Psyc 646 - Issues and Methods in Longitudinal/Developmental Research
Course Syllabus -
Spring 2003
Dr. Adam Winsler
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Instructor: Adam Winsler, Ph.D. Office: 2023 David King Hall
Phone: 703-993-1881 Office Hours: Wed 9-10:00am, Tues 4:15-5:30, + by appt.
Email: awinsler@gmu.edu URL: http://classweb.gmu.edu/awinsler
Class Schedule Tuesday 1:30–4:10 pm Location: ENT 279
Credit Hours: 3 Prerequisites: PSYC 611/612
Course Description
& Goals
Developmental psychology is all about studying stability and change in individual functioning over time. Other areas within psychology (i.e., I/O, Cognitive, Clinical, Biological) that have historically tended to study fixed, static, trait-like processes are now becoming increasingly interested in change processes over time within their respective units of analysis (groups, organizations, neuronal systems). Even developmental psychologists have, often, in the past, simply described what children are like at different ages rather than answered the more difficult questions of how and why individuals change over time. This is partly due to the fact that the field has only recently begun to develop sophisticated theories about the complex, dynamic nature of developmental systems. What is more, research methods and statistical procedures lag far behind contemporary developmental theory as most procedures have been based on static rather than dynamic models. Developmentalists (and others interested in change over time) must understand and make due with the methodological and statistical techniques that are currently available to the science. This course is designed to assist students as they grapple with such issues and has 4 major goals:
1) To help students think about the nature of development and different theories of change, and how these ideas may or may not fit well with current methods and statistics. We will explore current theoretical advances in understanding change over time, including contributions from developmental psychopathology, evolutionary psychology, behavior genetics, life-span development, and nonlinear, dynamic, transactional, and self-regulating developmental systems theories. Issues to be discussed for example, include probabilistic epigenesis, equifinality, intraindividual vs. interindividual stability and change over time, qualitative and quantitative development, linear vs. nonlinear change, reliability in measuring 'static' constructs vs. change over time in 'dynamic' constructs, unidimensional vs. multidimensional change, regressive and irreversible change, causality, mediation, moderation, and risk.
2) To understand current research methods, designs, and measurement issues in studying change. Topics include design options (cross-sectional, longitudinal, sequential, microgenetic, and cross-species experimental, quasi-experimental, and correlational designs) and measurement options (raw scores, difference/gain scores, covariates, individual growth curves, latent structural modeling...) for studying change over time.
3) To introduce students to various classic and relatively new statistical procedures available for analyzing individual change over time.
4) To provide students with hands-on experience analyzing a longitudinal/developmental data set from start to finish.
Required Reading
1) Selected articles (see reading list below – method of distribution to be determined)
2) Taris, T.W. (2000). A primer in longitudinal data analysis. Thousand Oaks, CA: Sage. (at bookstore)
3) Singer, J. D., & Willett, J.B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press. (at bookstore soon)
Additional free optional electronic resource that goes with the Singer/Willett text (Data sets and analysis code in SPSS and other stat packages) http://www.ats.ucla.edu/stat/examples/alda.htm
Course
Requirements and Assignments
The activities of this course are designed to provide students with scaffolded learning experiences engaging in the skills and activities required in the culture of academia (i.e. engaging in scholarly discourse and group discussion, critically evaluating research, conceiving of and conducting data analyses, interpreting statistics, writing up research reports, and collaborating with others).
1) Class Participation. This is an advanced doctoral seminar course which requires active discussion and contribution from each member of the class. Each student is expected to have had some (in some cases - extensive) experience with at least some of the issues discussed in the class and the course will be greatly enhanced if we can benefit from each individual student's expertise.
Students' participation grade will be based on the instructor's rating of a) the quantity and quality of students' verbal participation in weekly seminars. Obviously, if you are not in class (for whatever reason - even a good one), you cannot participate that day and your participation grade will be affected.
2) Theory of Change Paper.
Students will write a paper (≈ 15 pages) on the student's own specific theory of development/change for a domain/construct of particular interest to the student. Student will articulate their conception of change in the construct of interest and refer to as many of the relevant terms and concepts discussed in class to date as possible. This paper can be on the same construct that is the topic of the data analysis project but it does not have to be. Due in class on Tuesday– March 18.
