Psychometrika call for papers: Special issue on forecasting with intensive longitudinal data

Psychometrika - CALL FOR PAPERS

Special issue on: Forecasting with Intensive Longitudinal Data

The collection of longitudinal data in the social and behavioral sciences has been revolutionized by the widespread availability of information technologies such as smart phones, social media, physiological sensors, online games and simulations, virtual and augmented reality, and the internet more generally. We use the term intensive longitudinal data (ILD) inclusively, to encompass data coming from a broad range of data collection methods and research designs that are characterized by a relatively large number (e.g., > 60 time points) of multivariate observations collected over time from multiple respondents. ILD present challenges in the application of conventional “large N, small T” methods for longitudinal data (e.g., growth curves, panel models) as well as “small N, large T” methods such as time series analysis and signal processing. One challenge in particular that has received relatively little attention in the social science literature is that of forecasting constructs of interests such as behaviors (e.g., substance abuse) and emotions (e.g., depressive states). Forecasting allows for real-time inferences to be made on the basis of ongoing data collection, which is a key methodological step toward harnessing the full potential of ILD. The purpose of this special issue is to promote contributions that apply or develop new methods to address the problem of forecasting of future events in ILD.

The types of forecasting applications we have in mind are characterized by the following. This list is intended to be illustrative, not exhaustive.

  • Data-based protocols for clinical interventions (e.g., nudges, just-in-time interventions).

  • The use of educational technologies to support optimal learning trajectories.

  • Predicting emergent dynamical states in social networks, dyads, or individuals.

  • Improving real time feedback and adaptivity in human-computer interactions.

  • Anticipating heightened physiological reactivity (e.g., stress) before it occurs.

Potential extensions of existing forecasting methodology can be motivated by many characteristics of ILD. For example, the data can be:

  • high dimensional and multimodal (e.g., quantitative, textual, audio/visual),

  • characterized by both within-person and across-person variability,

  • sampled at irregular time intervals,

  • sampled using different technologies (self-report, machine-collected), and / or

  • subject to various issues involved with data collection from human participants (missing data, measurement error).

Manuscripts published in this special issue will be methodologically rigorous and illustrate the application of innovative forecasting methodology with one or more real data examples of general interest to social, psychological, neural, or behavioral scientists. Manuscripts may provide novel analytic developments (for consideration in the T&M section) or a novel application an existing method (for consideration in the ARCS section). Junior scholars are especially encouraged to submit their projects.

 

SUBMISSION GUIDELINES

Interested authors are asked to submit a short proposal (max. 1000 words) by January 15, 2020 (see link below). The proposal should describe the methodological contribution of the work to forecasting in ILD and the data application. Manuscript proposals can be submitted at the link below:

https://unc.az1.qualtrics.com/jfe/form/SV_3wOtDNbtSzZHZIx.

After reviewing the proposals, the guest editors will invite a subset of authors to submit a full manuscript to the special issue. This process is intended to ensure that submissions are aligned with the topic of the special issue. The guest editors may also offer suggestions on the intended projects to ensure a good fit to the special issue. The editors will get back to all authors with a decision by January 31, 2020.

The deadline for submission of invited manuscripts is June 1, 2020. All manuscripts submitted to the special issue will go through the regular peer-review process. Submissions must represent original material that has neither been submitted to, nor published in, any other outlet. Invited manuscripts must be submitted to the editorial manager submission system at https://www.editorialmanager.com/pmet/ and the authors should select the special issue “Forecasting with Intensive Longitudinal Data” during the submission process. Submissions must conform to the Psychometrika format guidelines available at https://www.springer.com/psychology/journal/11336.

 

GUEST EDITORS

Please direct all queries regarding this special issue to the guest editors.

Kathleen Gates
Associate Professor of Quantitative Psychology
University of North Carolina at Chapel Hill
Department of Psychology & Neuroscience
341A Davie Hall
Chapel Hill, NC 27599-3720
gateskm@email.unc.edu

 

Peter F. Halpin
Associate Professor of Quantitative Methods
University of North Carolina at Chapel Hill
School of Education
111 Peabody Hall
Chapel Hill, NC, 27599-3500
peter.halpin@unc.edu

 

Siwei Liu
Associate Professor of Human Development and Family Studies
Department of Human Ecology 
University of California at Davis
One Shields Avenue
Davis, CA 95616
sweliu@ucdavis.edu