Quantitative Research Methods I (EDMS 645)
Section 0101
Spring 2014
EDU 3315
Monday – 4:15-7:00pm
Instructor
Dr. Hong Jiao
1230C Benjamin Building
Phone: (301) 405-3627
Email: hjiao@umd.edu
Office Hours
Monday 1:00-3:00pm
Tuesday 1:00-3:00pm
or by appointment
Teaching Intern
Qiwen Zheng
0108M Cole Field House
Email: qzheng12@umd.edu
Office Hours
Thursday 1:00-3:00pm
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 a 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 international test or
survey data sets including the Program for International Student Assessment (PISA,
http://nces.ed.gov/surveys/pisa/), Progress in International Reading Literacy Study (PIRLS,
http://nces.ed.gov/surveys/pirls/), and Trends in International Mathematics and Science Study (TIMSS,
http://nces.ed.gov/timss/) to promote students using these existing international tests and survey data in
their research (http://www.aera.net/grantsprogram/res_training/diss_grants/DGFly.html).
Recommended Textbook
• Hinkle, D.E., Wiersma, W., & Jurs, S.G. (2003). Applied Statistics for the Behavioral Sciences,
Fifth Edition. Boston, MA: Houghton Mifflin.
• Lomax, R. G., & Hahs-Vaughn, D. L. (2012). An introduction to statistical concepts (3rd
ed.). New York: Routledge.
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
4
5
6
7
8
9
10
11
12
13
1/27
2/3
2/10
2/17
2/24
3/3
3/10
3/17
3/24
3/31
4/7
4/14
4/21
14
4/28
15
16
5/5
5/12
Topics
Introduction & research methods
Sampling & scale of measurement
Organizing and graphing data-SPSS training
Central tendency, variation, & percentiles
Normal distribution & scale transformation
Correlation
Simple Linear Regression
Spring break
Mid-term
Conference
Probability & Sampling distributions
Hypothesis testing: one-sample for mean
Hypothesis testing: one-sample for correlation
two-sample for mean (dependent samples)
Hypothesis testing:
two-sample for mean (independent samples)
Chi-square test for nominal data & review
Final exam
Readings
ECP
HW1
HW2
HW3
Schafer & Johnson, in-class notes
Hinkle – Ch. 7 & Ch. 1
Hinkle – Ch. 2-computer lab
Hinkle – Ch. 3 (CL)
Hinkle – Ch. 4 (CL)
Hinkle – Ch. 5 (CL)
Hinkle – Ch. 6
No class
P
HW4
HW5
No class
Hinkle – Ch. 7
Hinkle – Ch. 8 (CL)
Hinkle – Ch. 10 & Ch. 11 (CL)
Hinkle – Ch. 11 (CL)
P/ECP due 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 program supports SPSS versions 21.0 and higher (Windows and Macintosh). SPSS 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 the student version of SPSS that is licensed for
about a year. It is available for $35 at https://terpware.umd.edu/Windows/Package/2181. Note that you
will need to visit the Terrapin Technology store in Stamp Union to pick up a product key.
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Course Objectives
By the end of the course the student should have demonstrated the ability to:
1. Identify types of quantitative research methods and threats to internal and external validity.
2. Identify different sampling methods.
3. Identify differences between the four scales of measurement and the scale of measurement of a
variable.
4. 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. You
can choose to submit your individual homework. Or one copy of the homework for the group you
work together can be submitted for everyone. The maximum number of students allowed in a
group will be three for homework submission. Then each student in that group will get the same
score. We can not provide feedback on your answers to homework questions before you submit
your homework. 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.
Project
There will be one project which requires students to use a real data set to produce proper descriptive
statistics to summarize the data and to use one or several statistical methods to analyze the data.
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.
Another Extra Credit Opportunity
There will be 10 quizzes at the beginning of 10 classes. If you get 3 out of 5 or 2 out of 3 questions
correct for the quiz, you get 1 score point. You can use your accumulated quiz score points to replace
one of your lowest homework score.
