diff --git a/docs/mfv_russia/MJ_Manuscript_2024.md b/docs/mfv_russia/MJ_Manuscript_2024.md new file mode 100644 index 0000000..2b759dd --- /dev/null +++ b/docs/mfv_russia/MJ_Manuscript_2024.md @@ -0,0 +1,2029 @@ +# Validation of the Russian Version of the Realistic Moral Vignettes for Studies of Moral Judgments + +Zorina Rakhmankulova1, Rustam +Asgarov1,2, Eliana Monahhova3, Semyon +Mening3,4, Isak B. Blank1, Vasily +Klucharev1 + +1 International Laboratory of Social +Neurobiology, Institute for Cognitive Neuroscience, National Research +University Higher School of Economics, Moscow, Russia + +2 Food and Biotechnology Department, +Faculty of Engineering, Baku Engineering University, Baku, +Azerbaijan + +3 Centre for Cognition and Decision +Making, Institute for Cognitive Neuroscience, National Research +University Higher School of Economics, Moscow, Russia + +4 Laboratory for Cognitive Research, +Faculty of Social Sciences, National Research University Higher School +of Economics, Moscow, Russia + +Zorina Rakhmankulova +[https://orcid.org/0000-0001-9294-2983](https://orcid.org/0000-0001-9294-2983) + +Rustam Asgarov +[https://orcid.org/0000-0001-6739-6394](https://orcid.org/0000-0001-6739-6394) + +Eliana Monahhova +[https://orcid.org/0000-0001-9426-3642](https://orcid.org/0000-0001-9426-3642) + +Semyon Mening +[https://orcid.org/0009-0007-1740-4932](https://orcid.org/0009-0007-1740-4932) + +Vasily Klucharev +[https://orcid.org/0000-0002-5257-3789](https://orcid.org/0000-0002-5257-3789) + +Correspondence concerning this article should be addressed to Eliana +Monahhova or Rustam Asgarov, Center for Cognition and +Decision Making, Institute for Cognitive Neuroscience, National Research +University Higher School of Economics, Krivokolenny Pereulok 3, 101000 +Moscow, Russia. Email: +[e.monakhova@hse.ru;](mailto:e.monakhova@hse.ru) +[rustamasgarov@gmail.com](mailto:rustamasgarov@gmail.com) + +# Abstract + +Moral judgments and behavior are shaped by individual +experiences and cultural environments. In two online studies, we used a +standard set of moral vignettes to examine the generalizability of the +original factor structure of moral judgments by testing two independent +samples of the Russian population (Study 1, N = 247; Study 2, N = +223). In Study 1, the exploratory factor analysis revealed three +components that accounted for most of the variance: norm violation, +social affect, and intention. In Study 2, the factor structure of the +identified moral components was validated by confirmatory factor +analysis. Latent profile analysis revealed five distinct profiles of +moral scenarios: Peccadillo, Illegal-Antisocial, Controversial Act, +Prosocial, and a novel profile specific to the Russian sample—Social +Conflict—as compared to the previous study of the American population. +These findings suggest fundamental similarities in moral judgment +processes across cultures while also highlighting culture-specific +patterns in moral scenario categorization. This study +also provides researchers with a battery of vignettes that can be used +in cross-cultural studies of moral judgment. + +*Keywords:* vignette, event feature, moral judgment, moral component, +latent profile + +# + +# Validation of the Russian Version of the Realistic Moral Vignettes for Studies of Moral Judgments + +Human moral judgments and behavior have been extensively studied in +philosophy, sociology, psychology, and neuroscience. Moral judgments +enable people to evaluate others’ actions, attitudes, and even +personalities according to certain social, cultural, and universal +norms. Some scientific and philosophical frameworks suggest that moral +judgments and behavior are predominantly influenced and guided by an +innately operating moral sense of intuitions and emotions, while moral +reasoning is regarded only as a post hoc cognitive process for +justifying prior moral judgments when inquired (Haidt, 2001). +Importantly, an evolutionary perspective suggests that moral judgments +and morality support within-group altruism, cooperation, and normative +behavior (Greene & Haidt, 2002) and survival (Hawley, 2003). +Furthermore, the social intuitionist model proposes that the moral +judgments and moral behavior of an individual develop in a social and +cultural environment by the individual’s intuitions formed through +interpersonal processes and experience (Haidt, 2001). Therefore, social +and cultural experiences should consequently influence the individual’s +moral intuitions via reasoned persuasion and social persuasion effects +(Haidt, 2001). Thus, it is particularly important to develop +experimental tools to study moral judgments in different cultural +contexts. + +Studies of moral judgments have used different types of stimuli, from +complex moral dilemmas (Greene et al., 2001), sentences containing +social norm violations (Heekeren et al., 2003), and moral claims (Moll +et al., 2001) to highly structured moral scenarios (e.g., Young et al., +2007). Few seminal studies have introduced and validated a novel +experimental paradigm to assess the moral judgments of subjects with +American cultural background (Escobedo, 2009; Knutson et al., 2010; +Kruepke et al., 2018). Using three types of cue words for emotions, +actions, and superlatives, summaries of real-life events with positive +and negative moral experiences were collected from the participants’ +episodic memories in the form of first-person short moral vignettes +(Escobedo, 2009). It allowed the creation of a set of standardized +stimuli in the form of vignettes (Knutson et al., 2010), which are based +on real-life experiences and improve the ecological validity of future +studies on moral judgments. Importantly, the findings from 30 +individuals and 312 vignettes (Knutson et al., 2010) were later +replicated among 661 participants and 117 vignettes divided into three +subsets of 39 unique vignettes (Kruepke et al., 2018). The factor +analysis consistently indicated a three-component nature of ratings +during moral judgments, including norm violation, social affect, and +intention (Knutson et al., 2010; Kruepke et al., 2018). + +Importantly, cultures differ in moral judgments and moral behaviors +(Graham et al., 2016), and moral foundations theory (Graham et al., +2013) emphasizes the role of social learning in moral judgments. +Furthermore, the individualism–collectivism perspective suggests that +more individualistic cultures emphasize individual rights, while +collectivistic cultures reinforce communal obligations and spiritual +purity (Guerra & Giner-Sorolla, 2010; Graham et al., 2010). For example, +compared to participants in the United Kingdom, +Russians demonstrated more collectivistic attitudes (Tower et al., +1997), which have been associated with a higher tolerance of deceptive +behaviors used to avoid conflicts (Seiter et al., 2002). Some streams of +research have further suggested that compared to Western Europeans, +Eastern Europeans may hold the belief that multiple, even opposite, +truths are possible (e.g., Peng & Nisbett, 1999; Varnum et al., 2008). +Thus, it is possible that the Russian population might make moral +judgments different from those of the American population used in +studies that designed the standardized vignette sets currently +used to probe moral judgments. + +In the studies presented herein, we have attempted to replicate previous +findings using standard moral vignettes (Knutson et al., 2010; Kruepke +et al., 2018), further validate these vignettes, and examine the +generalizability of the original factor structure of moral judgments to +other cultures by testing two independent samples of the Russian +population. Our research question focuses on examining whether the +factor structure of moral judgments identified in the American samples +generalizes to the Russian population. This investigation is motivated +by prior research showing systematic differences between these cultures +in value systems and social cognition, particularly regarding +individualistic versus collectivistic orientations and variations in +power distance that could influence moral evaluations. We hypothesized +that while core aspects of moral judgment may be preserved across +cultures, culturally specific patterns may emerge in how moral scenarios +are categorized and evaluated. + +# Materials and Methods + +## Participants + +The studies included two samples of Russian-speaking participants +recruited using social media platforms. *Sample 1* (Study 1) was used as +a training sample to investigate factor structure via exploratory factor +analysis (EFA), while *Sample 2* (Study 2) was used as a test sample to +validate the model identified by EFA using confirmatory factor analysis +(CFA). Kline (2016) stated that the median sample size for uncomplex +models is around 200 participants, although having more than 200 +participants is usually preferred. The sample sizes in both Studies 1 +and 2 align with this recommendation. + +We invited 261 participants, (ranging in age from 18 to 66 years, mean +\[*M*\] = 25.0, standard deviation \[*SD*\] = 8.6, 190 females), and 236 +subjects (aged 18 to 66 years, *M* = 26.4, *SD* = 9.7, 162 females) for +Studies 1 and 2, respectively. Participants accessed the online +experiment and were provided with relevant online instructions. At the +beginning of the study, all individuals gave informed