Quantitative Research Methods I (EDMS 645)
Section 0102
Spring 2010
EDU 0212
Wednesday – 4:15-7:00pm
Instructor
Dr. Hong Jiao
1230B Benjamin Building
Phone: (301) 405-3627
Email: hjiao@umd.edu
Office Hours
Tuesday 1:00-3:00pm
or by appointment
Teaching Assistant
Fu Liu
0108R Cole Field House
Email: floraliu@umd.edu
Office Hours
Monday 1:00pm-2:30pm
Wed. 11:00am to 12:30pm
or by appointment
Course Description
This course is the first graduate-level applied statistics course. It starts with an introduction to
quantitative research methods, followed by descriptive statistics, and ends with inferential statistics.
Their applications in real research settings are exemplified throughout the course. It emphasizes the
correct understanding of basic statistics for quantitative data analysis: data representation; descriptive
statistics; and hypothesis testing. This course demonstrates some quantitative data analysis procedures
based on the commonly used statistical computer package: SPSS. Proper interpretation of the statistical
analysis results is one of the course foci. In addition, this course will introduce three existing
international test or survey data sets including the Program for International Student Assessment (PISA),
Progress in International Reading Literacy Study (PIRLS), and Trends in International Mathematics and
Science Study (TIMSS) to promote students using these existing international tests and survey data in
their research and be aware of the international dimension in their research.
Required Textbook
•
Hinkle, D.E., Wiersma, W., & Jurs, S.G. (2003). Applied Statistics for the Behavioral Sciences,
Fifth Edition. Boston, MA: Houghton Mifflin.
Additional Reference Textbooks
•
•
•
Agresti, A., & Finlay, B. (1997). Statistical methods for the social sciences (3rd ed.) Upper
Saddle River, NJ: Prentice Hall.
Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.)
Needham Heights, MA: Allyn and Bacon.
Howell, D.C. (2002). Statistical methods for psychology (5th edition). Pacific Grove, CA:
Duxbury Press.
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Course Topics and Readings
Course topics can be found: http://www.education.umd.edu/EDMS/courses/TopicsEDMS645.pdf
Or under Syllabus in ELMS
The following table lists the topics to be covered in this course. This
timetable is
tentative and subject to changes.
Week
Date
1
2
3
1/27
2/3
2/10
4
5
2/17
2/24
6
7
8
9
10
11
3/3
3/10
3/17
3/24
3/31
4/7
12
4/14
13
4/21
14
15
16
4/28
5/5
5/12
Topics
Introduction & research methods
Research methods & sampling
Scale of measurement &
organizing and graphing data-SPSS
Central tendency, variation, & percentiles
Normal distribution &
scale transformation
Project 1, Correlation
Simple Linear Regression
Spring Break
Midterm
Probability & Sampling distributions
Hypothesis testing: one-sample for mean
& for other statistics
Hypothesis testing:
two-sample for mean (dependent samples)
Hypothesis testing:
two-sample for mean (independent samples)
AERA Project 2
Chi-square test for nominal data & review
Final exam
Readings
ECP
Schafer & Johnson & in-class notes
Hinkle – Ch. 7 & Ch. 1
HW1 Hinkle – Ch. 1 & 2-computer lab
HW1 Hinkle – Ch. 3
HW2 Hinkle – Ch. 4
HW2 Hinkle – Ch. 5
HW3 Hinkle – Ch. 6
No class
Hinkle – Ch. 7
HW4 Hinkle – Ch. 8 & 10
Hinkle – Ch. 11
HW5 Hinkle – Ch. 11
No class
Hinkle – Ch. 21
Lecture notes, homework assignments, data, and announcements will be posted at
www.elms.umd.edu. Please log into the website to access to the course materials.
Statistical Software
EDMS department supports SPSS versions 11.0 and higher (Windows and Macintosh). Examples in
class will come from SPSS/Windows (17.0), which is available in the Benjamin Building’s computer lab
(0230) in the basement. Students may use whichever recent package they wish, but they should know
that slight differences may exist among versions. Students can



either use a campus lab to do SPSS assignments.
or buy a version of the Student/Ware package
or buy a version of the Grad Pack, which historically costs about $225 and manuals must be
purchased separately.
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Course Objectives
By the end of the course the student should have demonstrated the ability to:
1.
2.
3.
4.
Identify types of quantitative research methods and threats to internal and external validity.
Identify different sampling methods.
Identify differences between the four scales of measurement.
Differentiate between samples and populations and between parameters and random variables, and
know when each is used.
5. Identify different kinds of graphs (histograms, stem-and-leaf diagrams, boxplots, etc.) and know
proper uses (and misuses) of each kind of graph.
6. Properly construct each kind of graph when given a set of data.
7. Interpret graphic and tabular representations of data, recognizing important differences among
them.
8. Recognize and identify differences between various descriptive statistics, such as the mean,
median, variance, standard deviation, skewness, and kurtosis.
9. Recognize formulas for the above statistics (as well as others), and be able to write them in
summation notation.
10. Compute the above statistics.
11. Compute the descriptive statistics for linear transformations and combinations of variables, and
describe the influence of the transformations on the statistics.
12. Describe distributions of different types in terms of shape, location and variability, and match the
values of descriptive statistics to corresponding graphic representations of data.
13. Calculate the correlations and know when they are most appropriately applied.
14. Construct a prediction model using simple linear regression and interpret the resulting values.
15. Compute and interpret measures of explained variation.
16. Recognize differences in the properties of normal and nonnormal distributions, and identify the
consequences of the central limit theorem.
