Hierarchical factor analysis applied for interpreting chemical elements deposited with atmospheric precipitation in Karelia

2007 ◽  
Vol 32 (12) ◽  
pp. 761-769 ◽  
Author(s):  
V. M. Feoktistov ◽  
V. N. Kharin ◽  
E. N. Specktor
Keyword(s):  
Factor Analysis ◽  
Chemical Elements ◽  
Atmospheric Precipitation ◽  
Hierarchical Factor Analysis

Mining Spatial Patterns of Distribution of Uranium in Surface and Ground Waters in Ukraine

2017 ◽  
pp. 520-546
Author(s):  
Michael Govorov ◽  
Viktor Putrenko ◽  
Gennady Gienko
Keyword(s):  
Factor Analysis ◽  
Spatial Distribution ◽  
Spatial Patterns ◽  
Large Scale ◽  
Chemical Elements ◽  
Environmental Indicators ◽  
Local Factor ◽  
Correlation And Regression Analysis ◽  
Weighted Factor ◽  
Major Factors

A variety of geovisualization and spatial statistical methods can reveal spatial patterns in the distribution of chemical elements in surface and groundwater, and also identify major factors which define those patterns. This chapter describes a combination of modeling techniques to enhance understanding of large-scale spatial distribution of uranium in groundwater in Ukraine, by linking spatial patterns of several indicators and predictors. Factor, correlation, and regression analysis, including their spatial implementations, were used to describe the impacts of several environmental variables on spatial distribution of uranium. Local factor analysis (or Geographically Weighted Factor Analysis, GWFA) was proposed to identify major environmental factors which define the distribution of uranium, and to discover and map their spatial relationships. The study resulted in a series of maps to help visualize and explore the relationships between uranium and several environmental indicators.

GEOSTATISTICAL ANALYSIS OF THE ATMOGEOCHEMICAL FIELD IN THE PRIOR LAYER OF THE ATMOSPHERE OF NORTHERN UKRAINE (BRYOGEOCHEMICAL INDICATION DATA)

2019 ◽  
pp. 80-89
Author(s):  
Yu. Tyutyunnik ◽  
O. Shabatura ◽  
O. Blum ◽  
J. Daunis-i-Estadella
Keyword(s):  
Trace Elements ◽  
Spatial Variability ◽  
Atmospheric Pollution ◽  
Chemical Elements ◽  
Surface Wind ◽  
Atmospheric Precipitation ◽  
Good Method ◽  
Active Role ◽  
Geostatistical Analysis ◽  
Sampling System

The bryogeochemical indication of atmospheric pollution in the central part of northern Ukraine is based on the use of moss P.schreberi species. The sampling system provided an empirical reference the moss sample data to a particular physical and geographical zone, taking into account the distance from the source of atmospheric pollution. This approach allowed developing a gradation of natural and man-caused conditions for the accumulation of chemical elements in moss samples. The data on the content of chemical elements in the moss-indicator were analysed and checked for comprehensive geostatistics (factor analysis methods, mapping of spatial correlations of significant factors, centered triangular charts and composite biplot). Results made possible to form a mutual hyperspace of geochemical factors and signs for its subsequent interpretation.The leading factor of the bryogeochemical indication is air pollution by a large dust of natural terrigenous and man-caused origin. Second factor connected with a finely dispersed man-caused-condensation aerosols impact; the third factor is identified by the behavior of titanium and vanadium as the effect of heat and power (TPP, boiler room) on atmospheric emissions; the fourth factor is аn impact of biogenesis, which is determined by active role of trace elements – sulfur and phosphorus; the fifth factor is connected with biogenic migration of trace elements – boron, copper and cobalt. The sixth factor is weak, and probably due to the behavior of manganese and chromium, associated with Eh-pH parameters of atmospheric precipitation and hydrometeors. The regional geostatistical analysis of the bryogeochemical data shows that the factor of the terrigenous dust uplift within the studied territory exhibits a spatial variability due to differences in the granulometric composition of the Quaternary deposits, the degree of soil retention and plowing, and differences in surface wind speeds. Spatial heterogeneity is obvious due to aman-caused factor showing a distribution of the dispersion of the Fe-S-Pb and V-Cd-Pb clusters on bipolar charts. Instead, such a geochemical factor as "biogenesis" shows a low spatial variability. The bryogeochemical indication is a good method for the assessment and monitoring of atmospheric pollution of large and varied natural and man-causedarea as well as the proposed geostatistical mapping of the atmogeochemic field showing that it is efficient todivide the territories by type of pollution.

