Atnaujinkite slapukų nuostatas

Algorithms for Measurement Invariance Testing: Contrasts and Connections [Kietas viršelis]

(Wake Forest University, North Carolina), (Wake Forest University, North Carolina)
  • Formatas: Hardback, 94 pages, aukštis x plotis x storis: 235x155x10 mm, weight: 280 g, Worked examples or Exercises
  • Serija: Elements in Research Methods for Developmental Science
  • Išleidimo metai: 21-Dec-2023
  • Leidėjas: Cambridge University Press
  • ISBN-10: 100945417X
  • ISBN-13: 9781009454179
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 94 pages, aukštis x plotis x storis: 235x155x10 mm, weight: 280 g, Worked examples or Exercises
  • Serija: Elements in Research Methods for Developmental Science
  • Išleidimo metai: 21-Dec-2023
  • Leidėjas: Cambridge University Press
  • ISBN-10: 100945417X
  • ISBN-13: 9781009454179
Kitos knygos pagal šią temą:
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.

Daugiau informacijos

This Element gives developmental scientists the conceptual toolkit they need to understand and conduct measurement invariance analysis.
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.