Factor Analysis, Random Data, and Patterned Results

1981 ◽  
Vol 46 (2) ◽  
pp. 272-283 ◽  
Author(s):  
Robert K. Vierra ◽  
David L. Carlson
Keyword(s):  
Factor Analysis ◽  
Correlation Matrix ◽  
Statistical Significance ◽  
Principal Component ◽  
Multivariate Statistical Techniques ◽  
Random Data ◽  
Multivariate Statistical ◽  
Data Set ◽  
Bartlett’S Test ◽  
The Matrix

Multivariate statistical techniques such as factor analysis are capable of producing patterned results with most, if not all, data matrices. This paper demonstrates that patterned results are obtainable when principal component analysis is applied to a random data set. It is suggested that Bartlett's test for the statistical significance of a correlation matrix be employed in deciding whether a factor analysis of the matrix is justified.

scholarly journals Penerapan Kansei Engineering dalam Perbandingan Desain Aplikasi Mobile Marketplace di Indonesia

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Nucky Vilano ◽  
Setia Budi
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Mobile Application ◽  
Principal Component ◽  
Kansei Engineering ◽  
Multivariate Statistical ◽  
Christian University ◽  
Emotional Factors ◽  
Coefficient Corrélation ◽  
Major Factors

The company's application design is very important because it displays the company's image and to attract more users to purchase/utilize the application. This research applies Kansei Engineering Method to analyze the emotion or feelings of the user towards the design of a mobile application interface. Six Kansei Words and three specimens are utilised in this research, where Kansei words are selected from words related to user experience. The participants of this research consist of 54 students from Maranatha Christian University. Participants’ responses are studied using multivariate statistical analysis (e.g., Coefficient Correlation Analysis, Principal Component Analysis, and Factor Analysis). This study explores the emotional factors that occur in designing an application. This analysis shows that there are some major factors that greatly influence the design of a mobile application interface.

scholarly journals Oral Impact on Daily Performance (OIDP) scale for use in Sri Lankan Adolescents: a cross sectional modification and validation study

2019 ◽  
Author(s):  
Uttara Amilani ◽  
Prasanna Jayasekara ◽  
Irosha R Perera ◽  
Hannah E Carter ◽  
Sameera Senanayake ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Oral Health ◽  
Sri Lanka ◽  
Cultural Adaptation ◽  
Concurrent Validity ◽  
Principal Component ◽  
Sri Lankan ◽  
Oral Impact ◽  
Bartlett’S Test

Abstract Background Oral Health Related Quality of Life (OHRQoL) surveys play an important role in understanding subjective patient experiences in oral health care. The Oral Impact on Daily Performance (OIDP) scale is a validated OHRQoL tool that measures the impact and extent to which an individual’s daily activities may be compromised by their oral health. It is commonly used to facilitate oral health service planning. The aim of this study was to modify and validate a Sinhalese version of the OIDP for use in Sri Lankan adolescents. Methods The stage I involved cultural adaptation of the tool through translation and modification. After translation and cultural adaptation, the modified OIDP was tested on 220, 15-19 year secondary school students in the Gampaha district, Sri Lanka. The adolescents completed the modified OIDP scale along with questions on self-reported perceived oral health problems and treatment need which were used to assesses the concurrent validity of the modified OIDP scale. Stage II and III involved the exploring factor structure, validation and a reliability assessment. Factorability was assessed by inspection of correlation matrix and Kaiser-Meyer-Olkin and Bartlett's Test of Sphericity tests as a measure of sampling adequacy. An exploratory factor analysis was carried out using Principal Component Analysis method and factors were rotated using the oblimin method. Results 220 adolescents participated in factor analysis and validation studies. The most prevalent oral health impact related to chewing and enjoying foods, reported by 36.8% of respondents The Kaiser-Meyer-Olkin measure was 0.87 and Bartlett’s test of Sphericity was significant (p<0.001) Cronbach’s alpha was calculated as 0.88, indicating a high level of internal consistency. The principal component analysis produced two factors with Eigen values ranging from 1.12 to 4.40, explaining 70.0% of total variance. Concurrent validity was satisfactory as the OIDP score increased when the adolescents’ perceived oral health decreased.Conclusion This study showed that the modified OIDP scale is applicable for use among adolescents in Sri Lanka. It has promising psychometric properties but further research is required to use this tool in other cohorts.

scholarly journals Evaluation of the anthropogenic impact in Suat Ugurlu Dam lake using multivariate statistical techniques

2018 ◽  
Vol 20 (1) ◽  
pp. 161-168 ◽  
Keyword(s):  
Anthropogenic Impact ◽  
Sediment Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Cluster Number ◽  
Multivariate Statistical ◽  
Oxidation Reduction ◽  
Oxidation Reduction Potential ◽  
And Cluster Analysis

