Introduction

Extant evidence has shown that the use of emotion regulation strategies relates reliably to individual differences in psychopathological symptoms as well as resilience and wellbeing (Gross, 2015; Gross & Jazaieri, 2014). Considering the already high and rising prevalence of mental health problems worldwide (Vos et al., 2016), it is important to investigate possible risk and protective factors that lead to such individual differences. Given its clear link with psychopathology, one of the most frequently studied psychological factor is emotion regulation (Aldao et al., 2010; Sloan et al., 2017).

The Process Model of Emotion Regulation posits emotion regulation can generally be grouped into four different strategies: situational, attentional, cognitive, and response modulation (Gross, 2015; Gross et al., 2019). Two of the most commonly used emotion regulation strategies are expressive suppression and cognitive reappraisal, which are response modulation and cognitive strategies respectively. Expressive suppression relies on actively suppressing, or inhibiting emotional expressivity, such as not showing on one’s face how one is truly feeling (Gross & John, 2003). On the other hand, cognitive reappraisal involves reframing or reinterpreting situations or stimuli to change their potential emotional impact (Barrett & Gross, 2001). Gross and John (2003) developed a 10-item instrument, the Emotion Regulation Questionnaire (ERQ), which measures the use of both strategies. Emotion regulation assessed through this scale has been associated with other mood management constructs (Gross & John, 2003) such as mood repair (Salovey et al., 1995). Additionally, reappraisal and suppression have been associated with the coping styles (Compas et al., 2014; Skinner & Zimmer-Gembeck, 2007) reinterpretation and venting (Carver et al., 1989).

The ERQ has been used extensively in research due to its consistent association with mental health symptoms. Expressive suppression has been associated with increased self-reported psychopathology symptoms (Aldao et al., 2010; Gross & Jazaieri, 2014; John & Gross, 2004; Joormann & Gotlib, 2010; Moore et al., 2008). Alternatively, cognitive reappraisal has been shown to be negatively associated with symptoms of anxiety and depression (John & Gross, 2004; Joormann & Gotlib, 2010) as well as positively with overall well-being (Sloan et al., 2017). Theoretical and empirical work has suggested that risk for mental health problems may not be due to the initial response to a negative event, but the ability to recover from the effect of such event (Gross et al., 2019). Given the theoretical and the extensive empirical evidence linking certain emotion regulation strategies to improved mental health it is imperative to study these psychological processes and related constructs (Joormann & Gotlib, 2010; Sloan et al., 2017).

Both emotion regulation strategies assessed by the ERQ have also been reliably associated with personality facets in both the original English version and in various translations. Notably, expressive suppression has demonstrated a negative association with extraversion, agreeableness, and conscientiousness as well as a positive association with neuroticism (Ali & Alea, 2018; Balzarotti et al., 2010; Cabello et al., 2013; Gross & John, 2003). On the other hand, cognitive reappraisal has demonstrated a positive association with extraversion, agreeableness, and conscientiousness, and a negative association with neuroticism (Ali & Alea, 2018; Balzarotti et al., 2010; Cabello et al., 2013; Gross & John, 2003).

Studying emotion regulation strategy use in different cultural contexts, social scenarios, and age ranges has started to produce even broader understanding (Gullone & Taffe, 2012; Perez & Soto, 2011; Sala et al., 2012; Troy et al., 2017). The ERQ has been translated to at least 38 languages and implemented in various cultural contexts (Stanford Psychophysiology Laboratory, 2020). These translations have demonstrated to have acceptable reliability and validity in many countries and cultures worldwide. Cultural differences have also been explored (Matsumoto, Yoo, Nakagawa, et al., 2008), where internal consistency reliability for both ERQ subscales fluctuate between very poor to good (α = 0.35-0.86) depending on the country and context. Likewise, the 10-item 2-factor structure of ERQ has been replicated in some, but not all, studies. For instance, The 10-item 2-factor structure has been replicated in Chinese (Wang et al., 2009), Italian (Balzarotti et al., 2010; Sala et al., 2012), Spanish (Cabello et al., 2013), German (Abler & Kessler, 2009; Sala et al., 2012), Belgian (D’Argembeau & Van der Linden, 2006), Australian (Preece et al., 2020), and Peruvian samples (Gargurevich & Matos, 2010). However, others have found a better fit with a 9-item 2-factor structure in German, Australian, Finish, and U.K. samples (Rice et al., 2018; Spaapen et al., 2014; Westerlund & Santtila, 2018; Wiltink et al., 2011). Finally, some studies have found better fit with 8 items (Balzarotti, 2019) or have items that load onto both subscales (Wiltink et al., 2011). These studies demonstrate that translations of the ERQ have variable factor structure and demonstrate different internal consistency reliability.

