Eating disorders can be described as a prolonged disturbance of eating or related behavior such as selective or restrictive eating, compulsory eating behavior, binge eating episodes and/or compensatory behavior such as fasting, frequent vomiting, use of laxatives or diuretics as well as excessive and compulsory exercise (American Psychiatric Association
2013). Eating disorders are divided into sub-diagnoses anorexia nervosa, bulimia nervosa, binge eating disorder and other specified feeding or eating disorders, the latter including atypical anorexia nervosa, bulimia nervosa and binge eating disorder of low frequency and/or limited duration, rumination disorder, and purging disorder (American Psychiatric Association
2013). Overall eating disorder point prevalence has been reported between 0.5% and 5.3% for females and from 0.62% to 0.64% in males (Lindvall Dahlgren and Wisting
2016). In eating disorders, emotion regulation seems to play an important role. Previous research, for example, indicates that the risk of suicide attempts is highly elevated in individuals with eating disorders (Pisetsky et al.
2013) and a link between suicide attempts, non-suicidal self-injury and emotion dysregulation in individuals with eating disorders has been suggested (Gómez-Expósito et al.
2016; Vieira et al.
2016).
Emotion regulation has emerged as a central component of both theories of psychopathology and psychological interventions (Gross
2015). Emotion regulation can be conceptualized as the awareness, understanding and acceptance of one’s emotions, the ability to inhibit inadequate behaviors when emotionally aroused, and the ability to use adaptive regulation strategies in order to reach one’s goals (Gratz and Roemer
2004). Individuals with eating disorders have been found to display higher levels of emotion dysregulation compared to healthy controls, lower levels of emotional awareness, clarity, and recognition as well as problems regarding emotional inhibition and access to healthy emotion regulation strategies (e.g., Lavender et al.
2015; Monell et al.
2018). Regarding different eating disorder diagnostic subgroups, Lavender et al. (
2015) concludes in a review that global emotion regulation difficulties seems to be a transdiagnostic trait across the eating disorder spectrum, but that there might be some distinct patterns that potentially distinguishes the different eating disorders. Although numerous studies have reported relationships between eating disorders and emotion dysregulation (e.g., Brockmeyer et al.
2014; Lavender et al.
2015), a consistent conceptualization of emotion regulation seems to be lacking within the field of eating disorders. This is evidenced by the use of a wide range of different measures such as the subscale interoceptive awareness from the Eating Disorder Inventory (Garner et al.
1983), the Emotional Awareness Questionnaire (Rieffe et al.
2007), the Toronto Alexithymia Scale (Bagby et al.
1994), the Emotion Regulation Questionnaire (Gross and John
2003) and the Difficulties in Emotion Regulation Scale (DERS; Gratz and Roemer
2004).
Given the growing interest in emotion regulation in eating disorders, there is a need for valid and reliable measures. One widely used measure in both research and clinical practice is the aforementioned DERS (Gratz and Roemer
2004). Unlike most other measures that mainly focus on specific aspects of emotion regulation, the DERS was designed to comprehensively measure multiple dimensions of emotion regulation ability. The original version of the DERS includes 36 items formulated as assertions scored 1–5 yielding a total score as well as scores on six subscales (henceforth referred to as DERS-36). In the original study by Gratz and Roemer (
2004), exploratory factor analysis (EFA) revealed a correlated trait lower-order six-factor solution. The six factors or subscales were presented as distinct but related dimensions with adequate internal consistency. All items in the final exploratory factor solution had factor loadings of .40 or higher on the corresponding subscale and none of the items in the final version had significant loadings (above .40) on more than one factor (Gratz and Roemer
2004).
The six subscales were named Nonacceptance, Goals, Impulse, Awareness, Strategies, and Clarity. The Nonacceptance subscale (nonacceptance of emotional responses) is composed of items reflecting a tendency to experience negative secondary emotional responses or a nonaccepting reaction to distress, mainly shame, guilt, or self-blame regarding one’s own (negative) emotions. Items from the Goals subscale (difficulties engaging in goal directed behavior) reflect difficulties regarding concentration or accomplishing tasks when upset. The Impulse subscale (impulse control difficulties) reflect difficulties remaining in control of one’s behavior when feeling distress and also includes items reflecting the perception of emotions as overwhelming. The fourth subscale Awareness (lack of emotional awareness) comprises reverse scored items reflecting the tendency to pay attention to and acknowledge emotions. The subscale Strategies (limited access to emotion regulation strategies) consists of items reflecting a belief that there is nothing that can help regulate negative emotions, suggesting a kind of hopelessness when confronted with one’s feelings. The final subscale Clarity (lack of emotional clarity) addresses the ability to understand emotions; a high score indicates a high degree of confusion regarding emotions.
