scholarly journals Urban Regeneration: Construction of Factor model

2021 ◽  
Author(s):  
Sumana Jayaprakash ◽  
Vimala Swamy
Keyword(s):  
Factor Analysis ◽  
Principal Components ◽  
Planning Process ◽  
Factor Model ◽  
Urban Regeneration ◽  
Principal Component ◽  
Statistical Technique ◽  
Original Dataset ◽  
Monitoring Process ◽  
Wide Range

Abstract: Urban regeneration decision-making is a complex process, as it involves a wide range of decision-makers, public-private partnerships in finance and implementation, including the inevitable considerable amount of risk on a long-term basis. There are a multitude of stakeholders, the citizens being the key stakeholders. It is necessary to involve the citizens in the planning process. Such involvement allows the communities to express their needs and aspirations, which is useful in the policymaking, delivery of planning programs, and in the monitoring process. In such a context, Factor analysis was the statistical technique used (1) Carry out factor analysis based on the principal component analysis method using the software XLSTAT 2021.4.1.1205 - (2) Construct a factor model of Urban Regeneration. (3) Interpret and label the factor dimensions. The results of the analysis indicated that the first two principal components accounted for 60.04% of the total variance of the original dataset. All variables seemed to be, positively correlated to each other and contributed similarly to principal components PC1 &PC2. The observations were well clustered; except for very few outliers. The limitation of the work was that the perceptions of the citizens were limited to the variables derived by the researcher.

scholarly journals Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6370
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
David Mba ◽  
Xiaochuan Li ◽  
Tat-Hean Gan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Water Purification ◽  
Principal Component ◽  
Temporal Correlation ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Original Dataset ◽  
Monitoring Process ◽  
Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

scholarly journals Does the Order of Item Difficulty of the Addenbrooke's Cognitive Examination Add Anything to Subdomain Scores in the Clinical Assessment of Dementia

2015 ◽  
Vol 5 (1) ◽  
pp. 155-169 ◽  
Author(s):  
Sarah McGrory ◽  
John M. Starr ◽  
Susan D. Shenkin ◽  
Elizabeth J. Austin ◽  
John R. Hodges
Keyword(s):  
Factor Analysis ◽  
Factor Model ◽  
Item Difficulty ◽  
Principal Component ◽  
Scaling Analysis ◽  
Degenerative Disorders ◽  
Two Samples ◽  
Confirmatory Factor ◽  
The One ◽  
Addenbrooke’S Cognitive Examination

Background: The Addenbrooke's Cognitive Examination (ACE) is used to measure cognition across a range of domains in dementia. Identifying the order in which cognitive decline occurs across items, and whether this varies between dementia aetiologies could add more information to subdomain scores. Method: ACE-Revised data from 350 patients were split into three groups: Alzheimer's type (n = 131), predominantly frontal (n = 119) and other frontotemporal lobe degenerative disorders (n = 100). Results of factor analysis and Mokken scaling analysis were compared. Results: Principal component analysis revealed one factor for each group. Confirmatory factor analysis found that the one-factor model fit two samples poorly. Mokken analyses revealed different item ordering in terms of difficulty for each group. Conclusion: The different patterns for each diagnostic group could aid in the separation of these different types of dementia.

Dimensional Analyses of Taxonic Data

2007 ◽  
Vol 101 (2) ◽  
pp. 617-635 ◽  
Author(s):  
William M. Grove
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Data Structures ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Mixed Population ◽  
Single Population ◽  
Factor Analytic ◽  
Mixed Populations

Principal component analysis (PCA) and common factor analysis are often used to model latent data structures. Typically, such analyses assume a single population whose correlation or covariance matrix is modelled. However, data may sometimes be unwittingly sampled from mixed populations containing a taxon (nonarbitrary subpopulation) and its complement class. One derives relations between values of PCA parameters within subpopulations and their values in the mixed population. These results are then extended to factor analysis in mixed populations. As relationships between subpopulation and mixed-population principal components and factors sensitively depend on within-subpopulation structures and between-subpopulation differences, naive interpretation of PCA or factor analytic findings can potentially mislead. Several analyses, better suited to the dimensional analysis of admixture data structures, are presented and compared.

