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At least 19 recordsLinked to original sources
PubMed1986

[Left ventricular wall motion abnormalities evaluated by factor analysis as compared with Fourier analysis].

Factor analysis was applied to multigated cardiac pool scintigraphy to evaluate its ability to detect left ventricular wall motion abnormalities in 35 patients with old myocardial infarction (MI), and in 12 control cases with normal left ventriculography. All cases were also evaluated by conventional Fourier analysis. In most cases with normal left ventriculography, the ventricular and atrial factors were extracted by factor analysis. In cases with MI, the third factor was obtained in the left ventricle corresponding to wall motion abnormality. Each case was scored according to the coincidence of findings of ventriculography and those of factor analysis or Fourier analysis. Scores were recorded for three items; the existence, location, and degree of asynergy. In cases of MI, the detection rate of asynergy was 94% by factor analysis, 83% by Fourier analysis, and the agreement in respect to location was 71% and 66%, respectively. Factor analysis had higher scores than Fourier analysis, but this was not significant. The interobserver error of factor analysis was less than that of Fourier analysis. Factor analysis can display locations and dynamic motion curves of asynergy, and it is regarded as a useful method for detecting and evaluating left ventricular wall motion abnormalities.

Adult↗
PubMed1994

Figuring out factors: the use and misuse of factor analysis.

Factor analysis is a technique which is designed to reveal whether or not the pattern of responses on a number of tests can be explained by a smaller number of underlying traits or factors. Similarly, it can be used to indicate whether or not the various items on a questionnaire can be grouped into a few clusters with each cluster reflecting a different construct. As with all multivariate statistical tests, it is quite powerful and can provide much information about the instruments being used. Similarly, there are many ways it can be abused and misinterpreted. This paper will explain the basics of factor analysis and provide some guidelines relating to how the results should be reported.

Borderline Personality Disorder↗
PubMed1986

Comparison of phase analysis with factor analysis in equilibrium gated radionuclide angiography.

This study is the intercomparison of phase analysis (PA), factor analysis of dynamic structures (FADS) and Karhunen-Loeve analysis (KLA) in the diagnosis of regional wall motion abnormalities, RWMA, of the LV. One hundred and twenty eight patients with proven or suspected CAD have been investigated by both X-ray angiography and radionuclide equilibrium angiography performed in the LOA view. FADS and KLA are performed twice, once on the whole-image (WI-FADS, WI-KLA), and once on the LV ROI (LV-FADS, LV-KLA) as suggested by Pavel. Resulting images and factors are interpreted by a well trained observer. In an attempt to quantify LV-FADS images, two numeric parameters, P1 and P2, are defined. They measure the relative weight of the so-called ventricular factor for 2 and 3 factor analysis, respectively. A ROC curve is calculated for each method, taking X-ray angiography as the gold-standard. The areas under the ROC curves are estimated by the maximum likelihood method and are compared using a test described by Hanley which takes into account the correlation between the responses. The areas are: 0.90 for PA, 0.84 for WI-FADS, 0.86 for LV-FADS, 0.83 for WI-KLA, 0.86 for LV-KLA, 0.65 for P1 and 0.72 for P2. The observed differences are significant (at 5% level) between PA and WI-FADS and between FA and WI-KLA; whereas they are not between PA and LV-FADS, between PA and LV-KLA and between LV-FADS and LV-KLA. The diagnostic value of the two numeric parameters is poor.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1989

[Evaluation of hepatic function by hepatobiliary scintigraphy using factor analysis].

Factor analysis of hepatobiliary scintigraphy using 99mTc-N-pyridoxyl-5-methyltryptophan (99mTc-PMT) was performed, and functional factor of liver parenchyma (FA-hepatogram) was obtained. Two parameters (peak time, T 1/2) were calculated from this hepatogram. A good correlation was obtained between these parameters and ICG-R15; furthermore, these parameters were prolonged in patients with normal volunteers, chronic hepatitis, compensated and decompensated liver cirrhosis in this order. Conventional hepatogram by ROI method (ROI-hepatogram) was not suitable for the evaluation of global liver function in condition such as a large intrahepatic mass, intrahepatic gall bladder, dilatation of biliary tree, and severe liver cirrhosis. But, even in such cases, FA-hepatogram was useful for the evaluation of global liver function.

Biliary Tract↗
PubMed2001

Extracting parametric images from dynamic contrast-enhanced MRI studies of the brain using factor analysis.

Factor analysis of dynamic studies (FADS) is a technique that allows structures with different temporal characteristics to be extracted from dynamic contrast enhanced studies without making any a priori assumptions about physiology. These dynamic structures may correspond to different tissue types or different organs or they may simply be a useful way of characterising the data. This paper describes a method of automatically extracting factor images and curves from contrast enhanced MRI studies of the brain. This method has been applied to 107 studies carried out on patients with acute stroke. The results show that FADS is able to extract factor curves correlated to arterial and venous signal intensity curves and that the corresponding factor images allow a distinction to be made between areas of the brain with normal and abnormal perfusion. The method is robust and can be applied routinely to dynamic studies of the brain. The constraints described are sufficiently general to be applicable to other dynamic MRI contrast enhanced studies where an increase in contrast concentration produces an increase in signal intensity.

