Five factor score

Additional analyses of the five factor scores using t-tests show no gender differences at all, which is rmsurprising as none may have been predicted from country differences. However, age and seniority are inextricably linked together in terms of respect, loyalty etc for the Chinese culture, so age differences might differentiate the Chinese respondents from the two European groups. Age (as the midpoints of the... 

Ther utic strategies depend on disease severity, assessed by well-established indicators of severity and prognostic factors. On the basis of the FVSG prospective study on 342 patients (45), the five factor score (FFS) comprising proteinuria greater... 

The biplot in Fig. 37.3 has been constructed from the factor scores of the 12 compounds and the factor loadings of the five physicochemical and biological variables [42,43]. (The biplot graphic technique is explained in Section 31.2.) It is... 

Factor analysis was performed on the IR spectra of subfractions 1 to 13 using 28 nonzero wavenumber variables. Five of the orginal 33 variables were unique to spectrum 15 and were not used in the factor analysis of samples 1-13. Figure 4b shows the factor score plots of the IR data on subfractions 1-13 in the FI vs. F2 factor space. Samples 1-7 are very close together, implying that infrared spectroscopy does not detect much difference between these dominantly aliphatic mixutres in this space. Analysis of the underlying correlation between variables by means of the variance diagram method showed that component (a) (350 ) represents methyl and methylene absorptions such as 2870, 2850, 2920, 1460 and 720 cm". Component axes (b) (120 ) with peak 1516 cm l and (c) (160 ) with 3050,... 

The 55-item Likert scale was analysed using a principal components analysis and inspection of the scree plot indicated that six factors be rotated. A varimax rotation was performed on the six factors and the orthogonal factors generated are shown in Table 2. To create factor scores, item scores were aggregated for each factor and Cronbach s alphas were calculated to establish their reliability five of the factors had high enough alphas to allow the aggregated factors to be used in further analyses, and these factors are those shown in table 2. Factor 5 was difficult to interpret and had an alpha of only. 435, so it is omitted. 

These tables show that the traditional FMEA uses five scales and scores of one to ten, to measure the probability of occurrence, severity and the probability of detection. Though this simplifies the computation, converting the probability into another scoring system, and then finding the multiplication of factor scores may cause problems. From Tables 7.1 and 7.3 it can be seen that the relation between and the probability scale is non-linear, while it is linear for that between Sd and the probability scale. 

Cl, confidence interval Ml, myocardial infarction PVD, peripheral vascular disease TIA, transient ischemic attack. Hazard ratios derived from the model are used for the scoring system. The score for the five-year risk of stroke is the product the individual scores for each of the risk factors present. The score is converted into a risk with a graph. Source. Rothwell et at. (2005). 

Prediction of risk using models requires a computer, a pocket calculator with an exponential function or internet-access (the ECST model can be found at www.stroke. ox.ac.uk). As an alternative, a simplified risk score based on the hazard ratios derived from the relevant risk model can be derived. Table 27.3 shows a score for the five-year risk of stroke on medical treatment in patients with recently symptomatic carotid stenosis derived from the ECST model. As is shown in the example, the total risk score is the product of the scores for each risk factor. Fig. 27.7 shows a plot of the total risk score against the five-year predicted risk of ipsilateral carotid territory ischemic stroke derived from the full model and is used as a nomogram for the conversion of the score into a risk prediction. 

The coefficients of correlation between the same factor and five different IC forms were calculated and sorted by their absolute values, and given a score of 5, 4, 3, 2, 1 respectively, in the order of maximum (score 5) to minimum (score 1). Negative correlation indicates that this factor can improve IC dissolution, and more IC may take part in carbon cycling, and its score will be positive. Positive correlation indicates that this factor improves IC precipitation, so less IC participates in carbon cycling and its score will be negative. The sum of all... 

By the analysis of Table 7, we can see that Coimbra is the municipality that has a town with the lowest level in terms of capability support, due to its low score in factor 1 - water system performance. In Table 8 we can observe that Coimbra presents a deep territorial contrast in terms of capability support, in opposition with Fundao, and Proenpa-a-Novamunicipalities. The results of the Almeida municipality stress the relevant capability support in five towns, deriving from its high score in factor 1. 

For each of these three variables, there are five possible ratings, all using identical numerics 1, 3, 5, 7, and 9. Other selections are equally valid as long as they are proportional and do not give excessive consideration to one of the factors to be scored. The ratings follow in Tables 11-13. 

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