OPTIMAT Lund: Anonymised quantitative survey data on students’ perceptions of climate-friendly school meals
Description
This dataset contains anonymised quantitative survey data from the OPTIMAT Lund study, a mixed-methods study examining students’ perceptions of climate-friendly school meals. The study was exploratory and descriptive in nature and did not test a specific hypothesis. Instead, the quantitative data were collected to describe students’ reported attitudes, preferences, and perceptions related to school meals, including responses to climate-friendly menu changes. Data were collected from students in a school setting as part of the broader OPTIMAT project. Survey items captured students’ views on school meals, acceptability of menu changes, food preferences, and factors influencing meal choices. The dataset has been anonymised before publication, and variables that could identify individual participants or schools have been removed or recoded where necessary. The data can be used to understand patterns in students’ responses to climate-friendly school meal initiatives and to support interpretation of the associated article. The dataset should be interpreted alongside the published manuscript, which also includes qualitative findings from student qualitative sessions. Raw qualitative transcripts are not included in this dataset because they involve schoolchildren and contain contextual information that could compromise participant confidentiality even after anonymisation. Users of the dataset should note that the data are intended for descriptive analysis and contextual interpretation rather than causal inference. The dataset may be useful for researchers, school meal professionals, and policymakers interested in sustainable school meals, student acceptability, and food choice in school settings.
Files
Steps to reproduce
The dataset can be used to reproduce the descriptive quantitative results reported in the associated manuscript. 1. Download the anonymised quantitative dataset and accompanying variable/codebook file. 2. Open the dataset in a statistical software package such as SPSS, Stata, R, Excel, or similar. 3. Use the variable/codebook file to identify item wording, response categories, missing-value codes, and any recoded variables. 4. Exclude missing or non-valid responses according to the missing-value codes described in the codebook. 5. Calculate descriptive statistics for each survey item, including frequencies and percentages for categorical variables. 6. Where relevant, reproduce subgroup summaries using the grouping variables provided in the dataset. 7. Compare the resulting descriptive statistics with the tables and quantitative results presented in the associated manuscript. No specialised software, custom code, or model-based analysis is required to reproduce the descriptive results.
Institutions
- University of GothenburgVästra Götaland, Gothenburg