Cerebrovascular Circulation↗
PubMed1992

Principal component analysis and exploratory factor analysis.

In this paper we compare and contrast the objectives of principal component analysis and exploratory factor analysis. This is done through consideration of nine examples. Basic theory is presented in appendices. As well as covering the standard material, we also describe a number of recent developments. As an alternative to factor analysis, it is pointed out that in some cases it may be useful to rotate certain principal components if and when that is appropriate.

Animals↗
PubMed2003

Differences in preferences of entrees by elderly congregate meal participants according to age, gender, ethnicity and education and a factor analysis approach to group entrée preferences.

Congregate meal participants (n = 381), ages 60-100, provided preference ratings of 43 entrees utilizing a modified Food Rating Scale (FACT). Differences in preferences of entrees associated with age, gender, ethnicity and educational level were analyzed using X2 analysis. Factor analysis was used to group entrees which were associated according to preferences. Both males and females agreed with the top five preferred entrees. For other entrees, females had a higher acceptance level. Both age groups preferred the same top five entrees; for other entrees, the younger group was more accepting of these. Few differences were noted among the various ethnic groups (91% white and 7% African-American). Education level of respondents had a varying association with entree preference, depending upon the specific entree.

Black or African American↗
PubMed1991

[Analysis of clinical factors and IgE-RAST of Dermatophagoides farinae and rice in atopic dermatitis by multiple factor analysis of quantification theory type II].

Twelve clinical factors and IgE-RAST of Dermatophagoides farinae (DF) and rice were analysed in three hundred patients with atopic dermatitis by multiple factor analysis of quantification theory Type II. Atopic dermatitis was simply defined here, as a patient who had typical eczematous eruption on the flexural portions of the body, such as the cubital fossa and/or the tibial fossa and/or the neck. The results were as follows: 1) Five clinical items such as onset age, age, history of asthma, sex, and history of allergic rhinitis were selected as significant variables to discriminate the objective variable of DF RAST, and influenced the discrimination in the order. The category of "less than 10 years-old" in the item of onset age, mostly influenced on the positive DF RAST, followed by the categories of "more than 20 years-old" in age, "presence of asthma", "presence of allergic rhinitis", and so on, in the order. 2) Five clinical items such as duration of the disease, eruption on the face, eruption on the trunks and/or extremities, sex, and age were selected as significant variables to discriminate the objective variable of rice RAST, and influenced the discrimination in the order. The category of "more than 5 years" in the item of duration of the disease, mostly influenced on the positive rice RAST, followed by the categories of refractory signs such as "lichenification on the face", "lichenification on the trunks and/or extremities", and so on, in the order. Based on the results by the multiple factor analysis, rice antigen was strongly suspected to have relation with refractory patients with atopic dermatitis.

Adolescent↗
PubMed2001

Quantitative resolution of spectroscopic systems using computer-assisted target factor analysis (CAT).

Factor analysis (FA) is widely applicable in analytical chemistry. For suitable data structures, the generality of the approach is a strong point of FA. Using target testing procedures, a wealth of specific informations on the components in a chemical system can be extracted without stating a specific model. Target testing, however, is time-consuming and often heuristic. In the following, application of a computer-assisted target factor analysis (CAT) algorithm is described. CAT estimates a rotation matrix that transforms abstract factors into physically meaningful informations by using general constraints, i.e. non-negativity of absorptions and/or concentrations. Thus, the rotation step in factor analysis is automated and allows application of target factor analysis to situations where none or only very limited information on the real factors and their respective contributions is available. Thus, a target testing procedure of guessing physically meaningful factors and iteratively adapting these factors is performed automatically. Nevertheless, CAT is not a black box procedure. The relative importance of different optimization constraints is balanced interactively. CAT can be applied to all situations where factor analysis can be used. CAT is demonstrated using UV-Vis absorption spectra of a Nd(III) polyoxometalate cryptate system and the system U(VI)-CO2-H2O as examples.

Journal Article↗
PubMed1986

[Transformation of the factor loadings in inter-battery factor analysis].

The factor transformation problem in inter-battery factor analysis is not limited to ordinary factor rotation. In this paper, four methods of transformation in inter-battery factor analysis are proposed. The first one is a solution of Gibson's (1960) proposal and may be applied to obtain factor loadings for the transformation toward simple structure. The second one is a kind of Procrustes rotation in inter-battery factor analysis. These solutions are obtained by numerical methods. The third and the fourth ones consist of scale transformation of factor loadings and may be applied to cases where orthogonality of factor loadings are desirable to a definite interpretation (e.g. as a profile score which is independent of general ability). The transformed values of the third and fourth methods are obtained as the numerical solutions of quartic equations.

Factor Analysis, Statistical↗
PubMed1997

Dimensionality of an early childhood scale using rasch analysis and confirmatory factor analysis.

