Bibliographic record
Abstract
Goal pursuit is ubiquitous in everyday life, which has subsequently led to the proliferation of a multitude of theories and perspectives on what constitutes successful goal pursuit. While all of this work is indeed a testament to the centrality of goals in the context of motivational psychology, the problem, however, is that researchers often focus on only one particular theory while often ignoring other (possibly competing or overlapping) ideas. To address this concern, we conducted a prospective longitudinal study in order to determine which factors best predict goal progress over time. Participants (n = 799) were asked to set three week-long goals, as well as completed an extensive battery of measures, including 14 individual difference measures assessed at the between-person level and seven goal-specific measures assessed at the within-person level. Participants then reported how much progress they made on each of their goals at the end of the week. In keeping with best measurement practices, we first examined the validity of all self-report measures used in the study. Results indicate that the majority (92%) of individual difference measures demonstrated good internal consistency, although only a subset (71%) provided some evidence during more rigorous tests of validity. Upon examining the potential for overlapping constructs, three latent factors emerged providing evidence of substantial jangle-fallacies within the goal pursuit literature. Finally, using Bayesian model comparison we examined the extent to which these constructs predicted goal progress. Results indicate that people were more likely to make progress on the goals that they are committed to, have plans for, or that are more autonomous compared to their other goals. Additionally, we found that people who had specific plans for pursuing their goals, were more intrinsically oriented, experienced more competence in their daily life, experienced less frustration for their need for autonomy, and were able to re-engage in goals following failure made more progress on their goals compared to other people. The discussion focuses on implications of the present research on the field of self-regulation and goal pursuit, as well as measurement practices and theory development within social and personality psychology more broadly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".