Validating the Big Five Aspect Scales (BFAS) and the Short Form (BFAS-S) in German, French, and Italian

Abstract

Personality traits predict health, well-being, relationship success, and work-related outcomes, but most of the relevant evidence comes from English-speaking populations. The Big Five Aspect Scales (BFAS) are one of the most used English-language personality questionnaires, allowing to assess Big Five domains and aspects validly and reliably. In the present study, we validated German, French, and Italian translations of the BFAS and its short-form (BFAS-S) in a representative sample of Swiss adults (N = 4’457). Across the three languages, the translations of the BFAS and the BFAS-S showed satisfactory psychometric properties in terms of their factorial validity, external validity, reliability, and convergence of short- and long-version. Furthermore, we found partial weak measurement invariance across the three languages for all aspects and domains and strong invariance for some aspects. The translations of the BFAS and the BFAS-S may thus facilitate future research on personality traits in non-English speaking samples.

Full text

BFAS TRANSLATION
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Validating the Big Five Aspect Scales (BFAS) and the Short Form (BFAS-S) in German,
French, and Italian
Peter Haehner, Michael D. Krämer, Wiebke Bleidorn, Christopher J. Hopwood
Department of Psychology, University of Zurich, Switzerland
Author Note
Peter Haehner https://orcid.org/0000-0002-3896-6172
Michael D. Krämer https://orcid.org/0000-0002-9883-5676
Wiebke Bleidorn https://orcid.org/0000-0003-3795-8143
Christopher J. Hopwood https://orcid.org/0000-0001-6645-8645
The present study used data from the PERCIVAL study. Data collection of the
PERCIVAL study was funded by an SNSF Project Grant awarded to Wiebke Bleidorn [Grant
number 205026]. We thank Alexander G. Stahlmann for his work on the data collection of the
PERCIVAL study. We thank Colin DeYoung for sharing the French translation of the Big Five
Aspect Scales with us.
We have no conflicts of interest to report. Data, analysis scripts, and supplementary
materials are available at https://osf.io/8sv9k/. The study was preregistered at
https://osf.io/k7rjy.
Peter Haehner: Conceptualization (equal), Data curation (lead), Formal analysis (lead),
Methodology (lead), Writing-original draft (equal), Writing-review and editing (equal).
Michael D. Krämer: Conceptualization (equal), Methodology (supporting), Writing-review
and editing (equal). Wiebke Bleidorn: Conceptualization (equal), Investigation (lead),
Methodology (supporting), Supervision (equal), Writing-review and editing (equal).
Christopher J. Hopwood: Conceptualization (equal), Methodology (supporting), Supervision
(equal), Writing-original draft (equal), Writing-review and editing (equal).
Correspondence concerning this article should be addressed to Peter Haehner,
Department of Psychology, University of Zurich, Binzmühlestrasse 14/7, CH-8050 Zürich,
Switzerland. Email: p.haehner@psychologie.uzh.ch
Draft version March 7, 2025. This paper is submitted for publication but has not yet
been peer reviewed.

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Abstract
Personality traits predict health, well-being, relationship success, and work-related outcomes,
but most of the relevant evidence comes from English-speaking populations. The Big Five
Aspect Scales (BFAS) are one of the most used English-language personality questionnaires,
allowing to assess Big Five domains and aspects validly and reliably. In the present study, we
validated German, French, and Italian translations of the BFAS and its short-form (BFAS-S) in
a representative sample of Swiss adults (N = 4’457). Across the three languages, the translations
of the BFAS and the BFAS-S showed satisfactory psychometric properties in terms of their
factorial validity, external validity, reliability, and convergence of short- and long-version.
Furthermore, we found partial weak measurement invariance across the three languages for all
aspects and domains and strong invariance for some aspects. The translations of the BFAS and
the BFAS-S may thus facilitate future research on personality traits in non-English speaking
samples.
Word count: 147
Keywords: Big Five; personality traits; Big Five Aspect Scales; translation; culture
Public Significance
To advance internationalization and diversity in psychological science, we validated German,
French, and Italian translations of a widely-used personality questionnaire. The translations
performed well in a representative sample of Swiss adults.

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Validating the Big Five Aspect Scales (BFAS) and the Short Form (BFAS-S) in German,
French, and Italian
Research has established that personality traits predict a broad range of relevant life
outcomes, including physical and mental health, educational attainment, and relationship
success (Beck & Jackson, 2022; Bleidorn et al., 2019; Goodwin & Friedman, 2006; Kern &
Friedman, 2011; Soto, 2019). As such, personality traits are important research targets across
disciplines, including personality psychology, organizational psychology, and clinical
psychology. In clinical psychology, personality traits are, for example, studies as predictors of
stressful life events, risk factors of mental disorders, moderators of diathesis-stress transactions,
or even treatment outcomes (Barlow et al., 2014; Haehner, Sleep, et al., 2024; Haehner, Wright,
et al., 2024; Roberts et al., 2017; Santee et al., 2023; Sauer-Zavala et al., 2017; Vize et al.,
2024). However, most research on personality traits has been conducted in English-speaking
populations. To gain a better understanding of the generalizability of the nature, correlates, and
consequences of personality traits, validated measures are needed that can assess traits in
different languages (Rammstedt et al., 2024).
The most common model of personality in contemporary research organizes traits into
five domains: Neuroticism, Extraversion, Openness/Intellect, Agreeableness, and
Conscientiousness (Goldberg, 1990; John & Srivastava, 1999). These “Big Five” domains can
be parsed into lower-order traits, ranging from nuances over facets to aspects (Goldberg, 1993;
Mõttus et al., 2017). Aspects represent the meso-level of the trait hierarchy, with each Big Five
domain comprising two aspects: Neuroticism (Withdrawal, Volatility), Extraversion
(Enthusiasm, Assertiveness), Openness/Intellect (Openness, Intellect), Agreeableness
(Politeness, Compassion), and Conscientiousness (Industriousness, Orderliness). Considering
these lower-order traits, like aspects, is important because they can predict relevant life
outcomes above and beyond trait domains (DeYoung et al., 2007; Gallagher et al., 2023; Jang
et al., 2002; Mõttus et al., 2017). Furthermore, aspects allow fine-grained insights into the

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course and causes of personality development and, unlike many models with lower-order traits,
they were developed under the consideration of possible biological substrates (Bleidorn, 2024;
DeYoung et al., 2007; Jang et al., 2002).
The Big Five Aspect Scales (BFAS) are one of the most widely used English-language
personality questionnaires (DeYoung et al., 2007). The BFAS validation paper has been cited
in thousands of studies and existing research has shown reasonably good psychometric
properties. An additional advantage of the BFAS is that is connected to theories of motivational
processes that are theorized to drive personality variation, psychological development, and
adaptation (DeYoung, 2015).
In the last decade, the original 100-item English-version of the BFAS has been translated
to German (Mussel & Paelecke, 2018) and a 40-item (BFAS-S) version has been developed in
English (Gallagher et al., 2023). However, validated translations in other languages and a
validated German short-version are missing. The goal of the present study was to validate
German, French, and Italian translations of the BFAS and the BFAS-S. We used a representative
sample of Swiss adults to examine (1) the fit of measurement models in each language, (2)
measurement invariance across languages, (3) the correspondence of short and long versions
across languages, (4) reliabilities and internal consistencies, and (5) external validity.
Methods
Transparency and Openness
The present study used data from the PERCIVAL project (PERsonality and CIVic
Engagement across the Adult Lifespan), a nationally-representative longitudinal study of
personality and civic engagement conducted in Switzerland (Meisser et al., 2024). The data
collection was registered by local ethics committee of [BLINDED FOR REVIEW]. The dataset,
R scripts, a codebook, and a HTML document with supplementary materials can be found at
https://osf.io/8sv9k/?view_only=69369106185c4c24b97b1f478f83c1db. The study design of
PERCIVAL was not preregistered but the analysis plan was preregistered at

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https://osf.io/k7rjy/?view_only=d72e0324995d4253b6ae767ef248c0af. Deviations from the
preregistration are summarized in the analysis section and described in detail in the
Supplementary Materials.
Procedure
PERCIVAL is a longitudinal study conducted in Switzerland with five assessments each
spaced 6 months apart (W1 to W5). The stratified probability sample (see below for details)
included Swiss residents above the age of 18 who were invited to participate in the study via
mail. After providing informed consent, participants completed the first assessment and were
invited to the four subsequent waves of the study via email. All assessments were conducted
online using Qualtrics. At each wave, participants answered questions about their personality
traits, civic engagement, well-being, and self-esteem (see the codebook at OSF for a list of
measures). The questionnaires were provided in German, French, and Italian as the national
languages of Switzerland
1
. Unless not otherwise indicated, we used the data from the first wave.
Sample
Swiss residents were invited to take part in the PERCIVAL study based on a stratified
probability sample drawn from a sampling frame of the Federal Statistical Office. The sample
was stratified based on gender (male, female), age (18-34, 35-64, 65+ years), and the seven
regions of the Level-2 Nomenclature of Territorial Units for Statistics in Switzerland (Lake
Geneva Region, Espace Mittelland, Northwestern Switzerland, Zurich, Eastern Switzerland,
Central Switzerland, Ticino; European Commission, 2024).
The goal of the PERCIVAL study was to provide nationally-representative data on
personality development of at least 1’100 Swiss residents, with sample sizes for each language
reflective of the national population. Based on this target, invitations were sent to 16’052
1
The fourth national language, Romansh, is spoken by a small minority and all Romansh speakers can speak
another national language, so Romansh was not included in this study.

