Evidence map›Paper›PMID 36250345›Full record

ArticleThe British journal of mathematical and statistical psychology2023

A new goodness-of-fit measure for probit models: Surrogate R

Dungang Liu, Xiaorui Zhu, Brandon Greenwell, Zewei Lin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The British journal of mathematical and statistical psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. KineticEJNMMI physics · 2024
    Article
  2. Article
  3. A new goodness-of-fit measure for probit models: Surrogate RThe British journal of mathematical and statistical psychology · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Dungang LiuDepartment of Operations, Business Analytics and Information Systems, University of Cincinnati Carl H. Lindner College of Business, Cincinnati, Ohio, USA.ORCID 0000-0001-5340-9465
Xiaorui ZhuDepartment of Operations, Business Analytics and Information Systems, University of Cincinnati Carl H. Lindner College of Business, Cincinnati, Ohio, USA.
Brandon GreenwellDepartment of Operations, Business Analytics and Information Systems, University of Cincinnati Carl H. Lindner College of Business, Cincinnati, Ohio, USA.
Zewei LinDepartment of Operations, Business Analytics and Information Systems, University of Cincinnati Carl H. Lindner College of Business, Cincinnati, Ohio, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Probit models are used extensively for inferential purposes in the social sciences as discrete data are prevalent in a vast body of social studies. Among many accompanying model inference problems, a critical question remains unsettled: how to develop a goodness-of-fit measure that resembles the ordinary least square (OLS) R

Indexed as

Models, StatisticalLinear Modelscategorical datamodel comparisonOLS R2probit analysispseudo R2surrogate residual

Identifiers

PMID36250345
PMCPMC10092347

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.