Multivariate Statistics. VOLUME II. Interdependent 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
DOI: https://doi.org/10.55965/abib.2023.9786075939797

Keywords:

multivariate statistics, interdependent techniques, SPSS, social sciences

Synopsis

With greater computing capabilities and resource availability today, multivariate analysis is now featured in several software applications, increasing its potential use across various 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), to name a few.

Therefore, it is not surprising that in Social Sciences, there is an increasing trend in the publication of reports, articles, book chapters, or books that discuss various theoretical and empirical aspects and their interpretation based on these software applications.

In our case, we adopted IBM SPSS 20 for the development of the topics in this book. Based on this, we present this work with the main objectives:

  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. The basic development of techniques such as factor analysis, multidimensional analysis, correspondence analysis, and cluster analysis.

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

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 ofthe 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.

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Published

July 24, 2023

Series

GENERAL CATALOG: Statistics

Categories

Copyright (c) 2024 Juan Mejía Trejo

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Details about this monograph

ISBN-13 (15)

978-607-59397-9-7

doi

10.55965/abib.2023.9786075939797