ArticlePsychometrika2016
KSC-N: Clustering of Hierarchical Time Profile Data.
Article in Psychometrika, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Happy and threatening faces differentially influence facilitation and interference over time.Psychological research · 2026Article
- Full throttle: Demonstrating the speed, accuracy, and validity of a new method for continuous two-dimensional self-report and annotation.Behavior research methods · 2022Article
- The impact of self-distancing on emotion explosiveness and accumulation: An fMRI study.PloS one · 2018Article
- Clustering Vector Autoregressive Models: Capturing Qualitative Differences in Within-Person Dynamics.Frontiers in psychology · 2016Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quite a few studies in the behavioral sciences result in hierarchical time profile data, with a number of time profiles being measured for each person under study. Associated research questions often focus on individual differences in profile repertoire, that is, differences between persons in the number and the nature of profile shapes that show up for each person. In this paper, we introduce a new method, called KSC-N, that parsimoniously captures such differences while neatly disentangling variability in shape and amplitude. KSC-N induces a few person clusters from the data and derives for each person cluster the types of profile shape that occur most for the persons in that cluster. An algorithm for fitting KSC-N is proposed and evaluated in a simulation study. Finally, the new method is applied to emotional intensity profile data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.