Table 3.
Multiple regression analysis predicting blog influence by topic and credibility indicators
| Indicator | β | p |
|---|---|---|
| Main topic (ref: legal or ethical issues) | ||
| Health or self-knowledge | 0.325 | 0.051 |
| Life science | 0.272 | 0.090 |
| Technology | 0.048 | 0.737 |
| Product marketing | 0.156 | 0.264 |
| Genealogy or ancestry | 0.303 | 0.013* |
| Affiliation (ref: unaffiliated) | ||
| Educational or medical | –0.169 | 0.080 |
| Other not-for-profit | –0.048 | 0.602 |
| Other for-profit | –0.009 | 0.934 |
| Author first and last name | 0.074 | 0.505 |
| Author expertisea | ||
| Genetics (ref: none) | –0.005 | 0.969 |
| Life science (ref: none) | 0.290 | 0.021* |
| Technology (ref: none) | –0.101 | 0.346 |
| Medicine (ref: none) | –0.083 | 0.426 |
| Industry (ref: none) | 0.069 | 0.498 |
| Opportunity to contact | ||
| Online contact information (ref: no) | 0.085 | 0.378 |
| Comments allowed (ref: no) | 0.039 | 0.663 |
| Posting rate | 0.492 | 0.000* |
| Outbound links per post | 0.031 | 0.740 |
n = 94. ref = Reference category. A log-transformation of the influence variable was computed and used in these analyses.
a
Subcategories within this indicator are not mutually exclusive.
*
p < 0.05.