1104984


Course
Quantitative Measurement and Analysis - ONLINE

Faculty
Chee-Wee Tan, Michel Avital, and additional faculty members as needed.

Course Coordinator
Michel Avital and Chee-Wee Tan

Prerequisites


Prerequisite Statistical Software

Tools Prior to the first class, please obtain, install and get familiar (using the online tutorials) with the basic operation of the two required statistical software application: SPSS and SmartPLS. Required ▪ IBM SPSS [Downloadable from http://my.cbs.dk] ▪ SmartPLS [SmartPLS 3 purchasable from http://www.smartpls.de, but for this course, you can obtain a free copy of SmartPLS 2.0.M3 by entering your contact information in the fields located at the bottom of this URL: https://www.smartpls.com/smartpls2]
Optional ▪ LISREL [Purchasable from http://www.ssicentral.com/lisrel/index.html, but for this course, a free student copy can be downloaded via http://www.ssicentral.com/lisrel/student.html] ▪ fs/QCA [Downloadable from http://www.u.arizona.edu/~cragin/fsQCA/software.shtml]


Aim

The Quantitative Measurement and Analysis course is designed for doctoral students who are interested in pursuing quantitative research projects in social sciences. A primary objective of the course is to help participants acquire the necessary skills that will enable them to design, execute, report and critically review quantitative research in social sciences with an emphasis on management and administrative social science fields. Participants will gain foundational knowledge of quantitative research methods and the considerations that go into the design of empirical studies employing such methods.

Course content


The course is designed as a bi-weekly sequence of three 2-day residencies, each covering a key topic on quantitative research methods in social sciences. The meetings are in the form of participatory seminars that comprise class presentations, directed discussions and practical workshops. In addition to an appreciative and/or critical review of extant literature on quantitative research methods, the seminars seek to encourage constructive dialogue aimed at helping students to tackle research questions in a quantitative fashion, which builds on and extends contemporary knowledge. Meetings are held on a biweekly basis to allow sufficient time for in-depth reading and reflection. 
Given the aforementioned learning objectives, the course is designed with a heavy reading load. Reading the materials beforehand and participating actively in class assignments and dialogues are essential for getting a firm grasp of the course content. For each residency, students should read the assigned articles and be prepared to answer questions and discuss any other issues pertaining to the assigned reading material. Furthermore, students will be expected to prepare a take-home assignment that will be discussed in the next class. 

The course will run online using Zoom. Participants need to ensure that they are on a high-speed internet connection. They also commit for 100%s presence with face camera on, and active participation in discussions during the course days. If possible, we will also offer hybrid/blended mode of participation on campus.


Research Proposal Presentation [last class] 

For the last session of the course, each student will be expected to prepare a presentation that outlines the design of a quantitative empirical study for investigating their domain of interest or any other contemporary or emerging topic in social sciences. The purpose of the presentation is to familiarize students with the practical steps involved in conducting quantitative empirical studies.
The presentation should incorporate the following elements:
▪ Selected topic to be investigated via quantitative research models  
▪ Significance of the selected topic
▪ Prior research on the selected topic
▪ Research question(s) to be answered based on the selected topic ▪ Theoretical model and hypotheses for answering the research question(s)
▪ Quantitative research strategy being adopted to validate the theoretical model and hypotheses
     ➢ Instruments for data collection  
     ➢ Possible data source(s)
     ➢ Proposed data analytical technique(s) to be utilized
▪ Potential contributions to theory and practice 
 
 


Teaching style

Lecture plan

Week 11

Wednesday March 17th, 2021


9:00 - 12:00
Building Blocks in Context
: Theory and Theorizing, Modeling, Relationships and Hypotheses, Constructs and Variables

13:00 - 16:00
Measurement:
Measurement Properties, Construct Validity, Scale Development and Exploratory Factor Analysis (EFA)
Ex 1 Modeling 



