Nudging Meta-Analysis Data (Mertens et al., 2022)

Effect sizes from the meta-analysis of choice-architecture ("nudge") interventions by Mertens, Herberz, Hahnel and Brosch (2022). The data set was the basis of the debate about publication bias in the nudging literature (Maier et al., 2022) and serves as the real-data example of this package. Each row is one effect size; several effect sizes may stem from the same study and several studies from the same publication.

Usage

mertens_nudge

Format

A data frame with 447 rows and 24 columns. The columns needed for model fitting are cohens_d and variance_d. The variable descriptions below are taken from the codebook of Mertens et al. (2022), available at https://osf.io/49wtf/files/dvg3w.

publication_id: Publication ID.
study_id: Study ID. Studies were defined based on independent samples; where an experiment compared multiple intervention conditions to the same control condition, a single study ID was assigned.
es_id: Effect size ID.
reference: Descriptive reference of publication (i.e., authors + year of publication).
title: Title of publication.
year: Year of publication.
location: Geographical location of intervention: 0 = outside United States, 1 = inside United States.
domain: Behavioral domain of intervention: "health", "food", "environment", "finance", "pro-social" or "other" (domains not covered by the other categories).
intervention_category: Intervention category based on the taxonomy by Münscher et al. (2016): "information" (decision information), "structure" (decision structure) or "assistance" (decision assistance).
intervention_technique: Intervention technique based on the taxonomy by Münscher et al. (2016): "translation" (translate information), "visibility" (make information visible), "social_reference" (provide social reference point), "default" (change choice default), "effort" (change option-related effort), "composition" (change range or composition of options), "consequence" (change option consequences), "reminder" (provide reminders) or "commitment" (facilitate commitment).
type_experiment: Type of experiment as defined by Harrison and List (2004): "conventional_lab", "artefactual_field", "framed_field" or "natural_field".
population: Target population of intervention: 0 = children and/or adolescents, 1 = adults.
n_study: Overall sample size of study.
n_comparison: Sample size of control + intervention condition.
n_control: Sample size of control condition.
n_intervention: Sample size of intervention condition.
binary_outcome: Scale of outcome variable: 0 = continuous, 1 = binary.
mean_control: Mean of outcome variable in control condition.
sd_control: Standard deviation of outcome variable in control condition.
mean_intervention: Mean of outcome variable in intervention condition.
sd_intervention: Standard deviation of outcome variable in intervention condition.
cohens_d: Extracted effect size of intervention (Cohen's d).
variance_d: Variance around extracted effect size of intervention.
approximation: Approximation involved in effect size extraction: 0 = no, 1 = yes.

Source

Mertens, S., Herberz, M., Hahnel, U. J. J., & Brosch, T. (2022). The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains. Proceedings of the National Academy of Sciences, 119(1), e2107346118. doi:10.1073/pnas.2107346118

References

Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055.

Maier, M., Bartoš, F., Stanley, T. D., Shanks, D. R., Harris, A. J. L., & Wagenmakers, E.-J. (2022). No evidence for nudging after adjusting for publication bias. Proceedings of the National Academy of Sciences, 119(31), e2200300119. doi:10.1073/pnas.2200300119

Münscher, R., Vetter, M., & Scheuerle, T. (2016). A review and taxonomy of choice architecture techniques. Journal of Behavioral Decision Making, 29(5), 511–524. doi:10.1002/bdm.1897

Examples

data(mertens_nudge)
str(mertens_nudge[, c("domain", "cohens_d", "variance_d")])
#> 'data.frame':	447 obs. of  3 variables:
#>  $ domain    : chr  "food" "food" "food" "food" ...
#>  $ cohens_d  : num  3.08 3 2.77 2.65 2.24 ...
#>  $ variance_d: num  0.0485 0.2342 0.3142 0.0715 0.0556 ...

# z-statistics pile up just above the two-sided significance threshold
z <- mertens_nudge$cohens_d / sqrt(mertens_nudge$variance_d)
hist(z, breaks = 60, main = "Nudging meta-analysis", xlab = "z")
abline(v = qnorm(0.975), lty = 2)