3) Data Analysis Project. The other main assignment in this course is for students to analyze a longitudinal, developmental data set from beginning to end: from coming up with a brief theory of change for the phenomenon, to deciding on questions and hypotheses, to deciding on the data manipulation techniques and statistical procedures needed to do the analyses, to running the procedures, to interpreting the results, to writing the research report. Students may use their own data set (if one is available to them, if it is longitudinal, AND if it is approved by the instructor) or one provided by the instructor. The data must already be in some kind of electronic form (i.e., database, spreadsheet, or statistical package file). Because of the depth of the work and the fact that the end result of this project should be eventually worthy of publication, students are encouraged to obtain a data set with which they have some reasonable personal interest/commitment.
We will be using SPSS mostly for the course, but students may use whatever statistical package they like to analyze the data, however, two points qualify this statement : 1) the program needs to be able to do the analyses that are required to answer the questions well, and 2) the farther away you get from the packages I know well, the less help I will be able to give you.
Students may work collaboratively with one (1) other student for the data analysis project if it is a true collaboration and, in fact, are strongly encouraged to do so. In this situation, one project would be turned in with both students’ names on it. It is expected that multiple office meetings between students and the instructor will be needed in order to complete the assignment.
The project will be divided into the following meaningful chunks and turned in along the way as follows:
Phase
I - Definition/approval of the data set, variables, study design, and area
of study.
A
fairly-detailed, codebook-like description of all variables in the data set and
the nature of the variables, plus a brief description of the study, its design,
goals, and background. Due in class on Tuesday
– Feb 11.
Phase
II – Conceptualization of Change, Research Questions, Hypotheses, and
Data Analysis Plan
A
list of the main research questions, the main hypotheses, and a very specific
description of how you are going to go about testing the hypotheses (procedures
used on what X and Y variables etc…). Also needed here is an articulation
of how the construct(s) measured in your data set are conceptualized as
changing. (A smaller version of the theory of change paper) Due in class on Tuesday–
March 4.
Phase
III - Exploratory Data Analysis (EDA)
Annotated
output from your exploratory and preliminary analysis procedures, including
data cleaning, transformations, data reduction, and recoding procedures if any.
Also include a description of how the results of the EDA affect your data
analysis strategy. Due in class on Tuesday–
April 1.
Phase
IV - 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, an
expanded results section (in which you describe what was done, and why, and
what was found), and a brief discussion section. Due in class on Tuesday– April 29.
4) Oral
Presentation. At either the university-scheduled time
for the final exam (May 13) or another time toward the end of the course
negotiated by the class as a whole, students will give an oral presentation to
their fellow class members briefly summarizing their course project, described
above.
5) Final Activity. At the same meeting when we have the oral presentations (described above) students will complete/turn in/engage in some sort of final, cumulative, integrative review, activity/exercise/exam. The nature and format of this final activity will be decided upon and negotiated as a class and determined as soon as possible.
Grading Procedures
Students' final grades will be determined as follows:
• Project 50% • Theory of Change Paper 20%
- Part I 5% • Participation 10%
- Part II 10% • Oral Presentation 10%
- Part III 10% • Final Activity 10%
- Part IV 25%
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 (http://www.gmu.edu/departments/unilife/honorcode.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 collaborative papers, 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 and this instructor 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 the Disability
Resource Center ASAP (222 Student Union I) 993-2474 http://www.gmu.edu/student/drc/ 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.
Tentative Course Outline
|
Date |
Topic(s) |
Reading(s) |
|
Tuesday- Jan 21 |
Intro, Overview, and
Planning of the Course + SPSS |
|
|
Tuesday– Jan 28 |
Evolutionary, Wave, and
Life Span Perspectives on Development |
• Siegler (1996a) • Siegler (1996b) • Siegler (1996c) • Baltes et al.
