Time Series Analysis for Intensive Longitudinal Data

Time series data abound in the psychological, behavioral, and neural sciences. This newer type of data brings with it unique issues, data assumptions, and requirements that are not seen in cross-sectional data. This workshop seeks to provide you with the fundamentals for analyzing intensive longitudinal data.

Instructor:
Kathleen Gates (University of North Carolina - Chapel Hill)
Sandra Lee, PhD (University of North Carolina - Chapel Hill)

Workshop Dates and Times:
Monday, June 10th, 2024, 10:00am to 3:30pm ET
Tuesday, June 11th, 2024, 10:00am to 3:30pm ET

Workshop Format:
Two-Day Synchronous Online Workshop

Time series data is often obtained in an array of studies such as those using experience sampling methods, ecological momentary assessments, physiological collection, and observational data. Examples include self-report measures taken multiple times a day, daily diary, and passive data lifted from Smartphones or other devices. It also includes psychophysiological data such as functional MRI and heart rate measures.

The course covers an introduction to time series analysis with a focus on data issues, research questions, and methods that may be of most interest to behavioral, neural, and psychological scientists. The methods discussed here perform best with at least 60 time points per variable per person.

Given the heterogeneity across individuals in their dynamic processes, person-specific estimates will be obtained by conducting N of 1 time series analyses. Hands-on data examples using simulated data will be used throughout the workshop alongside lecture. Participants are welcome to use their own data. While all necessary code is provided, some familiarity with R will be helpful.

What you’ll learn

  • Foundations of time series analysis – Largely coming from econometrics and physics, we focus on topics such as: the autocovariance function; detecting trends; testing for stationarity and stability; and vector autoregression models.

  • Ergodicity – We discuss the technical and conceptual details of the assumption of ergodicity, and what it means for your analyses.

  • Dynamic factor analysis – You will learn how to measure latent constructs across time.

  • Inference making – Individual-level results can be difficult to interpret. We cover how to make accurate inferences for samples with N > 1 using meta analysis.

  • Network measures – Network measures are increasingly popular. We offer details on how to arrive at these, and also interpretational caveats.

Registration Options

Time Series Analysis for Intensive Longitudinal Data

  • Professional
  • $599
  • Baseline Price for Faculty,
    Staff, and Other Professionals
  • Click Register Below
  • Trainee
  • $599 $399
  • 33% Discount for
    Students and Postdocs
  • Use code "TRAINEE" at Checkout

Combo 1: Time Series Analysis for Intensive Longitudinal Data + Group Iterative Multiple Model Estimation

  • Professional
  • $1198 $959
  • Baseline Price for Faculty,
    Staff, and Other Professionals
  • 20% Combination Discount
  • Click Register Below
  • Trainee
  • $959 $639
  • 33% Discount for
    Students and Postdocs
  • 20% Combination Discount
  • Use code "TRAINEE" at Checkout

 FAQs

  • This workshop is intended for people who have (or plan to have) time series data and wish to make person-specific inferences or test individual differences. The data can be from ecological momentary assessments (e.g., self-report multiple times a day); passive collection via technology (e.g., Fitbit; texting behaviors); or psychophysiological data (e.g., fMRI; heart rate). Data requirements are at least 60 time points (more is better!).

  • Intermediate to advanced

  • It would be helpful to have some familiarity with R and to be comfortable with statistical testing and inferences.

  • R

  • Participants will be provided with a curated reading list and article pdfs along with recorded videos, slides, code, and toy data.