Environmental scientists’ support for public engagement strategy development is predicted by a range of factors, but mostly perceived benefits

Figures & data

Table 1. Descriptive statistics for belief measures.

	Mean	SD	N
Strategic engagement plan prioritization scale (a = .87)	3.51	0.76	329
Each LTER site should have an overall engagement strategy to help scientists help achieve priority goals.	4.12	0.94	323
How much time, if any, do you think your LTER site should put into developing a public engagement strategy^1	3.12	0.86	329
And how much of the LTER's financial resources do you think your LTER site should put into developing such a strategy?^2	2.73	0.87	328
Benefit beliefs scale (Attitudes; a = .87)	4.28	0.74	323
Having an engagement strategy would be an effective way for my LTER site to help achieve priority goals.	4.38	0.76	323
Having an engagement strategy would help my LTER site achieve priority goals more efficiently	4.29	0.85	323
It would be satisfying for my LTER site to have an engagement strategy to help achieve priority goals	4.15	0.89	322
Normative beliefs (Not combined)
Injunctive norm: My colleagues would approve of having an engagement strategy at our LTER site to help achieve priority goals	3.95	0.88	322
Descriptive norm: It is normal for places like LTER sites to have engagement strategies to help achieve priority goals.	3.53	1.01	323
Agency beliefs (Not combined)
I have the personal ability to contribute to an engagement strategy to help achieve priority site goals at my LTER site	3.42	1.24	323
People at my LTER site have the expertise to contribute to an engagement strategy to help achieve priority goals	3.77	1.05	323
Site resource belief scale (r = .59)	2.84	0.99	323
My LTER site has the financial resources to contribute to an engagement strategy to help achieve priority goals	2.91	1.11	323
People at my LTER site have the time to contribute to an engagement strategy to help achieve priority goals	2.76	1.12	323
Trust in public engagement staff (r = .81)^1	4.34	0.86	322
Desired role of engagement/outreach staff in developing engagement strategy	4.33	0.90	320
Desired role of engagement/outreach staff in implementing engagement strategy	4.35	0.90	316

Notes: Italics denote text used in Table 3. Unless noted, the response options were: 1 = Strongly disagree, 2 = Disagree, 3 = Neither agree, nor disagree, 4 = Somewhat agree, 5 = Strongly Agree. The allocation questions had response options of 1 = None/A very small amount of time/financial resources, 2 = A small amount of time/financial resources, 3 = A moderate amount of time/financial resources, 4 = A fairly substantial amount of time/financial resources, 5 = A very substantial amount of financial resources. Supplementary Table 1 provides additional related questions that provide further context into the role that scientists wanted various actors to play in the development and implementation of strategic engagement plans.

Table 2. Descriptive statistics for demographic measures.

Demographic item	Est.	SD	n
Identifies as a man	51%		317
Does not identify as White	15%		329
Career stage^2	2.69	1.36	317
Past engagement scale (alpha = .82)^3	2.26	1.05	312
Individual adult society members in the broader public (e.g. museum visitors, news readers/viewers, social media users, consumers, voters)	3.00	1.73	310
People in specific professional groups (e.g. environmental managers, healthcare workers, lawyers, trade-workers, social-workers, educators)	2.50	1.63	311
People from specific racial/cultural identity groups	2.27	1.68	307
Journalists and other media professionals (e.g. producers, filmmakers)	2.19	1.22	312
Policymakers and others involved in public policy (e.g. politicians, government employees, lobbying/advocacy organizations)	2.18	1.52	309
People from specific values-focused identity groups (e.g. liberals, conservatives, evangelicals, environmentalists)	1.90	1.44	308
For-profit businesses/people in the private sector (e.g. people who work for companies to develop, implement, and/or assess new products and services)- For-profit Actors	1.78	1.26	311
Previous consideration: I have previously thought a lot about the value an LTER engagement strategy to help achieve priority goals	3.25	1.27	324

Notes: Italics denote text used in Table 3. Responses were filtered to only include respondents for which there is strategy prioritization data. ¹Asked with belief questions reported in Table 2; response options were: 1 = Strongly disagree, 2 = Disagree, 3 = Neither agree, nor disagree, 4 = Somewhat agree, 5 = Strongly Agree. ²For career stage, 1 = Student, 2 = Junior (e.g. post-doc; assistant professor, entry-level researcher/analyst/technician), 3 = Mid-career (e.g. associate professor, mid-level administrator or researcher/analyst/technician), 4 = Senior (e.g. full professor, senior administrator or researcher/analyst/technician), 5 = Retired/Emeritus. ³Past engagement questions were preceded by ‘First, how often have you engaged with public audiences about science in the last year? We're interested in both engagement related to the LTERs as well as other engagement in which you may have participated. For each type of public engagement audience, please select the choice that best describes your amount of engagement in the last year through any channel (i.e. face-to-face, online, in writing, through audio/video, etc.).’ Response options were: 1 = Never, 2 = Once, 3 = 2-5 times, 4 = 6-11 times, 5 = About once a month, 6 = Multiple times per month, 7 = Once a week, or more. A separate youth/student category was not included in the scale.

Table 3. Mixed model results for environmental scientists’ views about prioritizing the development of a strategic engagement plan (Unstandardized estimates with 95% confidence intervals).

	Est.	95LL CI	95UL CI	p
Model 1: Intercept only
Intercept	3.48	3.33	3.62	.00
Random effects
Residual	0.50	0.42	0.58	.00
LTER Site	0.09	0.04	0.19	.02
n, -2LL, ICC, Marg./Cond. Pseudo R^2	329	754.39	0.15	.00/.15
Model 2: Demographics^++ (Full Model Results in Supplementary Table 2)
n, -2LL, ICC, Marg./Cond. Pseudo R^2	301	593.18	0.07	.28/.33
Model 3: Behavioral Predictors (Full Model Results in Supplementary Table 2)
n, -2LL, ICC, Marg./Cond. Pseudo R^2	311	501.37	0.07	.48/.52
Model 4: Complete Model
Fixed effects
Intercept	3.56	3.46	3.65	<.01
Identifies as man	−0.05	−0.17	0.07	.41
Does not identify as white	−0.08	−0.27	0.12	.44
Career stage	−0.08	−0.12	−0.03	<.01
Past engagement	0.06	0.00	0.13	.07
Previous consideration	0.13	0.07	0.18	<.01
Benefit Beliefs (Attitude; H1)	0.39	0.30	0.49	<.01
Injunctive Norm Belief (H2)	−0.01	−0.08	0.07	.86
Descriptive Norm Belief (H3)	0.06	0.00	0.12	.07
Personal Expertise Belief (H4)	0.06	0.00	0.12	.06
Site Expertise Belief (H5)	−0.09	−0.15	−0.02	.01
Site Resources Beliefs (H6)	0.14	0.07	0.21	<.01
Trust in Public Engagement Staff (H7)^1	0.14	0.07	0.21	<.01
Random effects
Residual	0.25	0.21	0.29	0 < .01
LTER Site	0.01	0.00	0.09	.45
n, -2LL, ICC, Marg./Cond. Pseudo R^2	288	448.46	0.03	.56/.57

Notes: ¹This is not a typo; they really were the same. 95LL CI = 95% Lower-level confidence interval; 95 UL CI 95% Upper-level confidence interval. ICC = Intraclass coefficient efficient (proportion of total variance accounted for by LTER site clusters). Marginal pseudo-R² represents fixed effects while conditional R² represents variance explained by fixed and random effects.