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EC 315 Quantitative Research Methods
Staff


Mission Statement: The mission of Park University, an entrepreneurial institution of learning, is to provide access to academic excellence, which will prepare learners to think critically, communicate effectively and engage in lifelong learning while serving a global community.

Vision Statement: Park University will be a renowned international leader in providing innovative educational opportunities for learners within the global society.

Course

EC 315 Quantitative Research Methods

Semester

F2T 2006 DLB

Faculty

Mahanty, Aroop

Title

Adjunct Professor of Business and Economics, U. Maryland Europe

Degrees/Certificates

B.S. Engineering, U. Wyoming
M.A. Economics, Colorado State College
Ph.D. Economics, Colorado State University; post-doc.training,U. Chicago, Graduate School of Business

Office Location

U.S. Embassy-Rome, Italy

Office Hours

08:00-15:00 CST

Other Phone

(Cell) 339-8655735

E-Mail

aroop.mahanty@park.edu

amahanty@faculty.ed.umuc.edu

Semester Dates

October 23- December 17, 2006

Class Days

TBA

Class Time

TBA

Prerequisites

MA 120 ans CS 140

Credit Hours

3


Textbook:

The primary text is:

Guide to Statistical Analysis for Business and Social Science by Peter Soule: ISBN 978-0-07-312532-9. This book can be acquired from your campus bokstore or ordered directly from MBS bookstore. Presumably, some of you will also research the offers made by Amazon com.

 

Textbooks can be purchased through the MBS bookstore

Textbooks can be purchased through the Parkville Bookstore

Additional Resources:

You may also consult the following books as reference:

Statistics for Business by Anderson , Williams and Sweeny

Econometrics: An Introductory Analysis by T. Hu.

A scientific calculator is also necessary. It need not be the expensive programmable kind but must posses the capability of processing data involving X-exponent y and Y- exponent x entries.

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Course Description:
This intermediate level statistics course covers the fundamentals of conducting quantitative research for the social and administrative sciences. The course is organized around a research project on quantitative analysis of data. PREREQUISITES: MA 120 and CS 140. 3:0:3

Educational Philosophy:

   The purpose of this course is to familiarize the student, especially someone pursuing a degree in either business or one of the social sciences, with the uses and usefulness of statistics in the discipline. Although some other topics are also discussed, the focus is on understanding the relationship among variables,  testing hypotheses, and constructing models with the tools of regression analysis.

   Although not difficult, at times calculations can either be cumbersome or confusing. As with other mathematical topics, "practice makes perfect". Consequently, the more you do and the more you practice, the easier it will get. You are encouraged to ask questions. Solutions to practice problems will be provided as necessary. There will be quizzes occasionally to keep you interested as well as to maintain a record of your progress.

   Mathematics, being what it is, thrives on precision. Thus, if a number is off by even one decimal point or if a (-) is put in place of a (+), the result will be unexpectedly bizarre. Recognizing that, I will be assigning more weight to the method as oposed to the numbers themselves. However, correct results will obviously earn more points. 

Learning Outcomes:
  Core Learning Outcomes

  1. Write a proposal for a research project on an original multiple regression model with four independent variables.
  2. Gather data and research articles related to the multiple regression model.
  3. Use a statistical software package to regress the original model.
  4. Write a formal research report that defines and analyzes the evaluative information provided by a statistical software package, including the adjusted R-square, F statistic, t statistics, and correlation coefficients for multicollinearity.
  5. Do hypothesis testing and determine confidence intervals using the t statistic. Also,  conduct hypothesis testing using the F and Chi-square statistics.


Core Assessment:

DESCRIPTION OF CORE ASSESSMENT FOR EC 315:


All Park University courses must include a core assessment that measures the course's Core Learning Outcomes.  The purpose of this assessment is to determine if expectations have been met concerning mastery of learning outcomes across all instructional modalities.  For this course, the core assessment is a research project that includes a written proposal and final research paper.  This project is worth 20 percent of the student's final grade and will assess students' mastery of four Core Learning Outcomes (outcomes 1, 2, 3, and 4 listed on this syllabus). Although guidelines for the research proposal are listed below, the Core Assessment Rubric refers to the grading of the final research paper only.


