Frontiers in Psychology 01 frontiersin.org
Anxiety in response to the climate
and environmental crises:
validation of the Hogg
Eco-Anxiety Scale in Germany
StephanHeinzel
1,2*, MiraTschorn
3, MichaelSchulte-Hutner
2,
FabianSchäfer
4,5, GerhardReese
6, CarinaPohle
5, FelixPeter
7,
MichaelNeuber
8, ShuyanLiu
9, JanKeller
2, MichaelEichinger
10,11
and MyriamBechtoldt
12
1 Institute of Psychology, Department of Educational Sciences and Psychology, TU Dortmund University,
Dortmund, Germany, 2 Department of Education and Psychology, Freie Universität Berlin, Berlin,
Germany, 3 Social and Preventive Medicine, Department of Sports and Health Sciences, University of
Potsdam, Potsdam, Germany, 4 Sustainable Development, Darmstadt University of Applied Sciences,
Darmstadt, Germany, 5 Klimabildung e.V., Bochum, Germany, 6 Department of Psychology, RPTU
Kaiserslautern Landau, Campus Landau, Landau, Germany, 7 Department of School Psychology, State
School Administration of Saxony-Anhalt, Halle (Saale), Germany, 8 Center for Technology and Society,
Technical University of Berlin, Berlin, Germany, 9 Department of Psychiatry and Psychotherapy (CCM),
Charité – Universitätsmedizin Berlin, Berlin, Germany, 10 Center for Preventive Medicine and Digital
Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 11 Institute of Medical
Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg-
University Mainz, Mainz, Germany, 12 Department of Management, EBS Universität für Wirtschaft und
Recht, Oestrich-Winkel, Germany
Background: As the climate and environmental crises unfold, eco-anxiety,
defined as anxiety about the crises’ devastating consequences for life on earth,
affects mental health worldwide. Despite its importance, research on eco-anxiety
is currently limited by a lack of validated assessment instruments available in
different languages. Recently, Hogg and colleagues proposed a multidimensional
approach to assess eco-anxiety. Here, weaim to translate the original English
Hogg Eco-Anxiety Scale (HEAS) into German and to assess its reliability and
validity in a German sample.
Methods: Following the TRAPD (translation, review, adjudication, pre-test,
documentation) approach, wetranslated the original English scale into German.
In total, 486 participants completed the German HEAS. We used Bayesian
confirmatory factor analysis (CFA) to assess whether the four-factorial model
of the original English version could be replicated in the German sample.
Furthermore, associations with a variety of emotional reactions towards the
climate crisis, general depression, anxiety, and stress were investigated.
Results: The German HEAS was internally consistent (Cronbach’s alphas 0.71–
0.86) and the Bayesian CFA showed that model fit was best for the four-factorial
model, comparable to the factorial structure of the original English scale (affective
symptoms, rumination, behavioral symptoms, anxiety about personal impact).
Weak to moderate associations were found with negative emotional reactions
towards the climate crisis and with general depression, anxiety, and stress.
Discussion: Our results support the original four-factorial model of the scale and
indicate that the German HEAS is a reliable and valid scale to assess eco-anxiety
in German speaking populations.
OPEN ACCESS
EDITED BY
Simone Grassini,
University of Bergen, Norway
REVIEWED BY
Léan O'Brien,
University of Canberra, Australia
Teaghan Hogg,
University of Canberra, Australia, in
collaboration with reviewer LO'B
Francisco Sampaio,
Escola Superior de Enfermagem do Porto,
Portugal
*CORRESPONDENCE
Stephan Heinzel
RECEIVED 14 June 2023
ACCEPTED 04 September 2023
PUBLISHED 21 September 2023
CITATION
Heinzel S, Tschorn M, Schulte-Hutner M,
Schäfer F, Reese G, Pohle C, Peter F, Neuber M,
Liu S, Keller J, Eichinger M and
Bechtoldt M (2023) Anxiety in response to the
climate and environmental crises: validation of
the Hogg Eco-Anxiety Scale in Germany.
Front. Psychol. 14:1239425.
doi: 10.3389/fpsyg.2023.1239425
COPYRIGHT
© 2023 Heinzel, Tschorn, Schulte-Hutner,
Schäfer, Reese, Pohle, Peter, Neuber, Liu, Keller,
Eichinger and Bechtoldt. This is an open-
access article distributed under the terms of
the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction
in other forums is permitted, provided the
original author(s) and the copyright owner(s)
are credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted which
does not comply with these terms.
