Syllabus - 780

Psyc 646 - Issues and Methods in Developmental Psychology

Course Syllabus - Fall 2000

Dr. Adam Winsler

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Instructor: Adam Winsler, Ph.D.
Office:     2011 David King Hall
Phone:     993-1881
Office Hours:     Tues 1:30-2:30, Thurs 11:00—12:00, + by appt.
Email: awinsler@gmu.edu
URL: http://classweb.gmu.edu/classweb/awinsler
Class Schedule T 7:20 - 10:00pm
Location: R B102
Credit Hours: 3
Prerequisites: PSYC 611/612 + 6 hours of graduate developmental psychology

Course Description & Goals

Developmental psychology is all about studying stability and change in human functioning over time. However, most developmental psychologists simply describe what children are like at different ages rather than explain how and why individuals change over time. This is partly due to the fact that the field has only recently begun to develop a sophisticated understanding of the complex, dynamic nature of developmental systems. What is more, research methods and statistical procedures lag far behind contemporary developmental theory as they are typically based on static rather than dynamic, transactional models. Developmentalists 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. This course 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, behavior genetics, evolutionary psychology, and nonlinear, dynamic, transactional, and self-regulating developmental systems theory. 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 developmental change. Topics include design options (cross-sectional, longitudinal, sequential, microgenetic, and cross-species experimental and quasi-experimental designs) and measurement options (difference/gain scores, covariates, individual growth curves, latent structural modeling...) for studying change over time.

3) To introduce students to various classic and novel statistical procedures available for analyzing change over time.

4) To provide students with hands-on experience analyzing a longitudinal developmental data set with SPSS for windows from start to finish.

Required Reading

1) Selected articles (see reading list below)

Format/Organization of Class Meetings

The primary format for the class meetings will be small group, seminar-type discussion with varying degrees of structure imposed by me depending on the content from week to week. The first hour and a half or so of class (7:20-8:50) will be spent discussing the readings and topics of the week. Then we will take a 5-10 minute break. The last hour of practically every class meeting (9:00-10:00pm) will be spent in a sort-of "hands-on lab," in which we will address particular practical and procedural issues/problems involved in research and statistics, such as database management, data analysis, software packages & internet resources for statistics, and doing/interpreting statistical procedures on the computer. We will use a laptop computer and real data sets for use in this portion of the class.

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) Data Analysis Project. The main assignment in this course is for students to analyze a longitudinal, developmental data set from beginning to end: from coming up with a 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 worthy of publication, students are encouraged to obtain a data set with which they have some reasonable personal interest/commitment.

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. It is expected that multiple office meetings between students and the instructor will be needed in order to complete the assignment. More details on the project will be given as needed.

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 on Tuesday — Sept 19.

Phase II - Theory of Change Paper

A short paper (Å 10 pages) on the student's specific theory of development in the domain being studied as well as their conception of change in the domain, using as many of the terms and concepts discussed in class as possible. Due on Tuesday — Oct 17.

Phase III - 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. Due on Tuesday — Oct 31.

Phase IV - 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 on Tuesday — Nov 14.

Phase V - 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 on Tuesday — Dec 5.

4) Oral Presentation. On either the university-scheduled night for the final exam (Dec 12) 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 70%
    - Part I 5%
    - Part II 20%
    - Part III 10%
    - Part IV 10%
    - Part IV 25 %
• Participation 10%
• Oral Presentation 10%
• Final Activity 10%

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 (pp. 24 of 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 helping with library research procedures, proofreading drafts, and discussing the assignment, but the student(s) whose name appears on the paper must be the author(s). 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 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.

