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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...
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Self-Report Tests of Personality

Self-report inventories are objective personality assessments that use multiple-choice items or numbered scales, typically ranging from 1 (strongly disagree) to 5 (strongly agree). They are often called Likert scales after Rensis Likert. These inventories are widely used due to their ease of administration and cost-effectiveness. One of the most prominent examples is the Minnesota Multiphasic Personality Inventory (MMPI), initially developed in the 1940s to assess abnormal personality traits.
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,...
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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
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Group Design02:01

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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 the two are due to...
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Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

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Computerized Adaptive Testing System of Functional Assessment of Stroke
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Using automatic item generation to create multiple-choice test items.

Mark J Gierl1, Hollis Lai, Simon R Turner

  • 1Department of Surgery, University of Alberta, Edmonton, Alberta, Canada. mark.gierl@ualberta.ca

Medical Education
|July 19, 2012
PubMed
Summary
This summary is machine-generated.

Automatic item generation (AIG) offers a solution for creating numerous multiple-choice test items efficiently. This method uses a three-stage process involving cognitive models, item models, and computer software to meet the growing demand for assessment content.

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Area of Science:

  • Medical Education
  • Assessment Science
  • Computer-Assisted Instruction

Background:

  • Multiple-choice items are crucial for assessing medical knowledge across various educational and professional levels.
  • Current methods for developing multiple-choice items are time-consuming and costly, failing to meet the increasing demand driven by computer-based testing.
  • There is a critical need for scalable and efficient item development strategies in medical education.

Purpose of the Study:

  • To introduce and describe a novel methodology for automatic item generation (AIG) of multiple-choice questions.
  • To demonstrate the application of this AIG methodology in the domain of surgical medical licensure testing.
  • To address the challenge of producing a large volume of high-quality assessment items.

Main Methods:

  • A three-stage approach to automatic item generation (AIG) was developed and implemented.
  • Stage 1: Content specialists create a cognitive model detailing the knowledge domain.
  • Stage 2: Item models are constructed based on the cognitive model.
  • Stage 3: Computer software generates multiple-choice items from the item models.

Main Results:

  • The described AIG methodology successfully generated a substantial number of multiple-choice items.
  • A single item model yielded 1248 multiple-choice items, demonstrating high efficiency.
  • The process effectively combined expert knowledge structuring with automated content assembly.

Conclusions:

  • Automatic item generation (AIG) provides a viable solution for the large-scale production of multiple-choice assessment items.
  • This methodology leverages computer technology to systematically generate new items based on expert-defined models.
  • AIG enhances the efficiency and scalability of test item development in medical education and licensure.