MARC details
| 000 -LEADER |
|---|
| fixed length control field |
01787nam a22002657a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
ZW-GwMSU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20221214112656.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
221214b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
MSU |
| Transcribing agency |
MSU |
| Description conventions |
rda |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kadengye, Damazo T. |
| Relator term |
author |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Direct likelihood analysis and multiple imputation for missing item scores in multilevel cross-classification educational data |
| Statement of responsibility, etc. |
created by Damazo T. Kadengye, Eva Ceulemans, Wim Van den Noortgate |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Belgium : |
| Name of producer, publisher, distributor, manufacturer |
Sage; |
| Date of production, publication, distribution, manufacture, or copyright notice |
2013 |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| Content type code |
txt |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| Media type code |
n |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| Carrier type code |
nc |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Volume/sequential designation |
Volume , number , |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Multiple imputation (MI) has become a highly useful technique for handling missing values in many settings. In this article, the authors compare the performance of a MI model based on empirical Bayes techniques to a direct maximum likelihood analysis approach that is known to be robust in the presence of missing observations. Specifically, they focus on handling of missing item scores in multilevel cross-classification item response data structures that may require more complex imputation techniques, and for situations where an imputation model can be more general than the analysis model. Through a simulation study and an empirical example, the authors show that MI is more effective in estimating missing item scores and produces unbiased parameter estimates of explanatory item response theory models formulated as cross-classified mixed models. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Explanatory item response models |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Missing data |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
ultiple imputation |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ceulemans, Eva |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Noortgate, Wim Van den |
| Relator term |
author |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
https://doi/10.1177/0146621613491138 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Journal Article |