Psychometric Study of the Cognitive Flexibility Inventory in a Colombian Sample.

Introduction: A cross-sectional descriptive study was conducted to evaluate the factor structure of Spanish translation and adaptation of the Cognitive Flexibility Inventory (CFI) in a sample of Colombian adults. Method: The sample of the study was n = 968. Respondents were aged between 18 and 52 years old (Mage=22.81, SD=4.42). Descriptive analyses, confirmatory factor analysis, and Cronbach's Alpha calculation were carried out.
Results: Internal consistency for the global scale was high (α=.89). Likewise, the coefficients of the Alternative factor and the Control factor were similar (α=.90, 95% CI=.89-.90 and α=.83, 95% CI=.81-.85, respectively). A two-factor structure performed best according to the results of model selection criteria. This model suggested the existence of two correlated factors, with correlated items within factors (Item19 Item20 and Item8 Item10). Conclusions: Overall, the results suggest that the CFI scale exhibits construct validity and adequate reliability, both for the general scale and the subscales in the Colombian sample, enabling their use in contexts such as clinical or research.

1. Introduction

Cognitive flexibility (CF) is an important variable to assess since it is an indicator of problem-solving ability, allows generating responses with an adequate degree of inhibitory control, and permits creating a significant number of alternatives from a single solution. Interpersonally, CF allows to establish personal relationships as it helps to combine ones wishes with others wishes or points of view and seeks lasting solutions (Maddio & Greco, 2010). CF has been widely studied and considered from different perspectives, as a skill and as a property of the cognitive system (Ionescu, 2017). Currently, there is no consensus on a single definition of this variable. However, it is generally conceived as the ability to modify cognitive strategies or the way of thinking in response to changes in the environment or context (Moore & Malinowski, 2009; Stemme et al., 2007). Consequently, it is the specific way in which competence is assessed that differentiates between the various measures of cognitive flexibility (Dennis & Vander Wal, 2010).

CF has been frequently conceived and evaluated as a neuropsychological variable (Nweze et al., 2020; Van Stockum & DeCaro, 2020; Webler et al., 2019). From this approach, it is considered one of the most demanding of the executive functions, related to cognitive control processes, through which a person can flexibly adapt to new tasks or demands, change perspective or approach regarding a problem, and alternate between mental sets or ways of thinking about stimuli (Dajani & Uddin, 2015; Diamond, 2013; Zaehringer et al., 2018). Further, CF also has been conceived as the ability to change cognitive content or thinking to adapt to changing environmental stimuli (Dennis & Vander Wal, 2010). In this sense, according to Dennis and Vander Wal (2010), CF includes a wide range of behaviors that allow people to have a more adaptive thinking and coping when facing situations or events that are stressful, which is an essential component in life. This is the definition considered in the present study.

As can be seen, this conception of CF is related to the concept of coping. In fact, Dennis and Vander Wal (2010) reported that there is evidence of convergent construct validity after finding correlations with some subscales of the Ways of Coping Checklist-Revised (WCCLR; Folkman & Lazarus, 1985). Studies such as Kruczek et al. (2020) and Muyan-Yılık and Demir (2020) have shown positive correlation and direct effects between these variables. However, some studies have established that they are different concepts at the theoretical and empirical levels, showing that cognitive flexibility is just a component of a larger concept known as coping flexibility, which refers to the ability to modify coping strategies according to the level of effectiveness when dealing with a stressful situation (Cheng, 2014; Góralska & Basiska, 2019; Kato, 2012; Kruczek et al., 2020).

In line with the foregoing, CF has tended to be assessed through neuropsychological measures that require a trained person for their administration, such as the Wisconsin Card Sorting Test (Heaton et al., 1993) and the Trail Making Test Part B (Reitan & Wolfson, 1993). Likewise, there are some self-report tests such as the Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al., 2000) that define cognitive flexibility as a neuropsychological variable. These measures generally configure deficits on CF as an indicator of alterations in the brain.

