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EC 315 Quantitative Research Methods
Genre, Raye Ann


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

F2GG 2010 MI

Faculty

Genre, Raye Ann

Degrees/Certificates

Master of Arts in Mathematics Education
Bachelor of Science in Mathematics Education

Daytime Phone

701-720-4853

Other Phone

office phone 727-0469

E-Mail

rgenre@park.edu

rayeann.genre@sendit.nodak.edu

Semester Dates

October - December

Class Days

----R--

Class Time

5:00 - 10:00 PM

Credit Hours

3


Textbook:
Douglas Lind,William Marchal and Samuel Wathen, Basic Statistics for Business & Economics - With Student CD.    ISBN-10: 0-07-738447-4  ISBN-13: 978-0-07-738447-0 .

Publisher: McGraw-Hill/Irwin; 7 edition (January 11, 2010) 

Additional Resources:

McAfee Memorial Library - Online information, links, electronic databases and the Online catalog. Contact the library for further assistance via email or at 800-270-4347.
Career Counseling - The Career Development Center (CDC) provides services for all stages of career development.  The mission of the CDC is to provide the career planning tools to ensure a lifetime of career success.
Park Helpdesk - If you have forgotten your OPEN ID or Password, or need assistance with your PirateMail account, please email helpdesk@park.edu or call 800-927-3024
Resources for Current Students - A great place to look for all kinds of information http://www.park.edu/Current/.


Course Description:
EC315 Quantitative Research Methods: 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. Prerequisite: MA120 and CS140. 3:0:3

Learning Outcomes:
  Core Learning Outcomes

  1. Derive an original regression model and run it using Microsoft Excel. Write a paper explaining this model and its outcome.
  2. Given an Excel regression output, correctly interpret the model statistics including the statistical significance of the independent variables and the R-square statistic of the model.
  3. Find the predicted value of the dependent variable given a regression output with independent variable coefficients plus values for the independent variables.
  4. Conduct hypothesis tests and confidence intervals on the mean and the difference between two means using the "t" statistic.
  5. Conduct hypothesis tests and confidence intervals on the binomial statistic and on the difference between two binomial statistics using the "t" statistic.


Core Assessment:

All Park University courses must include a core assessment that measures the course 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 final exam to be administered in all sections of EC 315. This exam is worth 30 percent of the student’s final grade and will test students’ mastery of core learning outcomes through short answer questions on specific knowledge, Regression analysis procedure, and hypothesis testing.

Link to Class Rubric

Class Assessment:


HW 20%

Project (Paper) 20%

MIDTERM 30%

Final Exam 30%

There will be a final exam that will account for 30 % of the total grade.

Grading:

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 variable

Class Assessment:

1 midterm, 1 comprehensible final, Homework and a major project. Midterm, final, quizzes and HW consist of question and mostly problems at the level of and suggested/based by textbook.

Problem Sets:  Problem sets are graded liberally and a solution set will be provided to the students.  The purpose of the problem sets is to assess your learning of the material.  The completion of the problems sets will facilitate your success on the examinations.  Should you have any questions about the problem sets, you will need to get it resolved before the examinations. 

Examinations:  The examinations will be primarily composed of applications and interpretations, in addition to a small set of multiple choice and/or true false questions.  The only reference allowed during the examinations will be the GUIDE.

Term Project:  The term project involves proposing a model and then writing a formal proposal for the research it involves.  The student will use Excel to regress data for their model.  The student will analyze the computer output and write and submit a written report.  See Guide for details on the various requirements for the written paper.  Each item turned in will have a cover paper with the student's name. 

Oral Report:  See additional Oral Report requirements and suggestions in Chapter 14 of the Guide, along with an Oral Report Checklist

     PowerPoint presentations should follow the 3-5-7 rule.  No more than 3 main points per slide, no more than 5 lines of dense text, and no more than seven words per line. 


Grading:

HW 10%
PROJECT 40% (Feasibility 3%, Proposal 10%, report 20%, presentation 7%)
MIDTERM 20%
FINAL 30 %

Grading Criteria             

Evaluation Item                                    Percent          Points

Homework                                                  10%                100

Term Project Topic and Feasibility Paper       3%                  30

Term Project Proposal                                1 0%                100

Mid-Term Examination                                 20%                200

Term Project Report                                    20%                200

Oral Report on Term Project                         7%                  70

Final Examination                                        30%                300

TOTAL                                                       100%              1000

Grading Scale:  A = 90-100;  B = 80-89;  C = 70-79; D = 60-69;  F = 0-59.


The course grade for students will be based on the overall average of homework and tests taken during the course in accordance with the weighting of the various requirements as stated in the syllabus.

All final exams in all School of Business courses will be comprehensive and will be closed book and closed notes. They will constitute 30% of the total course grade and will not be a take-home exam. They will be completed during the test week in the period designated by the registrar or by the Proctor in the case online courses. If calculators are allowed, they will not be multifunctional electronic devices that include features such as: phones, cameras, instant messaging, pagers, and so forth. Electronic Computers will not be allowed on final exams unless an exception is made by the Dean of the School of Business.