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individuals of whom 5’177 individuals agreed to participate and 4’520 individuals completed
the first assessment wave. To ensure data quality, we excluded data from participants who gave
the same answer to at least 80% of the items for at least two trait constructs (Yentes & Wilhelm,
2023). Thus, the final sample size was 4’457. Participants were on average 50.06 years old
(SD = 19.89); 51% of the sample were women; 42% had a university degree; 25% of the sample
were not born in Switzerland. Table 1 shows the sample characteristics separately for German,
French, and Italian participants.
Table 1
Sample Characteristics
Characteristic
Overall sample
German
French
Italian
N
4457
2955
767
735
Mean age (SD)
50.06 (19.89)
51.41 (19.97)
49.12 (19.34)
45.60 (19.42)
% female
51%
51%
49%
50%
% university degree
42%
42%
48%
32%
% not born in
Switzerland
25%
20%
32%
36%
Note. More details on the sample can be found in Section 1 of the Supplementary Materials.
Measures
Big Five Personality Traits and Aspects (W1 to W4)
Translated versions of the 100-item BFAS were administered at W1 and W2 and
translations of the 40-item BFAS-S were administered at W3 and W4. We used the Mussel and
Paelecke (2018) translation for the German participants, which was based on independent
translations from two research teams. The French translation was provided by DeYoung and
colleagues and checked in an independent back-translations. The Italian translation was created
by an external translation company and then checked and revised by independent survey
specialist from FORS (https://forscenter.ch/). The translated items can be found in the
Appendix. Items (e.g., “I tend to finish what I start”) were assessed on 5-point scale ranging

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from 1 (strongly disagree) to 5 (strongly agree). Items were reverse-coded if necessary and we
calculated mean scores for the five trait domains and the ten aspects.
Self-Esteem (W1 and W3)
Self-esteem was assessed using the Rosenberg Self-Esteem Scale (Rosenberg, 1965;
German translation: Collani & Herzog, 2003; French translation: Vallieres et al. (1990); Italian
translation: Prezza et al., 1997). Items (e.g., “On the whole I am satisfied with myself”) were
assessed on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). After
reverse-coding items, if necessary, we calculated mean scale scores for each of the three
language versions.
Life Satisfaction (W1 and W3)
Life satisfaction was assessed with the Satisfaction With Life Scale (Diener et al., 1985;
German translation: Glaesmer et al., 2011; French translation: Bacro et al., 2020; Italian
translation: Di Fabio & Gori, 2016). Items (e.g., “The conditions of my life are excellent”) were
assessed on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). We
calculated mean scores for each of the three language versions.
Political Orientation (W1)
Political orientation (“What is your political orientation?”) was assessed on a 5-point
scale ranging from 1 (strongly conversative/right) to 5 (strongly progressive/left).
Data Analysis
Data analyses were conducted in R (Version 4.3.2) using the lavaan (Rosseel, 2012),
semTools (Jorgensen et al., 2022), and psych (Revelle, 2024) packages. We used a significance
level of α = .05 for all analyses. Analyses comprised five steps.
Step 1: Factorial Validity of the BFAS and the BFAS-S
We evaluated the factorial validity of the three translations using confirmatory factor
analyses (CFA) separately for the BFAS and the BFAS-S using a two-step approach (e.g., see
Rammstedt et al., 2024). First, we estimated measurement models for each aspect in isolation,

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using the relevant items per aspect as indicators. Second, we examined the factorial validity at
the domain level by including all items of a domain as indicators while allowing for correlated
residuals across items from one aspect. This latter model represents a deviation from the
preregistration as we had preregistered to run a correlated five-factor model using the scale
scores of all aspects as indicators. However, it was not possible to run this model due to
identification problems.
We used full information maximum likelihood estimation to account for missing values
and the indicator variable method to identify measurement models (i.e., fixing the loading of
the first indicator to 1 and its intercept to 0). Model fit was evaluated using robust fit indices.
However, given recent criticisms of overgeneralizing fit cut-offs and binary accept-reject
decisions (Groskurth et al., 2023; Rammstedt et al., 2024), we used different criteria to evaluate
model fit. First, we applied standard cut-offs to identify acceptable (RMSEA < .08, CFI > .90)
and good (RMSEA < .05, CFI > .95) model fit (Hu & Bentler, 1999; Schermelleh-Engel et al.,
2003). Second, we compared model fit of the BFAS to the fit found in existing research using
the original English version of the BFAS and the BFAS-S (.78 ≤ CFI ≤ .93; .05 ≤ RMSEA ≤
.09; Gallagher et al., 2023; Hopwood et al., 2024). The latter strategy was chosen because model
fit of the English version can be seen as a fair benchmark for our translations. Furthermore, to
improve model fit, we explored modified measurement models when the adjustments were
justifiable based on item content (e.g., reverse coded or semantically similar items) and were
improving fit in all three languages.
Step 2: Measurement Invariance Across Languages
We next examined measurement invariance across the three languages at the aspect-
level (one model for each aspect) and the domain-level (one model for each domain). We
included all modifications to the measurement model that were added in Step 1 (e.g., correlated
residuals; see below). We first evaluated the fit of the configural model, which assumes the
same factor structure across languages and compared it to model fit for the three languages

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found in Step 1. We then tested weak and strong measurement invariance by examining whether
restricting loadings and intercepts, respectively, to be equal across languages reduced model
fit. If change in model fit was small (ΔCFI > −0.01 and ΔRMSEA < .015; Putnick & Bornstein,
2016), we considered the more restricted model to be adequate. If full weak or strong invariance
did not hold, we examined whether we could establish partial invariance by allowing certain
loadings and/or intercepts to differ across languages.
Step 3: Descriptive Statistics and Convergence of BFAS and BFAS-S
Using mean scale scores, we next examined descriptive statistics and intercorrelations
of the trait domains and aspects separately for the German, French, and Italian versions as well
as for the overall sample. We also examined convergence of the BFAS and the BFAS-S by
calculating correlations of domains and aspects assessed with the BFAS and the BFAS-S. Based
on the results reported by Gallagher et al. (2023), we expected the short- and the long-version
scales to correlate r ≥ .85.
Step 4: Reliability
We next examined internal consistencies (Cronbach’s α and McDonald’s ω) of the
BFAS and BFAS-S at the domain- and aspect-level in each language separately and for the
overall sample. Based on existing research, we expected internal consistencies to be above .70
for the BFAS and above .60 for the BFAS-S (DeYoung et al., 2007; Gallagher et al., 2023;
Mussel & Paelecke, 2018).
We also examined 6-month test-retest correlations using W1 and W2 data from
PERCIVAL. Existing research found test-retest correlations of r = .82 to .86 over 2 to 4 weeks
for the BFAS and the BFAS-S (DeYoung, 2015; Gallagher et al., 2023). However, over 6-
months, lower test-retest correlations are to be expected as they may reflect both reliability and
rank-order stability of personality traits. We thus expected values of r ≈ .70, similar to test-
retest correlations of other Big Five measures over 6 months (e.g., Haehner, Bleidorn, et al.,
2024).

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Step 5: External Validity
Finally, we examined correlations of the Big Five domains and aspects with self-esteem,
life satisfaction, and political orientation as indicators of external validity. For each of these
constructs, we tried to identify a publication that used the same measures to assess these
constructs and relied on a similar sample
2
. We expected correlations to be in a similar range (|r|
± .15) as those found in existing research (Gallagher et al., 2023; Krämer et al., 2024; Sun et
al., 2018) for each language separately and the overall sample.
Results
This manuscript is accompanied by an HTML document containing the analysis code
and supplementary results.
Factorial Validity of the BFAS and the BFAS-S
Factorial validity results of the German, French, and Italian BFAS and the BFAS-S are
described in detail in Section 1 of the Supplementary Materials. Measurement models without
any modifications for the Big Five aspects and domains resulted in poor to acceptable fit for
the BFAS (German: .74 ≤ CFI ≤ .95, .05 ≤ RMSEA ≤ .14, French: .79 ≤ CFI ≤ .92, .06 ≤ RMSEA
≤ .12, Italian: .76 ≤ CFI ≤ .95, .06 ≤ RMSEA ≤ .14) and mediocre to good model fit for the
BFAS-S (German: .85 ≤ CFI ≤ 1.00, .03 ≤ RMSEA ≤ .14, French: .85 ≤ CFI ≤ 1.00, .00 ≤
RMSEA ≤ .19, Italian: .84 ≤ CFI ≤ 1.00, .00 ≤ RMSEA ≤ .15). For most aspects and domains,
these fit results were similar to model fit found in previous studies (.78 ≤ CFI ≤ .93; .05 ≤
RMSEA ≤ .09; Gallagher et al., 2023; Hopwood et al., 2024), suggesting that our translations
performed about as well as the original English version of the BFAS and the BFAS-S.
2
For life satisfaction, the study by Sun et al. (2018) used the same measure and a comparable sample. For self-
esteem, there was no study that used the same self-esteem measure and had a similar sample so that we averaged
the correlations reported by Sun et al. (2018) and Krämer et al. (2024). Finally, for political orientation, we only
found one sample exampling these correlations (Gallagher et al., 2023).