(1998) |
|
Tuesday– Feb 4 |
Transactional, Emergent,
and Self-Organizing Developmental Systems |
• Gottlieb (1998) •
Rutter (1997) • Lewis (2000) • Sameroff (2000) • Rutter &
Pickles (1991) |
|
Tuesday– Feb 11 |
Risk, Causality, Mediation,
Moderation, Time, and Longitudinal Research Phase I Due |
• Kazdin et al. (1997) • Cicchetti (1994) • Rutter (1994) • Mitchell & James (2001) |
|
Tuesday– Feb 18 |
Variable-Based, Person-Oriented, and
Microgenetic Approaches to Studying Development
|
• Richters (1997) • Hinde (1998) • Bergman & Magnusson (1997) • Siegler (1996d) • Kuhn (1995) |
|
Tuesday– Feb 25 |
Conceptualizing and
Measuring Stability and Change - I |
• Taris (2000, Ch. 3) •
Collins (1991) • Widaman (1991) • Patterson (1995) |
|
Tuesday– Mar 4 |
Conceptualizing and
Measuring Stability and Change - II |
• Rogosa (1995) • Taris (2000, Ch. 4) • Cliff (1991) • van Geert & van Dijk (2002) |
|
Tuesday– Mar 11 |
No Class - Spring Break |
|
|
Tuesday– Mar 18 |
Developmental Research
Designs and Methodological Challenges Theory of Change Paper Due |
• Taris (2000, Ch. 1) • Van der
Kamp/Bijleveld (1998) • Kraemer (1994) • Cohen (1991) |
|
Tuesday– Mar 25 |
Longitudinal
Data: Specialized Problems and Classic
Analysis Tools |
• Taris (2000, Ch. 2) • Rovine & Delany (1990) • van der Kloot
(1998) • Olweus &
Alsaker (1991) |
|
Tuesday– April 1 |
Individual Growth Curves -
I Phase III Due |
•
Ployhart, Holtz & Bliese (2002) • Singer/Willett
(2003, Ch. 1) • Singer/Willett (2003, Ch. 2) • Singer/Willett (2003, Ch. 3) |
|
Tuesday– April 8 |
Individual Growth Curves -
II |
•
Singer/Willett (2003, Ch. 4) •
Singer/Willett (2003, Ch. 5) •
Bliese & Ployhart (2002) |
|
Tuesday– April 15 |
Survival Analysis - I
|
• Taris (2000, Ch. 6) • Singer/Willett
(2003, Ch. 9) • Singer/Willett
(2003, Ch. 10) |
|
Tuesday– April 22 |
Survival Analysis - II
|
• Singer/Willett
(2003, Ch. 11) • Singer/Willett
(2003, Ch. 12) |
|
Tuesday– April 29 |
Introduction to
Other Techniques for Analyzing
Longitudinal Data
-
Time Series Models, Sequential Analysis -
Correspondence Analysis, Log Linear Models -
Latent Class/Transition Analysis -
Configural frequency analysis Phase IV Due |
• Schmitz (1990) • Taris (2000, Ch. 7) • Von Eye &
Niedermeir (1999) • Collins et al.
(2000) • Von Eye (2002) |
|
Tuesday- May 13 |
Final Activity – TBA
(1:30-4:15) |
Oral Presentations/Activity |
Reading List (Required)
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Week 2 - Evolutionary, Wave, and Life Span
Perspectives on Development
1) Siegler,
R.S. (1996a). Emerging minds: The process of change in children’s
thinking. New York: Oxford University Press. (Chapter 1 - “Whose
children are we talking about?”)
2) Siegler,
R.S. (1996b). Emerging minds: The process of change in children’s
thinking. New York: Oxford University Press. (Chapter 2 - “Evolution
and cognitive development?”)
3) Siegler,
R.S. (1996c). Emerging minds: The process of change in children’s
thinking. New York: Oxford University Press. (Chapter 4 - “Strategic
Development: Trudging up the Staircase or Swimming with the Tide?”).
4) Baltes,
P.B., Lindenberger, U., & Staudinger (1998). Life-span theory in
developmental psychology. In W. Damon (Ed.). R.L. Lerner (Vol. Ed.), Handbook
of child psychology - 5th Edition - Volume 1: Theoretical models of human
development (pp. 1029-1144). New York: Wiley & Sons. (Just pp.
1029-1059)
Week 3 – Transactional, Emergent, and Self-Organizing Developmental Systems
5) Gottlieb,
G. (1998). Normally occurring environmental and behavioral influences on gene
activity: From central dogma to probabilistic epigenesis. Psychological
Review, 105, 792-802.
6) Rutter,
M. L. (1997). Nature-nurture integration: The example of antisocial behavior. American
Psychologist, 52, 390-398.
7) Lewis,
M. D. (2000). The promise of
dynamic systems approaches for an integrated account of human development. Child
Development, 71, 36-43.
8) Sameroff, A. J. (2000).
Developmental systems and psychopathology. Development and Psychopathology,
12, 297-312.
