Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: The Importance of Prior Distributions and Their Visualization

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...

Full description

Bibliographic Details
Main Authors: Ingrid Arts, Qixiang Fang, Rens van de Schoot, Katharina Meitinger
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-07-01
Series:Frontiers in Psychology
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyg.2021.624032/full
id doaj-08dfbc451a4f41e49173fd4c57b0dd75
record_format Article
spelling doaj-08dfbc451a4f41e49173fd4c57b0dd752021-07-22T19:52:26ZengFrontiers Media S.A.Frontiers in Psychology1664-10782021-07-011210.3389/fpsyg.2021.624032624032Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: The Importance of Prior Distributions and Their VisualizationIngrid ArtsQixiang FangRens van de SchootKatharina MeitingerNationwide 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2021.624032/fullmeasurement invariancevisualizationBayesgroup rankingMGCFAprior sensitivity
collection DOAJ
language English
format Article
sources DOAJ
author Ingrid Arts
Qixiang Fang
Rens van de Schoot
Katharina Meitinger
spellingShingle Ingrid Arts
Qixiang Fang
Rens van de Schoot
Katharina Meitinger
Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: The Importance of Prior Distributions and Their Visualization
Frontiers in Psychology
measurement invariance
visualization
Bayes
group ranking
MGCFA
prior sensitivity
author_facet Ingrid Arts
Qixiang Fang
Rens van de Schoot
Katharina Meitinger
author_sort Ingrid Arts
title Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: The Importance of Prior Distributions and Their Visualization
title_short Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: The Importance of Prior Distributions and Their Visualization
title_full Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: The Importance of Prior Distributions and Their Visualization
title_fullStr Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: The Importance of Prior Distributions and Their Visualization
title_full_unstemmed Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: The Importance of Prior Distributions and Their Visualization
title_sort approximate measurement invariance of willingness to sacrifice for the environment across 30 countries: the importance of prior distributions and their visualization
publisher Frontiers Media S.A.
series Frontiers in Psychology
issn 1664-1078
publishDate 2021-07-01
description 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.
topic measurement invariance
visualization
Bayes
group ranking
MGCFA
prior sensitivity
url https://www.frontiersin.org/articles/10.3389/fpsyg.2021.624032/full
work_keys_str_mv AT ingridarts approximatemeasurementinvarianceofwillingnesstosacrificefortheenvironmentacross30countriestheimportanceofpriordistributionsandtheirvisualization
AT qixiangfang approximatemeasurementinvarianceofwillingnesstosacrificefortheenvironmentacross30countriestheimportanceofpriordistributionsandtheirvisualization
AT rensvandeschoot approximatemeasurementinvarianceofwillingnesstosacrificefortheenvironmentacross30countriestheimportanceofpriordistributionsandtheirvisualization
AT katharinameitinger approximatemeasurementinvarianceofwillingnesstosacrificefortheenvironmentacross30countriestheimportanceofpriordistributionsandtheirvisualization
_version_ 1721291055151710208