consent and +completed eligibility screening questions. Upon completion of the +experiment, participants were compensated with 300 monetary units, +equivalent to approximately 10.4 USD when adjusted for purchasing power +parity (OECD, 2023). The study was conducted in accordance with the +Declaration of Helsinki and received approval from the Ethics Committee +of the HSE University. + +All participants self-identified as Russian citizens with permanent +residency in Russia and confirmed via self-report that they had no +history of psychiatric or neurological disorders and were not taking +medication for these conditions at the time of the study. A total of +223 participants (44.9%) reported completing a +high school diploma as their highest level of education. Of the +remaining 274 participants with higher education, 162 participants +(32.6%) held a bachelor’s degree, 105 participants (21.1%) held a +master’s degree, and 7 (1.4%) held a doctoral degree. The diverse range +of degrees spanned various fields, from law, politics, sociology, and +mathematics to chemistry and economics. + +To ensure data quality, we conducted an *insufficient effort responding* +(IER) analysis using the intra-individual response variability (IRV) +method (Dunn et al., 2018). We calculated each participant’s IRV score +by summing the standard deviations of their responses across all scales. +Potential outliers were identified using both the 1.5 IQR (interquartile +range) method and the *irv()* function from the ‘careless’ R package +(Ulitzsch et al., 2022). In Sample 1, we +identified 12 downward outliers (4.60% of the sample; IDs: 118, 119, +126, 141, 149, 197, 228, 236, 243, 252, 260, and 261). These +participants were removed from further analyses. In Sample 2, we +detected five downward outliers (2.12% of the sample; IDs: 47, 61, 64, +130, 158) and four upward outliers (1.69% of the sample; IDs: 13, 24, +104, 151). The downward outliers demonstrated potential +non-differentiation or straight-lining in ratings, while upward outliers +demonstrated excessive variability in ratings, possibly due to random or +inconsistent responses. All identified outliers were excluded from the +further statistical analyses to enhance the overall integrity of our +dataset. Additionally, ​​we conducted Mahalanobis distance analysis to +detect multivariate outliers in both samples that revealed two +multivariate outliers in Sample 1 (IDs: 2 and 74) and four multivariate +outliers in Sample 2 (IDs: 26, 35, 43, and 147) that were removed from +further statistical analyses. + +Overall, the final sample size used in all statistical analyses was n = +247 (age range: 18–66 years, *M* = 25.0, *SD* = 8.3, 183 females) for +Study 1 and n = 223 (age range: 18–66 years, *M* = 26.4, *SD* = 9.7, 156 +females) for Study 2. + +## Moral Vignettes and the Moral Judgment Task + +The vignette ratings were collected using an online moral judgment task +via an online questionnaire, with participants rating 39 vignettes +regarding 16 event features (dimensions), using 7-point Likert scales +with fixed-point sliding scales. Each scale was presented as a separate +question, following Kruepke's (2018) study. In the questionnaire, we +used moral vignettes from subset-3 (Kruepke et al., 2018), which were +previously tested on the American sample. Subset-3 was selected due to +its compatibility with Russian cultural contexts, requiring minimal +modifications. + +Out of 16 event features, the following 10 event features directly +represented the moral aspects of the vignettes (based on Kruepke et al., +2018): emotional intensity (1 = “Not at all emotionally intense” to 7 = +“Extremely emotionally intense”), emotional aversion (1 = “Not at all +aversive or unpleasant” to 7 = “Extremely aversive or unpleasant”), harm +(1 = “No harm to others” to 7 = “Extreme harm to others”), other-benefit +(1 = “No benefit to others” to 7 = “Extreme benefit to others”), +self-benefit (1 = “No benefit to main actor” to 7 = “Extreme benefit to +main actor”), premeditation (1 = “The action was completely unplanned” +to 7 = “The action was completely planned”), legality (1 = “The action +was extremely illegal” to 7 = “The action was extremely legal”), social +norms (1 = “This action breaks social rules” to 7 = “This action follows +social rules”), socialness (1 = “No other people involved in the action” +to 7 = “Other people are extremely involved in the action”), and moral +appropriateness (1 = “Extremely morally inappropriate” to 7 = “Extremely +morally appropriate”). + +Six additional features were more complementary and exploratory in +nature, providing additional context to the moral judgment aspects: +frequency (from 1 = “This type of event rarely occurs” to 7 = “This type +of event occurs all the time”), personal familiarity (from 1 = “Never +experienced this type of event” to 7 = “Frequently experienced this type +of event”), general familiarity (from 1 = “Never thought about this type +of event” to 7 = “Frequently think about this type of event”), self-harm +(from 1 = “No self-harm towards main actor” to 7 = “Extreme self-harm +towards main actor”), once vs. repeated event (from 1 = “One-time event” +to 7 = “Frequently”), and acted differently (from 1 = “Extremely +unlikely” to 7 = “Extremely likely”). + +The vignettes and rating scales (questions and their corresponding +anchor points) were translated from English into Russian and reviewed by +the expert to ensure accurate interpretation. Minor adaptations were +made to a few vignettes to make their content more familiar to the +Russian audience by adjusting names, places, countries, or currency (for +representative examples, see **Table 1**). Both the adopted and the +original versions of the vignettes, along with the event features +scales, are available online as Supplementary Materials on the Open +Science Framework: [https://osf.io/9k5b8/](https://osf.io/9k5b8/) +(**Tables S1** and **S2**). + +**Table 1** + +*Representative Vignettes Used in the Moral Judgment Task, Including the +Original Version and the Russian-Specific Version in Russian or English* + +| | | | | +|--------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Vignette No. | Original Version | Russian-Adapted Version | English Translation of Russian Version | +| 1 | When I first went to **dance** school in **New York** I lied about my age. There is a lot of pressure to be young in the dance industry. I was twenty and told people I was only sixteen. | Когда я только поступила в **школу моделей** в **Москве**, я солгала о своем возрасте. В индустрии моды очень важно быть молодым, поэтому я всем говорила, что мне шестнадцать, хотя в реальности мне было уже двадцать. | When I first went to **fashion** school in **Moscow** I lied about my age. There is a lot of pressure to be young in the **fashion** industry. I was twenty and told people I was only sixteen. | +| 8 | I am a very unfaithful person in general. I do not have a strong faith in God and I am constantly unfaithful to women. Recently I cheated on my girlfriend who comes from another **state** to see me. | Я в целом очень неверный человек. Я не особо верю в Бога и часто изменяю женщинам. Недавно я изменил своей девушке, которая приехала из другого **города**, чтобы повидаться со мной. | I am a very unfaithful person in general. I do not have a strong faith in God and I am constantly unfaithful to women. Recently I cheated on my girlfriend who comes from another **city** to see me. | +| 16 | I used to ride the bus everyday to work. One day I noticed a pregnant woman who did not have a seat. So I took her by the arm and helped her find a seat. | Раньше я ежедневно ездил на работу на автобусе. Как-то раз я заметил в автобусе беременную женщину, которой никто не уступал место. Тогда я взял ее за руку и помог найти свободное место. | I used to ride the bus everyday to work. One day I noticed a pregnant woman who did not have a seat. So I took her by the arm and helped her find a seat. | + +*Note.* The vignettes were adapted from Kruepke et al. (2018) with +cultural modifications for the Russian sample. + +The comparison of moral vignettes used for Russian and American +populations supported the validity of using the vignettes originally +designed for the American population for Russian participants. As shown +in **Table 2**, moral appropriateness, frequency, personal familiarity, +and general familiarity revealed close alignment between the Russian and +American samples. For instance, the measures of moral appropriateness +and frequency differed only slightly, indicating comparable perceptions +of the vignettes’ content. Additionally, the ranges for variables such +as the number of sentences and words remained consistent across both +samples, further supporting the comparability of the materials. While +differences in reading ease scores were observed—calculated using the +Flesch formula in Oborneva’s version for the Russian population +(Oborneva, 2006) for the Russian sample and the Flesch-Kincaid formula +(Kincaid et al., 1975; Flesch, 1948) for the American sample—these +differences were within acceptable limits, reflecting linguistic +variations rather than fundamental disparities in vignette +comprehensibility. + +**Table 2** + +*Characteristics of Moral Vignettes in Russian and American Samples* + + +++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Variable

