Replication Data for: 'Evaluating measurement invariance in categorical data latent variable models with the EPC-interest'
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Many variables crucial to the social sciences are not directly observed but instead are latent and measured indirectly. When an external variable of interest affects this measurement, estimates of its relationship with the latent variable will then be biased. Such violations of ``measurement invariance'' may, for example, confound true differences across countries in postmaterialism with measurement differences.To deal with this problem, researchers commonly aim at ``partial measurement invariance'', i.e. to account for those differences that may be present and important. To evaluate this importance directly through sensitivity analysis, the ``EPC-interest''   was recently introduced  for continuous data. However, latent variable models in the social sciences often use categorical data. The current paper therefore extends the EPC-inte' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Categorical data","Cross-national","Differential item functioning","Ideal point","Latent class analysis","Latent variable","Measurement invariance","Multilevel","Sensitivity analysis","Structural equation modeling"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Many variables crucial to the social sciences are not directly observed but instead are latent and measured indirectly. When an external variable of interest affects this measurement, estimates of its relationship with the latent variable will then be biased. Such violations of ``measurement invariance'' may, for example, confound true differences across countries in postmaterialism with measurement differences.To deal with this problem, researchers commonly aim at ``partial measurement invariance'', i.e. to account for those differences that may be present and important. To evaluate this importance directly through sensitivity analysis, the ``EPC-interest''   was recently introduced  for continuous data. However, latent variable models in the social sciences often use categorical data. The current paper therefore extends the EPC-inte' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Many variables crucial to the social sciences are not directly observed but instead are latent and measured indirectly. When an external variable of interest affects this measurement, estimates of its relationship with the latent variable will then be biased. Such violations of ``measurement invariance'' may, for example, confound true differences across countries in postmaterialism with measurement differences.To deal with this problem, researchers commonly aim at ``partial measurement invariance'', i.e. to account for those differences that may be present and important. To evaluate this importance directly through sensitivity analysis, the ``EPC-interest''   was recently introduced  for continuous data. However, latent variable models in the social sciences often use categorical data. The current paper therefore extends the EPC-inte
Categorical data; Cross-national; Differential item functioning; Ideal point; Latent class analysis; Latent variable; Measurement invariance; Multilevel; Sensitivity analysis; Structural equation modeling