Mixed Second Order Indicator Model: The First Order Using Principal Component Analysis and The Second Order Using Factor Analysis

9th Annual Basic Science International Conference 2019

自然科学（总论）

Hutahayan, Benny^1 ; Solimun^2^5 ; Rinaldo Fernandes, Adji Achmad^2^5 ; Armanu^3 ; Yanti, Indah^4^5 ; Astuti, Ani Budi^2^5 ; Nurjannah^2^5 ; Amaliana, Luthfatul^2^5

Department of Bussiness Administrative, Faculty of Administrative Sciences, University of Brawijaya, Malang, Indonesia^1

Department of Statistics, Faculty of Mathematics and Natural Sciences, University of Brawijaya, Malang, Indonesia^2

Department of Management, Faculty of Economy and Bussiness, University of Brawijaya, Malang, Indonesia^3

Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Brawijaya, Malang, Indonesia^4

Kelompok Kajian Unggulan Pemodelan Statistika di Bidang Manajemen, Faculty of Mathematics and Natural Sciences, University of Brawijaya, Malang, Indonesia^5

关键词: First order;    Latent variable;    Logical consequences;    Multi variate analysis;    Primary data;    Research results;    Second orders;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/546/5/052073/pdf
DOI  :  10.1088/1757-899X/546/5/052073

学科分类：自然科学（综合）

来源: IOP

【 摘 要 】

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.