17. Know the definition and properties of sampling distributions including Type I and Type II errors.
18. Use the sampling distribution of the sample mean to compute a test statistic and an interval
estimate for the population mean.
19. Use the sampling distribution of the difference between two sample means to compute a test
statistic and interval estimate for the population mean difference (independent groups).
20. Use the sampling distribution of the difference between two sample means to compute a test
statistic and interval estimate for the population mean difference (dependent groups).
21. Know the definition of power and factors affecting it.
22. Perform a test of goodness-of-fit, interpret results in terms of the hypothesis being tested
23. Perform a test of independence (“homogeneity”), interpret the results in terms of the hypothesis
being tested
They are in alignment with the course topics
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Formal Course Assessment
Homework Assignments (HW)
There will be 5 homework assignments spaced evenly throughout the semester to give students an
opportunity to apply and practice concepts learned in class. It is expected that students will be using
SPSS for their homework where computer work is required. When working the assignments, students
are expected to pull together the material from lecture, the text, and the supplemental notes where
applicable. In the assignments students should cut and paste relevant portions of the computer output into
the appropriate places in the homework to show how solutions are arrived. Assignments should be wellorganized and must be word-processed. Students are encouraged to work in pairs or groups. But
each student should write up their own answers to the homework questions. Late homework
assignments will be accepted with a penalty of 10% credit. Graded assignments will generally be returned
in the following class after they are submitted.
Projects
There will be two short projects. Project 1 requires students to come up with a research project. Then
they collect real data and use the descriptive statistics to summarize the data. Project 2 requires students
to use one or several statistical methods to analyze the data they collected.
Exams
There will be two in-class exams. The content of the exam will cover topics presented in class up to that
point. The exam will be closed book and closed class note; however, students may prepare and use a
reference sheet of a 8.5”x11” two-sided page of notes. Students should bring a calculator to the exams;
but calculator sharing between students will not be allowed. No cell phone calculators can be used in
exams.
Extra Credit Project (ECP)
There will be one project which is optional and give you an opportunity to replace one homework
assignment with the lowest score. If you are satisfied with your homework points accumulated, you do
not need to work on the extra credit project. If you choose to turn in the extra credit project and the score
for the extra credit project is higher than the lowest homework score, your homework assignment with
the lowest score will be dropped and the extra credit project score will be counted towards your total
homework points. If your extra credit project is graded with a score lower than the lowest homework
assignment’ score, no action will be taken.
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Course Grades
Students’ homework, quizzes and exam will be combined using a weighted average grading scheme
with the corresponding weights given below. Final letter grades will then be assigned based on the given
scale.
Assessment
Weight
Total homework points
50%
Total midterm exam points 20%
Total project points
10%
Total final exam points
20%
Overall Course Percent
100% - 93%
92 % - 88%
87% - 85%
84% - 81%
80% - 78%
77% - 75%
74% - 70%
69% - 65%
64% - 60%
59% - 55%
54% - 50%
 49%
Grade
A
AB+
B
BC+
C
CD+
D
DF
Incompletes
Incompletes for this course will be given on a case-by-case basis. The most valid reason for an
incomplete is an unforeseen event that gravely interferes with a student’s ability to perform at an
adequate level. Incompletes will not be given for unqualified poor performance.
Accommodations for Emergencies
If the University closes on the day of class, there will be no class. If the University does not close but
there is a threat of inclement weather, there will still have class unless notified otherwise. So, please
check email and/or the course website on blackboard, sometime during the day of class for any last
minute announcements. If you will be absent from class, please send me an email to notify. All students
are expected to take the quizzes and exams and to submit assignments on the specified dates. You must
contact me before an exam if you will be absent.
Academic Accommodations
In compliance with the Americans with Disabilities Act (ADA), I will work with you if you have a
documented disability that is relevant to successfully completing your work in this course. If you need
academic accommodation for a documented disability, please contact me as soon as possible to discuss
your needs. Students with documented needs for such accommodations must meet the same achievement
standards required of all other students, although the exact way in which achievement is demonstrated
may be altered. All requests for academic accommodations should be made as early as possible in the
semester. For further information concerning disability accommodations, please contact Dr. William
Scales at the Disability Support Service – (301) 314-7682.
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Academic Integrity
The University of Maryland, College Park has a nationally recognized Code of Academic
Integrity, administered by the Student Honor Council. This Code sets standards for academic
integrity at Maryland for all undergraduate and graduate students. As a student you are
responsible for upholding these standards for this course. It is very important for you to be
aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more
information on the Code of Academic Integrity or the Student Honor Council, please visit
http://www.shc.umd.edu.
To further exhibit your commitment to academic integrity, remember to sign the Honor Pledge
on all examinations and assignments: "I pledge on my honor that I have not given or received
any unauthorized assistance on this examination (assignment)."
For more information on the code of Academic Integrity or the Student Honor Council, please go to
http://www.shc.umd.edu for details.
On plagiarism -- It is important that the student synthesize pertinent information from the readings and
class lectures when writing up homework assignments. Synthesis does not occur when large blocks of
text are copied from the textbook or lecture notes and used to answer questions. You should avoid
extensive verbatim copying of information from the textbook or lecture notes when answering the longer
questions on the assignments.
Make-Up Examinations
The University policy states: “An instructor is not under obligation to offer a substitute assignment or to
give a student a make-up assessment unless the failure to perform was due to an excused absence, that
is, due to illness (of the student or a dependent), religious observance (where the nature of the
observance prevents the student from being present during the class period), participation in university
activities at the request of university authorities, or compelling circumstances beyond the student’s
control. Students claiming excused absence must apply in writing and furnish documentary support for
their assertion that absence resulted from one of these causes.”
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