Sussman_Obrien: Dataset of Sussman & O'Brien (2017) Study 3

In arashl1364/BFMediate: Mediation Analysis Using a Combination of Bayesian Estimation Methods, Latent Variable Models, and Bayes Factors

Dataset of Sussman & O'Brien (2017) Study 3

Description

Contains the data collected to investigate whether the effect of saving intention (e.g. for children's education vs. for a vacation) on the desire to withdraw from the saving account is mediated by a sense of personal responsibility.

Usage

Sussman_Obrien

Format

A dataframe with 408 observations and 6 variables:

...$CondGroup

Experimentally manipulated type of saving (X)

...$Irresponsible1

Perceived responsibility indicator 1 (m*_1)

...$Irresponsible2

Perceived responsibility indicator 2 (m*_2)

...$Irresponsible3

Perceived responsibility indicator 3 (m*_3)

...$IrresponsibleScale

Perceived responsibility composite measure

...$value_log

Compensation demanded (Y)

References

Sussman, A. B., & O'brien, R. L. (2016). Knowing when to spend: Unintended financial consequences of earmarking to encourage savings. Journal of Marketing Research, 53(5), 790-803.

Examples

data(Sussman_Obrien)

Data = NULL
Data$X = Sussman_Obrien$CondGroup
Data$M = Sussman_Obrien$IrresponsibleScale
Data$Y = Sussman_Obrien$BorrowAmount
Data$m_tilde = cbind(Sussman_Obrien$Irresponsible1, Sussman_Obrien$Irresponsible2, Sussman_Obrien$Irresponsible3)

# Saving the dataset to use in the Shiny app (https://bfmediate.shinyapps.io/bfmediate_app/)
save(Data,file = "~/Sussman_Obrien.rda")    # the file path can be changed by replacing ~

# Setting priors
A_M = c(100,100)
A_Y = c(100,100,1)

# Computing Bayes factor for the simple mediation model using the composite measure of the mediator
out = Mediate(Data = Data, Model = 'Simple', Prior = list(A_M = A_M, A_Y = A_Y),R=10000, burnin = 2000)

# Parameter estimates
colMeans(out$Simple$beta_M)
colMeans(out$Simple$beta_Y)
out$Simple$Indirect_CI
out$Simple$Direct_CI

# Bayes factor
out$Simple$BF
out$Simple$evidence

# Computing Bayes factor for the latent variable mediation model using the indicators of the mediator
out_lvm = Mediate(Data = Data, Model = 'MCat', Prior = list(A_M = A_M, A_Y = A_Y),R=10000, burnin = 2000)

# Parameter estimates 
colMeans(out_lvm$beta_M)
colMeans(out_lvm$beta_Y)
out_lvm$Indirect_CI
out_lvm$Direct_CI

# Bayes factor
out_lvm$BF 
out_lvm$evidence

# Computing Bayes factor for the latent variable mediation model with reverse causal ordering X->Y->M

# specifying Y as M, and Y indicators as M indicators
Data$y_tilde = Data$m_tilde
Data$m_tilde = NULL
Data$M = Data$Y
Data$Y = NULL

out_lvm_rev =  Mediate(Data = Data, Model = 'YCat', Prior = list(A_M = A_M, A_Y = A_Y),R=10000, burnin = 2000)

# Bayes factor
out_lvm_rev$BF
out_lvm_rev$evidence

arashl1364/BFMediate documentation built on Oct. 11, 2023, 5:54 p.m.