CONSTRUCT VALIDITY ANALYSIS OF THE GENETICS AND GENOMICS IN NURSING PRACTICE SURVEY: OVERCOMING CHALLENGES IN VARIABLE RESPONSE INSTRUMENTS.

Background and Purpose: We evaluated the construct validity of the Genetics and Genomics Nursing Practice Survey by investigating the factoral structure, while attempting to account for varied response structures of the items. Methods: Exploratory factor analyses provided insights into item loadings. Confirmatory factor analyses and a version of common methods bias analyses evaluated construct validity while considering the instrument's structural limitations. Structural equation models provided information regarding model fit. Results: The 7-factor model fit these data slightly better (AIC ≈ 169,405; RMSEA = .052) than did the 5-factor model (AIC ≈ 183,599; RMSEA = .063). Neither the CFI or TLI met commonly-accepted thresholds for adequate model fits. Conclusion: The response format of the GGNPS created challenges in evaluating the instrument for construct validity. Nonetheless, these results support the theory-based construct validity of the GGNPS.

Validity evaluation of the genetics and genomics in nursing practice survey.

AIM: To psychometrically test the Genetics and Genomics Nursing Practice Survey (GGNPS) for evidence of content, face and construct validity. DESIGN: This study was a secondary data analysis. METHOD: Data collected from the Method for Introducing a New Competency into Nursing Practice (MINC) study were used to evaluate the GGNPS for evidence of construct validity via structural equation modelling and confirmatory factor analysis. Face validity was evaluated via feedback from practicing RNs without specific experience with or knowledge of genetics/genomics. Content validity was evaluated via content expert feedback and assessment of a content validity index. RESULTS: The thresholds for evidence of content and face validity were met. However, we found less evidence for construct validity of the GGNPS; an exploratory factor analysis, conducted to gain additional insight into the theorized latent constructs, determined that the variables were more interrelated that previously predicted.