Psyc 612 001– Advanced Statistics and Research Methods
in Psychology II
Course Syllabus -
Spring 2002
Dr. Adam Winsler
---------------------------------------------------------------------------------------------------------------------------------------
Instructor: Adam Winsler, Ph.D. Office: 2011 David King Hall
Phone: (703) 993-1881 Office Hours: Thursday 1-3, + by appt.
Email: awinsler@gmu.edu Winsler URL: http://classweb.gmu.edu/awinsler
Course Schedule T R 3:00 -4:15pm Location: Robinson B228
Credit Hours: 4
Graduate Teaching Assistants (Lab Instructors)
Lisa Donahue Mike Ingerick Amy Madigan
Lab Section 612 201 Lab Section 612 204 Lab Section 612 202
DK 1005 DK 1005 ENT 420
Thurs 7:00-8:50 Friday 10:30-12:20 Fri 10:30-12:20
DK 1005 DK 1005 ENT 420
Office Hours: Office Hours: Office Hours:
Thurs 2-3 Fri 1-2 Tues 12:30-1:30
Robinson B209 Robinson B213 DK 2057
(703) 993-3706 x1 (703) 993-3706 x4 (703) 993-4050 x1
ldonahu1@gmu.edu mingeri1@gmu.edu amadigan@gmu.edu
Course Description
& Goals
This course is designed to a)
sensitively initiate students into the authentic activities that make up the
culture of applied data analyists in psychology, and b) to increase
students’ knowledge and skills as consumers of research and statistics.
No matter what students' career tracks are, they will almost certainly be
called upon at some point to either understand the findings of research to
improve applied practice, analyze a set of data either for local institutional
purposes or for academic publication purposes, conduct an empirical
investigation, or evaluate a program of research or service. So the goal of
this course is to help prepare students for these tasks. As everyone always
says, the best way to learn statistics is by doing them, so we will be
"doing them" as authentically as possible with the same, processes,
tools, and procedures that are used in the field. Through a combination of
lecture, discussion, presentation, projects, homeworks, and other activities,
we will significantly increase (p < .05?) student knowledge and skill with
regard to research methods and statistics, and inform students what else there
is that they might want to learn in this area and where to go to learn it.
Required Reading
1) Howell, D. C. (2002). Statistical
methods for psychology (5th ed.). Pacific Grove, CA:
Wadsworth/Duxbury.
2) Kerlinger, F. N., &
Lee, H. B. (2000). Foundations of behavioral research (4th ed.). Fort
Worth, TX: Harcourt.
1) Kinnear,
P.R., & Gray, C.D. (2000). SPSS for Windows Made Simple, Release 10. East Sussex, UK: Psychology
Press.
2) American Psychological
Association (2001). Publication manual (5th Ed.). Washington, DC:
Author.
3) Campion,
M.A. (1993). Article review checklist: A criterion checklist for reviewing
research articles in applied psychology. Personnel Psychology, 46,
705-718. (on electronic reserve - JC Library)
4)
Sample course projects,
sample article reviews, sample research proposals (on reserve - JC Library)
Course
Requirements/Assignments/Activities
1)
Data Analysis Project. The main assignment in this course is for students to
get first-hand experience analyzing a real, substantive data set from beginning
to end: from selecting the data set/topic area; to writing a very brief
introduction to the issues under investigation; to deciding on questions and/or
hypotheses; to deciding on the data reduction and manipulation techniques; to
data checking and cleaning; to choosing, running, and interpreting appropriate
statistical procedures for answering the questions; to interpreting the
results; and to writing the mini research report.
Students
will select one of the large scale SPSS datasets that we have acquired for use
for their project. If a student has access to an alternative, large scale,
(i.e., > 100 variables, > 200 cases), and appropriate data set that s/he
wishes to use for this project, this might be OK but would have to be seen
and approved by the instructor by the third week of class.
It is expected that multiple individual meetings between students and the instructor and/or TAs will be needed in order to complete the assignment well. The project is divided into the following meaningful chunks and turned in cumulatively along the way (and returned with feedback) as follows. Each phase turned in must also include the previous phase (revised as appropriate).
