• Development and Validation of the Retrospective Childhood Fantasy Play Scale

      Kirkham, Julie A.; Lloyd, Julian; Stockton, Hannah; University of Chester (SAGE Publications, 2018-08-16)
      This article describes the development and initial psychometric properties of the Retrospective Childhood Fantasy Play Scale (RCFPS), a brief 11-item retrospective self-report measure of reference for, and engagement with, fantasy play during childhood. Five studies were conducted to (a) develop the initial items for the scale (n =77), (b) determine the underlying factor structure (n = 200), (c) test the fit of the model (n= 530), and (d) and (e) ascertain construct validity (n = 200) and convergent validity (n = 263). Overall, the results suggest that the RCFPS is a unidimensional measure with acceptable fit and preliminary validity. The RCFPS may prove useful in educational and developmental research as an alternative to longitudinal studies to further investigate how childhood fantasy play relates to individual differences in adulthood (e.g., in the areas of creativity, theory of mind, and narrative skills).
    • Initial validation of the general attitudes towards Artificial Intelligence Scale

      Schepman, Astrid; Rodway, Paul; University of Chester
      A new General Attitudes towards Artificial Intelligence Scale (GAAIS) was developed. The scale underwent initial statistical validation via Exploratory Factor Analysis, which identified positive and negative subscales. Both subscales captured emotions in line with their valence. In addition, the positive subscale reflected societal and personal utility, whereas the negative subscale reflected concerns. The scale showed good psychometric indices and convergent and discriminant validity against existing measures. To cross-validate general attitudes with attitudes towards specific instances of AI applications, summaries of tasks accomplished by specific applications of Artificial Intelligence were sourced from newspaper articles. These were rated for comfortableness and perceived capability. Comfortableness with specific applications was a strong predictor of general attitudes as measured by the GAAIS, but perceived capability was a weaker predictor. Participants viewed AI applications involving big data (e.g. astronomy, law, pharmacology) positively, but viewed applications for tasks involving human judgement, (e.g. medical treatment, psychological counselling) negatively. Applications with a strong ethical dimension led to stronger discomfort than their rated capabilities would predict. The survey data suggested that people held mixed views of AI. The initially validated two-factor GAAIS to measure General Attitudes towards Artificial Intelligence is included in the Appendix.