Multivariate Statistics. VOLUME I. Dependent Techniques with SPSS in the Social Sciences

Authors

Juan Mejía Trejo
Research-Professor at the Centro Universitario de Ciencias Económico-Administrativas (CUCEA) Universidad de Guadalajara (UdeG), Guadalajara, Jalisco, México.
https://orcid.org/0000-0003-0558-1943

Keywords:

multivariate statistics, dependent techniques, spss, social sciences

Synopsis

Multivariate analysis is present in various software applications, thereby increasing its usability across different disciplines such as Administrative Sciences. Some of the most widely used software in both academic and professional fields worldwide include the 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). Given this, it is not surprising that Administrative Sciences support academic development through various postgraduate programs and in the professional world related to Social Sciences. Consequently, there is a growing trend in the publication of reports, articles, book chapters, or books discussing various theoretical and empirical aspects and their interpretation based on these software applications. In our case, we adopt IBM SPSS 20 for the development of the topics in this book.

Based on the above, we present this work with a triple purpose:

  1. To present a document that serves both those familiar and unfamiliar with the topic, who need to understand both the concepts addressed in this volume and how to manipulate the various commands offered by IBM SPSS 20 regarding the example problem cases presented.
  2. For a better understanding of case treatments, we follow the sequence proposed by Hair et al. (1999) of the six steps: objectives, design, assumptions, execution, interpretation, and validation, as the axis for presenting and resolving these cases.
  3. As the Coordinator of the Doctorate in Administrative Sciences at the University Center for Economic and Administrative Sciences (CUCEA) of the University of Guadalajara (UdG), to present the foundational book for the Quantitative Methods I and II courses.

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 this is not the case, it is hoped that at least it will serve as another useful step in achieving their academic and/or professional development.

         

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Author Biography

Juan Mejía Trejo, Research-Professor at the Centro Universitario de Ciencias Económico-Administrativas (CUCEA) Universidad de Guadalajara (UdeG), Guadalajara, Jalisco, México.

Dr. Juan Mejía Trejo

He is born in 1964 in CDMX, México.

As professional experience:

1986-1987. Quality Department Control in KOKAI Electrónica S.A.

1987-2008. Former Internal Plant Exploitation Manager at Teléfonos de México S.A.B. Western Division.

As academic experience :

1987. He earned his degree in Communications and Electronics Engineering from the Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional (ESIME at the IPN)

2004. He earned his master’s in Telecommunications Business Administration from INTTELMEX and France Telecom.

2010. He earned his doctorate in Administrative Sciences from the Escuela Superior de Comercio y Administración (ESCA at the IPN)

2011.He is a member of the Sistema Nacional de Investigadores (SNI) Level I of the Consejo Nacional de Ciencia y Tecnología (CONACYT) , México.

2010 to the present, he is Titular Research Professor B at the Department of Marketing and International Business at the Universidad de Guadalajara, México.

2015-2022.He earned the Coordination of the Doctorate in Management Sciences at the Universidad de Guadalajara.

2018-2020. He earned his master’s in Valuing Business in the Centro de Valores S.C. México.

2019.He earned Level II of the SNI/CONACYT.

2019. He is the Founder, the main Sponsor and Director of the AMIDI (Academia Mexicana de Investigacion y Docencia en Innovación SC) (https://amidi.mx/)

2021. He is the Founder, the main Sponsor and Editor-in-Chief of the Scientific Journal Scientia et PRAXIS (https://scientiaetpraxis .amidi.mx/index.php/sp)

2023. He is the Founder, the main Sponsor and Editor-in-Chief of the Digital Repository AMIDI.Biblioteca

(https://www.amidibiblioteca.amidi.mx/index.php/AB)

2024.He earned Level III of the SNI/CONAHCYT.

 

Currently, his line of research is Innovation Management, publishing articles and books that can be found on the Internet.

His ORCID is on https://orcid.org/0000-0003-0558-1943

Emails: jmejia@cucea.udg.mx; juanmejiatrejo@hotmail.com; direccion@amidi.mx; editorial@scientiaetpraxis.amidi.mx

ResearcherID: O-8416-2017

ResearcherID: HMW-2043-2023

References

Addelman, S. ( 1962), Orthogonal Main-Effects Plans for Asymmetrical Factorial Experiments. Techno metrics 4: 21-46.

Akaah, l. ( 1991 ), Predictive Performance of Self- Explicatcd, Traditional Conjoint, and Hybrid Conjoint Modcls undcr Alternative Data Collection Modcs. Journal of the Academy of Marketing Science 19:309-14.

Allenby, G. M., Arora, N., y Ginter, J. L. (1995), Incorporating Prior Knowledge into the Analysis of Conjoint Studies. Journal of Marketing Research 32 (May): 152-62.

Alpert, M. ( 1971), Definition of Determinan! Attributes: A Comparison of Methods. Journal of Marketing Research 8(2): 184-91.

Baalbaki, I. B., y Malhotra, N. K. (1995), Standardization versus Customization in Intemational Marketing: An Investigation Using Bridging Conjoint Analysis. Journal of the Academy of Marketing Science 23(3): 182-94.

Bretton-Clark (1988), Conjoint Analyzer. New York: Bretton-Clark. Carmone, F. J., Jr., y Schaffer, C. M. (1995),Review of Conjoint Software.

Journal of Marketing Research 32 (February): 113-20.

Carroll, J. D., y Green, P. E. (1995), Psychometric Methods in Marketing Research:

Part 1, Conjoint Analysis. Journal of Marketing Research 32 (November): 385-91. Conner, W. S., y Zelen, M. (1959), Fractional Factorial Experimental Designs for

Factors at Three Levels, Applied Math Series S4. Washington, D.C.: National Bureau of

Standards.

Elrod, T., Louviere, J. J. y Davey, K. S. (1992), An Empírical Comparison of Ratings-

Based and Choice- Based Conjoint Models. Journal of Marketing Research 29: 368-

Green, P. E. (1984), Hybrid Models for Conjoint Analysis: An Exploratory

Review. Journal of Marketing Research 21 (May): 155-69.

Green, P. E., Goldberg, S. M., y Montemayor, M. (1981), A Hybrid Utility Estimation

Model for Conjoint Analysis. Journal of Marketing 45 (Winter):33-41.

Green, P. E., Kreiger, A. M., y Agarwal M. K. (1991), Adaptive Conjoint Analysis: Sorne

Caveats and Suggestions. Journal of Marketing Research 28 (May): 215-22. Hahn, G. J., y Shapiro S. S. (1966), A Catalog and Computer Program for the Design

Huber, J. (1987), Conjoint Analysis: How We Got Here and Where We Are, In

and Analysis of Orthogonal Symmetric and Asymmetric Fractional Factorial Experiments, Report No. 66-C-165. Schenectady, N.Y.: General Electric Research and Development Center.

Hair , J.F.; Anderson, R.E.; Tatham, R.L.; Black W.C. .Análisis Multivariante.1a. Ed. Espana. Prentice (all.

Proceedings of the Sawtooth Conference on Perceptual Mapping, Conjoint Analysis and Computer lnterviewing, M. Metegrano, cd., Ketchum, Idaho: Sawtooth Software, pp. 2-6.

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Published

April 27, 2023

Details about this monograph

ISBN-13 (15)

978-607-59397-8-0

doi

10.55965/abib.2023.9786075939780