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This study presents a robust statistical procedure for evaluating validity coefficients, even with complex data issues like missing values and nonnormality. It enhances reliability in educational and behavioral research findings.

Keywords:
auxiliary variableconvergent validitycorrelationdiscriminant validityinterval estimationlatent variable modelingmaximum likelihoodmissing data

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Behavioral Statistics

Background:

  • Empirical research in education, behavioral, and social sciences often violates standard statistical assumptions.
  • Traditional methods for evaluating validity coefficients may fail under conditions of missing data, nonnormality, or clustering.
  • Latent variable modeling offers a flexible framework for addressing complex data structures.

Purpose of the Study:

  • To develop a procedure for evaluating validity-related coefficients and their differences when common statistical assumptions are violated.
  • To provide point and interval estimation for convergent and discriminant correlations and their differences.
  • To accommodate incomplete data, nonnormality, and clustering effects in validity analyses.

Main Methods:

  • Latent variable modeling methodology.
  • Full Information Maximum Likelihood (FIML) approach for model fitting and parameter estimation.
  • Inclusion of auxiliary variables and accounting for within-group correlations due to nesting effects.

Main Results:

  • The procedure successfully estimates convergent and discriminant correlations and their differences.
  • It handles incomplete data sets where data are not missing at random.
  • The method accounts for nonnormality and clustering effects without requiring multiple indicators for latent constructs.

Conclusions:

  • The developed procedure offers a valid and reliable method for assessing validity coefficients under challenging data conditions.
  • This approach enhances the accuracy of psychometric evaluations in educational, behavioral, and social research.
  • The method is applicable even with single indicators for latent constructs, broadening its utility.