Previous research has demonstrated support for the validity of the DERS. The DERS and its subscales has shown correlations with other scales measuring related aspects of emotion regulation, such as alexithymia, levels of positive and negative affect, experiential avoidance and suppression, in other populations including individuals with alcohol dependence, chronic pain, and aggression as well in psychiatric inpatient adolescents (e.g., Ghorbani et al.
2017; Kökönyei et al.
2014; Velotti et al.
2016; Venta et al.
2012). Levels of emotion regulation difficulties as measured by the DERS have also been found to be associated with different kinds of psychopathology such as personality disorders, posttraumatic stress disorders, depression and anxiety (e.g., Gratz et al.
2006; Sloan et al.
2017; Tull and Roemer
2007; Villalta et al.
2018; Visted et al.
2018). Regarding eating disorders, previous research has found associations between the DERS (total scale and all subscales) and levels of eating disorder pathology (e.g., Brockmeyer et al.
2014; Burns et al.
2012; Cooper et al.
2014; Haynos et al.
2015; Monell et al.
2015; Racine and Wildes
2015), and there is also evidence of relations to specific eating disorder behaviors (e.g., Burns et al.
2012; Monell et al.
2018). Furthermore, studies incorporating other emotion regulation measures in addition to DERS show that the relationships between the DERS and its subscales and levels of eating disorder pathology are fairly consistent with associations that were observed when using other measures that tap into similar constructs (e.g., Svaldi et al.
2012). Levels of emotion regulation difficulties in individuals with eating disorders, as measured by the DERS, have also been found to decrease following treatment (e.g., Mallorquí-Bagué et al.
2018; Sloan et al.
2017).
Since the original factor analytic study, a number of studies have examined the psychometric properties of the DERS-36. The majority of these studies have, however, been conducted in non-clinical samples. Several of the previous studies report acceptable or good fit for the original correlated traits six-factor solution in non-clinical adults (e.g., Bostan and Zaharia
2016; Ritschel et al.
2015) and adolescents (e.g., Neumann et al.
2010; Sarıtaş-Atalar et al.
2015). However, a few studies investigating the factor structure of the DERS-36 in non-clinical samples report trouble with the original six-factor solution, primarily relating to problems with the Awareness subscale (e.g., Bardeen et al.
2012; Lee et al.
2016). For example, Bardeen et al. (
2012) found support for a revised five-factor higher order solution and Lee et al. (
2016) suggested a five-factor lower order model – both excluded the six items from the Awareness subscale.
While the factor structure of the DERS-36 has been examined in non-clinical samples, only a handful of studies have examined the factor structure in clinical populations (Fowler et al.
2014; Hallion et al.
2018; Osborne et al.
2017; Perez et al.
2012; Wolz et al.
2015). Perez et al. (
2012) investigated the factor structure of DERS-36 in a sample of 218 adolescents with nonsuicidal self-injury. Confirmatory factor analysis (CFA) suggested acceptable fit for the six-factor original correlated traits solution. Fowler et al. (
2014) examined a sample of 592 adult in-patients with severe mental illness and reported acceptable and equivalent fit with the original correlated traits six-factor solution. In the recent study by Osborne et al. (
2017), the factor structure of the DERS-36 was examined in an adult population of 344 outpatients receiving Dialectical Behavior Therapy (DBT). Seven different models were tested, including two unidimensional models, three correlated traits models, one higher-order model and one bifactor model. Results showed good fit for the bifactor model including the original six factors, with a modification that prohibited items from the Awareness subscale from loading on the general factor but allowing the factor Awareness to be correlated with the Clarity subscale (Osborne et al.
2017). Support for a bifactor solution was replicated by Hallion et al. (
2018) using CFA in a study of 427 adults with emotional disorders. However, unlike the study by Osborne et al. (
2017), Hallion et al. (
2018) found support for a five-factor bifactor solution that excluded the subscale Awareness altogether.
The only study to our knowledge to examine the factor structure of the DERS-36 in patients with eating disorders, is the study by Wolz et al. (
2015). The study involved 74 healthy controls and 134 adult patients with a DSM-IV eating disorder (including anorexia nervosa, bulimia nervosa, binge eating disorder, and other specified feeding or eating disorders). The sample was analyzed with both EFA and CFA. EFA results suggested a one-dimension solution as well as a six-factor solution with close resemblance to the original factor structure. CFA results showed acceptable fit for a model comparable to the original six-factor correlated traits solution (Wolz et al.
2015).
In addition to the original 36-item version of the DERS, Bjureberg et al. (
2016) developed and evaluated a brief version of the DERS consisting of 16 items that generates a single global score for emotion regulation difficulties (henceforth referred to as the DERS-16). The DERS-16 showed good convergent and discriminant validity, excellent internal consistency and good test-retest reliability and was concluded to offer a valid and brief method for assessing emotion regulation difficulties (Bjureberg et al.