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As preregistered, we nonetheless evaluated whether including (a) a method factor for
reverse-coded items and (b) correlated residuals between conceptually related items improved
model fit. Model fits of these revised measurement models are summarized in Tables 2 and 3.
These modified models showed acceptable to good fit according to conventional criteria for the
BFAS and the BFAS-S, indicating that a measurement model with reasonable modifications is
supported for both long and short versions in all three languages.
Table 2
Model Fit of Modified Measurement Models of the BFAS in German, French, and Italian
Personality trait
German
French
Italian
CFI
RMSEA
CFI
RMSEA
CFI
RMSEA
Agreeableness+
0.92
0.05
0.91
0.06
0.92
0.06
Compassion*
0.93
0.08
0.95
0.07
0.97
0.05
Politeness*+
0.91
0.05
0.94
0.05
0.91
0.08
Conscientiousness+
0.91
0.06
0.92
0.06
0.92
0.06
Industriousness*
0.95
0.06
0.95
0.06
0.96
0.06
Orderliness+
0.95
0.06
0.96
0.06
0.93
0.08
Extraversion+
0.90
0.07
0.92
0.06
0.91
0.06
Assertiveness*
0.96
0.06
0.93
0.07
0.95
0.06
Enthusiasm*+
0.92
0.08
0.95
0.06
0.91
0.08
Neuroticism*
0.94
0.06
0.97
0.04
0.97
0.05
Volatility+
0.96
0.06
0.95
0.07
0.96
0.07
Withdrawal*
0.94
0.08
0.98
0.05
0.98
0.04
Openness/Intellect*
0.93
0.05
0.91
0.06
0.92
0.05
Intellect*
0.91
0.09
0.94
0.07
0.92
0.08
Openness*+
0.93
0.05
0.93
0.06
0.93
0.06
Note. For all aspects and domains labeled with an asterisk, we accounted for method variance of reverse coded
items. For all aspects and domains labeled with a plus, we additionally included correlated residuals between
conceptually-related items that were identified based on content considerations and modification indices (e.g., for
volatility, we included a correlated residual between Item 05 and 07 that both concern mood swings). More details
about these modifications and the results can be found in Section 2 of the Supplementary Materials.

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Table 3
Model Fit of Modified Measurement Models of the BFAS-S in German, French, and Italian
Personality trait
German
French
Italian
CFI
RMSEA
CFI
RMSEA
CFI
RMSEA
Agreeableness
0.98
0.03
0.98
0.04
1.00
0.01
Compassion+
1.00
0.03
1.00
0.03
1.00
0.00
Politeness
0.99
0.03
0.96
0.08
0.99
0.08
Conscientiousness
0.97
0.06
0.99
0.03
0.98
0.05
Industriousness
0.98
0.09
1.00
0.00
0.99
0.06
Orderliness+
1.00
0.00
1.00
0.00
1.00
0.02
Extraversion
0.92
0.08
0.95
0.06
0.91
0.08
Assertiveness+
0.99
0.05
0.96
0.10
1.00
0.00
Enthusiasm
0.99
0.05
1.00
0.03
1.00
0.00
Neuroticism
0.99
0.03
0.99
0.04
0.97
0.06
Volatility+
1.00
0.00
1.00
0.00
1.00
0.03
Withdrawal*
1.00
0.04
1.00
0.00
0.99
0.05
Openness/Intellect*
0.97
0.05
0.96
0.06
0.94
0.06
Intellect*
1.00
0.04
1.00
0.00
1.00
0.00
Openness*
1.00
0.03
0.98
0.08
0.98
0.07
Note. For all aspects and domains labeled with an asterisk, we accounted for method variance of reverse coded
items. For all aspects labeled with a plus, we additionally included correlated residuals between conceptually-
related items that were identified based on content considerations and modification indices (e.g., for compassion,
we included a correlated residual between Items 02 and 03 that both concern being sensible for other’s feelings).
More details about these modifications and the results can be found in Section 2 of the Supplementary Materials.

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Measurement Invariance Across Languages
We used these modified versions to examine configural, weak, and strong measurement
invariance across the German, French, and Italian versions of the BFAS and the BFAS-S. All
configural models fitted similarly to the data as the modified measurement models examined in
the previous step. However, most aspects and domains did not show full weak or strong
invariance across the three languages. Thus, we attempted to establish partial weak or strong
invariance by allowing certain loadings and/or intercepts to differ across languages. Results on
partial invariance for the Big Five aspects are summarized in Tables 4 and 5 (see Section 3 of
the Supplementary Materials for domain-level results). For the BFAS and the BFAS-S, we were
able to establish partial weak invariance for all aspects and domains, indicating that associations
with other variables can be compared across the three languages. However, partial strong
invariance could only be established for some aspects and domains, suggesting that the mean-
levels of several aspects and domains are not directly comparable across languages.

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Table 4
Results on Partial Measurement Invariance of the BFAS Across German, French, and Italian
Invariance
Freed parameters
RMSEA
ΔRMSEA
CFI
ΔCFI
Compassion*
Configural
0.072
0.942
Weak
none
0.068
-0.004
0.938
-0.003
Strong
Intercepts of Items 04, 06, 07, 09, 10
0.068
0.000
0.934
-0.005
Politeness*+
Configural
0.056
0.918
Weak
Loadings of Items 02, 03, 05, 07
0.055
-0.002
0.914
-0.004
Strong
Intercepts of Items 02, 03, 04, 05, 07
0.109
0.055
0.622
-0.292
Industriousness
Configural
0.073
0.919
Weak
Loadings of Item 07
0.071
-0.002
0.913
-0.006
Strong
Intercepts of Items 02, 04, 06, 07
0.071
0.001
0.903
-0.010
Orderliness+
Configural
0.060
0.950
Weak
Loadings of Items 03, 06
0.057
-0.003
0.948
-0.002
Strong
Intercepts of Items 02, 03, 04, 06, 08
0.061
0.005
0.935
-0.013
Assertiveness*
Configural
0.058
0.953
Weak
Loadings of Items 01, 08, 09, 10
0.058
0.000
0.947
-0.006
Strong
Intercepts of Items 01, 07, 08, 09, 10
0.078
0.020
0.899
-0.049
Enthusiasm*+
Configural
0.077
0.927
Weak
Loadings of Items 05, 06, 07
0.077
0.000
0.917
-0.010
Strong
Intercepts of Items 02, 04, 05, 06, 07
0.077
-0.001
0.912
-0.005
Volatility+
Configural
0.066
0.956
Weak
Loadings of Items 02, 10
0.067
0.001
0.949
-0.008
Strong
Intercepts of Items 02, 03, 07, 10
0.071
0.003
0.939
-0.009
Withdrawal*
Configural
0.071
0.951
Weak
Loadings of Item 07
0.071
0.001
0.941
-0.010
Strong
Intercepts of Items 04, 06, 07, 09, 10
0.081
0.009
0.917
-0.023
Intellect*
Configural
0.082
0.920
Weak
none
0.076
-0.006
0.918
-0.002
Strong
Intercepts of Items 03, 05, 09
0.074
-0.002
0.914
-0.004
Openness+
Configural
0.055
0.931
Weak
Loadings of Item 04
0.054
-0.001
0.923
-0.007
Strong
Intercepts of Items 02, 04, 06, 07, 10
0.060
0.006
0.899
-0.025
Note. The level of invariance that holds is indicated in bold. For all aspects labeled with an asterisk, we included
a method factor to improve model fit. For all aspects labeled with a plus, we additionally included correlated
residuals between conceptually-related items that were identified based on content considerations and modification
indices. The column Freed parameters indicates which intercepts and/or loadings were allowed to differ across
languages to establish partial invariance. More details on these results and domain-level findings can be found in
Section 3 of the Supplementary Materials.

