Essays 2019: Multivariate Analysis with a Dependent Approach in Management Sciences as a Basis for Innovation
Keywords:
multivariate analysis, independent approach, management, innovationSynopsis
The present work, Essays 2019: Multivariate Analysis with a Dependent Approach in Management Sciences as a Basis for Innovation, aims to bring together a series of essays developed by students of the Doctorate in Management Sciences (DCA) at the University Center for Economic and Administrative Sciences (CUCEA) of the University of Guadalajara (UdeG), based on what they learned in the Quantitative Research I course. These essays are primarily aimed at conducting a dissertation exercise that either strengthens the argumentation of their thesis in the methodological part or contributes to the subject matter. For both cases, the relevance of their writing from the introduction to developing the concepts and/or models justifying the basis of the opposing points to be discussed is highlighted as the foundation for conducting the discussion that clarifies the expected contribution. Finally, essential concluding points are presented to serve both the reader and the presenter for further studies.
Thus, this work is divided into ten essays, where the first essay: Statistical Power Calculation: Why is its calculation not common in scientific research? addresses the concepts of statistical power and its historical use in research. Its main contribution is the discovery of schools that have dealt with it and the scope of its consideration.
The second essay: Obsolescence of Quantitative Methods: Do past research lose their validity with the introduction of more refined methods? reflects on the methods that allow for more refined tests, revealing possibilities of inadequacy in demonstrating the obsolescence and validity of a model. It interestingly concludes on the convenience of considering the future application of refinement methods.
The third contribution: Considerations in Quantitative Methodologies for Economic-Administrative Sciences with the use of Multiple Linear Regression compiles knowledge on the application of linear regression and its usefulness in administrative sciences, suggesting more pros than cons, with its use being a guarantee of variable verification in model testing.
In the fourth section: Fuzzy Logic, Multiple Regression, Artificial Neural Network for use in Administrative Sciences. The main techniques for making projections in model treatment are presented, along with their advantages and disadvantages. It concludes with a description of the scope of each technique in order to select the most convenient one for research in management sciences.
In the fifth essay: Propensity Analysis in Social Sciences, there is an option for data analysis that is preliminarily entering administrative sciences given its initial application in health and social sciences. It closes with an interesting pros and cons table of this technique and reflections on its use in administrative sciences.
The sixth work: Simple Linear Regression, a current technique for obtaining results in quantitative research, allows visualizing the condition of using this relevant statistical technique through a bibliometric analysis. It concludes with the author's views on the advantages and disadvantages of the technique in order to consider them in future studies.
The seventh contribution: Specificities, Limitations, and Particularities of Logistic Regression in Administrative Sciences, refers to a comparison of the most extended uses of both techniques, concluding with recommendations for their application in administrative sciences research.
The eighth essay: Approaches and validity of bibliometric analysis as a simple linear regression tool to show the relationship between tourism and administrative sciences, demonstrates the author's ability to find the relationship between the use of regression technique with tourism and administrative sciences using traditional bibliometric techniques and those based on Leiden indicators. It provides concentration maps by country and keywords.
The ninth work: The Statistical Regression Model in clear concepts, for its dissemination and application in the administration area, makes a contribution of the main concepts supporting the technique and recommendations for use.
Finally, the essay: Data analysis using either logistic regression and/or linear regression in disability studies which analyzes both techniques and their applications in inclusion issues reported at the country level and describes various related works.
It is the desire of the coordination of this work that it contributes to the reader's enthusiasm for knowing the projects that are developed and informing about the opportunities that are shown, in order to follow up on their evolution during their stay in the postgraduate program.
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December 30, 2019
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Copyright (c) 2019 Juan Mejía-Trejo
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ISBN-13 (15)
978-607-98782-6-9
doi
10.55965/abib.9786079878269.2019b
