Keith, T. Z. (2015). Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling (2nd ed.). New York, NY: Taylor & Francis.

2016 ◽  
Vol 53 (2) ◽  
pp. 248-250
Author(s):  
Todd M. Milford
Keyword(s):  
New York ◽  
Structural Equation Modeling ◽  
Multiple Regression ◽  
Structural Equation ◽  
Equation Modeling

Which E-Lifestyle Avoids Internet Advertising More?

2014 ◽  
Vol 5 (4) ◽  
pp. 22-36
Author(s):  
Amir Abedini Koshksaray ◽  
Kambiz Heidarzadeh Hanzaee
Keyword(s):  
Structural Equation Modeling ◽  
Hypothesis Testing ◽  
Multiple Regression ◽  
Structural Equation ◽  
Research Data ◽  
Internet Advertising ◽  
Equation Modeling ◽  
Modeling Approach ◽  
First Time

This study aimed at finding out which e-lifestyles avoid internet advertising more. To this aim, a survey was conducted on 412 students working with internet. Structural Equation Modeling approach was used for estimating the validity of research constructs and multiple regression was utilized for hypothesis testing. According to the findings, individuals with interest-driven e-lifestyle avoid from internet advertising more than others. Novelty-driven, importance-driven, sociability-driven, need-driven, entertainment-driven, and uninterested or concern-driven e-lifestyles avoid from internet advertising, respectively. This study has considered e-lifestyle's avoidance from internet advertising for the first time. It is the first attempt to investigate which e-lifestyle avoids internet advertising more. Also, it is the first study modifying research data according to the significant effect of “the average hours of using internet” and controlling and analyzing the effect of this variable.

scholarly journals MANUFACTURING AND CONTINUOUS IMPROVEMENT AREAS USING PARTIAL LEAST SQUARE PATH MODELING WITH MULTIPLE REGRESSION COMPARISON

2014 ◽  
Vol 2 ◽  
pp. 15-26 ◽  
Author(s):  
Carlos Monge Perry ◽  
Jesús Cruz Álvarez ◽  
Jesús Fabián López
Keyword(s):  
Structural Equation Modeling ◽  
Multiple Regression ◽  
Continuous Improvement ◽  
Structural Equation ◽  
Statistical Power ◽  
Scientific Research ◽  
Small Sample ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling

Structural equation modeling (SEM) has traditionally been deployed in areas of marketing, consumer satisfaction and preferences, human behavior, and recently in strategic planning. These areas are considered their niches; however, there is a remarkable tendency in empirical research studies that indicate a more diversified use of the technique.  This paper shows the application of structural equation modeling using partial least square (PLS-SEM), in areas of manufacturing, quality, continuous improvement, operational efficiency, and environmental responsibility in Mexico’s medium and large manufacturing plants, while using a small sample (n = 40).  The results obtained from the PLS-SEM model application mentioned, are highly positive, relevant, and statistically significant. Also shown in this paper, for purposes of validity, reliability, and statistical power confirmation of PLS-SEM, is a comparative analysis against multiple regression showing very similar results to those obtained by PLS-SEM.  This fact validates the use of PLS-SEM in areas of untraditional scientific research, and suggests and invites the use of the technique in diversified fields of the scientific research

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