scholarly journals Pathologies of Between-Groups Principal Components Analysis in Geometric Morphometrics

2019 ◽  
Author(s):  
Fred L. Bookstein
Keyword(s):  
Geometric Morphometrics ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Null Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Wide Range ◽  
Pls Analysis ◽  
Components Analysis ◽  
First Time

AbstractGood empirical applications of geometric morphometrics (GMM) typically involve several times more variables than specimens, a situation the statistician refers to as “highp/n,” wherepis the count of variables andnthe count of specimens. This note calls your attention to two predictable catastrophic failures of one particular multivariate statistical technique, between-groups principal components analysis (bgPCA), in this high-p/nsetting. The more obvious pathology is this: when applied to the patternless (null) model ofpidentically distributed Gaussians over groups of the same size, both bgPCA and its algebraic equivalent, partial least squares (PLS) analysis against group, necessarily generate the appearance of huge equilateral group separations that are actually fictitious (absent from the statistical model). When specimen counts by group vary greatly or when any group includes fewer than about ten specimens, an even worse failure of the technique obtains: the smaller the group, the more likely a bgPCA is to fictitiously identify that group as the end-member of one of its derived axes. For these two reasons, when used in GMM and other high-p/nsettings the bgPCA method very often leads to invalid or insecure bioscientific inferences. This paper demonstrates and quantifies these and other pathological outcomes both for patternless models and for models with one or two valid factors, then offers suggestions for how GMM practitioners should protect themselves against the consequences for inference of these lamentably predictable misrepresentations. The bgPCA method should never be used unskeptically — it is never authoritative — and whenever it appears in partial support of any biological inference it must be accompanied by a wide range of diagnostic plots and other challenges, many of which are presented here for the first time.

scholarly journals Verification of the Unidimensionality of Academic Delay of Gratification Scale in the Indian Context

2019 ◽  
Vol 8 (5C) ◽  
pp. 1319-1324
Keyword(s):  
Factor Analysis ◽  
Undergraduate Students ◽  
Goodness Of Fit ◽  
Factor Model ◽  
Principal Component ◽  
Delay Of Gratification ◽  
Minority Community ◽  
Academic Delay ◽  
Variance Explained ◽  
Academic Delay Of Gratification

The present study verifies the three models on the dimensionality of the construct academic delay of gratification measured with the academic delay of gratification scale prepared by Bembenutty and Karabenick (1996). Sample of the study comprises of 488 professional courses undergraduate students of Muslim minority community (277 boys and 211 girls) from law, engineering, education and pharmacy faculties of Sultan Ul Uloom Education Society, Banjara Hills, Hyderabad, Telangana, India. Exploratory factor analysis was conducted on the responses of the 10 items provided by the sample using SPSS Statistics Ver.23 to extract the factors of the construct. Confirmatory factor analysis conducted using SPSS Amos Ver.23 provided the goodness of fit measures for each of the models. The unidimensional model produced excellent fit indices. Also, one factor model satisfied Gorsuch (1983) criterion to further verify the unidimensional nature of the construct, where the percentage of variance explained by factor 1 was nearly thrice when compared by the percentage of variance explained by the next subsequent factor 2. Monte Carlo principal component analysis method also revealed single factor for this variable. Implications of the findings are discussed.

scholarly journals Determination of genetic divergence in pointed gourd by principal component and non-hierarchical euclidean cluster analysis

