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Related Concept Videos

Group Design02:01

Group Design

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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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...
Stratified Sampling Method01:16

Stratified 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. 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.
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Related Experiment Video

Updated: Jun 24, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Setting standards for teaching evaluation data: an application of the contrasting groups method.

Judy A Shea1, Lisa M Bellini, Katherine S McOwen

  • 1Department of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104-6021, USA. sheaja@mail.med.upenn.edu

Teaching and Learning in Medicine
|March 31, 2009
PubMed
Summary
This summary is machine-generated.

Applying the Contrasting Groups method to faculty teaching evaluations provides precise pass-rates for retention and remediation decisions. This standard-setting approach enhances the reliability of faculty performance assessments.

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Last Updated: Jun 24, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

  • Medical Education
  • Faculty Development
  • Higher Education Assessment

Background:

  • Faculty teaching evaluations are crucial for retention and remediation decisions.
  • Current evaluation methods often rely on subjective, global assessments.
  • There is a need for more objective and precise methods in faculty performance review.

Purpose of the Study:

  • To apply the Contrasting Groups standard-setting methodology to faculty teaching dossiers.
  • To determine the resulting pass-rates and decision precision using this method.
  • To evaluate the effectiveness of a structured approach for faculty performance assessment.

Main Methods:

  • Ten faculty judges utilized the Contrasting Groups approach to set standards for teaching dossiers.
  • Blinded dossiers, encompassing clinical (N=47) and classroom (N=37) teaching, were categorized.
  • Categories included Unsatisfactory, Satisfactory, Excellent, and Superior, with cut-points based on aggregated judge performance.

Main Results:

  • For clinical teaching, distribution was 4.1% Unsatisfactory, 5.9% Satisfactory, 26.1% Excellent, and 63.9% Superior.
  • For classroom teaching, distribution was 6.6% Unsatisfactory, 24.8% Satisfactory, 44.1% Excellent, and 24.5% Superior.
  • The standard error of measurement ranged from 0.20 to 0.25 with 5 to 7 judges, indicating high precision.

Conclusions:

  • Standard-setting methodologies, like Contrasting Groups, yield precise outcomes when applied to faculty evaluation data.
  • Future research should investigate the stability and acceptability of these standards.
  • Developing methods to integrate evaluations across different teaching venues is recommended.