Hierarchical factor analysis

Jensen I don t know the answer to that. I know that there have been factor analyses of up to 50 different body measurements, but I can t recall the hierarchical analysis. There is a general factor in body measurements. These measurements still exist the British garment industry has collected about 50 body measurements on 10 000 women, and the correlation matrix exists. When I took a course on factor analysis, our final exam project was to factor analyse this huge correlation matrix which in those days took a 40 hour week to do on a desk calculator There was a big general factor and about four or five other factors that were large enough to be significant. 

The profiles of hydrocarbons, PCBs, organochlorine pesticides, and sterols in sediments from the northwestern Mediterranean Sea were studied by PCA, hierarchical cluster analysis, and positive matrix factorization [70]. Three sources could be distinguished anthropogenic, consisting mostly of PAHs rivers, containing mostly -alkanes, pesticides, and sterols and an unspecified background source, containing just some n-alkanes. 

L.) collected by beekeepers in apparently polluted and nonpolluted environments was performed by using inductively coupled plasma atomic emission spectrometry (ICP-AES) to measure significant concentrations of Ag, Ca, Cr, Co, Cu, Fe, Li, Mg, Mn, Mo, P, S, Zn, Al, Cd, Hg, Ni, and Pb. Fortunately, Cd, Hg, Ni, and Pb were not detected in the analyzed samples. Conversely, Ag, Cu, Al, Zn, and S were found in some samples located near industrial areas. Because a high variability was found in the concentration profiles, correspondence factor analysis was used to rationalize the data and provide a typology of the honeys based on the concentration of these different elements in the honeys. The results were confirmed by means of principal component analysis and hierarchical cluster analysis. Finally, the usefulness of the acacia honey as a bioindicator of heavy metal contamination is discussed. 

Coulomb potential functions and the standard Tripos CoMFA probes (the Csp probe was used for calculation of steric interactions and the probe for calculation of elec-bostatic interactions, respectively). A PCA (factor analysis without axes rotation) was done on the descriptor matrix and a classification of the heteroaromatic substiments into families was performed using the Sybyl hierarchical clustering procedure of the obtained principal component... 

The human factors literature is rich in task analysis techniques for situations and jobs requiring rule-based behavior (e.g., Kirwan and Ainsworth 1992). Some of these techniques can also be used for the analysis of cognitive tasks where weU-practiced work methods must be adapted to task variations and new circumstances. This can be achieved provided that task analysis goes beyond the recommended work methods and explores task variations that can cause failures of human performance. Hierarchical task analysis (Shepherd 1989), for instance, can be used to describe how operators set goals and plan their activities in terms of work methods, antecedent conditions, and expected feedback. When the analysis is expanded to cover not only normal situations but also task variations or changes in circumstances, it would be possible to record possible ways in which humans may fail and how they could recover from errors. Table 2 shows an analysis of a process control task where operators start up an oil refinery furnace. This is a safety-critical task because many safety systems are on manual mode, radio communications between control room and on-site personnel are intensive, side effects are not visible (e.g., accumulation of fuel in the fire box), and errors can lead to furnace explosions. 

The human inspection models of visual search and human decision making were shown to be particularly applicable leading to a task analytic framework using hierarchical task analysis (HTA). In this way, human factors knowledge could be applied systematically to observed FPI processes. Visits were made to several engine repair facilities owned by major air carriers and engine manufac-... 

Classification of lake sediments by means of factor analysis and hierarchical clustering has been reported by Hopke et. al. C1103- For a set of 79 sediment samples, 32 characteristic properties were determined (e.g. concentration of 15 elements, percent organic matter, criteria of the particle size distribution, water depth). Cluster analysis detected a single cluster for samples from the centre of the lake and three different clusters for samples from near the shore. 

Hierarchical Task Analysis (HTA) - a well-known human factors analysis approach the technique was followed as laid out by Shepherd and Stammers (2005). 

Annett, J. (2005). Hierarchical task analysis. In N.A. Stanton, A. Hedge, K. Brookhuis, E. Salas, and H. Hendrick (Eds.), Handbook of Human Factors and Ergonomics Methods (pp. 33.1-33.7). Boca Raton, FL CRC Press. 

FIGURE 7.5 Example of a hierarchical task analysis (HTA). (From Stanton, N.A. et al.. Human Factors Methods, Ashgate, Williston, VT, 2005. With permission.)... 

The statistical submodel characterizes the pharmacokinetic variability of the mAb and includes the influence of random - that is, not quantifiable or uncontrollable factors. If multiple doses of the antibody are administered, then three hierarchical components of random variability can be defined inter-individual variability inter-occasional variability and residual variability. Inter-individual variability quantifies the unexplained difference of the pharmacokinetic parameters between individuals. If data are available from different administrations to one patient, inter-occasional variability can be estimated as random variation of a pharmacokinetic parameter (for example, CL) between the different administration periods. For mAbs, this was first introduced in sibrotuzumab data analysis. In order to individualize therapy based on concentration measurements, it is a prerequisite that inter-occasional variability (variability within one patient at multiple administrations) is lower than inter-individual variability (variability between patients). Residual variability accounts for model misspecification, errors in documentation of the dosage regimen or blood sampling time points, assay variability, and other sources of error. 

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