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Examining the Effect of Reverse Worded Items on the Factor Structure of the Need for Cognition Scale
Reverse worded (RW) items are often used to reduce or eliminate acquiescence bias, but there is a rising concern about their harmful effects on the covariance structure of the scale. Therefore, results obtained via traditional covariance analyses may be distorted. This study examined the effect of t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4909292/ https://www.ncbi.nlm.nih.gov/pubmed/27305001 http://dx.doi.org/10.1371/journal.pone.0157795 |
Sumario: | Reverse worded (RW) items are often used to reduce or eliminate acquiescence bias, but there is a rising concern about their harmful effects on the covariance structure of the scale. Therefore, results obtained via traditional covariance analyses may be distorted. This study examined the effect of the RW items on the factor structure of the abbreviated 18-item Need for Cognition (NFC) scale using confirmatory factor analysis. We modified the scale to create three revised versions, varying from no RW items to all RW items. We also manipulated the type of the RW items (polar opposite vs. negated). To each of the four scales, we fit four previously developed models. The four models included a 1-factor model, a 2-factor model distinguishing between positively worded (PW) items and RW items, and two 2-factor models, each with one substantive factor and one method factor. Results showed that the number and type of the RW items affected the factor structure of the NFC scale. Consistent with previous research findings, for the original NFC scale, which contains both PW and RW items, the 1-factor model did not have good fit. In contrast, for the revised scales that had no RW items or all RW items, the 1-factor model had reasonably good fit. In addition, for the scale with polar opposite and negated RW items, the factor model with a method factor among the polar opposite items had considerably better fit than the 1-factor model. |
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