Shaddy_Fishbach: Dataset of Shaddy & Fishbach (2017) Study 5 (Scenario: lose)

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

Dataset of Shaddy & Fishbach (2017) Study 5 (Scenario: lose)

Description

Contains the data collected to investigate whether the effect of losing a product in a bundle (vs. in isolation) on the compensation demanded for the product loss is mediated by the need to replace that product

Usage

Shaddy_Fishbach

Format

A dataframe with 103 observations and 3 variables:

...$bundled_cond

Experimentally manipulated Bundling condition (X)

...$replacement

Perceived importance of replacement (M)

...$value_log

Compensation demanded (Y)

References

Shaddy, F., & Fishbach, A. (2017). Seller beware: how bundling affects valuation. Journal of Marketing Research, 54(5), 737-751.

Examples

data(Shaddy_Fishbach)

Data = NULL
Data$X = Shaddy_Fishbach$bundled_cond
Data$M = Shaddy_Fishbach$replacement
Data$Y = Shaddy_Fishbach$value_log

# Saving the dataset to use in the Shiny app (https://bfmediate.shinyapps.io/bfmediate_app/)
save(Data,file = "~/Shaddy_Fishbach.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
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)

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

# Computing the Bayes factor for the model with reverse MY causal direction (X->Y->M)

# Specifying Y as M and vice versa
temp = Data$M
Data$M = Data$Y
Data$Y = temp

out_rev = Mediate(Data = Data, Model = 'Simple', Prior = list(A_M = A_M, A_Y = A_Y),R=10000, burnin = 2000)
out_rev$Simple$BF
out_rev$Simple$evidence

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