EC315 Quantitative Research Methods

for F1J 2006

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EC 315 Quantitative Research Methods


F1J 2006 PV


Kelsay, Michael P.


Adjunct Faculty


PhD Economics University of Tennessee
MA Economics University of Missouri - Kansas City
BA Economics University of Missouri - Kansas City

Daytime Phone



Semester Dates

21 August - 10 October 2006

Class Days


Class Time

5:30 - 9:50 PM


MA120 and CS 140

Credit Hours


Soule, Peter, GUIDE to STATISTICAL ANALYSIS for Business and Social Sciences Using SPSS Version 12.0 and Microsoft Excel.  ISBN 0-07-312532-6 McGraw-Hill 2005.  The text is referred to as "Guide."

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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 Yx or Xy function.  The cost is apporximately $20.

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.
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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 provide students with the quantitative skills that are necessary for doing apppied work.   In order to accomplish that goal, the teacher must facilitate learning and the student must learn the required materials.  This requires feedback from the student to the teacher.  This feedback occurs in written work and classroom discussion.   

The homework is intended to assess what students know and what they are uncertain about. Homework is graded liberally and based on effort while the examinations and papers are graded on achievement. 

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.

  Instructor Learning Outcomes
  1. Give an oral report to the class on your research paper
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 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 (Learning Outcomes 1, 2, 3, and 4 listed on this syllabus).   

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).  This proposal should include the following four items. 

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 the primary independent 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).  They should be in this order: dependent variable, primary independent variable, and remaining three independent variables. 

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

For the primary independent variable, you must also cite two research sources that discuss the variable.  The sources need not be technical documents.  List these sources on the Works Cited Page.   

3)  Data Description 

You must have at least 30 observations of cross-sectional data for each of the five variables.  Thus for the final paper, you will have a data matrix that is at least 30 rows by 5 columns. 

In one paragraph, describe the data (i.e., which government agency supplies the data, what methods are used to compile them, when they were collected, etc.) and identify the specific data sources, including table numbers.  Include the data sources on your Works Cited page.  Attach a Xerox copy of the data set to the end of the proposal.  

4) Works Cited Page 

The final page of your proposal should be a Works Cited page on which you list the two research sources for the primary independent variable in the appropriate format.  You should also list the data sources, with a separate citation for each table of data. 

Final Research Paper 

Purpose Statement and Model 

1) In the introductory paragraph, state why you have chosen to analyze the dependent variable.  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 you've found that support your thesis, i.e., your model. (Definitions or citations from encyclopedias are not acceptable.) 

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.  State your expectations for each independent variable. (one paragraph for each variable, in numerical order, i.e., X1 first, then X2, etc.)You should address the following: 

<        How is the variable defined in the data source?

<        What unit of measurement is used?

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

<        What sign do you expect 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 did you take the data? (Citing a website is not acceptable.)

<        What 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 you have a low adjusted R2, how has your 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 your 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 you 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 your 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 your model. 

11) Other (not required): 

<        If you use any of the techniques for improving results discussed in class, discuss these at the end of the paper. 

Works Cited Page 

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


12) Research:  two sources 

13) Data: a separate citation for each variable



Link to Class Rubric

Class Assessment:

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 refererence 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 Criteria             

Evaluation Item                                    Percent          Points

Homework                                                10%                100

Term Project Topic and Feasibility Paper      3%                  30

Term Project Proposal                                10%               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.

Late Submission of Course Materials:

Problem sets are due at the start of class one week after it is assigned.  Place the problem sets on the instructor's desk as you enter the classroom.  The solutions will be passed out at the beginning of class and discussed.  Homework turned in late will be grade for completeness and will be given a maximum grade of 50%.

A studnet that misses an examination is allowed to make up the examination.  The make up examinations will be a different format that the in class examinations. 


Course Topic/Dates/Assignments:




Week 1 Introduction to Regression 1-5, 7-8, 170-178

(Project Topic and Feasibility Paper)

Week 2 Excel Regression 84-98, 124-141, 208-211

Week 3 Statistical Significance 142-154, 154-169

Week 4 MIDTERM EXAMINATION All Previous References

Week 5 Normal Distribution/ Sampling Distributions 13-16

Hypothesis Testing, Confidence Intervals 17-28

Paired Difference Tests 43-46

Week 6 Large Sample Test and Confidence Interval 29-36

Small Sample Test and Confidence Interval 36-48

(Term Project Written Proposal Due)

Week 7 F Test of Variance between Two Samples 49-52

ANOVA and Goodness of Fit Tests 52-57, 74-79

Week 8 Final Examination All Previous References

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 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.

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: .

Additional Information:
The final examination is not a take home test.  The final is closed book and closed notes.


CompetencyExceeds Expectation (3)Meets Expectation (2)Does Not Meet Expectation (1)No Evidence (0)
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 variables are appropriately chosen and no variables are measurable. 
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; one research article is cited. No data are appropriate or documented; no research articles are cited. 
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 Two to one 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 
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                                                                                                                                                                                                                                   
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                                                                                                                                                                                                                           
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                                                                                                                                                                                                                              
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 defined and analyzed for two to three t-tests. The p-value approach to hypothesis testing is defined and analyzed for one t-test. The p-value approach to hypothesis testing is not defined or analyzed for any t-test. 


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Last Updated:8/21/2006 3:05:00 PM