Intensive Longitudinal Methods - Online Course
A 4-Day Livestream Seminar Taught by
Donald HedekerTuesday, July 30 –
Friday, August 2, 2024
10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
Innovative methods of data collection often produce large numbers of repeated measurements for each individual. Variously known as ecological momentary assessments (EMA), experience sampling method (ESM), and daily diary (DD), these methods have been developed to record the momentary events and experiences of subjects in daily life. They usually involve self-reports from individuals, dyads, families, or other small groups over the course of hours, days, and weeks. Data produced by these methods are commonly referred to as intensive longitudinal data.
Although there is much to be learned from such data, conventional methods of analysis are often unsuited to the task. In this seminar, you will learn how to analyze intensive longitudinal data by way of mixed models, also known as multilevel or hierarchical linear models. The course begins with the basic 2- and 3-level model, and then proceeds to more extended uses of these models.
One of the extended uses of these models is to model the variances. In the standard mixed model, the error variance and the variance of the random effects are assumed to be constant across individuals. When there are many observations per individual, it becomes practical to allow those variances to vary randomly across individuals, as well as to depend on other covariates including time itself. Besides making the models more realistic, additional substantive insights can be gleaned by modeling both means and variances.
Starting July 30, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about the course content
In the seminar, you will learn to:
-
- Apply and interpret the results of 2- and 3-level mixed (i.e., multilevel) models with observations nested within days and days within subjects, or observations within waves and waves within subjects.
- Estimate descriptive statistics for time-varying variables in situations where the number of observations per subject can be quite varied across subjects.
- Include occasion-varying covariates in your analyses, and distinguish the within-subjects (WS) and between-subjects (BS) effects of such covariates.
- Model day of week and time of day effects.
- Model random subject intercept and slope heterogeneity in terms of covariates to learn what explains such heterogeneity.
- Extend your modeling of the mean response, by also modeling WS and BS variances in terms of covariates. This extended approach uses mixed location-scale (MELS) models that allow subject heterogeneity in both a subject’s mean and variance.
- Apply these methods using SAS, Stata, and the freeware MixRegLS & MixWILD computer software programs.
In the seminar, you will learn to:
-
- Apply and interpret the results of 2- and 3-level mixed (i.e., multilevel) models with observations nested within days and days within subjects, or observations within waves and waves within subjects.
- Estimate descriptive statistics for time-varying variables in situations where the number of observations per subject can be quite varied across subjects.
- Include occasion-varying covariates in your analyses, and distinguish the within-subjects (WS) and between-subjects (BS) effects of such covariates.
- Model day of week and time of day effects.
- Model random subject intercept and slope heterogeneity in terms of covariates to learn what explains such heterogeneity.
- Extend your modeling of the mean response, by also modeling WS and BS variances in terms of covariates. This extended approach uses mixed location-scale (MELS) models that allow subject heterogeneity in both a subject’s mean and variance.
- Apply these methods using SAS, Stata, and the freeware MixRegLS & MixWILD computer software programs.
Computing
In all cases, methods will be illustrated using software, with SAS, Stata, and MixRegLS & MixWILD examples and syntax. Some familiarity with reading in data and performing basic statistical analyses in either SAS or Stata is recommended.
This is a hands-on course featuring carefully structured and supervised assignments. To do the exercises, you will need a computer with a recent version of SAS or Stata and the free programs MixRegLS & MixWILD installed.
MixRegLS & MixWILD can be downloaded by clicking here. Stata users will want to install the runmixregls program, which can be downloaded by clicking here.
If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.
There is now a free version of SAS, called SAS OnDemand for Academics, that is available to anyone.
In all cases, methods will be illustrated using software, with SAS, Stata, and MixRegLS & MixWILD examples and syntax. Some familiarity with reading in data and performing basic statistical analyses in either SAS or Stata is recommended.
This is a hands-on course featuring carefully structured and supervised assignments. To do the exercises, you will need a computer with a recent version of SAS or Stata and the free programs MixRegLS & MixWILD installed.
MixRegLS & MixWILD can be downloaded by clicking here. Stata users will want to install the runmixregls program, which can be downloaded by clicking here.
If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.
There is now a free version of SAS, called SAS OnDemand for Academics, that is available to anyone.
Who should register?
If you plan on analyzing EMA or other forms of intensive longitudinal data, this course is for you. You should be thoroughly familiar with multiple linear regression, and some knowledge of mixed models (i.e. multilevel/HLM) is helpful, but not assumed.
If you plan on analyzing EMA or other forms of intensive longitudinal data, this course is for you. You should be thoroughly familiar with multiple linear regression, and some knowledge of mixed models (i.e. multilevel/HLM) is helpful, but not assumed.
Seminar outline
Day 1
- Estimating summary statistics accounting for clustering of observations within subjects, where the number of clustered observations varies across subjects.
- Mixed models for 2-level (observations within subjects) intensive longitudinal data.
