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PubMed2006

Using and understanding factor analysis: the Brief Pain inventory.

Factor analysis is useful for examining relationships among large numbers of variables and understanding the conceptual structure of an instrument The Brief Pain Inventory (BPI), which measures pain in terms of its severity and interference with function, has been widely used with cancer patients. A secondary analysis of BPI data in a sample of postoperative patients is used to illustrate the factor analytic method. The resulting two-factor solution of pain's interference with function and pain severity are discussed by Margarete Lieb Zalon in relation to the validity of the BPI along with considerations in using the instrument with surgical patients.

Activities of Daily Living↗
PubMed1986

The Reitan-Indiana Aphasia Screening Test: scoring and factor analysis.

This article presents a scoring system and factor analysis for the Reitan-Indiana Aphasia Screening Test. Eight content categories were clinically identified and scored (N = 197). Also, the interrelationships of these content areas were explored by factor analysis. The analysis revealed two major factors, a general language abilities factor and a sensorimotor coordination factor. The pattern of loadings was interpreted with reference to hypothesized relationships and previous research on the test. The findings also have implications for the clinical interpretation of test results.

Brain Diseases↗
PubMed2001

Spectroelectrochemical study of the oxidative doping of polydialkylphenyleneethynylene using iterative target transformation factor analysis.

Iterative target transformation factor analysis (ITTFA) was used to determine the spectra of the individual species generated during the oxidative p-doping of films of poly(para-phenyleneethynylene) (PPE). UV-visible spectra of PPE films on transparent electrodes were obtained in-situ during an anodic sweep. ITTFA identified 4 species present during the oxidation, which we assign as neutral polymer, polaron species, bipolaron species, and a species formed by further bipolaron reaction. The region of electrochemical stability for each of these species was identified and their potential-dependent profiles were obtained. This work is the first deconvolution of conjugated polymer spectroelectrochemistry.

Electrochemistry↗
PubMed1984

Factor analysis as a tool in community diagnosis.

For the family physician, community diagnosis can be likened to individual diagnosis by the application of methods of information acquisition and analysis. This paper describes the technique of factor analysis, applied to data from an urban inner-city area, for reducing a multitude of factors to a manageable number without an appreciable loss of information. The variables were selected from four broad categories which have been shown to influence both health states and health care needs: demography, socioeconomics, social disorganization and morbidity/mortality. A correlation matrix based on 40 of the most accessible indices served as input for a principal axes factor analysis. Six factors accounted for 74% of the total variance and were interpreted as: 'Poverty and social disorganization', 'Distribution and problems of the elderly', 'Ethnic composition', 'Fertility', 'Infant mortality' and 'Foetal mortality'. Representative indices from each factor were then mapped to identify and display census tract differences. Finally, the application of the factors to a specific community illustrated important differences within the community and identified areas of high risk and need.

Factor Analysis, Statistical↗
PubMed1992

Disease patterns of patients with systemic lupus erythematosus as shown by application of factor analysis.

Clinical and laboratory test data of 77 patients with systemic lupus erythematosus (SLE) were evaluated by factor analysis. Six factors representing disease patterns were extracted: cutaneous symptoms of alopecia, malar rash, rash and photosensitivity; renal involvement; the anticoagulant syndrome of phlebitis and partial thromboplastic time inversely related to platelet count; lymphopenia; viral or fibromyalgia symptoms of headache, nervousness, joint and muscle pain; and serology of anti-DNA antibodies and complement inversely related. Application of factor analysis reveals various clinical presentations of SLE.

Adult↗
PubMed1994

Valid parameters for investigation of the pupillary light reflex in normal and diabetic subjects shown by factor analysis and partial correlation.

Pupillary test data of 103 normal and 119 diabetic subjects (47 IDDM, 72 NIDDM) were evaluated by factor analysis. From a total of nine pupillary parameters three factors were extracted in the analysis. Factor 1 represents maximal pupillary area, contraction velocity at 1 s, dilation velocity at 6 s and minimal pupillary area--static and simple dynamic parameters; factor 2 amplitude of pupillary unrest, area under the detrended curve of pupillary unrest and period of pupillary unrest--parameters of pupillary unrest; factor 3 fusion frequency of pupillary response following flicker stimuli and latency time of pupillary light reflex--second order dynamic parameters. Factor analysis was then applied to investigate diabetic patients with a high percentage of autonomic neuropathic participants (about 39% had pupillary and about 35% had cardiorespiratory function disorders), which revealed the same three factors as those identified in normal subjects. Furthermore, an age-related database of parameters of pupillary unrest is given. It demonstrates that normal subjects and diabetic patients did not differ in the period of pupillary unrest (normal vs diabetic (mean +/- SEM): 1550 +/- 29 vs 1536 +/- 27 ms; 2p > 0.5). The difference in amplitude (47.8 +/- 2.8 vs 41.0 +/- 2.6% percentile; 2p = 0.071) and area under the detrended curve of pupillary unrest (47.9 +/- 2.8 vs 40.8 +/- 2.6% percentile, 2p = 0.062) seems to show a trend but was not significant. In conclusion, factor analysis revealed three different pupillary test factors.(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed1984

Factor analysis in gated cardiac studies.

