Quantitative Methods - PhD

Course: PQAP – Advanced Quantitative Research Procedures

Workload: 45 hours (3 credits)

Level: PhD

Professors: Prof. Dr. Graziela Rodrigues and Prof. Dr. Luiz Pinheiro

I - OBJECTIVES:

• Master the concepts of multivariate data analysis with a focus on research in administration.

• Develop the skills needed to plan and carry out statistical techniques and data analysis applied to research in administration.

II - SYLLABUS

• Multivariate analysis

• Building a quantitative matrix

• Construct and scale development and validation, and survey execution

• Cluster analysis (Hierarchical and K-means)

• Analysis of variance (t-test and ANOVA)

• Linear and Logistic Regression

• Exploratory factor analysis

• Confirmatory factor analysis

• Structural equation modeling

• Experimental models

III - METHODOLOGY

• Student-led presentations

• Participation in discussions and debates on theoretical-empirical studies,

• Critical reviews of articles,

• Exercises in Jamovi and PSRAT

• Questionnaire design, survey application and data analysis.

V – ASSESSMENT:

• Topic introduction (no PowerPoint) - 10

• Class participation - 5

• Submission of reviews and exercises - 10

• Survey application and data analysis - 40

• Research paper (submission and presentation) - 35

IV - DETAILED SCHEDULE

CLASS/DATE: 001 - Aug 26, 2026 (Wednesday)

TOPIC: Open Class (Prof. Graziela)

Required reading:

1) do Prado Pagotto, D., & Borges, C. (2023). Pesquisa quantitativa em empreendedorismo e o apoio do software R para análise de dados. REGEPE Entrepreneurship and Small Business Journal, 12(2).

Link: https://regepe.emnuvens.com.br/regepe/article/view/2257/62

2)  Li, J.; Yu, H.; Zhang, H. (2026). Investigating the microfoundation of dynamic managerial and organizational capabilities for business model innovation: a mediated-moderated model of artificial intelligence. Journal of Innovation & Knowledge, v. 17, p. 101058. https://doi.org/10.1016/j.jik.2026.101058

Agenda:

• Personal introductions: professor and students;

• Course presentation;

• Proposal presentation;

• Team formation;

• Discussion of models, variables, etc.

CLASS/DATE: 002 - Aug 29, 2026 (Saturday)

TOPIC: Multivariate Analysis (Prof. Pinheiro)

STUDENTS: All students.

Required readings:

1) Fiates, G. G. S., Serra, F. A. R., & Martins, C. (2014). A aptidão dos pesquisadores brasileiros pertencentes aos programas de pós-graduação stricto sensu em Administração para pesquisas quantitativas. Revista de Administração, 49(2), 384-398. https://www.sciencedirect.com/science/article/pii/S0080210716303533 

2) Straub, D. W., Gefen, D., & Recker, J., (2022) Quantitative Research in Information Systems http://www.janrecker.com/quantitative-research-in-information-systems

3)Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. Chapter 1: Overview of Multivariate Methods.

Link: https://eli.johogo.com/Class/CCU/SEM/_Multivariate%20Data%20Analysis_Hair.pdf

CLASS/DATE: 003 - Sep 16, 2026 (Wednesday)

TOPIC: Cluster Analysis and Analysis of Variance (Prof. Graziela)

STUDENT: All students

Required readings:

1) Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. Chapter 4: Cluster Analysis 

2) RUBIO-ANDRÉS, Mercedes; ABRIL, Carmen. Sustainability oriented innovation and organizational values: a cluster analysis. The Journal of Technology Transfer, v. 49, n. 1, p. 1-18, 2024.

Supplementary reading:

1)  James M. Crick & David Crick (2025) Designing robust electronic surveys in marketing research, Journal of Strategic Marketing, 33:5, 559-572, DOI: 10.1080/0965254X.2025.2468679.

CLASS/DATE: 004 - Sep 19, 2026 (Saturday)

TOPIC: Exploratory Factor Analysis (Prof. Pinheiro)

STUDENTS: Maysa and Gustavo Lara

Required readings:

1) Pinheiro Jr, L.; Frega, J. R., Desempenho da TI: Uma pesquisa no Faturamento Eletrônico nas Empresas (2016). Americas Conference on Information Systems, AMCIS, Proceedings, San Diego, USA.

2) Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8, 1-13.

