Bridging the gaps: Comparing structural equation models to network analysis models of depression, anxiety, and perfectionism
Bibliographic record
Abstract
Network models of psychopathology can identify specific items/symptoms that explain the connections among broader constructs such as depression, anxiety, and perfectionism. In two studies, we examine the dynamic interplay between depression, anxiety, and perfectionism symptoms among undergraduates using structural equation modeling (SEM) and network analysis. Participants in two independent samples (N = 774 and N = 759) completed online, cross-sectional questionnaires including measures of anxiety, depressive symptoms, and perfectionism (i.e., concerns over mistakes, doubts about actions, and personal standards). When analyzing data in the traditional fashion using SEM as a point of comparison, results from both samples were consistent with the existing literature. After controlling for all other perfectionism variables in the model, concerns over mistakes and doubts about actions were positively associated with depressive and anxiety symptoms (βs from .21 to .46), while personal standards showed negative associations with depressive symptoms (β = -.20 both samples) and non-significant associations with anxiety symptoms (βs from -.09. to -.03). Nonetheless, model fit for the confirmatory factor model was below ideal cutoffs in the second sample, suggesting other structures (e.g., a network model) might better represent the data. Network analyses revealed associations between constructs at the item level across both samples. Four key symptoms emerged as central nodes linking depression, anxiety, and perfectionism: difficulty taking initiative to do activities, feeling worthless, feeling close to panic, and doubts about simple everyday activities. This study underscores the importance of investigating item-level associations for a nuanced interpretation of these constructs.
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 | 0.024 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".