Principal Component Analysis, Factor Analysis, and Structural Equation Modeling

Groundwater is considered as one of the most important sources for water supply in Iran. The Fasa Plain in Fars Province, Southern Iran is one of the major areas of wheat production using groundwater for irrigation. A large population also uses local groundwater for drinking purposes. Therefore, in this study, this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis (CA), Discriminant Analysis (DA), and Principal Component Analysis (PCA). Water quality data was monitored at 22 different wells, for five years (2009-2014) with 10 water quality parameters. By using cluster analysis, the sampling wells were grouped into two clusters with distinct water qualities at different locations. The Lasso Discriminant Analysis (LDA) technique was used to assess the spatial variability of water quality. Based on the results, all of the variables except sodium absorption ratio (SAR) are effective in the LDA model with all variables affording 92.80% correct assignation to discriminate between the clusters from the primary 10 variables. Principal component (PC) analysis and factor analysis reduced the complex data matrix into two main components, accounting for more than 95.93% of the total variance. The first PC contained the parameters of TH, Ca2+, and Mg2+. Therefore, the first dominant factor was hardness. In the second PC, Cl-, SAR, and Na+ were the dominant parameters, which may indicate salinity. The originally acquired factors illustrate natural (existence of geological formations) and anthropogenic (improper disposal of domestic and agricultural wastes) factors which affect the groundwater quality.

Principal Component Analysis (PCA) and Factor Analysis (FA) are common multivariate techniques used for dimensionality reduction. With these techniques it is expected to identify actual number of dimensions while accounting almost all observed variability. Standard PCA is based either on correlation matrix (CORM) or covariance matrix (COVM). When it is based on CORM, scale dependency can be removed but inherent variability cannot be preserved. On the other hand, when PCA is based on COVM, inherent variability can be preserved but scale dependency cannot be removed. As a solution to this issue, this paper suggests scaling each indicator by its mean, resulting in new mean equal to 1 and standard deviation equal to the coefficient of variance (CV). This leads to PCs, which are scale independent while retaining the observed variability. The computation of PCs and factors under the suggested method is derived in the study. The procedure is illustrated using the lowest level administrative division census data of Western province of Sri Lanka.

We review a neural network implementation of the statistical technique of Principal Component Analysis (PCA) and Factor Analysis. We now derive a new method based on Kernel Principal Components Analysis (KPCA) and extend the Kernel PCA method to sparsified Kernel PCA. We then apply two methods to the data set which is composed of 10 faces in a mixture of poses. We wish to identify only the most significant poses on a data set. We found the better result from the sparsified Kernel PCA method.

Three-component seismic array data provide a multidimensional, spatially and temporally unaliased sampling of the seismic wavefield and the potential for a nearly comprehensive decomposition and characterization of the observed wavefield based on its frequency, wavenumber, and polarization characteristics. In this paper I outline a statistical array processing approach for three-component seismic array data that allows us to exploit and fully utilize the abundance of information provided by these unique data. My approach entails a succession of principal component analysis, factor analysis, and principal component analysis. The initial principal component analysis is conducted in an effort to separate the coherent and random components of the wavefield into orthogonal subspaces. The ensuing factor analysis allows us to isolate superimposed signals and estimate their frequency-wavenumber characteristics. The final principal component analysis provides estimates of the signals' polarization characteristics. This adaptive formulation provides high-resolution estimates of the signals' frequency, wavenumber, and polarization characteristics.

In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.

Multivariate methods and molecular properties are utilized to show similarity of pyridine-3-carboxylic acid (nicotinic acid) to seven drugs that penetrate the central nervous system. Multivariate methods applied include cluster analysis, discriminant analysis, correspondence analysis, self organizing tree algorithm (SOTA) analysis, factor analysis, and principal component analysis. Numerical values of properties for nicotinic acid showed very high correlation with the values from dihydropyridine, barbital, metharbital, phenobarbital, methohexital, 4-aminohex-5-enoic acid, and (4-chlorophenyl)(5-fluoro-2-hydroxyphenyl)methanone. Descriptive statistics of property values for these drugs showed overlapping numerical values for partition coefficients, index of refraction, and nOnN. Principal component analysis and factor analysis of molecular properties showed that nicotinic acid is highly similar to dihydropyridine as well as to other members of this group of drugs. Applying cluster analysis utilizing standard euclidean distance with single linkage and centroid linkage showed that nicotinic acid is highly similar to dihydropyridine and both are consistently grouped into the identical cluster (indicating high similarity). Both nicotinic acid and dihydropyridine show zero violations of the Rule of 5, which indicates good bioavailability characteristics. Discriminant analysis of molecular properties for these eight compounds could not demonstrate differentiation between nicotinic acid and dihydropyridine within the properties applied, this indicating high level of similarity. Neural cluster analysis of molecular properties showed that nicotinic acid and dihydropyridine are included into the same cluster (indicating very strong similarity). SOTA analysis also placed nicotinic acid and dihydropyridine into the same cluster unit. SOTA analysis of molecular properties indicates high similarity between nicotinic acid and dihydropyridine. Correspondence analysis was performed on the molecular properties and showed that there exists considerable association between dihydropyridine and nicotinic acid. Multiple regression analysis of these molecular properties produced a mathematical equation to predict the formula weight of similar compounds for use as drug carriers.

