8 Free Half-Day Methodological Workshops Will Be Offered:

Developmental Methods Conference Johathan
Developmental Methods Conference Johathan

Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research

Niall Bolger, Ph.D.

Professor of Psychology
Columbia University

Jean-Philippe Laurenceau, Ph.D.

Unidel A. Gilchrist Sparks III Chair and Professor of Psychological and Brain Sciences University of Delaware

Intensive longitudinal methods, often called experience sampling, daily diary, or ecological momentary assessment methods, allow researchers to study people’s thoughts, emotions, and behaviors in their natural contexts. Typically, they involve self-reports from individuals, dyads, families or other small groups over the course of hours, days, and weeks. Such data can reveal life as it is actually lived and provide insights that are not possible using conventional experimental or survey research methods. Intensive longitudinal data, however, present data analytic challenges stemming from the multiple levels of analysis and temporal dependencies in the data. In this half-day workshop, participants will (a) recognize the types of research questions that can be addressed using intensive longitudinal methods; (b) see example datasets and how to run basic longitudinal models using SPSS Mixed and Mplus; and (c) understand in broad strokes how complex topics such as dyadic, mediation, and power analyses can be handled using Mplus. We will assume that participants are familiar with multiple regression analysis and ANOVA.

RECOMMENDED READING: Bolger, N. & Laurenceau, J-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York: Guilford.

Data sets and syntax for analyses can be found here: www.intensivelongitudinal.com

Developmental Methods Conference Johathan

‘Translating back’ common statistical test into their graphical causal language ancestors

Emil Coman, Ph.D.

Research Associate, Health Disparities Institute
UConn Health

This workshop is a hands-on working session meant to give practical (and testable) meaning to theoretical causal models behind common statistical tests like: t-test; ANOVA; Pearson chi-square, by demonstrating their utility in models like regression, mediation or ‘causal’ mediation (mediation + moderation).

Open source Onyx and R codes in daggity, MIIVsem, and lavaan will be used, which will be provided before the training, along with the prepared ‘presentation’ content, so participants can (1) run ‘analyses’ in R (and Onyx) along with the trainer, and (2) annotate their own copy of the presentation content. The choice of these R modules is intentional, to: (1) Show the ease in implementing such models in R, with minimal R skills/experience; (2) Demonstrate the role of ‘modeling’ as a distinct a priori step before the estimation, or data crunching.

Developmental Methods Conference Johathan

Growth Mixture Modeling

Whitney Moore

Assistant professor, College of Education
Wayne State University
Abstract to come

Developmental Methods Conference Johathan

Program Evaluation

Mwarumba Mwavita

Associate professor, College of Education, Health and Aviation
Oklahoma State University
Abstract to come

Developmental Methods Conference Johathan

Continuous Time Modeling

Pascal R. Doboeck

Pascal R. Deboeck
Professor, Department of Psychology
The University of Utah
Abstract to come

Developmental Methods Conference Johathan

Participant Recruitment and Design in Large Multisite Longitudinal Studies

Amanda S. Morris

Professor, Human Development and Family Science
Oklahoma State University-Tulsa
Abstract to come

Developmental Methods Conference Johathan

Time-Varying Effect Models in Developmental Research

Sara Vasilenko

Assistant Professor, Human Development & Family Science
Syracuse University

A new analytic method, the time-varying effect model (TVEM), can expand research ondevelopmental processes by allowing researchers to examine how associationsbetween variables differ over the course of continuous time. TVEM is a flexiblemethodthat doesnot impose any particular parametric form, but flexibly estimates curves toidentify nuanced periods of change in outcomes and their associations with predictors(Lanza et al., 2014). TVEM can be used to answer questions such as: How do riskfactors differentially predict behavior at different ages throughout childhood andadolescence? How do processes underlying well-being changeover timeafterexperiencing a life transition? How does the effectiveness of a program change overtime since an intervention?The goal of this workshop is to allow attendees to learn to identify and address newresearch questions by applying TVEM tocross-sectional data, panel data, and intensivelongitudinal data. Participants will gain both conceptual and practical knowledge in theuse of SAS software to estimate a basic TVEM in order to examine the mean levelof anoutcome variable as a function of time; examine time-varying effects of both time-invariant (e.g., gender) and time-varying (e.g.,parental closeness) predictors onanoutcome; and include moderators (e.g. race/ethnicity) of time-varying effects. Inaddition, advanced features, such as incorporation of survey weights, will be discussed.Finally, the workshop will end with a brief, optional hands-on activity.Analyses will use the SAS TVEM macro. Participants who want to participate in thehands-on portion of the workshop can download the macroand sample dataat:https://www.methodology.psu.edu/downloads/tvem/

Developmental Methods Conference Johathan

Visualizing Developmental Data

Andrea Howard

Associate Professor, Department of Psychology
Carleton University

Developmental researchers working with longitudinal data sets often fit models to their data that include trajectories meant to capture change over time in some phenomenon as well as effects of variables hypothesized to influence rates of change. Trajectories are fitted as latent growth curve models, multilevel models, and latent class or growth curve mixture models, to name a few. Models of fitted trajectories lend themselves to intuitively interpretable diagrams. At the same time, diagrams showing simple lines or curves tracking change over time often mask important information about variability around typical fitted trajectories and don’t always speak to the magnitude or scope of change occurring over a developmental window.The goal of this workshop is to introduce some strategies for visualizing developmental data more effectively and comprehensively. Topics will include: (a) visualizing raw data to build an understanding of possible developmental trends present in the data, (b) visualizing fitted trajectories and incorporating model uncertainty with confidence bands (c) visualizing effect sizes with strategies for plotting time-invariant and time-varying covariate effects.Strategies for visualizing both continuous and categorical outcomes will be reviewed. The workshop will feature hands examples using sample data, so participants are encouraged to bring their laptop computers if they wish to follow along. Software examples will be presented using R Studio. Workshop slides, sample data, and R script files will be made available here: [link TBA]