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EC 315 Quantitative Research Methods
Barcus, Randall H.


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

S1T 2007 DL

Faculty

Barcus, Randall H.

Title

Adjunct Economics Instructor

Degrees/Certificates

B.S. Mathematics
B.S. Economics
M.S. Economics

Office Location

Online (Spokane, Washington Pacific Time)

Daytime Phone

I am not available by phone, just by email

E-Mail

Randall.Barcus@park.edu

randy.barcus@avistacorp.com

Semester Dates

15Jan07 to 11Mar07

Class Days

Online Monday through Sunday Midnight Central Time

Class Time

Please complete all of your work by Sunday midnight Central time so I can post scores for you in a timely manner

Prerequisites

MA 120 & CS 140

Credit Hours

3


Textbook:

Required Text:  GUIDE to QUANTITATIVE RESEARCH METHODS Using SPSS Version 14.0 and Microsoft Excel.

Author: Dr. Pete Soule
ISBN: 978-007-3396-29X (2007)

Order Texts at: http://direct.mbsbooks.com/park.htm

Textbooks can be purchased through the MBS bookstore

Additional Resources:

Scientific Calculator [This calculator must have the ability to raise a number to a power other than two. Such calculators usually have an XY or an YX key.] Cost about $15
 
Microsoft Word and Microsoft Excel software [homework assignments are only accepted in these two formats--other software may not be used--you may need to purchase these from Microsoft--I would recommend purchasing Microsoft Office Student Edition as a package--see link below)

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/.
Advising - Park University would like to assist you in achieving your educational goals. Please contact your Campus Center for advising or enrollment adjustment information.
Online Classroom Technical Support - For technical assistance with the Online classroom, email helpdesk@parkonline.org or call the helpdesk at 866-301-PARK (7275). To see the technical requirements for Online courses, please visit the http://parkonline.org website, and click on the "Technical Requirements" link, and click on "BROWSER Test" to see if your system is ready.
FAQ's for Online Students - You might find the answer to your questions here.


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:

Welcome to Quantitative Research Methods. Along with your fellow on-line students, you will learn how to conduct quantitative research using computer statistical analysis. You will be helped along the way by the other members of this on-line class as well as by me. I will provide you with numerous tutiorals and various handouts.  I will answer questions that you post in the Questions and Answers discussion thread and questions sent by e-mail.

The fundamental goal of this course is for you to learn how to conduct quantitative research and how to report the results in an effective manner. Since this is a learning exercise, it is not important how statistically significant your research is. Rather, it is important that you be able to use the computer software to generate printouts that you can correctly interpret.

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:

Class Participation
Term Project Topic 
Week 2 through Week 7 Homework (6 at 20 points each) 120 points or 12% of total
Open Book Mid Term
Project Written Proposal
Term Project Final Report
Whatsit Problems Practice Final Exam
Closed Book, Closed Notes Final Exam
 
PLEASE NOTE: The Term Project is three separate assignments, the Project Topic, the Written Proposal, and the Final Report which represents 42% of your grade (see grading plan below). Thus, it should be clear to you that the level of effort you put into this assignment should be very high.
 

Grading:

Class Participation (6 at 10 points each) 60 points or 6%
Week 1 Homework Term Project Topic 70 points or 7% of total
Week 2 through Week 7 Homework (6 at 20 points each) 120 points or 12% of total
Week 4 Open Book Mid Term Exam 150 points or 15% of total
Week 5 Term Project Written Proposal 150 points or 15% of total
Week 7 Term Project Final Report 200 points or 20% of total
Week 7 Whatsit Problems 50 points or 5% of total
Week 8 Closed Book, Closed Notes Final Exam 200 points or 20% of total
 
HELPFUL HINTS: Each homework assignment beginning with week 2 will have the answers posted on the Monday following the Sunday due date of each. Because I expect you to use the "Break Room" to help each other with figuring out the homework together, I necessarily will NOT be providing helpful hints or tips. In the case that someone is leading you down the wrong path, I expect you to challenge each other as to whether you are getting good or misleading help. In order for you to be successful on the closed book final exam, you will have to have learned this material yourself. Often the struggle to learn each assignment (and then seeing the correct response posted on Monday) is the most effective method. It may seem painful to you at the time, which is why the homework assignments have an important but low point weight. On the Monday of week 5 I will post the solutions to the mid term exam. I encourage you to review your responses side by side with the solutions provided, since the final exam is a comprehensive test over the entire course. Similarly, the Whatsit Problems solutions will be posted on Monday of week 8 shortly after midnight.
 