Although in its nascent stages, research has demonstrated that culture and language shapes how people use emotion regulation strategies and, if used, the degree to which they are adaptive (Ford & Mauss, 2015). While Spain and Mexico share the same basic linguistic foundation with substantial cultural similarity, there are clear differences in the way language is applied and its impact on the application of assessment instruments. The lexicon of Mexican Spanish is the result of an adaption of indigenous and English influences (Valdivia Vázquez et al., 2015). Numerous words and phrases do not translate directly or adequately capture the breadth of meaning across populations (Cotton, 2001).

The literature also suggests differences in emotion regulation depend on placement in the collectivisms-individualism scale (Matsumoto, Yoo, Nakagawa, et al., 2008). Mexicans samples tend to be more collectivist while Spanish respondents may favor individualistic cultural values (Carballeira et al., 2015). These differences may be reflected in the use and the effects of emotion regulation strategies (Matsumoto, Yoo, Fontaine, et al., 2008). As such, it is critical to examine psychometric properties across Spanish speaking populations (Carretero-Dios & Pérez, 2007).

Apart from cultural differences, it is also important to note that the scale was originally validated in a student sample (Gross & John, 2003) and until recently most subsequent studies have used well-educated or university student samples (Balzarotti et al., 2010; Matsumoto, Yoo, Nakagawa, et al., 2008). Many (Balzarotti, 2019; Rice et al., 2018; Spaapen et al., 2014; Westerlund & Santtila, 2018) although not all of the community samples did not replicate the 10-item 2-factor structure (Ali & Alea, 2018; Brady et al., 2019; Cabello et al., 2013; Preece et al., 2020) (See Table 1). It is crucial to understand whether educational status, age, or stage in life impact factor structure and other psychometric properties in validation samples.

The main objective of the present research was to validate the emotion regulation questionnaire in Northwest Mexico using a previous Spanish translation validated in Spain. Moreover, since most previous validations are of a university or well-educated samples, we then sought to validate the scale in a non-student Northwest Mexico sample (i.e., young school children guardians). Finally, given possible variation in the Spanish language comprehension we translated the original English scale to Mexican Spanish and validated it in a larger community Mexican sample (sample from 16 different Mexican states).

Methods

Participants

Participants were part of three separate samples. The first sample included social sciences graduate and undergraduate students (age: M = 23.08, SD = 5.55) from Northwestern Mexican universities. Participants were primarily women due to preference of social science majors in women (80.7 % women). Approximately 20% reported working in addition to studying. The second sample consisted of young parents (parents of 5-7 year old primary school students) form a North western Mexican city (age: M = 37.5, SD = 7.68). Given that in Mexico primary care givers are traditionally mothers the sample was more skewed toward female participants (86% women). The third sample was a community sample from various cities across Mexico. Participants in this sample were more evenly distributed in terms of gender (61.1% women and 38.7% men). The sample included participants from a wide range of ages (18 to 81; M = 31.98, SD =14.74) where less than half of the sample were students (46.8%). It is also important to note that although it was a community sample, the education level was still higher than the average in Mexico, where 46.7% of individuals reported having completed high school and 29.8% had a completed at least a bachelor’s degree. Table 2 shows descriptive statistics.

Questionnaires

Emotion regulation was assessed using a Spanish translation of the Emotion Regulation Questionnaire (ERQ (Cabello et al., 2013)) for the first two samples. The ERQ (Gross & John, 2003) consists of ten, seven-point Likert-style items ranging from “totally disagree” to “totally agree”. It evaluates two different emotion regulation strategies: Expressive Suppression (4 items) and Cognitive Reappraisal (6 items). For the third sample, an in-house translation was performed to have a version of the questionnaire that is adapted to the Mexican context. For the third sample, the ERQ was translated to Spanish by a bilingual researcher. A different bilingual researcher subsequently back translated it. The back-translated instrument was then compared to the original by a third bilingual researcher. Finally, two senior researchers checked the Spanish translation to ensure that Mexican participants would understand each of the items.

Anxiety and depression symptoms were assessed using a previously validated abbreviated version (Corral-Frías et al., 2019) of theMini-MASQ (Casillas & Clark, 2000). It consists of 26-items using a five-point Likert-style scale grouped into three subscales: General Distress (GD, 8 items), Anhedonic Depression (AD, 8 items), and Anxious Arousal (AA, 10 items). Internal consistency reliability was acceptable (α > 0.80).

Personality was measured using a previously validated Spanish translation of the Big Five Inventory (BFI), which evaluates five personality dimensions. It consists of 44 Likert-style items using a five-point scale (Benet-Martínez & John, 1998) which showed acceptable reliability for all subscales (α > 0.61) and were consistent with reliability in Spanish samples.

Emotional repair was assessed using a previously validated Spanish version of the Trait Meta-Mood Scale (TMMS-24) (Fernandez-Berrocal et al., 2004). The TMMS-24 (Salovey et al., 1995) consist of 24 Likert-style items on a five-point scale ranging from “completely disagree” to “completely agree”. The full scale features four distinct dimensions, of which only the Emotional Repair subscale was utilized for Sample 3 of this study (8 items). Reliability (α = .83), was comparable to the original Spanish translation (α = .85).