2017 ◽  
Vol 9 (4) ◽  
pp. 2421-2426
Author(s):  
Priyanka Verma ◽  
S. K. Maurya ◽  
Hridesh Yadav ◽  
Ankit Panchbhaiya
Keyword(s):  
Cluster Analysis ◽  
Principal Components ◽  
Genetic Divergence ◽  
Principal Component ◽  
Female Flower ◽  
Research Centre ◽  
Cluster Number ◽  
Wide Range ◽  
Total Divergence ◽  
Pointed Gourd

The present investigation was carried out at Vegetable Research Centre, Pantnagar to estimate the ge-netic divergence using Mahalanobis D2 statistics for twelve characters on 35 genotypes of pointed gourd. Cluster analysis and principal component analysis (PCA) were used to identify the most discerning trait responsible for greater variability in the lines and on the basis of mean performance, genotypes were classified into different groups. Five principal components (PC) have been extracted using the mean performance of the genotypes and 83.23 per cent variation is yielded by the first five principal components, among them high mean positive value or higher weight age was obtained was obtained for days to first female flower anthesis and days to first fruit harvest among all the vectors, indicates that these traits are important component of genetic divergence in pointed gourd. Non- hierarchical Euclidean cluster analysis grouped the genotypes into seven clusters and the highest number of genotypes were found in cluster number IV i.e. eleven whereas maximum inter-cluster distance was found between the cluster III and VI i.e. 74.250, it indicates that a wide range of genetic divergence is present between the genotypes present among these two clusters. And as per contribution toward total divergence, traits like fruit yield per hectare and number of fruit per plant contributed 92.64% toward total divergence. The high diversity found in the genotypes showed its great potential for improving qualitative as well as quantitative traits in pointed gourd.

scholarly journals Assessment of the genetic components and trait associations in diverse set of fieldpea (Pisum sativum L.) genotypes

2015 ◽  
Vol 43 (3) ◽  
pp. 323-330 ◽  
Author(s):  
AK Parihar ◽  
GP Dixit ◽  
V Pathak ◽  
D Singh
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Components ◽  
Agronomic Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Genetic Components ◽  
Wide Range ◽  
Cluster Distance ◽  
Biological Yield

One hundred and 40 genotypes of fieldpea were used to assess the genetic divergence for various agronomic traits. The study revealed that all the accessions were significantly different for the traits and a wide range of variability exists for most of the traits. Correlation studies exhibited that seed yield had positive significant correlation with most of the traits. Cluster analysis classified 140 genotypes into 12 distinct groups. A large number of genotypes (30) were placed in cluster IV followed by cluster III with 24 genotypes. The maximum inter-cluster distance was observed between clusters III and IV indicating the possibility of high heterotic effect if the individuals from these clusters are cross-bred. Principal component analysis yielded 12 Eigen vectors and PCA analysis revealed significant variations among traits with seven major principal components explaining about 90% of variations. The estimates of Eigen value associated with the principal components and their respective relative and accumulated variances explained 50.16% of total variation in the first two components. The characters with highest weight in component first were biological yield, pods/plant, yield/plant and branches/plant which explained 34.22% of the total variance. The results of principal component analysis were closely in line with those of the cluster analysis. The grouping of genotypes and hybridization among genetically diverse genotypes of different cluster would be helpful in broadening the genetic base of fieldpea and producing desirable recombinants for developing new fieldpea varieties. DOI: http://dx.doi.org/10.3329/bjb.v43i3.21605 Bangladesh J. Bot. 43(3): 323-330, 2014 (December)

scholarly journals Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoming Xu ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Principal Components ◽  
Information Entropy ◽  
Principle Component Analysis ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Principle Component ◽  
Original Dataset ◽  
Diagnosis Method

In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

DEVELOPMENT OF A CHINESE VERSION OF THE ZUCKERMAN-KUHLMAN PERSONALITY QUESTIONNAIRE: RELIABILITIES AND GENDER/AGE EFFECTS