2016). The factor structure of the DERS-16 has been investigated in five studies. Miguel et al. (
2017), Shahabi et al. (
2018), Westerlund and Santtila (
2018), and Yiugit and Guzey Yiugit (
2017) all found support for the five-factor solution proposed by Bjureberg et al. (
2016) in non-clinical populations. Hallion et al. (
2018) examined the factor structure of the DERS-16 in a clinical sample consisting of 427 adults with emotional disorders. Results indicated good fit for a bifactor model.
In summary, the factor structure of DERS-36 and DERS-16 has been investigated in several studies. However, most factor analytic studies are based on non-clinical samples and there is limited knowledge of the factor structure of both versions in clinical samples. To our knowledge, only one study has examined the DERS-36 in an eating disorder population (Wolz et al.
2015). That study, however, was marked with some limitations, most significantly the relatively small sample size for the analyses that were conducted in the study (i.e., CFA). The small sample size also precluded a careful examination of the factor structure in different subpopulations of eating disorders. Further, only one model was investigated using CFA meaning that there were no direct comparisons between different factor solutions. Also, given that both EFA and CFA was conducted using the same sample in the study, there is a clear need to confirm the factor structure using CFA in an independent larger sample of individuals with eating disorders.
The present study is the first to investigate the factor structure of both the 36-item and 16-item DERS in a large eating disorder sample. Based on previous examinations of the factor structure of the 36-item DERS in clinical and non-clinical samples, a series of CFA models were fitted and compared in the present study. Given previously identified problems with the Awareness subscale of the DERS-36, a priori modifications were made to each model that was tested. Specifically, four primary structures were fitted to the data and compared in terms of model fit: (1) a unidimensional model, (2) a six-factor correlated-traits model, (3) a higher-order factor model, and (4) a bifactor model. We were primarily interested in the bifactor model that has to date not been examined among individuals with eating disorder but has previously been successfully tested on the DERS-36 in two psychiatric samples. The bifactor model also has a clear advantage by examining the specific variance accounted for by the subfactors of DERS-36 over and above the variance accounted for by the general factor. Thus, information gained from the bifactor model can, for example, be used to determine the adequacy of the total score of the original DERS-36 and the DERS-16, as well as the additional value of scoring subscales as the correlations among all the items are accounted for by both a general factor and set of subfactors in the model. Finally, the present study is, to our knowledge, the first to test for measurement and structural invariance across subgroups of different eating disorders, that is, anorexia nervosa and bulimia nervosa.
Methods
Participants
The sample was drawn from the Swedish Stepwise clinical database (Birgegård et al.
2010) with nationwide data on treatment seeking individuals of all ages and both genders with eating disorder; however, in the present study only adult patients (≥18 years) were included. Data was extracted in October 2017. Stepwise is a longitudinal internet-based quality assurance register developed in 2005. Inclusion criteria for entry in the Stepwise database are a DSM-IV eating disorder (American Psychiatric Association
2000), medical or self-referral to a participating treatment unit, and intention to treat at the unit. All measures are collected during an initial assessment within the first three visits at the clinical unit. Assessment was carried out by eating disorder professionals with special training (a 2-day mandatory course). The Stepwise database includes structured diagnostic interviews for eating disorders and other DSM-IV Axis I disorders and contains a variety of both mandatory and optional assessment instruments (i.e. units can decide which of the optional instruments to include in their test battery). For a full description of Stepwise, see Birgegård et al. (
2010).
The DERS-36 was added as an optional assessment for units on 7th April 2014. Registrations prior to this date were thus excluded, leaving
N = 2625 patients from 30 units. The following exclusions were then made: no DERS ratings (
n = 1674), ratings from patients at units with <20 DERS-36 administrations (
n = 94), leaving a final simple size of 857 patients from 14 units. A previous study using the DERS-36 from the Stepwise register, largely sharing the present sample (
N = 999, only women), could not find any differences between patients with or without DERS-36 registration regarding variables such as age, body mass index, eating disorder characteristics, and psychiatric comorbidity (see Monell et al.
2018).
