TY - JOUR
T1 - Say farewell to bland regression reporting: Three forest plot variations for visualizing linear models
AU - Fries, Jonathan
AU - Oberleiter, Sandra
AU - Pietschnig, Jakob
N1 - Publisher Copyright:
© 2024 Fries et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2024/2/2
Y1 - 2024/2/2
N2 - Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.
AB - Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.
UR - http://www.scopus.com/inward/record.url?scp=85183798353&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0297033
DO - 10.1371/journal.pone.0297033
M3 - Article
SN - 1932-6203
VL - 19
JO - PLoS ONE
JF - PLoS ONE
IS - 2
M1 - e0297033
ER -