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Experimental Designs01:16

Experimental Designs

11.1K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
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Factorial Design02:01

Factorial Design

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Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
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Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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Crossover Experiments01:16

Crossover Experiments

2.7K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
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Group Design02:01

Group Design

8.9K
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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Longitudinal Research02:20

Longitudinal Research

11.8K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Related Experiment Video

Updated: May 30, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

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An Experimental Design to Investigate Item Parameter Drift.

Peter Baldwin1, Irina Grabovsky1, Kimberly A Swygert1

  • 1NBME, Philadelphia, PA, USA.

Applied Psychological Measurement
|January 27, 2025
PubMed
Summary

A new strategy uses unexposed items to detect item parameter drift, a common issue in testing. While suggestive of drift on a licensure exam, results were not statistically significant at the .05 level.

Keywords:
differential item functioninginvarianceitem parameter drifttest security

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Traditional methods for detecting item parameter drift are insufficient when all items are exposed.
  • Item parameter drift can significantly impact test validity and fairness.
  • High-stakes exams require robust methods to ensure score integrity.

Purpose of the Study:

  • To propose and illustrate a novel strategy for detecting item parameter drift using only unexposed items.
  • To address the limitations of existing drift detection methods in fully exposed item scenarios.
  • To evaluate the effectiveness of the proposed method in a real-world high-stakes testing context.

Main Methods:

  • A stratified random sampling method was employed to deploy unexposed items within an experimental design.
  • The strategy focuses on analyzing data from a subset of unexposed items to infer potential drift in exposed items.
  • The method was applied to investigate unexpected score increases observed on a licensure examination.

Main Results:

  • The application of the proposed method to the licensure exam data yielded results suggestive of item parameter drift.
  • Observed score increases indicated a potential shift in item characteristics.
  • Statistical significance was not reached at the conventional 0.05 alpha level, indicating inconclusive findings.

Conclusions:

  • The proposed strategy offers a viable approach for detecting item parameter drift, particularly when all items are at risk.
  • Further research and validation are needed to refine the method and confirm its sensitivity.
  • The findings underscore the importance of continuous monitoring for item parameter drift in standardized testing.