Sediments play an important role in the quality of aquatic ecosystems in the Dam Lake where they can either be a sink or a source of contaminants, depending on the management. This purpose of this study is to identify the sediment quality in order to find out the causes for the malodor and the eutrophication that is causing a bad scenario. Solutions for improving the dam are proposed. Multivariate statistical techniques, such as a principal component analysis (PCA) and cluster analysis (CA), were applied to the data regarding sediment quality in relation to anthropogenic impact in Suat Ugurlu Dam Lake. This data was generated during 2014-2015, with monitoring at four sites for 11 parameters. A PCA and CA were used in the study of the samples. The total variance of 84.1%, 74.3%, 87.4% and 91.5% suggest 4, 3, 3 and 4 principle components (PCs) in the four locations: LC1, LC2, LC3 and LC4, respectively. Also, a CA was applied to both the variables and the observations. Some variables and observations showed a high similarity based on the results of variables in the CA. Also, the similarity ratio of temperature-mercury (Hg) and oxidation reduction potential (ORP) was high and generally, the cluster number of variables was 5, according to the selected similarity level.

scholarly journals Micro-SME Characterization From Financial Statements

2012 ◽  
Vol 11 (13) ◽  
pp. 1507
Author(s):  
Guillermo Ceballos-Santamaria ◽  
Juan-Jose Villanueva-Alvaro ◽  
Jose Mondejar-Jimenez
Keyword(s):  
Factor Analysis ◽  
Statistical Analysis ◽  
Small Businesses ◽  
Financial Statements ◽  
Developed Countries ◽  
Financial Statement ◽  
Statistical Techniques ◽  
General Interest ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

In recent years, small businesses have created interest and research, because they represent the majority of the business fabric and account for over seventy per cent of jobs in developed countries. Governments of these countries share a general interest in knowing about Small and Medium-Sized Enterprises (SME). Based on this premise, the approach of this study is to characterize micro-SMEs in the province of Cuenca, Spain, by analysis of financial statements, specifically analyzing their structure in financial terms by use of univariate and multivariate statistical techniques allowing this kind of business in the province of Cuenca to be identified. The information used comes from the databases of SABI (Iberian Financial Statement Analysis Systems), DIRCE (Central Business Directory and CamerData, the database of the Chambers of Commerce. The statistical analysis is centered on a classic modal of exploratory factor analysis, and finally the main results arising from the study are presented.

scholarly journals Study of groundwater contamination and drinking suitability in basaltic terrain of Maharashtra, India through PIG and multivariate statistical techniques

2020 ◽  
Vol 69 (4) ◽  
pp. 398-414 ◽  
Author(s):  
Vasant Wagh ◽  
Shrikant Mukate ◽  
Aniket Muley ◽  
Ajaykumar Kadam ◽  
Dipak Panaskar ◽  
...  
Keyword(s):  
Major Ions ◽  
Principal Component ◽  
World Health ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Post Monsoon ◽  
Groundwater Samples

Abstract The integration of pollution index of groundwater (PIG), multivariate statistical techniques including correlation matrix (CM), principal component analysis (PCA), cluster analysis (CA) and various ionic plots was applied to elucidate the influence of natural and anthropogenic inputs on groundwater chemistry and quality of the Kadava river basin. A total of 80 groundwater samples were collected and analysed for major ions during pre- and post-monsoon seasons of 2012. Analytical results inferred that Ca, Mg, Cl, SO4 and NO3 surpass the desirable limit (DL) and permissible limit (PL) of Bureau of Indian Standards (BIS) and the World Health Organization (WHO) in both the seasons. The elevated content of total dissolved solids (TDS), Cl, SO4, Mg, Na and NO3 is influenced by precipitation and agricultural dominance. PIG results inferred that 52.5 and 35%, 30 and 37.5%, 12.5 and 20%, 2.5 and 5% groundwater samples fall in insignificant, low, moderate and high pollution category (PC) in pre- and post-monsoon seasons, respectively. PC 1 confirms salinity controlled process due to high inputs of TDS, Ca, Mg, Na, Cl and SO4. Also, PC 2 suggests alkalinity influence by pH, CO3, HCO3 and F content. PIG and statistical techniques help to interpret the water quality data in an easier way.

scholarly journals Creating and Validating the DESEA Questionnaire for Men and Women

2020 ◽  
Vol 9 (7) ◽  
pp. 2301
Author(s):  
Francisco Cabello-Santamaría ◽  
Marina A. Cabello-García ◽  
Jerónimo Aragón-Vela ◽  
F. Javier del Río
Keyword(s):  
Factor Analysis ◽  
Psychometric Properties ◽  
Sexual Desire ◽  
Spanish Speakers ◽  
Interpersonal Stress ◽  
Bartlett’S Test ◽  
Confirmatory Factor ◽  
The Matrix ◽  
Valid Instrument ◽  
Group A

In clinical practice, it is essential to be able to identify hypoactive sexual desire disorder (HSDD), with its different severity levels and assess the influence the subject’s relationship has on the issue. In order to do this, questionnaires are needed that comprise appropriate psychometric properties. We analyzed the psychometric properties and factorial structure of the Sexual Desire and Aversion (DESEA) questionnaire that evaluates sexual desire and interpersonal stress (relationship problems) in male and female couples. A pilot study was conducted with a group of 1583 people. Finally, it included 20,424 Spanish speakers who answered the questionnaire via an online link. The requirements for factor analysis were verified followed by the exploratory and confirmatory factor analysis. The Cronbach’s alpha coefficient calculated the reliability of the test scores at 0.834 in the pilot group and 0.889 in the final group. A 3-factor factorial design explains the 62.08% variance. The KMO (Kaiser-Meyer-Olkin) test (p = 0.904), Bartlett’s test of sphericity (126,115.3; p = 0.000010) and the matrix determinant (0.0020770) verified the appropriateness of the factor analysis. The results show that the DESEA questionnaire is a reliable and valid instrument for evaluating desire and interpersonal stress, both in women and men, in clinical and research contexts.