PurposeThough daycare centres are built to facilitate children's social and physical growth, divergent stakeholder perceptions yield conflicts while managing risks for each space due to the minimal guidelines in the Republic of Korea. Due to the unclearly defined knowledge and experience gap between representative stakeholders, teachers have difficulties in operating it with high satisfaction. This study therefore was conducted to systematically identify which parts of performance should be increased to preferentially increase end-user satisfaction in limited resources.Design/methodology/approachThis research conducted quantitative analysis gradually. Importance–performance analysis (IPA) grasped the perceptions of construction practitioners and end-users after identifying space composition and representative risks of daycare centres. Based on the factors influencing perception differences, principal component analysis and structural equation modelling (SEM) were conducted to determine which space improves the performance preferentially to increase overall end-user satisfaction.FindingsAs the utilisation of indoor spaces increases after COVID-19, improvement of indoor spaces except for indoor playroom is necessary, especially, advancement of the quality of teachers' rooms.Practical implicationsOutputs by reflecting the end-user experiences support to understand which spaces are managed from the limited resources.Originality/valueTheoretical framework for daycare centre was established in Korea where guidelines were insufficient, and differences in perception of each risk of stakeholders were identified. Moreover, gradual statistical analysis was set to construct end-user-focused centres by extracting where the performance of space should be improved in prior.

Construction Cost Index (CCI) is introduced to enhance cost forecasting in construction projects. However, the effectiveness and its application in the developing countries are limited. Therefore, a case study is conducted in Malaysia to investigate the effectiveness of CCI in developing country. In order to achieve the aim, the objectives of the study are to identify the CCI implementation factors, challenges of CCI that hindering its implementation and to justify its applicability in developing countries. Quantitative approach is selected for data collection. 1000 sets of questionnaires were distributed to the construction industry stakeholders. Principal Component Analysis (PCA) and Structural Equation Modelling (SEM) were employed to analyse the collected data. It is found that encouragement from local government plays a vital role in CCI concept. Meanwhile, the reliability and validity of CCI remain as the major CCI implementation challenges in the industry. From the SEM, a causal relationship framework of CCI implementation factors and CCI functions is developed. The framework is important to serve as an implementation guideline for the stakeholders. This study is significant to provide an insight overview of CCI to the stakeholders to enhance cost estimating efficiency.

The variability of the severity and regional distribution of pathological process in basal ganglia (BG) and brainstem-cerebellar systems results in clinical heterogeneity and represents the motor subtype of multiple system atrophy (MSA). This study aimed to quantify spatial patterns of multimodal MRI abnormalities in BG and stem-CB regions and define structural MRI findings that correlate with clinical characteristics. We simultaneously measured R2*, mean diffusivity (MD), and volume in the subcortical structures (BG, thalamus, brainstem-cerebellar regions) of 39 probable MSA and 22 control subjects. Principal component analysis (PCA) and structural equation modeling (SEM) were performed to show a model consisting of multiple inter-dependencies. Structural MRI alterations were found to be significantly interrelated within BG as well as brainstem-cerebellar regions in MSA patients. PCA extracted four factors: three factors reflected alterations in R2*, MD and volume of the BG region including the caudate nucleus, putamen, and pallidum, and the remaining one factor represented degenerative changes in MD and volume of stem-CB region. In SEM, a latent variable reflecting brainstem-cerebellar degeneration did not show a significant correlation with the other latent variables associated with BG degeneration. Putaminal MD values and a PCA-driven factor reflecting MD values in the BG showed a significant correlation with UPDRS and UMSARS scores. Multimodal structural MRI abnormalities in MSA appear to be segregated into BG and stem-CB-related factors that can be associated with the clinical phenotype and motor severity.

