scholarly journals Principal Components Analysis and Factorial Analysis to Measure Latent Variables in a Quantitative Research: A Mathematical Theoretical Approach

2013 ◽  
Vol 7 ◽  
pp. 3-12
Author(s):  
Arturo García-Santillán ◽  
Milka Escalera-Chávez ◽  
Francisco Venegas-Martínez
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Theoretical Approach ◽  
Latent Variables ◽  
Quantitative Research ◽  
Factorial Analysis ◽  
New Concepts ◽  
Components Analysis

The aim of this paper focuses on showing how the factorial analysis and principal components analysis are useful for measuring latent variables in a concise way and safely as a help to building for new concepts and theories.

Comparison of multivariate pattering methods in group/cluster identification regarding the science of educational research: Implicative Statistical Analysis vs. L’ Analysee Factorielle des Correspondances

2019 ◽  
Vol 6 (1) ◽  
pp. 238-245
Author(s):  
Sofia D Anastasiadou
Keyword(s):  
Statistical Analysis ◽  
Principal Components Analysis ◽  
Educational Research ◽  
Principal Components ◽  
Quantitative Research ◽  
Self Control ◽  
Clustering Methods ◽  
Technological Education ◽  
Cluster Identification ◽  
Components Analysis

Nowadays, there is a substantial improvement and utilisation of pattering methods in the science of educational research, a comparison of multivariate methods in group/cluster identification in the scientific domain of quantitative research has not been carried out. This study analyses two different statistical techniques: i.e., the principal components analysis (PCA) and the implicative statistical analysis (ASI). A survey was carried out using a structured questionnaire for a sample of 135 nurses which studied in the School of Pedagogical and Technological Education in order to be qualified in respect The study focuses on the presentation of the two main types of clustering methods, της ASI and L’ Analysee Factorielle des Correspondances (AFC). AFC’s results made it evident that Emotionality, Self-control, Sociability, General items of EI constructs are shaped attitudes and reveal the latent dimension of respondents psychological attributes related to EI conceptual constructs. Keywords: L’ Analysee Factorielle des Correspondances, principal components analysis, implicative statistical analysis, research.

scholarly journals Mapping Common Aphasia Assessments to Underlying Cognitive Processes and Their Neural Substrates

2017 ◽  
Vol 31 (5) ◽  
pp. 442-450 ◽  
Author(s):  
Elizabeth H. Lacey ◽  
Laura M. Skipper-Kallal ◽  
Shihui Xing ◽  
Mackenzie E. Fama ◽  
Peter E. Turkeltaub
Keyword(s):  
Principal Components Analysis ◽  
Language Processing ◽  
Principal Components ◽  
Latent Variables ◽  
Specific Pattern ◽  
Left Middle Cerebral Artery ◽  
Brain Anatomy ◽  
Language Functions ◽  
Components Analysis ◽  
Lesion Symptom Mapping

Background. Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments. Objective. To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests. Methods. Twenty-five behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high-resolution magnetic resonance image was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores. Results. The principal components analysis yielded 4 dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing. Conclusions. An extensive clinical aphasia assessment identifies 4 independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual’s specific pattern of deficits and preserved abilities.

scholarly journals Relationships between Association of Research Libraries (ARL) Statistics and Bibliometric Indicators: A Principal Components Analysis

2017 ◽  
Author(s):  
Dean Hendrix
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Quantitative Research ◽  
Bibliometric Indicator ◽  
Data Sets ◽  
Research Libraries ◽  
Association Of Research Libraries ◽  
Size Dependent ◽  
Dependent Variables ◽  
Components Analysis

This study analyzed 2005–2006 Web of Science bibliometric data from institutions belonging to the Association of Research Libraries (ARL) and corresponding ARL statistics to find any associations between indicators from the two data sets. Principal components analysis on 36 variables from 103 universities revealed obvious associations between size-dependent variables, such as institution size, gross totals of library measures, and gross totals of articles and citations. However, size-independent library measures did not associate positively or negatively with any bibliometric indicator. More quantitative research must be done to authentically assess academic libraries’ influence on research outcomes.

scholarly journals Component Structure of the Penn Inventory for Posttraumatic Stress Disorder (PTSD) Among Veterans Seeking Department of Veterans Affairs Disability Benefits: Concordance by Race/Ethnicity, Gender, Service Era, and Type of Military Exposure

2019 ◽  
Author(s):  
Karen Wahmanholm ◽  
Melissa A Polusny ◽  
Joseph Westermeyer ◽  
Maureen Murdoch
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Latent Variables ◽  
Survey Methods ◽  
Disability Benefits ◽  
Cross Sectional Survey ◽  
Component Structure ◽  
Penn Inventory ◽  
Race Ethnicity ◽  
Components Analysis

