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PreprintCC0PsyArXiv

On the size of the model selection problem in a conjoint measurement experiment

  • Kenneth Knoblauch
  • Frederic Devinck
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Abstract

We examine how the number of models respecting marginality in model selection using Maximum Likelihood Conjoint Measurement (MLCM) varies with the number of dimensions tested and explain the logic for determining this number. The results are related to Dedekind numbers, which increase super-exponentially, and, thus, suggest a limit to the number of dimensions that can be practically tested using the MLCM paradigm.

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Cite

BibTeXRIS
BibTeX
@misc{knoblauch2026size,
  title = {{On the size of the model selection problem in a conjoint measurement experiment}},
  author = {Kenneth Knoblauch and Frederic Devinck},
  howpublished = {PsyArXiv},
  year = {2026},
  month = oct,
  doi = {10.31234/osf.io/qwh64_v1},
  url = {https://osf.io/preprints/psyarxiv/qwh64_v1/},
  note = {CC0},
}
RIS
TY  - UNPB
TI  - On the size of the model selection problem in a conjoint measurement experiment
AU  - Kenneth Knoblauch
AU  - Frederic Devinck
PY  - 2026
DA  - 2026/10/31
PB  - PsyArXiv
DO  - 10.31234/osf.io/qwh64_v1
UR  - https://osf.io/preprints/psyarxiv/qwh64_v1/
AB  - We examine how the number of models respecting marginality in model selection using Maximum Likelihood Conjoint Measurement (MLCM) varies with the number of dimensions tested and explain the logic for determining this number. The results are related to Dedekind numbers, which increase super-exponentially, and, thus, suggest a limit to the number of dimensions that can be practically tested using the MLCM paradigm.
ER  -