A previously validated Spanish version of the COPE Inventory was used to assess coping strategies. Both the Spanish (Perczek et al., 2000) and the original English version (Carver, 1997) consists of 60 Likert-style items on a 4-point scale where items are grouped into 15 separate dimensions. Here the Positive Reinterpretation (PR) and Focus on and Venting of Emotions (FVE) (4 item) was used. Reliability in our study (FVE α =.65; PR α =.76) was acceptable.

Resilience was assessed using two separate scales, a translation of the Resilience Scale and the Connor-Davidson Resilience Scale (Connor & Davidson, 2003; Wagnild & Young, 1993). The first was used in sample 1(Corral-Frías et al., 2019), and assesses resilience using 25 items. The latter, which consisted of 25 Likert-style items, was used in sample 2. Internal consistency reliabilities were acceptable for both scales (α = 0.94 and α = 0.83 respectively).

Wellbeing was assessed using a Spanish translation (Díaz et al., 2006) of the Ryff’s Psychological Wellbeing scale (Ryff, 1989). This translation consists of thirty-nine 6-point (1= Totally Disagree to 6=Totally Agree). For the use of this paper, a single score was calculated including all items (α < 0.80). For a summary of descriptive statistics and internal consistency reliabilities of all scales see Supplemental Table 1.

Procedures

Participants were recruited from various Northwest Mexico universities (sample 1), primary schools (sample 2) and community centres (sample 3) through flyers, classroom announcements and online forums. Participants who were students were given extra credit for their participation. Participants in samples 1 and 3 signed an online informed consent form electronically. On the other hand, participants in sample 2 signed the consent form in paper form. All participants were informed of the research objective, risks and benefits of the study, and confidentiality of the data. Data were collected online using Qualtrics (Sample 1 and 3) or through paper questionnaires (sample 2).

Analysis

Univariate analyses were performed, including computation of means and standard deviations of continuous variables and frequencies of categorical variables, using the statistical package SPSS v24. Additionally, to determine reliability (internal consistency) Cronbach’s alphas were calculated. A Confirmatory Factor Analyses (CFAs) to assess the factor structure of the ERQ was run using the statistical software EQS v6. Two main types of fit index indicators were used to evaluate whether the data supported the proposed hypothetical model: practical and statistical. The chi square (χ 2) was used to measure the difference between the proposed model and the saturated χ 2. Given large sample sizes (200 participants or more each), the relative χ2 was used (calculated by dividing the χ2 fit index by the degrees of freedom) to reduce the dependence of χ2 on sample size. According to Schumacker and Lomax (2004) if this ratio is less than 5 the model is deemed to have good fit. The practical indicators used were the Comparative Fit Index (CFI), Bentler-Bonnet Normed Fit Index (NFI), and Tucker Lewis Index (TLI). To demonstrate good fit these indices should have a value higher than .90 (Bentler, 2007). Lastly, the Root Mean Square Error of Approximation (RMSEA) was used, which should have values lower than .09 (Browne & Cudeck, 1992). According to Satorra & Bentler (2001) and given that Mardia multivariate normalized coefficients values were greater than 7 (Sample 1 = 17.97; Sample 2 = 8.57; Sample 3 = 14.78), the robust maximum likelihood method was used.

Additionally, to measure concurrent construct validity, associations with previously related constructs were calculated using correlational analysis. Given that both ERQ measures showed a non-normal distribution (See Supplemental Table 2) non-parametric correlational analysis were run (i.e. Spearman correlations). Finally, to measure convergent and divergent validity in a more standardized manner we calculated the average variance extracted (AVE) as well as the difference between the square root of the AVE and covariances with other constructs. To calculate AVE, the factorial weights for each factor were extracted and squared (i.e., lambda squared). Afterwards each lambda squared was added and subsequently divided by the total number of indicators (or parcels) in each of the constructs. Finally, the square root of the summation was used to calculate the square root of the AVE. According to (Hair, 1995) convergent validity is satisfactory if the AVE of the measured construct is higher than 0.50. On the other hand, Henseler et. al., (2009) suggests that satisfactory discriminant validity can be met if the square root of the AVE is larger than covariance with other constructs.

Results

Reliability

Descriptive statistics for the ERQ in each sample are shown in Table 3. In all three samples, the cognitive reappraisal (CR) subscale (Sample 1 α=.67 and Sample 3 α=.69) and expressive suppression (ES) subscale (Sample 1 α=.78 and Sample 1 α=.77) demonstrated acceptable levels of internal consistency reliability (See Table 2). However, reliability for sample 2 was slightly lower (CR α= .63 and ES α= .62).

Factor structure

Confirmatory factor analyses of the ERQ partially replicated the 2-factor structure in all three samples (i.e. cognitive reappraisal and expressive suppression). The 2-factor model showed acceptable goodness of fit (for CFA fit index values see Table 4) in all three samples. However, the 10 item 2-factor structure was not replicated. Due to high residuals, two items were eliminated in Sample 1, three in sample 2, and one from sample 3 to improve fit indexes. After these exclusions, a 2-factor model showed acceptable fit indices.

