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Table 3 How to score each component of the IFLQ-19

From: Development, validation and item reduction of a food literacy questionnaire (IFLQ-19) with Australian adults

How to score each component of the IFLQ-19
1) Identify the raw score for each item: The final IFLQ-19 can be seen in Table 4. The response categories for each item are listed in the second column (Response categories (score)) and each response category is allocated a value. For example, Q1_1.1.1 has five response categories, where strongly disagree is valued at 1, disagree is valued at 2, neutral valued at 3, agree valued at 4 and strongly agree valued at 5
2) Sum the raw scores for items under each statistical component: All items listed under the statistical component headings need to be scored, where the response category to each item needs to be totaled. For example, in statistical component 1.1.1, items 1_1.1.1 through to 5_1.1.1 will be summed where a total of 5 to 23 can be achieved. This is the raw score
3) Determine the re-scaled score: Download the Appendix 3 document, and under column B, find the raw score for the statistical component achieved by the respondent to obtain their re-scaled score. For example, if an individual obtained a raw score of 5 (Column B, Row 4), the re-scaled score would be 10. Please note, raw scores and re-scaled scores are different for each statistical component
4) Interpreting the re-scaled score:
 a) If administered at a single time point, the rescaled score can be used to rank individuals on their level of food literacy for a given statistical component. It can also be used to compare respondents. For example, respondent one’s rescaled score of 10 (Column D, Row 4) compared to respondent two’s rescaled score of 43 (Column D, Row 13) suggests respondent two has a higher level of the latent trait (the statistical component of food literacy) compared to respondent one
 b) If administered across multiple time points, it can be used to represent an individual’s progress toward the maximum possible score on the statistical component, which provides a more accurate representation of the change. For example, if respondent one’s rescaled score changed from 10 (Column D, Row 4) to 38 (Column D, Row 9), this indicates a four-fold increase in the statistical component of food literacy
 c) Rescaled scores can also be compared to other variables. For example, a correlation with participant socio-demographics and statistical components of food literacy
5) For consideration: Further research is required to determine whether a score on one statistical component represents an equivalent score on another. In its current form, the IFLQ-19 is not designed for rescaled statistical component scores to be summed to obtain a score at the theoretical component, domain or total food literacy level