The present sample consisted of 820 women and 37 men with a mean age of 26. As is typical in the eating disorder population, mean age of eating disorder onset was in adolescence. The majority of the participants had graduated from upper secondary school and had an employment of at least half-time. Bulimia nervosa was the most common diagnosis in the sample (
n = 319), followed by purging disorder (
n = 189), anorexia nervosa (
n = 141; whereof
n = 90 for restricting subtype,
n = 32 for binge/purging subtype, and
n = 19 for anorexia nervosa except no amenorrhea), atypical anorexia nervosa (
n = 131), binge eating disorder (
n = 41), unspecified feeding or eating disorder (
n = 23) and rumination disorder (
n = 13). Demographic information for the final sample is presented in Table
1. Eating disorder diagnoses were recategorized to correspond to the diagnostic criteria in DSM-5 (American Psychiatric Association
2013); eating disorders not otherwise specified type 1 (anorexia nervosa except no amenorrhea) were recategorized into anorexia nervosa and eating disorders not otherwise specified type 3 (bulimia nervosa except binge/purge frequency lower than the DSM-IV threshold) into bulimia nervosa.
Table 1
Descriptive and clinical characteristics of the sample
Age | 26.37 | 8.23 | | 24.24 | 7.38 | | 27.09 | 8.19 | |
Age of ED onset | 16.45 | 5.60 | | 16.83 | 5.56 | | 15.65 | 4.05 | |
BMI | 22.94 | 6.16 | | 18.75 | 3.83 | | 25.32 | 5.57 | |
EDE-Q | 3.86 | 1.19 | | 3.66 | 1.36 | | 4.15 | 0.96 | |
Gender | | | 96 (820) | | | 94 (255) | | | 97 (308) |
Education | | | 80 (688) | | | 22 (60) | | | 31 (100) |
Employment | | | 79 (673) | | | 76 (207) | | | 83 (264) |
Living alone | | | 39 (337) | | | 34 (91) | | | 42 (134) |
Data Analytic Approach and Statistical Analysis
Results
Factor Structure of the DERS-36
Descriptive statistics for each item of the DERS-36 are presented in supplementary Table S
1 and descriptives for the total scale of DERS and subscales are presented in Table
2. Measures of skewness and kurtosis for both items and scales were generally low. In total, 8 models were estimated using CFA. Table
3 provides model fit indices for all estimated models in the full sample (
N = 857).
Table 2
Descriptive statistics for total and subscales of Difficulties in Emotion Regulation Scale (36 items) as a function of diagnostic groups
Total | 100.12 | 26.43 | 2.80 | 0.73 | 0.10 | 0.17 | 99.31 | 27.38 | 2.78 | 0.76 | 0.16 | −0.32 | 102.32 | 26.62 | 2.86 | 0.73 | −0.00 | −0.53 |
Non-accept | 16.15 | 6.22 | 2.69 | 1.04 | 0.37 | −0.72 | 16.15 | 6.10 | 2.69 | 1.02 | 0.30 | −0.69 | 16.17 | 6.34 | 2.69 | 1.06 | 0.39 | −0.84 |
Goals | 16.30 | 5.30 | 3.26 | 1.06 | −0.20 | −0.93 | 15.93 | 5.41 | 3.19 | 1.08 | −0.08 | −1.13 | 16.87 | 5.16 | 3.37 | 1.03 | −0.39 | −0.75 |
Impulse | 14.39 | 6.17 | 2.40 | 1.03 | 0.61 | −0.48 | 13.85 | 6.21 | 2.31 | 1.03 | 0.74 | −0.30 | 15.11 | 6.15 | 2.52 | 1.02 | 0.46 | −0.65 |
Awareness | 18.23 | 5.15 | 3.04 | 0.86 | −0.15 | −0.55 | 18.24 | 5.15 | 3.04 | 0.86 | 0.02 | −0.52 | 18.40 | 5.14 | 3.07 | 0.86 | −0.26 | −0.48 |
Strategies | 21.10 | 7.75 | 2.64 | 0.97 | 0.29 | −0.76 | 20.72 | 8.11 | 2.59 | 1.01 | 0.40 | −0.70 | 21.76 | 7.64 | 2.72 | 0.95 | 0.18 | −0.82 |
Clarity | 13.95 | 4.59 | 2.79 | 0.92 | 0.15 | −0.58 | 14.40 | 4.67 | 2.88 | 0.93 | 0.03 | −0.65 | 14.01 | 4.61 | 2.80 | 0.92 | 0.16 | −0.51 |
Table 3
Estimated confirmatory factor analysis models for DERS-36 using the MLR estimator
Unidimensional | Model 1.1 | 7320.013 | 594 | 0.592 | 0.111 | 0.115 | [0.113, 0.117] |
Model 1.2 | 5164.097 | 405 | 0.663 | 0.092 | 0.117 | [0.114, 0.120] |
Trait | Model 2.1 | 2440.130 | 579 | 0.887 | 0.065 | 0.061 | [0.059, 0.064] |
Model 2.2 | 1729.675 | 395 | 0.905 | 0.058 | 0.063 | [0.060, 0.066] |
Higher-order | Model 3.1 | 2756.743 | 588 | 0.869 | 0.082 | 0.066 | [0.063, 0.068] |
Model 3.2 | 2472.762 | 587 | 0.886 | 0.067 | 0.061 | [0.059, 0.064] |
Bivariate | Model 4.1 | 2382.028 | 558 | 0.889 | 0.073 | 0.062 | [0.059, 0.064] |
Model 4.2 | 2010.668 | 557 | 0.912 | 0.049 | 0.055 | [0.053, 0.058] |
Measurement invariance |
Bifactor | AN | 1047.596 | 557 | 0.914 | 0.059 | 0.057 | [0.052, 0.062] |
BN | 1153.135 | 557 | 0.906 | 0.051 | 0.058 | [0.053, 0.063] |
Configural | 2199.933 | 1114 | 0.910 | 0.055 | 0.057 | [0.054, 0.061] |
Scalar | 2293.930 | 1208 | 0.910 | 0.060 | 0.055 | [0.052, 0.059] |
