Modern psychometrics for assessing achievement goal orientation: A Rasch analysis
Bibliographic record
Abstract
BACKGROUND: A program of research is needed that assesses the psychometric properties of instruments designed to quantify students' achievement goal orientations to clarify inconsistencies across previous studies and to provide a stronger basis for future research. AIM: We conducted traditional psychometric and modern Rasch-model analyses of the Achievement Goals Questionnaire (AGQ, Elliot & McGregor, 2001) and the Patterns of Adaptive Learning Scale (PALS, Midgley et al., 2000) to provide an in-depth analysis of the two most popular instruments in educational psychology. SAMPLES AND METHODS: For Study 1, 217 undergraduate students enrolled in educational psychology courses participated. Thirty-four were male and 181 were female (two did not respond). Participants completed the AGQ in the context of their educational psychology class. For Study 2, 126 undergraduate students enrolled in educational psychology courses participated. Thirty were male and 95 were female (one did not respond). Participants completed the PALS in the context of their educational psychology class. RESULTS: Traditional psychometric assessments of the AGQ and PALS replicated previous studies. For both, reliability estimates ranged from good to very good for raw subscale scores and fit for the models of goal orientations were good. Based on traditional psychometrics, the AGQ and PALS are valid and reliable indicators of achievement goals. Rasch analyses revealed that estimates of reliability for items were very good but respondent ability estimates varied from poor to good for both the AGQ and PALS. These findings indicate that items validly and reliably reflect a group's aggregate goal orientation, but using either instrument to characterize an individual's goal orientation is hazardous.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.062 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".