This paper explores the use of Rasch analysis and linear confirmatory factor analysis as methods for investigating the dimensionality of an early childhood test (Gesell School Readiness Screening Test), taking into account the theoretical basis of test construction. The paper presents the results of empirical analyses using both approaches and discusses the theoretical and psychometric considerations that guide the selection and application of each technique.

Aptitude Tests↗
PubMed2003

Temperature-dependent near-infrared spectra of bovine serum albumin in aqueous solutions: spectral analysis by principal component analysis and evolving factor analysis.

Fourier transform near-infrared (FT-NIR) spectra have been measured for bovine serum albumin (BSA) in an aqueous solution (pH 6.8) with a concentration of 5.0 wt% over a temperature range of 45-85 degrees C. Not only conventional spectral analysis methods, such as second-derivative spectra and difference spectra, but also chemometrics, such as principal component analysis (PCA) and evolving factor analysis (EFA), have been employed to analyze the temperature-dependent NIR spectra in the 7500-5500 and 4900-4200 cm-1 regions of the BSA aqueous solution. Intensity changes of bands in the 7200-6600 cm-1 and 4650-4500 cm-1 regions in the difference spectra indicate variations of the hydration and secondary structure of BSA in the aqueous solution, respectively. The plot of a band intensity at 7080 cm-1 in the different spectra shows a clear turning point at 63 degrees C, revealing that a significant change in the hydration occurs at about 63 degrees C. The forward and backward eigenvalues (EVs) from EFA suggest that marked changes in the hydration and secondary structure of BSA take place in the temperature ranges of 61-65 degrees C and 59-63 degrees C, respectively. In addition, the temperature of 71 degrees C marked in the EFA plots may correspond to the onset temperature of increase in the intermolecular beta-sheet structure.

Animals↗
PubMed2006

A biological question and a balanced (orthogonal) design: the ingredients to efficiently analyze two-color microarrays with Confirmatory Factor Analysis.

BACKGROUND: Factor analysis (FA) has been widely applied in microarray studies as a data-reduction-tool without any a-priori assumption regarding associations between observed data and latent structure (Exploratory Factor Analysis).A disadvantage is that the representation of data in a reduced set of dimensions can be difficult to interpret, as biological contrasts do not necessarily coincide with single dimensions. However, FA can also be applied as an instrument to confirm what is expected on the basis of pre-established hypotheses (Confirmatory Factor Analysis, CFA). We show that with a hypothesis incorporated in a balanced (orthogonal) design, including 'SelfSelf' hybridizations, dye swaps and independent replications, FA can be used to identify the latent factors underlying the correlation structure among the observed two-color microarray data. An orthogonal design will reflect the principal components associated with each experimental factor. We applied CFA to a microarray study performed to investigate cisplatin resistance in four ovarian cancer cell lines, which only differ in their degree of cisplatin resistance. RESULTS: Two latent factors, coinciding with principal components, representing the differences in cisplatin resistance between the four ovarian cancer cell lines were easily identified. From these two factors 315 genes associated with cisplatin resistance were selected, 199 genes from the first factor (False Discovery Rate (FDR): 19%) and 152 (FDR: 24%) from the second factor, while both gene sets shared 36. The differential expression of 16 genes was validated with reverse transcription-polymerase chain reaction. CONCLUSION: Our results show that FA is an efficient method to analyze two-color microarray data provided that there is a pre-defined hypothesis reflected in an orthogonal design.

Antineoplastic Agents, Alkylating↗
PubMed1989

[The investigation of asthmatic children by multiple factor analysis. III. The changes in allergic factors selected by multiple factor analysis in 3 anti-allergic during treated groups over one year].

We investigated the difference in the influence on 10 allergic factors of 3 anti-allergic drugs: azelastine, tranilast, ketotifen. We determined the ranking of 10 allergic factors using the relative range ratio calculated by Multiple factor analysis. The ranking as follows: (1) Eosinophil count (2) IgE RAST score to H.D. (3) IgG4 antibody titers to egg white (4) IgE RAST score to egg white (5) IgE RAST score to D.f. (6) IgG4 antibody titers to milk (7) IgE RIST (8) IgG4 antibody titers to soybean (9) IgE RAST score to soybean (10) IgE RAST score to milk. Then we split 122 asthmatic children (mean age: 9.3) into 3 groups and gave each group a different one of the 3 anti-allergic drugs for one year. From daily records of asthma attacks and clinical findings, we classified the children into improved and not-improved groups within their respective anti-allergic drug group, and we investigated the changes in and the change ratios of the 10 allergic factors by the Student t method over this one year. The results were as follows. 1) There was no difference in the chang of 3 allergic factors (Eosinophil count, IgG4 antibody titers to egg white and IgE RIST) among the 3 anti-allergic drug treated groups. 2) The specific IgE antibodies to H.D. and D.f. had a remarkable decline tendency in treated groups by azelastine and tranilast. 3) The specific IgE antibody to egg white had a remarkable decline tendency in treated group by ketotifen.

Adolescent↗