This repository contains materials from The Change Lab's research collaborations. These are shared with intent to facilitate learning and teaching about analysis of intensive longitudinal data. Use lisences are in each folder.
If you have any questions about these project or this repository, please email The Change Lab at thechangelab@stanford.edu.
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├── AMIB
│ └── AMIB_persons.csv
│ └── AMIB_daily.csv
│ └── AMIB_daily2.csv
│ └── AMIB_interaction.csv
│ └── AMIB_interaction2.csv
├── Bolger_Laurenceau_2013
│ └── BL2013_data_categorical.csv
│ └── BL2013_data_intimacy.csv
│ └── BL2013_data_process.csv
├── Growth Modeling
├── The Cortisol Data
│ └── TheCortisolData.csv
The AMIB Data, collected as part of a multiple time-scale study, are useful for illustrating a variety of methods for modeling of i ntensive longitudinal data. The data include person-level dispositions, daily diary assessments, and ecological momentary assessments that were obtained after everyday social interactions (event-contingent sampling).
The data are shared with intent to facilitate learning about analysis of intensive longitudinal data (from experience sampling, daily diary, or EMA designs). The data are posted only for teaching purposes and should not be used for research.
Use requires citation and acknowledgement of both this website and the following paper:
Ram, N., Conroy, D. E., Pincus, A. L., Hyde, A. L., & Molloy, L. E. (2012). Tethering theory to method: Using measures of intraindividual variability to operationalize individuals' dynamic characteristics. In G. Hancock & J. Harring (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 81-110). New York: Information Age.
Please contact us with questions about the data and their use. We always appreciate knowing how the data are being used to facilitate learning and teaching. If you find something interesting, please let us know. We are happy to engage collaboration around a similar data set that is larger (in terms of both persons and occasions).
Cheers,
Nilam, David, Aaron
Use requires citation and acknowlegement of:
Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. Guilford Press.
Growth models are among the core methods for analyzing how and when people change. Discussing both structural equation and multilevel modeling approaches, Grimm, Ram & Estabrook (2016) leads readers step by step through applying each model to longitudinal data to answer particular research questions. It demonstrates cutting-edge ways to describe linear and nonlinear change patterns, examine within-person and between-person differences in change, study change in latent variables, identify leading and lagging indicators of change, evaluate co-occurring patterns of change across multiple variables, and more. User-friendly features include real data examples, code (for Mplus or NLMIXED in SAS, and OpenMx or nlme in R), discussion of the output, and interpretation of each model's results.
Tutorials can be found at: https://thechangelab.stanford.edu/tutorials/growth-modeling/
Use requires citation and acknowlegement of:
Grimm, K. J., Ram, N., & Estabrook, R. (2016). Growth Modeling: Structural Equation and Multilevel Modeling Approaches. Guilford Publications. https://books.google.com/books?id=MLW_CwAAQBAJ
The Cortisol Data is an N = 34, T = 9 time points data set we have used to illustrate a variety of growth modeling and mixture modeling methods. The data are shared with intent that others may find them useful for learning about growth modeling or developing new methods for analysis of change.
New publications based on these data require citation and acknowledgement of the full set of papers that have used the data, and …
Ram, N., & Grimm, K. (2007). Using simple and complex growth models to articulate developmental change: Matching theory to method. International Journal of Behavioral Development, 31(4), 303-316. https://doi.org/10.1177/0165025407077751
Ram, N., & Grimm, K. (2009). Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioral Development, 33(6), 565-576. https://doi.org/10.1177/0165025409343765
Ram, N., Grimm, K., Gatzke-Kopp, L. & Molenaar, P.C.M. (2011). Longitudinal mixture models and the identification of archetypes: Action-adventure, mystery, science fiction, fantasy, or romance? In B. Laursen, T. Little, & N. Card (Eds.) Handbook of Developmental Research Methods (pp. 481-500). New York: Guilford.
Grimm, K.J., Steele, J.S., Ram, N., & Nesselroade, J.R. (2013). Exploratory latent growth models in the structural equation modeling framework. Structural Equation Modeling, 20(4), 568-591. https://doi.org/10.1080/10705511.2013.824775
Thank you to John Nesselroade, Teresa Seeman, and Marilyn Albert for data collection and inspiration.
Please contact us with questions about the data and their use. We always appreciate knowing how the data are being used to facilitate learning and teaching. If you find something interesting, please let us know.
Nilam & Kevin