1
From: "Human Potential & Development."
Split Justification: Development fundamentally involves both our inner landscape (**Internal World**) and our interaction with everything outside us (**External World**). (Ref: Subject-Object Distinction)..
2
From: "Internal World (The Self)"
Split Justification: The Internal World involves both mental processes (**Cognitive Sphere**) and physical experiences (**Somatic Sphere**). (Ref: Mind-Body Distinction)
3
From: "Cognitive Sphere"
Split Justification: Cognition operates via deliberate, logical steps (**Analytical Processing**) and faster, intuitive pattern-matching (**Intuitive/Associative Processing**). (Ref: Dual Process Theory)
4
From: "Analytical Processing"
Split Justification: Analytical thought engages distinct symbolic systems: abstract logic and mathematics (**Quantitative/Logical Reasoning**) versus structured language (**Linguistic/Verbal Reasoning**).
5
From: "Quantitative/Logical Reasoning"
Split Justification: Logical reasoning can be strictly formal following rules of inference (**Deductive Proof**) or drawing general conclusions from specific examples (**Inductive Reasoning Case Study**). (L5 Split)
6
From: "Inductive Reasoning Case Study"
Split Justification: Induction involves forming general rules (**Hypothesis Generation**) and testing their predictive power (**Hypothesis Testing**). (L6 Split)
7
From: "Hypothesis Generation"
Split Justification: Generating a hypothesis requires identifying a pattern (**Observing Correlations**) and formulating a testable explanation (**Stating a Falsifiable Claim**).
8
From: "Observing Correlations"
Split Justification: This dichotomy separates the process of identifying relationships based on numerical data and statistical analysis from the process of discerning patterns and connections within non-numerical, descriptive, or categorical information. Together, these two categories comprehensively cover the fundamental modes of observing correlations in any form of data or experience for hypothesis generation.
9
From: "Observing Quantitative Correlations"
Split Justification: This split categorizes the observation of quantitative correlations based on the number of variables involved in the relationship. A quantitative correlation fundamentally involves either two variables (bivariate) or more than two variables (multivariate), making these categories mutually exclusive and jointly exhaustive for any observed quantitative relationship.
10
From: "Observing Bivariate Quantitative Correlations"
Split Justification: This split differentiates observed relationships based on whether the pattern of association between the two quantitative variables approximates a straight line or follows a curved or more complex form. This provides a fundamental and comprehensive dichotomy for categorizing the visual or conceptual structure of bivariate quantitative correlations.
11
From: "Observing Non-linear Bivariate Quantitative Correlations"
Split Justification: This split differentiates between identifying non-linear bivariate correlations through direct perceptual interpretation of data representations (e.g., visual inspection of scatter plots) versus identifying them through the application of quantitative methods, statistical models, and computational analysis. These represent distinct modes of human observation and hypothesis generation.
12
From: "Observing Statistically Derived Non-linear Bivariate Correlations"
Split Justification: This split categorizes the observation of non-linear bivariate correlations based on their monotonic property. Monotonic correlations show a consistent directional trend (always increasing or always decreasing) without changing direction, even if the rate of change varies. Non-monotonic correlations, in contrast, exhibit at least one turning point where the direction of the relationship reverses (e.g., U-shaped or inverted U-shaped). This dichotomy is fundamental to understanding the nature of non-linear effects, is mutually exclusive, and comprehensively covers all possible statistically derived non-linear bivariate correlations.
✓
Topic: "Observing Monotonic Statistically Derived Non-linear Bivariate Correlations" (W5647)