2000 ◽  
Vol 28 (3) ◽  
pp. 241-250 ◽  
Author(s):  
Yi -Xiao Wu ◽  
Wei Wang ◽  
Wu -Ying Du ◽  
Jing Li ◽  
Xiao-Fe n g Jiang ◽  
...  
Keyword(s):  
Sensation Seeking ◽  
Chinese Culture ◽  
Factor Model ◽  
Five Factor Model ◽  
Principal Component ◽  
The United States ◽  
Personality Questionnaire ◽  
Wide Range ◽  
Scale Scores ◽  
And Gender

A five-factor model of the Zuckerman-Kuhlman Personality Questionnaire (ZKPQ) was tried in a Chinese speaking area. Three hundred and thirty-three healthy subjects (217 women and 116 men) with a wide range of occupations attended this study and were divided into 5 age ranges. They were free of depression and answered with low dissimulation in ZKPQ. The principal component analysis detected 16 factors with eigenvalues larger than 1.5, the first 5 of which accounted for 21.0% of the variance. The five-factor solution analysis was, therefore, performed. The alpha internal reliabilities of the five personality scales ranged from 0.61 to 0.81. Sixty-one out of 89 items loaded larger than, or equal to, 0.3 on target factors. Scale scores were comparable to those reported in the United States, and the intercorrelations between five personality scales were lower. Gender and education level had little effect on the personality measures; the Impulsive Sensation Seeking declined with age only from 20 years on, in women. This study demonstrates the validity of the ZKPQ in Chinese culture.

scholarly journals 545 - Exploratory factor analysis of the Rowland Universal Dementia Assessment Scale.

2021 ◽  
Vol 33 (S1) ◽  
pp. 87-88
Author(s):  
J. Antonio Garcia-Casal ◽  
Natacha Coelho de Cunha Guimarães ◽  
Sofía Díaz Mosquera ◽  
María Alvarez Ariza ◽  
Raimundo Mateos Álvarez
Keyword(s):  
Factor Analysis ◽  
Construct Validity ◽  
Exploratory Factor Analysis ◽  
Internal Consistency ◽  
Principal Components ◽  
Principal Component ◽  
Assessment Scale ◽  
Cognitive Test ◽  
Five Factors ◽  
Bartlett’S Test

Background:Rowland Universal Dementia Assessment Scale (RUDAS) is a brief cognitive test, appropriate for people with minimum completed level of education and sensitive to multicultural contexts. It could be a good instrument for cognitive impairment (CI) screening in Primary Health Care (PHC). It comprises the following areas: recent memory, body orientation, praxis, executive functions and language.Research Objective:The objective of this study is to assess the construct validity of RUDAS analysing its internal consistency and factorial structure.Method:Internal consistency will be calculated using ordinal Cronbach’s α, which reflects the average inter-item correlation score and, as such, will increase when correlations between the items increase. Exploratory Factor Analysis will be used to arrange the variables in domains using principal components extraction. The factorial analysis will include the extraction of five factors reflecting the neuropsychological areas assessed by the test. The result will be rotated under Varimax procedure to ease interpretation.Exploratory factor analysis will be used to arrange the variables in domains using principal components extraction. The analysis will include Kaiser–Meyer–Olkin measure of sampling adequacy and Bartlett’s test of sphericity. Estimations will be based based on Pearson’s correlations between indicators using a principal component analysis and later replicated with a tetrachoric correlation matrix. The variance in the tetrachoric model will be analysed to indentify convergent iterations and their explicative power.Preliminary results of the ongoing study:RUDAS is being administered to 321 participants older than 65 years, from seven PHC physicians’ consultations in O Grove Health Center. The data collection will be finished by August 2021 and in this poster we will present the final results of the exploratory factor analysis.Conclusions:We expect that the results of the exploratory factor analysis will replicate the results of previous studies of construct validity of the test in which explanatory factor weights were between 0.57 and 0.82, and all were above 40%. Confirming that RUDAS has a strong factor construct with high factor weights and variance ratio, and 6-item model is appropriate for measurement will support its recommendation as a valid screening instrument for PHC.

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