A sample of 1,068 UK adults started the survey. The sample was recruited via Prolific (www.prolific.com) to closely mimic one that is representative of the UK population. To recruit a sample that approximates representativeness, Prolific uses data from the UK Office of National Statistics, and matches participants to the national population as closely as possible on age, gender, and ethnicity. We removed the data of 8 participants who gave consent to partaking but did not consent to the storage of their data, as well as 40 participants who only filled in the consent form and nothing else. We excluded a further 33 participants from data analysis due to incorrect responses to (one or both) attention check questions (e.g., Please select agree). The final sample consisted of 987 participants (463 males, 505 females, 2 participants indicated that they did not wish to share their sexFootnote 2), ages 18–86, M = 45.21, SD = 15.61. Seventeen participants only partially completed the survey, and their demographics details were thus missing. Participants were recruited via Prolific Academic and reimbursed £7.50 for their time. Across some of the analyses we were interested primarily in the responses of young adults, and hence completed them by including only the 375 participants who were aged 18–39 (M = 28.56, SD = 6.39, 184 males, 191 females).

Data was collected as part of a larger project in November, 2021 (see for further details: [9]. We created an online survey using Qualtrics software. Participants were first presented with an informed consent form and information sheet detailing their tasks throughout the study. They next completed several psychometric questionnaires, including measures focusing on the assessment of eating disorders and body dysmorphia described below. All scales were presented in a randomized order across participants. Finally, participants responded to demographic questions (sex, gender, age, ethnicity), were debriefed and thanked for their time.

Eating disorders were assessed using the 12-item short version of the EDE-QS [10], the 5-item SCOFF questionnaire [11], and the 22-item eating disorder diagnostic scale (EDDS, 23,24).

The EDE-QS [10] was completed by 972 participants. Participants responded to 10 items of the EDE-QS (e.g., On how many of the past 7 days have you had a definite fear that you might gain weight? ) on a 4-point scale with response options 0 = 0 days, 1 = 1–2 days, 2 = 3–5 days, 3 = 6–7 days; and to two items (e.g., Over the past 7 days, how dissatisfied have you been with your weight or shape? ) on a 4-point scale with response options 0 = not at all, 1 = slightly, 2 = moderately, 3 = markedly. Participants’ responses were summed, with higher scores indicating an increased presence of characteristics of eating disorders.

The SCOFF [11] was completed by 975 participants. Participants completed 5 items of the questionnaire (e.g., Do you make yourself sick because you feel uncomfortably full? ) using binary yes/no responses. We scored ‘yes’ responses as 1 and ‘no’ responses as 0, and summed participants’ answers, with higher scores indicating a greater likelihood for the presence of eating disorders.

The EDDS [26, 27] was completed by 974 participants. The 22 items which participants completed included a variety of response methods, e.g., questions asked participants to enter their weight and height, to respond to binary questions with yes/no responses (e.g., During the times when you ate an unusually large amount of food, did you experience a loss of control (feel you couldn’t stop eating or control what or how much you were eating)? ), or to respond to 15 point scales (e.g., How many times per week on average over the past 3 months have you made yourself vomit to prevent weight gain or counteract the effects of eating, with response options between 0 and 14), among others. We used existing code [27] to calculate index scores (raw eating disorder composite score and Z-transformed eating disorder composite score) based on participants’ responses, where higher scores indicate a greater likelihood for the presence of eating disorders. Note that as a diagnostic tool this scale corresponds directly to the DSM-IV rather than the DSM-V diagnostic criteria of eating disorders.

Body dysmorphia was assessed using the 4-item body dysmorphic disorder questionnaire (BDDQ, 25) and the 7-item DCQ [12]. The BDDQ [28] was completed by 997 participants. This scale is made up of four core questions, where each question is presented based on participants’ previous responses (e.g., the question ‘Is your main concern with how you look that you aren’t thin enough or that you might get too fat?’ is only presented if a participant responds ‘yes’ to the question ‘Are you worried about how you look?’). This scale functions as a diagnostic tool for eating disorders. Following the scoring guidelines, we coded participants either as being at risk of an eating disorder (coded 1, overall sample: N = 183 out of 987; young adults: N = 109 out of 375) or not (coded 0).

The DCQ [12] was completed by 977 participants. Participants responded to the 7 items of the DCQ (e.g., Have you ever been very concerned about some aspect of your physical appearance? ) on a 4-point scale with response options 0 = not at all, 1 = same as most people, 2 = more than most people, 3 = much more than most people. Participants’ responses were summed, with higher scores indicating increased body dysmorphia.

Weight bias internalisation was assessed using the 11-item [14] and 3-item [13] versions of the WBIS. The scales were completed by 978 participants. Participants responded to the items (e.g., I hate myself for my weight) on a 7-point Likert scale with response options ranging from 1 = strongly disagree to 7 = strongly agree. Participants’ responses on selected items were reverse scored and all scores were summed in a way that higher scores reflect increased weight bias internalisation.

