memory are consistent in their evaluation of memory function, and the extent to which these tests discriminate between different memory functions (e.g. recall/recognition and verbal/non-verbal memory). DESIGN: Fifty patients with selective memory impairment were tested on the WMS-R, WRMT and D&P. METHODS: Age-scaled scores from selective measures of these tests (WMS-R-verbal, WMS-R-visual, WMS-R-delay, WRMT-words, WRMT-faces, D&P-people, D&P-doors, D&P-shapes, D&P-names) were used as input to a factor analysis. RESULTS: Maximum likelihood factor analysis yielded a three-factor solution consistent with a theoretically motivated fractionation of memory function into recall and recognition components. Recognition performance, but not recall performance, showed dissociation into visual and verbal components. CONCLUSIONS: The WMS-R, WRMT and D&P are highly consistent in their assessment of memory function. The results of the factor analysis are consistent with a theoretically motivated fractionation of recall and recognition memory. They are also partially consistent with a dissociation between visual and verbal memory function.

Adolescent↗

Factor analysis of minimum-inhibitory concentrations for Escherichia coli isolated from feedlot cattle to model relationships among antimicrobial-resistance outcomes.

Factor analysis was used to assess relationships in the minimum-inhibitory concentration among 17 antimicrobials tested on isolates of Escherichia coli isolated from 360 faecal samples obtained from feedlot cattle. Six factors were extracted using maximum-likelihood factor analysis. The factors were interpretable antimicrobial groupings based on class of antimicrobial and previously described associations. New-generation cephalosporins, older-generation beta-lactams, fluoroquinolones and aminoglycosides grouped separately as classes of antimicrobials on four of the six factors. One of the remaining factors was a grouping of antimicrobials that had been identified as being related in previous feedlot studies. The last factor was a grouping of three of the five antimicrobials that comprise the antimicrobials found in penta-resistant strains of Salmonella Typhimurium. The factor analysis described patterns in the MIC data that would not have been apparent if only antimicrobial-resistance data categorized as susceptible-resistance had been analysed.

Animals↗