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AEJMC 2014 presentation
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Perceptions about Scientists: Comparing 2001 and 2012
John C. Besley, Ph.D.Ellis N. Brant Chair in Public Relations, Michigan State
Background
Background
Underlying concern:Poor image of scientists leads to fewer people choosing a science career, less influence for scientists, and less support for science
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
Background
• Image questions were back for 2012 Indicators• I served as
chapter author• Losh encouraged
me to re-analyze
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/ANOVAStep 2. GLM Model with interactions by survey year
ChallengesDescriptive Statistics and 2001-2012 Comparisons for Predictor Variables
Suggests slight decline in expected positive predictors
ChallengesDescriptive Statistics and 2001-2012 Comparisons for Criterion Variables
Slight increase in positive perceptions
Slight increase in negative perceptions
ChallengesGLM 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!
ChallengesGLM 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!
ChallengesGLM 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!
ChallengesGLM 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!
ChallengesGLM 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!
ChallengesGLM 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!
ChallengesGLM 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!
ChallengesGLM 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!
ChallengesGLM tests of between subject effects for views about scientists
Very limited variance explained
Note: These are F-scores, NOT parameter estimates!
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
Parameter Estimates