BFAS TRANSLATION
15
Table 5
Results on Partial Measurement Invariance of the BFAS-S Across German, French, and Italian
Invariance
Freed parameters
RMSEA
ΔRMSEA
CFI
ΔCFI
Compassion+
Configural
0.025
0.999
Weak
none
0.027
0.002
0.997
-0.002
Strong
Intercepts of Item 03, 04
0.040
0.013
0.993
-0.005
Politeness
Configural
0.055
0.983
Weak
Loadings of Item 02
0.051
-0.004
0.975
-0.008
Strong
Intercepts of Items 02, 08
0.118
0.067
0.842
-0.133
Industriousness
Configural
0.074
0.982
Weak
Loadings of Item 04
0.068
-0.006
0.975
-0.007
Strong
Intercepts of Items 03, 04
0.070
0.002
0.968
-0.007
Orderliness+
Configural
0.000
1.000
Weak
Loadings of Item 03
0.014
0.014
0.999
-0.001
Strong
Intercepts of Items 03, 07
0.046
0.032
0.991
-0.008
Assertiveness+
Configural
0.054
0.993
Weak
Loadings of Items 01, 03
0.039
-0.015
0.994
0.001
Strong
Intercepts of Items 01, 03
0.139
0.100
0.859
-0.135
Enthusiasm
Configural
0.040
0.993
Weak
none
0.038
-0.002
0.988
-0.005
Strong
Intercepts of Items 04, 07
0.068
0.031
0.954
-0.034
Volatility+
Configural
0.000
1.000
Weak
Loadings of Items 01, 10
0.000
0.000
1.000
0.000
Strong
Intercepts of Items 01, 10
0.050
0.050
0.993
-0.007
Withdrawal*
Configural
0.037
0.998
Weak
none
0.034
-0.003
0.994
-0.004
Strong
Intercepts of Items 06, 09
0.058
0.024
0.979
-0.015
Intellect*
Configural
0.024
0.999
Weak
Loading of Items 01
0.017
-0.006
0.999
0.000
Strong
Intercepts of Item 01, 03
0.019
0.002
0.998
-0.001
Openness+
Configural
0.049
0.990
Weak
Loadings of Items 03, 08
0.041
-0.008
0.989
-0.002
Strong
Intercepts of Items 03, 08
0.121
0.079
0.864
-0.125
Note. The level of invariance that holds is indicated in bold. For all aspects labeled with an asterisk, we included
a method factor to improve model fit. For all aspects labeled with a plus, we additionally included correlated
residuals between conceptually-related items that were identified based on content considerations and modification
indices. The column Freed parameters indicates which intercepts and/or loadings were allowed to differ across
languages to establish partial invariance. More details on these results and domain-level findings can be found in
Section 3 of the Supplementary Materials

BFAS TRANSLATION
16
Descriptive Statistics and Convergence of the BFAS and BFAS-S
Table 6 summarizes descriptive statistics of the aspects and domains assessed with the
BFAS and the BFAS-S in the overall sample and the three languages. To ease mean-level
comparison across languages even in the absence of strong invariance, we computed aligned
means as described by Asparouhov and Muthén (2014). These aligned means show, for
example, higher levels of Politeness but lower levels of Compassion in the Italian subsample
compared to the other two languages (see Section 4 of the Supplemental Materials).
Table 7 provides an overview of the intercorrelations across all aspects and domains as
well as convergent correlations of the BFAS and the BFAS-S in the overall sample (see Section
4 of the Supplemental Materials for language-specific results). For all aspects and domains,
except Openness, the convergent correlations of the BFAS and the BFAS-S were above ≥ .85.
Reliability
McDonald’s ω, Cronbach’s α, and 6-month test-retest correlations are shown in Table 8.
BFAS estimates were similar to those found in existing research (DeYoung et al., 2007;
Gallagher et al., 2023). Politeness had a slightly lower Cronbach’s α than the other aspects
(α = .62 in the overall sample) but McDonald’s ω and the test-retest correlation were in the
expected range for this aspect.
The Cronbach’s α estimates of BFAS-S domains and aspects were considerably lower,
ranging from α = .35 to α = .78. However, note that lower values for Cronbach’s α can be
expected (Rammstedt et al., 2020; Rammstedt & Beierlein, 2014; Ziegler et al., 2014) or even
preferred (Loevinger, 1954) for short measures of broad constructs like personality traits. Test-
retest correlations (all ≥ .63) and McDonald’s ω values (all ≥ .67) were in the expected range
for all domains and aspects. Overall, the translated versions of the BFAS and BFAS-S, thus,
have satisfactory reliabilities.
.

BFAS TRANSLATION
17
Table 6
Descriptive Statistics of the BFAS and the BFAS-S
Personality trait
Overall sample
German
French
Italian
BFAS
BFAS-S
BFAS
BFAS-S
BFAS
BFAS-S
BFAS
BFAS-S
M
SD
M
SD
M
SD
M
SD
M
SD
M
SD
M
SD
M
SD
Agreeableness
3.90
0.40
3.97
0.47
3.88
0.38
3.93
0.45
4.03
0.44
4.07
0.5
3.85
0.43
3.98
0.50
Compassion
3.97
0.52
3.94
0.6
3.99
0.49
3.96
0.58
4.03
0.58
3.98
0.67
3.85
0.57
3.84
0.63
Politeness
3.83
0.46
3.99
0.59
3.77
0.44
3.91
0.55
4.03
0.48
4.16
0.58
3.85
0.47
4.13
0.65
Conscientiousness
3.57
0.47
3.68
0.57
3.56
0.46
3.64
0.55
3.59
0.48
3.78
0.61
3.61
0.50
3.72
0.6
Industriousness
3.56
0.58
3.55
0.71
3.51
0.57
3.49
0.70
3.65
0.54
3.71
0.72
3.69
0.59
3.63
0.72
Orderliness
3.58
0.55
3.80
0.67
3.61
0.52
3.79
0.65
3.54
0.60
3.85
0.74
3.54
0.56
3.82
0.71
Extraversion
3.45
0.47
3.46
0.53
3.44
0.47
3.44
0.53
3.53
0.48
3.60
0.51
3.37
0.45
3.39
0.52
Assertiveness
3.42
0.56
3.52
0.61
3.43
0.56
3.53
0.60
3.48
0.56
3.65
0.59
3.31
0.52
3.37
0.64
Enthusiasm
3.47
0.55
3.39
0.70
3.45
0.55
3.34
0.70
3.58
0.58
3.56
0.70
3.44
0.55
3.41
0.66
Neuroticism
2.57
0.60
2.74
0.65
2.57
0.58
2.78
0.64
2.49
0.64
2.59
0.68
2.63
0.61
2.76
0.62
Volatility
2.51
0.65
2.58
0.75
2.48
0.61
2.57
0.72
2.47
0.73
2.53
0.80
2.68
0.70
2.68
0.75
Withdrawal
2.63
0.68
2.90
0.77
2.67
0.67
2.98
0.78
2.51
0.71
2.65
0.78
2.59
0.64
2.83
0.68
Openness/Intellect
3.57
0.46
3.58
0.49
3.55
0.45
3.56
0.48
3.61
0.49
3.59
0.50
3.61
0.45
3.62
0.49
Intellect
3.58
0.59
3.62
0.69
3.59
0.59
3.63
0.69
3.59
0.62
3.62
0.71
3.55
0.56
3.58
0.65
Openness
3.56
0.54
3.53
0.58
3.52
0.54
3.50
0.58
3.64
0.55
3.55
0.59
3.67
0.53
3.64
0.59
Note. More details on descriptive statistics including aligned means (Asparouhov and Muthén, 2014) can be found in Section 4 of the Supplementary Materials.

BFAS TRANSLATION
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Table 7
Correlations of the Big Five Aspects and Domains Assessed with the BFAS and the BFAS-S in the Overall Sample
Personality trait
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
01 - Agreeableness
0.90
0.79
0.78
0.20
0.20
0.12
0.21
0.03
0.30
-0.19
-0.30
-0.03
0.20
0.13
0.18
02 - Compassion
0.84
0.91
0.24
0.11
0.10
0.09
0.35
0.19
0.37
-0.03
-0.11
0.06
0.30
0.21
0.25
03 - Politeness
0.79
0.34
0.83
0.20
0.22
0.10
-0.02
-0.15
0.10
-0.27
-0.36
-0.11
0.01
-0.01
0.03
04 - Conscientiousness
0.14
0.10
0.12
0.92
0.84
0.81
0.25
0.23
0.18
-0.28
-0.19
-0.28
-0.03
0.10
-0.17
05 – Industriousness
0.14
0.12
0.12
0.84
0.89
0.36
0.30
0.26
0.23
-0.44
-0.31
-0.43
0.00
0.18
-0.21
06 – Orderliness
0.08
0.05
0.09
0.82
0.39
0.89
0.10
0.11
0.06
-0.01
0.00
-0.02
-0.04
-0.01
-0.06
07 – Extraversion
0.17
0.38
-0.14
0.29
0.38
0.09
0.92
0.78
0.84
-0.29
-0.17
-0.32
0.26
0.35
0.03
08 – Assertiveness
-0.03
0.21
-0.29
0.30
0.37
0.12
0.84
0.88
0.32
-0.23
-0.11
-0.27
0.27
0.39
-0.01
09 – Enthusiasm
0.32
0.43
0.06
0.19
0.27
0.04
0.84
0.42
0.89
-0.24
-0.17
-0.24
0.16
0.18
0.05
10 – Neuroticism
-0.15
-0.08
-0.17
-0.32
-0.51
0.00
-0.33
-0.29
-0.27
0.94
0.84
0.86
-0.07
-0.22
0.15
11 – Volatility
-0.25
-0.14
-0.28
-0.23
-0.38
-0.01
-0.2
-0.15
-0.18
0.89
0.90
0.45
-0.11
-0.20
0.05
12 – Withdrawal
-0.03
-0.01
-0.04
-0.33
-0.54
0.00
-0.39
-0.36
-0.31
0.90
0.61
0.92
0.00
-0.18
0.21
13 – Openness/Intellect
0.25
0.40
-0.02
0.06
0.13
-0.03
0.41
0.40
0.28
-0.13
-0.14
-0.10
0.86
0.80
0.71
14 – Intellect
0.12
0.27
-0.10
0.17
0.27
0.01
0.44
0.50
0.25
-0.27
-0.24
-0.24
0.82
0.91
0.15
15 – Openness
0.29
0.38
0.08
-0.09
-0.08
-0.07
0.20
0.13
0.20
0.07
0.02
0.10
0.78
0.29
0.74
Note. Convergent correlations of the BFAS and the BFAS-S are in the diagonal of the table (in bold). Below the diagonal are correlations of aspects and domains assessed with the
BFAS. Above the diagonal are correlations of aspects and domains assessed with the BFAS-S. Separate findings for the three languages can be found in Section 4 of the
Supplementary Materials.

