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Item response theory in affective instrument development: an illustration
Journal of Nursing Measurement
|July 27, 2001
Summary
Item Response Theory (IRT) analysis offers valuable insights into construct validity during instrument development. This method, demonstrated using the Facets program with the Postpartum Depression Screening Scale, enhances understanding of respondent performance.
Area of Science:
- Nursing Research
- Psychometrics
- Educational Measurement
Background:
- Item Response Theory (IRT) analysis is crucial for establishing construct validity in instrument development.
- IRT provides detailed interpretations of respondent performance, differentiating between low- and high-scoring individuals.
- A key IRT function is evaluating how well items assess the underlying attitude continuum within an instrument.
Purpose of the Study:
- To introduce nurse researchers to the benefits of IRT in developing affective instruments.
- To describe the Facets computer program, a tool for conducting IRT analysis.
- To illustrate the application of IRT using real-world data.
Main Methods:
- The study utilizes the Facets program, which employs a one-parameter Rasch measurement model (item difficulty).
- Data from a survey of 525 new mothers were analyzed.
- The psychometric properties of the Postpartum Depression Screening Scale were assessed using Facets.
Main Results:
- The Facets program effectively demonstrated the utility of IRT in analyzing the Postpartum Depression Screening Scale.
- The analysis provided empirical support for the construct validity of the scale.
- The results highlighted the potential for finer construct interpretations and descriptions of respondent performance.
Conclusions:
- IRT analysis, facilitated by programs like Facets, is a valuable yet underutilized approach in affective instrument development for nursing research.
- Increased awareness and adoption of IRT can enhance the quality and interpretability of affective measures.
- The study advocates for greater prominence of IRT in nursing research to improve instrument development and data interpretation.