Research Topic Proposal


As preparation for the final research paper, formulate an original theory about the correlation between four measurable independent variables (causes) and one measurable dependent variable (the effect).  The topic proposal should include the following four items which serve as the foundation for the final research paper after instructor feedback is given.


1)  Purpose Statement 


In one paragraph, state the correlation and identify the primary independent variable.


State the correlation:


“The dependent variable _______ is determined by independent variables ________, _________, ________, and ________.”


Identify and defend the “primary” independent variable, or the variable believed to have the strongest impact on the dependent variable:


 “The most important independent variable in this relationship is ________ because _________.”


2)  Definition of Variables 


For each variable, write a single definition paragraph (five paragraphs total).  Paragraphs should be in this order: dependent variable, primary independent variable, and remaining three independent variables.


In addition to defining the independent variables, defend why each determines the dependent variable.


For the primary independent variable, at least two research sources that discuss the variable also must be cited.  These sources need not be technical documents but should contain evidence to justify the relationship between the primary independent variable and the dependent variable.  List these sources on the Works Cited (reference) page.  Citations from encyclopedias, abstracts, or non-governmental websites are not acceptable research sources.


3)  Data Description


For each of the five variables, at least 30 observations of cross-sectional data must be obtained.  Thus for the final research paper, a data matrix that is at least 30 rows by five columns must be presented.


In one paragraph, identify the data sources and describe the data (i.e., which government agencies supply the data, which methods are used to compile them, when they were collected, etc.).  Attach a Xerox copy of the original data tables from which the data will be compiled after the proposal is reviewed and approved by the instructor. 


4) Works Cited Page


The final page of the proposal should be a Works Cited page listing the two research sources for the primary independent variable and the data sources, with a separate citation for each table of data, including specific table numbers for each of the five sources.  The appropriate format should be employed (see below).


Final Research Paper

Purpose Statement and Model


1) In the introductory paragraph, state why the dependent variable has been chosen for analysis.  Then make a general statement about the model:


The dependent variable _______ is determined by variables ________, ________, ________, and ________.”


2) In the second paragraph, identify the primary independent variable and defend why it is important.


“The most important variable in this analysis is ________ because _________.”   In this paragraph, cite and discuss the two research sources that support the thesis, i.e., the model. 


3) Write the general form of the model, with the primary independent variable as X1:


The model is:


Y =


Where 

Y: brief definition of Y


X1: brief definition of X1  [etc. for each variable]


Definition of Variables

4) Define and defend all variables, including the dependent variable, in a single paragraph for each variable.  Also, state the expectations for each independent variable.  These paragraphs should be in numerical order, i.e., dependent variable, X1, then X2, etc.  


In each paragraph, the following should be addressed:


<        How is the variable defined in the data source?


<        Which unit of measurement is used?


<        For the independent variables: why does the variable determine Y?


<        What sign is expected for the independent variable's coefficient, positive or negative?  Why?


Data Description

5) In one paragraph, describe the data and identify the data sources. 


<        From which general sources and from which specific tables are the data taken? (Citing a website is not acceptable.)


<        Which year or years were the data collected?


<        Are there any data limitations?


Presentation and Interpretation of Results
 

6) Write the estimated (prediction) equation:


The results are:


 =


7) Identify and interpret the adjusted R2 (one paragraph):


<        Define “adjusted R2.”


<        What does the value of the adjusted R2 reveal about the model?


<        If the adjusted R2 is low, how has the choice of independent variables created this result?


8) Identify and interpret the F test (one paragraph):


<        Using the p-value approach, is the null hypothesis for the F test rejected or not rejected?  Why or why not?


<        Interpret the implications of these findings for the model.


9) Identify and interpret the t tests for each of the coefficients (one separate paragraph for each variable, in numerical order):


<        Are the signs of the coefficients as expected?  If not, why not?


<        For each of the coefficients, interpret the numerical value.


<        Using the p-value approach, is the null hypothesis for the t test rejected or not rejected for each coefficient?  Why or why not?


<        Interpret the implications of these findings for the variable.


<        Identify the variable with the greatest significance.


10) Analyze multicollinearity of the independent variables (one paragraph):


<        Generate the correlation matrix.


<        Define multicollinearity.


<        Are any of the independent variables highly correlated with each other?  If so, identify the variables and explain why they are correlated.


<        State the implications of multicollinearity (if found) for the model.