Environmental and taxonomic variation in fingernail clam (Bivalvia: Pisidiidae) shell morphology

1983 ◽  
Vol 61 (12) ◽  
pp. 2781-2788 ◽  
Author(s):  
R. C. Bailey ◽  
E. H. Anthony ◽  
G. L. Mackie
Keyword(s):  
Correlation Matrix ◽  
Shell Morphology ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Morphometric Variation ◽  
Running Water ◽  
Components Analysis ◽  
Generic Groups ◽  
Fingernail Clams

Variation in the shell morphology of Sphaerium and Musculium fingernail clams was examined using multivariate statistical techniques. On the basis of shell measurements alone, clams from either genera which were collected in running-water habitats could be distinguished, with over 90% accuracy, from clams inhabiting standing water. The discrimination between the two groups was mainly due to the greater size and thickness of shells from clams living in running water. The pisidiid genera Sphaerium and Musculium were also morphologically distinguishable, mainly by size. Morphometric classification of these groups also resulted in over a 90% success rate. The morphometric variation within each of the above groups was further compared using a principal components analysis of each group's morphometric correlation matrix. This analysis revealed differences in growth-related changes in form between the pairs of habitat and generic groups studied. The techniques used to compare ecologically or taxonomically distinct shells appear to be promising for use in either biological monitoring or habitat selection studies.

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

Principal Component Analyses of Potential Repository Groundwaters

1999 ◽  
Vol 556 ◽  
Author(s):  
H. Sasamoto ◽  
P. Salter ◽  
M. Apted ◽  
M. Yuiv
Keyword(s):  
Radioactive Waste ◽  
Principal Component ◽  
Chemical Components ◽  
Compacted Clay ◽  
Multivariate Statistical ◽  
Data Set ◽  
High Level Radioactive Waste ◽  
Scenario Analyses ◽  
Statistical Analysis Technique ◽  
High Level

AbstractThe chemical composition of ambient groundwater for a geological, high level radioactive waste repository is of crucial significance to issues such as radioelement solubility limits, sorption, corrosion of the overpack, behavior of compacted clay buffers, and many other factors involved in repository safety assessment. At this time, there are no candidate repository sites established in Japan for the geological disposal of high-level radioactive waste, and only generic rock formations are under consideration. It is important that a small, but representative set of groundwater types be identified so that defensible models and data for generic repository performance assessment can be established. Over 15,000 separate analyses of Japanese groundwaters have been compiled into a data set for the purpose of evaluating the range of geochemical conditions for waste repositories in Japan. This paper demonstrates the use of a multivariate statistical analysis technique, principal component analysis (PCA), to derive a set of statistically based, representative groundwater categories from the multiple chemical components and temperature that characterize the deep Japanese groundwater analyses. PCA also can be used to guide the selection of groundwaters that could be used in scenario analyses of future geological events in Japan.

scholarly journals Association Plots: Visualizing associations in high-dimensional correspondence analysis biplots

2020 ◽  
Author(s):  
Elzbieta Gralinska ◽  
Martin Vingron
Keyword(s):  
Correspondence Analysis ◽  
Principal Component ◽  
Projection Methods ◽  
Three Dimensions ◽  
Data Matrix ◽  
Small Data ◽  
Complex Data ◽  
Science Data ◽  
Data Set ◽  
The Matrix

SummaryIn molecular biology, just as in many other fields of science, data often come in the form of matrices or contingency tables with many measurements (rows) for a set of variables (columns). While projection methods like Principal Component Analysis or Correspondence Analysis can be applied for obtaining an overview of such data, in cases where the matrix is very large the associated loss of information upon projection into two or three dimensions may be dramatic. However, when the set of variables can be grouped into clusters, this opens up a new angle on the data. We focus on the question which measurements are associated to a cluster and distinguish it from other clusters. Correspondence Analysis employs a geometry geared towards answering this question. We exploit this feature in order to introduce Association Plots for visualizing cluster-specific measurements in complex data. Association Plots are two-dimensional, independent of the size of data matrix or cluster, and depict the measurements associated to a cluster of variables. We demonstrate our method first on a small data set and then on a genomic example comprising more than 10,000 conditions. We will show that Association Plots can clearly highlight those measurements which characterize a cluster of variables.

Sign in / Sign up

Export Citation Format

Share Document