BFAS TRANSLATION
19
Table 8
Reliability and Internal Consistency of the BFAS and BFAS-S
Personality trait
Overall sample
German
French
Italian
BFAS
BFAS-S
BFAS
BFAS-S
BFAS
BFAS-S
BFAS
BFAS-S
ω
α
r
ω
α
r
ω
α
r
ω
α
r
ω
α
r
ω
α
r
ω
α
r
ω
α
r
Agreeableness
0.90
0.79
0.80
0.83
0.64
0.74
0.88
0.78
0.81
0.81
0.63
0.73
0.90
0.82
0.78
0.85
0.68
0.75
0.91
0.82
0.80
0.89
0.73
0.74
Compassion
0.92
0.83
0.80
0.94
0.73
0.74
0.92
0.82
0.80
0.94
0.71
0.73
0.93
0.84
0.78
0.95
0.75
0.74
0.94
0.86
0.82
0.95
0.77
0.75
Politeness
0.85
0.62
0.75
0.90
0.48
0.67
0.84
0.62
0.76
0.89
0.43
0.66
0.86
0.67
0.71
0.91
0.54
0.69
0.87
0.67
0.71
0.97
0.70
0.63
Conscientiousness
0.91
0.82
0.85
0.87
0.73
0.82
0.91
0.82
0.85
0.86
0.72
0.81
0.91
0.81
0.86
0.88
0.75
0.84
0.93
0.86
0.83
0.90
0.78
0.81
Industriousness
0.91
0.80
0.83
0.93
0.66
0.78
0.92
0.82
0.83
0.93
0.68
0.76
0.91
0.73
0.82
0.93
0.64
0.80
0.93
0.85
0.82
0.95
0.72
0.77
Orderliness
0.89
0.74
0.80
0.95
0.70
0.77
0.89
0.73
0.79
0.95
0.68
0.77
0.88
0.78
0.82
0.97
0.75
0.77
0.91
0.78
0.80
0.97
0.74
0.79
Extraversion
0.91
0.83
0.86
0.82
0.67
0.80
0.92
0.84
0.86
0.84
0.69
0.81
0.91
0.83
0.85
0.81
0.63
0.77
0.91
0.83
0.85
0.83
0.67
0.77
Assertiveness
0.91
0.80
0.84
0.91
0.57
0.74
0.91
0.81
0.85
0.92
0.59
0.74
0.91
0.79
0.83
0.91
0.51
0.68
0.89
0.79
0.82
0.94
0.66
0.76
Enthusiasm
0.88
0.76
0.83
0.92
0.62
0.77
0.88
0.77
0.83
0.93
0.64
0.78
0.88
0.77
0.83
0.92
0.62
0.75
0.87
0.77
0.82
0.91
0.58
0.71
Neuroticism
0.94
0.90
0.86
0.88
0.76
0.81
0.94
0.90
0.86
0.89
0.77
0.80
0.95
0.90
0.86
0.88
0.77
0.80
0.95
0.90
0.87
0.88
0.75
0.84
Volatility
0.93
0.85
0.82
0.95
0.72
0.74
0.93
0.84
0.80
0.95
0.71
0.72
0.94
0.86
0.83
0.94
0.74
0.79
0.95
0.87
0.85
0.95
0.75
0.78
Withdrawal
0.93
0.84
0.85
0.93
0.66
0.79
0.93
0.85
0.84
0.94
0.68
0.80
0.93
0.85
0.86
0.93
0.66
0.73
0.92
0.82
0.85
0.91
0.55
0.77
Openness/Intellect
0.88
0.80
0.85
0.79
0.57
0.76
0.89
0.80
0.85
0.80
0.58
0.76
0.89
0.82
0.88
0.67
0.53
0.78
0.91
0.81
0.82
0.80
0.60
0.77
Intellect
0.92
0.81
0.86
0.93
0.65
0.78
0.93
0.82
0.86
0.93
0.67
0.79
0.89
0.80
0.86
0.93
0.61
0.78
0.92
0.79
0.83
0.93
0.60
0.78
Openness
0.87
0.71
0.83
0.90
0.44
0.68
0.88
0.72
0.83
0.90
0.46
0.67
0.88
0.71
0.85
0.91
0.35
0.69
0.89
0.74
0.79
0.91
0.51
0.66
Note. Test-retest correlations (r) relied on W1 and W2 data from the PERCIVAL study, which took place 6 months apart. Values that were below the expected range (< .70 for BFAS
and < .60 for BFAS-S) are indicated in bold.

BFAS TRANSLATION
20
External Validity
To evaluate convergent and discriminant validity of the BFAS and the BFAS-S, we
examined correlations of the aspects and domains with life satisfaction, self-esteem, and
political orientation (Table 9) and compared these correlations to findings from existing
research (Gallagher et al., 2023; Krämer et al., 2024; Sun et al., 2018). Most correlations were
in the expected range (r ± .15 compared to existing findings; see Section 6 of the Supplementary
Materials for details), suggesting that the nomological nets of our translations were similar to
those of the original English version. However, there also were some potentially notable
exceptions. For example, we expected stronger associations between BFAS-S enthusiasm with
life satisfaction and self-esteem (Krämer et al., 2024; Sun et al., 2018). These differences could
indicate some slight changes in patterns of correlation across languages or point to other
methodological differences across studies.
Robustness Check: BFAS-S at Wave 3
To evaluate the robustness of our findings and to rule out the possibility that
psychometric properties of the BFAS-S change when its items are presented without the items
of the long version, we repeated all analysis for the BFAS-S using W3 data (instead of W1 data)
from PERCIVAL. Details on this robustness check can be found in Section 7 of the
Supplementary Material. Overall, this robustness check largely replicated the findings from our
main analysis. The only differences were that some aspects and domains that fulfilled the
criteria for strong partial invariance at W1 were only weakly partially invariant at W3 and that
there were fewer deviations from the expected correlation pattern when examining external
validity.

