Management Sciences and Multivariate Analysis: Research Projects, Analysis, and Discussion of Results Volume II: Interdependent Techniques
Keywords:
Administración, Análisis Multivariante, Técnicas interdependientesSynopsis
With greater capabilities and availability of computing resources today, multivariate analysis is now featured in various software applications, increasing its use across multiple disciplines such as Administrative Sciences. Some of the most widely used models in academic and professional fields worldwide include Statistical Package for the Social Sciences (SPSS, by IBM), Analytics, Business Intelligence and Data Management (SAS, by SAS Institute and/or World Programming), Statistica (by STATISTICA), and the R language (open-source software), to name a few. It is not surprising that Administrative Sciences support academic development in various postgraduate programs as well as in the professional realm related to Social Sciences. Consequently, there is a growing trend in the presentation of reports, articles, book chapters, or books that discuss various theoretical-empirical aspects and their interpretation based on these software applications. In our case, we have adopted IBM's SPSS 20 for developing the topics in this book.
Based on the above, we present the work: Administrative Sciences and Multivariate Analysis under the approach of Interdependent Techniques. Research Projects, Analysis, and Discussion of Results. Volume II, with the recommendation to have previously read the first three chapters of Volume I. The main objectives are:
-
To present a document that serves both those familiar and unfamiliar with the topic, who need to understand the concepts discussed in this volume and manipulate the various commands offered by IBM's SPSS 20 regarding the problem cases presented as examples.
-
For better comprehension of the treatment of cases, the sequence proposed by Hair et al. (1999) of the six steps: objectives, design, assumptions, execution, interpretation, and validation, is used as the framework for presenting and solving these cases.
-
As the Coordinator of the Doctorate in Administrative Sciences at the Centro Universitario de Ciencias Económico Administrativas (CUCEA) of the University of Guadalajara (UdG), to present the base book for the course Quantitative Methods I and II.
The author hopes to contribute to the reader's acquisition of knowledge that can be applied in the practical world and aid in its theoretical interpretation. If not, it is hoped that at least it serves as another useful step towards achieving their academic and/or professional formation.
Downloads
Download data is not yet available.
References
Anderson, J. C., Gerbing, D. W. y Hunter J. E. (1987), On the Assessment of Unidimensional Measurement: Internal and External Consistency and Overall Consistency Criteria. Journal of Marketing Research 24 (November): 432-37.
American Psychological Association (1985), Standards for Educational and Psychological Tests. Washington, D.C.: APA.
Bearden, W. 0., Netemeyer, R. G. y Mobic M. ( 1993), Handbook of Marketing Sea!es: Multi-ltem Measures for Marketing and Consumer Behavior. Newbury Park, Calif.: Sage.
Bentler, Peter M. (1992), EQS Structural EquationsProgram Manual. Los Angeles: BMDP Statistica1Software.
BMDP Statistical Software, Inc. ( 1992), BMDP Statistical Software Manual, Release 7, vols. 1 and 2,Los Angeles: BMDP Statistical Software.
Borgatta, E. F., Kercher, K. y Stull D. E. ( 1986), A Cautionary Note on the Use of Principal Components Analysis. Sociological Methods and Research 15:160- 68.
Bruner, G. C., y Hensel P. J. (1993). Marketing Scales Handbook, A Compilation of Multi- ltem Measures. Chicago: American Marketing Association.
Campbell, D. T., y Fiske D. W. (1959). Convergentand Discriminant Validity by the Multitrait Multimethod Matrix. Psychological Bulletin 56 (March): 81-105.
Cattell, R. B. (1966), The Scree Test for the Number of Factors. Multivariate Behavioral Research 1 (April): 245-76.
Cattell, R. B., Balear, K. R. Horn, J. L. y Nesselroade, J. R. ( 1969), Factor Matching Procedures: An Improvement of the s index; with tables. Educational and Psychological Measurement 29: 781-92.
Chatterjee, S., Jamieson, L. y Wiseman F. (1991), ldentifying Most Influential Observations in Factor Analysis. Marketing Science 10 (Spring): 145-60.
Churchill, G. A. (1979), A Paradigm for Developing Better Measures of Marketing Constructs. Journal of Marketing Research 16 (February): 64-73.
Downloads
Published
December 30, 2017
Series
Categories
Copyright (c) 2017 Juan Mejía-Trejo
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Details about this monograph
ISBN-13 (15)
978-607-742-774-2
doi
10.55965/abib.9786077427742.2017b