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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
Overall Course Percent
Grade
Total homework points
50%
100%-95%
A+
Total midterm exam points 20%
94% - 91%
A
Total project points
10%
90% - 88%
ATotal final exam points
20%
87% - 85%
B+
84% - 81%
B
80% - 78%
B77% - 75%
C+
74% - 70%
C
69% - 65%
C64% - 60%
D+
59% - 55%
D
54% - 50%
DF
 49%
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.
CLASS POLICIES
Academic integrity: The University of Maryland, College Park has a student-administered Honor Code
and Honor Pledge. For more information on the Code of Academic Integrity or the Student Honor
Council, please visit http://www.studenthonorcouncil.umd.edu/whatis.html. 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. The code prohibits students from
cheating, fabrication, facilitating academic dishonesty, and plagiarism. Instances of this include
submitting someone else’s work as your own, submitting your own work completed for another class
without permission, or failing to properly cite information other than your own (found in journals,
books, online, or otherwise). Any form of academic dishonesty will not be tolerated, and any sign of
academic dishonesty will be reported to the appropriate University officials.
Special needs: If you have a registered disability that will require accommodation, please see the
instructor so necessary arrangements can be made. If you have a disability and have not yet registered
with the University, please contact Disability Support Services in the Shoemaker Building
(301.314.7682, or 301.405.7683 TTD) as soon as possible.
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Religious observances: The University of Maryland policy on religious observances states that students
not be penalized in any way for participation in religious observances. Students shall be allowed,
whenever possible, to make up academic assignments that are missed due to such absences. However,
the must contact the instructor before the absence with a written notification of the projected absence,
and arrangements will be made for make-up work or examinations.
Course evaluations: As a member of our academic community, students have a number of important
responsibilities. One of these responsibilities is to submit course evaluations each term though
CourseEvalUM in order to help faculty and administrators improve teaching and learning at
Maryland. All information submitted to CourseEvalUM is confidential. Campus will notify you when
CourseEvalUM is open for you to complete your evaluations for fall semester courses. Please go
directly to the website (www.courseevalum.umd.edu) to complete your evaluations. By completing all
of your evaluations each semester, you will have the privilege of accessing online, at Testudo, the
evaluation reports for the thousands of courses for which 70% or more students submitted their
evaluations.
Missed single class due to illness: Once during a semester, a student’s self-authored note will be
accepted as an excuse for missing a minor scheduled grading event in a single class session if the note
documents the date of the illness, acknowledgement from the student that information provided in the
note is correct, and a statement that the student understands that providing false information is a
violation of the Code of Student Conduct. Students are expected to attempt to inform the instructor of
the illness prior to the date of the missed class.*
Major scheduled grading events: Major Scheduled Grading Events (MSGE) are indicated on the
syllabus. The conditions for accepting a self-signed note do not apply to these events. Written, signed
documentation by a health care professional, or other professional in the case of non-medical reasons
(see below) of a University-approved excuse for the student’s absence must be supplied. This
documentation must include verification of treatment dates and the time period for which the student
was unable to meet course requirements. Providers should not include diagnostic information. Without
this documentation, opportunities to make up missed assignments or assessments will not be provided.
Non-consecutive, medically necessitated absences from multiple class sessions: Students who
throughout the semester miss multiple, non-consecutive class sessions due to medical problems must
provide written documentation from a health care professional that their attendance on those days was
prohibited for medical reasons.
Non-medical excused absences: According to University policy, non-medical excused absences for
missed assignments or assessments may include illness of a dependent, religious observance,
involvement in University activities at the request of University officials, or circumstances that are
beyond the control of the student. Students asking for excused absence for any of those reasons must
also supply appropriate written documentation of the cause and make every attempt to inform the
instructor prior to the date of the missed class.
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