Thursday March 18th, 2021


9:00 - 12:00
Primary Data Collection: Survey Research and Sampling 

13:00 - 16:00
Experimental and Quasi-Experimental Research 



Week 13


Tuesday March 30th, 2021


9:00 - 12:00
Recap Measurement
(and go over exercise)
Ex 2 EFA

13:00 - 16:00
Structural Equation Modeling (SEM):
Confirmatory Factor Analysis (CFA), LISREL, Regression and Partial Least Squares (PLS) 



Wednesday, March 31st, 2021


9:00 - 12:00
Structural Equation Modeling (SEM):
Model Specification, Second-Order Constructs and Common Method Bias (CMB) 

13:00 - 16:00
Mediation and Moderation 


Week 15


Wednesday April 14th, 2021


9:00 - 12:00
Recap SEM
(and go over exercise)
Ex 3 SEM

13:00 - 16:00
Secondary Data Sets:
Secondary Data Analysis, Meta-Analysis and Qualitative Comparative Analysis  
 

Thursday April 15th, 2021

9:00 - 12:00
Econometrics Modeling

13:00 - 16:00
Project Presentations


* The time is subject to change. Please check for updates prior to the course. * Light breakfast will be served at 8:30.

*All daily sessions will take place between 9:00 – 12:00 and 13:00 – 16:00 unless noted otherwise.

*Ex 1 is due by the end of Day 1, Ex 2 is due prior to the second residency, Ex 3 is due prior to the third residency.  


Learning objectives


At the end of the course, students should be able to:

 ▪ Discuss the theories and methods that were presented in class and covered by the readings
 ▪ Design theoretically valid and methodologically rigorous quantitative studies
▪ Develop instruments for quantitative data collection  
▪ Identify and assess data sources and data collection methods for quantitative studies
▪ Assess the reliability and validity of measures
▪ Demonstrate an understanding of quantitative data analysis techniques
▪ Interpret analytical results from quantitative studies
▪ Articulate in writing a formal description of quantitative research design and analysis  
 


Exam

 
Individual take-home 15 pages written exam together with a research proposal presentation will form the basis for evaluating students’ performance. The written exam and the presentation will have equal weight in the course grade. Grading is based on the standard 7-step scale. A passing grade on three individual 5 pages written homework assignments is a prerequisite for taking the exam. 
 
Homework Assignments 

Three mandatory individual 5 pages written homework assignments are designed to reinforce key analytical technics and provide an opportunity for deeper learning and reflection. The assignments cover hypotheses development, exploratory factor analysis, and structural equation modeling. 
 
Written Exam 


The exam will take the form of an individual take-home 15 pages written exam that is designed to foster deep reflections on the quantitative research methods covered in the course. The exam involves data analysis procedures in SPSS and SmartPLS (or another structural equation modeling software of your choice). All work must be original material that is produced individually. The exam will be distributed after the last class of the course and will be due in one week. Re-take exam, if necessary, will be administered about a month later. 
 


Other

Start date
17/03/2021

End date
15/04/2021

Level
PhD

ECTS
6

Language
English

Course Literature


Required Text 

Textbooks

▪ DeVellis, R. F. Scale Development: Theory and Applications (Vol. 26), Sage Publications, 2011.
▪ Pedhazur, E. J., and Schmelkin, L. P. Measurement, Design, and Analysis: An Integrated Approach, Psychology Press, 1991. ▪ Hair Jr, J. F., Hult, G. T. M., Ringle, C., and Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage Publications, 2016.

Supplementary Readings:
See reading list under a separate cover. Additional articles and resources will be provided on a need-to basis. 
 


Fee
DKK 3,900 (online course fee)

Minimum number of participants
10

Maximum number of participants
18

Location

Copenhagen Business School
2000 Frederiksberg
Location: Online via Zoom (and if possible, hybrid/blended at Copenhagen Business School)

All daily sessions will take place between 9:00 – 12:00 and 13:00 – 16:00. Please see the course lecture plan.


Contact information
For any further information about the course please contact Nina Iversen, ni.research@cbs.dk

Registration deadline
31/01/2021

Please note that your registration is binding after the registration deadline.
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