Validity

Associations between the ERQ scales and other measures are presented in Table 5. ERQ scales were, as expected, also correlated with mood and anxiety symptoms. CR was negatively correlated with Mini MASQ scores (Sample 1: anhedonic depression rho = -.27 and general distress rho =-.21, p< .01; Sample 3: anxious arousal rho= -.08, p<.05 and anhedonic depression rho= -.22, p< .01). ES, on the other hand, was positively correlated with symptoms (Sample 1: anxious arousal rho=.14 and anhedonic depression rho=.15, p< .05; Sample 3: anxious arousal rho=.18, anhedonic depression rho=.22 and general distressrho=.23, p< .01). Consistent with previous studies, we found that the ERQ subscales were both significantly associated (albeit in opposite direction) with wellbeing (Sample 1: CR rho = .21, p< .01 and ES rho = -.33, p< .01; Sample 2: CR rho = .04, NS and ES rho = -.17, p< .05) and resilience (CR rho = .20, p< .01 and ES rho = -.17, p< .05; Sample 2: CR rho = .15, p< .05 and ES rho = -.02, NS).

Likewise, ERQ measures were correlated with different personality facets (see Table 5). Neuroticism was negatively associated with CR (Sample 1: rho = -.30, p< .01; Sample 3: rho = -.09, p< .05) and positively with ES (Sample 3: rho = .10, p< .01). Agreeableness was positively associated with CR (Sample 3: rho = .18, p< .01) and negatively with ES (Sample 1: rho = -.15, p< .05; Sample 3: rho = -.20, p< .01). Conscientiousness was positively associated with CR (Sample 1: rho = .18, p< .01; Sample 3: rho = .20, p< .01) and negatively with ES (Sample 3: rho = -.13, p< .01). Extraversion was positively associated with CR (Sample 1: rho = .18, p< .01; Sample 3: rho = .22, p< .01) and negatively with ES (Sample 1: rho = -.27, p< .01; Sample 3: rho = -.10, p< .01). The results also demonstrated a positive correlation between CR and openness (Sample 1: rho = .19, p< .01; Sample 3 rho = .12, p< .01).

To test convergence with other emotion related constructs we computed correlations between ERQ constructs and mood repair, positive reinterpretation and growth, as well as focus on and venting ofemotions (see Table 5). Our results demonstrated that there was a significant and positive correlation between CR, mood repair and positive reinterpretation and growth (rho = .34 and rho = .33, p< .01, respectively) and negatively between ES, mood repair, focus on and venting of emotions and positive reinterpretation and growth (rho = -.15, rho = -.32 and rho = -.11, p< .01, respectively).

Finally, to measure convergent and divergent validity in a more standardized manner we calculated the AVE and the difference between the square root of the AVE and correlations with other constructs. Only ES reached acceptable AVE scores in sample 1 (AVE SE: Sample 1 = .53, Sample 2 = .36, Sample 3= .40; AVE CR: Sample 1 = .31, Sample 2= .28, Sample 3 =. 31). Based on the tenet that satisfactory discriminant validity can be met if the square root of the AVE is larger than correlations with other constructs, both CR and ES met this criterion (See Supplemental Table 3 for differences in sample 3).

Discussion

The current manuscript shows evidence of reliability and validity of a previously validated Spanish ERQ translation in a college-age student Mexican sample. This same version of the ERQ showed lower fit indices in a sample of young school-age children guardians. Importantly, we found that a Mexican Spanish translation improved fit indices. Consistent with prior reports, albeit varying number of items, the instrument demonstrated a 2-factor solution in all three samples.

In congruence with previous evidence showing a reduced item 2-factor structure in some community samples (Balzarotti, 2019; Rice et al., 2018; Spaapen et al., 2014; Westerlund & Santtila, 2018), we found that a 7-item 2 factor solution had the best fit in a sample of young parents. Two items were eliminated from the CR subscale (5 and 10). Previous translations in community samples have eliminated items from this subscale, but most found problems with item 3 or high error correlations between item 1 and 3 (Balzarotti, 2019; Spaapen et al., 2014). A German version found that item 8 loaded onto both subscales (Wiltink et al., 2011). Only one previous translation found item 5 problematic (Westerlund & Santtila, 2018) and no previous studies report that item 10 is potentially problematic. We also eliminated item 9 from the ES subscale. Most previous translations have left this subscale intact except for one that eliminated a different item (item 6) (Rice et al., 2018). Data from the university sample, in comparison to the guardian sample, possibly exhibited better psychometric properties due to higher education and higher exposure to variants of the Spanish language. This hypothesis was supported by improved fit indices using a ERQ scale adapted to Mexican Spanish, where the factor structure was closer to the original using in a multiple state community sample. These results suggest that differences in fit and factor structure may be due to language comprehension. The instrument showed good divergent validity (SAVE>covariance with other constructs). However, convergent validity could be improved (AVE scores were only acceptable for the ES subscale in sample 1). This is further exemplified in the acceptable albeit low internal consistency reliability scores (α>.62) in all three samples.