Factor analysis of dynamic structures (FADS) can automatically provide "physiological" factors related to anatomical structures that have different temporal behavior, even if these structures overlap; it also yields images corresponding to the factors' spatial distributions. In normal patients, two significant cardiac factors, corresponding to the atria and the ventricles, may be extracted. A third significant factor can be obtained when additional dynamic structures exist. However, the method does not provide an estimate of the background. It becomes part of the factors, but it does not modify their shapes. FADS has been applied to 45 gated cardiac studies. Results obtained by FADS were compared with those obtained from the amplitude and the phase of first-harmonic Fourier analysis (FA). The joint results were compared with the final diagnosis, established by real-time echocardiography and/or ventriculography. In normal patients, good agreement was obtained between the two approaches. On the whole set of patients, FADS was significantly better than FA (by sign test: p less than 5%).

Atrial Function↗
PubMed2006

Random intercept item factor analysis.

The common factor model assumes that the linear coefficients (intercepts and factor loadings) linking the observed variables to the latent factors are fixed coefficients (i.e., common for all participants). When the observed variables are participants' observed responses to stimuli, such as their responses to the items of a questionnaire, the assumption of common linear coefficients may be too restrictive. For instance, this may occur if participants consistently use the response scale idiosyncratically. To account for this phenomenon, the authors partially relax the fixed coefficients assumption by allowing the intercepts in the factor model to change across participants. The model is attractive when m factors are expected on the basis of substantive theory but m + 1 factors are needed in practice to adequately reproduce the data. Also, this model for single-level data can be fitted with conventional software for structural equation modeling. The authors demonstrate the use of this model with an empirical data set on optimism in which they compare it with competing models such as the bifactor and the correlated trait-correlated method minus 1 models.

Factor Analysis, Statistical↗
PubMed2003

Imince: an unrestricted factor-analysis-based program for assessing measurement invariance.

In this article, a Windows program for analyzing measurement invariance in two different populations is described. Factor analysis is a common way of assessing measurement invariance, and restricted factor analysis is now the most popular method. However, applied researchers have usually found that the theoretical advantages of restricted factor analysis do not always apply in practical situations. For example, when the participant sample is large, as is the case in Internet-based questionnaires, the available software for restricted factor analysis might fail to converge on a solution. Our program is based on unrestricted factor analysis and considers the three parameters that define factor invariance: difficulties, discriminations, and residual variances. The statistical significance of the tests for evaluating invariance is obtained using Bootstrap resampling procedures. A real-life example demonstrates the usefulness of the program.

Adult↗
PubMed1992

Confirmatory factor analysis of reading and mathematics performance: a twin study.

Reading and mathematics performance data from a sample of 264 reading-disabled twin pairs and 182 matched control twin pairs were subjected to multivariate behavior genetic analysis. The factor structure of reading and math performance measures was found to be highly similar for both groups. Consistent with previous findings obtained using alternative methods, a significant heritable component to individual differences in reading performance was found both within the reading-disabled (h2 = 0.78) and control (h2 = 0.74) twin samples. In addition, a substantial genetic influence on mathematics performance was found (h2 = 0.51 and 0.60 in the reading-disabled and control samples, respectively), although shared environmental influences common to both members of a twin pair also contribute significantly to the variance in math scores of both groups (c2 = 0.44 and 0.37). Moreover, genetic influences accounted for 98% of the observed correlation between reading and math performance within the sample of reading-disabled twin pairs, and for 55% of the observed correlation in the control sample. Thus, individual differences in both reading and mathematics performance are highly heritable and appear to be caused by many of the same genetic influences.

Adolescent↗
PubMed1998

A confirmatory factor analysis of posttraumatic stress symptoms.

Investigators have recently identified a two-factor structure underlying posttraumatic stress symptoms through the use of exploratory factor analysis. [Taylor et al. (1988). The structure of posttraumatic stress symptoms. Journal of Abnormal Psychology, 107, 154-160]. These two factors, which were labeled as Intrusion and Avoidance, and Hyperarousal and Numbing, are consistent with current theoretical models of posttraumatic stress disorder--PTSD [e.g. Foa et al. (1992). Uncontrollability and unpredictability in post-traumatic stress disorder: An animal model. Psychological Bulletin, 112, 218-238]. However, the authors of the Taylor et al. study issued caution in interpreting their findings because there has yet to be a systematic replication of their results. This paper presents a confirmatory factor analysis of the two-factor structure of posttraumatic stress symptoms in 217 survivors of serious motor vehicle accidents with varying degrees of PTSD symptoms. A hierarchical, confirmatory-factor analysis conducted with a structural equation modeling statistics package confirmed that the model proposed by Taylor et al. can adequately account for the presentation of PTSD symptoms in this sample of motor vehicle accident survivors. The implications for the assessment and diagnosis of PTSD are discussed.

