Addressing Three Common Issues in Research on Youth Activities: An Integrative Approach for Operationalizing and Analyzing Involvement
Bibliographic record
Abstract
Youth activity involvement has been operationalized and analyzed using a wide range of approaches. Researchers face the challenges of distinguishing between the effects of involvement versus noninvolvement and intensity of involvement in a particular activity, accounting simultaneously for cumulative effects of involvement, and addressing multiple unique effects of individual activities. In the present work, we review and illustrate the conceptual and empirical implications of these issues using data from a study of activity involvement and successful development in early adolescence (N=537; M age=11.56, 52% female). An integrative solution is introduced based on a latent composite variable (LCV) model (Bollen & Lennox, 1991), which can be used to address all three issues simultaneously. Using this approach, we show that of the aggregate indices examined, breadth of involvement was uniquely and positively associated with multiple indices of successful development. Of the individual activities, a dichotomous score and residual frequency rating for involvement in out-of-school clubs were both uniquely associated with less positive development indicators. We concluded that an LCV approach provides a novel method for addressing several fundamental operational and analytic issues facing researchers who investigate youth activity involvement as a context for positive development.
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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 | 0.056 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".