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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Comparison Tests01:28

Comparison Tests

An infinite series composed of positive terms may either approach a finite value or increase without bound. Determining which outcome occurs is a central task in calculus, and comparison tests provide structured methods for making this determination. Rather than evaluating a series directly, these tests relate it to another series whose behavior is already known, allowing conclusions to be drawn through logical comparison.The direct comparison test applies to series with positive terms. If each...
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,...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Complementation Tests00:49

Complementation Tests

A complementation test is a simple cross to identify whether the two mutations are located on the same gene or different genes. It was first performed by Edward Lewis in the 1940s while working on fruit flies. He developed the test to identify the location and arrangement of different mutations on chromosomes.
Organisms heterozygous for different mutations are crossed pairwise in all combinations. If present on different genes, the mutations can complement each other by providing the missing...

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Related Experiment Video

Updated: Jul 5, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

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

Published on: September 11, 2021

Script concordance testing: more cases or more questions?

Robert Gagnon1, Bernard Charlin, Carole Lambert

  • 1CPASS, Faculty of Medicine, University of Montreal, Centre-Ville Station, Box 6128, Montreal, QC H3C3J7, Canada. robert.gagnon@umontreal.ca

Advances in Health Sciences Education : Theory and Practice
|May 16, 2008
PubMed
Summary
This summary is machine-generated.

The Script Concordance Test (SCT) relies heavily on nested questions within cases for reliable clinical reasoning assessment. Optimizing SCT reliability involves using 2-4 questions per case, with 15-25 cases recommended.

Related Experiment Videos

Last Updated: Jul 5, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

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

Published on: September 11, 2021

Area of Science:

  • Medical Education
  • Assessment and Evaluation

Background:

  • The Script Concordance Test (SCT) is a case-based clinical reasoning assessment.
  • Previous research suggests nested questions, rather than cases, enhance SCT reliability.

Purpose of the Study:

  • To quantify variance components in SCT related to cases and nested questions.
  • To identify optimal numbers and combinations of cases and questions for SCT.

Main Methods:

  • Utilized G study and D study methodology on SCT data from three fields.
  • Analyzed variance components to determine reliability drivers.

Main Results:

  • Nested questions accounted for over 70% of the score variance.
  • Optimal SCT design involves 15-25 cases with 2-4 nested questions per case, depending on desired reliability.

Conclusions:

  • Nested questions are critical for enhancing SCT score reliability.
  • Formulating up to 5 questions per case is an efficient strategy to optimize SCT reliability.