# An Evaluation of Healthy Eating Scale for Patients with Pre-Diabetes Using Rasch Analysis
## 4 Discussion  
This study investigated the psychometric performance of a healthy eating habit scale using a sample of pre-diabetic people employing Rasch analysis. This paper fills a gap in the literature due to the lack of studies that provide comprehensive assessment of eating pattern scales. The validity of the category scoring system, the fit of individual questions, and an assessment of the possible propensity of ethnicity, gender, and age groups are all included in this current study. The results from the Rasch analysis suggested that the scale required minor modifications. Some items with disordered thresholds were handled by collapsing response categories. Even though a significantly better fit was not achieved, the best scale appeared to be a 3-point scoring structure. The fact that a better fit was not achieved by collapsing response categories gives further evidence to the notion that disordered thresholds may not indicate a misfit of the data to the model, and it remains controversial [55]. The value of PSI which is analogous to [PERSON]'s alpha [49] was 0.57 in the initial model which indicates a problem with the scale. The PSI represents the power of the latent variable to discriminate among persons. A non-satisfactory PSI means that the top measure  
Figure 4: Persons, items: uncentralized thresholds. cannot be reliably distinguished from the bottom measure with confidence [49], essentially reflecting the ability to discriminate between different levels of the construct [59]. There are two types of DIF that RUMM2030 tests for, uniform and non-uniform. Uniform DIF occurs when the group exhibits consistent differences in their responses on an item [59]. According to [PERSON] and [PERSON] [49], items with significant DIFs could be removed because they may reflect misfit in the model. The scale showed no evidence of DIF by age and gender, but there was evidence of uniform DIF in item 6 (\"two serves of milk\") for the ethnicity variable. Table 4 shows that more than half of non-Chinese participants drink two serves of milk daily on most days, whereas 13/20 Chinese participants answered \"Rarely\" or \"Never\". This is aligned with findings from another study where the author found out that Chinese consumption of dairy products is relatively low compared to other countries [63]. This may be due to the fact that high proportion of Asians are lactose-intolerant [64]. The Rasch model showed some good psychometric characteristics such as good person and item fit, and good item-trait interaction statistic (\(\chi^{2}\)). There were some concerns about the low person separation index (PSI), the unidimensionality, and targeting of the scale. A low PSI may indicate that the power of the test to distinguish misfit between the responses and the model is weak. The issue with targeting, with the many participants at the high end of the scale indicating higher levels of persons with the trait of healthy eating, might be a result of social desirability. The tendency for people to present themselves in a generally favorable fashion could be mitigated by having a self-report scale rather than an in-person interview. The strength of this study is that Rasch analysis was used to evaluate the Rapid Eating Assessment for Participants (REAP), with Rasch analysis considered the gold standard with which the validity of scales can be evaluated [49] and overcoming limitations of the classical test theory approach. A limitation of this study is that it uses the dataset from [PERSON] et al. 's study in 2007 [27], which might not be up to date. A study conducted by Roy Morgan Research in 2017 [65] showed that Australians had changed their attitudes to food between 2006 and 2016 and some changes were worrisome. For instance, fewer people were concerned about their cholesterol level (36.8% in 2006 and 31.1% in 2016) and fewer people considered low-fat diet as a way of life (37.9% in 2006 and 29.9% in 2016). The investigation would be improved by obtaining a more recent sample.