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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Cochran's Q Test01:17

Cochran's Q Test

Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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...

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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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Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Extensions of a versatile randomization test for assessing single-case intervention effects.

Joel R Levin1, Venessa F Lall, Thomas R Kratochwill

  • 1University of Arizona, United States. jrlevin@u.arizona.edu

Journal of School Psychology
|January 11, 2011
PubMed
Summary
This summary is machine-generated.

This study validates a randomization test for comparing interventions in single-case research. The test maintains accuracy but requires sufficient data and large effects for reliable power in school settings.

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Area of Science:

  • Psychology
  • Educational Psychology
  • Research Methodology

Background:

  • The Levin-Wampold (1999) model is crucial for single-case intervention research.
  • Existing methods face challenges with multiple conditions and limited data in classroom settings.

Purpose of the Study:

  • To evaluate statistical properties of extended Levin-Wampold randomization tests.
  • To assess the test's adaptability for more than two conditions.
  • To examine its performance in data-limited classroom environments.

Main Methods:

  • Utilized Monte Carlo simulations across diverse scenarios.
  • Varied participant blocks, time periods, start points, autocorrelation, and effect sizes.
  • Investigated Type I error rates and statistical power.

Main Results:

  • Type I error probability remained consistently acceptable across all simulations.
  • Sufficient statistical power generally required large sample sizes, numerous start points, substantial effect sizes, and low autocorrelation.
  • The test demonstrated robustness in maintaining internal validity and statistical-conclusion validity.

Conclusions:

  • The comparative effectiveness randomization test is a versatile tool for single-case school psychology research.
  • Its application is feasible in various intervention research contexts.
  • Researchers should consider data quantity and effect size for optimal test performance.