Overview

Belief network analysis following Lydic, Torres-Grillo, Levine, Cosme et al. (2025). Pipeline: GLASSO partial correlation network → walktrap community detection → eigenvector centrality → combine with Hornik & Woolf PTG to identify priority targets.

Node selection note (2026-05-20): Subscale means are used only for batteries with internally consistent items (Cronbach α ≥ 0.65). Three batteries were revised: research_4 split into literacy/engagement (items 1–4, α = 0.750) and benefit beliefs (items 5, 7, 8, α = 0.677) — item 6 dropped (reverse-coded item that does not behave as expected empirically); govtfund_4 restricted to non-partisan stakeholder items (items 1–5, 11–13, α = 0.835) — partisan perception items (6–10) dropped; gotvfund_6 restricted to items 2–4, 7 (α = 0.694) — private-company comparison items and profit-motive item dropped. Node selection finalized at N = 300.

PTG adaptation note: H&W PTG is designed for single items on a common scale. For subscale means we dichotomize at the empirical 75th percentile as an approximation of “strongest desired position.” Thresholds based on full N = 300 sample.

1. Data

1.1 Load and clean

## N = 300

1.2 Scale levels and numeric recoding

1.3 Intent outcomes

2. Node construction

Nodes are defined here.

Two types: - Subscale means — item batteries aggregated to a single score per construct (α ≥ 0.65) - Individual items — standalone items or small batteries where items are conceptually distinct

govtfund_5 is split into positive (items 1–5: benefits of govt funding) and negative (items 6–10: costs/risks of govt funding) subscales because they have opposite valences. research_4 is split into science literacy/engagement (items 1–4) and science benefit beliefs (items 5, 7, 8); item 6 (“Science makes our way of life change too fast”) is excluded because reversing it reduces rather than increases internal consistency, suggesting it measures a distinct construct. govtfund_4 uses items 1–5 and 11–13 (workers, students, scientists, general public, community members, health researchers, pharma, tech) — partisan/political perception items 6–10 are excluded (α = 0.209 for those items alone). gotvfund_6 uses items 2–4, 7 (oversight, everyday tools, daily benefits, objectivity of government-funded research); private-company comparison items and profit-motive item excluded.

## Total nodes: 58

2.1 Node registry

Human-readable labels and block membership for each node.

## Registry: 58 nodes

2.2 Node inclusion decisions

Documents all inclusion decisions relative to the full survey instrument. Cronbach’s α reported for subscale means; product composites do not have a traditional α.

Subscale means and composites

Individual items

All 54 individual items retained in the network:

Dropped batteries and items

3. Correlations: nodes × intent outcomes

Spearman r used throughout (ordinal data on different scales).

4. Belief network

4.1 Correlation matrix

4.2 Network estimator

EBIC-GLASSO partial correlation network (EBICglasso via qgraph, gamma = 0.5). GLASSO requires approximately 5–10 observations per node (N/p ≥ 5–10). With 58 nodes and N = 300, N/p = 5.2 — within the acceptable range.

## GLASSO edges retained: 385 (of 1653 possible)

5. Community detection

Walktrap algorithm on absolute edge weights (follows Lydic et al., 2025).

## Number of communities detected: 11
## Modularity: 0.496

5.1 Community membership

5.2 Network plot with communities

6. Centrality

Eigenvector centrality: reflects both the number of connections and how influential those connections are. Predicts actual causal influence of nodes reasonably well (Dablander & Hinne, 2019).

6.1 Centrality plot

6.2 Centrality × correlation with composite

Replicates Figure 1C from Lydic et al. (2025): tests whether more central nodes are also more strongly correlated with the behavioral outcome.

## r(centrality, |r with composite|) = 0.45, p = 0.000

7. Percentage to gain (per node × per intent outcome)

Adaptation from H&W: Outcome dichotomy = “Definitely will” (7 of 7-point scale). Predictor dichotomy = empirical 75th percentile as “strong endorsement” — used for both subscale means and individual items to maintain consistency. At N = 10 (pilot), expect many boundary cells (rate_other ≈ 0 or NA).

8. Centrality × PTG: combined analysis

One plot per intent outcome. Upper-right quadrant = above-average on both centrality and PTG → highest-priority targets. Follows Figure 1C approach from Lydic et al. (2025).

8.1 Contact reps: oppose cuts

16 nodes above average on both metrics:

8.2 Talk to others about cuts

17 nodes above average on both metrics:

8.3 Share info online

17 nodes above average on both metrics:

8.4 Contact reps: support funding

15 nodes above average on both metrics:

9. Summary table

Full metrics for all nodes × all outcomes.

10. Export

## Saved: ~/Documents/GitHub/UniversityNews/Output/05_belief_network.csv
## Rows: 58 | Columns: 14