SAMPLE SYLLABUS FOR HYPOTHETICAL COURSE
“EMPIRICAL RESEARCH METHODS IN THE SOCIAL SCIENCES”
This is very similar to a course entitled “Empirical Research Methods in Business
Administration,” that I teach in 15-week Fall semesters for first year doctoral
students. This course draws students from a variety of disciplines in the social
sciences. Although the course is taught in the first semester, with variations, it
could well be taught in the second or third semester of a doctoral program. I have
indicated how the “Measurement Error and Research Design,” text can be used for
such a class. I will continue to add suggestions to this website on how the book
can be used for different types of research methods courses.
Overview
The course will aim to provide a foundation for designing and using methods to perform
empirical research in the social sciences. The seminar will be structured around a framework of
measurement principles covered in the first part of the course. Using these measurement
principles as a foundation, the second part of the course will be devoted to discussing issues
about specific methods listed below such as experimental designs and survey designs. The third
part of the course will cover some miscellaneous issues in research methods and presentations by
students.
Topics
At the heart of scientific inquiry is the ability to measure phenomena through the use of
research methods. Principles of measurement relate the abstract domain of ideas, theories and
hypotheses, to the operational domain of research methods. The first part of the course will cover
the basics of measurement in the social sciences.
Part I
Principles of measurement
(i) Introduction to measurement
(ii) Reliability in measurement
Indicators of reliability
Assessment of reliability using data sets
(iii) Validity in measurement
Indicators of validity
Assessment of types of validity including
convergent, discriminant, and nomological
validity
Assessment of dimensionality using
exploratory factor analysis
(iv) Understanding measurement
error
An in-depth examination of types
of measurement error, their causes, and their
consequences (this topic draws directly
from the unique orientation of the book
and is appropriate after covering some
fundamentals of measurement)
(v) Using Structural Equation
Modeling in measurement
Confirmatory factor analyses
Introduction to LISREL
Simultaneous assessment of measurement
and theory using structural equations
(v) Measurement applications in the social sciences
This portion of the course aims to provide a basis to view research methods from a
measurement perspective as well as a working knowledge of the assessment of reliability and
validity of measurement procedures. Using these measurement principles as a foundation the
second part of the course will be devoted to discussing issues about specific methods listed
below.
Part II Specific research methods
(i) Validity of research designs
Validity of research designs
Trade-offs between types of validity
(ii) Experimental research designs
Basics of experimental designs
Types of designs
Manipulation checks; demand artifacts, etc.
Applications in business
(iii) Survey research designs
Measurement error in surveys
Question wording effects
Response scale effects
Applications in business
(iv) Qualitative research designs
Introduction to qualitative designs
Applications in business
The third part of the course will cover some miscellaneous issues in research methods and papers
developed by students.
Part III
Paper presentations and miscellanous issues
Background required to take course
While the course will require students to have a background in statistics, in depth
knowledge of multivariate statistics is not necessary. The assignments will require knowledge of
a statistical package (preferably SPSS) to run programs such as reliability, and factor analyses.
Students in different areas of business administration are encouraged to bring in readings
of interest to them for class discussion. One assignment will specifically require students to
bring in applications of topic areas covered in the course.
Course requirements
(i)
Class participation
(The course will use a discussion format.
Students will be responsible for weekly
readings and will be expected to lead class
discussions)
40%
(ii)
Assignments
(The course will involve several assignments
throughout the semester including
analyses and interpretation of data sets,
designing research methods, and critically
assessing specific research designs.)
10%
(iii)
Paper/Presentation & Write-up
(The course requirements include the completion
of a project where students will choose a set of
hypotheses of interest and develop a research
method to test the hypotheses. A paper based
on this project as well as a class presentation
will be required. Various sections of the paper
will be due during the course of the semester.
Requests for extension will not be considered
except for valid medical or personal reasons.
The project will involve application of course
material in designing the method for a study and
providing rationale for it. For example, a survey
or an experiment or a qualitative method could
be used. Data collection is encouraged but not
necessary. Further details are provided under
the assignment schedule.)
50%
Text
Measurement Error and Research Design (2005) by Madhu Viswanathan, Sage
Publications.