Reliability and Explained Common Variance
Omega and OmegaH values obtained from the estimated final selected bifactor model are presented in Table
4. The overall reliability values (Omega), which can be thought of as weighted coefficient alpha, of the total and subscales were all high (range .796–.963). The OmegaH value for the total scale was likewise high, indicating that almost all of the reliable variance in total scores (.852/.962 = .885) could be attributed to the general factor, assumed to measure general emotion dysregulation. Despite high omega values for all subscales, low OmegaH values for the Goals, Impulse and Strategies subscales, however, indicate that most of the reliable variance on these subscales was accounted for by the reliable variance due to the general factor. The opposite was true for the Awareness and Clarity subscales that both had relatively high OmegaH and subsequently explained more of the reliable variance relative to the general factor (proportion = .94 and .70 respectively).
Table 4
Omega and Omega Hierarchical for total and subscales of the DERS-36
Total | 0.963 | 0.852 | 0.885 |
Non-Accept | 0.897 | 0.479 | 0.534 |
Goals | 0.895 | 0.249 | 0.278 |
Impulse | 0.918 | 0.261 | 0.284 |
Awareness | 0.796 | 0.749 | 0.941 |
Strategies | 0.904 | 0.098 | 0.108 |
Clarity | 0.842 | 0.589 | 0.700 |
The estimate of ECV statistics was .86. This suggested that most of the common variance was accounted for by the general factor and thus only a small proportion of the variance (1–.86 = .14) appeared to be explained by the subscale factors beyond the general factor.
Measurement and Structural Invariance across Anorexia Nervosa and Bulimia Nervosa
Factor Structure of the DERS-16
The bifactor model that was established as the best-fitted model for all the 36 items was used to evaluate the factor structure of the 16-item version of the DERS in the full sample. Given that this shortened version did not include the Awareness scale, no covariance could subsequently be added between this subfactor and the Clarity factor (as model 4.2 estimated in the full sample). In addition, due to the fact that the Clarity subscale only had two indicators, loadings were constrained to 1 for these items to make the model identified globally. The bifactor model provided a good fit to the data (χ2 (89) = 379.985, p < .001; RMSEA = .062 [95% CI .055, .068]; CFI = .954; SRMR = .04).
Criterion Validity and Diverging Associations with Eating Pathology
To examine the associations between dimensions of emotion dysregulation (i.e., latent factors) and eating pathology and whether the subfactors could account for unique variance over and above the general factor, we incorporated EDE-Q (as a measure of eating pathology) as an observed dependent variable into the best-fitted bifactor model. First, a model was run in which the EDE-Q was regressed on the general factor only (and all the subfactors effects on EDE-Q were constrained to zero.) Second, a model was run in which EDE-Q was regressed on all the subfactors and the general factor. The contribution of all subfactors ability to account for unique variance in the outcome above the contribution of the general factor was examined by improvement in global model fit between models using the scaled chi-square difference test. This analytic procedure was repeated for individuals with bulimia nervosa and individuals with anorexia nervosa.
For the anorexia nervosa subsample, the general factor was statistically significantly associated with scores on the EDE-Q (z = 5.099, p < .001) and accounted for approximately 15% of the variance (R2 = .154). The subfactors accounted for an additional 2% of the variance, but the overall contribution was not statistically significant, Δχ2(6) = 9.509, p = .147. For the bulimia nervosa subsample, the general factor was statistically significantly associated with scores on the EDE-Q (z = 5.245, p < .001) and accounted for approximately 14% of the variance (R2 = .144). The subfactors accounted for an additional 5.7% of the variance, a contribution that was statistically significant, Δχ2(6) = 13.055, p = .042. However, despite accounting for a significant contribution together, none of the estimates of the effects of subfactors on EDE-Q had a statistically significant individual contribution in the model (all p’s > .06; all standardized beta’s < .17).