Accidents, Traffic↗
PubMed2005

Higher-order factor analysis of an instrument with dichotomous data.

This article reports the use of higher-order factor analysis to examine the underlying dimensions of an instrument designed to measure community acceptance of nurse practitioners/physician's assistants. The instrument consisted of both dichotomous and Likert scale items. Use of factor analysis with dichotomous data is controversial. Higher-order factor analysis provides a potential solution to this dilemma. Following initial factor analysis using maximum likelihood extraction with oblique rotation, the factor correlation matrix was factored to obtain second order factors. The second order factors were interpreted using the factor pattern matrix and examining correlations of individual survey items with the second order factors. This procedure provides additional factorial validity of the underlying uni- and multidimensional concepts related to acceptance of NPs and PAs in rural settings: knowledge, competence, access, and trust.

Attitude to Health↗
PubMed1995

Applicability of factor analysis in solid state NMR.

Factor analysis has been used to deduce the composition of multicomponent magic angle spinning NMR spectra comprised of overlapping isotropic peaks. The technique has been used to determine the number of constituents present and was combined with a target transformation minimization procedure to identify the component MAS spectra. The new analysis procedure is compared to the conventional least-squares approach and is found to be superior in all cases.

Factor Analysis, Statistical↗
PubMed2002

Correction for ambiguous solutions in factor analysis using a penalized least squares objective.

Factor analysis is a powerful tool used for the analysis of dynamic studies. One of the major drawbacks of factor analysis of dynamic structures (FADS) is that the solution is not mathematically unique when only nonnegativity constraints are used to determine factors and factor coefficients. In this paper, a method to correct for ambiguous FADS solutions has been developed. A nonambiguous solution (to within certain scaling factors) is obtained by constructing and minimizing a new objective function. The most common objective function consists of a least squares term that when minimized with nonnegativity constraints, forces agreement between the applied factor model and the measured data. In our method, this objective function is modified by adding a term that penalizes multiple components in the images of the factor coefficients. Due to nonuniqueness effects, these factor coefficients consist of more than one physiological component. The technique was tested on computer simulations, an experimental canine cardiac study using 99mTc-teboroxime, and a patient planar 99mTc-MAG3 renal study. The results show that the technique works well in comparison to the truth in computer simulations and to region of interest (ROI) measurements in the experimental studies.

Algorithms↗
PubMed1978

A factor analysis of coping behaviors.

Undertook factor analysis on questionnaire data that deal with ways of diminishing tension. Data were obtained on 132 college students. Seven patterns of coping were identified tentatively: seeking social support, dysfunctional behavior, narcotizing anxiety, problem solving, reliance on professionals, bearing with discomfort, and escape.

Adaptation, Psychological↗
PubMed2006

An empirical basis for domains in the analysis of dependency in the activities of daily living (ADL): results of a confirmatory factor analysis of the Cleveland Scale for Activities of Daily Living (CSADL).

This study examines the empirical basis for dividing activities of daily living (ADLs) into broad behavioral domains. Following an exploratory factor analysis of results from the Cleveland Scale for Activities of Daily Living (CSADL), a two-factor model of ADLs was tested in a confirmatory factor analysis using two groups of demented patients. The model, which included a Basic and an Instrumental factor, was confirmed. Comparison of the item composition of the CSADL factors with a priori domains used by other ADL scales suggested that the results of the present study are broadly generalizable. The two factors are correlated, and cautions were raised concerning the likelihood that many ADL items may reflect both basic and instrumental domains.

Activities of Daily Living↗
PubMed1995

Confirmatory factor analysis of The Aggression Questionnaire.

A confirmatory factor analysis of the factor structure of The Aggression Questionnaire created by Buss and Perry (1992) [Journal of Personality and Social Psychology, 63, 452-459] was conducted to assess whether the scale's purported 4 factors emerged. The results generally supported the 4-factor model. However, the hostility factor may be improved if 2 items pertaining to suspicion are removed from the scale. These items had relatively low loadings on that factor and decreased the hostility scale's internal reliability.

Adult↗
PubMedDate not supplied

A longitudinal confirmatory factor analysis of indirect and physical aggression: evidence of two factors over time?

The purpose of the present study was to examine whether indirect aggression was distinct from physical aggression across developmental periods. Participants were 3,089 Canadian children aged 4 to 7 years (Time 1), 6 to 9 years (Time 2), and 8 to 11 years (Time 3). Confirmatory factor analysis using an accelerated longitudinal design confirmed a 2-factor model that was stable across cohorts, time, and sex. The longitudinal predictive links between indirect and physical aggression were also examined in a path analysis. Findings did not support the notion that maturation is associated with changes in the ways children aggress but rather suggest that children are consistent in the type of aggression they use over time, whether it be indirect or physical.

Aggression↗