However, there are other self-report measures that are oriented to assess CF as a reaction to affective states that indicate the type of skill required for the restructuring of maladaptive thoughts (Dennis & Vander Wal, 2010). One of them is the Cognitive Flexibility Inventory (CFI) by Dennis and Vander Wal (2010). This instrument consists of 20 items designed to assess CF aspects that allow people to think adaptively rather than maladaptively when facing stressful events. The inventory has two subscales or dimensions resulting from factor analysis: Control and Alternatives.

The first dimension refers to the ability or tendency to perceive difficult situations as controllable; the second one, to the ability to perceive multiple alternative explanations for human behavior and situations in life, and the ability to generate multiple alternative solutions to difficult situations. The instrument exhibits adequate levels of internal consistency with α values ranging between .84 and .91. Similarly, test-retest reliability evaluation for the total scale and the subscales has also proved to be adequate (r=.75-.81; p <.001; Dennis & Vander Wal, 2010). In addition, traditional tests that measure cognitive flexibility as performance require interactive relationships between test administrator and test participant or patient and need more time to administer and score. The CFI and, in general, the self-report measures offer some advantages such as not depending on an evaluator, greater practicality when using them in studies that involve some type of psychological treatment, brevity, and ease of administration and rating (Dennis & Vander Wal, 2010; Kurginyan & Osavolyuk, 2018). This explains why this instrument has been used in numerous studies in different contexts, and has been translated into various languages such as Turkish, Polish, Russian, and Japanese (Caldwell et al., 6; Demirtas & Yildiz, 2019; Kurginyan & Osavolyuk, 2018; Odac & Cikrikci, 2019; Rudnik et al., 2019; Shah, 2019; Shapero et al., 2018).

Regarding possible differences in the CFI in terms of sociodemographic variables, some studies such as Shapero et al. (2018) found no differences in terms of age and gender; others such as Eldesouky and English (2018) found negative relationships between age and CF; Rudnik et al. (2019) found negative relationships between the range of alternatives and age, but not in terms of gender. That is why studies such as those of Kurginyan and Osavolyuk (2018) point to the need to carry out more balanced studies at taking into account variables such as gender, in order to evaluate the differences in these variables in the total scale and its subscales.

However, no extant studies have sought to translate the CFI into Spanish and apply it to samples in Spanish speaking countries. Therefore, the present study aims to evaluate the factor structure of the Spanish translation and adaptation of the CFI in a sample of Colombian adults. We hypothesized that the CFI has construct validity and is a reliable instrument to assess cognitive flexibility as defined by Dennis and Vander Wal (2010). The analysis focuses on the factorial structure of the CFI, its internal consistency, and the assessment of the differences regarding sociodemographic variables such as gender and age.

2. Method

2.1 Participants

A total of 970 individuals from different Colombian (South America) territories participated in this study. Two of these individuals were removed from the sample because of missing data, resulting in a final sample size of 968. According to Bentler and Chou (1987), the factor analysis requires a minimum sample size of 10 observations per item to process data. Likewise, authors such as Morata et al. (2015) recommend samples greater than 250, in order to avoid Type I error, according to RMSEA. The original study that described and evaluated the psychometric properties of the CFI used a sample of 196 people (Dennis & Vander Wal, 2010). Based on the above, the authors decided, for the present study, to expand the sample size to maximize confidence in model fit.

The inclusion criteria for age and residence were the following: 18 years of age or older, so the participant was able to write and read, and to reside in Colombia. All participants who did not meet these criteria were excluded from the study. The sampling used in the study was non-probabilistic and intentional. The main collection points for the sample were university campuses.