OVERVIEW OF SCHEDULE
Part I
Principles of Measurement
September 1
Introduction to measurement
The Measure development process
Introduction to reliability
Reliability (con’td)
Introduction to factor analysis
Reliability assignment due
Reliability (cont’d)
Factor analysis (cont’d)
Introduction to validity
Factor analysis assignment due
Paper topic due
Validity (cont’d)
Summary of reliability, factor analysis and validity
Understanding measurement error
Validity assignment due
Error assignment due
Understanding measurement error
Front end of paper due
Understanding measurement error
Using Structural Equation Modeling in measurement
September 8
September 15
September 22
September 29
October 6
Part II
October 13
October 20
October 27
November 3
November 10
Part III
November 17
December 1
December 8
Specific Research Methods
Using Structural Equation Modeling in measurement cont’d
Measurement Applications
Validity of research designs
Participation in debate on validity
Validity of research designs (cont’d)
Experimental research methods (cont’d)
Overview of methods section due
Survey research methods
Survey research methods (cont’d)
Qualitative research methods
Details of methods sections due
Paper presentations and miscellaneous issues
Qualitative research methods (cont’d)
Miscellanoues issues
Paper Presentations
Final paper due
DETAILED SCHEDULE
PART I - PRINCIPLES OF MEASUREMENT
September 1
Reading assignment
Introduction to Measurement
Kerlinger, Fred N. (1986), "Constructs, Variables, and Definitions," Foundations of Behavioral
Research, New York: Holt, Rinehart and Winston, 26-44.
Nunnally, Jum C., and Ira H. Bernstein (1994), "Introduction," Psychometric Theory, New York:
McGraw Hill, Chapter 1, 3-30.
Viswanathan (2005), “What is Measurement?,” Measurement Error and Research Design,
CA: Sage Publications, Chapter 1, 1-10.
The Measure Development Process
Churchill, Gilbert A., Jr. (1979), "A Paradigm for Developing Better Measures of Marketing
Constructs," Journal of Marketing Research, 16 (February), 64-73, (Read pages 64-68,
i.e., pages 195-199 on the reprinted version).
Viswanathan (2005), “What is Measurement?,” Measurement Error and Research Design,
CA: Sage Publications, Chapter 1, 11-18.
Introduction to Reliability
Devellis, Robert F. (1991), Scale Development: Theory and Applications, Sage
Publications Inc., 51-90.
Viswanathan (2005), “What is Measurement?,” Measurement Error and Research Design,
CA: Sage Publications, Chapter 1, 18-29.
September 8
Reliability assignment due (Pages 14-15, 20-23 and 30 of the book are used for this
assignment. Datasets are available on the book website. These datasets offer students an
opportunity to experiment with a statistical package.)
Reading assignment
Reliability (cont’d)
Devellis, Robert F. (1991), Scale Development: Theory and Applications, Sage Publications Inc.,
12-42.
Viswanathan (2005), “What is Measurement?,” Measurement Error and Research Design,
CA: Sage Publications, Chapter 1, 18-29.
Introduction to Factor Analyses
Hair, Joseph F., Jr. et al. (1979), Multivariate Data Analysis, Tulsa, Oklahoma: Petroleum
Publishing Company, Chapter 6, 223-253.
Viswanathan (2005), “What is Measurement?,” Measurement Error and Research Design,
CA: Sage Publications, Chapter 1, 29-35.
September 15
Paper topic due
Factor analyses assignment due (Pages 14-15, and 37-38 of the book are used for this
assignment. Datasets are available on the book website)
Reading assignment
Reliability (cont’d)
Devellis, Robert F. (1991), Scale Development: Theory and Applications, Sage
Publications Inc., 12-42.
Viswanathan (2005), “What is Measurement?,” Measurement Error and Research Design,
CA: Sage Publications, Chapter 1, 18-29.
Factor Analyses (cont’d)
Hair, Joseph F., Jr. et al. (1979), Multivariate Data Analysis, Tulsa, Oklahoma: Petroleum
Publishing Company, Chapter 6, 223-253.
Viswanathan (2005), “What is Measurement?,” Measurement Error and Research Design,
CA: Sage Publications, Chapter 1, 29-35.
Introduction to Validity
Churchill, Gilbert A., Jr. (1979), "A Paradigm for Developing Better Measures of Marketing
Constructs," Journal of Marketing Research, 16 (February), 64-73, (Read pages 69-73,
i.e., pages 200-204 on the reprinted version).
Nunnally, Jum C., and Ira H. Bernstein (1994), “Validity,” Psychometric Theory (3rd ed.), New
York: McGraw-Hill, 83-113.