TYPE Brief Research Report
PUBLISHED 21 September 2023
DOI 10.3389/fpsyg.2023.1239425
Heinzel et al. 10.3389/fpsyg.2023.1239425
Frontiers in Psychology 02 frontiersin.org
KEYWORDS
environmental crisis, climate crisis, climate change, eco-anxiety, climate anxiety, Hogg
Eco-Anxiety Scale
1. Introduction
The climate and environmental crises pose existential threats to
human survival (Kates etal., 2012) and adversely affect mental health
worldwide (Hickman etal., 2021; Corvalan etal., 2022). Catastrophic
and more frequent extreme weather events as well as anticipated changes
in living conditions lead to considerable anxiety and other emotions,
even among people not yet adversely affected. In this context,
eco-anxiety has been defined as anxiety about the climate and
environmental crises’ devastating consequences for life on earth
(Pihkala, 2020; Hogg etal., 2021). Eco-anxiety is considered an umbrella
term and comprises “climate change anxiety,” i.e., anxiety specifically
related to the anthropogenic climate change (Clayton, 2020; Clayton and
Karazsia, 2020), as well as “anxiety about a multiplicity of environmental
calamities, which may or may not bedirectly caused by climate change,
including the elimination of entire ecosystems and plant and animal
species, global mass pollution and deforestation” (Hogg etal., 2021, p.3).
Expanding earlier conceptions of climate change and eco-anxiety
primarily focused on affective symptoms (Searle and Gow, 2010; Helm
etal., 2018), current measures acknowledge the multidimensionality
of these constructs (Clayton and Karazsia, 2020; Hogg etal., 2021),
including cognitions and behavioral impairments operationalized by
items such as “unable to stop thinking about losses to the environment”
or “difficulty sleeping.” The first validated multidimensional climate
anxiety scale was developed by Clayton and Karazsia (2020) and
comprised two dimensions: cognitive-emotional impairment (e.g., “I
find myself crying because of climate change”) and functional
impairment (e.g., “My concerns about climate change undermine my
ability to work to my potential”). Despite advancements compared to
earlier conceptions of climate change anxiety, the Climate Anxiety
Scale has certain limitations. First, the scale focuses on the climate
crisis as the sole cause of anxiety and disregards other devastating
environmental calamities caused by human activity, such as
deforestation or pollution. Second, the scale emphasizes different
impairments caused by the climate crisis but does not capture the
emotional experience of anxiety (Wullenkord etal., 2021). Third,
Wullenkord etal. (2021) were unable to replicate the factorial structure
of the Climate Anxiety Scale of Clayton and Karazsia (2020) in a
German sample and point to conceptual limitations. Supporting this
finding, a recent synthesis of psychometric properties of the Climate
Anxiety Scale and the Hogg Eco-Anxiety Scale (HEAS; Hogg etal.,
2023) indicated that psychometric performance of the Climate
Anxiety Scale was mixed and inconsistent (Larionow etal., 2022;
Mouguiama-Daouda etal., 2022; Simon etal., 2022; Tam etal., 2023).
To expand previous work, Hogg and colleagues proposed the HEAS
as an alternative measure to the Climate Anxiety Scale. Their validation
studies (Hogg etal., 2021) yielded a four-factorial model, comprising
affective symptoms, rumination, behavioral symptoms and anxiety of
one’s personal impact on the planet, that supports the multidimensionality
of eco-anxiety. Furthermore, they reported high internal consistency and
moderate associations with general anxiety, depression, and stress.
The global adverse effects of the climate and environmental crises
on mental health highlight the importance of making reliable and
valid scales of eco-anxiety available in different languages to facilitate
cross-country research. Since no validated eco-anxiety scale is
currently available in German, the aim of this study was to translate
the original English HEAS (Hogg etal., 2021) into German and to
assess its psychometric properties. This included an examination of its
internal consistency, factorial structure and associations with general
anxiety, depression, and stress as well as various emotional reactions
to the climate crisis.
2. Methods
We used a two-step approach in this study. In a first step,
we translated the original English HEAS into German using
standardized guidance (Dorer, 2018). In a second step, weconducted
a cross sectional study to assess the psychometric properties of the
German HEAS. The study was approved by the local ethics committee
of Freie Universität Berlin, Germany (No 036/2021 and Amendments
020/2022 and 023/2022 for samples 1 and 2) and by the local ethics
committee at University of Potsdam, Germany (No 51/2022 for
sample 3). Weobtained written informed consent from all participants.
2.1. Translation of the original English HEAS
into German
We translated the original English HEAS (Hogg etal., 2021) into
German using the TRAPD approach (translation, review, adjudication,
pre-test, documentation) as recommended by the European Social
Survey (Dorer, 2018). First, three German native speakers fluent in
English (SH, MS-H, and FP) independently translated the scale into
German. Second, the three versions were discussed in the research
team and integrated, yielding a pre-test version of the scale. Third,
following the back-translation approach (Brislin, 1970), two researchers
fluent in English (C2 level) and not involved in our study translated the
scale back into English. Based on the back-translations, wemade minor
adjustments to the German pre-test version. Forth, weconducted a
pilot survey with 33 participants to assess the comprehensibility of the
scale. Given good comprehensibility of all items in the pilot survey,
wedid not apply further changes to the German HEAS. The English
and German items of the HEAS are reported in Table1. The protocol
of the translation can berequested from the corresponding author.
