Local Warming Effect: A Meta-Analysis
Creators: name: Sugerman, Eli
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Business
subject: Marketing
subject: FOS: Economics and business
subject: Environmental Studies
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: 1. Working Title The Local Warming Effect: A Meta-Analysis 2. Anticipated Start Date &amp; Completion Date Start: January 21, 2021 End: January 25, 2021 3. Stage of Review at Completion of Pre-Registration Preliminary reading (COMPLETE) Preliminary scoping &amp; pilot search for studies (COMPLETE) Study Inclusion/Exclusion Criteria and Design (COMPLETE) Systematic Search &amp; Screening (COMPLETE) Data Extraction (IN PROGRESS) Quality Assessment of Data (PENDING DATA EXTRACTION) Analysis of Data (PENDING DATA EXTRACTION) Project Write-Up (PENDING DATA ANALYSIS) 4. Student Details Eli Sugerman; ers2257@gsb.columbia.edu PhD Student, Marketing Division Columbia University in the City of New York 5. Faculty Details Ye Li; ye.li@ucr.edu Assistant Professor of Management University of California, Riverside Eric Johnson; ejj3@gsb.columbia.edu Norman Eig Professor of Business, Marketing Columbia University in the City of New York 6. Rationale Although people are generally aware of global warming, their judgments (belief, concern, etc.) about it may be malleable and may be constructed in response to questions about global warming. As such, people’s judgments may reflect irrelevant but salient information, such as today’s temperature. The Local Warming Effect is the effect of recent temperature on people's judgments of (and subsequent actions regarding) climate change/global warming. A considerable body of research have documented the existence of the Local Warming Effect, including work by two of the current authors (Li, Johnson &amp; Zaval, 2011; Zaval, Keenan, Johnson &amp; Weber, 2014), and many papers both replicating and extending the Local Warming effect in a number of contexts and conditions. In the 2010s, dozens of Local Warming articles appeared in a wide array of academic journals. Motivated by these developments, the present work seeks to summarize and understand these myriad results by offering a novel, meta-analytic perspective on the size of the Local Warming effect and its moderators. 7. Research Question to be answered To what extent does local, recent, ambient temperature effect individuals’ judgements about global warming/climate change? 8. Sources to Search &amp; Search Strategy First and foremost, we collected every paper citing either Li, Johnson &amp; Zaval, 2011 or Zaval, Keenan, Johnson &amp; Weber, 2014 (according to Web of Science in January 2021). We evaluated each of these papers to assess whether they met basic requirements for inclusion in the meta-analysis. In a manner of spreading activation, we then explored relevant citations within these articles, compiling all relevant works. We solicited unpublished studies by sending emails to the Association for Consumer Research and the Society for Judgement and Decision Making listservs, offering a $10 donation to the student fund established in honor of the late Frank Yates (http://www.sjdm.org/join.html#yates) for each paper or unpublished work. Additionally, we shared preliminary lists of articles for inclusion with select, topic-relevant researchers, who recommended other works for consideration. 9. Inclusion/Exclusion Criteria for Studies INCLUDE: Studies that... a) Have a dependent variable that encapsulates a measure of participants’ belief in, perception of, or concern for global warming/climate change, or a measure of pro-environmental behavior relevant to climate change (such as donating to a climate change philanthropic organization). b) Have an independent variable that encapsulates recent, localized objective temperatures as measured by the research team or weather service, either absolute or relative to historic temperatures (e.g., historic averages or past weeks’); and/or respondents’ subjective perceptions of recent local temperature. c) Have reported relevant statistics such that effect size (Cohen’s D) can be calculated. d) Have been published in or translated into English. EXCLUDE: Studies that... a) Do not contain relevant dependent and independent variables or contain the relevant variables but hypothesize reversed directionality (that is, studies exploring the effect of global warming/climate change beliefs on perceptions of local weather). b) Leverage alternative weather metrics, such as a quantification of extreme weather events or risk/exposure to global warming/climate change-incited consequences. c) Have not reported relevant statistics or sufficient detail. d) Have not been published in or translated into English. 10. Data Extraction Qualified studies will be collected and subsequently synthesized into a database. Researchers will extract relevant qualifying information (variables, statistics, etc.), discarding research which fails to satisfy the criteria outlined above. For research papers that include more than one qualified study, Cohen’s D will be calculated for each of the studies. For research papers containing relevant work as well as irrelevant work, Cohen’s D will be calculated only for qualified studies. 11. Data Analysis All data analyses will be conducted using R, except for the calculation of Cohen’s D coefficients, which may be executed in Microsoft Excel prior to importing the data to R. In either case, we will compute Cohen’s D using the formulas in the widely used compute.es R package. We will conduct multilevel modeling using R, clustering data at both the article and study levels. Cohen’s D coefficients will serve as the dependent variable, and the independent variables (potential moderators) may include (but are not limited to): context (field or lab), temperature temporality (today, past 3 days, past week, past month, etc.), temperature measurement (absolute or relative), subjectivity (objective or subjective), nature of DV (concern for, belief in, or perception of global warming/climate change), and climate change term (“climate change” or “global warming”). Specification of independent variables is left deliberately incomplete, in order to allow for exploration into additional moderator variables that we expect to emerge through the data extraction process. Our intention is to report not only the aggregate Local Warming effect size, but to understand the potential heterogeneity of the effect as it pertains such moderators.