Abstract
Abstract
Background
Skepticism has traditionally been associated with critical thinking. However, philosophy has proposed a particular type of skepticism, termed naive skepticism, which may increase susceptibility to misinformation, especially when contrasting information from official sources. While some scales propose to measure skepticism, they are scarce and only measure specific topics; thus, new instruments are needed to assess this construct.
Objective
This study aimed to develop a scale to measure naive skepticism in the adult population.
Method
The study involved 446 individuals from the adult population. Subjects were randomly selected for either the pilot study (phase 2; n = 126) or the validity-testing study (phase 3; n = 320). Parallel analyses and exploratory structural equation modelling were conducted to assess the internal structure of the test. Scale reliability was estimated using Cronbach's alpha and McDonald's omega coefficients Finally, a multigroup confirmatory factor analysis was performed to assess invariance, and a Set- Exploratory Structural Equation Modeling was applied to estimate evidence of validity based on associations with other variables.
Results
The naive skepticism scale provided adequate levels of reliability (ω > 0.8), evidence of validity based on the internal structure of the test (CFI = 0.966; TLI = 0.951; RMSEA = 0.079), gender invariance, and a moderate inverse effect on attitudes towards COVID-19 vaccines.
Conclusions
The newly developed naive skepticism scale showed acceptable psychometric properties in an adult population, thus enabling the assessment of naive skepticism in similar demographics. This paper discusses the implications for the theoretical construct and possible limitations of the scale.
Funder
Agencia Nacional de Investigación y Desarrollo
Publisher
Springer Science and Business Media LLC
Reference61 articles.
1. Abad, F., Olea, J., Ponsoda, V., García, C. (2011). Medición en ciencias sociales y de la salud. Madrid: Síntesis. 26–38 p.
2. American Educational Research Association, American Psychological Association & National Council on Measurement in Education (2014). Standards for educational and psychological testing. Washington, DC.
3. Asparouhouv, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 397–438. https://doi.org/10.1080/10705510903008204
4. Ato, M., López-García, J., & Benavente, A. (2013). Un sistema de clasificación de los diseños de investigación en psicología. Anales De Psicología, 29(3), 1038–1059.
5. Baban, A., & Craciun, C. (2007). Changing health-risk behaviors: A review of theory and evidence-based interventions in health psychology. Journal of Evidence-Based Psychotherapies, 7(1), 45.