Discussion
Factor Structure of DERS
The factor structure of the DERS-36 in a large eating disorder sample was examined using CFA with eight different models. Results indicated that a modified bifactor model that allowed the subscale Awareness to correlate with the subscale Clarity (model 4.2) provided best fit. This model was the only model to reach acceptable or good fit on all indices. Further, it was the only model to reach good fit according to our primary measure of fit (i.e., RMSEA). A similar bifactor solution was also found to be a good fit for the DERS-16. The bifactor model suggest that the composite score has more than one source of variance, meaning that the scores of all items on the DERS-36 and DERS-16 are influenced by more than one distinct underlying construct (i.e. subscales) beyond the contributions from the general factor. A bifactor model of the DERS-36 and DERS-16 has previously been suggested in two studies (Hallion et al.
2018; Osborne et al.
2017). Similar to our model, both of these studies made modifications to the model due to identified problems with the Awareness subscale. Osborne et al. (
2017) tested a bifactor model that excluded items of Awareness scale from loading onto the total scale but permitted a correlation between this subfactor and the Clarity factor. Hallion et al. (
2018) tested a bifactor model with all items from the Awareness subscale excluded. Thus, while the overall structure differed slightly with previous tested bifactor models, our result aligns with previous examinations of the factor structure of DERS in other clinical populations.
In comparison with the other tested bifactor models in other clinical samples, our bifactor model also allowed us to explicitly test the contribution of the Awareness subscale in accounting for unique and shared variance. The Awareness concept or similar constructs are common features in several emotion regulation models as well as in several models of psychiatric pathology (D’Agostino et al.
2017) and specific models of eating disorder pathology (Lavender et al.
2015). Keeping the Awareness subscale is also in line with recommendations from the study by Fowler et al. (
2014). While their study did not test a bifactor solution, the authors concluded that removal of the Awareness subscale did nothing to improve fit in their sample of 592 adult patients with severe mental illness. The only previous study of the factor structure of the DERS-36 in an eating disorder sample also found acceptable fit for a (correlated-traits) solution that included Awareness (Wolz et al.
2015). As for the DERS-16, the results from the present study indicated a good fit for a bifactor model, a finding that is in line with the results from Hallion et al. (
2018) that is the only previous study to investigate the factor structure of the DERS-16 in a psychiatric clinical sample. While general recommendations on the factor structure are difficult to provide given different populations and methods in these studies, the present study adds additional support to this factor structure in another clinical population.
Reliability and Explained Common Variance
Reliability of DERS-36 for individuals with eating disorder was examined in terms of Omega (comparable to a weighted coefficient alpha), OmegaH (reliable variance accounted for by the particular scale excluding reliable variance accounted for by the general factor), and ECV (the degree of common variance explained by the general factor). Results showed that OmegaH and the explained proportion of variance were high for DERS total, indicating that most of the explained common variance can be attributed to the total scale. Regarding the subscales, results varied. The results from the present study as well as the study by Osborne et al. (
2017) are in line with earlier findings suggesting that the large majority of psychological multidimensional measures show similar tendencies with high OmegaH and ECV for the measures total scale and corresponding low OmegaH for subscales (Rodriguez et al.
2016a). There is no consensus in the field regarding specific benchmarks for evaluating OmegaH, but values greater than .50 or .75 have been suggested (Reise et al.
2013). Comparing with these cutoffs, our results indicate that half of the DERS subscales (Awareness, Clarity, and NonAcceptance) reach sufficient or close to sufficient OmegaH values and the other half present lower values. In this context it is important to note that all Omega values (comparable to a weighted coefficient alpha) were high for all subscales.
The subscales Goals, Impulse, and Strategies all exhibited low OmegaH, indicating that these subscales contribute with little unique explanatory value beyond the contributions of the total scale. The Awareness, Clarity and Nonacceptance subscales showed medium to relatively high OmegaH-values. Only one previous study by Osborne et al. (
2017) has presented OmegaH for the DERS. Results were similar with low OmegaH for Goals, Impulse and Strategies as well as high OmegaH for the subscale Clarity. Osborne et al. (
2017) did not provide OmegaH for the Awareness subscale as their modified bifactor model prevented Awareness items from loading onto the total scale. OmegaH for DERS total were similar to ours.