Our study further shows evidence of concurrent validity by showing associations between ERQ and related mood-regulation and coping strategies. Consistent with previous findings (John & Gross, 2004) positive reinterpretation and growth as well as mood repair were positively associated with CR and negatively with ES. Also consistent with previous findings, focus on and venting of emotions was only negatively associated with expressive suppression (Gross & John, 2003).

Significant associations between emotion regulation strategies assessed here with theoretically related constructs such as self-report psychopathology, resilience, and wellbeing provide additional evidence of validity. As expected, ERQ subscales were associated with mood and anxiety symptoms, where CR was negatively and ES was positively associated with self-reported psychopathology (John & Gross, 2004; Joormann & Gotlib, 2010; Moore et al., 2008). Moreover, ES showed incremental validity, where this type of emotion regulation predicted symptoms above and beyond other coping strategies and emotional repair (See Supplemental Tables 4-6). Finally, consistent with previous literature (Balzarotti, 2019; Haga et al., 2009) wellbeing and resilience were negatively associated with ES but positively associated with CR.

As in previous studies, ES was associated with different personality facets, notably a negative association with extraversion (Balzarotti et al., 2010; Cabello et al., 2013; Gross & John, 2003). Although some previous studies also report a positive association with neuroticism, this was only true in the larger community sample. There was also a negative association with agreeableness and conscientiousness (Ali & Alea, 2018; Balzarotti et al., 2010; Cabello et al., 2013; Gross & John, 2003). As in most previous studies, we found a negative association between CR and neuroticism and a positive association with extraversion (Balzarotti et al., 2010; Cabello et al., 2013; Gross & John, 2003) and conscientiousness (Balzarotti et al., 2010; Gross & John, 2003). Although previous studies have shown consistent associations (Ali & Alea, 2018; Balzarotti et al., 2010; Cabello et al., 2013; Gross & John, 2003), with agreeableness and openness we only found it to be true in the large community sample (sample 3).

As with all research, this study does not come without limitations. For instance, AVE scores only reached acceptable levels for ES in sample 1. This suggests that factor loadings are low, particularly as compared to those demonstrated in previous studies, and especially in Sample 2 (lowest educational background). Furthermore, while we strove for variability in our samples, most participants were still highly educated (See Table 1 and Sample section) and two of the samples included or was entirely a student population. Moreover, samples included mostly people from Northern Mexico and may not be representative of the Mexican population as a whole. Thus, although both Spanish versions were grammatically correct and true to the original, some further adaptations may be needed for less educated samples.

Our findings thus highlight the importance of validating scales in the specific context in which they will be used, even when same-language versions have been previously validated. The ERQ is increasingly being utilized in research with non-undergraduate samples (Brady et al., 2019; Spaapen et al., 2014) and different cultural contexts (Butler et al., 2007; Cabello et al., 2013; Gómez-Ortiz et al., 2016; Sala et al., 2012; Wang et al., 2009). Validation studies in non-student samples, consistent with our results, show variations in the number of items in each factor. For instance, Spaapen and colleagues (2014) did not replicate the 10-item two‐factor structure found in previous studies. A 9-item (removing item 3 from the reappraisal subscale) confirmatory factor analysis resulted in strong model fit in a diverse age sample. A recent study did replicate the 10-item two‐factor structure in an older community dwelling sample, however, it is important to note that participants were all well educated (Brady et al., 2019). In our study, the sample that showed the worst fit indices included participants with the lowest range and mean years of education. This highlights the importance of adapting scales depending on the language, culture or educational background of the sample. Thus, making modifications to scales, assessing factor structure and reporting psychometric properties for scales in different contexts is necessary.

Extensive empirical work has demonstrated that risk for numerous mental health problems, such as depression, is more related to the ability to recover from the effect of such event rather than the event itself (Aldao et al., 2010; Gross & Jazaieri, 2014; Marroquín & Nolen-Hoeksema, 2015). Given the power that different strategies of emotion regulation have on mental health, it is imperative to continue the study of these strategies and related constructs, and thus to ensure that measures are appropriate, understandable, and psychometrically sound in a variety of cultural contexts. Our study highlights the importance of adapting scale language to fit different contexts. It is important that these scales are validated in the context in which they will be used, particularly scales which may be utilized for clinical application.

Conflicts of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Abler, B., & Kessler, H. (2009). Emotion Regulation Questionnaire—Eine deutschsprachige Fassung des ERQ von Gross und John. [Emotion Regulation Questionnaire—A German version of the ERQ by Gross and John.]. Diagnostica, 55(3), 144–152. https://doi.org/10.1026/0012-1924.55.3.144

Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30(2), 217–237.