Viswanathan (2005), “What is Measurement?,” Measurement Error and Research Design,
CA: Sage Publications, Chapter 1, 61-75.
September 22
Validity assignment due (Pages 63-65 of the book are used for this assignment)
Reading assignment
Validity (cont’d)
Campbell, Donald T. and Donald W. Fiske (1959), "Convergent and Discriminant Validation by
the Multitrait-Multimethod Matrix," Psychological Bulletin, 56 (March), 100-122.
Reliability, Factor analysis, and Validity - Summary
Devellis, Robert F. (1991), Scale Development: Theory and Applications, Sage
Publications Inc., 12-42.
Hair, Joseph F., Jr. et al. (1979), Multivariate Data Analysis, Tulsa, Oklahoma: Petroleum
Publishing Company, Chapter 6, 223-253.
Churchill, Gilbert A., Jr. (1979), "A Paradigm for Developing Better Measures of Marketing
Constructs," Journal of Marketing Research, 16 (February), 64-73.
Nunnally, Jum C., and Ira H. Bernstein (1994), “Validity,” Psychometric Theory (3rd ed.), New
York: McGraw-Hill, 83-113.
Viswanathan (2005), “What is Measurement?,” Measurement Error and Research Design,
CA: Sage Publications, Chapter 1, 1-35; 61-75.
Understanding Measurement Error
(This topic draws directly from the unique orientation of the book and is appropriate after some
fundamentals of measurement have been covered. The discussion of types of measurement error
ties in with reading about validity and systematic error while cementing the fundamentals
covered in the first few weeks. It also provides a basis for the error assignment to be covered in
the next week. This topic directly runs through three weeks in the course as additional issues
such as confirmatory factor analyses are covered.)
Viswanathan (2005), “What is Measurement Error?,” Measurement Error and Research
Design, CA: Sage Publications, Chapter 2, 97-134.
Viswanathan (2005), “What Causes Measurement Error?,” Measurement Error and
Research Design, CA: Sage Publications, Chapter 3, 135-148.
September 29
Front end of paper due
Error assignment due (Pages 123-134 presents the assignment; After several weeks of basic
coverage of measurement, the aim here is to develop deeper understanding of measurement
error. In my experience, it is useful to revisit this assignment, allowing students multiple
aopportunites to add to it.)
Reading assignment
Understanding Measurement Error
Viswanathan (2005), “What is Measurement Error?,” Measurement Error and Research
Design, CA: Sage Publications, Chapter 2, 97-134.
Viswanathan (2005), “What Causes Measurement Error?,” Measurement Error and
Research Design, CA: Sage Publications, Chapter 3, 135-148.
Viswanathan (2005), “Can Empirical Procedures Pinpoint Types of Measurement Error?,”
Measurement Error and Research Design, CA: Sage Publications, Chapter 4, 149159.
October 6
Revisit error assignment
Reading assignment
Using Structural Equation Modeling in measurement
Viswanathan (2005), “What is Measurement?,” Measurement Error and Research Design,
CA: Sage Publications, Chapter 1, 35-61.
Ecob, Russell, and Peter Cuttance, “An overview of structural equation modeling,” Structural
Modeling By Example: Applications in Educational, Sociological, and Behavioral
Research, Peter Cuttance and Russell Ecob (eds.), NY: Cambridge University Press.
Long, Scott, Confirmatory Factor Analysis, Sage Publications Inc., 1983, pages 11-34.
Long, Scott, Covariance Structure Models: An Introduction to LISREL, Sage Publications Inc.,
1983, pages 11-24.
Anderson, James C. and David W. Gerbing (1988), "An Updated Paradigm for Scale
Development Incorporating Unidimensionality and Its Assessment," Journal of
Marketing Research, 25 (May), 186-192.
Judd, Charles M., Jessor, Richard, and Donovan, John E. (1986). Structural equation models
and personality research. Journal of Personality, 54 (1), 149-198.
Understanding Measurement Error cont’d
Viswanathan (2005), “How Can Measurement Error be Identified and Corrected For in
Measure Development?,” Measurement Error and Research Design, CA: Sage
Publications, Chapter 5, 161-196.
PART II - SPECIFIC RESEARCH METHODS
October 13
Using Structural Equation Modeling in measurement cont’d from previous part
Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural
equation
perspective. Psychological Bulletin, 110(2), 305–314.
Viswanathan (2005), “How Do Measures Differ?,” Measurement Error and Research
Design, CA: Sage Publications, Chapter 7, 228-238.
