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Related Concept Videos

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

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When ab ≠ c - c': published errors in the reports of single-mediator models.

John V Petrocelli1, Joshua J Clarkson, Melanie B Whitmire

  • 1Wake Forest University, Winston-Salem, USA. petrocjv@wfu.edu

Behavior Research Methods
|October 12, 2012
PubMed
Summary

Many psychology studies misreport mediation analyses, with over 24% of single-mediator models failing equivalence tests. This inaccuracy in statistical conclusions impacts causal inference and future research.

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Area of Science:

  • Psychology
  • Social Psychology
  • Behavioral Research

Background:

  • Accurate mediation analysis reporting is crucial for causal inference and evaluating research.
  • Previous work indicates around 15% of psychology articles contain statistical errors.
  • Inaccuracies in mediation reports raise concerns about the reliability of published findings.

Purpose of the Study:

  • To quantify the inaccuracy in mediation analysis reports in high-impact psychology journals.
  • To examine the prevalence of misreported regression coefficients in single-mediator models.

Main Methods:

  • Analysis of articles published in 2011 in three top-tier personality and social psychology journals.
  • Coding of 156 single-mediator models for accuracy using equivalence tests (ab = c - c').

Main Results:

  • Over 24% of the coded single-mediator models failed an equivalence test.
  • This suggests frequent misreporting of regression coefficients in mediation analyses.
  • Identified common sources of errors in statistical reporting.

Conclusions:

  • A significant proportion of published mediation analyses contain statistical inaccuracies.
  • Recommendations are provided to enhance accuracy in reporting single-mediator models.
  • Discusses implications of these findings for research methodology and interpretation.