Background: Although a common neurobiological mechanism for PTSD has been proposed, contextual issues related to race/ethnicity, gender, other sociohistorical factors, and the type of trauma experienced could influence how PTSD presents in any given individual. Objective: To analyze and compare the patterns of correlations (the component structure) of Veterans’ responses to the Penn Inventory for PTSD according to their race/ethnicity, gender, service era (e.g., World War II v. Vietnam Conflict), and military trauma exposures (i.e., combat v. sexual assault). Subjects: 3,337 nationally representative Veterans who applied for PTSD disability benefits between 1994 and 1998 and returned a mailed questionnaire between 1998 and 2000. Study Design: Secondary analysis of a cross-sectional survey. Methods: Using Principal Components analysis, component scores and component comparability coefficients were calculated for each pre-defined subgroup. Results: In the overall sample and across all subgroups, Principal Components analysis consistently identified 2 main latent variables: “alienation and numbing” and “re-experiencing and sleep disturbance.” These two components explained 46.3% of the variance in subjects’ responses. For most planned comparisons, the component comparability coefficient met or exceeded 0.80 (all ps < 0.001), suggesting that the patterns of correlations across subgroups’ responses were highly similar. Hispanic Veterans were a notable exception: component comparability coefficients between them and the other race/ethnicity categories ranged from 0.29 to 0.70. Conclusion: Except for Hispanic Veterans, subjects’ response patterns to the Penn Inventory for PTSD did not vary substantively by race/ethnicity, gender, trauma exposure, or service era. Implications are discussed.

scholarly journals Relationships between Association of Research Libraries (ARL) Statistics and Bibliometric Indicators: A Principal Components Analysis

2010 ◽  
Vol 71 (1) ◽  
pp. 32-41
Author(s):  
Dean Hendrix
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Quantitative Research ◽  
Bibliometric Indicator ◽  
Data Sets ◽  
Research Libraries ◽  
Association Of Research Libraries ◽  
Size Dependent ◽  
Dependent Variables ◽  
Components Analysis

This study analyzed 2005–2006 Web of Science bibliometric data from institutions belonging to the Association of Research Libraries (ARL) and corresponding ARL statistics to find any associations between indicators from the two data sets. Principal components analysis on 36 variables from 103 universities revealed obvious associations between size-dependent variables, such as institution size, gross totals of library measures, and gross totals of articles and citations. However, size-independent library measures did not associate positively or negatively with any bibliometric indicator. More quantitative research must be done to authentically assess academic libraries’ influence on research outcomes.

scholarly journals Principal Components Analysis Utility in the Livestock Field

2016 ◽  
Vol 73 (2) ◽  
pp. 251
Author(s):  
Ancuta Simona Rotaru ◽  
Ioana Pop ◽  
Anamaria Vatca ◽  
Luisa Andronie
Keyword(s):  
Factor Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Latent Variables ◽  
Principal Component ◽  
The Other ◽  
Data Sets ◽  
Method Factor ◽  
Two Factors ◽  
Components Analysis

Principal Component Analysis is a method factor - factor analysis - and is used to reduce data complexity by replacingmassive data sets by smaller sets. It is also used to highlight the way in which the variables are correlated with eachother and to determining the (less)latent variableswhich are behind the (more)measured variables. These latent variables are called factors, hence the name of the methodi.e. factor analysis. Our paper shows the applicability of Principal Components Analysis (PCA) in livestock area of study by carrying out a researchon some physiological characteristics in the case of tencow breeds.By using PCA only two factors have been preserved, concentrating over 80% of their information from the four variables in question, one factor concentrating weight and height and the other factor concentrating trunk circumference and weight at calving, respectively.

scholarly journals Relative Principal Components Analysis: Application to Analyzing Biomolecular Conformational Changes

2018 ◽  
Author(s):  
Mazen Ahmad ◽  
Volkhard Helms ◽  
Olga V. Kalinina ◽  
Thomas Lengauer
Keyword(s):  
Principal Components Analysis ◽  
Conformational Changes ◽  
Principal Components ◽  
Latent Variables ◽  
Drug Molecules ◽  
Leibler Divergence ◽  
Microscopic Features ◽  
Macroscopic States ◽  
Analysis Application ◽  
Components Analysis

AbstractA new method termed “Relative Principal Components analysis” (RPCA) is introduced that extracts optimal relevant principal components to describe the change between two data samples representing two macroscopic states. The method is widely applicable in data-driven science. Calculating the components is based on a unified physical framework which introduces the objective function, namely the Kullback-Leibler divergence, appropriate for quantifying the change of the macroscopic state as it is effected by the microscopic features. To demonstrate the applicability of RPCA, we analyze the thermodynamically relevant conformational changes of the protein HIV-1 protease upon binding to different drug molecules. In this case, the RPCA method provides a sound thermodynamic foundation for the analysis of the binding process. The relevant collective (global) conformational changes can be reconstructed from the informative latent variables to exhibit both the enhanced and the restricted conformational fluctuations upon ligand association. Moreover, RPCA characterizes the locally relevant conformational changes which can be presented on the structure of the protein.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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