Open AccessMixed Second Order Indicator Model: The First Order Using Principal Component Analysis and The Second Order Using Factor Analysis
Author(s) -
Benny Hutahayan,
. Solimun,
Adji Achmad Rinaldo Fernandes,
Armanu,
Indah Yanti,
Ani Budi Astuti,
. Nurjannah,
Luthfatul Amaliana
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/546/5/052073
Subject(s) - principal component analysis , order (exchange) , latent variable , goodness of fit , mathematics , variable (mathematics) , econometrics , structural equation modeling , formative assessment , statistics , economics , mathematical analysis , finance
The second order indicator model can be the first order having formative or reflective indicators of an underlying second order. The research used principal component analysis in the first order and factor analysis in the second order. The variable used in the research was ihsan behavior. This research aims to apply multivariate analysis, i.e. the principal component analysis in the first order and the factor analysis in the second order to obtain the latent variable data of ihsan behavior in the second order indicator model. The data used in this research were primary data by distributing questionnaires. Respondents of this research were lecturers of the Faculty of Economics and Business at the University of X. The research results generated latent variable data in the form of ihsan behavior. Ihsan behavior was reflected in six indicators, i.e. doing something perfectly, repaying goodness with more goodness, reducing optimally unpleasant consequences, as a solution when justice cannot be realized, as a logical consequence rather than faith, and as an investment in future success.