QMB7910 / GEB7910
Quantitative Research Methods
AMIN SHOJA, PHD
ashoja@fiu.edu
Introductions – About Me
Economist– University of Tehran
◦ Tose Sarmaye Vala
Ph.D. in Economics – Florida International University
◦ Microeconomics, Econometrics, International Trade
Ph.D. in Information Systems and Business Analytics – Florida International
University
◦ Technology Adoption and Diffusion, Research Methodology
Faculty at FIU (2018 – Present)
◦ Graduate-level teaching: Business Analytics in specialized MS and MBA, DBA courses
(theory development, research methods, doc res in bus admin)
Course Objectives
(1) Provide the statistical background and knowledge to enable you to critically
read/understand/evaluate published work
(2) Learn how to appropriately conduct and interpret the results of the
fundamental statistical analyses employed in business research
(3) Obtain the foundational statistical knowledge that will allow you to specialize
in more complex designs and analyses as needed
Materials
Expectations
This is a doctoral-level course; you should expect the workload to be significant
every week of the term
Weekly readings will include book chapter(s) and recently-published articles that
showcase the techniques or analyses of interest
There will be no tests or exams; rather, quite a bit of hands-on work, directed
first and then undirected later
Schedule for QMB7910
Week
1 Residency
Date
8/18/23 (6.9)
8/19/23 (6.8)
Assigned Chapters
2 Zoom
8/22/23 (6.9)
8/24/23 (6.8)
Chapter 5: Statistical Inference – Estimation
3 Zoom
8/29/23 (6.9)
8/31/23 (6.8)
Chapter 6: Statistical Inference – Significance Tests
4 Zoom
9/5/23 (6.9)
9/7/23 (6.8)
Chapter 7: Comparison of Two Groups
5 Residency
9/15/23 (6.9)
9/16/23 (6.8)
Chapter 9: Linear Regression and Correlation
Chapter 10: Introduction to Multivariate Relationships
Chapter 11: Multiple Regression and Correlation
6 Zoom
9/19/23 (6.9)
9/21/23 (6.8)
Chapter 8: Analyzing Associations between Categorical Variables
7 Zoom
9/26/23 (6.9)
9/28/23 (6.8)
Chapter 12: Regression with Categorical Predictors – Analysis of Variance Methods
8 Zoom
10/3/23 (6.9)
10/5/23 (6.8)
Chapter 13: Multiple Regression with Quantitative and Categorical Predictors
Chapter 1: Introduction
Chapter 2: Sampling and Measurement
Chapter 3: Descriptive Statistics
Chapter 4: Probability Distributions
Schedule for GEB7910
Week
1 Zoom
2 Residency
3 Zoom
4 Zoom
5 Zoom
6 Zoom
7 Zoom
8 Residency
Final Project
Date
10/10/23 (6.9)
10/12/23 (6.8)
10/20/23 (6.9)
10/21/23 (6.8)
10/24/23 (6.9)
10/26/23 (6.8)
10/31/23 (6.9)
11/02/23 (6.8)
11/07/23 (6.9)
11/09/23 (6.8)
11/14/23 (6.9)
11/16/23 (6.8)
11/28/23 (6.9)
11/30/23 (6.8)
12/08/23 (6.9)
12/09/23 (6.8)
Assigned Readings
(Agresti) Chapter 14: Model Building with Multiple Regression
Factor Analysis (read Exploratory Factor Analysis, by Finch)
(Agresti) Chapter 15: Logistic Regression – Modeling Categorical Responses
Non-response and Missing Data (see readings in Canvas)
Mediation (PROCESS) (see readings in Canvas)
Bootstrapping (see readings in Canvas)
No Class this Week – Happy Thanksgiving!
Final Project Review and Q&A
Nonparametric Tests (read Nonparametric Statistics – An Introduction, by Gibbons)
Introduction to Advanced Statistical Techniques: Path Analysis, Confirmatory Factor
Analysis, Structural Equation Modeling (see readings in Canvas)
Final Project Review and Q&A
Final Project Due – Thursday 12/14/23 by midnight
(No Exceptions!)
Why Statistics?
STATISTICS = EVIDENCE
Statistics is about collecting (“gathering”) and analyzing data:
◦ Design: planning how to gather data for a research study to investigate questions of
interest; a poor design cannot be fixed afterwards, no matter what you do
◦ Or how much data you collect, or how fancy your analyses are, or how unique your dataset is, etc.
◦ Description: summarize the data obtained in the study
◦ Inference: make predictions (“inferences”) based on the collected data (and making
some assumptions about them) to deal with uncertainty
Statistics is about Model Building
𝑜𝑢𝑡𝑐𝑜𝑚𝑒𝑖 = 𝑚𝑜𝑑𝑒𝑙 + 𝑒𝑟𝑟𝑜𝑟𝑖
The outcome (or score) of the ith unit (a person, a company, etc.) is a function of
your proposed model (that applies to all units) plus some error that will be
different for each unit
◦ Data we observed can be predicted from a model we choose to fit plus some error
◦ The “model” will vary in shape, complexity, etc. but the logic is the same
◦ Over time, we would like to progressively build better fitting models; that is, models
that explain more and more of an outcome of interest
◦ Model = ‘predicting an outcome from some variables’
Types of Research
Conceptual: Theory
Conceptual: Theory
Conceptual: Integrative Reviews
Conceptual: Integrative Reviews
Conceptual: Methodological
Qualitative: Ethnography
Qualitative: Case Study
Qualitative: Case Study
Flipping Coins
Quantitative: “Experiments”
Quantitative: “Experiments”
Quantitative: Surveys
Quantitative: Longitudinal
Quantitative: Meta-Analytical
The quantitative
research process
Results in a typical
structure for
quantitative
research studies