Tonietto_Malkoc: Dataset of Tonietto & Malkoc (2016) Study 4

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

Dataset of Tonietto & Malkoc (2016) Study 4

Description

Contains the data collected to investigate the serial mediation: Type of scheduling -> Free-flowing -> Work construal -> Anticipation utility (X->M1->M2->Y).

Usage

Tonietto_Malkoc

Format

A dataframe with 141 observations and 9 variables:

...$Condition

Experimentally manipulated type of scheduling (X)

...$Freeflowing

Free flowing indicator 1 (m_1*_1)

...$Flexible

Free flowing indicator 2 (m_1*_2)

...$Scale_Freeflowing

Free flowing composite measure

...$Chore

Work construal indicator 1 (m_2*_1)

...$Effortful

Work construal indicator 2 (m_2*_2)

...$WorkLike

Work construal indicator 3 (m_2*_3)

...$Scale_Work

Work construal composite measure

...$Excited

Anticipation utility indicator 1 (y*_1)

...$Thrilled

Anticipation utility indicator 2 (y*_2)

...$LookingForward

Anticipation utility indicator 3 (y*_3)

...$ResentfulR

Anticipation utility indicator 4 (y*_4)

...$UnenthusiasticR

Anticipation utility indicator 5 (y*_5)

...$ReluctantR

Anticipation utility indicator 6 (y*_6)

...$Scale_Anticipation_Utility

Anticipation utility composite measure

References

Tonietto, G. N., & Malkoc, S. A. (2016). The calendar mindset: Scheduling takes the fun out and puts the work in. Journal of Marketing Research, 53(6), 922-936.

Examples

data(Tonietto_Malkoc)

# Finding indices of observations that contain missing values
which(is.na(Tonietto_Malkoc),arr.ind = T)

# Removing (the two) observations that contain missing values from the dataset
Tonietto_Malkoc = Tonietto_Malkoc[-c(65,96),]

Data = NULL
Data$X = ifelse(Tonietto_Malkoc$Condition=="Impromptu",2,-1)    # C1 contrast coding of X (based on the paper)
Data$M1 = Tonietto_Malkoc$Scale_Freeflowing
Data$M2 = Tonietto_Malkoc$Scale_Work
Data$m_tilde = cbind(Tonietto_Malkoc$Chore,Tonietto_Malkoc$Effortful,Tonietto_Malkoc$WorkLike,8-Tonietto_Malkoc$Flexible,8-Tonietto_Malkoc$Freeflowing)
Data$y_tilde = cbind(Tonietto_Malkoc$Excited, Tonietto_Malkoc$Thrilled, Tonietto_Malkoc$LookingForward,Tonietto_Malkoc$ResentfulR, Tonietto_Malkoc$UnenthusiasticR, Tonietto_Malkoc$ReluctantR)
Data$Y = Tonietto_Malkoc$Scale_Anticipation_Utility


# Saving the dataset to use in the Shiny app (https://bfmediate.shinyapps.io/bfmediate_app/)
save(Data,file = "~/Tonietto_Malkoc.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 each of the simple mediation chains:
# X->M1->M2, X->M1->Y, X->M2->Y, and M1->M2->Y 
# Substantial evidence in favor of conditional independence for all the above chains is evidence in favor of 
# the serial mediation X->M1->M2->Y 

# X->M1->M2

Data1 = NULL
Data1$X = Data$X
Data1$M = Data$M1
Data1$Y = Data$M2

out1 = Mediate(Data = Data1, Model = 'Simple', Prior = list(A_M = A_M, A_Y = A_Y),R=10000, burnin = 2000)
out1$Simple$BF
out1$Simple$evidence

# X->M1->Y

Data2 = NULL
Data2$X = Data$X
Data2$M = Data$M1
Data2$Y = Data$Y

out2 = Mediate(Data = Data2, Model = 'Simple', Prior = list(A_M = A_M, A_Y = A_Y),R=10000, burnin = 2000)
out2$Simple$BF
out2$Simple$evidence

# X->M2->Y

Data3 = NULL
Data3$X = Data$X
Data3$M = Data$M2
Data3$Y = Data$Y

out3 = Mediate(Data = Data3, Model = 'Simple', Prior = list(A_M = A_M, A_Y = A_Y),R=10000, burnin = 2000)
out3$Simple$BF
out3$Simple$evidence

# M1->M2->Y

Data4 = NULL
Data4$X = Data$M1
Data4$M = Data$M2
Data4$Y = Data$Y

out4 = Mediate(Data = Data4, Model = 'Simple', Prior = list(A_M = A_M, A_Y = A_Y),R=10000, burnin = 2000)
out4$Simple$BF
out4$Simple$evidence

# BF shows evidence against conditional independence in the chain M1->M2->Y (the results are robust to a latent variable model specification using the indicators)
# therefore we cannot provide evidence in favor of serial mediation. 

# Next we explore a different data-generating process where M1 and M2 are indicators of a latent mediator

Data5 = NULL
Data5$X = Data$X
# using the indicators of (reverse) Freeflowing and work construal as indicators for a common latent mediator
Data5$m_tilde = Data$m_tilde
Data5$y_tilde = Data$y_tilde

# Computing Bayes factor for the new latent variable model
outnew = Mediate(Data = Data5, Model = 'MYCat', Prior = list(A_M = A_M, A_Y = A_Y),R=10000, burnin = 2000)
outnew$BF
outnew$evidence

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