The influence of emotions on our daily lives can hardly be overstated. They directs attention in our environment, facilitate decision-making, shape behavioural responses and impact memory formation to highlight a few functions (Gross, 2014). Such examples show that everyday functioning requires all individuals to engage in some minimum level of emotion regulation all the time (Davidson, 1998). While wonderfully useful for adaptive functioning in general, emotions can also be the cause of substantial harm.

Indeed, the inability to regulate emotions can produce serious disruptions to adaptive psychological functioning (Koole, 2009). Failure to regulate emotions effectively has been implicated in the emergence and maintenance of several forms of psychopathology, including depression and anxiety (Bebko, Ochsner, Franconeri, & Chiao, 2014), schizophrenia (Gross & Jazaieri, 2014), social anxiety (Klemanski, Curtiss, McLaughlin, & Nolen-Hoeksema, 2017), eating disorders (Mallorqui-Bague et al., 2017), personality disorders (Fitzpatrick, Khoury, & Kuo, 2018) and mood disorders (Gruber, Hay, & Gross, 2014; Joormann & Siemer, 2004). Efforts to better understand this aspect of our mental landscape are therefore not trivial.

Given the salience of emotion regulatory processes in psychological health, it has become a major field of research in recent years (Gross, 2014). Naturally, many measures seeking to measure individual differences in emotion regulation have been developed in the process. Examples include the Emotion Control Questionnaire (ECQ) (Roger & Nesshoever, 1987), the Emotion Regulation Questionnaire (ERQ) (Gross & John, 2003), Difficulties in Emotion Regulation Scale (DERS) (Gratz & Roemer, 2004), Regulation of Emotions Questionnaire (REQ) (Phillips & Power, 2007), Affective Style Questionnaire (ASQ) (Hofmann & Kashdan, 2010), the Emotion Regulation Profile-Revised (ERP-R) (Nelis, Quoidbach, Hansenne, & Mikolajczak, 2011), the Emotion Regulation of Others and Self (EROS) (Niven, Totterdell, Stride, & Holman, 2011) and the State Difficulties in Emotion Regulation Scale (S-DERS) (Lavender, Tull, DiLillo, Messman-Moore, & Gratz, 2015).

While this list is not exhaustive, it showcases a variety of measures that have been developed in an effort to investigate the antecedents and consequences of emotion regulation, or components thereof (John & Eng, 2014). Importantly, John and Eng (2014) differentiated among three individual difference approaches to the measurement of emotion regulation. The first approach represents measures based on Gross’s (1998) process model, the second on coping with stressors and the third is focused on emotional competences. The focus of the present study is on the Cognitive Emotion Regulation Questionnaire-short (CERQ-short). The CERQ is considered a measure of the second type, as its focus is on coping, or managing emotions in regards to stressors over a longer term (Garnefski, Kraaij, & Spinhoven, 2001; John & Eng, 2014). This approach is differentiated from the other two approaches, as it does not focus on changing immediate behavioural–expressive components of affect (approach 1), and is not focused on emotional competences aimed at appropriate socio-emotional behaviour (approach 3; John & Eng, 2014).

Garnefski et al.’s (2001) model for the CERQ categorises cognitive emotion regulation into nine different strategies, namely Self-Blame, Other-Blame, Positive Reappraisal, Rumination, Catastrophising, Putting-into-Perspective, Positive Refocusing, Acceptance and Refocus on Planning (Garnefski et al., 2001). These cognitive emotion regulation strategies have all been linked to the presence or absence of psychopathology. While some strategies are adaptive (Putting-into-Perspective, Positive Refocusing, Positive Reappraisal, Acceptance and Refocus on Planning), others are considered maladaptive (Self-Blame, Blaming Others, Rumination and Catastrophising), and are associated with psychological distress and psychopathology (Aldao, 2012; Garnefski et al., 2002; Garnefksi & Kraaij, 2007; Jermann, Van der Linden, d’Acremont, & Zermatten, 2006; Martin & Dahlen, 2005). In contrast, the adaptive strategies have been positively associated with higher levels of optimism and self-confidence and correlated negatively with several psychopathologies (Garnefski et al., 2002).

The original CERQ (Garnefski et al., 2001) contains 36 items, although a shorter 18-item version was created some years later (Garnefski & Kraaij, 2006), with two items per subscale instead of four. The CERQ-short is the focus of the present study. For the original version, support for construct validity came from a principal components analysis with nine factors extracted, consistent with the hypothesised model (Garnefski & Kraaij, 2007; Garnefski et al., 2001). Principal components analysis was similarly performed on the shortened version (Garnefski & Kraaij, 2006) to again assess construct validity. Confusingly, the authors used oblimin and varimax rotations inconsistently across these studies with no clear rationale provided for either option, although one would theoretically expect the factors to be somewhat correlated. Cronbach’s alpha reliability ranged between 0.75 and 0.87 for the four-item version and between 0.68 and 0.81 for the short version (Garnefski & Kraaij, 2006). Slightly weaker alpha reliabilities were reported in an earlier study for the full version (Garnefski et al., 2001).

