Zhang Xijuan, Noor Ramsha, Savalei Victoria
Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada.
PLoS One. 2016 Jun 15;11(6):e0157795. doi: 10.1371/journal.pone.0157795. eCollection 2016.
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.
反向计分(RW)项目常用于减少或消除默许偏差,但人们越来越担心它们对量表协方差结构的有害影响。因此,通过传统协方差分析获得的结果可能会被扭曲。本研究使用验证性因素分析检验了RW项目对简化的18项认知需求(NFC)量表因素结构的影响。我们对量表进行了修改,创建了三个修订版本,从无RW项目到全是RW项目不等。我们还操纵了RW项目的类型(完全相反与否定)。对于这四个量表中的每一个,我们拟合了四个先前开发的模型。这四个模型包括一个单因素模型、一个区分正向计分(PW)项目和RW项目的双因素模型,以及两个双因素模型,每个模型都有一个实质因素和一个方法因素。结果表明,RW项目的数量和类型影响了NFC量表的因素结构。与先前的研究结果一致,对于同时包含PW项目和RW项目的原始NFC量表,单因素模型拟合效果不佳。相比之下,对于没有RW项目或全是RW项目的修订量表,单因素模型拟合效果相当好。此外,对于同时包含完全相反和否定RW项目的量表,在完全相反项目中包含方法因素的因素模型比单因素模型拟合效果要好得多。