(This reading provides a discussion of formative versus reflective measures.)
Measurement Applications cont’d from previous part
Viswanathan (2005), “What are Examples of Measures and Measurement Across Various
Disciplines?,” Measurement Error and Research Design, CA: Sage Publications,
Chapter 8, 239-288.
(This chapter could be skimmed as needed but provides a series of stimulating examples
that may encourage creative thinking about measure development.)
Assignment: Debate on validity
Reading assignment
Validity of research designs
Cook, Thomas D. and Donald T. Campbell, "Validity," Quasi-Experimentation: Design &
Analysis Issues for Field Settings, 37-94.
Calder, Bobby J. et al. (1981), "Designing Research for Application," Journal of
Consumer Research, 8 (September), 197-207.
Lynch, John G., Jr. (1982), "On the External Validity of Experiments in Consumer Research,"
Journal of Consumer Research, 9 (December), 225-239.
Lynch, John G., Jr. (1983), "The Role of External Validity in Theoretical Research," Journal of
Consumer Research, 10 (June), 109-111.
Calder, Bobby J. et al. (1983), "Beyond External Validity," Journal of Consumer Research, 10
(June), 112-114.
October 20
Reading assignment
Validity of research designs (cont’d)
McGrath, Joseph E. and David Brinberg (1983), "External Validity and the Research Process: A
Comment on the Calder/Lynch Dialogue," Journal of Consumer Research, 10 (June),
115-124.
Berkowitz, Leonard and Edward Donnerstein (1982), "External Validity is More Than Skin
Deep," American Psychologist, 37 (March), 245-257.
Mook, Douglas G. (1983), "In Defense of External Invalidity," American Psychologist, (April),
379-387.
Ellsworth, Phoebe C. (1977), From Abstract Ideas to Concrete Instance: Some Guidelines for
Choosing Natural Research Settings,” American Psychologist, 604-615.
Viswanathan (2005), “How Does Measurement Error Affect Research Designs?,”
Measurement Error and Research Design, CA: Sage Publications, Chapter 10, 307310; 329-336; 343-346.
(This chapter strongly connects the earlier discussion on measurement to the broader issue
of research designs. It is appropriate during coverage of topics of validity of research
designs.)
October 27
Overview of methods section of paper due
Reading assignment
Experimental Research Designs
Perdue, Barbara C. and John O. Summers (1986), "Checking the Success of Manipulations in
Marketing Experiments," Journal of Marketing Research, 23 (November),317-326.
Greenwald, Anthony G. (1976), "Within Subjects Designs: To Use or Not to Use?"
Psychological Bulletin, 83(2), 314-320.
Viswanathan (2005), “How Does Measurement Error Affect Research Designs?,”
Measurement Error and Research Design, CA: Sage Publications, Chapter 10, 307310; 315-336; 340-343.
(The repetition of some material earlier assigned for validity of research designs is
deliberate to encourage revisiting these issues.)
November 3
Reading assignment
Survey Research Designs
Viswanathan (2005), “How Does Measurement Error Affect Research Designs?,”
Measurement Error and Research Design, CA: Sage Publications, Chapter 10, 310315; 337-339.
Fowler, Floyd, “Designing Questions to be Good Measures,” Survey Research Methods, Sage
Publications Inc., pages 69-93.
Schwarz, Norbert, and Hans-Jurgen Hippler (1991), "Response Alternatives: The Impact of their
Choice and Presentation Order," in Paul B. Biemer et al. (eds.), Measurement Error in
Surveys, 41-56, Wiley: NY.
Cox, Eli (1980), “The Optimal Number of Response Alternatives in a Scale: A Review,” Journal
of Marketing Research, 17, 407-422.
Viswanathan (2005), “How Do Measures Differ?,” Measurement Error and Research
Design, CA: Sage Publications, Chapter 7, 213-228.
(This reading provides a discussion of stimulus-centered versus respondent-centered
scales).
November 10
Details of methods section of paper due
Reading Assignment
Cox, Eli (1980), “The Optimal Number of Response Alternatives in a Scale: A Review,” Journal
of Marketing Research, 17, 407-422.
Qualitative Research methods
Hirschman, Elizabeth C. (1986), “Humanistic Inquiry in Marketing Research: Philosophy,
Method, and Criteria,” Journal of Marketing Research, 23, 237-49.
