Food addiction (FA) has received considerable attention in both laboratory and clinical research [1]. This concept refers to the idea that some foods, especially those with dense calories, heavy processing or high palatability, may promote addictive consumption. FA may be considered as a kind of behavioral addiction (related to eating) or an eating problem which may not constitute a psychiatric disorder. FA may overlap with binge-eating disorder, night-eating syndrome, bulimia nervosa or other conditions [2, 3]. A hypothesis that an addictive process related to neural features may contribute to excessive eating is an underlying conceptual feature of FA [4]. However, FA is not classified as a formal diagnosis in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), although it has been discussed as a possible psychiatric disorder [5]. People with FA often express symptoms such as considerable distress in relation to specific foods, eating more food than planned, eating more than needed to relieve hunger, feelings of lost control over food intake, unsuccessful attempts to reduce eating particular foods, and diminished interests in participating in some experiences due to fear of overeating [6, 7]. FA has been associated with multiple mental disorders including anxiety, depressive, attention-deficit/hyperactivity, post-traumatic stress and binge-eating disorders [7].

To date, there are limited data on the prevalence of FA globally. However, some general-population studies suggest that between 4 and 10% of people may experience FA, and it is more prevalent in females than males [2, 3]. Its prevalence among people with overweight or those seeking weight-loss may range between 16 and 30%, higher than in general populations [8]. A study conducted in college students in China revealed that nearly 7% of participants may have experienced mild to severe FA [9]. However, since nearly two-thirds of the world population has overweight/obesity, the role of FA as a contributor should be investigated and addressed [10]. The prevalence of obesity in China has risen from 3 to 8% during the last decade, and currently more than 90 million people live with obesity in this country [11].

Having a psychometrically sound instrument to measure FA is important for detection and timely intervention [12]. Historically, few instruments have been available to assess addictive eating behaviors, and most were not comprehensive for evaluating different aspects of addictive eating tendencies [13]. Consequently, the Yale Food Addiction Scale (YFAS) was developed to address this concern and revealed acceptable psychometric properties across different translations into languages including French, Italian, Persian, Chinese and Turkish [13,14,15,16,17]. An updated version of the YFAS (i.e., YFAS 2.0) was published in 2016. The YFAS 2.0 included four essential criteria to diagnose FA based on DSM-5-related criteria for substance-use disorders [18]. These included craving, consumption despite negative social/ interpersonal consequences, failure to perform role obligations, and consumption in physically dangerous settings [5, 18].

The YFAS 2.0 is a commonly employed measure of FA and has 35 items that assess 11 indicators of addictive behaviors, distress, and related clinical impairment [3]. An abbreviated version of this measure, the modified YFAS 2.0 (mYFAS 2.0), includes 13 items and is available for use as a short screening measure to assess FA [19]. Although, perhaps due to fewer questions, the mYFAS 2.0 is a less sensitive instrument than the YFAS 2.0 to measure addictive eating, it has demonstrated appropriate validity and reliability in several studies [9, 19, 20]. Having a valid and reliable instrument with fewer questions may help reduce burden on respondents during the screening process and can be time-saving for both participants and researchers [14]. Nevertheless, both scales currently serve as standard measures of FA with relatively similar results.

Although these instruments have been validated in many languages with acceptable psychometric properties, the validation process was mostly done based on traditional approaches using classical test theory (CTT) methods. In this approach, the quality of an item is assessed by the degree of the association between participants’ response pattern for that item and their scores for all items [21]. However, there are some shortcomings to this approach including test and sample size dependence, considering equal weights for all items while there may be differences in the difficulty levels between items, and using a constant standard error of measurement and ordinal values to compute total scores [22, 23]. These factors may influence accurate measurements. In contrast, the Rasch model applies a modern item response theory and has been recognized as a gold standard in validation processes. Thus, it may resolve many CTT-related shortcomings [21].

The Rasch model allows researchers to critically evaluate scales using parametric tests by transforming categorical data into quantitative data [24]. In the Rasch model, a scale is examined against a mathematical measurement model that clarifies what should be in the item responses using interval-based measures. The interval data versus ordinal values provide more robust and accurate findings on the structural validity and objectivity of the scale [21]. The model contains more quantitative information and a continuous scale of measurement compared to a CTT approach and assumes that each individual has a fixed latent tendency along with each item with a particular fixed difficulty [25]. The Rasch model also will help assess the unidimensionality of both the YFAS 2.0 and mYFAS 2.0, and both instruments have been found to be unidimensional [20, 26,27,28,29,30,31,32,33,34,35,36,37,38]. Moreover, both the YFAS 2.0 and mYFAS 2.0 were expected to create a single factor structure of FA to differentiate between those with or without FA [18, 19]. Therefore, we may confirm this feature by Rasch model indicating the appropriateness of the scale for such measurements. Therefore, examination of the factor structure of this scale using traditional methods like exploratory factor analysis may not be particularly helpful.

The differential item functioning (DIF) or item bias in subsamples also may be assessed using Rasch analysis. The presence of DIF suggests that the likelihood of a correct response among people who are assumed to be test-taking with equal abilities, in subgroups based on gender, race/ethnicity, income and other variables, may be different. Thus, DIF provides negative evidence for the validity of a scale across groups [24, 39].

A psychometrically sound scale should ideally be examined using various statistical techniques to provide greater empirical evidence supporting its validity [12]. To the best of our knowledge, Rasch analysis has not been previously used to assess the YFAS 2.0 and mYFAS 2.0. Thus, the current study aimed to assess the psychometric properties of the scales using this modern approach. Further, differential responses among groups based on gender and body mass index (BMI) were investigated with the hypotheses that both scales would demonstrate validity across groups.