2.2. Participants and data collection
We tested the German HEAS with 486 participants (121 male/357
female/8 diverse) in Germany (age M [SD] = 29.43 [10.63], median:
26 years, range: 18–73). To reach an adequate sample size for our
Heinzel et al. 10.3389/fpsyg.2023.1239425
Frontiers in Psychology 03 frontiersin.org
planned analyses, weused three different recruitment approaches:
158 participants (students) were recruited via online advertisements
and mailing-lists at 40 German universities (sample 1). One hundred
and ninety-six participants (students and university staff) were
recruited at Freie Universität Berlin via flyers, posters, and emails
(sample 2) and 132 participants (students) at University of Potsdam
via an online recruitment system for university students and online
advertisement (sample 3). Using a link/QR code on the study
invitation, participants accessed and completed an online survey
implemented in the survey software Unipark (Version 21.2, QuestBack
GmbH, Oslo, Norway).
2.3. Measures
2.3.1. HEAS
The Hogg Eco-Anxiety Scale (HEAS; Hogg etal., 2021) comprises
13 items (see Table 1 for the German items) and is intended to
measure four dimensions of anxiety related to the climate and
environmental crises: affective symptoms, rumination, behavioral
symptoms, and anxiety about one’s negative impact on the planet. For
each item, the frequency during the past 2 weeks was self-rated on a
4-point Likert scale (0 = not at all, 1 = several of the days, 2 = over half
the days, 3 = nearly every day). The validation study of the original
English HEAS confirmed the postulated four-factorial structure, with
all subscales being internally consistent (all Cronbach’s alphas >0.82).
2.3.2. DASS-21
In accordance with Hogg etal. (2021), weused the Depression
Anxiety Stress Scale 21 (DASS-21; Lovibond and Lovibond, 1995) to
assess associations between eco-anxiety and general depression,
anxiety, and stress. The DASS-21 measures self-reported symptoms
during the past 2 weeks on a 4-point Likert scale (from 0 “did not
apply to me at all” to 3 “applied to me very much or most of the time”)
with higher scores indicating higher symptom burden.
2.3.3. Emotional reactions in response to the
climate crisis
To investigate associations between eco-anxiety and other
emotional reactions towards the climate crisis, weincluded 18 positive
and negative emotions, each assessed with a single item based on work
by Hickman etal. (2021). Participants were asked to rate the current
strength of each emotion when thinking about the climate crisis on a
5-point Likert scale ranging from “not at all” to “extremely.”
All participants completed the German HEAS and items on
emotional reactions towards the climate crisis. Participants of sample
1 additionally completed the DASS-21. To reduce participant burden
and to achieve high response rates necessary for the planned analyses,
sample 2 and 3 did not complete the DASS-21. Respondents received
no financial compensation for participating in the study.
2.4. Data analysis
We calculated Cronbach’s alpha for all HEAS subscales to estimate
internal consistency. To explore concurrent and discriminant validity,
we investigated associations with the DASS-21 and emotional
reactions towards the climate crisis. Comparable to Hogg etal. (2021),
we fitted a multiple linear regression model to assess unique
associations between the DASS-21 anxiety subscale and each HEAS
subscale while controlling for the remaining HEAS subscales. To test
unique associations of emotional reactions with the HEAS affective
symptoms subscale, wefitted a second multiple linear regression
model. Furthermore, wecalculated bivariate Pearson correlations
TABLE1 Original English items of the Hogg Eco-Anxiety Scale (HEAS) and
their German translation.
Original English HEAS German translation of the
HEAS
Over the last 2 weeks, how often
have youbeen bothered by the
following problems, when
thinking about climate change and
other global environmental
conditions (e.g., global warming,
ecological degradation, resource
depletion, species extinction,
ozone hole, pollution of the
oceans, deforestation)?
Wie oft fühlten Sie sich im Verlauf der
letzten 2 Wochen durch die folgenden
Beschwerden beeinträchtigt, wenn Sie über
den Klimawandel und andere globale
Umweltbedingungen nachdachten (z.B.
globale Erwärmung, Umweltzerstörung,
Ressourcenerschöpfung, Artensterben,
Ozonloch, Verschmutzung der Ozeane,
Abholzung?)
1. Feeling nervous, anxious or on
edge
1. Nervosität, Ängstlichkeit oder
Anspannung
2. Not being able to stop or control
worrying
2. Nicht in der Lage sein, Sorgen zu stoppen
oder zu kontrollieren
3. Worrying too much 3. Übermäßige Sorgen
4. Feeling afraid 4. Gefühl der Angst
5. Unable to stop thinking about
future climate change and other
global environmental problems
5. Nicht in der Lage sein, das Nachdenken
über den zukünftigen Klimawandel und
andere globale Umweltprobleme zu stoppen
6. Unable to stop thinking about
past events related to climate
change
6. Nicht in der Lage sein, das Nachdenken
über vergangene Ereignisse zu stoppen, die
mit dem Klimawandel zusammenhängen
7. Unable to stop thinking about
losses to the environment
7. Nicht in der Lage sein, das Nachdenken
über Schäden für die Umwelt zu stoppen
8. Difficulty sleeping 8. Schwierigkeiten zu schlafen
9. Difficulty enjoying social
situations with family and friends
9. Schwierigkeiten soziale Situationen mit
Familie und Freund*innen zu genießen
10. Difficulty working and/or
studying
10. Schwierigkeiten zu arbeiten und/oder zu
lernen
11. Feeling anxious about the
impact of your personal behaviors
on the earth
11. Besorgnis über die Auswirkungen Ihrer
persönlichen Verhaltensweisen auf die Erde
12. Feeling anxious about your
personal responsibility to help
address environmental problems
12. Besorgnis über Ihre persönliche
Verantwortung beim Angehen von
Umweltproblemen
13. Feeling anxious that your
personal behaviors will do little to
help fix the problem
13. Besorgnis, dass Ihr persönliches
Verhalten wenig zur Lösung des Problems
beitragen wird
Response scale: 0 = not at all,
1 = several of the days, 2 = over half
the days, 3 = nearly every day.
