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Climate Change in the American Mind: April 2019


Appendix II: Survey Method

The data in this report are based on a nationally representative survey of 1,291 American adults, aged 18 and older. The survey was conducted March 29 – April 8, 2019. All questionnaires were self-administered by respondents in a web-based environment. The survey took, on average, 23 minutes to complete. The sample was drawn from the Ipsos (formerly GfK) KnowledgePanel®, an online panel of members drawn using probability sampling methods.

Prospective members are recruited using a combination of random digit dial and address-based sampling techniques that cover virtually all (non-institutional) resident phone numbers and addresses in the United States. Those contacted who would choose to join the panel but do not have access to the Internet are loaned computers and given Internet access so they may participate.

The sample therefore includes a representative cross-section of American adults – irrespective of whether they have Internet access, use only a cell phone, etc. Key demographic variables were weighted, post survey, to match U.S. Census Bureau norms.

From November 2008 to December 2018, no KnowledgePanel® member participated in more than one Climate Change in the American Mind (CCAM) survey. Beginning with the current survey (April, 2019), panel members who have participated in one or more of these surveys in the past, excluding the most recent two surveys (i.e., March and December 2018), may be randomly selected for participation. In the current survey, 464 respondents participated in a previous CCAM survey prior to 2018.

The survey instrument was designed by Anthony Leiserowitz, Seth Rosenthal, Matthew Ballew, Matthew Goldberg, Abel Gustafson, and Parrish Bergquist of Yale University, and Edward Maibach and John Kotcher of George Mason University.

 

Sample details and margins of error

All samples are subject to some degree of sampling error – that is, statistical results obtained from a sample can be expected to differ somewhat from results that would be obtained if every member of the target population were interviewed. Average margins of error for each wave, at the 95% confidence level, are plus or minus 3 percentage points except where noted.

  • April 2019: March 29 – April 8 (N = 1,291)
  • December 2018: Fielded November 28 – December 11 (N = 1,114)
  • March 2018: Fielded March 7 – March 24 (N = 1,278)
  • October 2017: Fielded October 20 – November 1 (N = 1,304)
  • May 2017: Fielded May 18 – June 6 (N = 1,266)
  • November 2016: Fielded November 18 – December 1 (N = 1,226)
  • March 2016: Fielded March 18 – 31 (N = 1,204)
  • October 2015: Fielded September 30 – October 19 (N = 1,330)
  • March 2015: Fielded February 27 – March 10 (N = 1,263)
  • October 2014: Fielded October 17 – 28 (N = 1,275)
  • April 2014: Fielded April 15 – 22 (N = 1,013)
  • November 2013: Fielded November 23 – December 9 (N = 830)
  • April 2013: Fielded April 10 – 15 (N = 1,045)
  • September 2012: Fielded August 31 – September 12 (N = 1,061)
  • March 2012: Fielded March 12 – March 30 (N = 1,008)
  • November 2011: Fielded October 20 – November 16 (N = 1,000)
  • May 2011: Fielded April 23 – May 12 (N = 1,010)
  • June 2010: Fielded May 14 – June 1 (N = 1,024)
  • January 2010: Fielded December 24, 2009 – January 3, 2010 (N = 1,001).
  • November 2008: Fielded October 7 – November 12 (N = 2,164).
    • Data were collected over two periods: from October 7 – October 20 and from October 24 – November 12. Margin of error plus or minus 2 percentage points.

 

Rounding error

For tabulation purposes, percentage points are rounded to the nearest whole number. As a result, percentages in a given chart may total slightly higher or lower than 100%. Summed response categories (e.g., ”strongly agree” + ”somewhat agree”) are rounded after sums are calculated (e.g., 25.3% + 25.3% = 50.6%, which, after rounding, would be reported as 25% + 25% = 51%).

 

Acknowledgment

A special “thank you” goes to Parrish Bergquist, Ph.D. and Matto Mildenberger, Ph.D. for creating an automated version of this report.