details
| Title |
Rapid guessing rates across administration mode and test setting |
|---|---|
| Authors |
Kröhne, Ulf   ;
Deribo, Tobias ;
Goldhammer, Frank |
| Source | Psychological test and assessment modeling 62 (2020) 2, S. 147-177  |
| Document | full text (867 KB) |
| License of the document | In copyright |
| Keywords (German) | Test; Bewertung; Innovation; Validität; Technologiebasiertes Testen; Design; Testkonstruktion; Testverfahren; Wirkung; Verhalten; Logdatei; Experiment; Student; Vergleichsuntersuchung |
| sub-discipline | Empirical Educational Research |
| Document type | Article (journal) |
| ISSN | 2190-0493; 21900493 |
| Language | English |
| Year of creation | 2020 |
| review status | Peer-Reviewed |
| Abstract (English): | Rapid guessing can threaten measurement invariance and the validity of large-scale assessments, which are often conducted under low-stakes conditions. Comparing measures collected under different administration modes or in different test settings necessitates that rapid guessing rates also be comparable. Response time thresholds can be used to identify rapid guessing behavior. Using data from an experiment embedded in an assessment of university students as part of the National Educational Panel Study (NEPS), we show that rapid guessing rates can differ across modes. Specifically, rapid guessing rates are found to be higher for un-proctored individual online assessment. It is also shown that rapid guessing rates differ across different groups of students and are related to properties of the test design. No relationship between dropout behavior and rapid guessing rates was found. (DIPF/Orig.) |
| other articles of this journal | Psychological test and assessment modeling Jahr: 2020 |
| Statistics |  Number of document requests
|
| Checksums | checksum comparison as proof of integrity |
| Date of publication | 05.01.2022 |
| Citation | Kröhne, Ulf; Deribo, Tobias; Goldhammer, Frank: Rapid guessing rates across administration mode and test setting - In: Psychological test and assessment modeling 62 (2020) 2, S. 147-177 - URN: urn:nbn:de:0111-pedocs-236307 - DOI: 10.25656/01:23630 |
share content at social platforms
Teile diese Seite:
