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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.
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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.
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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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The randomized marker method for single-case randomization tests: Handling data missing at random and data missing

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Behavior Research Methods
|February 8, 2022
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Summary
This summary is machine-generated.

The randomized marker method is a valid and powerful approach for handling missing data in single-case experiments across various missing data scenarios. It offers sufficient statistical power, making it a reliable alternative to multiple imputation.

Keywords:
Missing dataRandomization testsSimulationSingle-caseStatistical power

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Area of Science:

  • Psychology
  • Statistics
  • Research Methodology

Background:

  • Single-case experiments often encounter missing data, complicating analysis.
  • The randomized marker method is effective when data are missing completely at random.
  • Identifying the precise missing data mechanism in real-world studies is challenging.

Purpose of the Study:

  • To evaluate the randomized marker method's performance with data missing at random and missing not at random.
  • To compare the randomized marker method against multiple imputation for single-case randomization tests.
  • To assess the validity and statistical power of these methods under diverse missing data conditions.

Main Methods:

  • Simulated single-case experiments with varying missing data mechanisms (missing at random, missing not at random).
  • Calculated type I error rates and statistical power for the randomized marker method and multiple imputation.
  • Compared results against complete datasets and analyzed performance with correlated covariate data.

Main Results:

  • The randomized marker method demonstrated validity and sufficient statistical power across most simulated missing data conditions.
  • Multiple imputation showed an advantage specifically when covariate data were strongly correlated.
  • Both methods were evaluated against complete data benchmarks.

Conclusions:

  • The randomized marker method is a robust technique for handling missing data in single-case experiments, suitable for various missing data scenarios.
  • It provides a reliable alternative to multiple imputation, particularly when the missing data mechanism is uncertain.
  • Researchers can confidently apply the randomized marker method for valid and powerful single-case randomization tests.