Assessment the changes of surface water quality in different times throughout one year is an important aspect for evaluating temporal variation of river pollution due to natural or anthropogenic input of point and non-point sources to water environment that is gradually deteriorating. In this paper, principal component analysis & factor analysis (PCA & FA) is conducted according to the average value of 16 monitoring data taken from Shaanxi Reach of the Jinghe River in three years and dividing it into dry season, normal-water season and wet season. And the most important water quality parameters in different times is selected according to the standard that the correlation coefficient value of water quality variable of rotated factor loaded matrix is larger than 85%. Analysis shows that the most important parameters are mainly salinity indexes of natural input in dry season, the main parameters in wet season are indexes of non-point source pollution by anthropogenic input (including nitrogen nutritional index and salinity index) and the index of upstream organic pollution (volatile phenol) which is carried by large amount of runoff. No obvious regularity in other water period. And sulfate is always the most important water quality parameter in Jinghe River, Shaanxi Province in different water period.

This paper focused on the characteristics of software companies and their workers in Shandong Province, and then we divided factors that affect knowledge sharing into two parts: enterprise environment factors (including: incentives, communication platform, learning mechanism) and employee personal factors (career planning, responsibility, trust mechanism). Based on theoretical analysis, we carried out a questionnaire survey on some software enterprises employees in Shandong Province. Then we used the principal component analysis and factor analysis as the statistical method to test the significance of factors and then obtained the main factors. Finally we gave some advice on knowledge sharing of software enterprises in Shandong Province.

The modeling technology of combination evaluation drops the imperfectness of information in complex system. However, the fuzzy combination evaluation method unifying the Fuzzy Theory and combination makes the evaluation of the complex system more realistic. Firstly, adopting the TOPSIS evaluation method, Principal Component Analysis and Factor Analysis, deliberating the data of the Chinese listed companies' financial statements in the first quarter of 2011, this paper conducts the operating performance on the comprehensive evaluation and rank, which is from the 53 listed companies whose main business is power system in the Shanghai and Shenzhen Stock Market. Secondly, the article proves the consistency of the three evaluation methods through the nonparametric Spearman correlation coefficient test. Finally, establishing and applying the Fuzzy Borda Combination Evaluation Method, the article processes the operating performance afresh. The result makes the comprehensive evaluation and rank of the 53 enterprises more scientific and rational.

OBJECTIVESThis study was conducted to establish profiles of socioeconomic characteristics, dietary intake, and health status among Korean older adults by employing 3 multivariate analysis techniques.METHODSData were obtained from 1,352 adults aged 65 years and older who participated in the 2019 Korea National Health and Nutrition Examination Survey. Principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA) were utilized for profiling, with data preprocessing undertaken to facilitate these approaches.RESULTSPCA, FA, and CA yielded similar results, reflecting the high common variance among the variables. PCA identified 4 components, accounting for 71.6% of the accumulated variance. FA revealed 5 factors, displaying a Kaiser-Meyer-Olkin value of 0.51 and explaining 74.3% of the total variance. Finally, CA grouped the participants into 4 clusters (R2=0.465). Both PCA and FA identified dietary intake (energy, protein, carbohydrate, etc.), social support from family (incorporating family structure, number of family numbers, and engagement in social eating), and health status (encompassing oral, physical, and subjective health) as key factors. CA classified Korean older adults into 4 distinct typologies, with significant differences observed in dietary intake, health status, and household income (p<0.01).CONCLUSIONSThe study utilized PCA, FA, and CA to analyze profiling domains and derive characteristics of older adults in Korea, followed by a comparison of the results. The variables defining the clusters in CA were consistent with those identified by PCA and FA.

In this paper an approach to design controllers based on principal components analysis (PCA) models is presented. Closed-loop control can be formulated and implemented within the reduced space defined by a PCA model. This PCA controller, results in an integral controller, which can be used as an inferential controller when a measurement of a primary variable is not available. The main contributions of the paper are: a) the incorporation of an adjustable gain on the classical PCA controller; b) the proposal of a set of tuning rules; c) the performance evaluation of this new controller, in nominal operation conditions and in faulty situations. Some experimental results, obtained with the three tank benchmark (European COSY project), are presented.

A measure assessing an individual's ability to assert the use of condoms was developed using, N= 248, heterosexually active college men and women. Both principal component analysis (PCA) and structural equation modeling (SEM) procedures were performed. External validity for the assertion for condom use measure was established by integrating the measure with the stages of change dimension from the transtheoretical model of behavior change. The transtheoretical model posits that both the cessation of high‐risk behaviors and the acquisition of health behaviors involve the progression through five stages of change: precontemplation, contemplation, preparation, action, and maintenance. The results indicated that individuals were further along in the stages of change for using condoms with a casual partner, as compared to a steady partner. The degree to which assertive condom use behavior was engaged in was related to an individual's stage of readiness for using condoms with the two types of partners. The utility of stage‐matched intervention strategies, as opposed to the action‐oriented approaches to modify high‐risk sexual behavior, is discussed.