Tentative Course Outline

Date	Topic(s)	Reading(s)
Tues - Aug 29	Introduction, Overview, and Planning of the Course
	ISSUES:
Tues - Sept 5	Phylogenetic Development - Cross-species comparisons and their relevance to human development	• Rumbaugh & Savage- Rumbaugh (1996) • Tomasello et al. (1993) • Fairbanks (1996) • Siegler (1996a)
Tues - Sept 12	Ontogenetic Development - Contemporary Issues in Dev. Theory - I (Dynamic, transactional, self-regulating systems, Probabilistic epigenesis, Equifinality, Nature/nurture integration, developmental psychopathology)	• Siegler (1996b) • Gottlieb (1998) • Cicchetti (1994) • Richters (1997) • Rutter (1991)
Tues - Sept 19	Ontogenetic Development - Contemporary Issues in Dev. Theory - II (Causality, Risk, Mediation, Moderation, Self-Organization)	• Kazdin et al. (1997) • Baron & Kenny (1986) • Rutter (1994) • Rutter & Pickles (1991) Phase I Due
Tues - Sept 26	Microgenetic Development - Contemporary Issues in Dev. Theory — III (Microgenetic, Life Span Perspectives, Overlapping Waves, Gains and Losses)	• Siegler (1996c) • Siegler (1996d) • Kuhn (1995) • Baltes et al. (1998)
Tues - Oct 3	Conceptualizing Change - Intra- vs. interindividual stability/change - Qualitative vs. quantitative change - Linear vs. nonlinear change - Uni- vs. multidimensional change - Regressive vs. irreversible change	• Collins (1991) • Widaman (1991) • Bergman et al. (1991) • Nesselroade & Fetherman (1991)
Tues - Oct 10	No Class - Fall Break
	METHODS:
Tues - Oct 17	Classic Developmental Designs & Issues - Cross sectional, Longitudinal, Cohort-Sequential, Single subject, Between- vs. within-subjects, Counterbalancing, External Validity	• Van der Kamp & Bijleveld (1998) • McGuigan (1990) • Keren (1993) • Reese (1997) • Mook (1983) Phase II Due
Tues - Oct 24	Longitudinal Data - Specialized Problems - Timing of data collection, covariates, planning - Missing data and subject attrition - Secondary data analysis	• Cohen (1991) • Rovine & Delany (1990) • Kraemer (1994) • Bergman & Magnusson (1997) • Brooks-Gunn et al. (2000)
Tues - Oct 31	Measuring Change - Difference/gain scores: Myths and realities - Stability within change - Individual growth curves - Ordinal methods, Odds ratios	• Rogosa (1995) • Francis et al. (1991) • Patterson (1995) • Cliff (1991) • Von Eye & Schuster (2000) Phase III Due
	STATISTICS:
Tues - Nov 7	General Statistical Issues I - Data Quality and Screening - Exploratory Data Analysis (EDA) - Outliers	• Smith et al. (1986) • Allison et al. (1993) • Tabachnick & Fidell (1996) • Wilkinson et al. (1999)
Tues - Nov 14	General Statistical Issues II - Hypothesis Testing - Effect Sizes - Power	• Cortina & Dunlap (1997) • Cortina & Folger (1998) • McCartney & Rosenthal (2000) • Cortina & Nouri (in press) • Cohen (1992) Phase IV Due
Tues - Nov 21	Classic Statistics for Longitudinal Data - Repeated-Measures ANOVA, MANOVA, ANCOVA, MANCOVA - Nonparametric Approaches	• Games (1990) • Olweus & Alsaker (1991) • Zimmerman & Zumbo (1993a) • Zimmerman & Zumbo (1993b)
Tues - Nov 28	New Techniques for Analyzing Longitudinal Data - Survival Analysis - Individual Growth Curve Modeling	• Willett et al. (1998) • Willett & Singer (1995) • Singer et al. (1998) • Singer (1998)
Tues - Dec 5	Other Techniques for Analyzing Longitudinal Data - Repeated-Measures Regression - Structural Equation Modeling - Time Series Models (ARIMA, Spectral analysis) - Latent Class/Transition Analysis - Log Linear Models for Repeated Measures - Prediction Analysis - Configural frequency analysis - Convergence Analysis - Correspondence Analysis - Dynamic Factor Analysis - Sequential Analysis	• McArdle & Bell (2000) • Sackett & Shortt (1995) • Collins et al. (2000) • Schmitz (1990) • Von Eye & Niedermeir (1999) Phase V Due
Tuesday - Dec. 12	Final Activity — TBA (7:30-10:15pm)	Oral Presentations + ?

