Body Appreciation Scale (BAS-2): measurement invariance across genders and item response theory examination

Abstract

Background The present study considers a measure of positive body image, the Body Appreciation Scale-2, which assesses acceptance and/or favourable opinions towards the body (BAS-2). Potential variations of the psychometric properties of the scale across males and females, as well as across its different items invite for further investigation. The present study contributes to this area of knowledge via the employment of gender Measurement Invariance (MI) and Item Response Theory (IRT) analyses. Methods A group of 386 adults from Australia, Canada, New Zealand, Ireland, the United Kingdom, and the United States of America (USA) were assessed online (N = 394, 54.8% men, 43.1% women, Mage = 27.48; SD = 5.57). Results MI analyses observed invariance across males and females at the configural level, and non-invariance at the metric level. Further, the graded response model employed to observe IRT properties indicated that all items demonstrated, although variable, strong discrimination capacity. Conclusions The items showed increased reliability for latent levels of ∓ 2 SD from the mean level of Body Appreciation (BA). Gender comparisons based on BAS-2 should be cautiously interpreted for selected items, due to demonstrating different metric scales and same scores indicating different severity. The BAS-2 may also not perform well for clinically low and high BA levels. Thus, it should optimally be accompanied by clinical interviews for formal assessment in such cases.

Problematic Shopping Behavior: An Item Response Theory Examination of the Seven-Item Bergen Shopping Addiction Scale

“…Moreover, all IRT parameters showed interesting variability across items when considering different levels of shopping addiction risk. In line with previous literature, variations in α indicated that the GRM provided the optimal solution to fit the data (Marmara et al, 2021 ; Zarate et al, 2021 ). Much like previous literature investigating behavioral addictions, tolerance demonstrated the highest α highlighting the item’s ability to detect subtle changes in the risk of shopping addiction (Gomez et al, 2019 ; Kircaburun et al, 2020 ; Primi et al, 2021 ).…”

“…Subsequently, the best fitting model was determined based on Δ χ 2Loglikelihood (Gomez et al, 2021b ). Secondly, following past recommendations (Zarate et al, 2021 ), DIF statistics using Wald tests were obtained for all items with p < 0.05 as indication of non-invariance. Subsequently, to avoid increasing type 1 error, invariant items were anchored, and only non-invariant items were assessed.…”

“…Accordingly, IRT models can be estimated based on research needs, including “Rasch” models assuming equality constraints on α (Mellenbergh, 1994 ) or the graded response model assuming different α across items (GRM; Samejima, 1996 ). Although other models could be estimated (e.g., generalized partial credit, GPC; Muraki, 1992 ; nominal model for nominal and ordinal responses), the present study focuses on the Rasch and GRM due to its suitability for ordered polytomous items (Gomez et al, 2019 ; Marmara et al, 2021 ; Zarate et al, 2021 ).…”

Application of Item Response Theory (IRT)-Graded Response Model (GRM) to Entrepreneurial Ecosystem Scale

“…The IRT shows the relationship between an item and respondent, with the relationship having a certain level of latent traits with a probability that the respondent will endorse a particular item; this is shown in the ICCC or item characteristic curve-this curve represents the change in the probability of selecting an item as a function of three parameters, e.g., pseudo guessing (c), item difficulty (β), and discrimination (α) [29]. The differential capability of the items is represented by discrimination; in terms of item difficulty parameters, the easier items have lower (β) values, the rate of positive responses changes with an individual's latest trait (α), and pseudo-guessing (c) is the probability of guessing a right answer [30].…”

“…The policy variable asked respondents to what extent their national, state, and local government support and advocate entrepreneurship. This could be support of intellectual property, permissions for business, favorable regulations, taxes, and companies [29,30]. There are six items in this policy variable.…”