Russians, N = 470

+

(Present study)

Americans, N = 225

+

(Kruepke et al., 2018)

+

M

+
+

SD

+
Range
+

M

+
+

SD

+
+

Range

+
Moral appropriateness3.881.711.7–6.6
+

3.56

+
+

1.47

+
+

1.8–6.3

+
Frequency4.720.773.2–6.64.630.633.0–5.7
Personal familiarity3.370.902.1–6.22.720.851.8–4.9
General familiarity3.320.722.2–5.22.950.662.0–4.7
Number of sentences2.80.52–43.00.22–4
Number of words36.97.120–5342.36.131–56
Number of characters217.641.4117–306204.929.5154–261
Reading ease51.517.00–8584.79.762–99
+ +*Note.* *M* = mean across 39 vignettes; *SD* = standard deviation. +Ranges represent minimum and maximum observed values for each variable. +The reading ease index ranges from 0 to 100, where higher numbers +indicate more readable text. + +## Statistical Analysis + +Two prior studies (Knutson et al., 2010; Kruepke et al., 2018) served as +the foundation for the statistical analysis. As in these previous +studies, we began with EFA. Given potential cultural differences between +Russian and American samples, we hypothesized that the factor structure +might differ between our study and past findings (Knutson et al., 2010; +Kruepke et al., 2018). Next, extending previous studies, we applied both +CFA and exploratory structural equation modeling (ESEM) as a more robust +approach to confirm the factor structure suggested by EFA and to +identify the best-fitting model. Finally, we conducted a latent profile +analysis (LPA) and compared these results with those of the American +population. + +Overall, our data analysis followed three sequential steps, all +implemented within the R software environment (version 4.2.2; R Core +Team, 2022), as described below. + +1) EFA on the first sample (N = 247; Study 1) was used to identify the + factor structure for the concurrent ratings of the vignettes for the + Russian population and to compare it to the factor structure of + ratings of the vignettes for the American population. + +2) CFA and ESEM was performed on the second sample (N = 223; Study 2) + to validate the EFA results and to identify the model with the best + fit. + +3) LPA on the aggregate sample (N = 470) was conducted to characterize + patterns of the Russian participants’ responses to the vignettes and + to compare these profiles with those found for the American + population. + +Given our focus on the moral components of the vignettes, all of our +analyses, similar to Knutson et al. (2010), were conducted on the 10 +event features—emotional intensity, emotional aversion, harm, +other-benefit, self-benefit, premeditation, legality, social norms, +socialness, and moral appropriateness. + +# Results + +## Exploratory Factor Analysis (Study 1) + +The EFA was performed using the “psych” (Revelle, 2023) and the +“EFAtools” (Steiner & Grieder, 2020) R packages. The ratings of the 10 +moral event features were averaged across participants to obtain mean +values for the event features per vignette. To account for the skewness +of our data, detected by Mardia’s test of multivariate normality (*p* \< +.001; Mardia, 1970), we proceeded with a Spearman correlation matrix. +The Kaiser-Meyer-Olkin measure (.72), above the recommended minimum +value of .50 (Kaiser & Rice, 1974) and significant Bartlett’s test of +sphericity (χ2(45) = 445.99, *p* \< .001; Bartlett, 1951), +indicated satisfactory overall sampling adequacy and significant +correlations among the event feature variables. The optimal number of +factors to extract was determined through several eigenvalue-based +criteria: Cattell’s scree test (Cattell, 1966), the Kaiser-Guttman rule +(Guttman, 1954; Kaiser, 1960), and a more recent adaptation of parallel +analysis via principal axis factoring (see **Figure 1**; Crawford et +al., 2010; Lim & Jahng, 2019). All these tests collectively pointed to a +three-factor solution, accounting for 76% of the total variance, as the +most plausible fit for our data. + +**Figure 1** + +*Scree Plot with Parallel Analysis Results* + + + +*Note.* The solid line represents the scree plot derived from the actual +data, while the dashed line illustrates the scree plot for randomly +simulated data with the same dimensions as the actual data. This figure +clearly indicates that three factors should be retained, as the first +three factors are distinctly above the random scree plot. + +The factor loadings for the 10 event features obtained using the +principal axis factoring method with Varimax rotation and Kaiser +normalization (Kaiser, 1958) are displayed in **Table 3**. Additionally, +**Table 3** includes the factor loadings identified in the two prior +studies (Knutson et al., 2010; Kruepke et al., 2018) on the American +population, allowing for a direct comparison of the two populations. +Similar to the previous studies (Knutson et al., 2010; Kruepke et al., +2018), the strongest common factors derived from EFA can be interpreted +as norm violation (factor 1), social affect (factor 2) and intention +(factor 3). Norm violation, with positive loadings from social norms, +moral appropriateness, legality, and other-benefit event features, along +with negative loadings from harm and emotional aversion event features, +explained the largest part of total variance (46%). Social affect, which +is primarily characterized by positive loadings from emotional +intensity, emotional aversion, socialness, harm event features, and a +negative loading from the self-benefit event feature, accounted for 17% +of the total variance. The third factor, intention, explained only 12% +of the variance and was driven by positive loadings from premeditation +and self-benefit event features. + +**Table 3** + +*Results of Exploratory Factor Analyses (EFA) for the Russian (current +study) and American Populations (from Knutson et al., 2010; Kruepke et +al., 2018)* + + ++++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Event Features

Russians, N= 247

+

Subset-3: 39 vignettes

+

(Present study)

Americans, N=225

+

Subset-3: 39 vignettes

+

(Kruepke et al., 2018)

Americans, N=30

+

Full set: 312 vignettes

+

(Knutson et al., 2010)

Norm ViolationSocial AffectIntentionNorm ViolationSocial AffectIntentionNorm ViolationSocial AffectIntention
Social norms.970-.160-.024.956-.179-.023.947.154.144
Moral appropriateness.968-.208.023.951-.193-.042-.956-.102-.120
Legality.814.154-.083.785.335-.046.737-.288.115
Other-benefit.806.080-.026.898-.111-.054-.883.046.051
Harm-.864.436-.022-.814.460-.048.803.473.009
Emotional aversion-.788.588-.035-.521.788-.012.336.762-.258
Emotional intensity-.132.698-.018-.213.896.067.024.896-.066
Socialness.030.572-.007.087.712-.116-.115.763.154
Premeditation-.138.134.799-.001.201.914-.002.175.859
Self-benefit.181-.529 .731-.069-.371.813.244-.304.772
Variance explained46%17%12%48%21%14%40%24%15%
+ +*Note.* Loadings of \> 0.4 are in bold. In the Kruepke et al. (2018) and +present studies scales for social norms and illegality were reversed in +comparison with the study of Knutson et al. (2010), so that higher +scores mean higher compliance with social norms and higher legality, +respectively. However, despite the difference in loading direction for +the norm violation factor, the results reflect the same factor structure +and interpretation. + +Furthermore, we used one extra orthogonal (*geominT*) rotation and +several different oblique (*promax, geominQ, oblimin*) rotations. These +alternative rotations yielded very similar three-factor solutions, as +detailed in supplementary **Table S3** (see Supplementary Materials). +However, the relatively weak correlations among the extracted factors +(ranging from 0.01 to 0.33; *M* = 0.14, *SD* = 0.16) obtained with the +oblique rotations led us to consider the factors as independent; +therefore, orthogonal rotations seemed to be more appropriate for our +dataset. + +According to the loading cutoff value of 0.4 (Hair et al., 2010), we +observed three moderate-size cross-loadings +for emotional aversion, harm, and self-benefit event features. Emotional +aversion and harm loaded on norm violation and social affect factors, +while self-benefit loaded on social affect and intention factors. It is +important to highlight that Kruepke et al. (2018) also demonstrated +cross-loadings for emotional aversion and harm. The cross-loading for +the self-benefit variable was not observed in the American population, +given a threshold of 0.4, although its contribution to social affect was +only slightly below the cutoff (-.371). The overall similarity of the +factor loadings between the Russian and American subjects was assessed +using Tucker’s index of factor congruence (Burt, 1948; Tucker, 1951). +All three factors exhibited very high values of Tucker’s index ($\geq$ +0.95; **Table S4** in Supplementary Materials), suggesting a high degree +of similarity of the factor structures and loadings (Lorenzo-Seva & Ten +Berge, 2006) across the studies with Russian and American samples. + +Furthermore, given the gender disparity in our sample (74% females) and +the potential influence of gender on moral judgments, we examined the +factor congruence between the male and the female groups in our dataset. +The correspondence was very high, ranging from 1.00 to 0.98 (**Table +S4** in Supplementary Materials), implying that the gender imbalance in +our sample should not be seen as having a distorting effect on the EFA +results. + +## Confirmatory Factor Analysis (Study 2) + +To our knowledge, CFA has not been presented in the previous studies of +moral vignettes. Since EFA does not provide a detailed assessment of +error terms or a rigorous test of how well the identified structure fits +the observed data, CFA is required to confirm the +correctness of the model obtained during the exploratory phase of +the data analysis. + +In standard CFA, it is often assumed that there is a simple factor +structure: each factor is determined by its unique set of indicators, +ideally without an overlap, leading to most or all cross-loadings being +fixed at zero (McDonald, 1985). However, even minor model inaccuracies +(e.g., not accounting for small cross-loadings such as .10 or .15) can +significantly affect the rest of the model, resulting in a bias toward +higher correlations between CFA factors and a poor fit to the data that +has been demonstrated for simulations (Asparouhov & Muthén, 2009; Marsh +et al., 2013) and real data (Marsh et al., 2010; Spooren et al., 2010). +The presence of at least three moderate cross-loadings in our +exploratory factor structure indicated that a simple CFA structure might +be too restrictive for our dataset (Morin et al., 2013). Therefore, in +addition to standard CFA, we also applied ESEM (Asparouhov & Muthén, +2009), which integrates the advantages of both EFA and CFA (Marsh et +al., 2014). Unlike the traditional approach, ESEM does not require the +elimination of even small cross-loadings, which could be theoretically +justified. This makes ESEM generally more suitable for complex factor +structures with multiple cross-loadings between factors. Importantly, +ESEM allowed us to achieve proper model estimation by avoiding +overfitting related to the misuse of residual covariances, convergence +problems, and goodness-of-fit challenges. + +The СFA and ESEM were performed using the “*lavaan*” (Rosseel, 2012) and +“*esemComp*” (Silvestrin & de Beer, 2024) R packages. Prior to these +analyses, similar to EFA, the ratings of the 10 moral event features +were averaged across participants to obtain mean values for each event +feature for each vignette. In the CFA, the event features were assigned +to their predefined constructs (i.e., norm violation, social affect, or +intention) based on loadings exceeding 0.4, as identified in the EFA in +Study 1 (see **Table 2**). Consequently, the CFA model included only +three cross-loadings—harm, emotional aversion, and +self-benefit—consistent with those observed in the EFA. By contrast, in +the ESEM, the event features were freely estimated and allowed to +cross-load onto multiple factors. A *geominT* rotation was employed in +the ESEM, as it does not prioritize the elimination of cross-loadings +(Asparouhov & Muthén, 2009). Consistent with the EFA results, the +factors in the models were assumed to be independent. + +All models were estimated using a robust version of the maximum +likelihood estimator (MLR) with standardized latent variables, where the +variance of each latent variable was fixed at 1.0 (Brown, 2015). Models’ +goodness-of-fit was assessed using several traditional indices, +including the scaled chi-square statistic (scaled χ2; Yuan & +Bentler, 2000), robust comparative fit index (CFI), robust Tucker-Lewis +index (TLI), robust standardized root mean square residual (SRMR), and +robust root mean square error of approximation (RMSEA) with its 90% +confidence interval. Traditionally, a good fit is indicated by values +less than .08 for RMSEA and SRMR (Hu & Bentler, 1999) and by values of +.95 or higher for CFI and TLI, although values around .90 can be +acceptable (Hoyle, 1995). The scaled chi-square statistic tests the +model’s exact fit to the data (Weston & Gore, 2006). In addition, we +used the scaled chi-square difference test (Satorra & Bentler, 2001), +based on the standard chi-square statistic, to compare nested competing +models in CFA and ESEM. + +The goodness-of-fit indices for the CFA and ESEM models, presented in +**Table 4**, indicated that neither solution generally fit the data +satisfactorily. Specifically, the CFA showed poor fit across all +indices, whereas the ESEM demonstrated acceptable fit on the CFI, TLI, +and SRMR indices but not on the RMSEA. An inspection of the modification +indices (MI) suggested that inclusion of four residual covariances with +MI greater than or equal to five in the ESEM model (socialness and +legality, socialness and other-benefit, social norms and other-benefit, +and other-benefit and harm) and four residual covariances in the CFA +model (the first three as in the ESEM model, plus social norms and +legality) significantly improved model fit (CFA: +χ2diff = 31.87, df diff = 4, *p* \< +.001; ESEM: χ2diff = 25.83, df diff = +4, *p* \< .001). + +**Table 4** + +*Goodness-of-Fit Statistics for ESEM and CFA Models* + + +++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Modelscaled χ2dfCFITLISRMRRMSEA
+(90% CI)
CFA122.914, p < .00135.828.778.138.256 (.208; .306)
ESEM50.413, p = .00124.948.903.046.169 (.103; .234)
CFA,
+4 residual covariances*		92.935, p < .00131.877.821.130.230 (.177; .284)
ESEM,
+4 residual covariances*		20.237, p = .443201.000.999.031.017 (.000; .137)
+ +*Note.* Optimal models are marked with an asterisk. Scaled χ2 += scaled chi-squared statistic; χ2 = normal chi-squared +statistic; df = degrees of freedom; CFI = Comparative Fit Index; TLI = +Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual; RMSEA += Root Mean Square Error of Approximation. + +The inclusion of residual covariances in the ESEM and CFA models was not +only statistically motivated but also theoretically grounded. +Covariances reflect key relationships: socially involved actions are +more likely to align with laws and benefit others due to their +regulation and alignment with collective goals (Cialdini et al., 1990; +Tyler, 2006; Axelrod & Hamilton, 1981). Similarly, laws often formalize +social norms that promote altruism and reduce harm (Haidt, 2012; +Schwartz, 1977). Actions benefiting others are less likely to be viewed +as harmful, aligning with dualistic moral perceptions (Fehr & +Fischbacher, 2003). + +Although the two solutions that included residual covariances produced +the expected factor structure (see **Table 5**), only the ESEM model +displayed adequate goodness-of-fit statistics, including a +non-significant chi-squared test of exact fit, CFI/TLI \> 0.95, and +SRMR/RMSEA \< 0.05 (see **Table 4** for details). These results +emphasize the superiority of the ESEM solution over CFA in achieving a +better and more acceptable model fit. + +**Table 5** + +*Standardized Loadings for the CFA and ESEM Optimal Models* + + +++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Event Features
+

