Keith
Markus' Urban Sprawl:
http://web.jjay.cuny.edu/~kmarkus
PSYC U76000 GC
Introduction to Psychometrics
Registration Number 51980
This course is equivalent to EPSY U73000 Psychometric Methods
Syllabus
Fall 2024
Time: Wednesday 6:30-8:30
PM
Room: Graduate
Center 3309
Section: 01
Contact Information:
Professor Keith A. Markus
kmarkus@aol.com (This is
the best way to contact me.)
212-237-8784
Office: 10.65.04
New Building, John Jay College
Address: Psychology Department, 10th Floor
John Jay College of Criminal Justice, CUNY
524 W59th Street, New York, NY, 10019
Office Hours: Please contact me by email.
Most questions can be answered more quickly by email. When
that is not the case, we can use email to schedule an in-person
appointment.
Course Description: The course offers a general
introduction to psychometric methods primarily emphasizing classical
test theory, test construction and validation, and test use.
The emphasis lies with developing a firm understanding of basic
psychometric concepts. This course lays a foundation for more
advanced courses in specific topics introduced here. The course
understands psychometrics and testing as applying broadly, not just
to paper and pencil tests but also to performance assessments,
behavioral observations, measured variables in experiments and
quasi-experiments, surveys, and other forms of behavioral data
collection. However, much of the material will emphasize measurement
involving multiple indicators of a common construct.
Course Objectives: The
course assumes a foundation in basic statistics and a healthy
curiosity but little more. The more you put into the course, the
more you will get out of the course. The course design reflects
the following objectives.
1. Students will gain a basic understanding of the foundations of
test theory that will prepare them to pursue more advanced topics
(e.g., item response theory, structural equation modeling).
2. Students will gain the background and confidence to critically
read technical manuals and other documentation in conjunction with
use of published tests.
3. Students will gain facility with conceptual tools for thinking
through issues of validity and reliability as applied to all
measures from dependent variables in experiments to large scale
testing programs.
4. Students will gain a level of comfort with algebraic
representations of test scores and the use of these to think
through applied problems related to test use and interpretation.
5. Students will gain an increased sensitivity to the fallibility
of educational and psychological tests and the limits to their use
and interpretation.
6. Students will gain exposure to the use of statistical software
for conducting psychometric analyses and some experience with such
analyses.
Text Book:
Bandalos,
D. L. (2018). Measurement Theory and Applications for the Social
Sciences. New York: Guildford.
Additional
Reading:
Recommended Reading:
American Psychological Association (2020). Publication manual
of the American Psychological Association (7th ed.).
Washington, DC: Author. (Project papers should be written in
APA style and format.)
Markus, K. A. & Borsboom, D. (2013). A theory of test
score interpretation. (from Markus, K. A. & Borsboom, D.,
2013. Frontiers of test validity theory. New York:
Routledge. A copy of the chapter will be provided.)
Course Flow: Familiarize yourself with the reading
material before the corresponding lecture. Lectures will
summarize and clarify the reading. In general, I would rather
answer your questions than lecture. I will use class time to
illustrate and amplify particularly tricky points based on past
experience. It is not possible to cover all the material in class.
I will illustrate psychometric concepts primarily using
spreadsheets and R. Prior familiarity with the software is
not a requirement for taking the course. However, learning
psychometrics simply by reading about it is akin to learning to
swim, ski, or play a musical instrument simply by reading about
it. Actual practice is a much more effective method. Whether you
use a simple calculator, a spreadsheet, or advanced statistical
software, it is a good habit to play around with the material by
constructing concrete examples and taking a try-and-see attitude
toward the material. If something seems puzzling, make up an
example and try it out. If something seems counter-intuitive to
you, try to construct a counterexample. The more concrete you make
psychometrics, the more comfortable you will feel with the
material, the better you will understand it, and the more skills
you will develop that you can apply outside of the class. None of
the this is required for the course, but it will make it more fun,
more interesting, and more valuable at a practical level.
Course Project:
The course project is broken into four parts: topic, and Parts 1 to
3. Details of the assignment along with R scripts will be
provided through Blackboard. Due dates are listed below on the
course schedule. The provided scripts are just starter code
meant to save you some time and effort, they are not meant as
complete plug-and-play scripts that you can use unaltered. You
will need to tailor them to fit the specifics of your project.
