'Truth Biases for Scientist and Skeptic Claims, 2020' (AsPredicted #47,923)
Author(s) This pre-registration is currently anonymous to enable blind peer-review. It has 2 authors.
Pre-registered on 2020/09/17 19:46 (PT)
1) Have any data been collected for this study already? No, no data have been collected for this study yet.
2) What's the main question being asked or hypothesis being tested in this study? People rate repeated, more familiar claims as more true than claims they see for the first time, a phenomenon known as the illusory truth effect (ITE; meta-analysis: Dechene, Stahl, Hansen, & Wanke, 2010). While many studies have examined this phenomenon, to our knowledge there is a lack of studies testing the truth effect with controversial claims, though social psychology literatures have shown that underlying beliefs are an important factor in truth reasoning and judgment (e.g., Strickland, Taber, & Lodge, 2011; Oyserman, 2011).
We examine susceptibility to the truth effect in the context of belief consistency by using claims related to climate change. In particular, we use piloted claims supporting the beliefs of a climate scientist (scientist claims), supporting the belief of a climate science skeptic (skeptic claims), and neutral weather claims. Belief consistency is determined by measuring level of belief in climate science prior to participating in the ITE component of the study.
Our key research question is whether claims inconsistent with our own beliefs will be less susceptible to the effects of repetition. It is hypothesised that relative to claims that are consistent with our own beliefs, claims inconsistent with our own beliefs will be related to a decreased truth effect.
3) Describe the key dependent variable(s) specifying how they will be measured. Mean truth ratings on a 6-point scale from Definitely True to Definitely False.
The dependent variable can be captured by examining the size of the illusory truth effect (mean truth ratings for old claims – mean truth rating for new claims). We can also examine the ITE across conditions by comparing old ratings to new ratings across conditions (as a within-subject factor).
4) How many and which conditions will participants be assigned to? This is a 2 (claim familiarity: old or new) x 2 (claim type: skeptic or scientist) fully within-subjects design. We will use climate science endorsement as a continuous or categorical (between-subjects) variable in examining the variability in the ITE by claim and climate science beliefs/attitudes.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. We will analyse how belief in climate science predicts the size of the illusory truth effect for skeptic and science claims in two ways:
One, as a 2 (claim familiarity: old or new) x 2 (claim type: skeptic or scientist) repeated measures design with belief/attitudes in climate science as the continuous predictor, or two, as a mixed design with 2 (claim familiarity: old or new) x 2 (claim type: skeptic or scientist) as within variables and belief/attitudes in climate science 2 (belief: high/low) as a between variable.
We will use a combination of spotlight and paired samples t-test to further explore any detected interactions.
We will run the same exploratory analyses for the weather claims and ITE.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. We will exclude answers from people who have said yes to searching for answers online (for the test phase). We will also exclude submissions that fail the bot check, submissions with identical answers in the testing phase (e.g. all true).
7) How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about exactly how the number will be determined. We will post 100 HITs on MTurk.
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) We will examine how ITE for weather claims (our seemingly innocuous filler items) varies according to climate change science/attitudes as above.
We will also include an exploratory measure of individual variation in chronic trust.
We may also consider an analysis considering upper and lower quartiles of the belief in climate science measure.