Regarding the gender of the participants, 52.79% identified themselves as women, 45.97% as men, and 1.24% as other. The ages of the participants were between 18 and 52 years old (M age=22.81, SD=4.42). About the occupation of the participants, 67% were students, 18.70% were employed, 8.26% were self-employed, 4.03% were studying and working at the same time, and 1.34% were unemployed. Regarding religious belief, 485 participants (50.10%) did not practice any religion, 386 (39.88%) identified themselves as Catholic, 69 (7.13%) as Evangelicals, and 28 of them (2.89%) practiced another religion. In terms of place of residence, 601 (62.09%) lived in cities in the north of Colombia; 194 (20.04%), in cities in the central western area of Colombia; and 173 (17.87%), in the capital city. In this study, gender and age were considered to analyze possible differences in the CFI, as mentioned in the introduction of this manuscript. On the other hand, the variables of religious beliefs, place of residence, and occupation played a descriptive role in the sample.

2.2. Procedure

A cross-sectional descriptive study was performed to evaluate the factor structure of the CFI Spanish version in Colombia. A translation of the CFI from English to Spanish was carried out (See Table S1). Following bestpractice guidelines (Escobar-Pérez & Cuervo-Martínez, 2008; González-Consuegra & Verdú, 2010; Hambleton & Zenisky, 2010; Muñiz et al., 2013), this translation was carried out by a native English speaker and then the Spanish version was back-translated into English by a native speaker. There were no significant differences in meaning between the two versions, so these were synthesized into one. Then, the translated CFI was reviewed by seven bilingual expert judges (six of them with a Ph.D. degree and one was a Ph.D. candidate). The next stage involved the application of the instrument to a group of 10 people with similar characteristics to the study sample to assess the understanding of the items. All the participants understood all items in this version of the CFI, according to the cognitive interviews which were carried out.

The application of the CFI was carried out in classrooms and places that were arranged for the process. Each participant read and completed the informed consent, where the purposes, and aspects such as confidentiality, anonymity, voluntariness, and that their participation did not contemplate any direct reward were explained. Consequently, participants completed a short sociodemographic questionnaire and the CFI. In some cases, participants completed the questionnaire using a computer or smartphone. The questionnaire had a digital version that, regarding the content and response options, was equivalent to the paper and pencil version. Contact was maintained in real-time with the person completing the questionnaire to be able to resolve doubts and manage the application time. The questionnaire session required approximately 10 minutes to complete.

2.3 Instrument

CFI (Dennis & Vander Wal, 2010) is a scale of 20 selfreport items. It was designed to measure the tendency to perceive difficult situations as controllable, the ability to perceive multiple alternatives or solutions to difficult situations, and the ability to generate multiple alternative solutions for difficult situations. Each statement is rated by the participant from 1 (Strongly disagree) to 7 (Strongly agree). In their original work, Dennis and Vander Wal (2010) found a two-factor structure (Control and Alternatives) and adequate internal consistency when evaluated in two stages (test-retest). Cronbach’s alpha for the scale is .90 (time 1) and .91 (time 2); for the alternative subscale .91 (time 1 and 2); and for the control subscale .86 (time 1) and .84 (time 2). Some examples in the Alternatives dimension are “I consider multiple options before making a decision” and “It is important to look at difficult situations from many angles”. Some Control dimension items are “I feel I have no power to change things in difficult situations” and “I am capable of overcoming the difficulties in life that I face” (See Table S1). Studies by Kurginyan and Osavolyuk (2018) and Shareh et al. (2014) confirmed that the scale has adequate psychometric properties.

2.4 Ethical Approval

The present study corresponded to an investigation with minimal risk as established in resolution 8430 of 1993, chapter I, article 11 of the Ministry of Health of Colombia (Ministerio de Salud, 1993), since it only requires the registration of data through an inventory/psychological test in which no intervention was performed. Further, the application of the CFI was contemplated within a research project that was approved by the Ethics Committee of the Universidad Del Norte in Barranquilla, Colombia (act # 172). Therefore, it was deemed that there were no procedures that could affect the physical or psychological integrity of the participants. All the people who voluntarily decided to participate in the research previously completed an informed consent document where they were made aware of the conditions and characteristics of the study.