Phase I (10% of project grade)- Specification of the data set to be used, list and definition of relevant variables, description of study design and procedures, and brief introduction to the area/topic of study. (This will eventually become the intro and method sections of your final project)
This will consist of about a 5-page written
description of the above and a detailed, codebook/table of all of the variables
in the data set that you are interested in exploring (NOT all of the variables
in the dataset), including their names, meaning, and nature (i.e., type of data
[categorical, ordinal, continuous], range, and coding definitions [1=male,
2=female, etc]). Phase 1 is due at the beginning of lecture on Tuesday
Feb 12. This first phase (only) may
be done collaboratively with one other student.
Phase II (20% of project grade) - Research Questions and/or Hypotheses and Data Analysis Plan.
Students
will list their specific research questions and/or hypotheses to be
answered/tested in a hierarchical numerical-bulleted fashion. Students should
include at least some hypotheses if they list predominately questions. Research
questions should be as coherent, interesting, and meaningful as possible. There
should be about 7-15 questions/hypotheses. Organized by the students list of
questions/hypotheses, students will also give a very specific, detailed
description of how they are going to go about testing each of the
questions/hypotheses (variables used, statistical procedure used, specification
of which variables are serving as what in each analysis…). If the method
of analysis for one question depends on what happens in a previous analysis,
then give such contingency plans. Due at the beginning of lecture on Tuesday,
March 5.
Students
can pick whatever questions they are interested in pursuing with the chosen
data set, with the only constraint being that the procedures used to answer the
questions have to be a good representative sample of the many procedures
discussed in 611 and 612 this year. Obviously, the procedure used in each case
will depend on the type of data and the research question. So the general idea
is to do as many of the following different procedures as is reasonably
possible:
One-sample t-test, independent samples t-test, correlated samples t-test, one-way ANOVA, factorial ANOVA, ANCOVA, MANOVA, MANCOVA, Factor analysis, simple regression, multiple regression, Kruskal-Wallis, Mann-Whitney, Wilcoxon signed-rank, Friedman, chi-square, internal consistency reliability, MTMM, various types of correlations, and RM ANOVA. Interested students are welcome to take this opportunity to master other more advanced analyses as well, such as loglinear models, logistic regression, discriminant function analysis, cluster analysis, SEM…, but only if desired.
Minimum requirements are, however: at least 1 of each of the following: any t-test, one-way ANOVA, factorial or mixed anova, nonparametric analysis (not counting chi-square or Spearman r), chi-square, any correlation, multiple regression, and some kind of MANOVA.
Phase III (15% of project grade) - Exploratory Data Analysis (EDA)
Annotated output and written description of your exploratory and preliminary analysis procedures, including data cleaning, transformations, data reduction, distributional graphs, missing data, outlier analysis, and recoding procedures as necessary. Also include a description in the text of how, if at all, the results of the EDA affect your data analysis strategy. Due at the beginning of lecture on Tuesday, April 2.
Phase IV (55% of project grade) - Final Report
An
APA style research report of the results of your analyses, complete with a
brief introduction to the topic, a brief method and procedures section (revised
phase I with final questions/hypotheses [phase II] clearly listed at end of
intro), a much-expanded results section in which you describe what exactly was
done, and why, what was found, and what it means, an executive summary (2-3
pages) of the main findings, and lots of organized, annotated appendices
referring to the relevant SPSS output. Due at the beginning of lecture
on Thursday, May 7.
The
final report must also contain at least one presentation-style graph and one
presentation-style table in the results text. Also students will calculate and
report at least 2 different effect sizes (1 d-family, 1 r-family) from their
analyses. The executive summary consists of hierarchically organized bullet
points about the main findings from your analyses, implications for practice of
your findings, some mention of the extent to which practical/clinical significance
was demonstrated, and future issues/directions/research needs.
2) On-Line Discussion. We will be using WebCT to facilitate our discourse both inside and outside of class this semester. Students are encouraged to post questions, issues, problems, suggestions, whatever, as often as they like throughout the semester. This open ended, unmoderated, online discussion can be used to discuss the readings and course content, ask questions about things that were unclear in class or in the readings, find a partner for collaborative assignments, or discuss questions/issues that come up. Participation in the online discussion is completely voluntary, however, each reasonable, substantive post submitted to the online discussion forum will count as .33 extra credit points on top of your final grade (5 extra credit point maximum, however).
