Abstract
The purpose of this research is to develop structural modeling with metric and nonmetric measurement scales. Also, this study compares the level of efficiency between the first order and second-order models. The application of structural modeling in agriculture is the satisfaction of farmers in East Java. The data used in this study are about perceptions by distributing questionnaires to farmers in East Java Province in 2020. The respondents in this study were 155 districts in East Java Province. Therefore, the sampling technique chosen is probability sampling, which is a proportional area random sampling. The results are obtained that the first-order model is better than the second-order model because it has the lowest MSE value and the highest R2. The results of the path analysis for the first order and second-order models produce the same results that there 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.
Highlights
Where the change of one variable is assumed to result in changes in other variables, besides it is related to the relationship between variables and the model of indicators using simultaneous [25]
Measurement of the variable of gratitude uses factor analysis. This is because the gratitude variable has a reflective indicator, where the variable is reflected by the indicators
The results of the path analysis for the first order and second-order models produce the same results that there is a significant positive effect between the gratitude variables on the farmer satisfaction variable
Summary
Where the change of one variable is assumed to result in changes in other variables, besides it is related to the relationship between variables and the model of indicators using simultaneous [25]. Based on the measurement process, variables can be categorized into manifest variables and latent variables [13]. The variable is determined as a variable that cannot be accessed directly, but the variable must go through indicators that reflect and build it [10]. In a study there is a hierarchy of latent variables that only have indicators as well as items that are often referred to as first-order indicator models. Latent variables that have indicators and items are named second. In a more complex hierarchy, latent variables have dimensions, indicators, and items so they are named third, and so on [5]
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