Open AccessStructural equation modelling on Latent Variables to identify farmers satisfaction in East Java using Mixed-Scale Data
Author(s) -
Adji Achmad Rinaldo Fernandes,
. Solimun,
Retno Ayu Cahyoningtyas
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1872/1/012022
Subject(s) - gratitude , structural equation modeling , java , latent variable , statistics , scale (ratio) , variables , psychology , metric (unit) , mathematics , econometrics , computer science , social psychology , geography , engineering , operations management , cartography , programming language
Structural Equation Modeling (SEM) which is able to predict structural models as well as measurement models simultaneously and be able to accommodate the form of reciprocal relationships. This research is to develop structural modeling with metric and nonmetric measurement scales. In addition, this study aims also to identify satisfaction of farmer in East Java. The variables used were demographic variables, gratitude variable, and satisfaction farmer. The data used are primary data with respondents who are Farmer in East Java using proportional area random sampling. Measurement method used is the main component score method and the factor analysis. The number of respondents in this study were 155 districts in East Java Province. The results is a significant positive effect between the gratitude variables on the farmer satisfaction variable. That is, the more gratitude felt by farmers, the satisfaction will be increased by East Java Farmers. On the other hand, the test results showed that demographic variables did not significantly influence gratitude variables. The coefficient of total determination (R 2 ) is 0.8714, meaning that the diversity of data (farmer satisfaction) that can be explained by the model in the first order analysis is 87.1% while the remaining 12.9% is explained by other variables not contained in the model.