Factor Structure of the College Adjustment Scales

2000 ◽  
Vol 86 (1) ◽  
pp. 79-84 ◽  
Author(s):  
Michael H. Campbell ◽  
Shawn T. Prichard
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
College Adjustment ◽  
Correlation Matrix ◽  
College Counseling ◽  
Demographic Characteristics ◽  
Underlying Structure ◽  
Counseling Centers ◽  
Components Analysis ◽  
College Counseling Centers

The present study examined the underlying structure of the College Adjustment Scales via principal components analysis. A correlation matrix of the nine subscales showed significant multicolinearity. A subsequent principal components analysis demonstrated that one factor accounted for 57% of the total variance and that the majority of subscales were moderately correlated with this single factor. The results suggest that the College Adjustment Scales may measure the same underlying construct and that the clinical distinctions implied by subscale scores should be regarded with caution. Conclusions are constrained by sample size and demographic characteristics, but the results suggest the need for further empirical validation of the College Adjustment Scales, which may be useful in college counseling centers.

Anhedonia and Alexithymia: Distinct or Overlapping Constructs

1997 ◽  
Vol 84 (2) ◽  
pp. 415-425 ◽  
Author(s):  
Gwenolé Loas ◽  
Didier Fremaux ◽  
Patrice Boyer
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Correlation Matrix ◽  
Healthy Subjects ◽  
Factor Solution ◽  
Toronto Alexithymia Scale ◽  
Factor Loadings ◽  
Components Analysis ◽  
Physical Anhedonia ◽  
The Relationship

The aim was to examine the relationship between alexithymia, anhedonia, and capacity for displeasure in a group of 133 healthy subjects using principal components analysis. A correlation matrix comprised of items from both the Communication and Identification scale of the 20-item Toronto Alexithymia Scale and the Physical Pleasure-Displeasure Scale yielded a four-factor solution (one Communication-Identification, two Pleasure, and one Displeasure factor) with no overlap of the significant factor loadings for the items from each scale. Moreover, there were no positive significant correlations between the Communication and Identification Scales and the Physical Anhedonia Scale. Our findings support the view that physical anhedonia is a construct distinct and separate from alexithymia.

A FAMILY OF MODELS EXPLAINING THE LEVEL-SLOPE-CURVATURE EFFECT

2003 ◽  
Vol 06 (03) ◽  
pp. 239-255 ◽  
Author(s):  
LILIANA FORZANI ◽  
CARLOS TOLMASKY
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Correlation Matrix ◽  
Yield Curve ◽  
Correlation Structure ◽  
Spectral Structure ◽  
Curvature Effect ◽  
Correlation Matrices ◽  
The Us ◽  
Components Analysis

One of the most widely used methods to build yield curve models is to use principal components analysis on the correlation matrix of the innovations. R. Litterman and J. Scheinkman found that three factors are enough to explain most of the moves in the case of the US treasury curve. These factors are level, steepness and curvature. Working in the context of commodity futures, G. Cortazar and E. Schwartz found that the spectral structure of the correlation matrices is strikingly similar to those found by R. Litterman and J. Scheinkman. We observe that in both cases the correlation between two different contracts maturing at times t and s is roughly of the form ρ|t-s|, for a certain (fixed) 0 ≤ ρ ≤ 1. Assuming this correlation structure we prove that the observed factors are perturbations of cosine waves and we extend the analysis to multiple curves.

Reanalysis of Halstead's Biological Intelligence Factor Matrix

1973 ◽  
Vol 37 (3) ◽  
pp. 699-705 ◽  
Author(s):  
Elbert W. Russell
Keyword(s):  
Factor Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Correlation Matrix ◽  
Factor Matrix ◽  
Perceptual Factor ◽  
Visual Threshold ◽  
Components Analysis

A recent study by Russell did not duplicate Halstead's factors of biological intelligence. As an approach to understanding this finding, Hal-stead's original correlation matrix was subjected to the same orthogonal principal components analysis used in Russell's study as well as an orthogonal and an oblique factor analysis using communalities. All of Halstead's factors appeared in these analyses. The failure to duplicate Halstead's work was evidently not due to use of different factoring methods. In a second analysis which reduced the number of Halstead's variables to the number used by Russell, one of Halstead's factors (P) did not appear. This factor represented tests measuring the visual threshold, and so it appears to be primarily a perceptual factor.

