Using a Gaussian Graphical Model to Explore Relationships Between Items and Variables in Environmental Psychology Research

Frontiers in Psychology

article

Nitin Bhushan¹  Florian Mohnert²  Daniel Sloot³  Lise Jans³  Casper Albers¹  Linda Steg³ 

[1] Department of Psychometrics and Statistics, University of Groningen;Faculty of Science, Informatics Institute, Universiteit van Amsterdam;Department of Environmental Psychology, University of Groningen

关键词: graphical model;    exploratory analyses;    subgroup analysis;    community energy initiatives;    data visualization methods;   

DOI  :  10.3389/fpsyg.2019.01050

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Exploratory analyses are an important first step in psychological research, particularly in problem-based research where various variables are often included from multiple theoretical perspectives not studied together in combination before. Notably, exploratory analyses aim to give first insights into how items and variables included in a study relate to each other. Typically, exploratory analyses involve computing bivariate correlations between items and variables and presenting them in a table. While this is suitable for relatively small data sets, such tables can easily become overwhelming when datasets contain a broad set of variables from multiple theories. We propose the Gaussian graphical model as a novel exploratory analyses tool and present a systematic roadmap to apply this model to explore relationships between items and variables in environmental psychology research. We demonstrate the use and value of the Gaussian graphical model to study relationships between a broad set of items and variables that are expected to explain the effectiveness of community energy initiatives in promoting sustainable energy behaviors.

【 授权许可】

CC BY