Antwortalternativen: 0 = Überhaupt nicht,
1 = An einzelnen Tagen, 2 = An mehr als der
Hälfte der Tage, 3 = Beinahe jeden Tag
HEAS comprises the following four subscales: affective symptoms (true mean of items 1–4),
rumination (true mean of items 5–7), behavioral symptoms (true mean of items 8–10),
anxiety about personal impact (true mean of items 11–13).
Heinzel et al. 10.3389/fpsyg.2023.1239425
Frontiers in Psychology 04 frontiersin.org
between HEAS subscales and DASS-21 subscales as well as emotional
reactions towards the climate crisis.
We performed a Bayesian confirmatory factor analysis (CFA) to
examine the factorial structure of the German HEAS. Bayesian CFA,
unlike conventional CFA, offers certain benefits: Bayesian CFA
employs probabilistic methods to effectively estimate parameters even
when sample sizes are modest, mitigating issues associated with
statistical power and enhancing the robustness of findings. Another
notable feature of Bayesian CFA is its capacity to incorporate existing
knowledge or beliefs into the analysis through “informed priors.” This
means that researchers can introduce relevant information about
parameter values before analyzing the data. Such a priori knowledge
enhances the precision of parameter estimates and refines the accuracy
of model outcomes. Furthermore, Bayesian CFA offers a more versatile
approach for modeling cross-loadings of items on latent factors.
Unlike conventional CFA, which only allows for substantial loadings
of items on their respective factors, Bayesian CFA accounts for the
possibility that items may have subtle yet meaningful relationships
with other factors (near-zero loadings). This permits a more nuanced
understanding of the relationships between variables, resulting in a
more comprehensive representation of the underlying constructs
(Depaoli, 2021). These attributes collectively establish Bayesian CFA
as a valuable analytical tool, extending researchers’ capabilities to
address challenges posed by limited samples, to leverage existing
knowledge, and to effectively model complex relationships within
the data.
We chose Bayesian CFA to incorporate information from prior
work including data about the factorial structure and item loadings
reported by Hogg et al. (2021) in our analysis. Considering this
knowledge in the estimation process enabled us to include pre-existing
information about the model parameters and update these
assumptions (Depaoli and van de Schoot, 2017). Even small to
moderate samples are sufficient to obtain accurate results if, as in this
case, there is prior knowledge (Depaoli and van de Schoot, 2017).
Model fit was assessed using posterior predictive checks, which
compared the observed data with the estimated model, i.e., the
posterior predictive distribution (Muthén and Muthén, 2017).
The statistics program Mplus (version 8, Muthén and Muthén,
2017) produces a confidence interval for the posterior predictive
checks, which, if they include zero indicate that the hypothesized
model structure adequately fits the observed data. The posterior
predictive (PP) value of p indicates the proportion of replicated data
that exceeds the original data. Low PP values of p indicate poor fit.
Models with values <0.10 should berejected, whereas values around
0.5 indicate excellent model fit (Cain and Zhang, 2019).
We compared a four-factorial model in which the main loadings
of the items on their hypothesized factors were specified to a four-
factorial model in which near-zero cross-loadings of items from the
three factors rumination, behavioral symptoms and anxiety about
personal impact were allowed on the affective symptoms factor
(Muthén and Asparouhov, 2012). Wedid this to account for the fact
that in a principal component analysis by Hogg etal. (2021), the
affective symptoms factor was found to explain 50% of the item
variance. To evaluate the assumption of minor cross-loadings of items
from the rumination, behavioral symptoms and anxiety about
personal impact factor on affective symptoms, wechecked the prior-
posterior predictive (PPP) value of p (Asparouhov and Muthén, 2017).
The PPP value of p differs from the PP value of p: The PPP value of p
is suitable for testing whether the assumptions of near-zero priors with
small variances hold. This way, individual parameters are tested rather
than the fit of the overall model. If the PPP value of p is close to zero,
the hypothesis that these cross-loadings are minor is rejected, which
may indicate model misspecification. Any cross-loadings that are not
minor would contradict the intended clear assignment of items to
their respective factors. In addition, wecompared the four-factorial
solution to three-, two-and one-factorial models where rumination,
behavioral symptoms and anxiety about personal impact were merged
with the affective symptoms factor, respectively.