Ali, S., & Alea, N. (2018). Validating the emotion regulation questionnaire (ERQ) in Trinidad. Journal of Psychiatry and Behavioral Sciences, 1(1). https://doi.org/10.33582/2637-8027/1003

Balzarotti, S. (2019). The emotion regulation questionnaire: Factor structure and measurement invariance in an Italian sample of community dwelling adults. Current Psychology. https://doi.org/10.1007/s12144-019-00426-3

Balzarotti, S., John, O. P., & Gross, J. J. (2010). An Italian adaptation of the Emotion Regulation Questionnaire. European Journal of Psychological Assessment, 26(1), 61–67. https://doi.org/10.1027/1015-5759/a000009

Barrett, L. F., & Gross, J. J. (2001). Emotional intelligence: A process model of emotion representation and regulation. In Emotions: Currrent issues and future directions (pp. 286–310). Guilford Press.

Benet-Martínez, V., & John, O. P. (1998). Los Cinco Grandes across cultures and ethnic groups: multitrait multimethod analyses of the Big Five in Spanish and English. Journal of Personality and Social Psychology, 75(3), 729–750.

Bentler, P. M. (2007). On tests and indices for evaluating structural models. Personality and Individual Differences, 42(5), 825–829. https://doi.org/10.1016/j.paid.2006.09.024

Brady, B., Kneebone, I. I., & Bailey, P. E. (2019). Validation of the Emotion Regulation Questionnaire in older community-dwelling adults. British Journal of Clinical Psychology, 58(1), 110–122. https://doi.org/10.1111/bjc.12203

Browne, M. W., & Cudeck, R. (1992). Alternative Ways of Assessing Model Fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005

Butler, E. A., Lee, T. L., & Gross, J. J. (2007). Emotion regulation and culture: are the social consequences of emotion suppression culture-specific? Emotion (Washington, D.C.), 7(1), 30–48. https://doi.org/10.1037/1528-3542.7.1.30

Cabello, R., Salguero, J. M., Fernández-Berrocal, P., & Gross, J. J. (2013). A Spanish adaptation of the Emotion Regulation Questionnaire. European Journal of Psychological Assessment, 29(4), 234–240. https://doi.org/10.1027/1015-5759/a000150

Carballeira, M., González, J.-Á., & Marrero, R. J. (2015). Diferencias transculturales en bienestar subjetivo: México y España. Anales de Psicología, 31(1), 199–206. https://doi.org/10.6018/analesps.31.1.166931

Carretero-Dios, H., & Pérez, C. (2007). Standards for the development and review of instrumental studies: Considerations about test selection in psychological research. International Journal of Clinical and Health Psychology, 7(3), 863–882.

Carver, C. S. (1997). You want to measure coping but your protocol’s too long: consider the brief COPE. International Journal of Behavioral Medicine, 4(1), 92–100. https://doi.org/10.1207/s15327558ijbm0401_6

Carver, C. S., Scheier, M. F., & Weintraub, J. K. (1989). Assessing coping strategies: a theoretically based approach. Journal of Personality and Social Psychology, 56(2), 267–283.

Casillas, A., & Clark, L. A. (2000). The Mini Mood and Anxiety Symptom Questionnaire (Mini- MASQ). 72nd Annual Meeting of the Midwestern Psychological Association.

Compas, B. E., Jaser, S. S., Dunbar, J. P., Watson, K. H., Bettis, A. H., Gruhn, M. A., & Williams, E. K. (2014). Coping and Emotion Regulation from Childhood to Early Adulthood: Points of Convergence and Divergence. Australian Journal of Psychology, 66(2), 71–81. https://doi.org/10.1111/ajpy.12043

Connor, K. M., & Davidson, J. R. T. (2003). Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC). Depression and Anxiety, 18(2), 76–82. https://doi.org/10.1002/da.10113

Corral-Frías, N. S., Velardez Soto, S. N., Frías-Armenta, M., Corona-Espinosa, A., & Watson, D. (2019). Concurrent Validity and Reliability of Two Short Forms of the Mood and Anxiety Symptom Questionnaire in a Student Sample from Northwest Mexico. Journal of Psychopathology and Behavioral Assessment, 41(2), 304–316. https://doi.org/10.1007/s10862-019-09738-x

Cotton, E. G. (2001). Spanish in the Americas. Georgetown University Press.

D’Argembeau, A., & Van der Linden, M. (2006). Individual differences in the phenomenology of mental time travel: The effect of vivid visual imagery and emotion regulation strategies. Consciousness and Cognition, 15(2), 342–350. https://doi.org/10.1016/j.concog.2005.09.001

Díaz, D., Rodríguez-Carvajal, R., Blanco, A., Moreno-Jiménez, B., Gallardo, I., Valle, C., & van Dierendonck, D. (2006). [Spanish adaptation of the Psychological Well-Being Scales (PWBS)]. Psicothema, 18(3), 572–577.