Non-Graphical Solutions for Cattell’s Scree Test

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Gilles Raîche ◽  
Theodore A. Walls ◽  
David Magis ◽  
Martin Riopel ◽  
Jean-Guy Blais
Keyword(s):  
Factor Analysis ◽  
Principal Components Analysis ◽  
Simulation Study ◽  
Principal Components ◽  
Correlation Matrix ◽  
Numerical Solutions ◽  
Number Of Components ◽  
Components Analysis ◽  
Low Dimensional ◽  
Scree Test

Most of the strategies that have been proposed to determine the number of components that account for the most variation in a principal components analysis of a correlation matrix rely on the analysis of the eigenvalues and on numerical solutions. The Cattell’s scree test is a graphical strategy with a nonnumerical solution to determine the number of components to retain. Like Kaiser’s rule, this test is one of the most frequently used strategies for determining the number of components to retain. However, the graphical nature of the scree test does not definitively establish the number of components to retain. To circumvent this issue, some numerical solutions are proposed, one in the spirit of Cattell’s work and dealing with the scree part of the eigenvalues plot, and one focusing on the elbow part of this plot. A simulation study compares the efficiency of these solutions to those of other previously proposed methods. Extensions to factor analysis are possible and may be particularly useful with many low-dimensional components.

Estimating the Strength of a General Factor: Coefficient Omega Hierarchical

2015 ◽  
Vol 8 (3) ◽  
pp. 434-438 ◽  
Author(s):  
Gilles E. Gignac
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Correlation Matrix ◽  
Principal Component ◽  
Accurate Solution ◽  
General Factor ◽  
Verbal Comprehension ◽  
Components Analysis ◽  
Coefficient Omega ◽  
Factor Coefficient

Relying on work described by Jackson (2003), Ree, Carretta, and Teachout (2015) recommended researchers use the first unrotated principal component associated with a principal components analysis (PCA) to estimate the strength of a general factor. Arguably, such a recommendation is based on rather old work. Furthermore, it is not a method that can be relied on to yield an accurate solution. For example, it is well known that the first component extracted from a correlation matrix of the Wechsler intelligence subtests is biased toward the verbal comprehension subtests (Ashton, Lee, & Vernon, 2001).

Relationship Aspects of Hypnotic Susceptibility

1980 ◽  
Vol 51 (3_suppl2) ◽  
pp. 1032-1034 ◽  
Author(s):  
David P. Fourie
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Correlation Matrix ◽  
Point Of View ◽  
Rater Agreement ◽  
Hypnotic Susceptibility ◽  
The Subject ◽  
Components Analysis ◽  
The Relationship

If hypnosis is seen from an interpersonal point of view it could be hypothesized that aspects of the relationship existing between the hypnotist and the subject prior to hypnosis would probably be related to the subject's susceptibility to the hypnosis. A study was conducted to test this hypothesis. 19 volunteer subjects were individually tested for hypnotic susceptibility using the Stanford Hypnotic Susceptibility Scales, Form A. In each case this was preceded by a short tape-recorded non-directive interview. Four independent raters rated the interviews on 10 behavioral dimensions plus general “hypnotizability,” using seven-point scales. Although the coefficients of inter-rater agreement were generally low, the signs of the correlations between mean ratings and Stanford scores were as predicted in all 11 cases. Three of the correlations were significant. From a principal components analysis on the inter-correlation matrix, three factors emerged of which one was labelled Hypnotic Susceptibility.

