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

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

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalFrontiers in psychology
Volume10
Issue numberMAY
DOIs
Publication statusPublished - 9 May 2019

Keywords

  • probabilistic graphical models
  • data science
  • exploratory research
  • Subgroup analysis
  • Graphical model
  • Exploratory analyses
  • Data visualization methods
  • Community energy initiatives

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