Evidence map›Paper›PMID 38923520›Full record

ArticleThe British journal of mathematical and statistical psychology2025

Are alternative variables in a set differently associated with a target variable? Statistical tests and practical advice for dealing with dependent correlations.

Miguel A García-Pérez

Abstract read
In one paragraph

Article in The British journal of mathematical and statistical psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Miguel A García-PérezDepartamento de Metodología, Facultad de Psicología, Universidad Complutense, Madrid, Spain.ORCID 0000-0003-2669-4429

Funding

Ministerio de Ciencia e Innovación PID2019-11083GB-I00
6 · The paper itself

Abstract

The analysis of multiple bivariate correlations is often carried out by conducting simple tests to check whether each of them is significantly different from zero. In addition, pairwise differences are often judged by eye or by comparing the p-values of the individual tests of significance despite the existence of statistical tests for differences between correlations. This paper uses simulation methods to assess the accuracy (empirical Type I error rate), power, and robustness of 10 tests designed to check the significance of the difference between two dependent correlations with overlapping variables (i.e., the correlation between X

Indexed as

Computer SimulationModels, StatisticalCorrelation of DataData Interpretation, StatisticalHumansPsychometricsStatistics as Topicdependent correlationsMonte Carlo simulationpowerrobustnessstatistical testsType I error

Identifiers

PMID38923520
PMCPMC11701387

What OpenQuestion holds

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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.