Aejmc 2014 presentation views about scientists
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Aejmc 2014 presentation views about scientists
- 1. Perceptions about Scientists: Comparing 2001 and 2012 John C. Besley, Ph.D. Ellis N. Brant Chair in Public Relations, Michigan State
- 2. Background
- 3. Background Underlying concern: Poor image of scientists leads to fewer people choosing a science career, less influence for scientists, and less support for science
- 4. Background • Used 1983 and 2001 data from the NSF S&T surveys • Found that images of scientists driven by: • Year (2001 = better) • Demographics • Faith vs. science
- 5. Background • Image questions were back for 2012 Indicators • I served as chapter author • Losh encouraged me to re-analyze
- 6. Current Project • Not feasible to include 1983 data in new analysis • As of 2006, NSF S&T survey is part of General Social Survey (GSS) • Not much overlap between 1983 and 2012 surveys • Losh (2010) include a single “images” of scientist index • Initial analysis suggested that available items do not scale into a single index • Also potential for additional predictor variables aimed at understanding origins of views about scientists Two questions: • Is there evidence of a change by descriptive statistics? (While being wary of survey mode change) • Did predictors of views change by year? The data and the analysis … • 2001: n = ~1,000-1,200 (RDD telephone) • 2012: n = ~400-900 (Face-to-Face) Step 1. Mean comparisons using t-tests/ANOVA Step 2. GLM Model with interactions by survey year
- 7. Challenges Descriptive Statistics and 2001-2012 Comparisons for Predictor Variables Suggests slight decline in expected positive predictors
- 8. Challenges Descriptive Statistics and 2001-2012 Comparisons for Criterion Variables Slight increase in positive perceptions Slight increase in negative perceptions
- 9. Challenges GLM tests of between subject effects for views about scientists Year had very little impact on the underlying relationships Note: These are F-scores, NOT parameter estimates!
- 10. Challenges GLM tests of between subject effects for views about scientists Women were slightly less likely to have negative views (b = -.12 and -.11) Note: These are F-scores, NOT parameter estimates!
- 11. Challenges GLM tests of between subject effects for views about scientists Young people and those who took more math and science courses tended to have fewer negative views Note: These are F-scores, NOT parameter estimates!
- 12. Challenges GLM tests of between subject effects for views about scientists Those with more interest are more likely to hold positive images (b = .15, but less so in 2001) Note: These are F-scores, NOT parameter estimates!
- 13. Challenges GLM tests of between subject effects for views about scientists Small negative relationship between knowledge and perceptions (b = -.02 to -.04) Note: These are F-scores, NOT parameter estimates!
- 14. Challenges GLM tests of between subject effects for views about scientists No museum visit or lots of museum visits associated with seeing more danger in 2012 (i.e., non-linear) Note: These are F-scores, NOT parameter estimates!
- 15. Challenges GLM tests of between subject effects for views about scientists Indicating that newspaper were the primary source of S&T was associated with lower perceptions of danger and working alone Note: These are F-scores, NOT parameter estimates!
- 16. Challenges GLM tests of between subject effects for views about scientists Indicating that newspaper were the primary source of S&T was associated with lower perceptions of danger and working alone (b = -.18 and -.20) Note: These are F-scores, NOT parameter estimates!
- 17. Challenges GLM tests of between subject effects for views about scientists Very limited variance explained Note: These are F-scores, NOT parameter estimates!
- 18. Discussion • Factors associated with scientist views include: • Age and gender • Experience/Knowledge of science BUT … • NSF S&T Survey is meant to be a key source of S&T knowledge and attitude data. AND … • Models based on available questions explain limited variance in scientist perceptions SO … • We need to encourage NSF to continue to think about the questions included in the S&T survey • Better communication variables, including exposure/attention to various sources of science content, as well as interpersonal discussion • Consistent issue specific and general attitude measures Next steps … • Redo the analysis in MPlus using multi-group modelling approach • Build in criterion variables related to impact of views