REPEAT NOTE on Final Exam: You must score at least 60% or higher on the proctored final exam in order to pass the course regardless of the overall average of your other assignments. There will be no exceptions to this policy.

All final exams will be comprehensive and will be closed book and closed notes.  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 Associate Dean.

The Proctored final exam for online courses must be passed with a grade of 60% or higher in order to pass the course regardless of the overall average.  The grade for students who pass the proctored final will be based on the overall average of homework and tests taken during the course.  The proctored final exam must address only material which the student has been taught in class.

Late Submission of Course Materials:
Since the solutions to the assignments will be available on the Monday following the week of the assignment, there is no opportunity for you to provide the homework, mid term exam, or Whatsit Problems later than midnight Central time on each Sunday.
The term paper topic, proposal, and final report will be penalized 10% for each day or partial day the assignment is late. This means if you submit these assignment after midnight Central time, even if it is one minute, you will lose 10%, and there will be no exceptions. So the emphasis here is on getting your assignments completed on time or early--think of it this way: If you were my employee and you were late on an important assignment, Donald Trump's "Your Fired" words would be coming from my mouth. The term paper with its three parts is the most important assignment you will produce for this course. I do not know how I can be any more clear on this matter.

Classroom Rules of Conduct:

Throughout this course, you will be respectful to the other members of the class including the instructor--not matter how irritated you might get with the difficulty of this course, and believe me, this is a difficult subject.

Course Topic/Dates/Assignments:

Jan 15-21 Week 1 Term Project Topic
Jan 22-28 Week 2 Homework (begin sign up for final exam proctor)
Jan 29-Feb 4 Week 3 Homework (continue sign up for final exam proctor)
Feb 5-Feb 11 Week 4 Homework and Mid Term Exam (sign up for final exam procotr)
Feb 12-Feb 18 Week 5 Homework and Term Project Proposal (please sign up for final exam proctor)
Feb 19-Feb 25 Week 6 Homework (last chance to sign up for final exam proctor)
Feb 26-Mar 4 Week 7 Homework and Whatsit Problems and Term Project Report
Mar 5-Mar 11 Week 8 Proctored Final Exam

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
Be sure to re-read this if you have any question at all regarding how you might consider sharing homework solutions or other assignments in this course. In no case will you share solutions to the mid term exam, as this should be your own work only.

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
Be particularly careful with the citations you provide for the data that you will be using in your Term Project Report. If you get the data from a website, please include the URL in your bibliography--you can never provide too much information in a bibliography in this class.

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
The above information is the standard attendance policy--because adequate feedback of solutions to the homework, mid term and Whatsit Problems will occur each Monday, there will be no late work.

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:

No matter what you may have heard about this course from other students, please keep a positive attitude about your success opportunities in this class. This is a brand new book for this class, and the syllabus has been updated to reflect feedback and suggestions from students and faculty members.

The most important thing to do in this course is to attack the information early in the week--it make take you several days of study and figuring to respond to the homework assignments. You are always encouraged to "google" or otherwise search for examples or alternative explanations of each and every written assignment. That is what I do when I'm solving statistical problems at my employer--I never guess, I always want to be absolutely sure I'm doing my assignment correctly. If you falter on an assignment, simply learn from the solution.

I wish each of you to do the best that you can possibly do. Everyone passed my last class, and each of them will tell you to a person it was the most difficlut course they had taken--and they learned more than they ever learned in any course, too.

Randy Barcus, Park University Adjunct Economics Instructor



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:12/25/2006 8:50:48 PM