2.5 Statistical Analysis

All statistical analyses were performed in R version 3.6.1 (The R Foundation, 2019); confirmatory factor analysis was performed using the Lavaan and Psych packages (Rosseel, 2012) available for that software. The internal consistency of the CFI was calculated using Cronbach’s α. Following salient recommendations in the extant CFI literature, the scoring procedures specified for the scale require reverse scoring of select items (Items 2,4, 7, 9, 11, and 17).

The result of the KMO test was .93, Bartlett’s test applied to the correlation matrix was χ 2 = 8196.783, p < .001 (from principal components analysis PCA) (Figure 1), and the Mardia test result did not confirm a multivariate normal distribution (skew = 9236.53, p < .001; kurtosis = 111.91, p < .001). Given this, it is also not possible to ensure that FC behaves as a continuous variable. Therefore, we used a robust version of maximum likelihood (ML), due to its relatively unbiased standard error estimates, good recovery of the population inter-factor correlations (Cheng-Hsien, 2015), better control of Type I error, and better performance if it is compared with ML in non-normal distribution conditions (Bandalos, 2014), because it considers the deviation of the multinormality in the variables (Herrero, 2010).

Figure 1

Scree plot for CFI factors

We developed eight models and evaluated their performance to determine which of them had the best fit. We used confirmatory factor analysis (CFA) to determine the goodness of fit of models. To maximize confidence in parameter estimation and model fit, without depending only on the sample size criterion based on the statistical significance of χ 2, we invoked other fit indices that allowed us to penalize complexity measured in terms of the number of adjustable parameters of the asymptotic chi-square test: the Bayesian information criterion (BIC), Akaike’s information criterion (AIC), the comparative fit index (CFI), the non-normed fit index (NNFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR).

It was assumed that RMSEA values over .10 would suggest an unacceptable model fit; values below .05 would indicate a close model fit. SRMR values below .08 suggest an acceptable model fit. CFI and NNFI are bound between 0 and 1; values greater than .95 suggest a close model fit. The model with the lowest value of AIC and BIC among the candidate factorial models was selected as the best model (Cangur & Erca, 2015; Kline, 2016; MacCallum et al., 1996; Schulz et al., 2011).

3. Results

3.1 Confirmatory Factor Analysis

We developed eight models with different structures and assessed their goodness of fit using criteria described in the Method section. Values of χ 2 and df are displayed for each model (see Table 1).

Table 1

Confirmatory Factor Analysis of the CFI in a Colombian Sample

Model	Description	x 2	df	NNFI	CFI	RMSEA SRMR		AIC	BIC
1	2 independent factors from PCA	690.201	170	.897	.907	.056	.134	60489.17	60684.18
2	2 correlated factors from PCA	569.918	169	.914	.923	.050	.048	60319.22	60519.11
3	2 independent factors from original scale	746.495	170	.876	.889	.059	.149	60570.86	60765.87
4	2 correlated factors from original scale	617.214	169	.903	.914	.052	.063	60387.71	60587.60
5	Model 4 plus Ilem19~Ilem20	555.350	168	.916	.926	.049	.063	60297.47	60502.23
6	Model 4 plus Ileml9~Ilem2 , 0. Ilem8~Ileml0	513.619	167	.924	.934	.046	.062	60239.37 60449.01
7	2 correlated factors from original scale with a global entity	613.562	168	.903	.915	.052	.063	60389.71	60594.47
8	2 independent factors from original	613.562	168	.903	.915	.052	.063	60389.71	60594.47

Note. df =degrees of freedom; χ 2=chi-square test statistic; NNFI=non-normed fit index; CFI=comparative fit index; RMSEA=root mean square error of approximation; SRMR=standardized root means square residual; AIC=Akaike’s information criterion; BIC=Bayesian information criterion. The “ ” sign denotes “correlated”.