The reporting of the analyses follows the recommendations by
Depaoli and van de Schoot (2017). The CFA was performed using
Bayesian estimation in Mplus (version 8; Muthén and Muthén, 2017).
Two Markov chains were implemented for each parameter. A Markov
chain is a computational algorithm to iteratively approximate the
model parameters. Its characteristics imply that each new parameter
value is conditional only on the preceding one, irrespective of the
entire history of the chain. Through numerous iterations, Markov
chains gradually converge towards a more accurate approximation of
the true parameter values. To assess chain convergence, the Gelman
and Rubin convergence diagnostic (Gelman and Rubin, 1992a,b) was
implemented as described in the Mplus manual with a stricter
convergence criterion than the default setting (0.01 instead of 0.05).
To establish stable calculations, weinitiated a preliminary phase of
50,000 iterations (initial burn-in phase) without recording the results,
followed by a fixed number of 50,000 iterations with recorded
outcomes (postburn-in iterations). The Gelman and Rubin (1992a,b)
diagnostic indicated that convergence was obtained with these fixed
iterations for each of the two chains. Next, the trace plots for each
model parameter were visually inspected. For each of the model
parameters, both chains showed a constant mean and variance in the
postburn-in portion of the chain. To further endorse convergence,
weestimated the model again but with the number of burn-in and
postburn-in iterations doubled (i.e., 200,000 iterations in total). Again,
convergence was obtained and the model parameters were almost
identical for all main factor loadings and factor covariances, i.e., the
percent of relative deviation was less than 1%. However, this was not
true for minor cross-loadings of items on the affective symptoms
factor (see below). The magnitude of their factor loadings was
substantially greater in the model with doubled iterations than in the
original model, with a relative deviation of up to 300%. Wetherefore
report the results of models with doubled iterations.
When implementing the informative priors for the main loadings
of items on their respective factors, i.e., using the estimates from
Table 5in Hogg etal. (2021), wefollowed the recommendations by
Mplus and assumed that they followed a normal distribution. As Hogg
and colleagues did not provide variances for the factor loadings,
weconducted sensitivity analyses with different variance priors (0.001,
0.01, 0.1, 1.0 and 10) to test the robustness of our findings. Werelied
on the Mplus default prior settings for error variances of items and the
latent factor covariance matrix.
Recognizing the relatively infrequent use of Bayesian CFA, wealso
offer model fit indices of a conventional CFA to enhance interpretation.
In evaluating model fit, weapplied the following criteria: χ
2
/df ≤ 2,
Comparative Fit Index (CFI; Bentler, 1990) ≥0.95, Tucker Lewis Index
(TLI; Tucker and Lewis, 1973) ≥0.95, Root Mean Square Error of
Approximation (RMSEA; Steiger, 1980) ≤0.05, and Standardized Root
Mean Squared Residual (SRMR; Hu and Bentler, 1999) ≤0.08.
Heinzel et al. 10.3389/fpsyg.2023.1239425
Frontiers in Psychology 05 frontiersin.org
However, since conventional CFA does not permit the modeling of
near-zero loadings for items 5–13 on the affective symptoms factor,
the Bayesian model is more complex and not entirely congruent with
the four-factorial conventional CFA.
3. Results
3.1. Internal consistency of the German
HEAS
The HEAS subscales affective symptoms (M [SD] = 0.69 [0.60]),
rumination (M [SD] = 0.60 [0.67]), and anxiety about personal impact
(M [SD] = 1.20 [0.70]) showed good internal consistency (Cronbach’s
alphas = 0.83; 0.86; 0.83, respectively). The internal consistency of the
subscale behavioral symptoms (M [SD] = 0.33 [0.50]) was acceptable
(Cronbach’s alpha = 0.71).
3.2. Structural validity of the German HEAS
Overall, model fit was best for the four-factorial model with
variance priors of 0.1 (results for models with different variance priors
are available upon request). The model fit was acceptable based on a
PP value of p of 0.187, and the four-factorial model showed better fit
than the three-, two-, and one-factorial solutions where items loaded
onto the affective symptoms factor instead of their content-specific
factors. Furthermore, the Bayesian posterior predictive checking
utilized χ
2
likelihood ratio tests to compare the observed-data test
statistic with the replicated-data test statistic. Since its 95% confidence
interval included zero, this result indicated that there was no
significant difference between the observed-data χ
2
values and the
replicated-data χ
2
values. Finally, the PPP value of p of 0.902 was
excellent and suggested that the assumption of near-zero cross-
loadings in the four-factorial model was valid. The findings are
presented in Table2.
Model fit indices from conventional frequentist CFA support the
conclusion of good model fit with exception for the χ
2
value:
χ
2
/df = 2.45 (144.47/59), p ≤ 0.001, CFI = 0.983, TLI = 0.977,
RMSEA = 0.055 (90% CI, 0.043–0.066), and SRMR = 0.024.
