Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise de Confiabilidade de Testes Adaptativos Computadorizados× | Teste Adaptativo Computadorizado baseado na Teoria de Resposta ao Item (CAT-IRT)× | |
|---|---|---|
| Área | Psicometria | Psicometria |
| Família | Latent structure | Latent structure |
| Ano de origem | 1970s–1980s | 1970s–1980s |
| Autor original≠ | David J. Weiss and IRT psychometricians | Lord, F. M.; further developed by Wainer, van der Linden, and others |
| Tipo≠ | Reliability estimation under adaptive testing | Adaptive measurement / sequential testing |
| Fonte seminal≠ | Weiss, D. J. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21(4), 361–375. DOI ↗ | Wainer, H. (Ed.). (2000). Computerized Adaptive Testing: A Primer (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805835113 |
| Outros nomes | CAT reliability, adaptive test reliability, IRT-based reliability estimation, marginal reliability in CAT | CAT-IRT, adaptive testing, IRT-based CAT, computerized adaptive testing |
| Relacionados | 4 | 4 |
| Resumo≠ | CAT reliability analysis quantifies measurement precision in computerized adaptive tests where each examinee receives a unique, individually tailored subset of items. Rather than a single classical coefficient, it uses item response theory to express precision as conditional standard error of measurement at each ability level, and marginal reliability as a global summary across the ability distribution. | Computerized adaptive testing based on item response theory is a sequential measurement procedure in which a computer algorithm selects successive test items tailored to each examinee's estimated ability level. Drawing on IRT to model item characteristics and ability estimation, CAT delivers precise scores with far fewer items than fixed-length tests, making it efficient for high-stakes assessments, clinical screening, and large-scale surveys. |
| ScholarGateConjunto de dados ↗ |
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