3) On-Line
Study Guide. Students are required to write a minimum of 5 multiple
choice or true/false or could-this-be-true quiz/exam items throughout the
semester from the material discussed in lectures and submit these items
electronically for inclusion in the course’s online study guide. (See
"Online Course Materials and Tools" below). Items must be original,
creative, and high quality in order to count. The correct answer must also
be submitted with the item. Students will only get credit for one
test item per week and the item has to come from material covered in lectures
(not material that appeared only in the textbook or elsewhere) within the same
week of the item submission (i.e., items are submitted from Tuesday to Sunday
of each week and they have to come from that week’s content). Items
submitted are first reviewed privately by Dr. Winsler, and acceptable ones will
become part of the on-line study guide forum available to all students. Students
will receive one point for each acceptable item that appears in the
online study guide. Items not acceptable will be returned privately to the
student with feedback given. One (particularly good) item submitted by a
student each week will be selected as a question that will definitely appear
on the next quiz. A good number of other student-submitted items will also
appear on quizzes. Items must be submitted by 10:00pm Sunday night in order to
count for the week that is ending.
Students are encouraged to submit as many additional items as they want since it is a good learning experience and all students benefit by studying via the on-line study guide. Students can earn up to 5 final exam extra credit points (1 point for each additional good item submitted) by submitting additional items at any time to the on-line study guide. However, you don’t get the XC points until after you have completed your required 5.
4) Quiz. On March 21-22, in lab, students will take a quiz over the content covered to date in the course. The quiz will likely include a variety of item formats, including multiple choice, T/F, could this be true, and short answer.
5) Article Review/Critique. Students will choose one empirical article from a professional journal on a topic of their choice within Psychology and provide a 4-8 page summary, review, and critique of the article. Students may wish to get approval on the article from the instructor ahead of time to make sure the article is indeed empirical, relevant, and appropriate for the assignment. The study reviewed can be one that was going to be read by the student(s) anyway either in the context of the course project or the research proposal. Students may choose to do the article critique together with one (1) other student if they wish as long as it is a true collaboration. Both students would get the same grade on the assignment. The review is due in lecture on Thursday, Feb 21. Students should consult Campion (1993) [optional reading list] for a list of potential issues to consider in evaluating the article.
6) Oral Presentation. On April 25-26, in lab, students will give a brief (5-10 min.) oral presentation to their fellow lab members briefly summarizing the results of their analyses for the data analysis project (above). This will serve three goals: (a) students will get experience with yet another authentic activity within the culture of academic researchers, namely – presenting the results of data analysis, (b) students will learn and profit from hearing what other classmates did on their projects, and (c) because of the timing of the presentations, this will be an opportunity for students to get some feedback on how their data analyses and conclusions are going before they turn in their final project. Some type of visual aids/handouts/slides is required.
7) Final Exam. At the university-scheduled time for the final, Tuesday, May 14 (1:30 – 4:15pm), students will complete an in class final exam. The exam will likely include a variety of familiar item formats, including multiple choice, T/F, could this be true, and short answer.
8) Research
Proposal. All doctoral students, and MA students who have chosen
to do the research proposal option, will continue to work with their research
advisor this semester to turn the literature review paper they turned in at the
end of 611 into a full-fledged research proposal. This means a) revising the
lit review to make sure it sets up the rationale for the particular study being
proposed, b) adding a focussed set of research questions/hypotheses at the end
of the lit review, c) adding a standard APA style method section, with particular
attention paid to methodological design issues discussed in 611-612 all year
(i.e., internal and external validity; reliability; between vs. within
subjects; controlling, measuring or matching on relevant variables; power;
sample size; ethics…), and d) adding a proposed data analysis section
which gives the reader some idea of how you are going to answer the research
questions. Proposals are due to advisors and TAs by 5:00pm, May 7th.
9)
Lab Activities.
Students will complete a few short,
hands-on lab in class activities and homeworks in the lab sections throughout
the semester. These can be completed collaboratively with one other student
in the same lab.
Online
Course Materials and Tools
Five important online resources are located for students at the new 612 Course Website (NOT the same 611 site) located at: http://webct.gmu.edu/
1) Course Materials - Various course materials (syllabus, lecture notes, lab handouts, assignments, guidelines/grading criteria for assignments…) are/will be available from this site.
2) Online Resources for Statistics – We have compiled a variety of excellent web resources for research methods and statistics and listed them on the course website.
3) Online discussion - Discussion of and reflection on course content, inside and outside of class, is critical for sustained student learning and motivation. This semester, students in this course will no longer be limited to the discussion which occurs in the classroom. Using GMU’s WebCT platform, students in this course may also participate in electronic discussions in which students type in messages that are stored in a central web location and are accessible for all other students in the course (and the instructor) to read and respond to. (See course requirement # 2 above).