Table3 displays the factor loadings of items in the four-factorial
model with minor cross-loadings of items from the rumination,
behavioral symptoms and anxiety about personal impact factor on the
affective symptoms factor.
The intercorrelations of the latent factors were high. Affective
symptoms correlated with rumination (0.79), behavioral symptoms
(0.74), and anxiety about personal impact (0.65). Further correlations
were: rumination with behavioral symptoms (0.55) and anxiety about
personal impact (0.61); behavioral symptoms with anxiety about
personal impact (0.43).
To examine the effects of informed priors for factor loadings on
model estimates, wecompared the results of the four-factorial model
with informed priors with the model parameters that resulted based
on diffuse priors for factor loadings with normal distribution.
Differences in main factor loadings and factor covariances were,
according to Depaoli and van de Schoot (2017), moderate (1–10%).
But the levels of deviation were larger for the cross-loadings of all
items on the affective symptoms factor although their model
parameters were modeled as diffuse in both analyses. In the alternative
model without informed priors, particularly the items 7 (“Unable to
stop thinking about losses to the environment”), 9 (“Difficulty
enjoying social situations with family and friends”), and 13 (“Feeling
anxious that your personal behaviors will do little to help fix the
problem”) had stronger than near-zero (>0.1) loadings on the affective
symptoms factor, with l
7
= 0.16, l
9
= 0.19, and l
13
= 0.11. Thus, prior
assumptions about factor loadings slightly affected the analyses but
the conclusions regarding the number and structure of factors
remained unchanged.
TABLE2 Model fit comparisons.
Posterior
predictive check
95% CI
Posterior
predictive
p-value
Prior
posterior
predictive
p-value
Lower
2.5%
Upper
2.5%
4-Factorial
model,
informed
priors, no
cross-
loadings
−20.596 62.997 0.166 0.987
4-Factorial
model,
informed
priors, near
zero cross-
loadings
−23.007 61.230 0.187 0.902
4-Factorial
model,
diffuse
priors, near
zero-cross-
loadings
−19.774 64.523 0.151 0.000
3-Factorial
model,
informed
priors and
near-zero
cross-
loadings
38.02 133.949 0.000 0.973
2-Factorial
model,
informed
priors and
near-zero
cross-
loadings
61.853 163.564 0.000 0.906
1-Factorial
model,
informed
priors
212.347 331.619 0.000 0.424
N = 486; CI, confidence interval.
Heinzel et al. 10.3389/fpsyg.2023.1239425
Frontiers in Psychology 06 frontiersin.org
3.3. Association between the HEAS
subscales and the DASS-21in sample 1
Scores of the DASS-21 depression (n = 158, M[SD] = 4.97 [4.41]),
anxiety (3.01 [3.67]), and stress subscales (6.32 [4.32]) were below
clinical thresholds. A multiple regression model investigating unique
associations of each HEAS subscale and the DASS-21 anxiety subscale
showed that the affective symptoms subscale (standardized β = 0.29,
p = 0.011) and the behavioral symptoms subscale (standardized
β = 0.25, p = 0.009) were significantly associated. Bivariate correlations
between all HEAS subscales and all DASS-21 subscales are reported
in Table4.
3.4. Associations between emotional
reactions towards the climate crisis and the
HEAS affective symptoms subscale
When entering all 18 emotional reactions towards the climate
crisis into the multiple linear regression model to test unique
relationships with the HEAS affective symptoms subscale, only the
emotions “anxious” (standardized β = 0.19, p < 0.001), “desperate”
(standardized β = 0.19, p = 0.001), and “hurt” (standardized β = 0.19,
p < 0.001) were significantly related when controlling for all other
emotions. See Table4 for bivariate correlations between all HEAS
subscales and emotional reactions.
4. Discussion
In this study, wetranslated the original English HEAS into German
and assessed its psychometric properties. We measured the internal
consistency and investigated whether our data supported the original four-
factorial model of the scale. Furthermore, weassessed associations of the
HEAS with emotional reactions towards the climate crisis and general
depression, anxiety, and stress. In line with the original English HEAS
(Hogg etal., 2021), a replication study (Hogg etal., 2023), and recent
translations into Turkish (Uzun etal., 2022) and Portuguese (Sampaio,
n.d.), the German HEAS showed good reliability (internal consistency)
and construct validity, confirming the multidimensional nature of the
construct. Results of the Bayesian CFA indicated a good model fit for the
four-factorial solution with minor cross-loadings of items from the
rumination, behavioral symptoms and anxiety about personal impact
factors on the affective symptoms factor. Only three items (item 7: “Unable
to stop thinking about losses to the environment”; item 9: “Difficulty
enjoying social situations with family and friends”; item 13: “Feeling
anxious that your personal behaviors will do little to help fix the problem”)
showed minor (>0.1) cross-loadings on the affective symptoms factor.
Moreover, the four-factorial model showed a better fit to the data than the
three-, two-, and one-factorial solutions. An additional conventional CFA
supported the good model fit of the four-factorial model. In summary,
wewere able to reproduce the four-factorial structure of the original
English version for the German translation of the HEAS.