Depression, anxiety, and psychological distress were also assessed as part of the survey, though these scales are examined in detail elsewhere [9]. The 10-item K10 scale and the 6-item K6 scale embedded in it [29], the 9-item version of the Malaise Inventory [30, 31], the PHQ-9 [32, 33], PHQ-2 [34], GAD-7 [35], and GAD-2 [36] were included (see the Supplementary Materials for further details).

We used MPlus version 8.7 [37] to explore measurement properties with a latent variable modelling approach. To test the latent structure of each self-report measure we used confirmatory factor analyses with a robust mean and variance adjusted weighted least squares (WLSMV) estimator, with either a model for binary (Yes vs. No responses) or ordered categorical data (questionnaires with multiple ordered response options) depending on the type of responses used for each scale. Because each of the self-report questionnaires which we focus on here have well-established factor structures, we relied on confirmatory factor analyses. We used the root mean square error of approximation (RMSEA, [38]), the comparative fit index (CFI, [39]), and the Tucker-Lewis Index (TLI, [40]) to determine model fit. We interpreted RMSEA values up to 0.05 as indicating good fit, and values up to 0.08 as indicating adequate fit [41]. In the cases of CFI and TLI, we interpreted values greater than 0.90 as indicating adequate, and those greater than 0.95 as indicating good model fit [42].

Finally, we plotted test information functions (TIF) to evaluate the precision of measurement of the self-report questionnaires using MPlus version 8.7 [37]. TIF plots illustrate Fischer information - i.e., an indicator of the precision or reliability of the measure due to their inverse relationship with the standard error of measurement - at different levels of the underlying latent variable [43]. All analyses exploring the properties of the self-report questionnaires were conducted on the complete sample as well as on the young adult subsample.

We aimed to optimise two of the eating disorder measures, the EDE-QS and SCOFF, and two of the body dysmorphia measures, the DCQ and WBIS, by shortening them using item response theory. The diagnostic measures, the EDDS and BDDQ, served instead as measures against which we could validate the emerging results. We relied on the factor analyses conducted for the EDE-QS, SCOFF, DCQ, and WBIS to examine their general properties. Our approach was to take a small number of items which load the highest on the underlying factors (i.e., those with the highest discrimination parameter, ideally three items) to create the short scale, while ensuring that the TIF remains as similar as possible to that of the original scale and that internal consistency also remains optimal.

As the measures included in the present study may be used to screen clinical populations, certain items may provide limited information in the general population despite being important in clinical samples. As here we aimed to develop short measures for use in nonclinical samples, we additionally took into consideration the thresholds related to the items. This way, we attempted to avoid the inclusion of any items which may be less informative in the target sample. Where item thresholds were very high, thus resulting in low item endorsement and, subsequently, low variability in a general (not clinical) population like that of MCS, lower item loadings but thresholds closer to the centre of the distribution of latent factor scores were preferred. Unless otherwise noted, the thresholds did not suggest that items should not be retained.

To determine whether the measurement properties of the scales were equivalent across sex and age groups, we used a measurement invariance testing strategy. To compare ages, we split the sample according to younger adults (18–39 years) and older adults (40 + years), as in the previous analyses. We tested measurement invariance to explore any potential bias within the self-report questionnaires across sexes or age groups caused by measurement error [18, 19, 44, 45]. We conducted the analyses across four groups (sex * age: younger males, older males, younger females, and older females). We used a WLSMV estimator and tested two levels of invariance: configural invariance, without constraining any measurement parameters to be equal across the groups, and scalar invariance, where the items’ loadings as well as their thresholds are constrained to be equal across the groups. We compared the goodness-of-fit indices of the two models. Since the chi-square difference test is very sensitive to sample size, invariance was also informed by additional fit indices. Models where the loss of fit was less than 0.01 for CFI and 0.015 for RMSEA met the criteria for invariance [46, 47]. These analyses were conducted using MPlus version 8.7 [37].

Note that this type of strategy could not be implemented in scales with three or less items, since in those cases the configural model is just-identified at best, thus resulting in non-meaningful goodness-of-fit indices that cannot be compared to those from models with invariance constraints. It was thus not possible to test measurement invariance in the short versions of the scales comprised of only three items. We performed the analyses on the 12- and 5-item EDE-QS, 5-item SCOFF, 7-item DCQ, and 11-item WBIS scales. This allowed us to detect potential differences in the measurement properties of the larger scales that may impact the shorter versions.

We first explored scale properties by examining descriptive statistics. To test whether any differences exist in the sample on key measures among sex and age groups (i.e., 18–39 year olds vs. 40 + year olds), we ran independent samples t-tests. We also conducted 2 × 2 ANOVAs to explore any interactions across sex and age groups. The two participants who did not disclose their sex were excluded from the analyses where sex differences were tested. We used SPSS 27.0 to conduct these analyses. We used the Omega macro for SPSS [48] to test the internal consistency of the scales with McDonald’s omega total (ωt) coefficient [49].

We conducted bivariate correlations between the long and short versions of the eating disorder and body dysmorphia, and between these measures and those of depression, anxiety, and psychological distress. This way, we were able to explore the equivalence in the rank ordering across the measures, along convergent and discriminant validity.