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 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.

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 Principal Components Analysis of Multispectral Image Data

2004 ◽  
Vol 12 (5) ◽  
pp. 36-39 ◽  
Author(s):  
Brent Neal ◽  
John C. Russ
Keyword(s):  
Principal Components Analysis ◽  
20Th Century ◽  
Principal Components ◽  
Principal Axis ◽  
Image Data ◽  
Data Sets ◽  
Principal Axes ◽  
Source Data ◽  
Components Analysis ◽  
The One

Principal components analysis of multivariate data sets is a standard statistical method that was developed in the early halt or the 20th century. It provides researchers with a method for transforming their source data axes into a set of orthogonal principal axes and ranks. The rank for each axis in the principal set represents the significance of that axis as defined by the variance in the data along that axis. Thus, the first principal axis is the one with the greatest amount of scatter in the data and consequently the greatest amount of contrast and information, while the last principal axis represents the least amount of information.

scholarly journals Morphological Differentiation of the Genus Niphargus (Amphipoda) on Corsica — A Numerical Approach

1984 ◽  
Vol 54 (1) ◽  
pp. 147-155
Author(s):  
W. Hovenkamp ◽  
F. Hovenkamp ◽  
J.J. van der Heide
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Morphological Differentiation ◽  
Taxonomic Status ◽  
Numerical Approach ◽  
Short Introduction ◽  
Size Dependent ◽  
Methods Of Analysis ◽  
Components Analysis

A short introduction is provided on the taxonomic status of the genus Niphargus, especially on the species related to N. longicaudatus corsicanus. Previous findings and descriptions are mentioned. An attempt is made to clarify the relationships between Corsican Niphargus populations by means of a cluster analysis and a principal components analysis combined with a cluster analysis. Special attention has been paid to the size-dependent variability of most of the characters. The results of both methods of analysis are compared with each other and evaluated. The morphological differentiation between populations is, on the average, greater than within populations. This, along with the large amount of character variability, makes it very difficult to fit populations into, or to distinguish them from, any of the — often poorly described — taxa of Niphargus.

Attribute Requirements for a Simulated Flight Scenario Microcomputer Test

1982 ◽  
Vol 26 (11) ◽  
pp. 959-963 ◽  
Author(s):  
R. H. Shannon ◽  
M. Krause ◽  
R. C. Irons
Keyword(s):  
Construct Validity ◽  
Principal Components Analysis ◽  
Motor Skills ◽  
Principal Components ◽  
Video Game ◽  
Factor Solution ◽  
Independent Variables ◽  
Dependent Variables ◽  
Components Analysis ◽  
Tool Position

Eighteen subjects practiced a video game of bombing and air combat maneuvering, Phantoms Five®, on an APPLE® microcomputer for 10 minutes a day for 15 days. The dependent variable was the combined score for number of hits and number of targets. Performance stabilized from Days 8–15 with a pooled reliability of .904. Eight reference tests which theoretically measure cognitive, perceptual, quantitative, and motor skills were selected and used as independent variables. Stabilized performance on these tests was observed after a period of practice which was predetermined from previous experimentation. Attributes of the Phantoms Five® were isolated using a structured job analytic tool (Position Analysis Questionnaire, PAQ). A principal components analysis of the measures that correlated with the dependent variable resulted in a one factor solution explaining 66 percent of the variance. It was concluded that construct validity was established since there was a strong similarity between the attribute requirements attained by correlating the stabilized scores of independent and dependent variables and by the PAQ analysis of task functions.

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.

scholarly journals Haplotype Classification Using Copy Number Variation and Principal Components Analysis

2013 ◽  
Vol 7 (1) ◽  
pp. 19-24
Author(s):  
Kevin Blighe
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Large Scale ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Reduction Techniques ◽  
Number Variation ◽  
Components Analysis

Elaborate downstream methods are required to analyze large microarray data-sets. At times, where the end goal is to look for relationships between (or patterns within) different subgroups or even just individual samples, large data-sets must first be filtered using statistical thresholds in order to reduce their overall volume. As an example, in anthropological microarray studies, such ‘dimension reduction’ techniques are essential to elucidate any links between polymorphisms and phenotypes for given populations. In such large data-sets, a subset can first be taken to represent the larger data-set. For example, polling results taken during elections are used to infer the opinions of the population at large. However, what is the best and easiest method of capturing a sub-set of variation in a data-set that can represent the overall portrait of variation? In this article, principal components analysis (PCA) is discussed in detail, including its history, the mathematics behind the process, and in which ways it can be applied to modern large-scale biological datasets. New methods of analysis using PCA are also suggested, with tentative results outlined.

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