MES:  Quantitative Analysis and Research Methods

Ken Tabbutt & Nobi Suzuki

 

 

Class Schedule

                Tuesday                                6:00 – 10:00                            Lecture Hall 3                                                        Lecture

                Thursday                               6:00 – 10:00                            Computer Applications Lab (CAL)                   Lab

 

Quantitative Analysis class will meet Tuesday and Thursday evenings.  Tuesday will be predominantly lectures, workshops, and periodic seminars, and Thursday will be devoted to Excel labs in the CAL.  In addition to attending class regularly, students are expected to complete the assigned reading prior to class, complete weekly homework assignments, read and analyze articles for seminar discussions, participate in seminar discussions, write a research proposal, and pass the mid-term (take-home) and final exams.

 

 

Tuesday  

Thursday

Week 1

Sept 30 – Oct 4

Statistics:  Introduction, Descriptive Statistics

  Reading: 1, 2 & 3

Research Methods: Scientific Method                

  Reading: Paper to be handed out

 

Lab: Graphic Descriptive Statistics

Numerical Descriptive Statistics

 

Quantitative Resource Center Introduction by Louis Nadelson

Week 2

Oct 7 – Oct 11

Statistics:  Probability

  Reading: 4., 5, 6, 7, 8 & 9

Research Methods: Research Design

  Reading: Papers to be handed out

Discuss NSF proposal assignment

Assignment: Hypothesis of NSF Research Proposal is due.

 

Lab: Probability, Randomization

Sampling Methods, Estimation

 

 

Week 3

Oct 14 – Oct 18

Statistics:  Hypothesis Testing and Inference of Populations

  Reading: 10, 11 & 12

Research Methods: How to write Scientific and Review Papers. 

Seminar: Presentation of NSF proposals.

Assignment: submit a copy of an article from the popular press that has some numerical or statistical content that you would like to analyze for discussion in seminar.

 

Lab: t-test for a single population, Independent-sample t-test

Paired t-test, comparison of variance and proportions between populations.

Week 4

Oct 24 – Oct 25

Statistics:  Inference of Population, Transformation of Data

  Reading: 13

Research Methods: Power Analysis and Sample Size. 

  Reading: Assigned readings on Power Analysis (Peterman 1990 and Stidle et al. 1997, See the reading list)

Seminar: Discuss popular paper

 

Lab: t-test continued from previous week. Assessment of Normality and Data transformation for Independent sample t-test and paired t-test.

Week 5

Oct 28 – Nov 1       

Mid-Term Exam Due

Statistics:  Analysis of Variance

  Reading: 14

Assignment: submit a copy of an article from a scientific journal that has some numerical or statistical content, that you would  like to  analyze for  discussion in seminar.

Seminar: Discuss popular paper

 

Lab: single factor ANOVA, randomized block ANOVA, two-factor ANOVA

Guest Speaker: Elizabeth Minnich

Week 6   

Nov 4 – Nov 8

Statistics: Analysis of Variance and Experimental Design 

  Reading: 14 and assigned readings in

  Pseudoreplication (Hulbert 1984 See the

  reading list).

Research Methods: Principles of Sound Experimental Planning (with emphasis on statistics)

Seminar: Discuss journal articles

 

No Class – Faculty Retreat

 

Week 7

Nov 11 – Nov 15

Statistics:  Nonparametric Tests         

  Reading: 16          

Assignment: Review Paper Due          

Seminar: Discuss journal articles

 

Lab: Wilcoxon rank sum test, Sign test, Wilcoxon signed rank sum test, Kruskal-Wallis test, Friedman test.

 

Week 8

Nov 18 – Nov 22    

Statistics:  Regression I

  Reading: 17 & 18

Research Methods: TBA

Seminar: Discuss review paper

 

Lab: Simple linear regression

Regression diagnostics and transformation

 

Thanksgiving Break

 

 

Week 9                  

  Dec 2 – Dec 6        

 

Statistics:  Regression II                      

  Reading: 17 & 18

Research Methods: TBA

Assignment: NSF Research Proposals Due

 

Lab: Multiple linear regression and non-linear regression

Week 10                

Dec 9 – Dec 13                                      

Final Exam

*Bring a pocket calculator     

Presentations: Oral presentations of NSF proposals

Evaluation Week

  Dec 16 – Dec 20

 

 

 

Faculty

Nobi Suzuki           Lab I 2015                              360-866-6000 ext. 5493         suzukin@evergreen.edu

Ken Tabbutt         Lab II 2264                             867-6558                 tabbuttk@evergreen.edu   

Office Hours: Prior to class, after class or by appointment

 

Texts

Keller, Gerald.  2001.  Applied Statistics with Microsoft Excel.  Duxbury,  Pacific Grove,  CA.

Optional Texts

Motulsky, Harvey.  1995.  Intuitive Biostatistics.  Oxford University Press, New York, NY.

Ford, E. David.  1999.  Ecological Research.  Cambridge University Press, New York, NY.

Norusis, Marija.  1999.  SPSS 9.0 Guide to Data Analysis.  Prentice Hall, Upper Saddle River, NJ.

SPSS 11.0 for Windows Student Verion.  2002.  Prentice Hall, Upper Saddle River, NJ.

 

Program Subdirectory (Folder)

PRG_QuantMethods in workspace on Masu (Masu/workspace/PRG_QuantMethods)

 


Assignmnets

 

NSF Resarch Proposal (Due on Dec. 3rd)

Research proposals are a critical component of scientific research today.  Any project that requires funding will inevitably find itself competing with other projects for resources.  It is the goal of the written proposal to clearly state the objectives of the study and to justify it.  This means that scientific research proposals have two functions:

1.        State a hypothesis (thesis) and describe the method(s) that will be employed to validate or reject that hypothesis.  This means that the researcher(s) must demonstrate that the methods proposed will reach a conclusion, either validating or rejecting the hypothesis.