4) Online Grade Checking Mechanism - Students may get an update of their current course grades at any time during the course from the website (24 hours a day, 7 days a week!). Students can see their own grades for all assignments, including extra credit points earned to date. The WebCT password that students will designate on their first day of use provides assurance that others can not access your grades. Students will only see their own grades - not anyone else's and not class distributions. The goal of this service is to give students a mechanism for getting immediate feedback about their progress in the course.
5) Online Study Guide - Also
available from the website is the online student study guide which contains the
student-submitted (and instructor-approved) multiple choice and true/false exam
items that can and should be used to study for quizzes and exams (See course
requirement # 3 above on submitting exam items and receiving extra credit for
sending additional items).
The standard 93-100% = A, 90-92 = A-, 87-89% = B+, 83-86 = B, 80-82 = B-, 77-79% = C+, 70-76 = C, 60-69% = D, <60% = F scale will be used. Students' final grades will be determined as follows:
Students DOING research proposal: Students NOT DOING the research proposal :
•
Project 35%
•
Project 40%
•
Article Review 5% •
Article Review 10%
•
Quiz 10% •
Quiz 10%
•
Final Exam 15% •
Final Exam 15%
•
Lab Grade 25% •
Lab Grade 25%
•
Research Proposal 10%
|
Date |
Topic(s) |
Reading/Assignment |
(In Lab) |
|
Tues - Jan 22 |
Introduction / Overview of
Course / The Big Picture |
|
• WebCT • Project Datasets • Review 611 Final |
|
Thurs - Jan 24 |
Overview of Research
Designs - Types of Experimental Research, Quasi-Experimental Research, and Nonexperimental/Correlational Research - Applied Research in Community Settings |
• Winsler (1991) • K & L - Ch. 18
(Review) • K & L - Ch. 19
(Review) • K & L - Ch. 20
(Review) |
|
|
|
|
|
|
|
Tues - Jan 29 |
Quasi-Experimental Research
- Program Evaluation - Single-Subject Designs |
Posavac & Carey (1992) • K & L - Ch. 22 |
• Project Datasets • Observational Activity |
|
Thurs – Jan 31 |
Nonexperimental/Correlational
Research and Field Studies - Observational Methods |
• K & L - Ch. 23 • K & L - Ch. 24 • K & L - Ch. 31 |
|
|
|
|
|
|
|
Tues - Feb 5 |
•
Nonexperimental/Correlational Research and Field Studies - Interobserver Agreement and Reliability • Reliability -
Revisited |
• Cortina (1993) • K & L - Ch. 27
(Review) • Schmidt &
Hunter (1996) |
• Calculating Reliabilities • Assistance w/ Phase I • Article Reviews • Obs HW Due |
|
Thurs – Feb 7 |
Survey Research - Interviews - Focus Groups - Questionnaires - Internet Surveys |
• K & L - Ch. 25 • K & L - Ch. 29 • Hinkin (1998) • Simsek & Viega
(2001) |
|
|
|
|
|
|
|
Tues - Feb 12 |
Qualitative Research - Participant Observation/Ethnography/Case Studies |
Project Phase I Due |
• Multitrait- Multimethod Matrices |
|
Thurs - Feb 14 |
Validity – Revisited - Multitrait-Multimethod Matrices |
• K & L - Ch. 28
(Review) • Mook (1983) • Messick (1995) |
|
|
|
|
|
|
|
Tues - Feb 19 |
So Now What Do We Do with
These Data? The Big Data Analysis
Picture - Univariate vs. Multivariate - Independent vs. Dependent Variables - Categorical vs. Ordinal vs. Bad / Good Continuous Data - Questions?, Goals? (i.e., Mean Diffs, Associations, Prediction, Causality, Mediation, Moderation,
Model Fit) |
• Howell - Ch. 11
(Review) • Howell - Ch. 12
(Review) • Howell - Ch. 13
(Review) • K & L - Ch 13
(Review) • K & L - Ch 14
