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.
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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 DesignReading: 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 |
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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 10Dec 9 Dec
13
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Final Exam *Bring a pocket calculator |
Presentations: Oral
presentations of NSF proposals |
Evaluation
Week Dec 16 Dec 20 |
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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:
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 NSFs 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.
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 cant collaborate
with other students
5.
Group Proposals
Probably not
applicable.
6.
Proposals for
Renewed Support Probably not
applicable.
F.
Biographical
Sketch(es) Not
needed
G.
Budget Not
needed.
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).
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
**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.
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.
**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.