Supplementary reading:

1) Rogers, P. (2022). Melhores práticas para sua análise fatorial exploratória: tutorial no factor. Revista de Administração Contemporânea, 26, e210085.

2) Dias, P. D. S., Silva, H. V. R. S., & de Cássia Macedo, R. (2019). Estatísticas multivariadas na administração: importância e aplicação da análise fatorial exploratória. Revista Eletrônica de Administração e Turismo - ReAT, 13(1), 1807-1828.

CLASS/DATE: 005 - Sep 30, 2026 (Wednesday)

TOPIC: Confirmatory Factor Analysis (Prof. Graziela)

STUDENTS: Natalia

Required readings:

1) Brown, T. A. (2015). Confirmatory factor analysis for applied research. 2nd ed. Guilford Pr. Chapter 3: Introduction to CFA

2) Freitas, A. D. G., Lacerda, F. M., Santos, E. A., Alvarenga, M. A., & Bizarrias, F. S. (2022). Políticas de recursos humanos, aprendizagem e intenção de rotatividade nas organizações. RAM. Revista de Administração Mackenzie, 23.

Supplementary reading:

1) Hair, J. F., Lopes, E. L., Gabriel, M. L., Bizarrias, F. S., & da Silva, D. (2025). Coffee, Data and Jamovi: The Perfect Recipe for great Statistical Analysis. Journal of Sustainable Institutional Management, 12, e0178-e0178

CLASS/DATE: 006 - Oct 3, 2026 (Saturday)

TOPIC: Linear and Logistic Regression (Prof. Pinheiro)

STUDENTS: Renata and André

Required readings:

1) Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. Chapter 9: Multiple Regression Analysis

2) Bajumpaa, A., Borden, D., Taylor, C., Silva, P., & Fiondella, L. (2026). A predictive system resilience assessment tool (PSRAT). SoftwareX, 34, 102576.

3) Silva, P., Hidalgo, M., Hotchkiss, M., Dharmasena, L., Linkov, I., & Fiondella, L. (2024). Predictive resilience modeling using statistical regression methods. Mathematics, 12(15), 2380.

CLASS/DATE: 007 - Oct 28, 2026 (Wednesday)

TOPIC: Experimental Models (Prof. Graziela)

STUDENT: Kátia Biscouto

Required readings:

1)  da Costa Hernandez, J. M., Basso, K., & Brandão, M. M. (2014). Pesquisa experimental em marketing. Revista brasileira de marketing, 13(2), 98-117.

2) 2) RODRIGUES, G. P.; PRADO, P. H. M. When Does Negative Feedback Cause Engagement in the Early Stage of Goal Pursuit? The roles of Goal Progress, Effort, and Goal Attainability. In: Anais do XLIX Encontro da ANPAD. Aracaju-SE. 2025.

Supplementary reading:

 1)     KISHIMOTO, Tomoko et al. Single online self-compassion writing intervention reduces anxiety: With the feedback of ChatGPT. Internet Interventions, v. 39, p. 100810, 2025.

CLASS/DATE: 008 - Oct 31, 2026 (Saturday)

TOPIC: Structural Equation Modeling (Prof. Pinheiro)

STUDENTS: Cleiton

Required readings:

1) Bido, D. de S., & Souza, C. A. (2026). Structural Equation Modeling: Is it Still Worth Learning? Brazilian Administration Review, 23(3), e260020. https://doi.org/10.1590/1807-7692bar2026260020

2) Zyphur, M. J., Bonner, C. V., & Tay, L. (2023). Structural equation modeling in organizational research: The state of our science and some proposals for its future. Annual Review of Organizational Psychology and Organizational Behavior, 10(1), 495-517.

3) Reis, J. F. dos. (2025). Inteligência artificial nos bancos brasileiros: resolvendo os problemas percebidos pelos clientes [Master's thesis, Universidade Positivo]. Available at: https://repositorio.cruzeirodosul.edu.br/handle/123456789/7597 

CLASS/DATE: 009 - Nov 11, 2026 (Wednesday)

TOPIC: Paper Development – Quantitative Research and Research Proposal (Prof. Graziela)

CLASS/DATE: 010 - Nov 14, 2026 (Saturday)

TOPIC: Presentation of the research/paper data analysis (Prof. Gustavo Lara)

STUDENTS: All students.