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- All Subjects: Assessment
- Creators: Levy, Roy
- Creators: Redman, Aaron
- Creators: Wharton, Christopher (Christopher Mack), 1977-
Results showed that falsely fitting bifactor data using unidimensional models or failing to account for DIF in item parameters resulted in estimation bias in the general factor mean difference, while treating ordinal data as continuous had little influence on the estimation bias as long as there was no severe model misspecification. The extent of estimation bias produced by misspecification of bifactor datasets with unidimensional models was mainly determined by the degree of unidimensionality (i.e., size of specific factor loadings) and the general factor mean difference size. When the DIF was present, the estimation accuracy of the general factor mean difference was completely robust to ignoring noninvariance in specific factor loadings while it was very sensitive to failing to account for DIF in threshold parameters. With respect to ignoring the DIF in general factor loadings, the estimation bias of the general factor mean difference was substantial when the DIF was -0.15, and it can be negligible for smaller sizes of DIF. Despite the impact of model misspecification on estimation accuracy, the power to detect the general factor mean difference was mainly influenced by the sample size and effect size. Serious Type I error rate inflation only occurred when the DIF was present in threshold parameters.
Businesses, as with other sectors in society, are not yet taking sufficient action towards achieving sustainability. The United Nations recently agreed upon a set of Sustainable Development Goals (SDGs), which if properly harnessed, provide a framework (so far lacking) for businesses to meaningfully drive transformations to sustainability. This paper proposes to operationalize the SDGs for businesses through a progressive framework for action with three discrete levels: communication, tactical, and strategic. Within the tactical and strategic levels, several innovative approaches are discussed and illustrated. The challenges of design and measurement as well as opportunities for accountability and the social side of Sustainability, together call for transdisciplinary, collective action. This paper demonstrates feasible pathways and approaches for businesses to take corporate social responsibility to the next level and utilize the SDG framework informed by sustainability science to support transformations towards the achievement of sustainability.