9) Rutter,
M., & Pickles, A. (1991). Person-environment interactions: Concepts,
mechanisms, and implications for data analysis. In D. Wachs & R. Plomin
(Eds.), Conceptualization and measurement of organism-environment
interaction (pp. 105- 141). Washington, DC: APA.
10) Kazdin,
A.E., Kraemer, H.C., Kessler, R.C., Kupfer, D.J., & Offord, D.R. (1997).
Contributions of risk-factor research to developmental psychopathology. Clinical
Psychology Review, 17, 375-406.
11) Cicchetti,
D. (1994). Integrating developmental risk factors: Perspectives from
developmental psychopathology. In C.A. Nelson (Ed.), Threats to optimal
development: Integrating biological, psychological, and social risk factors
(pp. 285-325). Hillsdale, NJ: Lawrence Erlbaum Associates.
12) Rutter,
M. (1994). Beyond longitudinal data: Causes, consequences, change, and
continuity. Journal of Consulting and Clinical Psychology, 62, 928-940.
13) Mitchell,
T.R., & James, L.R. (2001). Building better theory: Time and the
specification of when things happen. Academy of Management Review, 26,
530-547.
14) Richters, J.E. (1997). The Hubble hypothesis and the
developmentalist’s dilemma. Development and Psychopathology, 9,
193-229.
15) Hinde, R.A. (1998). Through
categories toward individuals: Attempting to tease apart the data. In R,B
Cairns, L.R. Bergman & J. Kagan (Eds.), Methods and models for studying
the individual (pp. 11-31). Thousand Oaks, CA: Sage.
16) Bergman,
L.R., & Magnusson, D. (1997). A person-oriented approach in research on
developmental psychopathology. Development and Psychopathology, 9,
291-319.
17) Siegler,
R.S. (1996d). Emerging minds: The process of change in children’s
thinking. New York: Oxford University Press. (Chapter 7 - “How
children generate new ways of thinking”).
18) Kuhn,
D. (1995). Microgenetic study of change: What has it told us? Psychological
Science, 6, 133-139.
Week 6 - Conceptualizing and Measuring Stability and Change I
19) Taris
(2000). Chapter 3 – Measuring Concepts Across Time: Issues of Stability
and Meaning”
20) Collins,
L. (1991). Measurement in longitudinal research. In L.M. Collins & J.L.
Horn (Eds.). Best methods for the analysis of change (pp. 137-148).
Washington, DC: American Psychological Association.
21) Widaman,
K.F. (1991). Qualitative transitions amid quantitative development: A challenge
for measuring and representing change. In L.M. Collins & J.L. Horn (Eds.). Best
methods for the analysis of change (pp. 204-225, with commentaries).
Washington, DC: American Psychological Association.
22) Patterson,
G.R. (1995). Orderly change in a stable world: The antisocial trait as a
chimera. In J.M. Gottman (Ed.), The analysis of change (pp. 83-101).
Mahwah, NJ: Lawrence Erlbaum Associates.
Week 7 - Conceptualizing and Measuring Stability and Change II
23) Rogosa,
D. (1995). Myths and methods: "Myths about longitudinal research"
plus supplemental questions. In J.M. Gottman (Ed.), The analysis of change
(pp. 3-66). Mahwah, NJ: Erlbaum.
24) Taris (2000). Chapter 4
– “Issues in Discrete Time Panel Analysis”
25) Cliff,
N. (1991). Ordinal methods in the assessment of change. In L.M. Collins &
J.L. Horn (Eds.), Best methods for the analysis of change (pp. 34-46).
Washington, DC: American Psychological Association.
26) van
Geert, P., & van Dijk, M. (2002). Focus on variability: New tools to study
intra-individual variability in developmental data. Infant Behavior &
Development, 25, 340-374.
Week 8 - Developmental Research Designs and Methodological Challenges
27) Taris
(2000). Chapter 1 – “Longitudinal Data and Longitudinal
designs”
28) Van
der Kamp, L.J., & Bijleveld, C.C.J.H. (1998). Methodological issues in
longitudinal research. In C.C.J.H. Bijleveld & L.J. Van der Kamp (Eds.), Longitudinal
data analysis: Designs, models, & methods (pp. 1-45). Thousand Oaks,
CA: Sage Publications.
29) Kraemer,
H. C. (1994). Special methodological problems of childhood developmental
follow-up studies: Focus on planning. In S.L. Friedman & H.C. Haywood
(Eds.), Developmental follow-up: Concepts, domains, and methods (pp.