CFA, 4 residual covariances

+
ESEM, 4 residual covariances
Norm ViolationSocial AffectIntention(resid)Norm ViolationSocial AffectIntention(resid)
Social norms
+

.992

+
+

(.016)

+
+

.997

+
+

-.004

+
+

.003

+
+

(.006)

+
Moral appropriateness
+

.993

+
+

(.014)

+
+

.986

+
+

-.147

+
+

.029

+
+

(.005)

+
Legality
+

.690

+
+

(.523)

+
+

.732

+
+

.287

+
+

.210

+
+

(.337)

+
Other-benefit
+

.795

+
+

(.368)

+
+

.773

+
+

-.024

+
+

-.214

+
+

(.356)

+
Harm
+

-.910

+
+

.327

+
+

(.064)

+
+

-.902

+
+

.355

+
+

-.061

+
+

(.056)

+
Emotional aversion-.898
+

.409

+
+

(.026)

+
+

-.885

+
+

.456

+
+

.015

+
+

(.008)

+
Emotional intensity
+

.738

+
+

(.455)

+
+

-.086

+
+

.702

+
+

.031

+
+

(.499)

+
Socialness
+

.449

+
+

(.798)

+
+

-.013

+
+

.453

+
+

-.014

+
+

(.795)

+
Premeditation
+

.789

+
+

(.378)

+
+

-.182

+
+

.042

+
+

.828

+
+

(.280)

+
Self-benefit
+

-.605

+
+

.584

+
+

(.293)

+
+

.315

+
+

-.551

+
+

.612

+
+

(.222)

+
+ +*Note.* Loadings of \> 0.4 are in bold; (resid) = event feature +residual. + +Overall, the best-fitting ESEM model confirmed the factor structure and +variable loadings from the EFA, with most event features loading +strongly (\> 0.4; p \< .001) onto their respective factors. Three +cross-loadings were identified: emotional aversion onto norm violation +(β = -.90) and social affect (β = .41), self-benefit onto intention (β = +.61) and social affect (β = -.55), and harm onto norm violation (β = +-.90) and weakly onto social affect (β = .36). Although these +cross-loadings generally aligned with the American sample, emotional +aversion had a stronger loading on norm violation in the Russian sample +than in the American sample, where it was primarily associated with +social affect (see **Table 3** for details). Additionally, the +cross-loading of self-benefit was more pronounced in the Russian sample, +whereas that of harm was stronger in the American sample. **Figure 2** +shows the graphical representation of the best-fitting ESEM model. + +**Figure 2** + +*ESEM Optimal Model with Four Residual Covariances* + + + +*Note.* Latent factors (NormV = norm violation, SocAf = social affect, +Inten = intention) are in circles; event features (ScNorm = social +norms, MrApp = moral appropriateness, Legal = legality, OtBen = +other-benefit, EmAv = emotional aversion, EmInt = emotional intensity, +Socil = socialness, Premd = premeditation, SlfBn = self-benefit) are in +rectangles. Positive and negative loadings are colored in green and red, +respectively, with opacity reflecting the magnitude of the loadings. + +## Latent Profile Analysis + +Following EFA and CFA, we also conducted an LPA using concurrent event +feature ratings of the vignettes averaged across the participants. The +goal of this analysis was to explore unique patterns of the +participants’ responses to vignette stories based on their event feature +ratings. We included ratings of the event features of emotional +intensity, emotional aversion, harm, other-benefit, self-benefit, +premeditation, legality, social norm, and socialness as indicator +variables and excluded ratings of the event feature moral +appropriateness for empirical categorization of the vignette stories +(i.e., clustering of the vignette responses) in the form of latent +profiles. The event feature moral appropriateness was excluded from our +model-based clustering and classification analysis to explore it as an +external variable and assess/compare the profile memberships predicted +by this variable (Fraley & Raftery, 1998; +2002). We used the “*mclust*” package (Scrucca et +al., 2016) to identify the best fitting model and profile numbers +using the Bayesian information criterion (BIC; Schwarz, 1978). Model +fitting and cluster analysis of the data by simultaneous application of +all 14 Gaussian mixture models of the “*mclust*” package revealed two +models with the lowest absolute BIC values–model VII and model VEI–with +various numbers of profiles. The best-fitting models with the +corresponding BIC values and profile numbers are presented in **Table +6**. + +**Table 6** + +*Gaussian Mixture Modeling of the Ratings of the Vignette Event Feature* + + +++++ + + + + + + + + + + + + + + + + + + + + + + +
Gaussian Mixture Models

