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Large-sample confidence intervals for validity and reliability coefficients.

J L Mendoza1, K L Stafford, J M Stauffer

  • 1Department of Psychology, University of Oklahoma, Norman 73019, USA. jmendoza@ou.edu

Psychological Methods
|September 27, 2000
PubMed
Summary
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This study presents confidence intervals (CI) for reliability and validity. Accurate CIs for reliability, validity, and unattenuated validity are achievable with a selection ratio of at least 20% and a sample size of 100 or more.

Area of Science:

  • Psychometrics
  • Statistical Inference

Background:

  • Confidence intervals (CI) are crucial for estimating reliability and validity in research.
  • Unattenuated validity estimation requires careful consideration of sample selection biases.

Purpose of the Study:

  • To develop and evaluate large-sample confidence intervals (CI) for reliability, validity, and unattenuated validity.
  • To investigate the accuracy of these CIs across different sampling conditions.

Main Methods:

  • The study utilizes the Bonferroni inequality for constructing the CI for unattenuated validity.
  • Four distinct sampling scenarios were examined: random samples for both, mixed samples, and selected samples for both.
  • A simulation study was employed to assess the performance and accuracy of the proposed CIs.

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Main Results:

  • Confidence intervals for reliability, validity, and unattenuated validity demonstrate accuracy under specific conditions.
  • Accuracy is maintained when the selection ratio is 20% or greater and the selected sample size is 100 or larger.
  • A selection ratio below 20% tends to result in an underestimation of the parameters.

Conclusions:

  • The proposed large-sample confidence intervals provide reliable estimates for psychometric properties.
  • Researchers can confidently use these CIs when sample selection ratios and sizes meet the specified criteria.
  • Awareness of potential underestimation is necessary when working with selection ratios below 20%.