BFAS TRANSLATION
21
Table 9
Convergent and Discriminant Correlations of the BFAS and the BFAS-S in the Overall Sample
Personality trait
Overall sample
German
French
Italian
BFAS
BFAS-S
BFAS
BFAS-S
BFAS
BFAS-S
BFAS
BFAS-S
LS
SE
PO
LS
SE
PO
LS
SE
PO
LS
SE
PO
LS
SE
PO
LS
SE
PO
LS
SE
PO
LS
SE
PO
Agreeableness
.13
.14
.16
.15
.15
.15
.13
.14
.15
.14
.16
.15
.10
.11
.18
.14
.17
.15
.16
.19
.17
.19
.25
.15
Compassion
.16
.19
.18
.15
.15
.16
.18
.19
.19
.15
.14
.17
.07
.12
.20
.09
.11
.18
.16
.17
.14
.16
.18
.11
Politeness
.04
.03
.07
.09
.09
.06
.02
.03
.04
.07
.11
.06
.08
.06
.09
.13
.16
.03
.09
.13
.14
.14
.22
.12
Conscientiousness
.35
.37
-.21
.34
.36
-.20
.36
.41
-.22
.36
.40
-.21
.33
.42
-.21
.31
.41
-.20
.38
.38
-.18
.34
.34
-.16
Industriousness
.44
.48
-.18
.40
.44
-.16
.46
.55
-.20
.43
.50
-.17
.42
.57
-.19
.36
.51
-.18
.47
.48
-.12
.39
.41
-.09
Orderliness
.14
.13
-.17
.16
.14
-.17
.12
.11
-.17
.15
.14
-.17
.15
.16
-.15
.16
.18
-.15
.17
.16
-.20
.17
.17
-.19
Extraversion
.39
.48
-.03
.34
.42
-.02
.38
.49
-.03
.33
.43
-.02
.37
.52
-.02
.35
.48
-.01
.41
.45
-.05
.36
.40
-.07
Assertiveness
.30
.43
-.06
.25
.37
-.06
.28
.41
-.04
.23
.35
-.05
.32
.53
-.09
.28
.43
-.05
.29
.41
-.11
.23
.36
-.12
Enthusiasm
.36
.38
.01
.30
.32
.02
.37
.41
-.01
.30
.35
.02
.30
.34
.05
.28
.34
.03
.40
.34
.02
.35
.27
.00
Neuroticism
-.47
-.61
.09
-.40
-.52
.07
-.50
-.65
.10
-.42
-.57
.08
-.40
-.60
.12
-.34
-.54
.10
-.47
-.55
.00
-.40
-.49
-.02
Volatility
-.34
-.47
.02
-.25
-.36
-.01
-.37
-.50
.05
-.25
-.38
.00
-.24
-.39
.03
-.19
-.32
.02
-.32
-.41
-.07
-.27
-.38
-.07
Withdrawal
-.51
-.62
.13
-.43
-.53
.13
-.53
-.67
.13
-.46
-.58
.14
-.46
-.67
.19
-.40
-.60
.16
-.53
-.59
.07
-.42
-.47
.05
Openness/Intellect
.09
.17
.20
.04
.09
.18
.09
.20
.22
.03
.11
.20
.07
.16
.20
.03
.05
.15
.13
.24
.13
.10
.21
.11
Intellect
.18
.29
.10
.15
.23
.11
.18
.30
.12
.14
.23
.12
.15
.28
.08
.13
.21
.08
.19
.32
.06
.19
.29
.09
Openness
-.04
-.02
.23
-.11
-.12
.17
-.05
.00
.24
-.12
-.10
.20
-.05
-.03
.26
-.12
-.17
.15
.01
.06
.16
-.05
.02
.08
Note. We compared these convergent and discriminant correlations of the BFAS and BFAS-S to respective correlations found in existing research (Gallagher et al., 2023; Krämer
et al., 2024; Sun et al., 2018). Correlations that differed by more than r ± .15 from correlations in existing research are indicated in bold. More details on these analyses can be
found in Section 6 of the Supplementary Materials. LS = life satisfaction, SE = self-esteem, PO = political orientation.

BFAS TRANSLATION
22
Discussion
English-speaking samples are over-represented in psychological assessment and clinical
research. Because French, German and Italian are collectively spoken by more than 500 million
people throughout the world, the translation of measurement tools into those languages can
enhance, expand, and help integrate research findings. The BFAS is one of the most used
English-language personality questionnaires, featuring scales to assess both Big Five domains
and two aspects apiece (DeYoung et al., 2007). In this study, we found that translations of full
and short versions of the BFAS to German, French, and Italian functioned reasonably well from
a psychometric perspective.
Factor analytic models of individual domain and aspect scales fit well in each language,
albeit with some model modifications. Configural and partial weak invariance were established,
indicating that the scales can be interpreted as representing the same underlying psychological
traits and that correlations can be meaningfully compared across languages. Partial strong
measurement invariance was identified for some but not all aspects and domains, indicating
that only some of the scale scores can directly be compared across languages. Although some
scholars have recently argued that missing invariance should not be over interpreted and that
the applied criteria may be overly strict (Funder & Gardiner, 2024), adjustment methods may
be useful to increase comparability across languages (Asparouhov & Muthén, 2014; Muthén &
Asparouhov, 2018). Furthermore, both the internal consistency estimates and the test-retest-
correlations supported the expected levels of reliability. The scale intercorrelations and the
correlations with external criteria were similar to those reported in previous studies (Gallagher
et al., 2023; Krämer et al., 2024; Sun et al., 2018). Finally, aspects and domains assessed with
the long and short versions of the instrument were strongly correlated. These patterns
generalized well across all three languages.
Overall, these results indicate that the BFAS can be used to measure personality traits
in German, French, and Italian participants. There are now multiple personality trait measures

BFAS TRANSLATION
23
available in each of these languages (e.g., Lignier et al., 2023; Rammstedt et al., 2020, 2024);
offering researchers the possibility to assess personality traits with the measure that best aligns
with their research goals. The BFAS may be the measure of choice if researchers want a
questionnaire that offers a useful balance between psychometric quality and efficiency or are
interested in Big Five aspects. Accumulating evidence about similar constructs assessed with
different measures, even across studies, contributes to a better understanding of construct
validity by parsing method from trait. Thus, the availability of multiple tools appropriate for
different groups, in this case questionnaires across three common languages, helps advance
psychological research.
There are also advantages and disadvantages to using long and short versions of the
BFAS. The longer version is slightly more reliable and could have better content validity, to the
extent that items trimmed from the short version nevertheless capture important and unique
elements of each construct. Both higher reliability and stronger content validity could make
external correlations stronger and more accurate. Although the current results suggested
differences in estimates with the BFAS and BFAS-S would not be large, they also suggested
that deviations from existing results in terms of external validity are more likely with the BFAS-
S. All in all, measurement of Big Five traits is slightly more precise with the BFAS than with
the BFAS-S. At the same time, shorter scales are often a good choice for practical reasons like
reducing costs of a study, limiting participant burden, or creating space for questions from other
measures in the same study. It would also be possible to use subsets or individual scales from
the BFAS depending on the goals of a study, although this limits opportunities to examine
discriminant validity.
Limitations and Directions for Future Research
The present study has several limitations. First, all measures were self-report. The use
of multiple assessment methods is desirable for capturing a more accurate portrait of

BFAS TRANSLATION
24
personality, and informant report measures have been validated for many personality trait
instruments (Balsis et al., 2015; Mõttus et al., 2020).
Second, this study did not provide evidence for the factor structure of the overall BFAS
model. This is a challenging issue because no measurement model of any common
multidimensional personality measure fits the data well according to conventional values
(Hopwood & Donnellan, 2010). This is arguably because strict models that assert, for instance,
that variation in every item must be explained by one trait alone, are probably stricter than our
best personality assessment models and measures psychometrically work. People also argue
that structural validity is overrated as a psychometric consideration (Revelle, 2024). It is thus
not clear what standard one should apply to try to answer this question; in this study we
attempted to fit a correlated five-factor model using the aspects as indicators but ran into
identification issues. Thus, these results do not speak to the validity of the overall BFAS
organization of traits and domains.
Third, although all translations were carefully conducted, the translation procedures
differed across the three languages and did in some cases not adhere to best-practice guidelines
that recommend multiple translations, back-translations, and discussions with language experts
(Beaton et al., 2000). Thus, even though all translations performed reasonably well, future
research investigating revisions of our translations may further improve their psychometric
quality.
Constraints on Generality
Although the sample was representative so that inferences from these data can be
generalized to the Swiss adult population, it is possible that differences would be observed in
French, German, or Italian participants sampled elsewhere. This is in part, because cultural
differences are not the same as language differences; it is possible that Swiss people are similar
in certain ways that transcend language by virtue of shared nationality.

BFAS TRANSLATION
25
Conclusion
In summary, this study provides psychometric support for translations of the 100- and
40-item versions of the BFAS for German, French, and Italian-speaking participants, giving
researchers studying people from those populations a new way to measure personality traits
from a Big Five perspective. The items can be found in the Appendix.