Grading: The course project is the only graded
assignment. The topic is worth 10%, Part 1 25%, Part 2 30% and
Part 3 35%. Letter grades will be assigned as indicated
below.
|
Letter Grade
|
Percent Grade
|
|
A
|
92-100
|
|
A-
|
84-91
|
|
B+
|
76-83
|
|
B
|
68-75
|
|
B-
|
60-67
|
|
C+
|
52-59
|
|
C
|
44-51
|
|
C-
|
36-43
|
|
F
|
0-35
|
Special Needs:
To request accommodations please contact the Office of the Vice
President for Student Affairs (Room 7301 Graduate Center; (212)
817-7400). Information about accommodations can be found in the
Graduate Center Student Handbook 05-06, pp. 51-52).
Academic Honesty:
The Graduate Center of The City University of New York is committed
to the highest standards of academic honesty. Acts of academic
dishonesty include—but are not limited to—plagiarism, (in drafts,
outlines, and examinations, as well as final papers), cheating,
bribery, academic fraud, sabotage of research materials, the sale of
academic papers, and the falsification of records. An individual who
engages in these or related activities or who knowingly aids another
who engages in them is acting in an academically dishonest manner
and will be subject to disciplinary action in accordance with the
bylaws and procedures of The Graduate Center and the Board of
Trustees of The City University of New York.
Each member of the academic community is expected to give full,
fair, and formal credit to any and all sources that have contributed
to the formulation of ideas, methods, interpretations, and findings.
The absence of such formal credit is an affirmation representing
that the work is fully the writer’s. The term “sources” includes,
but is not limited to, published or unpublished materials, lectures
and lecture notes, computer programs, mathematical and other
symbolic formulations, course papers, examinations, theses,
dissertations, and comments offered in class or informal
discussions, and includes electronic media. The representation that
such work of another person is the writer’s own is plagiarism.
Care must be taken to document the source of any ideas or arguments.
If the actual words of a source are used, they must appear within
quotation marks. In cases that are unclear, the writer must take due
care to avoid plagiarism.
The source should be cited whenever:
(a) a text is quoted verbatim
(b) data gathered by another are presented in diagrams or tables
(c) the results of a study done by another are used
(d) the work or intellectual effort of another is paraphrased by the
writer
Because the intent to deceive is not a necessary
element in plagiarism, careful note taking and record keeping are
essential in order to avoid unintentional plagiarism.
For additional information, please consult
“Avoiding and Detecting Plagiarism,” available in the Office of the
Vice President for Student Affairs, the Provost’s Office, or at
http://web.gc.cuny.edu/provost/pdf/AvoidingPlagiarism.pdf.
(From The Graduate Center Student Handbook 05-06, pp. 36-37)
Schedule
Date
|
Topics
|
Reading Due
|
Assignments Due
|
Week 1 W 8/28
|
Course overview & Models of test scores
"R in an Hour"
|
Bandalos Ch. 1(B1) |
|
Week 2 W 9/4
|
Scale development
|
B3 & Standards Ch. 4 (S4) |
|
Week 3 W 9/11
|
Norms and standard scores
|
B2 & S5 |
Post Choice of Project Topic
|
Week 4 W 9/18
|
Cognitive items
|
B4 |
|
Week 5 W 9/25
|
Non-cognitive items
|
B5 |
|
Week 6 W 10/9
(Class does not meet 10/2)
|
Item analysis
|
B6 & S7 |
Project Part 1 |
|
Week 7 W 10/16
|
Reliability (part one)
|
B7 & S2 |
|
Week 8 W 10/23
|
Reliability (part two)
|
B8
|
|
Week 9 W 10/30
|
Validity (part one)
|
B11 (pp. 254-265) & S1
|
|
Week 10 W 11/6
|
Validity (part two)
|
B11 (pp. 265-297)
|
|
Week 11 W 11/13
|
Exploratory Factor Analysis |
B12 |
Project Part 2
|
Week 12 W 11/20
|
Item Response Theory
|
B14
|
|
Week 13 W 12/4
(Class does not meet 11/27)
|
Test equating
|
B18 (recall S5) |
|
|
Week 14 W 12/11
|
Test bias & Test fairness
|
B16 & S3 |
|
Week 15 W 12/18
(no meeting)
|
Open office hours for project questions
(virtual)
|
|
|
12/21 (no meeting)
|
(Last day of final exam week) |
|
Project Part 3 |
Source: https://www.gc.cuny.edu/registrar/academic-calendar
Created 27 January 2008
Updated 7 Sept 2024, 19 August 2024
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