2.        The proposal must convey the importance of this project.  The question, “why is this research important?” must be addressed.  Proposals are intended to be convincing documents, they should not be review papers.

Over the next 9 weeks you will be writing a research proposal based on the National Science Foundation (NSF) guidelines.  All research proposals tend to contain similar content, we have chosen to follow the specific format of the NSF for this exercise.  This proposal may reflect an actual project that you are working on, or intend to work on (thesis?), or it may be a fictional research project framed around a topic of interest to you.  This assignment has four deadlines during the quarter:

  1. A statement of your hypothesis or research question must be submitted in writing next week (October 8th). 
  2. We will discuss the proposals during seminar of week 3 (October 15th). 
  3. Your written research proposals are due week 9 (December 3rd).
  4. Oral presentations on Thursday of week 10 (December 12th)

The NSF Proposal Guidelines can be found online:

http://www.nsf.gov/pubs/2003/nsf032/start.htm

The specific sections that we expect you to write are listed below.  A description of the content expected in each section can be found in the NSF Proposal Guidelines (above).  There are some sections of the proposal that will not be required or have been slightly modified, these changes are listed to the right of the section. 

 

Note, FastLane is NSF’s online proposal submission and review system.  We will not be using FastLane, you will be writing the same content on paper.

I.            Sections of the Proposal                                                  Modifications

                   A.            Cover Sheet                                                     Identify applicable division and program, DUNS is not needed.

                   B.            Project Summary

                   C.            Table of Contents

                   D.            Project Description

                                            1.            Content

                                            2.            Page Limitations                              Project description will be limited to 5 pages, not 15.

                                            3.            Results from Prior NSF Support       Probably not applicable

                                            4.            Collaborations                                  Probably not applicable.  You can’t collaborate with other students

                                            5.            Group Proposals                               Probably not applicable.

                                            6.            Proposals for Renewed Support        Probably not applicable.

                   E.            References Cited                             

                   F.            Biographical Sketch(es)                                  Not needed

                  G.            Budget                                                             Not needed.

 

Review paper assignment (Due on Nov. 12th)

The articles in the reading list below discuss some issues concerning application of statistics in natural sciences.  Read at least 10 papers (6 required + 4 of your choice). Write a review paper that discusses limitations and appropriate use of statistics in a Masters thesis project.  Basically, you are asked to synthesize what you learned from these 10 papers, make your conclusions objectively, and advise new graduate students on the best way to apply statistics in a thesis project.  As long as you read and use 6 required papers, you may choose 4 or more papers from any journal sources, except from the internet.  Please follow a proper citation format used in scientific journals; you must cite these journal articles in the text and provide a literature citation section at the end.  Discussions among students are encouraged to further student understandings of statistical issues in natural science.  Plagiarism, however, is strictly prohibited.  3 pages maximum (double-spaced with font size 11-12pt).

 

Mid-Term and Final Exams

You will be asked to provide your own data or find a data set from various sources.  These data will be the focus of the exams.  The mid-term exam will require you to summarize your data using descriptive statistics, ask research questions, and analyze the data using a t-test.  The final exam will require you to use additional inferential statistics (ANOVA, regression).  On the exams you will be asked to:

Supplemental exam questions will be provided to evaluate your complete knowledge in application of statistics and research methods covered in lectures and labs

 

Reading list for the review paper assignment and lectures

**indicates required reading  These articles will be available at the closed reserve in the library.

 

Hypothesis Testing

**Anderson, D. R., K. P. Burnham, W. R. Gould, and S. Cherry. 2001. Concerns about finding effects that are actually spurious. Wildlife Society Bulletin 29:311-316.

 

**Anderson, D., K. Burnham, and W. Thompson. 2000. Null hypothesis testing: Problems, prevalence, and an alternative. Journal of Wildlife Management 64:912-923.

 

Johnson, D. H. 1999. The insignificance of statistical significance testing. Journal of Wildlife Management 63:763-772.

 

Johnson, D. H. 2002. The role of hypothesis testing wildlife science. Journal of Wildlife Management 66:272-276.

 

Mallows, C. 1998. The zeroth problem. The American Statistician 52:1-9.

 

**Robson, D. H., and H. Wainer.  2002.  On the past and future of null hypothesis significance testing.  Journal of Wildlife management 66:263-271.

 

Power Analysis

Di Stefano, J.  2001.  Power analysis and sustainable forest management.  Forest Ecology and Management.  154:141-153.

 

**Peterman, R. M.  1990.  The importance of reporting statistical power: The forest decline and acidic deposition example.  Ecology 71: 2024-2027.

 

**Steidl, R. J., J. P Hayes, and E. Schauber.  1997.  Statistical power analysis in wildlife research.  Journal of Wildlife Management. 61:270-279.

 

Pseudoreplication

**Hulbert, S. H.  1984.  Pseudoreplication and the design of ecological field experiments.  Ecological Monographs 54:187-211

 

Oksanen, L.  2001.  Logic of experiments in ecology: is pseudoreplication a pseudoissue?

Oikos 94:27-38.

 

Riley, J., and P. Edwards.  1998.  Statistical aspects of aquaculture research: pond variability and pseudoreplication.  Aquaculture Research 29:281-288.

 

Van Mantgem, P., M. Schwartz, and M. Keifer.  2001.  Monitoring Fire Effects for Managed Burns and Wildfires: Coming to terms with pseudoreplication.  Natural Areas Journal 21:266-273.