Similar to the results of Hogg etal. (2021), correlations between
subscales of the German HEAS and the DASS-21 ranged from weak
to moderate, indicating that the dimensions of eco-anxiety were
distinct from general depression, anxiety, and stress, but shared a
significant proportion of variance. This is in line with prior work
reporting weak to moderate but consistent positive associations
between negative eco-emotions and poor mental health (Stanley etal.,
2021; Stewart, 2021; Ogunbode etal., 2023). In a multiple linear
regression model only the affective and behavioral symptoms
subscales of the HEAS were related to general anxiety, emphasizing
the multidimensional properties of eco-anxiety. Moderate correlations
between eco-anxiety and other negative, but not positive emotions
suggest that engaging with the climate and environmental crises
simultaneously evokes a variety of negative emotions, as proposed by
other studies (Hickman etal., 2021; Ojala etal., 2021). A multiple
linear regression model of 18 emotions on the affective symptoms
subscale showed that only the emotions anxious, desperate and hurt
uniquely predicted the affective symptoms subscale which supports
the concurrent and discriminant validity of this subscale.
TABLE3 Item factor loadings of the four-factorial solution with informed
priors and near-zero cross loadings.
Estimate SD p-
value
(one-
tailed)
95% CI
Lower
2.5%
Upper
2.5%
Affective
symptoms
Item 1 0.807 0.023 <0.001 0.760 0.849
Item 2 0.796 0.027 <0.001 0.738 0.844
Item 3 0.847 0.023 <0.001 0.799 0.887
Item 4 0.826 0.024 <0.001 0.774 0.869
Item 5 0.064 0.116 0.285 −0.179 0.284
Item 6 −0.107 0.129 0.195 −0.374 0.133
Item 7 −0.069 0.115 0.273 −0.16 0.293
Item 8 0.066 0.165 0.348 −0.265 0.376
Item 9 0.126 0.156 0.216 −0.206 0.402
Item 10 −0.017 0.179 0.462 −0.371 0.330
Item 11 −0.001 0.138 0.497 −0.277 0.264
Item 12 0.007 0.136 0.479 −0.261 0.274
Item 13 0.076 0.119 0.263 −0.169 0.301
Rumination
Item 5 0.845 0.094 <0.001 0.676 1.053
Item 6 0.933 0.105 <0.001 0.745 1.158
Item 7 0.866 0.093 <0.001 0.692 1.058
Behavioral
symptoms
Item 8 0.723 0.125 <0.001 0.515 0.996
Item 9 0.602 0.132 <0.001 0.372 0.891
Item10 0.823 0.135 <0.001 0.586 1.112
Anxiety
about
personal
impact
Item 11 0.876 0.093 <0.001 0.717 1.082
Item 12 0.897 0.089 <0.001 0.740 1.088
Item 13 0.705 0.085 <0.001 0.558 0.894
N = 486; CI, confidence interval; SD, standard deviation. Values in bold indicate significant
estimates (p < 0.001).
Heinzel et al. 10.3389/fpsyg.2023.1239425
Frontiers in Psychology 07 frontiersin.org
The current study contributes to a growing international research
interest in understanding the emotional consequences of the climate and
environmental crises and highlights that eco-anxiety is more complex
than just feeling anxious or concerned. Rather, eco-anxiety comprises a
complex and intertwined set of ruminations, potential impairments, and
deep concerns. Saying that, however, the simple solution of “reducing
anxiety” that is usually a goal in the treatment of anxiety disorders may
not prove fruitful in the case of eco-anxiety. In contrast to anxiety
disorders such as agoraphobia, eco-anxiety is not an inadequate or
exaggerated reaction to an objectively harmless situation because the
dimensions of the climate and environmental crises are overwhelming,
and the threat is real. Thus, in most cases, eco-anxiety can beseen as a
reasonable response to these excessive crises (Heinzel, 2022). Further
research will berequired to define cut-off values identifying severe cases
of eco-anxiety (e.g., when professional support to deal with the anxiety
would berecommended). Quantifying the level of impairment and
frequency of symptoms as done in the HEAS is a good starting point for
this line of research. Weanticipate that climate-and eco-anxiety will
increase globally as the climate and environmental crises unfold in the
upcoming years. Thus, precisely operationalizing these constructs and
developing reliable and valid scales is important not only for research
but also for practice. Having adequate measures can increase awareness
for these types of anxiety in society and potentially support clinicians to
identify severe cases that may need help.
Investigating behavioral consequences of eco-anxiety including
the relationship between eco-anxiety and pro-environmental behavior
is another important line of research. Prior work suggests that the
experience of climate- or eco-anxiety is often associated with
motivation for pro-environmental action and policy support
(Wullenkord etal., 2021; Whitmarsh etal., 2022; Ogunbode etal.,
2023). However, further research is required to understand under
which conditions this anxiety transforms into action, rather than into
cognitive suppression/ assimilation or emotional apathy (Lamb etal.,
2020). We believe that eco-anxiety can beovercome if comprehensive
and effective measures to counteract the climate crisis and protect the
environment will beimplemented worldwide (Heinzel, 2022).
