AUTHORITARIANISM REVISITED: EVIDENCE FOR AN AGGRESSION FACTOR

A principal components factor analysis was performed on the scores of 245 undergraduates to a short version of the F Scale and measures of prejudice, attitude toward welfare, toleration of political deviance, punitiveness toward criminals, and support of the Vietnam War. The analysis produced two factors. One was an authoritarian aggression factor which was consistent with the Berkeley levels of personality approach and previous factorial research. All of the measures except the welfare items had substantial loadings on this factor. The second was an attitude toward welfare factor which appeared to be unrelated to authoritarianism.

Download Full-text

Determining the Hierarchical Structure of a Multidimensional Body of Information

Perceptual and Motor Skills ◽

10.2466/pms.1971.32.3.735 ◽

1971 ◽

Vol 32 (3) ◽

pp. 735-746 ◽

Cited By ~ 3

Author(s):

Albert Zavala

Keyword(s):

Factor Analysis ◽

Hierarchical Structure ◽

Principal Components ◽

Principal Axis ◽

Multidimensional Measurement ◽

Graphic Form ◽

Two Factors ◽

The Hierarchical Structure ◽

Largest Eigenvalues

A method for extracting the hierarchical structure of a body of information is described. The method uses factor analysis to extract the principal components of a multidimensional measurement space. The principal axis solution of the factor analysis is rotated, starting with the two factors having the largest eigenvalues, and in successive rotations, adding facors with the highest eigenvalue from the remaining unrotated factors. The results are then presented in graphic form. The method is called rotation to hierarchical structure (Rotohist) and uses a single computer run for the analysis.

Download Full-text

Factor Structure, Reliability, and Validity of the Death Attitude Profile-Revised

OMEGA - Journal of Death and Dying ◽

10.2190/xff0-c6ua-58pc-phpb ◽

2000 ◽

Vol 40 (3) ◽

pp. 453-463 ◽

Cited By ~ 13

Author(s):

Richard Clements ◽

Linda A. Rooda

Keyword(s):

Factor Analysis ◽

Factor Structure ◽

Internal Consistency ◽

Principal Components ◽

Concurrent Validity ◽

Reliability And Validity ◽

Fear Of Death ◽

Hospice Nurses ◽

Structure Reliability ◽

Two Factors

The Present Study Examined The Factor Structure, reliability, and validity of the Death Attitude Profile-Revised (DAP-R; Wong, Reker, & Gesser, 1994) using a sample of 403 hospital and hospice nurses. A principal-components factor analysis of the DAP-R indicated that the DAP-R may consist of six factors instead of the five originally reported by Wong et al. The first four factors reported by Wong et al., which correspond to the subscales that they labeled Fear of Death, Death Avoidance, Approach Acceptance, and Escape Acceptance, were replicated in the present study, and these subscales were found to have acceptable levels of internal consistency and to possess some degree of concurrent validity. However, the items which loaded on the fifth factor in Wong et al.‘s study (their “Neutral Acceptance” subscale) were split across two factors in the present study, suggesting that this subscale may not be measuring a unitary construct.

Download Full-text

Dimensions of Rosenburg's Self-Esteem Scale

Psychological Reports ◽

10.2466/pr0.1976.38.2.583 ◽

1976 ◽

Vol 38 (2) ◽

pp. 583-584 ◽

Cited By ~ 65

Author(s):

Wayne E. Hensley ◽

Mary K. Roberts

Keyword(s):

Factor Analysis ◽

Principal Components ◽

Analysis Data ◽

Self Esteem ◽

Factor Solution ◽

Response Set ◽

Two Factors

The dimensions of Rosenburg's scale of self-esteem (1965) were investigated via principal components and oblique factor analysis. Data from 479 students in a basic speech course yielded a two-factor solution. As the two factors appeared to identify only a response set, it was concluded the scale was unidimensional.

Download Full-text

Principal Components Analysis Utility in the Livestock Field

Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca Animal Science and Biotechnologies ◽

10.15835/buasvmcn-asb:12231 ◽

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.

Download Full-text

A Further Validation of the Children's Behavioral Classification Project Inventory

Psychological Reports ◽

10.2466/pr0.1997.81.1.259 ◽

1997 ◽

Vol 81 (1) ◽

pp. 259-271

Author(s):

Ralph Mason Dreger

Keyword(s):

Factor Analysis ◽

Confirmatory Factor Analysis ◽

Phase I ◽

Principal Components ◽

Two Samples ◽

Confirmatory Factor ◽

Two Factors ◽

Behavioral Classification ◽

Components Analysis ◽

Classification Project

To add evidence of the validity of the Children's Behavioral Classification Inventory, the responses of parents and teachers were compared on a set of 110 items of the inventory which describe behaviors equally observable to both groups. The two sets of data were subjected in Phase I to principal components analysis to extract two factors in each case which were compared by means of the Ahmavaara transformation and found to be highly comparable. In Phase II, predictions were made from factor weights of 36 items measuring six factors in the original standardization of the inventory to both sets of respondents' data by means of confirmatory factor analysis. The formal indices of comparability were not generally in acceptable ranges; however, substantively the six factors were definitely replicated in the two samples.

Download Full-text