Estimating the Multilevel Rasch Model: With the lme4 Package
Traditional Rasch estimation of the item and student parameters via marginal maximum likelihood, joint maximum likelihood or conditional maximum likelihood, assume individuals in clustered settings are uncorrelated and items within a test that share a grouping structure are also uncorrelated. These...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2007-02-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | http://www.jstatsoft.org/v20/i02/paper |
| _version_ | 1818345542814531584 |
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| author | Harold Doran Douglas Bates Paul Bliese Maritza Dowling |
| author_facet | Harold Doran Douglas Bates Paul Bliese Maritza Dowling |
| author_sort | Harold Doran |
| collection | DOAJ |
| description | Traditional Rasch estimation of the item and student parameters via marginal maximum likelihood, joint maximum likelihood or conditional maximum likelihood, assume individuals in clustered settings are uncorrelated and items within a test that share a grouping structure are also uncorrelated. These assumptions are often violated, particularly in educational testing situations, in which students are grouped into classrooms and many test items share a common grouping structure, such as a content strand or a reading passage. Consequently, one possible approach is to explicitly recognize the clustered nature of the data and directly incorporate random effects to account for the various dependencies. This article demonstrates how the multilevel Rasch model can be estimated using the functions in R for mixed-effects models with crossed or partially crossed random effects. We demonstrate how to model the following hierarchical data structures: a) individuals clustered in similar settings (e.g., classrooms, schools), b) items nested within a particular group (such as a content strand or a reading passage), and c) how to estimate a teacher × content strand interaction. |
| first_indexed | 2024-12-13T17:04:02Z |
| format | Article |
| id | doaj.art-0cdab9aafbaa4195a5ba94b56ade185e |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-13T17:04:02Z |
| publishDate | 2007-02-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-0cdab9aafbaa4195a5ba94b56ade185e2022-12-21T23:37:44ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602007-02-01202Estimating the Multilevel Rasch Model: With the lme4 PackageHarold DoranDouglas BatesPaul BlieseMaritza DowlingTraditional Rasch estimation of the item and student parameters via marginal maximum likelihood, joint maximum likelihood or conditional maximum likelihood, assume individuals in clustered settings are uncorrelated and items within a test that share a grouping structure are also uncorrelated. These assumptions are often violated, particularly in educational testing situations, in which students are grouped into classrooms and many test items share a common grouping structure, such as a content strand or a reading passage. Consequently, one possible approach is to explicitly recognize the clustered nature of the data and directly incorporate random effects to account for the various dependencies. This article demonstrates how the multilevel Rasch model can be estimated using the functions in R for mixed-effects models with crossed or partially crossed random effects. We demonstrate how to model the following hierarchical data structures: a) individuals clustered in similar settings (e.g., classrooms, schools), b) items nested within a particular group (such as a content strand or a reading passage), and c) how to estimate a teacher × content strand interaction.http://www.jstatsoft.org/v20/i02/papergeneralized linear mixed modelsitem response theorysparse matrix techniques |
| spellingShingle | Harold Doran Douglas Bates Paul Bliese Maritza Dowling Estimating the Multilevel Rasch Model: With the lme4 Package Journal of Statistical Software generalized linear mixed models item response theory sparse matrix techniques |
| title | Estimating the Multilevel Rasch Model: With the lme4 Package |
| title_full | Estimating the Multilevel Rasch Model: With the lme4 Package |
| title_fullStr | Estimating the Multilevel Rasch Model: With the lme4 Package |
| title_full_unstemmed | Estimating the Multilevel Rasch Model: With the lme4 Package |
| title_short | Estimating the Multilevel Rasch Model: With the lme4 Package |
| title_sort | estimating the multilevel rasch model with the lme4 package |
| topic | generalized linear mixed models item response theory sparse matrix techniques |
| url | http://www.jstatsoft.org/v20/i02/paper |
| work_keys_str_mv | AT harolddoran estimatingthemultilevelraschmodelwiththelme4package AT douglasbates estimatingthemultilevelraschmodelwiththelme4package AT paulbliese estimatingthemultilevelraschmodelwiththelme4package AT maritzadowling estimatingthemultilevelraschmodelwiththelme4package |