Model 1 considered the existence of two independent factors from PCA, F1, and F2 with no correlation between them allowed, whilst Model 2 allowed these correlations. Model 3 considered the existence of two independent factors from the original scale distribution of items, F1 and F2 with no correlation between them allowed, whilst Model 4 allowed these correlations. Models 5 and 6 were similar to Model 4, but in addition to allowing correlations between factors, they also include correlations between items within factors. Specifically, in Model 5 we considered a two-factor structure and allowed items 19 and 20 to be correlated; in Model 6 these correlations were allowed and also the correlations between items 8 and 10. These correlations were allowed following the model modification indices obtained by the Lavaan package (Rosseel, 2012). We followed the recommendations of MacCallum et al. (1992) and Whittaker (2012), who consider this process appropriate as long as it has theoretical coherence. In this case, the items were part of the same factor and had a high similarity regarding format and content. Model 7 considered the ex-

Thus, for instance, “Item19 Item20” means that items 19 and 20 in the CFI are allowed to be correlated in a model. Best performance indicators are emboldened. In all models, p <.001 for the test statistic. istence of two factors and a global factor that depends on them with no correlation between them allowed, whilst Model 8 allowed these correlations.

Considering the various performance measures, Model 6 was selected as the best. This model suggests the existence of two correlated factors with correlated items within factors. The index values were AIC=60239.37, BIC=60449.01, NNFI=.924, CFI=.934, RMSEA=.046, and SRMR=.062. From the values obtained in these indices, we can interpret that the model fits the data well and that it was possible to select the best model, so it is viable to draw valid conclusions from this data set. Figure 2 and Table 2 show the estimated structure of the two-factor model along with the existing correlation among composing factors. F1 is comprised of items 1, 3, 5, 6, 8, 10, 12, 13, 14, 16, 18, 19, and 20 (support from Alternatives, Table 3). F2 is comprised of items 2, 4, 7, 9, 11, 15, and 17 (support from Control, Table 3).

Figure 2

Structure of the Two-Factor Model

Table 2

Factor Matrix for the CFI Scale in a Colombian Sample

Item	Factor Alternatives Control		h 2
1	.426		.182
2		.535	.287
3	.582		.338
4		.745	.555
5	.623		.388
6	.590		.348
7		.778	.605
8	.530		.281
9		.637	.406
10	.441		.195
11		.811	.658
12	.506		.256
13	.787		.620
14	.787		.619
15		.378	.142
16	.653		.426
17		.536	.287
18	.741		.548
19	.754		.569
20	.817		.668
Note. Only loadings > .35 are shown. h 2 measures

Note. Only loadings > .35 are shown. h2 measures communality.

Table 3

Internal Consistency of the CFI in a Colombian Sample

Item	Internal Consistency (95% CI)
	Factor/dimension Correlation	Global Correlation	If an item is dropped	Global
Alternatives
1	.49*	.47 *	.89	.90
3	.62*	.51 *	.89
5	.67 *	.59 *	.89
6	.65 *	.56 *	.89
8	.64 *	.49 *	.89
10	.55 *	.48 *	.90
12	.55 *	.48 *	.89
13	.78 *	.66 *	.88
14	.79 *	.68 *	.88
16	.68 *	.61 *	.89
18	.73 *	.48 *	.88
19	.76 *	.71 *	.88
20	.80 *	.73 *	.88
Control
2	.65 *	.46 *	.82	.83
4	.78 *	.61 *	.79
7	.79 *	.63 *	.79
9	.71 *	.56 *	.81
11	.82 *	.67 *	.78
15	.52 *	.59 *	.83
17	.65 *	.48 *	.82
Total Scale				.89

Note. ∗p < .01.

3.2 Internal Consistency

As noted in Table 3, internal consistency for the global scale was α=.89 (95% CI=.88-.90). Alternatives factor and Control factor showed similar coefficients: α=.90 (95% CI=.89-.90 and α=.83 (95% CI=.81-.85), respectively. The correlations between the items, the factors/dimensions, and the overall score were considered suitable. In addition, when specific items were dropped from each dimension, none of the alpha values were greater than the whole scale alpha or factors alpha values. Internal consistency coefficients remained within acceptable limits, which means that there is no need to drop any item.