(Review) |
• Fitting Research Questions w/ Designs • EDA Activity • Assistance w/ Phase II |
|
Thurs - Feb 21 |
Exploratory Data Analysis
(EDA) - Data Cleaning, Recoding, Reduction, Aggregation - Graphing Data |
||
|
|
|
|
|
|
Tues – Feb 26 |
Exploratory Data Analysis
(EDA) - Missing Data, Outliers, Distributions, Transformations |
• Tabachnick &
Fidell (1996) |
• Calculating Effect Sizes • EDA HW Due |
|
Thurs - Feb 28 |
Effect Size -
Clinical/Practical Significance - Types of Effect Sizes and Their Calculation |
• McCartney &
Rosenthal (2000) • Cortina & Nouri
(2000) |
|
|
|
|
|
|
|
Tues - Mar 5 |
Power |
• Howell - Ch. 8 • Cohen (1992) • K & L - Ch. 12
(Review) Project Phase II Due |
• Power Activity • Assistance w/ Phase III |
|
Thurs - Mar 7 |
The Big 5 - Significance Testing, Effect Size, Power, N, & Meta-Analysis |
• Nickerson (2000) |
|
|
|
|
|
|
|
Mar 12-14 |
NO CLASSES – SPRING BREAK! |
|
|
|
Tues - Mar 19 |
• Review of Analysis
of Variance (ANOVA) • Analysis of
Covariance (ANCOVA) |
• Howell - Ch. 16 • Porter &
Raudenbush (1987) |
• Quiz • Power HW Due |
|
Thurs - Mar 21 |
• Paired Samples T
Test • Repeated Measures
(RM) ANOVA |
• Howell - Ch. 7
(Review) • Howell - Ch. 14 • K & L - Ch. 15 |
|
|
|
|
|
|
|
Tues - Mar 26 |
Nonparametric Analyses - Sign, Wilcoxon/Mann-Whitney, Wilcoxon Signed Rank,
Kruskal-Wallis, Friedman |
|
• Go Over Quiz • RM ANOVA Activity |
|
Thurs - Mar 28 |
Analysis of
Categorical/Count Data - One-way c2 - Two-way c2 |
||
|
|
|
|
|
|
Tues – April 2 |
• Analysis of
Categorical/Count Data - Odds Ratios - Intro to Log-Linear Models and Logistic Regression • Intro to the
Multivariate World |
• Von Eye & Schuster (2000) Project Phase III Due |
|
|
Thurs - April 4 |
• Factor Analysis |
• K & L - Ch. 34 • Tinsley &
Tinsley (1987) |
|
|
|
|
|
|
|
Tues – April 9 |
• Factor Analysis • Multivariate
Analysis of Variance (MANOVA) |
• Haase & Ellis
(1987) |
• Factor Analysis • c2 HW Due |
|
Thurs- April 11 |
• Multivariate
Analysis of Variance (MANOVA) • Multivariate
Analysis of Covariance (MANCOVA) • Repeated Measures
and Mixed MANOVA Models |
• Howell - Ch. 9 (Review) • Howell - Ch. 15
(Review) • K & L - Ch. 32
(Review) |
|
|
|
|
|
|
|
Tues - April 16 |
Multiple Regression - Model Basics |
• Wampold &
Freund (1987) |
• MANOVA Activity • MR Activity • Assistance w/ Project • FA HW Due |
|
Thurs- April 18 |
Multiple Regression - Assumptions, Problems and Diagnostics |
• Stevens (1984) |
|
|
|
|
|
|
|
Tues - April 23 |
Multiple Regression - Moderation, Mediation, Suppression, Multiple Models |
• Baron & Kenny
(1986) |
•Presentations • Reg HW Due |
|
Thurs- April 25 |
Longitudinal Research - Longitudinal vs. Cross Sectional Designs - Panel Studies, Sequential Designs - Time Series Designs. Microgenetic Designs |
• Miller (1998) • Wilkinson et al. (1999) |
|
|
|
|
|
|
|
Tues - April 30 |
Alternatives to RM ANOVA
for Longitudinal Data - Measures of Stability and Change - Change/Difference Scores - Individual Growth Curves |
• Francis et al.
(1991) |
• Review |
|
Thurs- May 2 |
• What Else is Out
There?: A Look Ahead • The Big Data
Analysis Picture Revisited |
• K & L - Ch. 33 Final Project Due |
|
|
|
|
(Research Proposal Due – 5/7) |
|
|
Tues – May 14 |
Final Exam (1:30
– 4:15pm) |
|
|
(* = also
available on the course website)
1) *Winsler,
A. (1991). A Vygotskian/Sociohistorical approach to the teaching of graduate
statistics in education and the behavioral sciences. Unpublished
manuscript. Stanford University.