Hierarchical Factoring in a Standardized Achievement Battery

1985 ◽  
Vol 45 (2) ◽  
pp. 343-351 ◽  
Author(s):  
David J. Martin ◽  
Stephen B. Dunbar
Keyword(s):  
Factor Analysis ◽  
Cognitive Abilities ◽  
Visual Information ◽  
General Factor ◽  
Factorial Validity ◽  
Cognitive Abilities Test ◽  
Language And Mathematics ◽  
Information Group ◽  
And Mathematics ◽  
Hierarchical Factor Analysis

This study was concerned with the factorial validity of the Iowa Tests of Basic Skills (ITBS). Previous research identified a strong general factor for this battery, which was taken as evidence of redundancy among the subtests. Hierarchical factor analysis was done with a subset of the standardization data to explore the presence of second-order group factors. The results supported the construct validity of the Language and Mathematics subscales, though a degree of factorial complexity was found in both. Verbal and Visual Information group factors were also identified. Extension of the ITBS general and group factors to subtests of the Cognitive Abilities Test supported the interpretations made of the various group factors.

Higher-Order Analysis of 16 PF Scores: An Alternative Method

1985 ◽  
Vol 57 (2) ◽  
pp. 564-566 ◽  
Author(s):  
Ellen K. Reuter ◽  
James M. Schuerger ◽  
Fred H. Wallbrown
Keyword(s):  
Factor Analysis ◽  
Private Practice ◽  
Counseling Psychology ◽  
Higher Order ◽  
Levels Of Analysis ◽  
Order Analysis ◽  
Alternative Method ◽  
Hierarchical Factor Analysis

For a sample of 132 clients from a counseling psychology private practice, 16 PF primary scores were subjected to hierarchical factor analysis with resulting replication of Cattell's higher order factors at secondary and tertiary levels of analysis.

The Hierarchical Structure of Attitudes Toward the Exceptional

1974 ◽  
Vol 40 (6) ◽  
pp. 430-435 ◽  
Author(s):  
Reginald L. Jones
Keyword(s):  
Factor Analysis ◽  
Interpersonal Relationships ◽  
Common Core ◽  
Social Distance ◽  
General Factor ◽  
Women College Students ◽  
The Disabled ◽  
The Hierarchical Structure ◽  
Separate Factor ◽  
Hierarchical Factor Analysis

The structure of attitudes toward the exceptional was investigated using hierarchical factor analysis. The subjects were 132 men and 132 women college students who completed a 78 item social distance questionnaire (6 interpersonal situation and 13 categories of exceptionality-nonexceptionality). The results revealed a general factor concerning attitudes toward the disabled. This factor represented a common core of attitudes which cut across categories of disability and interpersonal situation. The general facto was differentiated into attitudes toward the physically disabled (with certain special emphases), attitudes toward the psychologically disabled, and attitudes toward the mildly retarded-nonexceptional. The last named factor was particularly provocative, indicating that, except for the closest interpersonal relationships (marriage and acceptance as close kin by marriage), the mildly retarded shared in the same configuration of attitudes as are held toward the nonexceptional. Attitudes toward the gifted emerged as a separate factor, isolated in virtually all instances from those held toward the disabled.

Fluid and Crystallized Intelligence and the Berlin Model of Intelligence Structure (BIS)

2002 ◽  
Vol 18 (2) ◽  
pp. 97-112 ◽  
Author(s):  
André Beauducel ◽  
Martin Kersting
Keyword(s):  
Factor Analysis ◽  
Processing Speed ◽  
Personnel Selection ◽  
Processing Capacity ◽  
Crystallized Intelligence ◽  
Confirmatory Factor ◽  
German Speaking ◽  
Assessment Of Intelligence ◽  
Hierarchical Factor Analysis ◽  
Fluid And Crystallized Intelligence

Summary: Assessment of intelligence is often based on fluid (gf) and crystallized intelligence (gc), and - in the German-speaking countries - the Berlin Model of Intelligence Structure (BIS). As yet, however, the two approaches have not been systematically related to each other. The present study therefore aims to identify possible relationships between the approaches. We hypothesize that gf is related to “processing capacity” and “memory” in the BIS, whereas gc is related to “fluency” and “knowledge” and, to a lesser degree, to “processing capacity.” We also assume “processing speed” to be related to both gf and gc. All components of the BIS that are relevant to the present study were measured by means of the BIS-r-DGP test, which, together with “knowledge” scales, was administered to 9,520 persons in the context of personnel selection. The following results were obtained: First, the BIS was replicated by factor analysis of the BIS-r-DGP test. Second, “knowledge” was shown to form an additional component. Third, gf and gc emerged clearly from hierarchical factor analysis. Finally, with the exception of the relation of “fluency” to gc, all hypotheses were confirmed by confirmatory factor analysis.

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