Food waste of Visiting Friends and Relatives Dataset

Published: 31 July 2023| Version 1 | DOI: 10.17632/mg756z68hn.1
Kangrong Chen


The research article “Referring the Food Waste by Over-Ordering Behavior in Visiting Friends and Relatives Sector on Behavioral Reasoning Theory in China” explores the food waste reduction from Visiting Friends and Relatives in China. Data were collected by outsourcing the sampling to, an online survey platform, and had 396 effective questionnaires from Mainland China. The survey company conducted a random sampling method following the requests and carried out the random sampling in its huge population database, like Toluna in the USA. The target populations are those who had entertained guests of visiting friends and relatives within 3 years. The questionnaire for survey has been tested for reliability and validity to ensure the appropriate measurements of constructs in the research model. A statistical modeling software of Mplus was applied to structural equation model (SEM) with dataset for confirmative factor analysis (CFA) and hypotheses examination. The outcomes indicated that the dataset fits to be repeatable research for the same or similar subjects.


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Table 1 displays the methods from theoretical model development to statistical analysis. In phase 1, many research methods for the same subject of food waste from literature were screened and the appropriate was selected for this study. This gave the direction for the experimental research. In phase 2, the model and hypotheses were developed based on the previous phase. In phase 3, the measurements for all variables in the constructs were designed, considering the fitness for model and food waste conventions. The sampling mothed of random sampling and sampling platform were chosen in phase 4 and 5. After the data collection, the raw data was tested through validity and reliability in phase 6 and the structure equation modeling analysis was following in phase 7. The statistics processing software was picked as Mplus. Table 1 The experimental design process Process Design Criteria or purpose Phase 1 Literature Review Method reference Phase 2 Model and hypotheses Theoretical support Phase 3 Measurement scales development Reliability and validity analysis Phase 4 Sampling method: Random sampling Representative of the population Phase 5 Sampling platform: Sample size Phase 6 Data curation Valid data through SPSS Phase 7 Data analysis methods: SEM Hypotheses proof and model fitness test Phase 8 Statistics software: Mplus Statistical support During these phases, the raw data was generated through a survey online that collected 446 samples randomly on 396 samples were filtered. Strictly, every single mark from the raw data was scanned and remove those have same marks or other problems. In terms of demographics, the gender was balanced by 44.7% of male and 55.3% of female that the ratio of gender was 0.8:1. In terms of age, the largest group was age 31-40 by 40.2%. Following was the age 26-30 by 32.6%. These two main age groups accounted for over 72% of the whole groups. Looking at education background, 87.3% was undergraduates or junior college. Turn to occupation, 68% was working in private companies, while there were no soldiers or retired people. About incomes earned per month, 46% of respondents were in the group of 5001-10000 RMB/month, and 23% belonged to the group of 10001-15000 RMB/month. Totally, 67% of respondent economically can afford food to entertain VFR guests. The raw data of Excel format was then transformed into a dat. format by SPSS for reliability and validity test. Before CFA was conducted, the Kaiser-Mayer-Olkin (KMO) was 0.962, which was much greater than 0.6. The p value of KMO was 0.000 which was significant. This dataset was suitable for CFA to examine convergent validity and discriminant validity. Generally, the good-of-fitness of a model can be checked by the criteria below in Table 3. All these tests above are proved that the data are suitable and ready for SEM as measurement model and structural model to examine the hypotheses.


City University of Macau


Tourism, Hospitality, Food Waste