Descriptive statistics and zero-order correlations among the subscales are reported in Table 1. Statistically significant correlations ranged between 0.08 (Self-Blame and Positive Reappraisal) and 0.51 (Refocus on Planning and Positive Reappraisal). It is worth noting that while there is variation with regard to the intercorrelations of this study compared to Garnefski and Kraai’s (2006) findings, the pattern is quite similar. Strong and weak associations were consistently observed among the same variables in both studies. For example, while the previous correlation observed between Acceptance and Positive Reappraisal was 0.43, it is 0.46 in the present study, and whereas Catastrophising and Refocus on Planning previously correlated 0.09, they correlated 0.11 in the present study.

The purpose of this study was to examine the reliability and internal factor structure of the CERQ-short. While previous research has found fairly good support for the reliability and construct validity of the CERQ-full and short versions in a different population (Garnefski & Kraaij, 2006, 2007; Garnefski et al., 2001), no studies have been conducted to examine its utility in the South African context. This article sought to do this for the CERQ-short.

In terms of reliability, most scales yielded acceptable to good reliability; however, Rumination (α = 0.58), Refocusing on Planning (α = 0.65) and Putting-into-Perspective (α = 0.64) had reliability coefficients that were weaker than expected. This is substantially lower than what was previously found by Garnefski and Kraaij (2006), who reported an alpha coefficient of 0.79 for Rumination, Positive Refocusing and Putting-into-Perspective. The remaining scales had acceptable reliabilities, ranging between 0.71 and 0.81, which are consistent with previous findings (Garnefski & Kraaij, 2006).

Regarding the factor structure of the CERQ-short, results of the basic nine-factor model provided reasonable support for the separation of items into nine different scales. However, a nine-factor higher order model did not provide adequate fit for the data, and suggests that cognitive emotion regulation as measured by the CERQ-short is best considered a multidimensional rather than a unidimensional construct. This is reflected by the weak goodness-of-fit values observed for the nine-factor higher order model which does not support the idea that cognitive emotion regulation can be considered a unidimensional latent construct. However, the present data suggest that it can be indexed using a multidimensional approach as represented by the nine-factor model. This model supports the CERQ-short as a measure comprising meaningful constructs, with all items having strong loadings on their respective factors.

Importantly, this study examined the CERQ-short using CFA. Previous research mostly used principal components analysis, with one exception in Garnefski and Kraaij (2007), whose findings were insufficiently reported. Principal components analysis represents formative modelling, whereas CFA comprises reflective modelling (Fleuren, Van Amelsvoort, Zijlstra, De Grip, & Kant, 2018; Howell, Breivik, & Wilcox, 2007; Jarvis, MacKenzie, & Podsakoff, 2003). The use of principal components analysis rather than CFA, arguably, represents theoretical misspecification. A clear a priori theoretical structure (Garnefski & Kraaij, 2006; Garnefski et al., 2001) along with a consideration of typical criteria for reflective modelling (Fleuren et al., 2018) suggested that CFA was a more appropriate method with which to investigate the factor structure of the CERQ-short.

Although previous research conducted in a different country reported satisfactory measurement properties for the CERQ-short (Garnefski & Kraaij, 2006), its functioning in South Africa was found to be slightly weaker in general. While the reliability for six out of nine scales was acceptable, reliability for the three remaining scales was not satisfactory – especially problematic was the Rumination scale. It is possible that a latent variable estimate of reliability, such as McDonald’s (1999) omega, could perhaps paint a different picture than the Cronbach’s alpha coefficients; however, with two items per scale, it was not possible to compute.

This study has some limitations that should be considered when interpreting the results. The data collected were from an urban student population in one part of South Africa. English was not the first language of all participants, which may have impacted how questions were understood and interpreted. The sample consisted of university students who may not represent the general South African population and thus may limit the generalisability of the results.

While this study focused on the CERQ-short, future research using similar analyses is required on the full version of the measure. As such, the findings of this study should not be extrapolated to the full version. Future research should also explore if the measure functions equivalently across different gender, ethnic and language groups. This would subsequently allow investigation of group differences in cognitive emotion regulation.

Considered together, the results of this study showed reasonable support for the reliability and construct validity of the CERQ-short, with noted exceptions. It should be borne in mind that these findings likely represent a best-case scenario given the use of polychoric correlations. In comparison to the polychoric-based results, the Pearson-based reliability estimates reported in Table 2 were much less promising. The goodness-of-fit statistics, in particular the incremental fit indices (CFI and TLI), are also not above reproach. Nevertheless, the CERQ-short offers some promise for use in the South African context. Without data on the full version, one can only speculate about how it might function in South Africa. From previous work conducted elsewhere, it seems likely that the full version would provide more stable measurement than the short version. However, in the absence of empirical data, this remains a conjecture, and the short version is recommended for use in South Africa until research that supports the full version is produced.