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

Nitin Bhushan*, Florian Mohnert, Daniel Sloot, Lise Jans, Casper Albers, Linda Steg

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

26 Citations (Scopus)
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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 behaviours.
Original languageEnglish
Article number1050
Number of pages12
JournalFrontiers in Psychology
Publication statusPublished - 9-May-2019


  • Graphical model
  • exploratory analyses
  • subgroup analysis
  • community energy initiatives
  • data visualization methods
  • SELF
  • NEED

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