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Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: the Importance of Prior Distributions and Their Visualization
[journal article]
Abstract Nationwide opinions and international attitudes toward climate and environmental change are receiving increasing attention in both scientific and political communities. An often used way to measure these attitudes is by large-scale social surveys. However, the assumption for a valid country comparis... view more
Nationwide opinions and international attitudes toward climate and environmental change are receiving increasing attention in both scientific and political communities. An often used way to measure these attitudes is by large-scale social surveys. However, the assumption for a valid country comparison, measurement invariance, is often not met, especially when a large number of countries are being compared. This makes a ranking of countries by the mean of a latent variable potentially unstable, and may lead to untrustworthy conclusions. Recently, more liberal approaches to assessing measurement invariance have been proposed, such as the alignment method in combination with Bayesian approximate measurement invariance. However, the effect of prior variances on the assessment procedure and substantive conclusions is often not well understood. In this article, we tested for measurement invariance of the latent variable "willingness to sacrifice for the environment" using Maximum Likelihood Multigroup Confirmatory Factor Analysis and Bayesian approximate measurement invariance, both with and without alignment optimization. For the Bayesian models, we used multiple priors to assess the impact on the rank order stability of countries. The results are visualized in such a way that the effect of different prior variances and models on group means and rankings becomes clear. We show that even when models appear to be a good fit to the data, there might still be an unwanted impact on the rank ordering of countries. From the results, we can conclude that people in Switzerland and South Korea are most motivated to sacrifice for the environment, while people in Latvia are less motivated to sacrifice for the environment.... view less
Keywords
ISSP; measurement; visualization; Bayesian statistics; ranking; climate change; environmental protection
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Ecology, Environment
Free Keywords
measurement invariance; group ranking; MGCFA; prior sensitivity; Bayesian approximate measurement invariance (BAMI); ISSP 2010
Document language
English
Publication Year
2021
Page/Pages
p. 1-18
Journal
Frontiers in Psychology, 12 (2021)
DOI
https://doi.org/10.3389/fpsyg.2021.624032
ISSN
1664-1078
Status
Published Version; peer reviewed