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Related Concept Videos

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

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Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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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...
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Related Experiment Video

Updated: May 3, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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New tools for evaluating LQAS survey designs.

Lauren Hund1

  • 1Department of Family and Community Medicine, University of New Mexico, 2400 Tucker Avenue Northeast, Albuquerque NM 87131, USA. lbhund@gmail.com.

Emerging Themes in Epidemiology
|February 18, 2014
PubMed
Summary

This study introduces a Bayesian framework to enhance Lot Quality Assurance Sampling (LQAS) survey design for global health. The proposed methods improve classification accuracy and inform intervention decisions in healthcare applications.

Area of Science:

  • Global Health
  • Biostatistics
  • Survey Methodology

Background:

  • Lot Quality Assurance Sampling (LQAS) is widely used in global health.
  • Traditional LQAS design can be improved by integrating prior information.
  • Bayesian approaches offer potential enhancements for LQAS survey design.

Purpose of the Study:

  • To propose a joint frequentist and Bayesian framework for evaluating LQAS classification accuracy.
  • To inform the selection of design parameters and decision rules in LQAS surveys.
  • To provide practical software tools for calculating predictive values and assessing survey designs.

Main Methods:

  • Development of a joint frequentist and Bayesian framework for LQAS.
  • Creation of software tools for calculating positive and negative predictive values.

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  • Application of the framework to real-world data from Oral Rehydration Solution (ORS) preparation surveys.

    • Main Results:

    • The proposed framework effectively evaluates LQAS classification accuracy.
    • Software tools facilitate the calculation of predictive values based on coverage distributions.
    • Analysis clarified the impact of benchmark selection and grey region width on accuracy for ORS preparation.

    Conclusions:

    • Integrating Bayesian methods into LQAS design improves parameter selection and decision-making.
    • The provided tools aid in quantifying classification accuracy and guiding interventions.
    • Estimating coverage distribution post-survey enhances accuracy assessment and intervention planning.