Associations among the Subtests: A Short-Form CART

Principal Components ◽

Scientific Reasoning ◽

Large Scale ◽

Short Form ◽

Statistical Reasoning ◽

Rational Thinking ◽

The Core ◽

Chapter 12 describes a large-scale study of the short-form version of the CART. The short-form is composed of 11 of the 20 subtests and can be completed in less than two hours by most subjects. The short-form CART includes both the Probabilistic and Statistical Reasoning and the Scientific Reasoning subtests, as both are at the core of most definitions of rational thinking. All four subtests that directly tap the avoidance of miserly processing are included in the short form. The Probabilistic Numeracy subtest is included in the short-form CART because it is statistically quite potent for the amount of time that it takes. All four subtests assessing contaminated mindware are included in the short-form. Chapter 12 reports the results of a study of short-form performance involving 372 subjects. Reliabilities of all the subtests are reported, as well as correlations with cognitive ability and the Actively Open-Minded Thinking scale. Correlations among all the subtests are reported as well as a principal components analysis of the subtests.

Associations among the Subtests: The Full-Form CART

The Rationality Quotient ◽

10.7551/mitpress/9780262034845.003.0013 ◽

2016 ◽

Author(s):

Keith E. Stanovich ◽

Richard F. West ◽

Maggie E. Toplak

Keyword(s):

Cognitive Ability ◽

Principal Components ◽

Large Scale ◽

Short Form ◽

Large Scale Study ◽

Full Form ◽

Components Analysis ◽

Thinking Disposition

This chapter describes a large-scale study of the full-form version of the CART involving 747 subjects. Reliabilities of all the subtests are reported, as well as correlations with measures of cognitive ability and the four thinking disposition scales of the CART. Correlations among all the subtests are reported as well as a principal components analysis of the subtests. Comparisons between the full-form CART and the short-form CART are presented as well as comparisons with even briefer forms of the test than the short form.

FAULT DETECTION FOR LARGE SCALE SYSTEMS USING DYNAMIC PRINCIPAL COMPONENTS ANALYSIS WITH ADAPTATION

IFAC Proceedings Volumes ◽

10.3182/20060830-2-sf-4903.00039 ◽

2006 ◽

Vol 39 (14) ◽

pp. 220-225

Author(s):

Jesús Mina ◽

Cristina Verde

Keyword(s):

Fault Detection ◽

Principal Components ◽

Large Scale ◽

Large Scale Systems ◽

Short Forms of the Marlowe-Crowne Social Desirability Scale

Psychological Reports ◽

10.2466/pr0.1992.71.3f.1155 ◽

1992 ◽

Vol 71 (3_suppl) ◽

pp. 1155-1160 ◽

Cited By ~ 158

Author(s):

Rebecca Ballard

Keyword(s):

Social Desirability ◽

Principal Components ◽

Short Form ◽

Full Scale ◽

Social Desirability Scale ◽

Short Forms ◽

Scale Scores ◽

Three short forms of the Marlowe-Crowne Social Desirability Scale were constructed from the results of principal components analysis ( N = 399). Those subscales were compared with short forms developed by previous researchers who used the same methodology. Examination of the subscales indicated that 13 of the scale's 33 items were isolated by at least two of the three reported studies. Those items were used to construct a composite subscale, which appeared to offer a useful alternative to the full scale. Further analysis of the subscale's contents, however, raised questions about the dimensionality of the Marlowe-Crowne scale. Caution was urged in the use and interpretation of both the full inventory and the short form until the meaning of scale scores can be clarified.

On-the-fly model reduction for large-scale structural topology optimization using principal components analysis

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-019-02485-3 ◽

2020 ◽

Vol 62 (1) ◽

pp. 209-230 ◽

Cited By ~ 6

Author(s):

Manyu Xiao ◽

Dongcheng Lu ◽

Piotr Breitkopf ◽

Balaji Raghavan ◽

Subhrajit Dutta ◽

...

