Creativity Self Efficacy Mediation

Creativity Self-Efficacy Mediation Analysis

Purpose

This skill encodes expert methodological knowledge for analyzing the psychological mechanisms through which AI tools (ChatGPT, web search) affect human creativity. Specifically, it covers SEM-based mediation analysis with creative self-efficacy as a mediator, and moderation analysis using baseline creativity. A general-purpose programmer could run a mediation analysis package, but would not know why creative self-efficacy is the theoretically motivated mediator, how to measure it, what the RAT measures and why it is the appropriate baseline, or how to interpret the indirect effect in the context of creativity theory.

When to Use This Skill

• Investigating why an intervention affects creativity (not just whether it does)
• Testing whether creative self-efficacy mediates the effect of AI/tool use on creative output
• Examining whether baseline creativity moderates the effect of AI assistance
• Designing a study that needs both mediation and moderation analysis for creativity outcomes
• Specifying SEM models for creativity research using lavaan (R)

Research Planning Protocol

Before executing the domain-specific steps below, you MUST:

1. State the research question — What mechanism or moderator is being tested?
2. Justify the method choice — Why SEM-based mediation (not Baron & Kenny, not PROCESS)? What alternatives were considered?
3. Declare expected outcomes — What pattern of indirect/direct effects would support vs. refute the hypothesis?
4. Note assumptions and limitations — What does SEM assume? Where could cross-sectional mediation mislead?
5. Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

Theoretical Framework

Creative Self-Efficacy as Mediator

Creative self-efficacy (CSE) = an individual's belief in their ability to produce creative outcomes (Tierney & Farmer, 2002). It is grounded in Bandura's (1997) self-efficacy theory: people who believe they can be creative are more likely to attempt, persist at, and succeed in creative tasks.

Hypothesized causal chain (Lee & Chung, 2024):

AI tool use → ↓ Creative Self-Efficacy → ↓ Creative Output

Mechanism: Using AI to generate ideas may undermine the user's
belief in their own creative ability, leading to reduced creative
effort and output on subsequent tasks.

Baseline Creativity as Moderator

Baseline creativity moderates how much AI assistance affects creative output:

• High-creativity individuals: May benefit less from AI (ceiling effect) or be harmed more (self-efficacy threat)
• Low-creativity individuals: May benefit more from AI (scaffolding) or show less effect (floor effect)

Lee & Chung (2024) found that ChatGPT use disproportionately reduced creativity for individuals with higher baseline creativity (measured by RAT).

Measurement Instruments

Creative Self-Efficacy Scale (Tierney & Farmer, 2002)

3 items, 5-point Likert scale (1 = strongly disagree, 5 = strongly agree):

1. "I have confidence in my ability to solve problems creatively"
2. "I feel that I am good at generating novel ideas"
3. "I have a knack for further developing the ideas of others"

Critical timing note: CSE must be measured after the manipulation (e.g., after ChatGPT use) and before the outcome measure. Measuring CSE before the manipulation captures trait CSE, not the mediated state change.

Remote Associates Test — RAT (Mednick, 1962)

Used as the baseline creativity measure for moderation analysis.

Example item: FALLING / ACTOR / DUST → answer: STAR

Why RAT as baseline: RAT measures convergent thinking (a creativity component independent of divergent thinking), so it serves as a baseline creativity indicator without directly measuring the same construct as the AUT outcome (Lee & Chung, 2024).

SEM Mediation Model Specification

Model Structure (Lee & Chung, 2024)

 Creative Self-Efficacy (M)
 ↗ a b ↘
AI Condition (X) Creativity Score (Y)
 ————— c' —————→

• Path a: Effect of AI condition on CSE
• Path b: Effect of CSE on creativity, controlling for condition
• Path c': Direct effect of condition on creativity, controlling for CSE
• Indirect effect: a × b (the mediated portion)
• Total effect: c = c' + a × b

lavaan Specification (R)

library(lavaan)

mediation_model <- '
 # Measurement model (if using latent variables)
 # CSE =~ cse1 + cse2 + cse3 # Uncomment for latent CSE

 # Structural model
 cse ~ a * condition # Path a: X → M
 creativity ~ b * cse + # Path b: M → Y
 cprime * condition # Path c': X → Y (direct)

 # Indirect and total effects
 indirect := a * b # Mediated effect
 total := cprime + a * b # Total effect
'

fit <- sem(mediation_model, data = df, se = "bootstrap", bootstrap = 5000)
summary(fit, ci = TRUE)

Key Specification Decisions

Moderation Analysis

Baseline Creativity × Condition Interaction

Two approaches (Lee & Chung, 2024 used both):

Approach 1: Median Split (Descriptive)

1. Compute median RAT score across all participants
2. Split into high-creativity (above median) and low-creativity (below median) groups
3. Run separate ANOVAs or t-tests within each subgroup
4. Report condition effects separately for high vs low creativity

Limitation: Median split loses information and reduces power (MacCallum et al., 2002). Use for visualization/description; rely on continuous moderation for inference.

Approach 2: Continuous Moderation (Inferential)

# In lavaan or linear regression
moderation_model <- '
 creativity ~ b1 * condition +
 b2 * rat_score +
 b3 * condition:rat_score # Interaction term
'
# b3 = moderation effect
# Probe interaction at ±1 SD of RAT score (Aiken & West, 1991)

Interpreting Moderation Results

Simple Slopes Visualization

Plot creativity scores against condition, separately for high (+1 SD) and low (-1 SD) baseline creativity:

library(emmeans)
# For interaction probing
emtrends(model, ~ condition, var = "rat_score")
# Or Johnson-Neyman technique for regions of significance