A Guide to Visualizing Trajectories of Change With Confidence Bands and Raw Data

IF 15.6 1区 心理学 Q1 PSYCHOLOGY
Andrea L. Howard
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引用次数: 2

Abstract

This tutorial is aimed at researchers working with repeated measures or longitudinal data who are interested in enhancing their visualizations of model-implied mean-level trajectories plotted over time with confidence bands and raw data. The intended audience is researchers who are already modeling their experimental, observational, or other repeated measures data over time using random-effects regression or latent curve modeling but who lack a comprehensive guide to visualize trajectories over time. This tutorial uses an example plotting trajectories from two groups, as seen in random-effects models that include Time × Group interactions and latent curve models that regress the latent time slope factor onto a grouping variable. This tutorial is also geared toward researchers who are satisfied with their current software environment for modeling repeated measures data but who want to make graphics using R software. Prior knowledge of R is not assumed, and readers can follow along using data and other supporting materials available via OSF at https://osf.io/78bk5/. Readers should come away from this tutorial with the tools needed to begin visualizing mean trajectories over time from their own models and enhancing those plots with graphical estimates of uncertainty and raw data that adhere to transparent practices in research reporting.
用置信带和原始数据可视化变化轨迹指南
本教程旨在研究重复测量或纵向数据的研究人员,他们有兴趣增强模型隐含的平均水平轨迹的可视化,这些轨迹随时间随置信带和原始数据绘制。目标受众是已经使用随机效应回归或潜在曲线建模对实验、观察或其他重复测量数据进行建模的研究人员,但他们缺乏全面的指导来可视化随时间变化的轨迹。本教程使用了一个从两个组绘制轨迹的示例,如在随机效应模型中看到的那样,随机效应模型包括Time x Group相互作用和将潜在时间斜率因子回归到分组变量的潜在曲线模型。本教程还面向那些对当前的重复测量数据建模软件环境感到满意,但想要使用R软件制作图形的研究人员。本文不要求读者具备R的先验知识,读者可以使用OSF网站https://osf.io/78bk5/上提供的数据和其他支持材料。读者应该从本教程中获得所需的工具,开始从他们自己的模型中可视化平均轨迹,并通过不确定性的图形估计和坚持透明实践的原始数据来增强这些图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
21.20
自引率
0.70%
发文量
16
期刊介绍: In 2021, Advances in Methods and Practices in Psychological Science will undergo a transition to become an open access journal. This journal focuses on publishing innovative developments in research methods, practices, and conduct within the field of psychological science. It embraces a wide range of areas and topics and encourages the integration of methodological and analytical questions. The aim of AMPPS is to bring the latest methodological advances to researchers from various disciplines, even those who are not methodological experts. Therefore, the journal seeks submissions that are accessible to readers with different research interests and that represent the diverse research trends within the field of psychological science. The types of content that AMPPS welcomes include articles that communicate advancements in methods, practices, and metascience, as well as empirical scientific best practices. Additionally, tutorials, commentaries, and simulation studies on new techniques and research tools are encouraged. The journal also aims to publish papers that bring advances from specialized subfields to a broader audience. Lastly, AMPPS accepts Registered Replication Reports, which focus on replicating important findings from previously published studies. Overall, the transition of Advances in Methods and Practices in Psychological Science to an open access journal aims to increase accessibility and promote the dissemination of new developments in research methods and practices within the field of psychological science.
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