Order-constrained inference: a nuanced approach to hypothesis testing

Chen, Meichai

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https://hdl.handle.net/2142/124465 Copy

Description

Title

Order-constrained inference: a nuanced approach to hypothesis testing

Author(s)

Chen, Meichai

Issue Date

2024-05-03

Director of Research (if dissertation) or Advisor (if thesis)

Regenwetter, Michel

Committee Member(s)

Koehn, Hans Friedrich

Department of Study

Psychology

Discipline

Psychology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Order-constrained inference
• Nuanced hypotheses
• Model competition

Abstract

Many statistical analyses performed in psychological studies add extraneous assumptions that are not part of the theory. These added assumptions could adversely influence the conclusions one derives from the analyses. Order-constrained inference allows researchers to avoid unnecessary assumptions, translate verbal predictions into direct testable hypotheses, and run model selection among competing theories. We reanalyzed data from two separate case studies to highlight how one can use order-constrained modeling to formulate more nuanced hypotheses and test these hypotheses jointly. To further leverage order-constrained inference, we compared the performance of competing theories using Bayesian model selection methods in the second case study. We observe that order-constrained inference not only provides us with a coarse view of all the hypotheses at the joint level, it also offers a fine-grained perspective of all the hypotheses at the item level that might otherwise stay hidden if we only assessed trends at the aggregate level.

Graduation Semester

2024-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2024 Meichai Chen

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