Intensive Longitudinal Methods

A 2-Day Seminar Taught by Niall Bolger, Ph.D. & Jean-Philippe Laurenceau, Ph.D.


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.

The goal of this workshop is to provide a full-cycle treatment of two fundamental research questions that can be addressed using intensive longitudinal methods: (a) What is the time course of the outcome variable, and (b) what is the within-person causal process that underlies the time course? A full-cycle treatment will take workshop participants through five stages of answering each research question: (1) Design study & collect data, (2) Visualize, (3) Analyze, (4) Write up results, and (5) Power the next study.

 

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Material for this workshop will be drawn from introductory sections of the presenters’ 2013 Guilford Press book, Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research.  


Who Should Attend

Workshop participants are assumed to be familiar with basic linear regression analysis and looking for a practical introduction to intensive longitudinal methods. Participants are also assumed to have collected, or be interested in collecting, intensive longitudinal data to answer research questions. An emphasis will be placed on interpretation and presentation of intensive longitudinal findings for publication purposes and planning a future intensive longitudinal study for replication and/or grant applications.

Note that this workshop will not cover the more advanced topics in the instructors’ book (i.e., categorical outcomes, mediation, dyadic data, psychometrics).


COMPUTING

The course will include lectures, software demonstrations, and data analysis practice with example datasets. Two software packages will be used: SPSS and Mplus (there will be a brief Mplus tutorial). Participants should bring laptops on which they have installed full or trial versions of these packages; they can follow along using the printed output provided or conduct analyses along with the instructors. Participants will be also shown where they can find equivalent code for conducting the analyses in R, Stata, SAS, and STAN. Power outlets will be available at each seat.


LOCATION, FORMAT AND MATERIALS

The seminar meets Friday, November 11 and Saturday, November 12 at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. The class will meet from 9 to 5 each day with a 1-hour lunch break. 

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking. 


REGISTRATION AND LODGING

The fee of $995 includes all course materials.

Lodging Reservation Instructions

A block of rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut St., Philadelphia, PA for $149 per night. This hotel is about a 5-minute walk from the seminar location. To register, you must call 203-905-2100 during business hours and identify yourself with Statistical Horizons by using group code STA011 or click here. For guaranteed rate and availability, you must reserve your room no later than October 9, 2016.  

If you make reservations after the cut-off date ask for the Statistical Horizon’s room rate (do not use the code) and they will try to accommodate your request.


Course outline

Day 1:

  •  Essential guidelines for modeling intensive longitudinal data
    • How longitudinal data are different from multilevel data
    • Why you should always consider random effects
    • Multiple ways to take the influence of time into account
    • Within- and between-person levels of analysis should always be distinguished
    • How to choose the right degrees of freedom for inferential tests
  •  Modeling the time course of continuous outcomes
    • (1) Design study (number of persons and time points) & collect data
    • (2) Visualize using person-by-person panel plots of time course
    • (3) Analyze using multilevel model for longitudinal data
    • (4) Write up for publication: prepare tables and figures (e.g., spaghetti plots); provide statistical and conceptual interpretation of results
    • (5) Power a replication study: revise the number of persons and/or time points

Day 2:

  • Modeling within-person causal processes for continuous outcomes 
    • (1) Design study (number of persons and time points) & collect data
    • (2) Visualize using scatterplots and panel plots
    • (3) Analyze: distinguish between- and within-person levels of analysis in multilevel model for longitudinal data; center predictor variables; adjust for confounding factors (such as time)
    • (4) Write up for publication: prepare tables and figures (e.g., spaghetti plots); provide statistical and conceptual interpretation of results
    • (5) Power a replication study: revise the number of persons and/or time points

COMMENTS FROM RECENT PARTICIPANTS 

“A cutting edge course on long panel data. I have an econometric background so this course allows me to see how to handle intensive longitudinal data with a completely different approach using multilevel models. Prepared software scripts helped to save time in rerunning similar models.”
  Rafaele Zanoli, Organization Università

“This course was FANTASTIC! I learned so much, and I am already using the code that Drs. Laurenceau and Bolger provided in my own work. The course was geared towards a variety of knowledge levels, providing background to people who haven’t done growth modeling before, while also teaching the foundations in a way that I learned something new, even though it was familiar material to me. The more advanced material, they walked us through carefully, and I never felt lost. I cannot recommend this course highly enough to other people.”
  Acacia Parks, Hiram College