Keyword(s):

Topology Optimization ◽

Model Reduction ◽

Principal Components ◽

Large Scale ◽

Structural Topology Optimization ◽

Structural Topology ◽

Development of a Short-Form Conservatism Scale Suitable for Mail Surveys

Psychological Reports ◽

10.2466/pr0.1993.72.2.419 ◽

1993 ◽

Vol 72 (2) ◽

pp. 419-422 ◽

Cited By ~ 15

Author(s):

David M. Collins ◽

Peter F. Hayes

Keyword(s):

Factor Structure ◽

Principal Components ◽

Short Form ◽

Coefficient Alpha ◽

Varimax Rotation ◽

Mail Surveys ◽

Five Factors ◽

Analysis of data from 255 U.S. pharmacists provides support for the consistency and validity of this short-form conservatism scale. The scale returned a coefficient alpha of 0.82, and principal components analysis yielded a strong general conservatism factor. Varimax rotation produced five factors consistent with the factor structure of the original Conservatism Scale.

Haplotype Classification Using Copy Number Variation and Principal Components Analysis

The Open Bioinformatics Journal ◽

10.2174/1875036201307010019 ◽

2013 ◽

Vol 7 (1) ◽

pp. 19-24

Author(s):

Kevin Blighe

Keyword(s):

Principal Components ◽

Large Scale ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Data Set ◽

Reduction Techniques ◽

Number Variation ◽

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.

Searching for the core variables in principal components analysis

Brazilian Journal of Probability and Statistics ◽

10.1214/17-bjps361 ◽

2018 ◽

Vol 32 (4) ◽

pp. 730-754 ◽

Cited By ~ 1

Author(s):

Yanina Gimenez ◽

Guido Giussani

Keyword(s):

Principal Components ◽

The Core ◽

Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS)

Bioinformatics ◽

10.1093/bioinformatics/btaa649 ◽

2020 ◽

Author(s):

Ruey Leng Loo ◽

Queenie Chan ◽

Henrik Antti ◽

Jia V Li ◽

H Ashrafian ◽

...

Keyword(s):

Spectral Data ◽

Principal Components ◽

Large Scale ◽

Principal Component ◽

Population Based ◽

Correlation Spectroscopy ◽

Supplementary Information ◽

Components Analysis ◽

Statistical Spectroscopy

Abstract Motivation Large-scale population omics data can provide insight into associations between gene–environment interactions and disease. However, existing dimension reduction modelling techniques are often inefficient for extracting detailed information from these complex datasets. Results Here, we present an interactive software pipeline for exploratory analyses of population-based nuclear magnetic resonance spectral data using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS) within the R-library hastaLaVista framework. Principal component analysis models are generated for a sequential series of spectral regions (blocks) to provide more granular detail defining sub-populations within the dataset. Molecular identification of key differentiating signals is subsequently achieved by implementing Statistical TOtal Correlation SpectroscopY on the full spectral data to define feature patterns. Finally, the distributions of cross-correlation of the reference patterns across the spectral dataset are used to provide population statistics for identifying underlying features arising from drug intake, latent diseases and diet. The COMPASS method thus provides an efficient semi-automated approach for screening population datasets. Availability and implementation Source code is available at https://github.com/cheminfo/COMPASS. Supplementary information Supplementary data are available at Bioinformatics online.

Factor Structure of the Short Form Measure of Self-Actualization in a Black Sample

Psychological Reports ◽

10.2466/pr0.1991.69.3.871 ◽

1991 ◽

Vol 69 (3) ◽

pp. 871-877 ◽

Cited By ~ 2

Author(s):

Raymond K. Tucker ◽

Ronnie Dyson

Keyword(s):

Ethnic Groups ◽

Factor Structure ◽

Principal Components ◽

Short Form ◽

Varimax Rotation ◽

Five Factors ◽

Self Actualization ◽

Components Analysis ◽

Black Sample

The present study sought to assess the factor invariance of Jones and Crandall's short form measure of self-actualization on a sample of 213 black undergraduates. A principal components analysis followed by a varimax rotation yielded five factors, four of which were interpretable. The obtained structure essentially replicated that of Jones and Crandall; however, there were differences that indicate the test cannot be assumed to be invariant across ethnic groups.