A Practical Guide to Impact Threshold of a Confounding Variable (ITCV) and Robustness of Inference to Replacement (RIR).

Ken Frank; Guan Saw; Qinyun Lin; Ran Xu; Joshua Rosenberg; Spiro Maroulis; Bret Staudt Willet

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Peer reviewed

ERIC Number: ED674656

Record Type: Non-Journal

Publication Date: 2025-Aug

Pages: 81

Abstractor: As Provided

ISBN: N/A

ISSN: N/A

EISSN: N/A

Available Date: 0000-00-00

A Practical Guide to Impact Threshold of a Confounding Variable (ITCV) and Robustness of Inference to Replacement (RIR)

Ken Frank¹; Guan Saw²; Qinyun Lin³; Ran Xu⁴; Joshua Rosenberg⁵; Spiro Maroulis⁶; Bret Staudt Willet⁷

Grantee Submission

This is a practical guide for applying the Impact Threshold for a Confounding Variable and the Robustness of Inference to Replacement using the konfound packages in Stata and R as well as the R-shiny app. It includes motivation worked examples, and tutorials.

Descriptors: Robustness (Statistics), Statistical Inference, Programming Languages, Computer Software, Social Science Research, Research Methodology, Causal Models

Publication Type: Reports - Descriptive; Guides - General

Education Level: N/A

Audience: N/A

Language: English

Sponsor: Institute of Education Sciences (ED)

Authoring Institution: N/A

IES Funded: Yes

Grant or Contract Numbers: R305D220022

Department of Education Funded: Yes

Author Affiliations: ¹Michigan State University, East Lansing, MI, USA; ²Claremont Graduate University, Claremont, CA, USA; ³University of Gothenburg, Gothenburg, Sweden; ⁴University of Connecticut, Storrs, CT, USA; ⁵University of Tennessee at Knoxville, Knoxville, TN, USA; ⁶Arizona State University, Tempe, AZ, USA; ⁷Florida State University, Tallahassee, FL, USA