As for gender, we found differences in the total scale (χ 2=11.103, df = 2, p=.004, η 2=.008), in the Alternatives subscale (χ 2=8.042, df = 2, p= .018, η 2=.005), and in the control subscale (χ 2=10.637, df =2, p=.005,2 η =.008). Post-hoc tests showed that women scored.

Each item is an important determinant of scale perfor mance. These results suggest a good-to-acceptable internal consistency of the CFI when applied to Colombian adults (see Table 1).

3.3 Gender and Age Differences Testing

Data from the FC construct was not normally distributed (K.S=.41, p <.05). Accordingly, differences in mean lower than men on the total scale and the two subscales. However, as seen above, the effect sizes are very low. In terms of age, differences were found in the total scale (χ 2=8.407, df =2, p=.015, η 2=.006) and in the Alternatives subscale (χ 2=6.976, df =2, p=.031, η 2=.004). Nevertheless, the effect sizes were very low and post-hoc tests did not show a significant difference.

4. Discussion

The aim of this study was to assess the factor structure and psychometric properties of the Spanish translation and adaptation of the CFI in a sample of Colombian adults. The original instrument exhibits adequate levels of internal consistency with α values ranging between .84 and .91. Similarly, the test-retest reliability evaluation for the total scale and the subscales also proved to be adequate (r=.75-.81; p <.001; Dennis & Vander Wal, 2010).

The results showed that the internal consistency for the global scale was α=.89 (95% CI=.88-.90). Alternatives factor and Control factor showed similar coefficients: α=.90 (95% CI=.89-.90) and α=.83 (95% CI=.81 -.85), respectively. Other validation studies carried out around the world have found similar results (Kurginyan & Osavolyuk, 2018; Shareh et al., 2014).

Regarding the number of factors, it was found that model 6 was the best fit. This model suggested the existence of two correlated factors, with correlated items within factors (Item 19 Item 20 and Item 8 Item 10). Both the original study (Dennis & Vander Wal, 2010) and another study conducted in Russia (Kurginyan & Osavolyuk, 2018) found the same number of factors and distribution of items. However, in a study carried out with an Iranian sample (Shareh et al., 2014), it was found that a three-factor model was the best fit, which is suggestive of cultural aspects playing an important role in the results of factor analysis.

Regarding the discriminating power of the scale in terms of variables such as gender and age, we did not observe significant differences on the effect sizes in the total scale and the subscales in the study sample. Some studies have shown similar results, finding no differences in gender, although they report negative relationships between the alternative subscale and age (Rudnik et al., 2019). Further studies should continue to review these differences in diversified samples.

There are currently several areas where CF is recognized as an important variable in different human processes. For example, in the clinical and mental health area, there are some studies that positively relate CF to psychological well-being and family communication environments (Fu & Chow, 2017; Koesten et al., 2009). Likewise, other studies have established negative correlations with depressive symptoms (Wadsworth et al., 2004), psychological stress (Palm & Follette, 2011), and being a mediator between depressive symptoms and negative events in life (Fresco et al., 2007).

Furthermore, the growing number of studies that have been carried out in recent decades, where social and cognitive variables are integrated and cognitive flexibility plays a mediating role between different processes, is highlighted. For instance, in Cardom (2016), CF plays a mediating role between Cross-Race Interactions and psychological well-being, based on theoretical postulates by Crisp and Turner (2011). Brewster et al. (2013) found that CF moderates the relationship between experiences of prejudice and mental health in bisexual people and, more recently, Zuo et al. (2019) have identified the mediating role of CF in the influence of counter-stereotypes on creativity. In fact, in Spanish, some studies such as Angosto and Martínez (2004), Navarro and Mebarak (2014), and Enesco et al. (2014) have highlighted the importance of CF in interventions that seek to reduce prejudice towards stigmatized groups. What these trends show is that instruments such as the CFI have the potential to be used in different contexts such as clinical and research for the evaluation of cognitive flexibility.