Late Submission of Course Materials:
Attendance is mandatory and absences must be excused by the instructor or it will be an unexcused absence resulting in a zero for the class period.

Classroom Rules of Conduct:
Students will respect the teacher, their class mates and the education center.

Course Topic/Dates/Assignments:

 

Topic/Dates/Assignments:

EC 315 Assignments  Fall II 2010

Week one read chapters 1-3 do problem 47 on page 53 and problems 57-70 on pages 89-91.  We will be in the computer lab for class to get to familiarize with the computer programs needed for this class.

Week 2 read chapters 6 and 7 do problems 1-7 on page 171-173. Do problems 9-18 on page 180. 
note: book uses pi symbol for probability
do problems 1-4 page 20. Problems13-16 page 212, problems 18-20 on page 215 and problems 23-27 on page 217.

Week 3 read chapters 8 and 9.  There will be a handout for homework this week as well as problem 4 on page 232,  problems 5-8 on page 237, problems 15-18 on page 276 and problems 23-28 on page 282.

Week 4 Midterm Exam
read chapter 10 do problmes 1-8 on page 305 and 15-18 on page 313.

Week 5 Read chapter 13 do problems 1-6 on page 399. and problems 13-18 on page 409.  You will also be finding a data set and using it to derive an original regression model alnd run it using excel spreadsheet.   You will write a pater explaining it that will be due week 7.

Week 6 Read chapter 14. Do problems 1-4 page 445
and problems 9-12 pages 468-469.

Week 7 Read chapter 11.  Do problems 1-6 page 332-333. Do problems 7-10 page 337-338.
Do problems 14, 16, 18 page 342-343. Do problems 20, 22 pages 349-350.

Week 8 Review and Final exam .























































Week 4

Multiple-Sample Hypothesis Test

 Midterm


Chapter 11

Discussion and guidelines for  report

Descriptive Statistics

Homework will be assigned

Review  and discussed Research Project





Week 5

Simple Regression Analysis

Hypothesis Testing


Chapter 13

Linear Regression Section of Homework Project


 

Homework will be assigned






Week 6

Multiple Regression Analyses

Linear Regression


Chapter 14

Multiple Regression


 Hypothesis Testing

Homework will be assigned






Week 7

Interpreting Regression Analyses

Review Draft Projects Before Going Final.


Chapter 14





 Week 8                                       

Multiple  Regression

Multiple Regression





 





 Week 9

Presentations
Comprehensive final exam

Data reports are due




* Homework assignments will be assigned during class sessions

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 students and faculty members are encouraged to take advantage of the University resources available for learning about academic honesty (www.park.edu/current or http://www.park.edu/faculty/).from Park University 2010-2011 Undergraduate Catalog Page 92

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. from Park University 2010-2011 Undergraduate Catalog Page 92-93

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 "F".
  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.

Park University 2010-2011 Undergraduate Catalog Page 95-96

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 .



Rubric

CompetencyExceeds Expectation (3)Meets Expectation (2)Does Not Meet Expectation (1)No Evidence (0)
Critical Thinking                                                                                                                                                                                                                                          
Outcomes
Short answer questions with a Maximum value of 166 Points                                                                                                                                                                                                                                                                                                                                                                                                                                                            
Hypothesis tests and confidence intervals nearly perfectly conducted. (149 points or more of 166 points) Most causes or processes of economic phenomena are correctly identified and stated. (115 to148 points of  166 points) Most causes or processes of economic phenomena are not correctly identified and stated. (83 to 114 points  of 166 points) No causes or processes of economic phenomena are stated clearly. (0 to 82 points of  166 points) 
Effective Communication                                                                                                                                                                                                                                    
Outcomes
Short-answer questions on regression procedures and hypothesis testing with a maximum value of 52 points.                                                                                                                                                                                                                                                                                                                                                                                                            
All definitions of curves or items identified on graphs are stated nearly perfectly.  (47  points or more of 52 points) Most definitions of curves or items identified on graphs are stated correctly. (36 to 46 points of 52 points) Most definitions of curves or items identified on graphs are not stated correctly. (26 to 35 points of 52 points) No definitions of curves or items on graphs are stated clearly.



(0 to 25 points of 52 points)



 
Tools and Methods of Economics                                                                                                                                                                                                                             
Outcomes
This examines regression analysis including running and interpreting regressions.  It has a maximum value of 82 points.                                                                                                                                                                                                                                                                                                                                                                                              
All calculations and explanations are nearly perfect.  (75 points or more of 82 points) Most calculations and explanations are correct. (59 to 74 points of 82 points) Most calculations and explanations are not correct. (42 to 58 points of 82 points) None of the calculations and explanations is correct.



(0 to 41 points of 82 points)



 

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Last Updated:10/13/2010 11:15:18 AM