Latent Profiles/

+

Clusters

BIC
VEI (diagonal, varying volume, equal shape)8 profiles-899.21
VEI (diagonal, varying volume, equal shape)5 profiles-929.19
VII (spherical, unequal, volume)5 profiles-929.40
+ +For further LPA, we chose the second best-fitting model VEI with five +profile solutions, as the model VEI with eight profile solutions did not +reveal a statistically meaningful and theoretically interpretable +profile solution for our vignette ratings data. Subsequent clustering +and classification analysis of vignette’s ratings data with the model +VEI showed five latent profiles (BIC = -929.19) as the best solution, +followed by seven profiles (BIC = 951.26) and 6 profiles (BIC = +-969.38). Five-profile solution clustered 17 vignettes in latent profile +1 and 3 in latent profile 2, while latent profiles 3 and 4 each +clustered 7 different vignettes and latent profile 5 clustered the +remaining 5 vignettes together. The cluster plots of the vignettes based +on the model VEI with five profiles are shown in **Figure 3**. The +latent profile classification of the vignettes across all five latent +profiles (**Table S1**), as well as BIC value plots for all Gaussian +mixture models’ profile solutions (**Figure S2**), are provided in the +Supplementary Materials. + +**Figure 3** + +*The Cluster Plot for the Vignettes Across Five Latent Profiles Based on +the Model VEI* + + + +*Note.* Individual vignettes are denoted by red dots, marsh triangles, +green squares, blue pluses, or pink crosses stand for classification in +the latent profiles 1–5, respectively. The profiles are depicted with +color-coded ellipses and ellipsoidal centers; each profile is denoted by +the cluster number in the figure legend. Сlusters 1:5 in the figure +legend denote profiles 1:5, respectively. + +The identified latent profiles were labeled Peccadillo, Illegal & +Antisocial, Controversial Act, Prosocial, and Social Conflict based on +the previous literature. The profile Social Conflict was not identified +in Kruepke et al.’s (2018) original study; however, it emerged as a +highly robust category in our Russian sample, consistently appearing +across both VEI and VII models. Notably, our analysis did not yield the +Deception profile found in Kruepke et al.’s (2018) study; most vignettes +previously categorized as Deception were absorbed into the Peccadillo +profile in our sample. **Figure 4** presents boxplots comparing the +distribution of ratings across each event feature for the observed +latent profiles, allowing for a direct comparison of how profiles differ +on individual moral dimensions. The vignette ratings for moral +appropriateness were combined with the latent profile classification +results to demonstrate both the quantitative and qualitative results. + +**Figure 4** + +*Gaussian Mixture Modeling for Classification of the Vignette Ratings +across Nine Event Features as Indicator Variables* + + + +*Note.* Five latent profiles were identified and labeled as Peccadillo, +Social Conflict, Controversial Act, Prosocial, and Illegal & Antisocial, +based on a theoretical interpretation of the results. The vignettes’ +ratings of moral appropriateness are included to illustrate the original +behavioral results. + +Thus, 21 vignettes out of a total of 39 vignettes were clustered and +classified in the Peccadillo profile based on the participant ratings. +The profile is characterized by relatively low ratings of emotional +aversion, harm, and other-benefit. The Social Conflict profile clustered +only three vignettes, depicting situations with interpersonal conflicts +that occurred accidentally and unintentionally. Thus, the Social +Conflict profile is characterized by relatively low ratings of +premeditation, self-benefit, moral appropriateness, social norms, and by +relatively high harm and socialness. The Controversial Act profile +clustered five vignettes for which actions were perceived as having +elevated levels of harm and premeditation, violation of social norms, +and low moral appropriateness. The Prosocial profile clustered five +vignettes for which actions were perceived as having nearly neutral +emotional intensity, relatively low emotional aversion, high +other-benefit (as compared to self-benefit), high socialness, legality, +moral appropriateness, and compliance with social norms. Finally, the +Illegal & Antisocial profile clustered five vignettes for which the +actions were rated by participants as demonstrating high levels of harm, +high emotional aversion, relatively high self-benefit (as compared to +other-benefit), low legality and moral appropriateness, violating social +norms. + +Finally, we conducted multinomial logistic regression analyses to +explore how profile membership (identified in LPA) was predicted by +moral appropriateness ratings as an external variable. Using the +vignettes’ profile numbers, profile probability values, and averaged +ratings ("nnet" package; Venables & Ripley, 2002), we sequentially +assigned each profile as a reference category. For example, the results +revealed that vignettes with lower moral appropriateness ratings had +significantly higher odds of being classified in the Controversial Act +and Illegal & Antisocial profiles compared to the Peccadillo profile (p += *.038* and p = *.039*, respectively). Full results of the multinomial +regression analyses are provided in supplementary **Table S5** in +Supplementary materials. + +# Discussion + +We conducted two online studies of Russian participants (Study 1, N = +247 and Study 2, N = 223) who rated 39 moral vignettes on 10 moral event +features (dimensions). We translated and validated +standard sets of vignettes (Knutson et al., 2010; Kruepke et al., +2018) that provide a tool to assess the main event +features known to influence moral judgments and can be further used in +behavioral, neuroimaging, and cross-cultural studies. The +original vignettes were developed based on real-life episodic memories, +and each vignette event in the sets is characterized by previously +well-defined key moral features (Rusbult & Van Lange, 1996; Haidt, +2007). Our results demonstrate the reproducibility of ratings of +vignettes’ moral features for Russian, American and Iranian +participants, but they also indicate some cross-cultural differences. +Using qualitative and quantitative statistical analyzes, we observed +highly similar responses in the evaluation of the vignettes’ event plots +and event features by our Russian participants (current study) compared +to American and Iranian individuals (Knutson et al*.*, 2010; Kruepke et +al., 2018; Yazdanpanah et al., 2021). + +Similar to previous studies, our EFA of the event feature ratings +confirmed norm violation, social affect, and intention as fundamental +components of moral judgement. The +significance of our findings for understanding the basis of human moral +judgement is severalfold. Our findings regarding cross-culturally shared +moral motives confirm the role of universally recognized basic moral +concerns or intuitions (Shweder & Sullivan, 1993; Shweder et al., 1997; +Graham et al., 2011). Our results support the +multi-dimensionality of moral judgments and further demonstrate the +importance of previously identified moral factors, including norm +violation (Shweder et al.,1997; Haidt, 2007), social affect (Moll +et al., 2001; Moll et al., 2002), and the intention (Koster-Hale et al., +2013). Similar to the American and Iranian +populations, Russian participants demonstrated that social norms play a +key role in moral judgments, followed by social emotions, and then +intentions. Overall, the moral judgments of the participants in our +study can be explained by their moral intuitions, as suggested by the +moral foundations theory (Haidt, 2007; Graham et al., 2013). These moral +intuitions support rapid, effortless, associative, and heuristic +cognition developed in evolution and varyingly shaped by socio-cultural +experiences from childhood (Graham et al., 2013). + +Our findings are also supported by the literature on moral psychology. +Social norms and values have been previously reported as influential +factors in the development of moral psychology and the behavior of +individuals in different societal environments (Kohlberg, 1963b; +Schwartz, 1992; Schwartz & Bilsky, 1987; Schwartz & Bilsky, 1990). +Violation of social norms evokes moral judgment across various +situations, as demonstrated by the norm violation factor in our +findings. Similarly, the finding of the social affect factor is also in +line with the literature on moral cognition and psychology. This +component includes the event features of emotional aversion, emotional +intensity, and socialness, and it demonstrates that individuals make +moral evaluations through cognitive and socioemotional judgment of +various moral features associated with different events and situations +encountered in everyday real-life circumstances (Greene & Haidt, 2002; +Haidt, 2007; Graham et al., 2009). Emotional brain responses have also +been demonstrated in the functional magnetic resonance imaging (fMRI) of +a task involving passive visual attention to images of morality-evoking +scenes (Moll et al., 2002). Our results confirmed the intention as the +third component for the moral judgment of our participants. This +component includes the event features of self-benefit and premeditation. +Intention is generally defined as the instrumentality of the action, +omission, or the character of the protagonist for self-benefit in moral +events or situations by premeditation and planning of the protagonist. +An fMRI study of a task for subjects reading narratives with accidental +or intentional harms showed a distinct pattern of neural responses for +accidental versus intentional harms, demonstrating that the +intentionality of an action is an important component in moral judgment +and behavior (Koster-Hale et al., 2013). The role of intentionality is +even more pronounced in the analysis of intuitions in classic moral +dilemmas, such as trolley dilemmas (Thomson, 1985; Waldmann & Dieterich, +2007; Waldman & Wiegmann, 2010). + +Our ESEM modeling results generally confirmed the factor structure and +variable loadings identified in the EFA. Three cross-loadings were also +supported: (i) cross-loading of emotional aversion onto norm violation +and social affect factors, (ii) cross-loading of self-benefit onto +intention and social affect, and (iii) cross-loading of harm onto norm +violation and social affect factors. Overall, these cross-loading +profiles align with those reported in the American sample (Kruepke et +al., 2018); however, emotional aversion had a somewhat stronger loading +on norm violation than on social affect in the Russian sample, whereas +in the American one, it was initially presumed to contribute more +strongly to social affect (see **Tables 3** and **5** for details). +Additionally, the negative cross-loading of self-benefit to social +affect was more pronounced in the Russian sample, whereas the positive +cross-loading of harm was stronger in the American sample. + +Previous studies indicated that that compared to +Western Europeans, Eastern Europeans may hold the belief that multiple, +even opposite, truths are possible (e.g., Peng & Nisbett, 1999; Varnum +et al., 2008), which can potentially result in differences in moral +judgments in different cultures. Among other characteristics, Russian +and American populations also differ in collectivism and power distance. +According to Hofstede’s model and its revision, the United States (IDV = +91) is an individualistic country, whereas Russia (IDV = 39) is +collectivistic. Furthermore, the Hofstede Cultural Dimensions model +assigns Russia a score of 93 (out of 100) for the power distance, while +the United States showed a score of 40. A prominent cross-cultural study +investigated perceptions of the appropriateness of various responses to +a violation of a cooperative norm and to atypical social behaviors and +showed that appropriateness ratings of physical confrontation and social +ostracism were negatively correlated with individualism and positively +correlated with power distance (Eriksson et al., 2021). These findings +are in line with the stronger loading of emotional aversion onto the +Norm Violation factor in the Russian sample (in our study) +compared to the American sample (Kruepke et al., 2018). The high level +of collectivism may also explain the stronger negative cross-loading of +self-benefit (fairness) to Social Affect in the Russian sample. + +Based on interpretations from available literature for sociomoral +reasoning and behavior, we also identified the following latent +profiles: Peccadillo, Illegal & Antisocial, Controversial Act, +Prosocial, and Social Conflict. Interestingly, the Social Conflict +profile was not identified in the original study of the American +population (Kruepke et al., 2018), yet it consistently emerged in both +our VEI and VII models as a highly robust category for our Russian +sample. The cross-cultural differences in collectivism may perhaps +explain some differences in the latent profiles in the Russian and +American samples. Notably, our analysis of the Russian population also +did not yield the Deception profile found in Kruepke et al.’s (2018) +study of the American population. In our sample, most vignettes +previously categorized as Deception were absorbed into the Peccadillo +profile. Collectivistic attitudes have been linked to +a tolerance of deceptive behavior when it is used to avoid conflict and +support harmony (Seiter et al. 2002), whereas in individualistic +cultures, saying the truth is often an important norm (Hall & Whyte, +2008). Furthermore, Eastern Europeans often report the belief that +multiple and contradictory truths are possible (e.g., Peng & Nisbett, +1999; Spencer-Rodgers et al., 2010; Varnum et al., 2008), which may +affect their views on deception in various social contexts. Future +studies are clearly needed to explain and replicate the cross-cultural +differences in some aspects of moral judgments reported in the current +study. + +Our moral study has some limitations due to project timeline +constraints. The participants’ personality traits, emotional states, +moral developmental levels, and other nonmoral cognitive functions were +not assessed as additional measures. Therefore, future studies of +morality should also include assessment of personality traits to examine +the differential effects of traits and affective states on moral +judgment and behavior. It should also be noted that 73% of our study +participants were female. The statistical analysis did not demonstrate a +significant effect of gender on moral judgments in our study; however, +future studies should aim for gender-balanced samples. + +**Conclusion** + +In two (main and confirmatory) large-scale behavioral studies, we +further validated a research tool that probes moral judgment using a +battery of realistic vignettes representing both positive and negative +moral experiences. Using qualitative and quantitative statistical +analyses, we observed highly similar responses in the evaluation of the +vignettes’ event plots and event features by Russian participants +(current study) compared to American individuals (Knutson et al., 2010; +Kruepke et al., 2018). Similar to previous studies, our EFA of the event +feature ratings revealed norm violation, social affect and intention as +fundamental components of moral judgment. Although the cross-loading +profiles largely aligned with the previously published American sample +(Kruepke et al., 2018), emotional aversion had a stronger loading on +norm violation in the Russian sample than in the American sample. +Notably, our LPA of the Russian sample did not yield the Deception +profile found in Kruepke et al.’s (2018) study of the American +population, whereas Social Conflict emerged as a highly robust category +only in our Russian sample. Thus, our results call for additional +cross-cultural studies for a more detailed validation of the realistic +moral vignettes used in studies of moral judgments. Overall, our +findings demonstrate the feasibility and reliability of the previously +published vignettes (Knutson et al., 2010; Kruepke et al., 2018) +narrating real-life moral scenarios for the assessment of moral +judgment. The set of vignettes provides investigators with effective +tools to ensure broad coverage of the key factors implicated in moral +judgments in different cultures. + +# Declarations + +## Funding + +This article is an output of a research project +implemented as part of the Basic Research Program at the National +Research University Higher School of Economics (HSE University). + +## Conflicts of interest + +The authors declared that they had no conflict of +interest with respect to their authorship or the publication of this +article. + +## Ethics approval + +The study was conducted in accordance with the Declaration of Helsinki +and approved by the Ethics Committee of the HSE University. + +## Consent to participate + +All participants were provided with brief instructions and gave informed +consent before participating in this study. + +## Consent for publication + +Not applicable. + +## Open practices statement + +Supplemental Material and data presented in this +study, as well as minimal code to reproduce the main findings, +are openly and freely available online via the Open +Science Framework (OSF): +[https://osf.io/9k5b8/](https://osf.io/9k5b8/). None of +the experiments in this study were preregistered. + +## Authors’ contributions + +Studies 1 and 2 were supervised by Isak B. Blank and Vasily Klucharev. +Study 1 was established and planned by Isak B. Blank, Vasily Klucharev, +and Rustam Asgarov. Study 2 was established and planned by Isak B. +Blank, Vasily Klucharev, and Zorina Rakhmankulova. Rustam Asgarov +prepared the Google questionnaire and collected the data for Study 1, +while Zorina Rakhmankulova performed these tasks for Study 2. Zorina +Rakhmankulova and Rustam Asgarov conducted the statistical analyses and +prepared the relevant scripts for Study 1, whereas Zorina Rakhmankulova +and Semyon Mening performed these tasks for Study 2. Zorina +Rakhmankulova and Semyon Mening processed the experimental data for both +studies and generated all final scripts, tables, figures, and +supplementary materials. The initial manuscript was drafted by Zorina +Rakhmankulova and Rustam Asgarov. The final manuscript was reviewed, +edited, and written by Zorina Rakhmankulova, Eliana Monahhova, and +Vasily Klucharev. All authors reviewed and approved the final +manuscript. + +## Acknowledgements + +We express our gratitude to Anna Tokmovtseva from the Russian Orthodox +University of Saint John the Divine for her invaluable assistance in +calculating the reading ease index for the Russian-language version of +the vignettes and preparing the supplemental materials. We also thank +our colleagues Anna Shepelenko, Nina Kazanina, Ksenia Panidi, and Anna +Shestakova from the Institute for Cognitive Neuroscience of the HSE +University for their valuable help with the Russian-language translation +of the vignettes and event feature scales, as well as their assistance +with the recruitment of the participants. + +# References + +Asparouhov, T., & Muthén, B. (2009). Exploratory +structural equation modeling. *Structural equation modeling: A +Multidisciplinary Journal*, *16*(3), 397–438. +[https://doi.org/10.1080/10705510903008204](https://doi.org/10.1080/10705510903008204) + +Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. +*Science*, *211*(4489), 1390–1396. +[https://doi.org/10.1126/science.7466396](https://doi.org/10.1126/science.7466396) + +Bartlett, M.S. (1951). The effect of standardization +on a χ2 approximation in factor analysis, *Biometrika*, +*38*(3-4), 337–344. +[https://doi.org/10.2307/2332580](https://doi.org/10.2307/2332580) + +Brown, T. A. (2015). *Confirmatory factor analysis for applied research* +(2nd ed.). The Guilford Press. + +Burt, C. (1948). The factorial study of temperament +traits. *British Journal of Psychology*, *1*, 178–203. +[https://doi.org/10.1007/BF02288799](https://doi.org/10.1007/BF02288799) + +Cattell, R. B. (1966). The scree test for the number of factors. +*Multivariate Behavioral Research*, *1*(2), 245–276. +[https://doi.org/10.1207/s15327906mbr0102_10](https://doi.org/10.1207/s15327906mbr0102_10) + +Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory +of normative conduct: Recycling the