11) Other (not required):


<        If any additional techniques for improving results are employed, discuss these at the end of the paper.


Works Cited Page


12) Use the proper format to list the works cited under two headings:


 Research:  two sources


 Data: a separate citation for each of the five variables

Link to Class Rubric

Class Assessment:

   Your performance will be judged by how well you do in:

quizzes (unannounced!), assignments, term project and examinations (midterm and final). The details concerning points are indicated in the next section.

Grading:

   The grade for the course will be determined by your performance in assignments, quizzes, term papers and examinations according to following weights:

Quizzes: 10% ( 2 at 5% each)-- 100 points

Assignments: 10% ( 2 at 5% each)--100 points

Mid-term examination: 30%--300 points

Final examination: 35%--350 points

Term project: 15%--150 points

GRAND TOTAL : 1,000 points.

900-1,000 = A ; 800-899 = B; 700-799 = C ; 600-699 = D; <600 = F.

Late Submission of Course Materials:
   Homework is due NLT the date specified. I may permit the submission of late work subject to (substantial) penalty. Students missing examination(s) will need to provide valid documents which I must appove before the examination(s) can be made up.

Classroom Rules of Conduct:
   I do not need to lecture you on classroom conduct because I expect decent behavior from all of us. The idea is to create a healthy classroom atmosphere conducive to learning.

Course Topic/Dates/Assignments:

WEEK 1: Introductions ; introduction to class and basic review of statistical concepts; data discussion and some well-known probability distributions; testing hypotheses with the "t" statistic. Chapters 1-2. Assignment 1 handed out.

WEEK 2: Review as necessary ; tests of difference between two means; testing hypotheses with the F statistic. Chapters 3-4. Assignment 1 due.

WEEK 3: The binomial model and the X2 distribution; Binomial hypothesis testing;

contingency matrix and tests using the chi-squared statistic. Chapters 5-6.

WEEK 4: MID-TERM EXAMINATION based on chapters 1-6 and other material covered/discusssed until now.

After the examination: introduction to regression; obtaining simple linear equations: goodness-of-fit considerations. Using software to obtain linear regressions. Chapters 7-8

WEEK 5: Review; introduction to multiple regression; using SPSS or Excel to obtain multiple regression estimates. Chapters 9-10  Assignment 2 handed out.

WEEK 6: Evaluating regression results; studying constants, coefficients and R2 ; statistical

significance and the Durbin-Watson test. Chapters 11-12 Assignment 2 due.

WEEK  7:Review: term projects due: graphs, equations etc; miscellaneous. Chapter 15

WEEK 8: Final Examination

Academic Honesty:
Academic integrity is the foundation of the academic community. Because each student has the primary responsibility for being academically honest, students are advised to read and understand all sections of this policy relating to standards of conduct and academic life.   Park University 2006-2007 Undergraduate Catalog Page 87-89

Plagiarism:
Plagiarism involves the use of quotations without quotation marks, the use of quotations without indication of the source, the use of another's idea without acknowledging the source, the submission of a paper, laboratory report, project, or class assignment (any portion of such) prepared by another person, or incorrect paraphrasing. Park University 2006-2007 Undergraduate Catalog Page 87

Attendance Policy:
Instructors are required to maintain attendance records and to report absences via the online attendance reporting system.

  1. The instructor may excuse absences for valid reasons, but missed work must be made up within the semester/term of enrollment.
  2. Work missed through unexcused absences must also be made up within the semester/term of enrollment, but unexcused absences may carry further penalties.
  3. In the event of two consecutive weeks of unexcused absences in a semester/term of enrollment, the student will be administratively withdrawn, resulting in a grade of "W".
  4. A "Contract for Incomplete" will not be issued to a student who has unexcused or excessive absences recorded for a course.
  5. Students receiving Military Tuition Assistance or Veterans Administration educational benefits must not exceed three unexcused absences in the semester/term of enrollment. Excessive absences will be reported to the appropriate agency and may result in a monetary penalty to the student.
  6. Report of a "F" grade (attendance or academic) resulting from excessive absence for those students who are receiving financial assistance from agencies not mentioned in item 5 above will be reported to the appropriate agency.
ONLINE NOTE: An attendance report of "P" (present) will be recorded for students who have logged in to the Online classroom at least once during each week of the term. Recording of attendance is not equivalent to participation. Participation grades will be assigned by each instructor according to the criteria in the Grading Policy section of the syllabus.