Fernandez-Berrocal, P., Extremera, N., & Ramos, N. (2004). Validity and Reliability of the Spanish Modified Version of the Trait Meta-Mood Scale. Psychological Reports, 94(3), 751–755. https://doi.org/10.1016/j.concog.2005.09.001

Ford, B. Q., & Mauss, I. B. (2015). Culture and emotion regulation. Current Opinion in Psychology, 3, 1–5. https://doi.org/10.1016/j.copsyc.2014.12.004

Gargurevich, R., & Matos, L. (2010). Propiedades Psicométricas Del Cuestionario De Autorregulación Emocional adaptado Para El Peru (ERQP). Revista Psicología, 12, 192–215.

Gómez-Ortiz, O., Romera, E. M., Ortega-Ruiz, R., Cabello, R., & Fernández-Berrocal, P. (2016). Analysis of Emotion Regulation in Spanish Adolescents: Validation of the Emotion Regulation Questionnaire. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.01959

Gross, J. J. (2015). The Extended Process Model of Emotion Regulation: Elaborations, Applications, and Future Directions. Psychological Inquiry, 26(1), 130–137. https://doi.org/10.1080/1047840X.2015.989751

Gross, J. J., & Jazaieri, H. (2014). Emotion, Emotion Regulation, and Psychopathology: An Affective Science Perspective. Clinical Psychological Science, 2(4), 387–401. https://doi.org/10.1177/2167702614536164

Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348.

Gross, J. J., Uusberg, H., & Uusberg, A. (2019). Mental illness and well‐being: an affect regulation perspective. World Psychiatry, 18(2), 130–139. https://doi.org/10.1002/wps.20618

Gullone, E., & Taffe, J. (2012). The Emotion Regulation Questionnaire for Children and Adolescents (ERQ–CA): A psychometric evaluation. Psychological Assessment, 24(2), 409.

Haga, S. M., Kraft, P., & Corby, E.-K. (2009). Emotion Regulation: Antecedents and Well-Being Outcomes of Cognitive Reappraisal and Expressive Suppression in Cross-Cultural Samples. Journal of Happiness Studies, 10(3), 271–291. https://doi.org/10.1007/s10902-007-9080-3

Hair, J. F. (Ed.). (1995). Multivariate data analysis with readings (4th ed). Prentice Hall.

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (Eds.). (2009). Advances in International Marketing (Vol. 20). Emerald Group Publishing. https://doi.org/10.1108/S1474-7979(2009)0000020014

John, O. P., & Gross, J. J. (2004). Healthy and unhealthy emotion regulation: Personality processes, individual differences, and life span development. Journal of Personality, 72(6), 1301–1334.

Joormann, J., & Gotlib, I. H. (2010). Emotion Regulation in Depression: Relation to Cognitive Inhibition. Cognition & Emotion, 24(2), 281–298. https://doi.org/10.1080/02699930903407948

Marroquín, B., & Nolen-Hoeksema, S. (2015). Emotion regulation and depressive symptoms: Close relationships as social context and influence. Journal of Personality and Social Psychology, 109(5), 836–855. https://doi.org/10.1037/pspi0000034

Matsumoto, D., Yoo, S. H., Fontaine, J., Anguas-Wong, A. M., Arriola, M., Ataca, B., Bond, M. H., Boratav, H. B., Breugelmans, S. M., Cabecinhas, R., Chae, J., Chin, W. H., Comunian, A. L., Degere, D. N., Djunaidi, A., Fok, H. K., Friedlmeier, W., Ghosh, A., Glamcevski, M., … Grossi, E. (2008). Mapping expressive differences around the world: The relationship between emotional display rules and individualism versus collectivism. Journal of Cross-Cultural Psychology, 39(1), 55–74. https://doi.org/10.1177/0022022107311854

Matsumoto, D., Yoo, S. H., Nakagawa, S., & 37 members of the Multinational Study of Cultural Display Rules. (2008). Culture, emotion regulation, and adjustment. Journal of Personality and Social Psychology, 94(6), 925–937. https://doi.org/10.1037/0022-3514.94.6.925

Moore, S. A., Zoellner, L. A., & Mollenholt, N. (2008). Are Expressive Suppression and Cognitive Reappraisal Associated with Stress-Related Symptoms? Behaviour Research and Therapy, 46(9), 993–1000. https://doi.org/10.1016/j.brat.2008.05.001

Perczek, R., Carver, C. S., Price, A. A., & Pozo-Kaderman, C. (2000). Coping, mood, and aspects of personality in Spanish translation and evidence of convergence with English versions. Journal of Personality Assessment, 74(1), 63–87. https://doi.org/10.1207/S15327752JPA740105

Perez, C. R., & Soto, J. A. (2011). Cognitive reappraisal in the context of oppression: implications for psychological functioning. Emotion, 11(3), 675–680. https://doi.org/10.1037/a0021254

Preece, D. A., Becerra, R., Robinson, K., & Gross, J. J. (2020). The Emotion Regulation Questionnaire: Psychometric Properties in General Community Samples. Journal of Personality Assessment, 102(3), 348–356. https://doi.org/10.1080/00223891.2018.1564319

