{"title":"人工智能土耳其人生活满意度量表的效度与信度分析。","authors":"Servet Ati̇k, Nuri Erdemi̇r","doi":"10.1186/s40359-025-03246-2","DOIUrl":null,"url":null,"abstract":"<p><p>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 was tested on three independent samples of Turkish university students: 503 for Exploratory Factor Analysis (EFA), 301 for Confirmatory Factor Analysis (CFA), and 79 for test-retest reliability. EFA identified a unidimensional structure, accounting for 67.50% of the total variance (factor loadings .75-.89). CFA confirmed adequate model-data fit (e.g., χ<sup>2</sup>/sd = 2.63, RMSEA = 0.07). The scale demonstrated high internal consistency (Cronbach's α = .88) and temporal stability (test-retest correlation = .95). Criterion validity was supported by strong positive correlations with established Life Satisfaction (r = .74) and General Well-Being (r = .63) scales. These findings indicate that AI can expedite scale development while yielding robust psychometric instruments. This research underscores the innovative potential of AI-supported psychometric tools in the social sciences and offers valuable insights for future scale development.</p>","PeriodicalId":37867,"journal":{"name":"BMC Psychology","volume":"13 1","pages":"1139"},"PeriodicalIF":3.0000,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12522903/pdf/","citationCount":"0","resultStr":"{\"title\":\"Validity and reliability analysis of the Turkish life satisfaction scale developed through artificial intelligence.\",\"authors\":\"Servet Ati̇k, Nuri Erdemi̇r\",\"doi\":\"10.1186/s40359-025-03246-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 was tested on three independent samples of Turkish university students: 503 for Exploratory Factor Analysis (EFA), 301 for Confirmatory Factor Analysis (CFA), and 79 for test-retest reliability. EFA identified a unidimensional structure, accounting for 67.50% of the total variance (factor loadings .75-.89). CFA confirmed adequate model-data fit (e.g., χ<sup>2</sup>/sd = 2.63, RMSEA = 0.07). The scale demonstrated high internal consistency (Cronbach's α = .88) and temporal stability (test-retest correlation = .95). Criterion validity was supported by strong positive correlations with established Life Satisfaction (r = .74) and General Well-Being (r = .63) scales. These findings indicate that AI can expedite scale development while yielding robust psychometric instruments. This research underscores the innovative potential of AI-supported psychometric tools in the social sciences and offers valuable insights for future scale development.</p>\",\"PeriodicalId\":37867,\"journal\":{\"name\":\"BMC Psychology\",\"volume\":\"13 1\",\"pages\":\"1139\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12522903/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1186/s40359-025-03246-2\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1186/s40359-025-03246-2","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
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 was tested on three independent samples of Turkish university students: 503 for Exploratory Factor Analysis (EFA), 301 for Confirmatory Factor Analysis (CFA), and 79 for test-retest reliability. EFA identified a unidimensional structure, accounting for 67.50% of the total variance (factor loadings .75-.89). CFA confirmed adequate model-data fit (e.g., χ2/sd = 2.63, RMSEA = 0.07). The scale demonstrated high internal consistency (Cronbach's α = .88) and temporal stability (test-retest correlation = .95). Criterion validity was supported by strong positive correlations with established Life Satisfaction (r = .74) and General Well-Being (r = .63) scales. These findings indicate that AI can expedite scale development while yielding robust psychometric instruments. This research underscores the innovative potential of AI-supported psychometric tools in the social sciences and offers valuable insights for future scale development.
期刊介绍：
BMC Psychology is an open access, peer-reviewed journal that considers manuscripts on all aspects of psychology, human behavior and the mind, including developmental, clinical, cognitive, experimental, health and social psychology, as well as personality and individual differences. The journal welcomes quantitative and qualitative research methods, including animal studies.
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