Papers from guest speakers
November 17
Reading assignment
Qualitative research (cont’d)
Hirschman, Elizabeth C. (1986), “Humanistic Inquiry in Marketing Research: Philosophy,
Method, and Criteria,” Journal of Marketing Research, 23, 237-49.
Viswanathan (2005), “What is the Role of Measurement in Science?,” Measurement Error
and Research Design, CA: Sage Publications, Chapter 11, 347-382.
(This chapter provides some discussion of qualitative research as well as some broader
issues in the role of measurement in science that are appropriate near the end of the
course).
Papers from guest speakers
December 1
Paper Presentations
December 8
Final paper due
ASSIGNMENT SCHEDULE
This assignment schedule excludes reading assignments which are listed in detail earlier.
September 8
Reliability assignment due
September 16
Factor analyses assignment due
Paper topic due
September 22
Validity assignment due
September 29
Error assignment due
Front end of paper due (i.e., conceptualization & hypotheses)
October 13
Participation in debate on validity
October 27
Overview of methods section due (i.e., rough draft)
November 10
Details of methods section due (i.e., stimuli, questionnaire, etc.)
December 1
Paper Presentations
December 8
Final paper due
Informal assignments include the provision of papers which are applications of a
particular topic area to you for class discussion. These papers can ideally be given to me 1-2
weeks before scheduled class discussion on that topic. Areas in which papers are invited include
(i) measurement applications, (ii) experimental research methods, (iii) survey research methods,
and (iv) qualitative research methods. In addition, students are encouraged to suggest papers for
any topic covered during the semester as well as for any additional topics.
At the beginning of the semester, each student can give me 3-5 papers that reflect your
present research interests. This is important to enable me to educate myself on your interests.
Choose papers that are different enough to cover a range of your interests.
Throughout the latter part of the semester, we will also have discussions of each person’s
class project. These discussions will provide a useful forum to obtain feedback on your
individual projects and monitor your own progress.
DESCRIPTION OF ASSIGNMENTS
Details of some weekly assignments will be provided during the course of the semester.
Reading assignments
In terms of reading assignments, students are expected to read assigned material and be
prepared to lead class discussion. As you learn the material, write down questions that come to
mind. Also, try to work through examples during reading and raise these examples in class
discussion. I will provide a set of discussion questions for each week about a week in advance.
However, we do not need to be constrained by these questions alone. Students are strongly
encouraged to raise questions at appropriate times in the discussion. Class participation will
involve both raising questions and attempting to answer questions raised.
Project
The project will consist of several phases that are listed below. You are encouraged to
discuss your project with me during the course of the semester. Several assignments pertaining
to the project are due during the course of the semester. These assignments are intended to
facilitate feedback and ensure completion of the paper based on the project.
(i) Identify theory/past research that will form the basis for your paper. While in-depth
discussion and theorizing is not central to the purpose of this paper, it is important that you are
clear about the rationale/theory for the hypotheses for purposes of designing the method.
Further, you need to know past research in terms of methodological issues in order to provide
support for your own method.
(ii) Develop and state the hypotheses that you are going to test. Parts i and ii are due on
September 29.
(iii) Develop the overall design and provide the rationale for choosing it to test your
hypotheses.
(iv) Clearly describe the independent and dependent variables and their
operationalizations. As a part of the paper you are required to develop a multiple-item measure
for at least one variable which should have at least five items.
Parts iii and iv are due on October 27.
(v) Provide complete details of all materials to be used. If you are using a questionnaire,
the complete questionnaire must be presented. If you are conducting an experiment, all materials
should be presented. The paper should provide support for choice of materials. The final paper
should contain an appendix where all materials are presented. The reader should be able to go
out and collect data immediately using the information provided.
Describe all details such as the participants in the study, the exact procedure to be
employed, etc., along with rationale for your choice.
As you consider your paper, make sure you have addressed the issues we cover in class
such as reliability, validity, internal versus external validity, etc.. These principles should be
used to develop your method and also to provide support for your choices.
Part v is due on November 10.
(vi) Describe the data analyses that you would perform on the data including assessment
of reliability, validity, usage of LISREL, etc.
(vii) The final presentation and paper should be of good quality that is reflective of work
done throughout the course of the semester. The paper should be organized into headings and
sub-headings similar to published work. Good writing is very important.
(viii) The final paper is due on December 8. In order to be fair to all students in applying
consistent standards, requests for extension of the deadline will not be considered except for
certain special circumstances.