- Decomposing the within-subjects and between-subjects effects of time-varying predictors in mixed models.
- Day of week and time of day effects.
Day 2
- Multivariate mixed models for testing if effects are the same on different outcomes, and to decompose the correlation of the outcome in terms of within- and between-subjects association.
- Estimating random slopes of time-varying predictors in mixed models.
- Heterogeneous mixed models allowing covariates to affect intercept and slope variances.
Day 3
- Mixed effects location scale (MELS) models allowing covariates to affect means, between-subject variance, and within-subject variance.
- Software for the MELS model: MIXREGLS standalone program, and via Stata and R.
- MixWILD software for extended MELS models; models with random intercepts, slopes, and scale.
Day 4
- Using MixWILD to allow the random effects from EMA data analysis to predict future subject-level or multilevel outcomes.
- 3-level models for observations within days and days within subjects.
- Modeling repeated waves of intensive longitudinal data with 3-level models
Day 1
- Estimating summary statistics accounting for clustering of observations within subjects, where the number of clustered observations varies across subjects.
- Mixed models for 2-level (observations within subjects) intensive longitudinal data.
- Decomposing the within-subjects and between-subjects effects of time-varying predictors in mixed models.
- Day of week and time of day effects.
Day 2
- Multivariate mixed models for testing if effects are the same on different outcomes, and to decompose the correlation of the outcome in terms of within- and between-subjects association.
- Estimating random slopes of time-varying predictors in mixed models.
- Heterogeneous mixed models allowing covariates to affect intercept and slope variances.
Day 3
- Mixed effects location scale (MELS) models allowing covariates to affect means, between-subject variance, and within-subject variance.
- Software for the MELS model: MIXREGLS standalone program, and via Stata and R.
- MixWILD software for extended MELS models; models with random intercepts, slopes, and scale.
Day 4
- Using MixWILD to allow the random effects from EMA data analysis to predict future subject-level or multilevel outcomes.
- 3-level models for observations within days and days within subjects.
- Modeling repeated waves of intensive longitudinal data with 3-level models
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
References
Ma, Q. & Hedeker, D. (2020). Modeling of between- and within-subject variances using mixed effects location scale (MELS) models. SAS 2020 Global Forum Proceedings, Paper 4181-2020. pdf file
Dzubur, E., Ponnada, A., Nordgren, R., Yang, C.-H., Intille, S., Dunton, G., & Hedeker, D. (2020). MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data. Behavior Research Methods, 52:1403–1427. pdf file
Hedeker, D. & Mermelstein, RJ (2022). Modeling Variation in Intensive Longitudinal Data. In A.A. O’Connell, D. B. McCoach, & B. Bell (Eds.), Multilevel Modeling Methods with Introductory and Advanced Applications. Information Age Publishing. pdf file
Hedeker D & Mermelstein RJ (2012). Mood changes associated with smoking in adolescents: An application of a mixed-effects location scale model for longitudinal Ecological Momentary Assessment (EMA) data. In G. R. Hancock & J. Harring (Eds.), Advances in Longitudinal Methods in the Social and Behavioral Sciences (pp. 59-79). Information Age Publishing, Charlotte, NC. pdf file
Hedeker D, Mermelstein RJ, Berbaum ML, & Campbell RT (2009). Modeling mood variation associated with smoking: An application of a heterogeneous mixed-effects model for analysis of ecological momentary assessment (EMA) data. Addiction, 104:297-307. pdf file supplementary materials
Ma, Q. & Hedeker, D. (2020). Modeling of between- and within-subject variances using mixed effects location scale (MELS) models. SAS 2020 Global Forum Proceedings, Paper 4181-2020. pdf file
Dzubur, E., Ponnada, A., Nordgren, R., Yang, C.-H., Intille, S., Dunton, G., & Hedeker, D. (2020). MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data. Behavior Research Methods, 52:1403–1427. pdf file
Hedeker, D. & Mermelstein, RJ (2022). Modeling Variation in Intensive Longitudinal Data. In A.A. O’Connell, D. B. McCoach, & B. Bell (Eds.), Multilevel Modeling Methods with Introductory and Advanced Applications. Information Age Publishing. pdf file
Hedeker D & Mermelstein RJ (2012). Mood changes associated with smoking in adolescents: An application of a mixed-effects location scale model for longitudinal Ecological Momentary Assessment (EMA) data. In G. R. Hancock & J. Harring (Eds.), Advances in Longitudinal Methods in the Social and Behavioral Sciences (pp. 59-79). Information Age Publishing, Charlotte, NC. pdf file
Hedeker D, Mermelstein RJ, Berbaum ML, & Campbell RT (2009). Modeling mood variation associated with smoking: An application of a heterogeneous mixed-effects model for analysis of ecological momentary assessment (EMA) data. Addiction, 104:297-307. pdf file supplementary materials