Does Choice Foster Knowledge?
GIST
To analyze whether knowledge differed between the “no choice” and the “choice” condition, we ran a linear mixed-effects model with random intercepts for participants. The outcome variable was knowledge and the predictors were time (T1 v. T2; effect-coded as T1 = −0.5, T2 = +0.5) and knowledge type (verbatim v. gist; effect coded as verbatim = −0.5, gist = +0.5), the between-subjects factors condition (“no choice” v. “choice”; effect coded as no choice = −0.5, choice = +0.5), representation (numbers v. graphs; effect coded as numbers = −0.5, graphs = +0.5), graph literacy (centered), and all possible interactions. Numeracy (centered) was included as a covariate. Note that including graph literacy as a predictor variable, yet numeracy only as a covariate, was done in line with a related study,As a robustness check, we nevertheless also ran the same model with numeracy as a predictor (see Furthermore, for a better interpretation of the interaction of 2 predictors, we additionally tested simple effects by analyzing the effect of 1 predictor separately in the subgroups of the other predictor. We use a mixed-effects model because it enables us to both account for within-subjects factors (i.e., time and knowledge type) and to include a continuous predictor (i.e., graph literacy), as suggested by Magezi.Gist and verbatim knowledge scores for numbers versus graphs with separate lines for people with high and low graph literacy, separate panels for T1 (upper panels a and b) and T2 (lower panels c and d), and separate panels for conditions “no choice” (left panels a and c) and “choice” (right panels b and d). Error bars represent 1 standard error of the mean.