BANOVA (version 1.2.1)

bernlogtime: Data for analysis of effects of typicality, blur and color on gist perception of ads

Description

Data from a mixed design experiment, where respondents were exposed to 32 ads, for 100 millisec. The ads were either typical or atypical (typical: 1 or 2). Respondents were exposed to ads that were either in full color or black-and-white (color: 1 or 2), and at different levels of blur (1=normal,5 = very high blur). These are between-subjects factors. The dependent variables are the response 0/1, and the response time. Typicality is a within-subjects variable.

Usage

data(bernlogtime)

Format

This R object contains within-subject variable: $typical is a factor with 2 levels "0" (typical ads) and "1"(atypical ads); between-subjects variables: $blur is a factor with two levels (1=normal,5 = very high blur). $color denotes a factor with 2 levels "1"(full color) and "2"(grayscale). $subject is the ID of subjects. $response denotes if the ad is correctly identified. $logtime is the response time.

$bernlogtime: 'data.frame': 3072 obs. of 6 variables: … $ subject : int 5 5 5 5 5 5 5 5 5 5 ... … $ typical : Factor w/ 2 levels "1","2": 1 2 1 1 1 2 2 2 2 1 ... … $ blur : Factor w/ 2 levels "1","5": 1 1 1 1 1 1 1 1 1 1 ... … $ color : Factor w/ 2 levels "1","2": 2 2 2 2 2 2 2 2 2 2 ... … $ response: int 1 1 1 1 1 1 1 1 1 1 ... … $ logtime : num 0.977 1.73 1.784 1 1.149 ...

References

Wedel, M and R. Pieters (2015). The Buffer Effect: The Role of Color when Advertising Exposures are Brief and Blurred, Marketing Science, Vol. 34, No. 1, pp. 134-143.

Examples

# NOT RUN {
data(bernlogtime)
# model using the dependent variable : log of the response time(logtime) 
res1 <- BANOVA.Normal(logtime~typical, ~blur + color, bernlogtime, 
bernlogtime$subject, burnin = 1000, sample = 1000, thin = 1)
summary(res1)
table.predictions(res1)

# model using the dependent variable : response
res2 <- BANOVA.Bernoulli(response~typical, ~blur + color, bernlogtime, 
bernlogtime$subject, burnin = 1000, sample = 1000, thin = 1)
summary(res2)
table.predictions(res2)
# }