Nurs Res. 2022;71(6):469-476. doi: 10.1097/NNR.0000000000000614. Epub 2022 Aug 20.
Measures in nursing research frequently use Likert scales that yield ordinal data. Confirmatory factor analysis using Pearson correlations commonly applies to such data, although this violates ordinal scale assumptions.
The aim of this study was to illustrate the application of polychoric correlations and polychoric confirmatory factor analysis as a valid alternative statistical approach using data on family members' perceived support from nurses as an exemplar.
A primary analysis of cross-sectional data from a sample of 800 participants using data collected with the Iceland-Family Perceived Support Questionnaire was conducted using polychoric versus Pearson correlations, analysis of variance, and confirmatory factor analysis.
A two-factor measurement model was compatible with data from family members in the Ugandan care settings. Two contextual factors (cognitive and emotional support) constituted the family support measurement model. A factor correlation indicated that the two factors reflected distinct but closely related aspects of family support. Polychoric correlation revealed 13.8% (range: 5.5%-25.2%) higher correlations compared to Pearson correlations. Moreover, the polychoric agreed with the data, whereas the Pearson confirmatory factor analysis did not fit based on multiple statistical criteria. Analyses indicated a difference in emotional and cognitive support perception across two family characteristics: education and relationship to the patient.
A polychoric correlation suggests stronger associations, and consequently, the approach can be more credible with an ordinal Likert scale than Pearson correlations. Hence, polychoric confirmatory factor analysis can address a larger proportion of variance. In nursing research, polychoric confirmatory factor analysis can confidently be utilized when conducting confirmatory factor analysis of ordinal variables in Likert scales. Furthermore, when a Pearson confirmatory factor analysis is used for ordinal Likert scales, the researcher should carefully evaluate the difference between the two approaches and justify their methodological choice. Even though we do not suggest dispensing with Pearson correlations entirely, we recommend using polychoric correlation for ordinal Likert scales.
护理研究中的测量工具经常使用产生等级数据的李克特量表。尽管这种方法违反了等级量表的假设，但使用皮尔逊相关的验证性因子分析常用于此类数据。
本研究旨在说明使用多项式相关和多项式验证性因子分析作为一种有效的替代统计方法的应用，以护士感知的家庭成员支持为例。
使用冰岛家庭感知支持问卷对 800 名参与者的横断面数据进行了主要分析，使用多项式相关和皮尔逊相关、方差分析和验证性因子分析进行了分析。
在乌干达护理环境中，有两个因素的测量模型与家庭成员的数据相匹配。两个情境因素（认知和情感支持）构成了家庭支持测量模型。因素相关表明，这两个因素反映了家庭支持的不同但密切相关的方面。与皮尔逊相关相比，多项式相关显示出高出 13.8%（范围：5.5%-25.2%）的相关性。此外，多项式相关与数据一致，而皮尔逊验证性因子分析则不符合多项统计标准。分析表明，在教育和与患者的关系这两个家庭特征方面，情感和认知支持的感知存在差异。
多项式相关表明关联性更强，因此与皮尔逊相关相比，多项式相关在等级李克特量表中更可信。因此，多项式验证性因子分析可以解决更大比例的方差。在护理研究中，当对李克特量表中的等级变量进行验证性因子分析时，可以自信地使用多项式验证性因子分析。此外，当使用皮尔逊验证性因子分析对等级李克特量表进行分析时，研究人员应仔细评估这两种方法之间的差异，并证明其方法选择的合理性。尽管我们不建议完全放弃皮尔逊相关，但我们建议对等级李克特量表使用多项式相关。