There is no consensus in the literature regarding the use of the DERS total score or the use of subscale scores in clinical populations. For example, Fowler et al. (
2014) cautions against the use of DERS total, since the results from the higher-order models in their study showed a weak fit, suggesting that future use should focus primarily on the six subscales, whereas Osborne et al. (
2017) recommended the use of a general or total score from the modified DERS with only five subscales (excluding the Awareness subscale). Taken together, the results from the present study indicate that there may be reasons for being cautious when interpreting scores of some of the DERS subscales in an eating disorder population as most of the reliable variance might be accounted for by a general factor representing shared variance among all the items. This is especially true for Strategies. Regarding Awareness, the high OmegaH values indicate that the subscale contributes a high proportion of unique variance over and above the variance explained by the general factor. This could lead to questions regarding the underlying latent construct, as it is possible that Awareness measures some different aspect of emotion regulation than the rest of the items of the DERS (Bardeen et al.
2012; Lee et al.
2016). The present findings dovetail with previous studies that have found small intercorrelations between the Awareness factor and the other factors (Fowler et al.
2014; Osborne et al.
2017; Perez et al.
2012; Wolz et al.
2015). On the other hand, the Awareness subscale is the only one to meet the preferred benchmark of .75 or higher OmegaH suggested by Reise et al. (
2013). It might thus be possible to use this scale on its own, but more difficult to know whether the scale actually taps into to a more general emotion regulation ability as measured by the DERS.
Measurement and Structural Invariance across Anorexia Nervosa and Bulimia Nervosa
The present study is the first to test for measurement and structural invariance for the DERS-36 across the eating disorder subgroups anorexia nervosa and bulimia nervosa. Results showed that strong measurement invariance assumption was met. This suggests that the DERS-36 measures the same underlying latent construct in both groups, meaning that it is possible to make relevant comparisons between patients with anorexia nervosa and bulimia nervosa in terms of emotion regulation difficulties as measured by the DERS-36. This result is of importance in the field as comparisons and correlations between emotion regulation difficulties measured by the DERS in different eating disorder subgroups is a common study aim in eating disorder research (e.g. Brockmeyer et al.
2014; Lavender et al.
2015; Segal and Golan
2016; Svaldi et al.
2012). Further analyses showed that the dispersion in factor scores was similar across groups. Regarding mean factor score differences between anorexia nervosa and bulimia nervosa the results indicated small but statistically significant differences for DERS total as well as Clarity, with higher total mean and lower mean on Clarity in the bulimia nervosa group. No other comparisons were statistically significant. Taken together, patients with anorexia nervosa and bulimia nervosa seem to have fairly similar emotion regulation difficulties as measured by the DERS-36, a finding that is consistent with previous research (e.g., Brockmeyer et al.
2014; Lavender et al.
2015; Monell et al.
2018; Svaldi et al.
2012).
Criterion Validity
Results from bifactor models revealed that the general factor of emotion dysregulation as measured with the DERS in the best-fitted bifactor model was strongly associated with eating psychopathology in both anorexia nervosa and bulimia nervosa subsamples (15% and 14% respectively). These findings are in line with previous research that has shown that emotion dysregulation is an important feature of eating pathology across eating disorders (e.g., Brockmeyer et al.
2014; Lavender et al.
2015). Interestingly, once the variance by the general factor was accounted for in the model, the overall contribution of the subfactors was relatively small in both diagnostic groups (2% and 5%) and was not statistically significant in the anorexia nervosa subsample. None of the subfactors had a statistically significant contribution on their own in accounting for eating pathology. By incorporating an external criterion of relevance for eating disorders (eating disorder psychopathology), these findings corroborate our other results showing that most of the reliable variance was accounted for by the general factor. Of course, one might argue that it is difficult to know what it actually means to be high (or low) on a subscale that is supposed to tap into a specific emotion regulation skill when partielling out general emotion ability. Indeed, concerns in terms of the interpretation of a bifactor model have been raised (Bonifay et al.
2017; Murray and Johnson
2013). On the other hand, if most of the reliable variance is indeed accounted for by a general factor, and in the absence of evidence for reliability of the subscale scores, it is difficult to provide a meaningful interpretation of the associations between observed subscales scores of the DERS and other important variables. At this time point, it is difficult to make definitive conclusions as more research on the validity of the DERS in individuals with eating disorders is warranted. Still, in this subsample of individuals with eating disorders, our results highlight that the subfactors cannot be considered to reflect broad, independent abilities because they include a strong contribution of the general factor.
Strengths and Limitations
The findings of this study should be interpreted considering some strengths and limitations. First, a major strength is the large psychiatric eating disorder sample that provides an ecologically valid context for assessment. The DERS was developed for populations with clinical problems regarding emotion regulation and different kinds of psychopathology (Gratz and Roemer
2004). The present study makes important contributions regarding the psychometric properties and use of the DERS-36 and DERS-16 in the field of clinical psychiatric eating disorders. Second, the present study investigates the latent structure of the DERS with several models, including a bifactor model that has previously been suggested to be suitable for measures that are assumed to have a multidimensional structure (such as the DERS). The use of a bifactor model also allowed for investigations of reliability in terms of Omega, OmegaH, and explained common variance, giving important information regarding the previously identified problems with certain subscales. To our knowledge, there is only one previous study that has reported OmegaH for the DERS and the present study is the first to present OmegaH for all six subscales. Lastly, the test for measurement invariance is an important strength as this provides essential information regarding comparisons of emotion regulation across eating disorder diagnostic subgroups.
