AI in Psychometrics
Validity and reliability analysis of the Turkish life satisfaction scale developed through artificial intelligence
This study evaluates the validity and reliability of a Turkish Life Satisfaction Scale developed using artificial intelligence (ChatGPT) to explore AI's potential in creating psychometric tools. The scale, tested on three independent samples of Turkish university students, showed a unidimensional structure with 67.50% total variance explained and high internal consistency (Cronbach's α = .88). Confirmatory Factor Analysis confirmed adequate model-data fit (RMSEA = 0.07), and temporal stability was strong (test-retest correlation = .95). These findings suggest AI can expedite scale development while yielding robust psychometric instruments, offering valuable insights for future scale development in social sciences.
Key Enterprise Impact
Leveraging AI in psychometric development offers significant gains in efficiency and robustness, as evidenced by these core metrics.
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Total Variance Explained
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Internal Consistency (Cronbach's α)
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Temporal Stability (Test-Retest)
Deep Analysis & Enterprise Applications
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Scale Development
Validity & Structure
Reliability & Item Analysis
AI Implications
The scale was developed using ChatGPT-4, with a simple and broad prompt. Expert validation involved three faculty members assessing linguistic and cultural adaptation, clarity, and understandability. The Content Validity Index (CVI) was calculated, with Scale-level CVI/Ave at 0.87, exceeding the 0.80 threshold. Items 2 and 4, with I-CVI values below 0.78, were noted for linguistic simplification and conceptual clarity but were used in original form to align with the study's objective.
Exploratory Factor Analysis (EFA) confirmed a unidimensional structure, accounting for 67.50% of the total variance, with factor loadings ranging from 0.75 to 0.89. Confirmatory Factor Analysis (CFA) demonstrated excellent model-data fit (e.g., χ²/sd = 2.63, RMSEA = 0.07). Criterion validity was supported by strong positive correlations with established Life Satisfaction (r=0.74) and General Well-Being (r=0.63) scales. Measurement invariance across gender was confirmed at configural, metric, and scalar levels, supporting gender-based comparisons.
The scale exhibited high internal consistency with Cronbach's α = 0.88 (EFA data) and 0.89 (CFA data). Composite Reliability (CR) was 0.90, and Average Variance Extracted (AVE) was 0.66, both meeting acceptable thresholds. Temporal stability was robust, with a test-retest correlation of 0.95 over an 18-day interval. Item analysis via Item-Test Correlation showed values from 0.62 to 0.77, and t-tests for upper/lower 27% groups confirmed significant discriminatory power for all items.
AI, particularly large language models like ChatGPT, hold transformative potential for accelerating psychometric tool development. This study demonstrates that AI-generated scales can achieve robust psychometric properties, mirroring results from human-developed instruments. While AI enhances efficiency and precision, limitations include potential lack of originality, bias from training data, and limited critical thinking. Future research should focus on prompt optimization, diverse samples, and longer test-retest intervals.
67.50%
Total Variance Explained by the single factor
0.88
Cronbach's Alpha (Internal Consistency)
0.95
Test-Retest Correlation (Temporal Stability)
Enterprise Process Flow
Prompt Generation
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Expert Linguistic & Cultural Review
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Content Validity Index (CVI) Calculation
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Exploratory Factor Analysis (EFA)
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Confirmatory Factor Analysis (CFA)
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Reliability & Item Analysis
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Measurement Invariance Testing
| Fit Index | Acceptable Fit | Perfect Fit | AI Scale Result |
|---|---|---|---|
| x²/sd | 2 ≤ x²/sd ≤ 5 | 0 ≤ x²/sd < 2 | 2.63 (Acceptable Fit) |
| GFI | 0.90 < GFI < 0.95 | 0.95 ≤ GFI ≤ 1.00 | 0.99 (Perfect Fit) |
| RMSEA | 0.05 < RMSEA ≤ 0.08 | 0 ≤ RMSEA < 0.05 | 0.07 (Acceptable Fit) |
| SRMR | 0.05 < SRMR ≤ 0.08 | 0 < SRMR < 0.05 | 0.03 (Perfect Fit) |
AI's Transformative Potential in Psychometrics
This research provides compelling evidence that AI-powered tools, specifically ChatGPT, can significantly accelerate and enhance the development of psychometric scales. The Artificial Intelligence Turkish Life Satisfaction Scale, generated with minimal human intervention for item creation, demonstrated robust validity and reliability equivalent to established human-developed scales. This efficiency gain suggests a future where social science researchers can leverage AI to streamline early-stage instrument development, freeing up resources for deeper theoretical exploration and contextual validation.
However, the study also highlights crucial considerations. While AI excels at generating coherent content based on vast training data, it may lack the nuanced understanding required for culturally specific contexts or truly novel conceptualizations. The initial lower I-CVI scores for some items underscore the need for rigorous expert oversight and validation, even with AI-generated content. Therefore, a hybrid approach combining AI's generative power with human expertise remains optimal for ensuring both efficiency and scientific rigor in psychometric scale development.
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Your AI Implementation Roadmap
A structured approach to integrating AI for psychometric development, ensuring a smooth transition and measurable outcomes.
AI Integration Strategy
Define specific use cases for AI in scale development, identify key psychological constructs, and establish clear ethical guidelines.
Prompt Engineering & Item Generation
Develop refined AI prompts, generate initial item pools, and conduct preliminary linguistic and cultural reviews.
Psychometric Validation & Refinement
Apply EFA, CFA, reliability, and validity analyses; refine items based on psychometric properties and expert feedback.
Cross-Cultural Adaptation & Generalization
Conduct translation, adaptation, and validation studies across diverse populations and languages to enhance applicability.
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