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Related Concept Videos

Surveys02:16

Surveys

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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Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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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...
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Asking sensitive questions: a statistical power analysis of randomized response models.

Rolf Ulrich1, Hannes Schröter, Heiko Striegel

  • 1Department of Psychology, Universität Tübingen, Tübingen, Germany. ulrich@uni-tuebingen.de

Psychological Methods
|August 29, 2012
PubMed
Summary

This study introduces power curves for randomized response models to assess low prevalence rates, aiding in selecting optimal survey designs for detecting sensitive behaviors like doping.

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Area of Science:

  • Statistics
  • Survey Methodology
  • Social Science Research

Background:

  • Assessing sensitive behaviors with low prevalence (e.g., doping) requires specialized survey methods.
  • Randomized response models are designed to protect respondent privacy while estimating population parameters.
  • Existing models lack comprehensive power analysis tools for low prevalence scenarios.

Purpose of the Study:

  • To derive statistical power curves for Wald tests applicable to various randomized response models.
  • To enable robust power analysis for survey designs targeting rare events.
  • To facilitate the selection of optimal models, sample sizes, and parameters in sensitive surveys.

Main Methods:

  • Derivation of power curves for Wald tests within the framework of randomized response models.
  • Application of the general framework to specific models including Warner's, crosswise, unrelated question, forced-choice, item count, and cheater detection models.
  • Inclusion of numerical examples in the appendix for practical demonstration.

Main Results:

  • The study provides a generalizable method for calculating statistical power for randomized response models.
  • Power curves are demonstrated for several established randomized response techniques.
  • The analysis supports informed decisions in survey design for low prevalence estimation.

Conclusions:

  • The derived power curves offer a crucial tool for researchers studying sensitive topics with low prevalence rates.
  • This framework enhances the efficiency and reliability of survey data collection in fields like anti-doping.
  • The methodology aids in optimizing survey parameters for accurate estimation and effective model selection.