13 research outputs found
Procedure of the IAT response task.<sup>1</sup>
1<p>The 2 target categories were: I, Me, My, Myself, Self, Mine versus His, Her, They, Them, Their, Others. Positive versus negative attribution categories were: Fun, Nice, Positive, Good, Worthy, Clever versus Pathetic, Stupid, Negative, Bad, Worthless, Unintelligent (In Dutch these words were translated as: Leuk, Aardig, Positief, Goed, Waardevol, Slim versus Onaardig, Stom, Negatief, Slecht, Waardeloos, Dom).</p
Flow diagram of the recruitment procedure.
<p>Flow diagram of the recruitment procedure.</p
Standardized parameter coefficients for the path models to test the interaction effects on candy intake (kcal).
<p>Model 1 presents âno versus low and high intake conditionâ and model 2 âlow versus no and high intake conditionâ for the self-esteem measures.</p><p>Note: â marginal significant pâ=â.059, *p<.05, **p<.01.</p>1<p>Model 1: Reference is no intake versus low and high experimental intake condition.</p>2<p>Model 2: Reference is low intake versus no and high experimental intake condition.</p
Screenshot showing the new question submittal page on the crowdsourcing website.
<p>Screenshot showing the new question submittal page on the crowdsourcing website.</p
What crowd-suggested childhood markers for adult BMI are significant?<sup>1</sup>
1<p>For the list of all original questions and their correlations with BMI, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087756#pone.0087756.s001" target="_blank">Appendix S1</a>.</p>*<p>New dimensions for (existing) constructs or operationalizations of potential predictors of obesity.</p
Questions with highest correlations with BMI.
<p>Questions with highest correlations with BMI.</p
Leveraging Crowdsourcing for Research Insights.
<p>Leveraging Crowdsourcing for Research Insights.</p
Example of behavioral data of a low-mimicry dyad.
<p>Example of behavioral data of a low-mimicry dyad.</p