259-276). San Diego, CA: Academic Press.
30) Cohen,
P. (1991). A source of bias in longitudinal investigations of change. In L.M.
Collins & J.L. Horn (Eds.), Best methods for the analysis of change
(pp. 18-33, including commentaries). Washington, DC: APA.
Week 9 - Longitudinal Data: Specialized Problems and Classic Analysis Tools
31) Taris
(2000). Chapter 2 – “Nonresponse in Longitudinal Research”
32) Rovine,
M.J., & Delany, M. (1990). Missing data estimation in developmental
research. In A. Von Eye (Ed.), Statistical methods in longitudinal research
Vol. 1: Principles and structuring change (pp. 35-79).San Diego, CA:
Academic Press.
33) van
der Kloot W. (1998). Univariate and multivariate analysis of variance of
longitudinal data. In C.C.J.H. Bijleveld & L.J. Van der Kamp (Eds.), Longitudinal
data analysis: Designs, models, & methods (pp. 155-206). Thousand Oaks,
CA: Sage Publications.
34) Olweus,
D., & Alsaker, F.D. (1991). Assessing change in a cohort-longitudinal study
with hierarchical data. In D. Magnusson, L. Bergman, G. Rudinger, & B.
Torestad (Eds.), Problems and methods in longitudinal research (pp.
107-132). Cambridge, UK: Cambridge University Press.
35) Ployhart,
R.E., Holtz, B.C., & Bliese, P.D. (2002). Longitudinal data analysis:
Applications of random coefficient modeling to leadership research. The
Leadership Quarterly, 13, 455-486.
36) Singer
& Willett (2003). Chapter 1 – “A Framework for Investigating
Change Over Time”
37) Singer
& Willett (2003). Chapter 2 – “Exploring Longitudinal Data on
Change”
38) Singer
& Willett (2003). Chapter 3 – Introducing the Multilevel model For
Change”
39) Singer
& Willett (2003). Chapter 4 – “Doing Data Analysis with the
Multilevel Model For Change”
40) Singer
& Willett (2003). Chapter 5 – “Treating TIME More
Flexibly”
41) Bliese,
P.D., & Ployhart, R.E. (2002). Growth
modeling using random coefficient models: Model building, testing, and
illustrations. Organizational Research Methods, 5, 362-387.
42) Taris (2000). Chapter 6
– “Analyzing Durations”
43) Singer & Willett
(2003). Chapter 9 – “A Framework for Investigating Event
Occurrence”
44) Singer & Willett
(2003). Chapter 10 “Describing Discrete-Time Event Occurrence Data”
45) Singer & Willett
(2003). Chapter11 – “Fitting Basic Discrete-Time Hazard
Models”
46) Singer & Willett
(2003). Chapter 12 – “Extending the Discrete-Time Hazard
Model”
Week 14 - Other Techniques
for Analyzing Longitudinal Data (Configural Frequency Analysis, Correspondence
Analysis, Sequential Analysis, Time Series Models, Loglinear Models)
47) Schmitz,
B. (1990). Univariate and multivariate time-series models: The analysis of
intraindividual variability and intraindividual relationships. In A. Von Eye
(Ed.), Statistical methods in longitudinal research Vol. II: Time series and
categorical data (pp. 351-386). San Diego, CA: Academic Press.
48) Taris (2000, Ch. 7) “Analyzing sequences”
49) Von
Eye, A., & Niedermeir, K.E. (1999). Statistical analysis of longitudinal
categorical data in the social and behavioral sciences. Mahwah, NJ:
Erlbaum. (Ch. 1 – “Describing Categorical Variables,” Ch. 2
– “Log-linear Modeling,” & Ch. 3 – Log-linear
Models for Repeated Observations”)
50) Collins,
L.M., Hyatt, S.L., & Graham, J.W. (2000). Latent transition analysis as a
way of testing models of stage-sequential change in longitudinal data. In T.D.
Little, K.U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and
multilevel data (pp. 147-161). Mahwah, NJ: Erlbaum.
51) Von Eye, A. (2002). Configural
frequency analysis: Methods, Models, and applications. Mahwah, NJ: Erlbaum.
(Chapter 1- “Introduction: The goals and steps of configural frequency analysis”)
PSYC
646 – Spring 2003 - Student Information
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