4.1. Limitations and future perspectives
Some limitations need to beconsidered when interpreting our
results. First, the study samples were not representative for the general
TABLE4 Correlations between HEAS subscales and DASS-21 subscales and emotional reactions towards the climate crisis.
HEAS affective
symptoms subscale
HEAS rumination
subscale
HEAS behavioral
symptoms subscale
HEAS anxiety about
personal impact
subscale
DASS-21 anxiety subscale 0.41*** 0.23** 0.40*** 0.28***
DASS-21 depression
subscale
0.46*** 0.27*** 0.49*** 0.28***
DASS-21 stress subscale 0.43*** 0.23** 0.42*** 0.29***
Emotional reactions to the climate crisis
Anxious 0.47*** 0.27*** 0.16*** 0.25***
Distressed 0.40*** 0.25*** 0.21*** 0.32***
Worried 0.38*** 0.23*** 0.19*** 0.31***
Sad 0.38*** 0.29*** 0.24*** 0.34***
Angry 0.37*** 0.29*** 0.24*** 0.31***
Powerless 0.26*** 0.12** 0.09*0.16***
Helpless 0.36*** 0.22*** 0.16*** 0.28***
Ashamed 0.35*** 0.25*** 0.26*** 0.26***
Desperate 0.52*** 0.38*** 0.28*** 0.33***
Hurt 0.40*** 0.31*** 0.34*** 0.24***
Depressed 0.46*** 0.28*** 0.27*** 0.34***
Frustrated 0.40*** 0.30*** 0.20*** 0.38***
Disgusted 0.38*** 0.28*** 0.24*** 0.24***
Guilty 0.32*** 0.24*** 0.26*** 0.35***
Indifferent −0.09 −0.08 0.04 −0.10*
Calm −0.23*** −0.15*** −0.01 −0.20***
Optimistic −0.08 0.03 0.01 −0.01
Confident −0.09 0.02 0.03 −0.10*
Estimated correlations based on bivariate Pearson correlation coefficients. Data for DASS-21 was only available for sample 1 (n = 158). DASS-21, Depression, Anxiety, Stress Scale 21; HEAS,
Hogg Eco-Anxiety Scale; *** p < 0.001; ** p < 0.01; * p < 0.05.
Heinzel et al. 10.3389/fpsyg.2023.1239425
Frontiers in Psychology 08 frontiersin.org
population given that large proportions of the sample were young,
female and highly educated which is due to recruitment in university
settings. Thus, future research should bebased on representative
samples to assess whether our results can be generalized to the
German population. Second, while wewere able to investigate several
important psychometric properties of the German HEAS in this study,
other properties such as test–retest reliability remain to beinvestigated
in future studies (Mokkink etal., 2010).
Finally, given the confirmatory approach of the current study,
other possible conceptions of eco-anxiety were not investigated. As
suggested by Hogg etal. (2021), eco-anxiety may appear differently in
groups or societies already more strongly affected by the negative
consequences of the climate and environmental crises, highlighting
the need for further research on eco-anxiety in the Global South,
indigenous people and other exposed populations such as farmers.
Moreover, future work should investigate the overlap between
eco-anxiety and related emotions including, but not limited to, the
anxiety of societal impacts on the environment - as opposed to
personal impacts- as well as frustration and anger about a lack of
effective policies.
4.2. Conclusion
The German translation of the HEAS was tested in a sample of 486
participants in Germany and the scale and its subscales were found to
be as reliable as the original English version. The Bayesian CFA
confirmed the multidimensionality of the construct supporting a four-
factorial model of eco-anxiety (affective symptoms, rumination,
behavioral symptoms, anxiety about personal impact). Regression
results and weak to moderate associations between the HEAS subscales
and the DASS-21 subscales indicate that eco-anxiety is distinct from
general depression, anxiety, and stress, but shares a significant
proportion of variance. Taken together, the study suggests that the
German HEAS is a reliable and valid scale to measure eco-anxiety.
Data availability statement
The raw data supporting the conclusions of this article will
bemade available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by local ethics
committee of Freie Universität Berlin, Berlin, Germany. The studies
were conducted in accordance with the local legislation and
institutional requirements. The participants provided their written
informed consent to participate in this study.
Author contributions
SH, FS, GR, FP, JK, and ME contributed to conception and design
of the study. SH, MS-H, and FP translated the HEAS scale. MT, MS-H,
FS, CP, and JK coordinated the study administration and data
acquisition. SH, MS-H, and MB performed the statistical analyses. SH
and MB wrote the first draft of the manuscript. GR, FP, and ME wrote
sections of the manuscript. All authors contributed to the article and
approved the submitted version.
Acknowledgments
We thank Georg Hosoya from the Psychological University Berlin,
Germany, for consultation regarding our Bayesian CFA models.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
beconstrued as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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