2)
*Posavac, E.J., &
Carey, R.G. (1992). Program evaluation: Methods and case studies. (4th
ed.). Englewood Cliffs, NJ: Prentice Hall. (Chapter 1 – “Program
Evaluation and Organizations – pp. 1-22)
3)
Cortina, J.M. (1993).
What is coefficient alpha? An examination of theory and applications. Journal
of Applied Psychology, 78, 98-104.
4)
Schmidt, F.L., &
Hunter, J.E. (1996). Measurement error in psychological research: Lessons from
26 research scenarios. Psychological Methods, 1, 199-223.
5)
Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in
survey questionnaires. Organizational Research Methods, 1, 101-121.
6)
Simsek, Z., & Viega,
J. F. (2001). A primer on Internet organizational surveys. Organizational
Research Methods, 4, 218-235.
7)
Mook, D.G. (1983). In
defense of external invalidity. American Psychologist, 38, 379-387.
8)
Messick, S. (1995). Validity of psychological
assessment: Validation of inferences from persons’ responses and
performances as scientific inquiry into score meaning. American
Psychologist, 50, 741-749.
9)
Smith, P.C., Budzeika,
K.A., Edwards, N.A., Johnson, S.M., & Bearse, L.N. (1986). Guidelines for
clean data: Detection of common mistakes. Journal of Applied Psychology, 71,
457-460.
10)
Behrens, J. T. (1997). Principles and procedures of exploratory data analysis. Psychological Methods, 2,
131-160.
11)
Tabachnick, B.G., &
Fidell, L.S. (1996). Using multivariate statistics (3rd ed.). New York:
Harper Collins. (Chapter 4- “Cleaning up your act: Screening data prior
to analysis”)
12)
McCartney, K., &
Rosenthal, R. (2000). Effect size, practical importance, and social policy for
children. Child Development, 71, 173-180.
13)
Cortina, J., &
Nouri, H. (2000). Effect size for ANOVA designs. Newbury Park, CA: Sage
Publications.
14)
Cohen, J. (1992). A
power primer. Psychological Bulletin, 112, 155‑159.
15)
Nickerson, R.S. (2000).
Null hypothesis significance testing: A review of an old and continuing
controversy. Psychological Methods, 5, 241-301.
16)
Porter, A. C., &
Raudenbush, S. W. (1987). Analysis of covariance: Its model and use in
psychological research. Journal of Counseling Psychology, 34, 383-392.
17)
Von Eye, A., &
Schuster, C. (2000). The odds of resilience. Child Development, 71,
563-566.
18)
Tinsley, H.E.A., &
Tinsley, D.J. (1987). Uses of factor analysis in counseling psychology
research. Journal of Counseling Psychology, 34, 414-424.
19)
Haase, R. F., &
Ellis, M. V. (1987). Multivariate analysis of variance. Journal of
Counseling Psychology, 34, 404-413.
20)
Wampold, B.E., &
Freund R.D. (1987). Use of multiple regression in counseling psychology; A
flexible research strategy. Journal of Counseling Psychology, 34,
372-382.
21)
Stevens, J. P. (1984).
Outliers and influential data points in regression analysis. Psychological
Bulletin, 95, 334‑344.
22)
Baron, R.M., &
Kenny, D.A. (1986). The moderator-mediator variable distinction in social
psychological research: Conceptual, strategic, and statistical considerations. Journal
of Personality and Social Psychology, 51, 1173-1182.
23)
Miller, S. (1998). Developmental
research methods (2nd ed.). Englewood Cliffs, NJ: Prentice Hall. (Chapter 3
- “Design”).
24)
Wilkinson, L., & the
Task Force on Statistical Inference. (1999). Statistical methods in psychology
journals. American Psychologist, 54, 594-604.
25)
Francis, D.J., Fletcher,
J.M., Stuebing, K.K., Davidson, K.C., & Thompson, N.M. (1991). Analysis of
change: Modeling individual growth. Journal of Consulting and Clinical
Psychology, 59, 27-37.
The Honor Code
Students in this course are expected to behave at all times in a manner consistent with the GMU Honor Code. The Honor Code (in the GMU University Catalog and http://www.gmu.edu/facstaff/handbook/aD.html provides good definitions of lying, stealing, cheating, and plagiarism. For purposes of clarity, the following guidelines for plagiarism will be used in this course for the writing of the paper:
Plagiarism =
•
Copying, word for word, greater than about 25% of a sentence from someone
else's work and having the words appear to be your
own words. [Note: This is regardless of 1) the type of other person's work
(whether or not it was published) and 2)
whether or not you have given the person a citation after the text or a
reference in the bibliography].