Additionally, similar self-report questionnaires that assessperceptionofcognitive functioning have beenshown to be indicators of people’s quality of life and social functioning, which is an advantage of this kind of assessments (Daugherty et al., 2020). Another point to consider is that self-reporting scales are easy to manage, interpret and reduce time and costs, allowing the evaluation of larger samples of subjects (McDonald, 2008).

It is worth mentioning some limitations that could be addressed in future work. First, this sample only came from one Hispanic country (Colombia); thus, these results could not be generalized to the rest of the Spanishspeaking countries. Similarly, this study is across-sectional study that could make possible to assess the stability of the measure. Finally, self-report instruments may conduct to response bias, which may be a phenomenon to consider.

Future studies should consider clinical samples, to test the utility of the instrument in clinical settings and to yield broader and more generalizable results. Overall, this study showed that the scale exhibits adequate reliability, both for the general scale and for the subscales, which reveals item intercorrelations, indicating that each subscale accounts for a psychologically interpretable construct and that these can consistently be used to generate a composite score for cognitive flexibility.

5. Conclusion

We highlight that the CFI is an adequate measure for the evaluation of the type of cognitive flexibility necessary for individuals to successfully replace maladaptive thoughts with more adaptive thinking, when encountering stressful life events in Colombian adults. This means that this inventory is a useful tool to assess cognitive flexibility and offer some advantages such as brevity, and ease of administration, and rating. Also providing two factors of cognitive flexibility, it allows assessing possible difficulties in one of them, separately, and carrying out more specific interventions.

Table S1

Translated Version of the Cognitive Flexibility Inventory

	Muy en desacuerdo	En Desacuerdo	Algo en desacuerdo	Neutral	Algo de acuerdo	De acuerdo	Totalmente de acuerdo
1. Soy Bueno/a analizando situaciones	1	2	3	4	5	6	7
2. Me cuesta tomar decisiones cuando me enfrento a situa ciones difíciles	1	2	3	4	5	6	7
3. Considero múltiples alternati vas antes de tomar una decisión	1	2	3	4	5	6	7
4. Cuando me enfrento a situaciones difíciles, siento que pierdo el control	1	2	3	4	5	6	7
5. Me gusta ver las situaciones difíciles desde diferentes ángulos	1	2	3	4	5	6	7
6. Busco información adicional que no está inmediatamente disponible antes de atribuir causas a un comportamiento	1	2	3	4	5	6	7
7. Cuando me enfrento a situa ciones difíciles, me estreso tanto que no puedo pensar en una forma de resolver la situación	1	2	3	4	5	6	7
8. Trato de pensar sobre difer entes cosas desde el punto de vista de otra persona	1	2	3	4	5	6	7
9. Me resulta problemático que haya tantas formas diferentes de lidiar con situaciones difíciles	1	2	3	4	5	6	7
10. Soy bueno/a poniéndome en los zapatos de los demás	1	2	3	4	5	6	7
11. Cuando me enfrento a situa ciones difíciles, simplemente no sé qué hacer	1	2	3	4	5	6	7
12. Es importante mirar a las situaciones difíciles desde difer entes ángulos	1	2	3	4	5	6	7
13. Cuando me enfrento a situa ciones difíciles, considero múlti ples opciones antes de decidir cómo voy a actuar	1	2	3	4	5	6	7
14. A menudo observo una situación desde diferentes pun tos de vista	1	2	3	4	5	6	7
15. Soy capaz de superar las di ficultades a las que me enfrento en la vida	1	2	3	4	5	6	7
16. Considero todos los he chos y la información disponible cuando atribuyo causas a un comportamiento	1	2	3	4	5	6	7
17. Siento que no tengo poder para cambiar las cosas en situa ciones difíciles	1	2	3	4	5	6	7
18. Cuando me enfrento a situa ciones difíciles, me detengo y trato de pensar en varias man eras de resolverlas	1	2	3	4	5	6	7
19. Puedo pensar en más de una manera de resolver una situación difícil con la que me esté enfrentando	1	2	3	4	5	6	7
20. Considero múltiples alterna tivas antes de responder a situa ciones difíciles	1	2	3	4	5	6	7