concept of norms to reduce littering +in public places. *Journal of Personality and Social Psychology*, +*58*(6), 1015–1026. +[https://doi.org/10.1037/0022-3514.58.6.1015](https://doi.org/10.1037/0022-3514.58.6.1015) + +Crawford, A. V., Green, S. B., Levy, R., Lo, W. L., Scott, L., Svetina, +D., & Thompson, M. S. (2010). Evaluation of parallel analysis methods +for determining the number of factors. *Educational and Psychological +Measurement*, *70*(6), 885–901. +[https://doi.org/10.1177/0013164410379332](https://doi.org/10.1177/0013164410379332) + +Dunn, A. M., Heggestad, E. D., Shanock, L. R., & Theilgard, N. (2018). +Intra-individual response variability as an indicator of insufficient +effort responding: Comparison to other indicators and relationships with +individual differences. *Journal of Business and Psychology*, *33*, +105-121. +[https://doi.org/10.1007/s10869-016-9479-0](https://doi.org/10.1007/s10869-016-9479-0) + +Eriksson, K., Strimling, P., Gelfand, M., Wu, J., Abernathy, J., Akotia, +C. S., Aldashev, A., Ananyeva, K. I., Andersson, P. A., Arikan, G., +Batanova, M., Becker, M., Birney, M. E., Boehnke, K., Bortolini, T., +Choi, H., Chu, Q., Chuang, S., Collins, E., ... Van Lange, P. A. M. +(2021). Perceptions of the appropriate response to norm violation in 57 +societies. *Nature Communications*, *12*(1), 1481. +[https://doi.org/10.1038/s41467-021-21602-9](https://doi.org/10.1038/s41467-021-21602-9) + +Escobedo, J. R. (2009). *Investigating moral events: Characterization +and structure of autobiographical moral memories* \[Doctoral +dissertation, California Institute of Technology\]. +[https://resolver.caltech.edu/CaltechETD:etd-11112008-122002](https://resolver.caltech.edu/CaltechETD:etd-11112008-122002) + +Fehr, E., & Fischbacher, U. (2003). The nature of human altruism. +*Nature*, *425*(6960), 785–791. +[https://doi.org/10.1038/nature02043](https://doi.org/10.1038/nature02043) + +Flesch, R. (1948). A new readability yardstick. *Journal of Applied +Psychology*, *32*, 221–233. +[https://doi.org/10.1037/h0057532](https://psycnet.apa.org/doi/10.1037/h0057532) + +Fraley, C., & Raftery, A. E. (1998). How many clusters? Which clustering +method? Answers via model-based cluster analysis. *The Computer +Journal*, *41*(8), 578–588. +[https://doi.org/10.1093/comjnl/41.8.578](https://doi.org/10.1093/comjnl/41.8.578) + +Fraley, C., & Raftery, A. E. (2002). Model-based clustering, +discriminant analysis, and density estimation. *Journal of the American +Statistical Association*, *97*(458), 611–631. +[https://doi.org/10.1198/016214502760047131](https://doi.org/10.1198/016214502760047131) + +Graham, J., Haidt, J., & Nosek, B. A. (2009). Liberals and conservatives +rely on different sets of moral foundations. *Journal of Personality and +Social Psychology*, *96*(5), 1029–1046. +[https://doi.org/10.1037/a0015141](https://doi.org/10.1037/a0015141) + +Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S. P., & +Ditto, P. H. (2013). Moral foundations theory: The pragmatic validity of +moral pluralism. In P. G. Devine & A. Plant (Eds.), *Advances in +experimental social psychology* (Vol. 47, pp. 55–130). Academic Press. +[https://doi.org/10.1016/B978-0-12-407236-7.00002-4](https://doi.org/10.1016/B978-0-12-407236-7.00002-4) + +Graham, J., Meindl, P., Beall, E., Johnson, K. M., & Zhang, L. (2016). +Cultural differences in moral judgment and behavior, across and within +societies. *Current Opinion in Psychology*, *8*, 125–130. +[https://doi.org/10.1016/j.copsyc.2015.09.007](https://doi.org/10.1016/j.copsyc.2015.09.007) + +Graham, J., Meyer, L. H., McKenzie, L., McClure, J., & Weir, K. F. +(2010). Maori and Pacific secondary student and parent perspectives on +achievement, motivation and NCEA. *Assessment Matters*, *2*, 132–157. + + +Graham, J., Nosek, B. A., Haidt, J., Iyer, R., Koleva, S., & Ditto, P. +H. (2011). Mapping the moral domain. *Journal of Personality and Social +Psychology*, *101*(2), 366–385. +[https://doi.org/10.1037/a0021847](https://doi.org/10.1037/a0021847) + +Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., & +Cohen, J. D. (2001). An fMRI investigation of emotional engagement in +moral judgment. *Science*, *293*(5537), 2105–2108. +[https://doi.org/10.1126/science.1062872](https://doi.org/10.1126/science.1062872) + +Greene, J., & Haidt, J. (2002). How (and where) does moral judgment +work?. *Trends in Cognitive Sciences*, *6*(12), 517–523. +[https://doi.org/10.1016/S1364-6613(02)02011-9](https://doi.org/10.1016/S1364-6613(02)02011-9) + +Guerra, V. M., & Giner-Sorolla, R. (2010). The Community, Autonomy, and +Divinity Scale (CADS): A new tool for the cross-cultural study of +morality. *Journal of Cross-Cultural Psychology*, *41*(1), 35–50. +[https://doi.org/10.1177/0022022109348919](https://doi.org/10.1177/0022022109348919) + +Guttman, L. (1954). Some necessary conditions for common-factor +analysis. *Psychometrika*, *19*(2), 149–161. +[https://doi.org/10.1007/BF02289162](https://doi.org/10.1007/BF02289162) + +Haidt, J. (2001). The emotional dog and its rational tail: A social +intuitionist approach to moral judgment. *Psychological Review*, +*108*(4), 814–834. +[https://doi.org/10.1037/0033-295X.108.4.814](https://doi.org/10.1037/0033-295X.108.4.814) + +Haidt, J. (2007). The new synthesis in moral psychology. *Science*, +*316*(5827), 998–1002. +[https://doi.org/10.1126/science.1137651](https://doi.org/10.1126/science.1137651) + +Haidt, J. (2012). *The righteous mind: Why good people are divided by +politics and religion*. New York Pantheon. + +Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). +*Multivariate data analysis* (7th ed.). Prentice Hall. + +Hall, E. T., & Whyte, W. F. (2008). Intercultural communication. In C. +D. Mortensen (Ed.), *Communication theory* (2nd ed., pp. 17–33). +Routledge. +[https://doi.org/10.4324/9781315080918-32](https://doi.org/10.4324/9781315080918-32) + +Hawley, P. H. (2003). Strategies of control, aggression, and morality in +preschoolers: An evolutionary perspective. *Journal of Experimental +Child Psychology*, *85*, 213–235. +[https://doi.org/10.1016/s0022-0965(03)00073-0](https://doi.org/10.1016/s0022-0965(03)00073-0) + +Heekeren, H. R., Wartenburger, I., Schmidt, H., Schwintowski, H. P., & +Villringer, A. (2003). An fMRI study of simple ethical decision-making. +*Neuroreport*, *14*(9), 1215–1219. +[https://doi.org/10.1097/00001756-200307010-00005](https://doi.org/10.1097/00001756-200307010-00005) + +Hoyle, R. H. (1995). *Structural equation modeling: Concepts, issues, +and applications*. Sage Publications. + +Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in +covariance structure analysis: Conventional criteria versus new +alternatives. *Structural Equation Modeling: A Multidisciplinary +Journal*, *6*(1), 1–55. +[https://doi.org/10.1080/10705519909540118](https://doi.org/10.1080/10705519909540118) + +Kaiser, H. F. (1958). The varimax criterion for analytic rotation in +factor analysis. *Psychometrika*, *23*(3), 187–200. +[https://doi.org/10.1007/BF02289233](https://doi.org/10.1007/BF02289233) + +Kaiser, H. F. (1960). The application of electronic computers to factor +analysis. *Educational and Psychological Measurement*, *20*(1), 141–151. +[https://doi.org/10.1177/001316446002000116](https://doi.org/10.1177/001316446002000116) + +Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. *Journal of +Educational and Psychological Measurement*, *34*(1), 111–117. +[https://doi.org/10.1177/001316447403400115](https://doi.org/10.1177/001316447403400115) + +Kincaid, J. P., Fishburne, R. P., Jr., Rogers, R. L., & Chissom, B. S. +(1975). *Derivation of new readability formulas (Automated Readability +Index, Fog Count and Flesch Reading Ease Formula) for Navy enlisted +personnel* (Research Branch Report No. 8-75). Naval Technical Training +Command, Research Branch. +[https://doi.org/10.21236/ADA006655](https://doi.org/10.21236/ADA006655) + +Kline, R. B. (2016). *Principles and practice of structural equation +modeling* (4th ed.). Guilford Press. + +Knutson, K. M., Krueger, F., Koenigs, M., Hawley, A., Escobedo, J. R., +Vasudeva, V., *et al*. (2010). Behavioral norms for condensed moral +vignettes. *Social Cognitive and Affective Neuroscience*, *5*(4), +378–384. +[https://doi.org/10.1093/scan/nsq005](https://doi.org/10.1093/scan/nsq005) + +Kohlberg, L. (1963b). Moral development and identification. *Teachers +College Record*, *64*(9), 277–332. +[https://doi.org/10.1037/13101-008](https://doi.org/10.1037/13101-008) + +Koster-Hale, J., Saxe, R., Dungan, J., & Young, L. L. (2013). Decoding +moral judgments from neural representations of intentions. *Proceedings +of the National Academy of Sciences*, *110*(14), 5648–5653. +[https://doi.org/10.1073/pnas.1207992110](https://doi.org/10.1073/pnas.1207992110) + +Kruepke, M., Molloy, E. K., Bresin, K., Barbey, A. K., & Verona, E. +(2018). A brief assessment tool for investigating facets of moral +judgment from realistic vignettes. *Behavior Research Methods*, *50*(3), +922–936. +[https://doi.org/10.3758/s13428-017-0917-3](https://doi.org/10.3758/s13428-017-0917-3) + +Lim, S., & Jahng, S. (2019). Determining the number of factors using +parallel analysis and its recent variants. *Psychological Methods*, +*24*(4), 452–467. +[https://doi.org/10.1037/met0000230](https://doi.org/10.1037/met0000230) + +Lorenzo-Seva, U., & Ten Berge, J. M. (2006). Tucker's congruence +coefficient as a meaningful index of factor similarity. *Methodology*, +*2*(2), 57–64. +[https://doi.org/10.1027/1614-2241.2.2.57](https://doi.org/10.1027/1614-2241.2.2.57) + +Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis +with applications. *Biometrika*, *57*(3), 519–530. +[https://doi.org/10.1093/biomet/57.3.519](https://doi.org/10.1093/biomet/57.3.519) + +Marsh, H. W., Lüdtke, O., Muthén, B., Asparouhov, T., Morin, A. J. S., +Trautwein, U., & Nagengast, B. (2010). A new look at the big five factor +structure through exploratory structural equation modeling. +*Psychological Assessment*, *22*(3), 471–491. +[https://doi.org/10.1037/a0019227](https://doi.org/10.1037/a0019227) + +Marsh, H. W., Lüdtke, O., Nagengast, B., Morin, A. J., & Von Davier, M. +(2013). Why item parcels are (almost) never appropriate: Two wrongs do +not make a right—camouflaging misspecification