Factor Analysis communality

Ross, C. A., Joshi, S., Currie, R. (1991). Dissociative experiences in general population A factor analysis. Hospital and Community Psychiatry, 42, 297-301. 

The communality is introduced as a mathematical measure of this common feature variance. The communality is the part of the variance of one feature which is described by the common factor solution in the factor analysis. High communalities, hj, mean that this feature variance is highly explained by the factor solution. Low communalities for one feature detect either a specific feature variance or high random error. 

Such a definition of the aim of factor analysis affects also the eigenvalue solution as the way of extracting the factors. Therefore the diagonal elements of the original correlation matrix R, which are all identical to unity were substituted into the communalities of the features ... 

The purpose of application of factor analysis (FA) is the characterization of complex changes of all observed features in partial systems of the environment by determination of summarized factors which are more comprehensive and causally explicable. The method extracts the essential information from a data set. The exclusive consideration of common factors in the reduced factor analytical solution seems to be particularly promising for the analytical process. The specific variances of the observed features will be separated from the reduced factor analytical results by means of the estimation of the communalities. They do not falsify the influence of the main pollution sources (see also Tab. 7-2). The mathematical fundamentals of FA are explained in detail in Section 5.4.3 (see also [MALINOWSKI, 1991 WEBER, 1986]). 

Weintraub, S. T. Pinckard, R. N. Hail, M. 1991. Electrospray ionization for analysis of platelet-activating factor. Rapid Commun. Mass Spectrom., 5,309-311. 

Table 3. Factor analysis of the Keasling/Moffett data. Factor loadings aik o/the tests with respect to the three orthogonally varimax-rotated common factors, communality and uniqueness of the variables (++, high loadings +, moderate loadings)...

PCA has been often employed to explore the relationships among variables in a data set (19 20). Nevertheless, it is generally accepted that Factor Analysis (FA) is better suited than PCA to study these relationships (1 7). This is because FA algorithms are designed to distinguish between shared and unique variability. The shared variability, the so-called communalities in the FA community, reflect the common factors-common variability-among observable variables. The unique variability is only present in one observable variable. The common factors make up the relationship structure in the data. PCA makes no distinction between shared and unique variability and therefore it is not suited to find the structure in the data. 

The procedure of psychometric testing varies between the different studies included in this chapter. While many conducted no psychometric tests at all, two studies conducted a confirmatoiy factor analysis (CFA), two performed an exploratory factor analysis (EFA), and 10 studies did both. Despite this difficulty, we found only one study (Piyseley 2008) out of 10 which performed a CFA in order to test the original factor stracture of the HSPSC questionnaire, with several unacceptable thresholds for both absolute and incremental fit indices. With regards to the EFAs, the dimensions Staffing , Communication openness , Organisational learning and Teamwork across hospital units , appeared to be less stable. 

Since the questionnaire was still in a formative stage of development and factor analysis work had focussed only on the general section, it was not possible to determine a final model from the factor analyses but eight safety culture elements were proposed to refiect the results of the factor analyses and the themes derived from the literatme and interviews. The eight elements were Commitment, Involvement, Reporting and Learning, Teamwork, Communication, Risk Awareness, Trust and Responsibility. The first five of these elements had strong support from the factor analyses, while the last three had support from the literature and seemed to fit items from the occupation-specific sections of the questionnaire. 

Some questions cross-loaded onto more than one factor in factor analysis (e g. Teamwork and Communications), or loaded on different factors in different factor analyses, suggesting that the item may not fit consistently into a coherent model of safety culture. Many of these concerned issues that were better covered by other items statistical analysis was used to determine the best - most precise - questions. An engineering-related example was I sometimes have to do workarounds to compensate for lack of resources (equipment, manpower or time) . Wording was another reason for removal of certain questiotmaire items. For instance, controllers pointed out that they could not be sure what would constitute sufficient system checks by maintenance staff when asked whether Maintenance staff perform sufficient system checks . 

The human factors audit was part of a hazard analysis which was used to recommend the degree of automation required in blowdown situations. The results of the human factors audit were mainly in terms of major errors which could affect blowdown success likelihood, and causal factors such as procedures, training, control room design, team communications, and aspects of hardware equipment. The major emphasis of the study was on improving the human interaction with the blowdown system, whether manual or automatic. Two specific platform scenarios were investigated. One was a significant gas release in the molecular sieve module (MSM) on a relatively new platform, and the other a release in the separator module (SM) on an older generation platform. 

Biotic indices that are relatively simple and inexpensive to apply can be very useful for identifying environmental problems caused by pollutants. Serious effects of pollutants can cause departures from normal profiles. The problem is, however, identifying which pollutants—or which other enviromnental factors—are responsible for significant departures from normality. This dilemma illustrates well the importance of having both a top-down and a bottom-up approach to pollution problems in the field. Chemical analysis and biomarker assays can be used to identify chemicals responsible for adverse changes in communities detected by the use of biotic indices. 

One of the key aspects in developing a method for regulatory analysis is method ruggedness. The more rugged a method, the less susceptible it is to failure or to excessive variations due to differences in equipment, analyst technique, and other differences that are typically present among laboratories. Several factors contribute to poor method ruggedness insufficient testing by the developer, excessive method complexity, and a failure of the developer to identify and communicate critical points. 

Carburization of rhenium filaments has been used to optimize Th and Pa ionization efficiency for TIMS analysis on single filaments (Esat 1995). ReC has a greater work function than Re metal, and elemental oxidation state is maintained in the reduced or metal state by the presence of carbon in the filament. Using this method and a mass spectrometer with improved ion optics, Esat (1995) was able to improve Th transmission and ionization efficiency by about a factor of 30 over conventional methods. Using more conventional mass spectrometry, Murrell et al. (personal communication) were able to improve ionization efficiency for Pa and Th by a factor of 5-10 over conventional graphite sandwich loads on Re filaments (Goldstein et al. 1989 Pickett et al. 1994). For Pa analysis, one drawback is that Pa and U ionization commonly overlap using this... 

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