Park University 2006-2007 Undergraduate Catalog Page 89-90

Disability Guidelines:
Park University is committed to meeting the needs of all students that meet the criteria for special assistance. These guidelines are designed to supply directions to students concerning the information necessary to accomplish this goal. It is Park University's policy to comply fully with federal and state law, including Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Act of 1990, regarding students with disabilities. In the case of any inconsistency between these guidelines and federal and/or state law, the provisions of the law will apply. Additional information concerning Park University's policies and procedures related to disability can be found on the Park University web page: http://www.park.edu/disability .

Additional Information:

   Course runs Monday-Sunday each week. It is best if you participate regularly in a timely manner throughout the week instead of waiting till 11:59 p.m. Sunday. The week's conference will not be accessible after midnight of Sunday and you will no longer be able to post anything after that time.


   If you have urgent PERSONAL commnunications, use e-mail to contact me. Otherwise, please DO NOT contact me on course-related matters via e-mail. If you must write to me, expect a response within two(2) working days.


   Occasionally I will be posting a Q&A conference in the Discussion area where you may pose questions or make comments. 



Rubric

CompetencyExceeds Expectation (3)Meets Expectation (2)Does Not Meet Expectation (1)No Evidence (0)
Evaluation                                                                                                                                                                                                                                                 
Outcomes
1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Four independent variables are appropriately chosen and are measurable. Two to three independent variables are appropriately chosen and are measurable. One independent variable is appropriately chosen and is measurable. No independent variable is appropriately chosen and no variable is measurable. 
Synthesis                                                                                                                                                                                                                                                  
Outcomes
2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Data for five variables are appropriate and documented; two research articles are cited. Data for two to four variables are appropriate and documented; two research articles are cited. Data for one variable are appropriate and documented; OR less than two research articles are cited. No data are appropriate or documented; no research articles are cited. 
Analysis                                                                                                                                                                                                                                                   
Outcomes
4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
All of the following statistics are perfectly analyzed: R2, F statistic, four t statistics, and correlation coefficients for multicollinearity Three to seven of the following statistics are perfectly analyzed: R2, F statistic, four t statistics, and correlation coefficients for multicollinearity Less than three of the following statistics are perfectly analyzed: R2, F statistic, four t statistics, and correlation coefficients for multicollinearity None of the following statistics is analyzed: R2, F statistic, four t statistics, and correlation coefficients for multicollinearity 
Application                                                                                                                                                                                                                                                
Outcomes
3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
All statistical results are generated with no errors. All statistical results are generated with only one error. All statistical results are generated with two or more errors. Statistical results are not generated. 
Content of Communication                                                                                                                                                                                                                                   
Outcomes
4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Works Cited page is properly formatted and complete. Works Cited page has one to two errors. Works Cited page has three or more errors. Works Cited page is not present. 
Technical Skill in Communicating                                                                                                                                                                                                                           
Outcomes
4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
All of the following statistics are perfectly defined: R2, F statistic, t statistic, and correlation coefficients/multicollinearity Two to three of the following statistics are correctly defined: R2, F statistic, t statistic, and correlation coefficients/multicollinearity One of the following statistics is correctly defined: R2, F statistic, t statistic, and correlation coefficients/multicollinearity None of the following statistics is defined: R2, F statistic, t statistic, and correlation coefficients/multicollinearity 
First Disciplinary Competency                                                                                                                                                                                                                              
Outcomes
4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
The p-value approach to hypothesis testing is perfectly defined and analyzed for all four t-tests. The p-value approach to hypothesis testing is correctly defined and analyzed for two to three t-tests. The p-value approach to hypothesis testing is correctly defined and analyzed for one t-test. The p-value approach to hypothesis testing is not defined or analyzed for any t-test. 
Second Disciplinary Competency                                                                                                                                                                                                                             
Outcomes
4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
The p-value approach to hypothesis testing is perfectly defined and analyzed for the F-test. The p-value approach to hypothesis testing is defined and analyzed with at least one error for the F-test. The p-value approach to hypothesis testing is not defined and analyzed with at least one error for the F-test. The p-value approach to hypothesis testing is neither defined nor analyzed for the F-test. 

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Last Updated:10/17/2006 5:26:31 AM