Reading List (Required)

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Phylogenetic Development

1) Rumbaugh, D.M., & Savage-Rumbaugh, E.S. (1996). Biobehavioral roots of language: Words, apes, and a child. In B.M. Velichkovsky & D.M. Rumbaugh (Eds.), Communicating meaning: The evolution and development of language (pp. 257-274). Mahwah, NJ: Erlbaum.

2) Tomasello, M., Kruger, A.C., & Ratner, H.H. (1993). Cultural learning. Behavioral and Brain Sciences, 16, 495-552.

3) Fairbanks, L.A. (1996). Individual differences in maternal style: Causes and consequences for mothers and offspring. Advances in the Study of Behavior, 25, 579-611.

4) Siegler, R.S. (1996a). Emerging minds: The process of change in children’s thinking. New York: Oxford University Press. (Chapter 2 - "Evolution and cognitive development?")

Ontogenetic Development - Contemporary Issues in Developmental Theory - I

5) Siegler, R.S. (1996b). Emerging minds: The process of change in children’s thinking. New York: Oxford University Press. (Chapter 1 - "Whose children are we talking about?")

6) Gottlieb, G. (1998). Normally occurring environmental and behaioral influences on gene activity: From centra dogma to probabilistic epigenesis. Psychological Review, 105, 792-802.

7) 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.

8) Richters, J.E. (1997). The Hubble hypothesis and the developmentalist’s dilemma. Development and Psychopathology, 9, 193-229.

9) Rutter, M. (1991). Nature, nurture, and psychopathology: A new look at an old topic. Development and Psychopathology, 3, 125-136.

Ontogenetic Development - Contemporary Issues in Developmental Theory - II

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) 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.

12) Rutter, M. (1994). Beyond longitudinal data: Causes, consequences, change, and continuity. Journal of Consulting and Clinical Psychology, 62, 928-940.

13) 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.

Microgenetic/Life Span Perspectives

14) 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?").

15) 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").

16) Kuhn, D. (1995). Microgenetic study of change: What has it told us? Psychological Science, 6, 133-139.

17) 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.s 1029-1059)

Conceptualizing Change 18) 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.

19) 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.

20) Bergman, L.R., Eklund, G., & Magnusson, D. (1991). Studying individual development: Problems and methods. In D. Magnusson, L. Bergman, G. Rudinger, & B Torestad (Eds.), Problems and methods in longitudinal research (pp. 1-27). Cambridge, UK: Cambridge University Press.

21) Nesselroade, J.R., & Fetherman, D.L. (1991). Intraindividual variability in older adults' depression scores: Some implications for developmental theory and longitudinal research. In D. Magnusson, L. Bergman, G. Rudinger, & B Torestad (Eds.), Problems and methods in longitudinal research (pp. 47-66). Cambridge, UK: Cambridge University Press.

Classic Developmental Designs

22) 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.

23) McGuigan, F.J. (1990). Experimental psychology: Methods of research (5th ed.). Englewood Cliffs, NJ: Prentice Hall. [Chapter 12 - "Experimental Design: Single Subject (N = 1) Research"].

24) Keren, G. (1993). Between- or within-subjects design: A methodological dilemma. In G. Keren & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences: Methodological issues (pp. 257-272). Hillsdale, NJ: Erlbaum.

25) Reese, H. W. (1997). Counterbalancing and other uses of repeated-measures Latin-square designs: Analyses and interpretations. Journal of Experimental Child Psychology, 64, 137-158.

26) Mook, D.G. (1983). In defense of external invalidity. American Psychologist, 38, 379-387.

Longitudinal Data: Specialized Problems

27) 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: American Psychological Association.

28) 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.

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 Bergman, L.R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291-319.