BFAS TRANSLATION
26
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Appendix
German, French, and Italian Translation of the BFAS and the BFAS-S
Aspect
English
German
French
Italian
Short
Key
Compassion
Am not interested in other
people's problems.
Ich interessiere mich nicht für die
Probleme anderer.
Je ne m’intéresse pas aux
problèmes des autres.
Non sono interessato/a ai
problemi degli altri.
0
1
Compassion
Feel others' emotions.
Ich habe ein Gespür für die Gefühle
anderer.
Je suis sensible aux émotions
des autres.
Percepisco le emozioni degli
altri.
1
0
Compassion
Inquire about others' well-
being.
Ich erkundige mich nach dem
Wohlbefinden anderer.
Je m’inquiète du bien-être des
autres.
Mi informo sul benessere degli
altri.
1
0
Compassion
Can't be bothered with other's
needs.
Die Belange anderer Menschen
interessieren mich nicht.
Je ne m’occupe pas des besoins
des autres.
Non mi importa dei bisogni
degli altri.
1
1
Compassion
Sympathize with others'
feelings.
Ich kann die Gefühle anderer
nachempfinden.
Je fais preuve d’empathie.
Empatizzo con i sentimenti
degli altri.
0
0
Compassion
Am indifferent to the feelings
of others.
Ich stehe den Gefühlen anderer
gleichgültig gegenüber.
Les sentiments des autres me
laissent indifférent(e).
Sono indifferente ai sentimenti
degli altri.
0
1
Compassion
Take no time for others.
Ich nehme mir keine Zeit für
andere.
Je ne prends pas de temps pour
les autres.
Non perdo tempo per gli altri.
0
1
Compassion
Take an interest in other
people's lives.
Ich interessiere mich für das Leben
anderer.
Je m’intéresse à la vie des
autres.
Mi interesso alla vita degli altri.
1
0
Compassion
Don't have a soft side.
Mir fehlt eine weiche Seite.
Je n’ai pas un côté doux.
Non ho un lato tenero.
0
1
Compassion
Like to do things for others.
Es bereitet mir Freude, etwas für
andere zu tun.
J’aime faire des choses pour les
autres.
Mi piace fare cose per gli altri.
0
0
Politeness
Respect authority.
Ich respektiere Autoritäten.
Je respecte l’autorité.
Rispetto l'autorità.
0
0
Politeness
Believe that I am better than
others.
Ich fühle mich anderen überlegen.
Je me crois meilleur(e) que les
autres.
Credo di essere migliore degli
altri.
1
1
Politeness
Hate to seem pushy.
Es ist mir unangenehm,
aufdringlich zu erscheinen.
Je n’aime pas m’imposer.
Odio sembrare insistente.
0
0
Politeness
Take advantage of others.
Ich nutze andere aus.
Je profite des autres.
Approfitto degli altri.
0
1
Politeness
Avoid imposing my will on
others.
Ich vermeide es, anderen meinen
Willen aufzuzwingen.
J’évite d’imposer aux autres ma
volonté.
Evito di imporre la mia volontà
sugli altri.
0
0
Politeness
Rarely put people under
pressure.
Ich setze andere nur selten unter
Druck.
Je mets rarement la pression
aux autres.
Raramente metto le persone
sotto pressione.
1
0
Politeness
Insult people.
Manchmal beleidige ich andere
Leute.
J’insulte les autres.
Insulto gli altri.
1
1

BFAS TRANSLATION
34
Politeness
Seek conflict.
Ich suche Streit.
Je recherche le conflit.
Cerco il conflitto.
1
1
Politeness
Love a good fight.
Ich mag Diskussionen und
Auseinandersetzungen.
J’adore me battre.
Adoro un buon litigio.
0
1
Politeness
Am out for my own personal
gain.
Ich schaue auf meinen persönlichen
Vorteil.
Je ne recherche que mon profit
personnel.
Perseguo il profitto personale.
0
1
Industriousness
Carry out my plans.
Ich führe meine Vorhaben durch.
Je vais jusqu’au bout de mes
projets.
Porto a termine i miei piani.
0
0
Industriousness
Waste my time.
Ich verschwende meine Zeit.
Je perds mon temps.
Perdo tempo.
1
1
Industriousness
Find it difficult to get down to
work.
Es fällt mir schwer, mich an die
Arbeit zu machen.
J’ai des difficultés à me mettre
au travail.
Trovo difficile mettermi al
lavoro.
1
1
Industriousness
Mess things up.
Manche Dinge vermassele ich
einfach.
Je mets toujours la pagaille.
Incasino le cose.
1
1
Industriousness
Finish what I start.
Was ich beginne, bringe ich auch zu
Ende.
Je finis ce que je commence.
Finisco ciò che inizio.
0
0
Industriousness
Don't put my mind on the task
at hand.
Manchmal bin ich mit meinen
Gedanken nicht bei der Sache.
Je n’arrive pas à me concentrer
sur ce que je fais.
Non riesco a concentrarmi sui
compiti che devo svolgere..
0
1
Industriousness
Get things done quickly.
Ich erledige meine Aufgaben rasch.
Je fais les choses en vitesse.
Porto a termine le cose
velocemente
0
0
Industriousness
Always know what I am doing.
Ich weiss immer, was ich tue.
Je sais toujours ce que je fais.
So sempre cosa sto facendo.
0
0
Industriousness
Postpone decisions.
Ich schiebe Entscheidungen auf.
Je repousse le moment de
prendre des décisions.
Rimando le decisioni.
0
1
Industriousness
Am easily distracted.
Ich lasse mich leicht ablenken.
Je suis facilement distrait(e).
Mi distraggo facilmente.
1
1
Orderliness
Leave my belongings around.
Ich lasse meine Sachen
herumliegen.
Je laisse traîner mes affaires
partout.
Lascio le mie cose in giro.
0
1
Orderliness
Like order.
Ich mag Ordnung.
J’aime l’ordre.
Mi piace l’ordine.
1
0
Orderliness
Keep things tidy.
Ich räume meine Sachen stets auf.
J’aime que les choses soient
bien rangées.
Tengo le mie cose in ordine.
1
0
Orderliness
Follow a schedule.
Ich halte mich an Zeitpläne.
Je respecte mon emploi du
temps.
Seguo un programma preciso.
0
0
Orderliness
Am not bothered by messy
people.
Es stört mich nicht, wenn andere
Menschen unordentlich sind.
Les gens désordonnés ne me
dérangent pas.
Le persone disordinate non mi
danno fastidio.
0
1
Orderliness
Want everything to be “just
right.”
Ich wünsche mir, dass alles perfekt
ist.
Je veux que tout soit “parfait”.
Voglio che tutto sia come si
deve.
0
0
Orderliness
Am not bothered by disorder.
Unordnung stört mich nicht.
Le désordre ne me dérange pas.
Non sono infastidito dal
disordine.
1
1
Orderliness
Dislike routine.
Routine mag ich nicht.
Je n’aime pas la routine.
Non mi piace la routine.
0
1

BFAS TRANSLATION
35
Orderliness
See that rules are observed.
Ich achte darauf, dass Regeln
eingehalten werden.
Je m’assure que les règles sont
respectées.
Voglio che le regole vengano
rispettate.
1
0
Orderliness
Want every detail taken care
of.
Ich lege grossen Wert auf Details.
Je veux que chaque détail soit
pris en compte.
Voglio che ogni dettaglio venga
curato.
0
0
Assertiveness
Take charge.
Ich übernehme Verantwortung.
Je suis responsable.
Prendo il comando nelle
situazioni.
1
0
Assertiveness
Have a strong personality.
Ich habe eine starke Persönlichkeit.
J’ai une forte personnalité.
Ho una personalità forte.
0
0
Assertiveness
Lack the talent for influencing
people.
Ich bin nicht gut darin, andere zu
beeinflussen.
Je ne parviens pas à influencer
les autres.
Mi manca il talento per
influenzare le persone.
1
1
Assertiveness
Know how to captivate people.
Ich kann andere faszinieren.
Je sais comment captiver les
autres.
So come affascinare le persone.
0
0
Assertiveness
Wait for others to lead the way.
Ich überlasse es anderen, den Weg
zu weisen.
J’attends que l’on me montre le
chemin.
Aspetto che gli altri facciano
strada.
1
1
Assertiveness
See myself as a good leader.
Ich bin gut darin, Führung zu
übernehmen.
Je me considère comme un(e)
bon(ne) meneur/meneuse.
Mi vedo come un/a buon/a
leader.
1
0
Assertiveness
Can talk others into doing
things.
Ich kann andere dazu bringen,
Dinge zu tun, die ich will.
Je sais convaincre les autres de
faire des choses.
So convincere gli altri a fare
delle cose.
0
0
Assertiveness
Hold back my opinions.
Ich halte mich mit meiner Meinung
eher zurück.
Je refoule mes opinions.
Tengo le mie opinioni per me
stesso/a.
0
1
Assertiveness
Am the first to act.
Ich übernehme häufig die Initiative.
Je suis toujours le/la
premier(ère) à agir.
Sono il /la primo/a ad agire.
0
0
Assertiveness
Do not have an assertive
personality.
Ich bin nicht durchsetzungsfähig.
Je ne suis pas sûr(e) de moi.
Non ho una personalità
assertiva.
0
1
Enthusiasm
Make friends easily.
Es fällt mir leicht, neue
Freundschaften zu schliessen.
Je me fais facilement des amis.
Stringo amicizie facilmente.
0
0
Enthusiasm
Am hard to get to know.
Mich näher kennenzulernen fällt
schwer.
Je suis difficile à approcher.
È difficile imparare a
conoscermi.
0
1
Enthusiasm
Keep others at a distance.
Ich halte andere auf Distanz.
Je garde les autres à distance.
Tengo gli altri a distanza.
1
1
Enthusiasm
Reveal little about myself.
Ich gebe wenig über mich preis.
Je révèle peu de choses sur moi.
Rivelo poco di me stesso/a.
1
1
Enthusiasm
Warm up quickly to others.
Ich lerne schnell neue Leute
kennen.
Je suis très vite chaleureux(se)
avec les autres.
Mi affeziono alle persone
facilmente.
1
0
Enthusiasm
Rarely get caught up in the
excitement.
Ich lasse mich nicht durch den
Trubel der Ereignisse mitreissen.
Je me laisse rarement emporter
dans l’agitation générale.
Mi faccio prendere raramente
dall’eccitazione.
0
1

