Orthogonality - Revision history

Flc: /* GRI Factors */

2026-03-18T20:54:02Z

GRI Factors

← Older revision		Revision as of 20:54, 18 March 2026
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	=GRI Factors=		=GRI Factors=
	[[File:Factors maping.png\|right\|250px]]		[[File:Factors maping.png\|right\|250px]]
	GRI has four factors ~~devised~~ to be as ~~distant from each other~~ as possible. They cover the social behavior "map" in a way that can be more practical to analyze and use when locating, representing, modeling, and describing behaviors. Factor 1 contains as little as possible from factors 2, 3, and 4. The same goes for Factor 2, which contains as little as possible from factors 3 and 4. And the same between factors 3 and 4.		GRI has four factors designed to be as far apart as possible. They cover the social behavior "map" in a way that can be more practical to analyze and use when locating, representing, modeling, and describing behaviors. Factor 1 contains as little as possible from factors 2, 3, and 4. The same goes for Factor 2, which contains as little as possible from factors 3 and 4. And the same between factors 3 and 4.

	Unlike the map, the adaptive profiles display the four factors layered on top of each other. This is because we are dealing with a four-dimensional space, and for practical reasons, the four factors need to be shown close together.

			Unlike the map, the adaptive profiles display the four factors layered on top of each other. This is because we are dealing with a four-dimensional space. For practical reasons, the four factors need to be shown close together for analyzing their interactions.

	[[Category:Notes]]		[[Category:Notes]]
	[[Category:Assessment]]		[[Category:Assessment]]

Flc: /* Introduction */

2025-12-12T01:45:37Z

Introduction

← Older revision		Revision as of 01:45, 12 December 2025
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	In factorial analysis, "orthogonality" refers to variables that are independent and uncorrelated. When two factors are orthogonal, you can study their effects as if they are happening at a right angle to each other, rather than overlapping. This leads to a clearer understanding of each factor's contribution to the outcome. The opposite of orthogonal factors are oblique factors.		In factorial analysis, "orthogonality" refers to variables that are independent and uncorrelated. When two factors are orthogonal, you can study their effects as if they are happening at a right angle to each other, rather than overlapping. This leads to a clearer understanding of each factor's contribution to the outcome. The opposite of orthogonal factors are oblique factors.

	The ~~idea~~ of orthogonality in personality factors ~~came from the~~ studies on the Five Factor Model, ~~and~~ factors ~~that~~ may be extracted as orthogonal or oblique when analyzing a large number of nouns and adjectives describing personality in various languages.		The concept of orthogonality in personality factors originated in studies of the Five Factor Model, in which factors may be extracted as orthogonal or oblique when analyzing a large number of nouns and adjectives describing personality across various languages.

	=Map Analogy=		=Map Analogy=

Flc: /* Introduction */

2025-11-01T05:32:31Z

Introduction

← Older revision		Revision as of 05:32, 1 November 2025
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	In factorial analysis, "orthogonality" refers to variables that are independent and uncorrelated. When two factors are orthogonal, you can study their effects as if they are happening at a right angle to each other, rather than overlapping. This leads to a clearer understanding of each factor's contribution to the outcome. The opposite of orthogonal factors are oblique factors.		In factorial analysis, "orthogonality" refers to variables that are independent and uncorrelated. When two factors are orthogonal, you can study their effects as if they are happening at a right angle to each other, rather than overlapping. This leads to a clearer understanding of each factor's contribution to the outcome. The opposite of orthogonal factors are oblique factors.

	The idea of orthogonality in personality factors came from the studies on the Five Factor Model, and factors that may be extracted as orthogonal or oblique when analyzing a large number of ~~personality~~ nouns and adjectives in various languages.		The idea of orthogonality in personality factors came from the studies on the Five Factor Model, and factors that may be extracted as orthogonal or oblique when analyzing a large number of nouns and adjectives describing personality in various languages.

	=Map Analogy=		=Map Analogy=

Flc: /* GRI Factors */

2025-09-29T23:20:22Z

GRI Factors

← Older revision		Revision as of 23:20, 29 September 2025
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	GRI has four factors devised to be as distant from each other as possible. They cover the social behavior "map" in a way that can be more practical to analyze and use when locating, representing, modeling, and describing behaviors. Factor 1 contains as little as possible from factors 2, 3, and 4. The same goes for Factor 2, which contains as little as possible from factors 3 and 4. And the same between factors 3 and 4.		GRI has four factors devised to be as distant from each other as possible. They cover the social behavior "map" in a way that can be more practical to analyze and use when locating, representing, modeling, and describing behaviors. Factor 1 contains as little as possible from factors 2, 3, and 4. The same goes for Factor 2, which contains as little as possible from factors 3 and 4. And the same between factors 3 and 4.

	~~The main difference between~~ the adaptive profiles ~~and the map is that~~ the four factors ~~are represented~~ on top of each other. ~~That's~~ because we are dealing with a four-dimensional space, and for practical reasons, we need to ~~represent the four dimensions~~ close ~~to each other~~.		Unlike the map, the adaptive profiles display the four factors layered on top of each other. This is because we are dealing with a four-dimensional space, and for practical reasons, the four factors need to be shown close together.


	[[Category:Notes]]		[[Category:Notes]]
	[[Category:Assessment]]		[[Category:Assessment]]

Flc: /* Introduction */

2025-09-29T16:13:18Z

Introduction

← Older revision		Revision as of 16:13, 29 September 2025
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	In factorial analysis, "orthogonality" refers to variables that are independent and uncorrelated. When two factors are orthogonal, you can study their effects as if they are happening at a right angle to each other, rather than overlapping. This leads to a clearer understanding of each factor's contribution to the outcome. The opposite of orthogonal factors are oblique factors.		In factorial analysis, "orthogonality" refers to variables that are independent and uncorrelated. When two factors are orthogonal, you can study their effects as if they are happening at a right angle to each other, rather than overlapping. This leads to a clearer understanding of each factor's contribution to the outcome. The opposite of orthogonal factors are oblique factors.