31) Brooks-Gunn, J., Berlin, L.J., Leventhal, T., & Fuligni, A.S. (2000). Depending on the kindness of strangers: Current national data initiatives and developmental research. Child Development, 71, 257-268.

Measuring Change 32) 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.

33) 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.

34) 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.

35) 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.

36) Von Eye, A., & Schuster, C. (2000). The odds of resilience. Child Development, 71, 563-566. General Statistical Issues I — EDA and Common Sense

37) 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.

38) Allison, D. B., Gorman, B. S., & Primavera, L. H. (1993). Some of the most common questions asked of statistical consultants: Our favorite responses and recommended readings. Genetic, Social, and General Psychology Monographs, 119 , 155-185.

39) 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")

40) Wilkinson, L., & the Task Force on Statistical Inference. (1999). Statistical methods in psychology journals. American Psychologist, 54, 594-604.

General Statistical Issues II — Hypothesis Testing, Effect Sizes, & Power

Cortina, J.M., & Dunlap, W.P. (1997). On the logic and purpose of significance testing. Psychological Methods, 2,

161-172. 42) Cortina, J.M., & Folger, R.G. (1998). When is it acceptable to accept a null hypothesis: No way Jose? Organizational Research Methods, 1, 334-350.

43) McCartney, K., & Rosenthal, R. (2000). Effect size, practical importance, and socal policy for children. Child Development, 71, 173-180.

44) Cortina, J., & Nouri, H. (2000). Effect size for ANOVA designs. Newbury Park, CA: Sage Publications.

45) Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.

Classic Statistics for Longitudinal Data — RM ANOVA

46) Games, P.A. (1990). Alternative analyses of repeated-measure designs by ANOVA and MANOVA. In A. Von Eye (Ed.), Statistical methods in longitudinal research Vol. 1: Principles and structuring change (pp. 81-121). San Diego, CA: Academic Press.

47) 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.

48) Zimmerman, D.W., & Zumbo, B.D. (1993a). The relative power of parametric and nonparametric statistical methods. In G. Keren & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences: Methodological issues (pp. 481-517). Hillsdale, NJ: Lawrence Erlbaum Associates.

49) Zimmerman, D.W., & Zumbo, B.D. (1993b). Relative power of the Wilcoxon test, the Friedman test, and repeated-measures ANOVA on ranks. Journal of Experimental Education, 62, 75-86.

New Techniques for Analyzing Longitudinal Data 50) Willett, J.B., Singer, J.D., & Martin, N. (1998). The design and analysis of longitudinal studies of development and psychopathology in context: Statistical models and methodological recommendations. Development and Psychopathology, 10, 395-426. (Available online at: http://gseweb.harvard.edu/~faculty/singer/ )

51) Willett, J.B., & Singer, J.D. (1995). Investigating onset, cessation, relapse, and recovery: Using discrete-time survival analysis to examine the occurrence and timing of critical events In J.M. Gottman (Ed.), The analysis of change (pp. 203-259). Mahwah, NJ: Erlbaum.

52) Singer, J.D., Fuller, B., Keiley, M., & Wolf, A. (1998). Early child-care selection: Variation by geographic location, maternal characterstics, and family structure. Developmental Psychology, 34, 1129-1144. (Available online at: http://gseweb.harvard.edu/~faculty/singer/ )

53) Singer, J.D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models and individual growth models. Journal of Educational and Behavioral Statistics, 24, 323-355. (Available online at: http://gseweb.harvard.edu/~faculty/singer/

Other New Techniques for Analyzing Longitudinal Data 54) McArdle, J.J., & Bell, R.Q. (2000) An introduction to latent growth models for developmental data analysis. In T.D. Little, K.U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multilevel data (pp. 69-107). Mahwah, NJ: Erlbaum.

55) Sackett, G.P., & Shortt, J.W. (1995). Hierarchical regression analysis with repeated-measure data. In J.M. G. Gottman (Ed.), The analysis of change (pp. 67-82). Mahwah, NJ: Erlbaum.

56) 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.

57) 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.

58) 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")