BFAS TRANSLATION
36
Enthusiasm
Am not a very enthusiastic
person.
Ich bin keine besonders
enthusiastische Person.
Je ne suis pas une personne très
enthousiaste.
Non sono una persona
particolarmente entusiasta.
1
1
Enthusiasm
Show my feelings when I'm
happy.
Wenn ich glücklich bin, dann zeige
ich das auch.
Je montre mes sentiments
quand je suis heureux(se).
Quando sono felice mostro i
miei sentimenti.
0
0
Enthusiasm
Have a lot of fun.
Ich habe viel Spass.
Je m’amuse beaucoup.
Mi diverto molto.
0
0
Enthusiasm
Laugh a lot.
Ich lache viel.
Je ris beaucoup.
Rido molto.
0
0
Volatility
Get angry easily.
Ich werde schnell ärgerlich.
Je me mets facilement en
colère.
Mi arrabbio facilmente.
1
0
Volatility
Rarely get irritated.
Ich bin selten irritiert.
Je me mets rarement en colère.
Mi innervosisco raramente.
0
1
Volatility
Get upset easily.
Ich gerate leicht aus der Fassung.
Je me vexe facilement.
Mi irrito facilmente.
0
0
Volatility
Keep my emotions under
control.
Ich kann meine Gefühle gut
kontrollieren.
Je garde mes émotions sous
contrôle.
Tengo sotto controllo le mie
emozioni.
0
1
Volatility
Change my mood a lot.
Meine Stimmung ändert sich
häufig.
Je change d’humeur très
souvent.
Il mio umore cambia spesso.
0
0
Volatility
Rarely lose my composure.
Es kommt selten vor, dass ich die
Fassung verliere.
Je perds rarement mon calme.
Raramente perdo la calma.
0
1
Volatility
Am a person whose moods go
up and down easily.
Meine Stimmung verändert sich
leicht.
Je suis quelqu’un dont les
humeurs changent très
rapidement.
Sono una persona il cui umore
cambia facilmente.
0
0
Volatility
Am not easily annoyed.
Ich bin nicht schnell verärgert.
On ne m’agace pas facilement.
Non mi infastidisco facilmente.
1
1
Volatility
Get easily agitated.
Ich bin schnell aufgewühlt.
Je m’agite facilement.
Mi agito facilmente.
1
0
Volatility
Can be stirred up easily.
Ich bin schnell gerührt.
On peut me provoquer
facilement.
Mi sento facilmente provocato.
1
0
Withdrawal
Seldom feel blue.
Ich bin selten deprimiert.
Je ne me sens jamais
déprimé(e).
Mi sento raramente triste.
0
1
Withdrawal
Am filled with doubts about
things.
Ich bin häufig beunruhigt.
Je doute toujours de tout.
Sono pieno di dubbi su tutto.
0
0
Withdrawal
Feel comfortable with myself.
Ich bin mit mir zufrieden.
Je suis bien dans ma peau.
Mi sento a mio agio con me
stesso.
0
1
Withdrawal
Feel threatened easily.
Ich fühle mich schnell bedroht.
Je me sens facilement
menacé(e).
Mi sento minacciato/a
facilmente.
0
0
Withdrawal
Rarely feel depressed.
Es kommt selten vor, dass ich mich
niedergeschlagen fühle.
Je me sens rarement
déprimé(e).
Mi sento raramente depresso.
1
1
Withdrawal
Worry about things.
Ich mache mir häufig Sorgen.
Je m’inquiète de tout.
Mi preoccupo di tutto.
1
0
Withdrawal
Am easily discouraged.
Ich lasse mich schnell entmutigen.
Je me décourage facilement.
Mi scoraggio facilmente.
0
0
Withdrawal
Am not embarrassed easily.
Mir ist selten etwas peinlich.
Je ne m’embarrasse pas
facilement.
Non mi imbarazzo facilmente.
1
1

BFAS TRANSLATION
37
Withdrawal
Become overwhelmed by
events.
Manchmal ist mir alles zu viel.
Je me laisse submerger par les
évènements.
Mi faccio sopraffare dagli
eventi.
1
0
Withdrawal
Am afraid of many things.
Ich sorge mich über viele Dinge.
J’ai peur de beaucoup de
choses.
Ho paura di molte cose.
0
0
Intellect
Am quick to understand things.
Ich habe eine gute
Auffassungsgabe.
Je comprends vite.
Sono svelto/a a capire le cose.
1
0
Intellect
Have difficulty understanding
abstract ideas.
Ich habe Schwierigkeiten, abstrakte
Ideen zu verstehen.
J’ai du mal à comprendre les
notions abstraites.
Ho difficoltà nel comprendere
idee astratte.
1
1
Intellect
Can handle a lot of
information.
Ich kann mit einer grossen Menge
an Informationen umgehen.
J’arrive à gérer de nombreuses
informations en même temps.
Sono in grado di gestire
numerose informazioni.
1
0
Intellect
Like to solve complex
problems.
Es bereitet mir Freude, komplexe
Probleme zu lösen.
J’aime résoudre des problèmes
compliqués.
Mi piace risolvere problemi
complessi
0
0
Intellect
Avoid philosophical
discussions.
Ich vermeide philosophische
Diskussionen.
J’évite les discussions
philosophiques.
Evito discussioni filosofiche.
0
1
Intellect
Avoid difficult reading
material.
Ich vermeide schwierigen
Lesestoff.
J’évite les lectures
compliquées.
Evito letture difficili.
1
1
Intellect
Have a rich vocabulary.
Ich habe einen reichen Wortschatz.
J’ai un vocabulaire très riche.
Ho un vocabolario ampio.
0
0
Intellect
Think quickly.
Ich habe eine schnelle
Auffassungsgabe.
Je réfléchis vite.
Penso velocemente.
0
0
Intellect
Learn things slowly.
Ich brauche manchmal länger, um
etwas Neues zu lernen.
J’apprends lentement.
Imparo le cose lentamente.
0
1
Intellect
Formulate ideas clearly.
Ich kann meine Gedanken klar
formulieren.
Je formule clairement mes
idées.
Formulo chiaramente le idee
che voglio esprimere.
0
0
Openness
Enjoy the beauty of nature.
Es bereitet mir Freude, die
Schönheit der Natur zu geniessen.
J’apprécie la beauté de la
nature.
Mi piace la bellezza della
natura.
0
0
Openness
Believe in the importance of
art.
Kunst ist sehr wichtig für mich.
Je crois en l’importance de
l’art.
Credo nell’importanza dell’arte.
0
0
Openness
Love to reflect on things.
Es bereitet mir Freude, über die
Dinge nachzudenken.
J’aime réfléchir avant d’agir. a
Amo riflettere sulle cose.
1
0
Openness
Get deeply immersed in music.
Ich gehe manchmal völlig in einer
Musik auf, die ich höre.
Je me laisse profondément
emporter par la musique.
Mi immergo profondamente
nella musica.
0
0
Openness
Do not like poetry.
Poesie beeindruckt mich wenig
oder gar nicht.
Je n’aime pas la poésie.
Non mi piace la poesia.
0
1
Openness
Seldom notice the emotional
aspects of paintings and
pictures.
Gemälde oder Fotografien berühren
mich nicht.
Je remarque rarement les
aspects émotionnels des
tableaux et des images.
Noto raramente gli aspetti
emotivi nei dipinti e nelle
immagini.
0
1

BFAS TRANSLATION
38
Openness
Need a creative outlet.
Ich muss mich kreativ betätigen.
J’ai besoin d’un exutoire
créatif.
Ho bisogno di uno sfogo
creativo.
0
0
Openness
Seldom get lost in thought.
Es passiert selten, dass ich meinen
Gedanken nachhänge.
Je me perds rarement dans mes
pensées.
Raramente mi perdo nei miei
pensieri.
1
1
Openness
Seldom daydream.
Es kommt selten vor, dass ich
Tagträumen nachgehe.
Je rêvasse rarement.
Raramente sogno ad occhi
aperti.
1
1
Openness
See beauty in things that others
might not notice.
In manchen Dingen sehe ich etwas
Schönes, das anderen verborgen
bleibt.
Je vois dans les choses une
beauté que les autres ne
remarquent pas.
Vedo il bello in cose che gli
altri potrebbero non notare.
1
0
Note. The German translation has first been published by Mussel and Paelecke (2018) under CC-BY-NC-ND 4.0 license, cite accordingly. The column Short indicates whether an
item is included in the BFAS-S (0 = no, 1 = yes). The column Key indicates whether an item needs to be reversed before running the analyses (0 = no, 1 = yes).
a For this item, an independent check after the data collection took place indicated that the translation “J’aime réfléchir aux choses” may reflect the meaning of the English version
even better. Future research using the French version of the BFAS may thus prefer this translation.