School of Medicine, Queen's University Belfast, Belfast, UK.
School of Psychology, Queen's University Belfast, Belfast, UK.
Early Interv Psychiatry. 2022 Mar;16(3):239-246. doi: 10.1111/eip.13147. Epub 2021 Mar 24.
To examine the structure of the Prodromal Questionnaire (PQ-16) in a non-help-seeking population through exploratory factor analysis and confirmatory factor analysis. Previous studies have not looked at the structure of this self-report measure outside clinical settings.
Participants (n = 1045) were recruited through Amazon's Mechanical Turk (MTurk), and then completed the PQ-16. The data set was split randomly in two, one being used for exploratory factor analysis (EFA) and the other for confirmatory factor analysis (CFA). A polychoric correlation matrix was created and EFA was used to explore the factor structure of the PQ-16. Four models were tested through CFA to determine best fit: one, two, three and four-factor models were all analysed.
EFA indicated a two-factor structure in the PQ-16 in a non-help-seeking population (with a mean age = 29.7 years). Factor 1 represented perceptual abnormalities/hallucinations and factor 2 general symptoms associated with psychosis-risk. CFA indicated that all the proposed models were suitable fits for the dataset. Fit indices for the three-factor model (factor 1 representing perceptual abnormalities/hallucinations, factor 2 unusual thought content, and factor 3 negative symptom) indicated that it appeared to be a better fit for the data than the one, two, and four factor models.
This study suggests that a three-factor model of the PQ-16 is a better fit than other proposed models in a non-help-seeking population. Future research of the structure of the PQ-16 in this population may benefit from recruiting subjects with a lower mean age than the current study.
通过探索性因子分析和验证性因子分析,考察前驱期问卷(PQ-16)在非求助人群中的结构。先前的研究并未在临床环境之外研究这种自我报告测量的结构。
通过亚马逊的 Mechanical Turk(MTurk)招募参与者(n=1045),然后让他们完成 PQ-16。数据集随机分为两部分,一部分用于探索性因子分析(EFA),另一部分用于验证性因子分析(CFA)。创建了一个偏相关矩阵,并使用 EFA 来探索 PQ-16 的因子结构。通过 CFA 测试了四个模型来确定最佳拟合:分析了一个、两个、三个和四个因素模型。
EFA 表明,在非求助人群中(平均年龄为 29.7 岁),PQ-16 具有两因素结构。第一个因素代表知觉异常/幻觉,第二个因素代表与精神病风险相关的一般症状。CFA 表明,所有提出的模型都适合数据集。三因素模型(因素 1 代表知觉异常/幻觉,因素 2 代表异常思维内容,因素 3 代表阴性症状)的拟合指数表明,它似乎比一、二和四因素模型更适合数据。
这项研究表明,在非求助人群中,PQ-16 的三因素模型比其他提出的模型更适合。未来对该人群中 PQ-16 结构的研究可能受益于招募年龄低于本研究的受试者。