Expanding NAEP and TIMSS Analysis to Include Additional Variables or a New Scoring Model Using the <i>R</i> Package <i>Dire</i>
The <i>R</i> packages <i>Dire</i> and <i>EdSurvey</i> allow analysts to make a conditioning model with new variables and then draw new plausible values. This is important because results for a variable not in the conditioning model are biased. For regression-type...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Psych |
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| Online Access: | https://www.mdpi.com/2624-8611/5/3/58 |
| _version_ | 1797577477977014272 |
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| author | Paul Dean Bailey Blue Webb |
| author_facet | Paul Dean Bailey Blue Webb |
| author_sort | Paul Dean Bailey |
| collection | DOAJ |
| description | The <i>R</i> packages <i>Dire</i> and <i>EdSurvey</i> allow analysts to make a conditioning model with new variables and then draw new plausible values. This is important because results for a variable not in the conditioning model are biased. For regression-type analyses, users can also use direct estimation to estimate parameters without generating new plausible values. <i>Dire</i> is distinct from other available software in <i>R</i> in that it requires fixed item parameters and simplifies calculation of high-dimensional integrals necessary to calculate composite or subscales. When used with <i>EdSurvey</i>, it is very easy to use published item parameters to estimate a new conditioning model. We show the theory behind the methods in <i>Dire</i> and a coding example where we perform an analysis that includes simple process data variables. Because the process data is not used in the conditioning model, the estimator is biased if a new conditioning model is not added with <i>Dire</i>. |
| first_indexed | 2024-03-10T22:08:53Z |
| format | Article |
| id | doaj.art-e5a4292a3aee4508b2c83f2f073bad0c |
| institution | Directory Open Access Journal |
| issn | 2624-8611 |
| language | English |
| last_indexed | 2024-03-10T22:08:53Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Psych |
| spelling | doaj.art-e5a4292a3aee4508b2c83f2f073bad0c2023-11-19T12:43:39ZengMDPI AGPsych2624-86112023-08-015387689510.3390/psych5030058Expanding NAEP and TIMSS Analysis to Include Additional Variables or a New Scoring Model Using the <i>R</i> Package <i>Dire</i>Paul Dean Bailey0Blue Webb1American Institutes for Research, 1400 Crystal Drive, 10th Floor, Arlington, VA 22202-3289, USAAmerican Institutes for Research, 1400 Crystal Drive, 10th Floor, Arlington, VA 22202-3289, USAThe <i>R</i> packages <i>Dire</i> and <i>EdSurvey</i> allow analysts to make a conditioning model with new variables and then draw new plausible values. This is important because results for a variable not in the conditioning model are biased. For regression-type analyses, users can also use direct estimation to estimate parameters without generating new plausible values. <i>Dire</i> is distinct from other available software in <i>R</i> in that it requires fixed item parameters and simplifies calculation of high-dimensional integrals necessary to calculate composite or subscales. When used with <i>EdSurvey</i>, it is very easy to use published item parameters to estimate a new conditioning model. We show the theory behind the methods in <i>Dire</i> and a coding example where we perform an analysis that includes simple process data variables. Because the process data is not used in the conditioning model, the estimator is biased if a new conditioning model is not added with <i>Dire</i>.https://www.mdpi.com/2624-8611/5/3/58large-scale assessmentconditioning modelplausible valuesNAEPTIMSSmarginal maximum likelihood |
| spellingShingle | Paul Dean Bailey Blue Webb Expanding NAEP and TIMSS Analysis to Include Additional Variables or a New Scoring Model Using the <i>R</i> Package <i>Dire</i> Psych large-scale assessment conditioning model plausible values NAEP TIMSS marginal maximum likelihood |
| title | Expanding NAEP and TIMSS Analysis to Include Additional Variables or a New Scoring Model Using the <i>R</i> Package <i>Dire</i> |
| title_full | Expanding NAEP and TIMSS Analysis to Include Additional Variables or a New Scoring Model Using the <i>R</i> Package <i>Dire</i> |
| title_fullStr | Expanding NAEP and TIMSS Analysis to Include Additional Variables or a New Scoring Model Using the <i>R</i> Package <i>Dire</i> |
| title_full_unstemmed | Expanding NAEP and TIMSS Analysis to Include Additional Variables or a New Scoring Model Using the <i>R</i> Package <i>Dire</i> |
| title_short | Expanding NAEP and TIMSS Analysis to Include Additional Variables or a New Scoring Model Using the <i>R</i> Package <i>Dire</i> |
| title_sort | expanding naep and timss analysis to include additional variables or a new scoring model using the i r i package i dire i |
| topic | large-scale assessment conditioning model plausible values NAEP TIMSS marginal maximum likelihood |
| url | https://www.mdpi.com/2624-8611/5/3/58 |
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