•
Using greater than 25% of the words in someone else's sentence by switching
around the order of words or phrases and having
the words appear to be your own words (same notes apply, as above).
• Paraphrasing
someone else's ideas or findings or sentences without giving them a citation
and reference.
•
Using the same paper for this course which has been (or will be) turned in for
another course.
Students are encouraged to collaborate and study together as much as possible throughout the course. For the project, students can assist each other in the form of defining and narrowing down the data set, discussing the assignment, proofreading drafts, and doing analyses together, but the student whose name appears on Phase 2-4 of the project must be the author, and the research questions asked, analyses performed, and interpretation given need to be individual. For collaborative papers and article review, both students must contribute equally to the project, including relatively equal contributions to the actual writing. Violations of the Honor Code will not be tolerated in this course and will be immediately reported according to GMU procedures.
Accommodation for
Students with Disabilities
It is the policy of the
University to make reasonable accommodations for qualified individuals with
disabilities. Students who may have special needs because of a physical or
learning disability are encouraged to contact Disability Support Services
ASAP (234 Student Union I) at
993-3247. Students with disabilities who are in need of accommodation relative
to class attendance/arrival, course requirements, or related aspects of course
performance and who have already processed the necessary paperwork with
Disability Support Services must initiate such a request in writing
immediately, and prior to any anticipated need, to the instructor. Such
requests will be accommodated within the reasonable constraints of fairness and
timeliness with regard to the instructor and the other students enrolled in the
course.
A Few Notes About
the Labs
Lab grades - will consist of the following:
•
Homeworks 50%
•
Oral Presentation 20%
•
In-Class Lab Activities
/
Participation / Attendance 25%
•
Item Submission 5%
Lab Activities/Participation and Lab Switching– In order to receive credit for the lab activity/participation on a given week, students must attend their own scheduled/registered lab. Switching labs without prior approval and arrangements made with both lab instructors, will result in the student not receiving credit for attending/ participating in that week’s lab. If you have an urgent need to switch labs, email your lab instructor and the lab instructor whose lab you would like to attend with your reason and await approval.
Collaboration – If desired, students may collaborate with a maximum of one other student (from the same lab section) on any and all in-class lab activities and lab homeworks. With this option, students turn in one copy of their work with both names. Partners can be renegotiated for each week/assignment (i.e., working together once with one person doesn’t mean that you have to team up this way each time).
Late Policy – No late work will be accepted. All homework will be due on the dates clearly listed on the syllabus. All in-class lab activities will be due at the end of the lab session. Students with truly exceptional circumstances may request but are not guaranteed exceptions to this policy. (NOTE – The same policy holds for assignments in the lecture [article review, project phases…])
Grading of Lab Participation - For each lab session, students will receive a score of 0 to 4. 1 point is given for attendance, 1 point is given for the quality of student discussion/participation in lab, 1 point given for completing and turning in the lab activity of the day, and 1 point is given for the quality of the assignment turned in (0 for poor/minimal quality, 1 for good quality) .
Redo Policy – Due to the nature of the assignments this semester, re-doing work for additional points will not be an option this semester. All grades will be based on what was originally turned in.
Word Processing – All work that is done outside
of lab and turned in must be typed/word processed (except for the rare instance
of hand calculations – however even with these the interpretation part of
the assignment must be typed). Five points will be deducted from work turned in
that is not typed. Work done in-class/lab (and turned in the same day) can be
hand written.
Supplies. Due to the fact that students will be working extensively with a large database in SPSS, it is strongly recommended that they obtain a ZIP disk on which to save work this semester. Also, bring your course notes and text books/readings to lab each week.
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Name ___________________________________________
SS# ___________________________________________
Program/Year __________________________________________
Phone Number(s) ___________________________________________
___________________________________________
Primary Email Address ___________________________________________
GMU (Mason) Username ___________________________________________
(i.e., awinsler)
Previous (Pre PSYC 611) Statistics Coursework Taken:
Course Taken _______________________ Year ___________ Grad / Undergrad
Course Taken _______________________ Year ___________ Grad / Undergrad
Course Taken _______________________ Year ___________ Grad / Undergrad
Course Taken _______________________ Year ___________ Grad / Undergrad