	The idea of orthogonality in personality factors came from the studies on the Five Factor Model, and factors that may be extracted as orthogonal or oblique when analyzing a large number of personality nouns or adjectives in various languages.		The idea of orthogonality in personality factors came from the studies on the Five Factor Model, and factors that may be extracted as orthogonal or oblique when analyzing a large number of personality nouns and adjectives in various languages.

	=Map Analogy=		=Map Analogy=

Flc: /* Introduction */

2025-09-29T16:13:04Z

Introduction

← Older revision		Revision as of 16:13, 29 September 2025
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	In factorial analysis, "orthogonality" refers to variables that are independent and uncorrelated. When two factors are orthogonal, you can study their effects as if they are happening at a right angle to each other, rather than overlapping. This leads to a clearer understanding of each factor's contribution to the outcome. The opposite of orthogonal factors are oblique factors.		In factorial analysis, "orthogonality" refers to variables that are independent and uncorrelated. When two factors are orthogonal, you can study their effects as if they are happening at a right angle to each other, rather than overlapping. This leads to a clearer understanding of each factor's contribution to the outcome. The opposite of orthogonal factors are oblique factors.

	The idea of orthogonality in personality factors came from the studies on the Five Factor Model, and factors that may be extracted as orthogonal or oblique.		The idea of orthogonality in personality factors came from the studies on the Five Factor Model, and factors that may be extracted as orthogonal or oblique when analyzing a large number of personality nouns or adjectives in various languages.

	=Map Analogy=		=Map Analogy=

Flc at 16:10, 29 September 2025

2025-09-29T16:10:47Z

← Older revision		Revision as of 16:10, 29 September 2025
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	The main difference between the adaptive profiles and the map is that the four factors are represented on top of each other. That's because we are dealing with a four-dimensional space, and for practical reasons, we need to represent the four dimensions close to each other.		The main difference between the adaptive profiles and the map is that the four factors are represented on top of each other. That's because we are dealing with a four-dimensional space, and for practical reasons, we need to represent the four dimensions close to each other.


			[[Category:Notes]]
			[[Category:Assessment]]

Flc at 16:03, 29 September 2025

2025-09-29T16:03:07Z

← Older revision		Revision as of 16:03, 29 September 2025
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	=GRI Factors=		=GRI Factors=
			[[File:Factors maping.png\|right\|250px]]
	GRI has four factors devised to be as distant from each other as possible. They cover the social behavior "map" in a way that can be more practical to analyze and use when locating, representing, modeling, and describing behaviors. Factor 1 contains as little as possible from factors 2, 3, and 4. The same goes for Factor 2, which contains as little as possible from factors 3 and 4. And the same between factors 3 and 4.		GRI has four factors devised to be as distant from each other as possible. They cover the social behavior "map" in a way that can be more practical to analyze and use when locating, representing, modeling, and describing behaviors. Factor 1 contains as little as possible from factors 2, 3, and 4. The same goes for Factor 2, which contains as little as possible from factors 3 and 4. And the same between factors 3 and 4.

	The main difference between the adaptive profiles and the map is that the four factors are represented on top of each other. That's because we are dealing with a four-dimensional space, and for practical reasons, we need to represent the four dimensions close to each other.		The main difference between the adaptive profiles and the map is that the four factors are represented on top of each other. That's because we are dealing with a four-dimensional space, and for practical reasons, we need to represent the four dimensions close to each other.

Flc: /* Map Analogy */

2025-09-29T15:54:42Z

Map Analogy

← Older revision		Revision as of 15:54, 29 September 2025
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	=Map Analogy=		=Map Analogy=
	[[File:Orthogonal factors.png\|right\|~~300px~~]]		[[File:Orthogonal factors.png\|right\|250px]]
	[[File:Oblique factors.png\|right\|~~300px~~]]		[[File:Oblique factors.png\|right\|250px]]
	Consider the world map on the right. You may want to locate an individual on the map with the longitude and latitude. Those two dimensions are orthogonal.		Consider the world map on the right. You may want to locate an individual on the map with the longitude and latitude. Those two dimensions are orthogonal.

Flc: /* Map Analogy */

2025-09-29T15:53:44Z

Map Analogy

← Older revision		Revision as of 15:53, 29 September 2025
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	Consider the world map on the right. You may want to locate an individual on the map with the longitude and latitude. Those two dimensions are orthogonal.		Consider the world map on the right. You may want to locate an individual on the map with the longitude and latitude. Those two dimensions are orthogonal.

	You may want to locate the individual by two oblique factors as well, like with this other map on the right. The oblique factors may seem more practical for locating some people in some places of the map, rather than in others ~~where there~~ are ~~fewer people~~.		You may want to locate the individual by two oblique factors as well, like with this other map on the right. The oblique factors may seem more practical for locating some people in some places of the map, rather than in others, which are out of focus for some analyses.

	The first orthogonal system is simply more practical than the second one to navigate, communicate about, remember, and use.		The first orthogonal system is simply more practical and generic than the second one to navigate, communicate about, remember, and use.

	The same goes for behavioral dimensions that can be located by two, three, or four factors~~, like we do at GRI~~. Orthogonal factors allow for the location and description of behaviors when the factors are as distant from each other as possible.		The same goes for behavioral dimensions that can be located by two, three, four factors or more. Orthogonal factors allow for the location and description of behaviors when the factors are as distant from each other as possible.

	=GRI Factors=		=GRI Factors=