Rice, S. M., Treeby, M. S., Gersh, E., Ogrodniczuk, J. S., & Kealy, D. (2018). The Emotion Regulation Questionnaire: ERQ-9 factor structure and measurement invariance in Australian and Canadian community samples. TPM - Testing, Psychometrics, Methodology in Applied Psychology, 25, 369–377. https://doi.org/10.4473/TPM25.3.3

Ryff, C. (1989). Happiness Is Everything, or Is It? Explorations on the Meaning of Psychological Well-Being. Journal of Personality and Social Psychology, 57(6), 1069–1081. https://doi.org/10.1037/0022-3514.57.6.1069

Sala, M. N., Molina, P., Abler, B., Kessler, H., Vanbrabant, L., & van de Schoot, R. (2012). Measurement invariance of the Emotion Regulation Questionnaire (ERQ). A cross-national validity study. European Journal of Developmental Psychology, 9(6), 751–757. https://doi.org/10.1080/17405629.2012.690604

Salovey, P., Mayer, J. D., Goldman, S. L., Turvey, C., & Palfai, T. P. (1995). Emotional attention, clarity, and repair: Exploring emotional intelligence using the Trait Meta-Mood Scale. In J. W. Pennebaker (Ed.), Emotion, disclosure, & health. (pp. 125–154). American Psychological Association. https://doi.org/10.1037/10182-006

Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514. https://doi.org/10.1007/BF02296192

Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling (Second Edition). Lawrence Erlbaum Associates.

Skinner, E. A., & Zimmer-Gembeck, M. J. (2007). The development of coping. Annual Review of Psychology, 58, 119–144. https://doi.org/10.1146/annurev.psych.58.110405.085705

Sloan, E., Hall, K., Moulding, R., Bryce, S., Mildred, H., & Staiger, P. K. (2017). Emotion regulation as a transdiagnostic treatment construct across anxiety, depression, substance, eating and borderline personality disorders: A systematic review. Clinical Psychology Review, 57, 141–163. https://doi.org/10.1016/j.cpr.2017.09.002

Spaapen, D., Waters, F., Brummer, L., Stopa, L., & Bucks, R. (2014). The Emotion Regulation Questionnaire: Validation of the ERQ-9 in Two Community Samples. Psychological Assessment, 26(1), 46–54. https://doi.org/10.1037/a0034474

Stanford Psychophysiology Laboratory. (2020). Stanford Psychophysiology Laboratory Resources. https://spl.stanford.edu/resources

Troy, A. S., Ford, B. Q., McRae, K., Zarolia, P., & Mauss, I. B. (2017). Change the things you can: Emotion regulation is more beneficial for people from lower than from higher socioeconomic status. Emotion (Washington, D.C.), 17(1), 141–154. https://doi.org/10.1037/emo0000210

Valdivia Vázquez, J. A., Rubio Sosa, J. C. A., & French, B. F. (2015). Examination of the Spanish Trait Meta-Mood Scale–24 Factor Structure in a Mexican Setting. Journal of Psychoeducational Assessment, 33(5), 473–482. https://doi.org/10.1177/0734282914552052

Vos, T., Allen, C., Arora, M., Barber, R. M., Bhutta, Z. A., Brown, A., Carter, A., Casey, D. C., Charlson, F. J., Chen, A. Z., Coggeshall, M., Cornaby, L., Dandona, L., Dicker, D. J., Dilegge, T., Erskine, H. E., Ferrari, A. J., Fitzmaurice, C., Fleming, T., … Murray, C. J. L. (2016). Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1545–1602. https://doi.org/10.1016/S0140-6736(16)31678-6

Wagnild, G. M., & Young, H. M. (1993). Development and psychometric evaluation of the Resilience Scale. Journal of Nursing Measurement, 1(2), 165–178.

Wang, L., Shi, Z., & Li, H. (2009). Neuroticism, extraversion, emotion regulation, negative affect and positive affect: The mediating roles of reappraisal and suppression. Social Behavior and Personality, 37(2), 193-194. https://doi.org/10.2224/sbp.2009.37.2.193

Watson, D., Clark, L. A., Weber, K., Assenheimer, J. S., Strauss, M. E., & McCormick, R. A. (1995). Testing a tripartite model: II. Exploring the symptom structure of anxiety and depression in student, adult, and patient samples. Journal of Abnormal Psychology, 104(1), 15.

Westerlund, M., & Santtila, P. (2018). A Finnish adaptation of the emotion regulation questionnaire (ERQ) and the difficulties in emotion regulation scale (DERS-16). Nordic Psychology, 70(4), 304–323. https://doi.org/10.1080/19012276.2018.1443279

Wiltink, J., Glaesmer, H., Canterino, M., Wölfling, K., Knebel, A., Kessler, H., Brähler, E., & Beutel, M. E. (2011). Regulation of emotions in the community: suppression and reappraisal strategies and its psychometric properties. Psycho-Social Medicine, 8, Doc09. https://doi.org/10.3205/psm000078

Notas de autor

Correspondence about this article should be addressed Nadia Corral-Frías: nadia.corral@unison.mx