with item parcels in CFA +models. *Psychological Methods*, *18*(3), 257–284. +[https://doi.org/10.1037/a0032773](https://doi.org/10.1037/a0032773) + +Marsh, H. W., Morin, A. J., Parker, P. D., & Kaur, G. (2014). +Exploratory structural equation modeling: An integration of the best +features of exploratory and confirmatory factor analysis. *Annual Review +of Clinical Psychology*, *10*, 85–110. +[https://doi.org/10.1146/annurev-clinpsy-032813-153700](https://doi.org/10.1146/annurev-clinpsy-032813-153700) + +McDonald, R. P. (1985). *Factor analysis and related methods*. Lawrence +Erlbaum Associates. +[https://doi.org/10.4324/9781315802510](https://doi.org/10.4324/9781315802510) + +Moll, J., Eslinger, P. J., & Oliveira-Souza, R. D. (2001). Frontopolar +and anterior temporal cortex activation in a moral judgment task: +Preliminary functional MRI results in normal subjects. *Arquivos de +Neuro-psiquiatria*, *59*, 65–664. +[https://doi.org/10.1590/S0004-282X2001000500001](https://doi.org/10.1590/S0004-282X2001000500001) + +Moll, J., de Oliveira-Souza, R., Eslinger, P. J., Bramati, I. E., +Mourao-Miranda, J., Andreiuolo, P. A., & Pessoa, L. (2002). The neural +correlates of moral sensitivity: A functional magnetic resonance imaging +investigation of basic and moral emotions. *Journal of Neuroscience*, +*22*(7), 2730–2736. +[https://doi.org/10.1523/JNEUROSCI.22-07-02730.2002](https://doi.org/10.1523/JNEUROSCI.22-07-02730.2002) + +Morin, A.J.S., Marsh, H.W., & Nagengast, B. (2013). Exploratory +structural equation modeling. In G.R. Hancock & R.O. Mueller (Eds.), +*Structural Equation Modeling: A Second Course* (pp. +395–436). Information Age Publishing. + +Oborneva, I. V. (2006). *Automated assessment of the complexities of +educational texts based on statistical parameters* (Unpublished doctoral +dissertation). + +Peng, K., & Nisbett, R. E. (1999). Culture, dialectics, and reasoning +about contradiction. *American Psychologist*, *54*(9), 741-754. +[https://doi.org/10.1037/0003-066X.54.9.741](https://doi.org/10.1037/0003-066X.54.9.741) + +R Core Team (2022). *R: A language and environment for statistical +computing* \[Computer software\]. R Foundation for Statistical +Computing. +[https://www.R-project.org/](https://www.r-project.org/) + +Revelle, W. (2023). *psych: Procedures for psychological, psychometric, +and personality research* (R package version 2.3.6) \[Computer +software\]. Northwestern University. +[https://CRAN.R-project.org/package=psych](https://cran.r-project.org/package=psych) + +Rosseel, Y. (2012). lavaan: An R Package for Structural Equation +Modeling. *Journal of Statistical Software*, *48*(2), 1–36. +[https://doi.org/10.18637/jss.v048.i02](https://doi.org/10.18637/jss.v048.i02) + +Rusbult, C. E., & Van Lange, P. A. M. (1996). Interdependence processes. +In E. T. Higgins & A. W. Kruglanski (Eds.), *Social psychology: Handbook +of basic principles* (pp. 564–596). Guilford Press. + +Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square +test statistic for moment structure analysis. *Psychometrika*, *66*, +507–514. +[https://doi.org/10.1007/BF02296192](https://doi.org/10.1007/BF02296192) + +Schwartz, S. H. (1977). Normative influences on altruism. *Advances in +Experimental Social Psychology*, *10*, 221–279. +[https://doi.org/10.1016/S0065-2601(08)60358-5](https://doi.org/10.1016/S0065-2601(08)60358-5) + +Schwartz, S. H. (1992). Universals in the content and structure of +values: Theoretical advances and empirical tests in 20 countries. In M. +P. Zanna (Ed.), *Advances in experimental social psychology* (Vol. 25, +pp. 1–65). Academic Press. +[https://doi.org/10.1016/S0065-2601(08)60281-6](https://doi.org/10.1016/S0065-2601(08)60281-6) + +Schwartz, S. H., & Bilsky, W. (1987). Toward a universal psychological +structure of human values. *Journal of Personality and Social +Psychology*, *53*(3), 550–562. +[https://doi.org/10.1037/0022-3514.53.3.550](https://doi.org/10.1037/0022-3514.53.3.550) + +Schwartz, S. H., & Bilsky, W. (1990). Toward a theory of the universal +content and structure of values: Extensions and cross-cultural +replications. *Journal of Personality and Social Psychology*, *58*(5), +878–891. +[https://doi.org/10.1037/0022-3514.58.5.878](https://doi.org/10.1037/0022-3514.58.5.878) + +Schwarz, G. (1978). Estimating the dimension of a model. *The Annals of +Statistics*, *6*(2), 461–464. +[https://doi.org/10.1214%2Faos%2F1176344136](https://doi.org/10.1214%2Faos%2F1176344136) + +Seiter, J. S., Bruschke, J., & Bai, C. (2002). The acceptability of +deception as a function of perceivers' culture, deceiver's intention, +and deceiver‐deceived relationship. *Western Journal of Communication*, +*66*(2), 158–180. +[https://doi.org/10.1080/10570310209374731](https://doi.org/10.1080/10570310209374731) + +Shweder, R. A., & Sullivan, M. A. (1993). Cultural psychology: Who needs +it? *Annual Review of Psychology*, *44*(1), 497–523. +[https://doi.org/10.1146/annurev.ps.44.020193.002433](https://doi.org/10.1146/annurev.ps.44.020193.002433) + +Shweder, R. A., Much, N. C., Mahapatra, M., & Park, L. (1997). The "big +three" of morality (autonomy, community, divinity) and the "big three" +explanations of suffering. In A. M. Brandt & P. Rozin (Eds.), *Morality +and health* (pp. 119–169). Routledge. + +Silvestrin, M. T., & de Beer, L. (2022). *esemComp: ESEM-withinCFA +syntax composer* (R package version 0.2) \[Computer software\]. +[https://mateuspsi.github.io/esemComp](https://mateuspsi.github.io/esemComp) + +Spencer-Rodgers, J., Williams, M. J., & Peng, K. (2010). Cultural +differences in expectations of change and tolerance for contradiction: A +decade of empirical research. *Personality and Social Psychology +Review*, *14*(3), 296–312. +[https://doi.org/10.1177/1088868310362982](https://doi.org/10.1177/1088868310362982) + +Spooren, P., Mortelmans, D., & Van Loon, F. (2012). Exploratory +Structural Equation Modelling (ESEM): Application to the SET-37 +questionnaire for students' evaluation of teaching. *Procedia-Social and +Behavioral Sciences*, *69*, 1282–1288. +[https://doi.org/10.1016/j.sbspro.2012.12.063](https://doi.org/10.1016/j.sbspro.2012.12.063) + +Steiner, M., & Grieder, S. (2020). EFAtools: An R package with fast and +flexible implementations of exploratory factor analysis tools. *Journal +of Open Source Software*, *5*(53), 2521. +[https://doi.org/10.21105/joss.02521](https://doi.org/10.21105/joss.02521) + +Thomson, J. J. (1985). The Trolley Problem. *The Yale Law Journal*, +*94*(6), 1395–1415. +[https://doi.org/10.2307/796133](https://doi.org/10.2307/796133) + +Tower, R. K., Kelly, C., & Richards, A. (1997). Individualism, +collectivism and reward allocation: A cross‐cultural study in Russia and +Britain. *British Journal of Social Psychology*, *36*(3), 331–345. +[https://doi.org/10.1111/j.2044-8309.1997.tb01135.x](https://doi.org/10.1111/j.2044-8309.1997.tb01135.x) + +Tucker, L. R. (1951). *A method for synthesis of factor analysis +studies* (Personnel Research Section Report No. 984). Department of the +Army. + +Tyler, T. R. (2006). *Why people obey the law*. Princeton University +Press. + +Ulitzsch, E., Pohl, S., Khorramdel, L., Kroehne, U., & von Davier, M. +(2022). A response-time-based latent response mixture model for +identifying and modeling careless and insufficient effort responding in +survey data. *Psychometrika*, *87*(2), 593–619. +[https://doi.org/10.3102/10769986231173607](https://doi.org/10.3102/10769986231173607) + +Varnum, M., Grossmann, I., Katunar, D., Nisbett, R., & Kitayama, S. +(2008). Holism in a European cultural context: Differences in cognitive +style between Central and East Europeans and Westerners. *Journal of +Cognition and Culture*, *8*(3-4), 321–333. +[https://doi.org/10.1163/156853708X358209](https://doi.org/10.1163/156853708X358209) + +Waldmann, M. R., & Dieterich, J. H. (2007). Throwing a bomb on a person +versus throwing a person on a bomb: Intervention myopia in moral +intuitions. *Psychological Science*, *18*(3), 247–253. +[https://doi.org/10.1111/j.1467-9280.2007.01884.x](https://doi.org/10.1111/j.1467-9280.2007.01884.x) + +Waldmann, M. R., & Wiegmann, A. (2010). A double causal contrast theory +of moral intuitions in trolley dilemmas. *Proceedings of the Annual +Meeting of the Cognitive Science Society*, *32*, 2589–2594. +[https://escholarship.org/uc/item/1vc6w370](https://escholarship.org/uc/item/1vc6w370) + +Weston, R., & Gore, P. A. (2006). A brief guide to structural equation +modeling. *The Counseling Psychologist*, *34*(5), 719–751. +[https://doi.org/10.1177/0011000006286345](https://doi.org/10.1177/0011000006286345) + +Yazdanpanah, A., Soltani, S., Mirfazeli, F. S., Shariat, S. V., +Jahanbakhshi, A., GhaffariHosseini, F., Rezaei, Z., & Grafman, J. +(2021). Iranian and American Moral Judgments for Everyday Dilemmas Are +Mostly Similar. *Frontiers in Psychology*, *12*, 640620. +[https://doi.org/10.3389/fpsyg.2021.640620](https://doi.org/10.3389/fpsyg.2021.640620) + +Young, L., Cushman, F., Hauser, M., & Saxe, R. (2007). The neural basis +of the interaction between theory of mind and moral judgment. +*Proceedings of the National Academy of Sciences*, *104*(20), 8235–8240. +[https://doi.org/10.1073/pnas.0701408104](https://doi.org/10.1073/pnas.0701408104) + +Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for +mean and covariance structure analysis with nonnormal missing data. +*Sociological Methodology*, *30*(1), 165–200. +[https://doi.org/10.1111/0081-1750.00078](https://doi.org/10.1111/0081-1750.00078) diff --git a/docs/mfv_russia/media/image1.png b/docs/mfv_russia/media/image1.png new file mode 100644 index 0000000..6faa497 Binary files /dev/null and b/docs/mfv_russia/media/image1.png differ diff --git a/docs/mfv_russia/media/image2.jpg b/docs/mfv_russia/media/image2.jpg new file mode 100644 index 0000000..64d9434 Binary files /dev/null and b/docs/mfv_russia/media/image2.jpg differ diff --git a/docs/mfv_russia/media/image3.jpg b/docs/mfv_russia/media/image3.jpg new file mode 100644 index 0000000..3b21f92 Binary files /dev/null and b/docs/mfv_russia/media/image3.jpg differ diff --git a/docs/mfv_russia/media/image4.png b/docs/mfv_russia/media/image4.png new file mode 100644 index 0000000..531c7e1 Binary files /dev/null and b/docs/mfv_russia/media/image4.png differ