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 Multivariate Quantitative Correlations"
Split Justification: This dichotomy distinguishes between correlations immediately apparent from raw data or simple direct calculations (e.g., a correlation matrix, direct scatterplot analysis) and those that are inferred or revealed through more complex statistical modeling of underlying, unobserved (latent) variables or mediating relationships.
11
From: "Observing Latent or Indirect Multivariate Quantitative Correlations"
Split Justification: This dichotomy separates the two primary ways correlations can be "latent or indirect". Child 1 focuses on situations where the variables themselves are unobserved constructs (latent variables), and the task is to infer these variables and observe their quantitative interrelationships. Child 2 focuses on situations where the quantitative relationships between variables (which may be observed or latent) are not direct, but instead operate through mediating or moderating pathways, thereby making the overall correlation indirect. This covers the two distinct challenges implied by "latent *or* indirect" in the parent node.
12
From: "Observing Correlations Through Indirect Pathways"
Split Justification: This dichotomy separates the two fundamental ways an observed correlation between two variables can arise through an indirect pathway. "Observing Mediated Causal Structures" refers to scenarios where one variable influences another through an intermediate variable, forming a sequential chain of influence (e.g., X causes M, and M causes Y). "Observing Common Cause Structures" refers to situations where two variables are correlated because they are both influenced by a shared antecedent variable, thereby creating an indirect, non-causal correlation between them (e.g., Z causes X, and Z causes Y, leading to a correlation between X and Y). These two categories are mutually exclusive in their underlying structural mechanism for indirectness and together comprehensively cover the primary forms of indirect pathways that lead to observed correlations.
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Topic: "Observing Common Cause Structures" (W7951)