Factorial Study of Achievement-Related Motives

1977 ◽  
Vol 41 (3) ◽  
pp. 795-801 ◽  
Author(s):  
Hirotsugu Yamauchi ◽  
Kiyoharu Doi
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Correlation Matrix ◽  
Psychological Aspects ◽  
Components Analysis

The purpose of this investigation was to find out the factorial dimensions among achievement-related motives. 77 subjects were administered 11 tests which measured achievement-related motives. Four factors were extracted by the principal components analysis from the correlation matrix and the factors were rotated by a normalized varimax criterion. The findings suggested that achievement-related motivation is not a unitary construct. There were three psychological aspects as follows: (1) the motive to achieve (Ms), (2) the motive to avoid failure (Maf), and (3) the personality component of the resultant achievement-oriented tendency (Ms—Maf).

scholarly journals Principal Components Analysis (PCA) of Monument Stone Decay by Rainwater: A Case Study of “Basílica da Estrela” Church, Portugal

Proceedings ◽  
2018 ◽  
Vol 2 (10) ◽  
pp. 560
Author(s):  
Carlos Figueiredo ◽  
Carlos Alves
Keyword(s):  
Electrical Conductivity ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Ionic Species ◽  
Underlying Structure ◽  
Stone Decay ◽  
Extended Version ◽  
Stepwise Approach ◽  
Components Analysis

An extended version of Principal Components Analysis (PCA) of monument stone decay phenomena occurring at “Basilica da Estrela” church, Lisbon, Portugal, is now presented. The PCA rationale and general methodological procedure is presented, as a first step of a stepwise approach to the eigenvector methods of data analysis. PCA, as others “Eigenvector Methods”, seeks to reveal the underlying structure that might exist within a set of multivariate observations. Temperature, pH, electrical conductivity and main ionic species were measured on several seepage samples over three years inside the monument. PCA results are discussed in the perspective of a nondestructive tool.

Analyzing Inspection Results of Port State Control by Using PCA

2014 ◽  
Vol 686 ◽  
pp. 730-735
Author(s):  
Lian Feng Zhang ◽  
Long Hui Gang ◽  
Zheng Jiang Liu
Keyword(s):  
Data Processing ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Correlation Matrix ◽  
Safety Management ◽  
State Control ◽  
Analysis Method ◽  
Shipping Company ◽  
Components Analysis ◽  
Set Up

The paper applied Principal Components Analysis Method to analyze the PSC inspection results in the area of T-MOU and P-MOU. Set up the assessment of ship detention, the ships' main deficiencies of detentions were found out by the standardization of data processing and correlation matrix calculating. Provide the basis for shipping company to master the safety management focus and pass the PSC inspection.

ON THE SPECTRAL DECOMPOSITION OF EMPIRICAL CORRELATION MATRICES

2001 ◽  
Vol 10 (08) ◽  
pp. 1201-1213 ◽  
Author(s):  
LILIANA FORZANI ◽  
CARLOS F. TOLMASKY
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Spectral Decomposition ◽  
Correlation Matrix ◽  
Yield Curve ◽  
Empirical Correlation ◽  
Spectral Structure ◽  
Correlation Matrices ◽  
The Us ◽  
Components Analysis

One of the most widely used methods to build yield curve models is to use principal components analysis on the correlation matrix of the innovations. R. Litterman and J. Scheinkman found that three factors are enough to explain most of the moves in the case of the US treasury curve. These factors are level, steepness and curvature. Working in the context of commodity futures, G. Cortazar and E. Schwartz found that the spectral structure of the correlation matrices is strikingly similar to those found by R. Litterman and J. Scheinkman. We observe that in both cases the correlation between two different contracts maturing at times t and s is roughly of the form ρ|t-s|, for a certain (fixed) 0≤ρ≤1. Assuming this correlation structure we prove that the observed factors are perturbations of cosine waves.

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