There are also a few limitations with the current study. The DERS was added as an optional measure in the Stepwise register and criteria for clinics’ decisions in inclusion are not recorded. A previous study using the DERS from the Stepwise register (
N = 999, only women) could not find any differences between patients with or without DERS registration regarding variables such as age, body mass index, eating disorder characteristics and psychiatric comorbidity (see Monell et al.
2018). The fact that the present study largely shares this sample makes it plausible that this should be true for this study as well. Another related aspect concerns the nesting of individuals within clinics that could potentially have influenced the results. It is however important to note that sensitivity analyses were run that controlled for the nesting of observations within units and results were not altered materially from those presented.
Second, although the bifactor model comes with certain advantages, some concerns have also been raised. The bifactor model has shown tendencies to outperform other models in terms of fit statistics, possibly due to statistical bias, making model comparisons and interpretation of fit indices more challenging (Bonifay et al.
2017; Murray and Johnson
2013). It is also important to note that the differences in fit between some of the factor models were not substantial and even though the bifactor model provided an adequate fit to the data in the current sample, we cannot know for sure if this is a valid representation of the structure in the population. Additionally, conceptual concerns have also been raised regarding fitting bifactor models to psychological constructs, for example related to interpretation of the meaning of a bifactor structure (Bonifay et al.
2017; Murray and Johnson
2013). Notwithstanding these concerns, as pointed out by Bonifay et al. (
2017) and others, the bifactor model still might be of importance for the development and evaluation of measurements given that it offers a unique opportunity to calculate various useful indices (e.g., OmegaH, EVC) that can assist in determining whether a measure has an acceptable true score variance and the extent to which subscales scores are reliable after accounting for the general factor.
Our study did not examine validity by comparing the DERS with other established emotion regulation measures. It should, however, be noted that the validity of the DERS has previously been examined by associations with other relevant self-assessed variables in both nonclinical and clinical populations, including eating disorders (e.g., Brockmeyer et al.
2014; Monell et al.
2015; Svaldi et al.
2012). It would be most informative if future research also makes efforts to establish the validity of the DERS in other ways than merely studying correlations among self-report assessments, for example, using data from multimethod and longitudinal studies or experiments to assess the evidence of criterion relevance, discriminative validity and responsiveness to treatment among individuals with different eating disorders.
The sample in the current study was limited to treatment seeking adults (mostly females) with eating disorder without any reference group (e.g., normal controls). This, naturally, makes it difficult to determine the extent to which these results can be generalized to other clinical and nonclinical populations. Also, regarding measurement and structural invariance for diagnostic subgroups, the present study is limited to comparisons between individuals with anorexia nervosa and bulimia nervosa. For future research, the final bifactor solution needs replication as this is the first study to test that specific model in a clinical sample of eating disorder. There is also a need to broaden the investigation between subgroups to different kinds of eating disorders as well as comparisons between subgroups defined in other ways than by the diagnostic manual. The eating disorder diagnostic criteria have been criticized (e.g., Forbush and Wildes
2017) due to the overlap of behavioral symptoms and the fact that individuals with eating disorders tend to move between different sub-diagnoses over time. The findings of this study also warrant replication in a sample of younger patients.
Conclusions
The present study is the first to examine the factor structure of the DERS-36 and DERS-16 in a large clinical eating disorder sample, and to test a bifactor model as well as measurement invariance across diagnostic subgroups in this population. Results indicated good fit for a bifactor model with permitted correlations between the subscales Awareness and Clarity. Analyses of reliability suggest that the DERS-36 and the DERS-16 total scale are reliable, while results from the reliability analyses of the subscales varied. The interpretation of some specific subscales is less clear as most of the reliable variance accounted for by these subscales overlapped with the variance accounted for by the general factor. The general factor also accounted for most of the variance in eating pathology and none of the subfactors had a statistically significant contribution on their own. Finally, results also showed that criteria for strong measurement invariance were met, indicating that meaningful comparisons of the eating disorder subgroups (i.e., anorexia nervosa and bulimia nervosa) using the DERS are possible. Taken together, findings indicate that the use of the total scale can be recommended when administered in an eating disorder population.
Acknowledgements
Open access funding provided by Linköping University.
Compliance with Ethical Standards
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.