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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

8.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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McNemar's Test01:23

McNemar's Test

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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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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...
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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
225
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
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Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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A Randomization Permutation Test for Single Subject Mediation.

David P MacKinnon1, Heather L Smyth1, Jennifer Somers1

  • 1Department of Psychology, Arizona State University7864, Tempe, AZ, USA.

Evaluation & the Health Professions
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new randomization permutation (RP) test for analyzing mediation mechanisms in single subjects. This method allows for statistical evaluation of treatment effects on outcomes for an individual, aiding personalized medicine.

Keywords:
active ingredientsidiographic methodsmediating mechanismspermutation testssingle subject

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Area of Science:

  • Psychology
  • Statistics
  • Biomedical Research

Background:

  • Assessing individual-level treatment effects is crucial for personalized interventions.
  • Existing statistical methods often focus on group-level analyses, limiting insights into single-subject processes.
  • Understanding intraindividual change mechanisms is vital for tailoring treatments.

Purpose of the Study:

  • To introduce and describe a novel randomization permutation (RP) test for evaluating mediation mechanisms in a single subject.
  • To extend existing permutation test methodologies to individual-level data for mediation analysis.
  • To provide a statistical tool for elucidating treatment-outcome pathways within individuals.

Main Methods:

  • The proposed method involves random assignment to treatment levels at each measurement occasion.
  • It requires repeated measures of both the mediator and outcome variable from a single subject.
  • Statistical evaluation is performed using the permutation mediation test and permutation confidence intervals for residuals.

Main Results:

  • A simulation study indicated that at least eight repeated measures are necessary to adequately control Type I error rates.
  • Sufficient statistical power (approaching 0.8) requires larger sample sizes, even when effect sizes are substantial.
  • The RP mediation test demonstrated its potential for evaluating intraindividual treatment effects.

Conclusions:

  • The randomization permutation (RP) mediation test is a valuable new method for analyzing mediation in single subjects.
  • This approach can statistically evaluate how treatments influence outcomes at the individual level.
  • Findings support the application of RP mediation tests in personalized medicine and tailored treatment development.