Improving Fit Assessment and Incomplete Data Diagnostics in Structural Equation Modeling

改进结构方程建模中的拟合评估和不完整数据诊断

• 批准号: RGPIN-2021-02958
• 负责人: Savalei, Victoria
• 金额: $ 1.31万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764136
• 关键词: Improving; Fit; Assessment; Incomplete; Data

The proposed research has the goal of developing and evaluating new methodologies to deal with missing data. Missing data are common in psychological research, particularly in studies that take place over time, take place in the real world rather than in the lab, and that collect a lot of data from participants. When analyzing a dataset with missing data, conclusions may be biased if missing data are not treated using appropriate statistical techniques, particularly when missingness is not random. Other challenging conditions are also often present in social science datasets, such as nonnormality, i.e., deviation from bell-shaped distributions. How to estimate and evaluate the fit of theoretical models that are fit to normal and nonnormal data with missing values is the focus of the present proposal. The proposal is in the context of structural equation models (SEMs), a class of statistical models that are very popular in psychology. These models can include latent variables, i.e., that are not directly observable but instead only their consequences are observable. Such variables are frequently viewed as appropriate representations for psychological constructs, such as personality traits and psychological states. SEMs include more standard statistical models such as regression as a special case. When evaluating how well a proposed SEM describes the data, model fit statistics are used. These statistics will be biased if the dataset contains missing values, and more so if some variables in the dataset are nonnormal. How to adjust the computations of model fit statistics so that they are no longer biased under these challenging circumstances is the first research direction outlined in this proposal. The second direction is to develop measures that evaluate the overall impact of missing data on models and datasets in a single summary statistic. Such statistics have not been developed or studied. Their development will help researchers gauge the overall impact of missing data on the quality of estimation of their models, and it will also help other methodologists better capture the robustness to missing data when evaluating new statistical methods. Ultimately, being able to draw correct statistical conclusions from challenging datasets will improve the quality of the science in psychology and related fields.

拟议研究的目标是开发和评估处理缺失数据的新方法。缺失数据在心理学研究中很常见，尤其是那些在真实的世界而不是实验室中进行的研究，以及从参与者那里收集大量数据的研究。当分析具有缺失数据的数据集时，如果未使用适当的统计技术处理缺失数据，则结论可能会有偏差，特别是当缺失不是随机的时。其他具有挑战性的条件也经常存在于社会科学数据集中，例如非正态性，即，偏离钟形分布。如何估计和评价理论模型对正态和非正态数据的拟合度是本文研究的重点。 该建议是在结构方程模型（SEM）的背景下提出的，这是一类在心理学中非常流行的统计模型。这些模型可以包括潜在变量，即，它们是不可直接观察的，而只是它们的后果是可观察的。这些变量通常被视为心理结构的适当表示，如人格特质和心理状态。SEM包括更标准的统计模型，例如回归作为特殊情况。当评估所提出的SEM如何描述数据时，使用模型拟合统计。如果数据集包含缺失值，这些统计量将有偏差，如果数据集中的某些变量是非正态的，则更是如此。如何调整模型拟合统计量的计算，使它们在这些具有挑战性的情况下不再有偏见，是本提案中概述的第一个研究方向。第二个方向是制定措施，在单一汇总统计中评估缺失数据对模型和数据集的总体影响。这类统计数据尚未编制或研究。它们的发展将帮助研究人员衡量缺失数据对其模型估计质量的总体影响，也将帮助其他方法学家在评估新的统计方法时更好地捕捉缺失数据的鲁棒性。最终，能够从具有挑战性的数据集中得出正确的统计结论将提高心理学和相关领域的科学质量。