Algorithms for Measurement Invariance Testing
Contrasts and Connections
Seiten
2023
Cambridge University Press (Verlag)
978-1-009-45417-9 (ISBN)
Cambridge University Press (Verlag)
978-1-009-45417-9 (ISBN)
This Element introduces the testing of measurement invariance/DIF through nonlinear factor analysis and uses models to formulate different definitions of measurement invariance and DIF and different procedures for locating and quantifying these effects.
Latent variable models are a powerful tool for measuring many of the phenomena in which developmental psychologists are often interested. If these phenomena are not measured equally well among all participants, this would result in biased inferences about how they unfold throughout development. In the absence of such biases, measurement invariance is achieved; if this bias is present, differential item functioning (DIF) would occur. This Element introduces the testing of measurement invariance/DIF through nonlinear factor analysis. After introducing models which are used to study these questions, the Element uses them to formulate different definitions of measurement invariance and DIF. It also focuses on different procedures for locating and quantifying these effects. The Element finally provides recommendations for researchers about how to navigate these options to make valid inferences about measurement in their own data.
Latent variable models are a powerful tool for measuring many of the phenomena in which developmental psychologists are often interested. If these phenomena are not measured equally well among all participants, this would result in biased inferences about how they unfold throughout development. In the absence of such biases, measurement invariance is achieved; if this bias is present, differential item functioning (DIF) would occur. This Element introduces the testing of measurement invariance/DIF through nonlinear factor analysis. After introducing models which are used to study these questions, the Element uses them to formulate different definitions of measurement invariance and DIF. It also focuses on different procedures for locating and quantifying these effects. The Element finally provides recommendations for researchers about how to navigate these options to make valid inferences about measurement in their own data.
1. Algorithms for measurement invariance testing: Contrasts and connections; 2. Latent variable models; 3. What is measurement invariance? What is DIF?; 4. Codifying measurement non-invariance and differential item functioning in different latent variable frameworks; 5. Models for measurement non-invariance and differential item functioning; 6. Consequences of measurement non-invariance and differential item functioning; 7. Detecting measurement non-invariance and differential item functioning; 8.Recommendations for best practices; 9. References.
| Erscheinungsdatum | 02.11.2023 |
|---|---|
| Reihe/Serie | Elements in Research Methods for Developmental Science |
| Zusatzinfo | Worked examples or Exercises |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Gewicht | 274 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung | |
| ISBN-10 | 1-009-45417-X / 100945417X |
| ISBN-13 | 978-1-009-45417-9 / 9781009454179 |
| Zustand | Neuware |
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