| bpndata {BANOVA} | R Documentation |
Eye-movement data for analysis of print ad designs
Description
Data were collected in an experimental study in which 88 participants freely paged through a magazine at home or in a waiting room. While flipping through pages at their own pace, participants' eye-movements were recorded with infra-red corneal reflection eye-tracking methodology. In a subsequent memory task, participants were asked to identify the target brand in the ad as soon as possible by touching the correct brand name on the screen. Accuracy (accurate=1, inaccurate =0) of brand memory and response time were recorded for each ad and participant.
Usage
data(bpndata)Format
This R object contains 3080 observations in the data (35 ads x 88 participants). The goal is to examine the effects of several ad design variables on both eye movements and memory. The variables include:
1. RESPONDENT_ID: ID number of a respondent;
2. AD_ID: ID number of an ad;
3. PAGE_NUM: page number in the magazine where an ad appears (1,2,3,...);
4. PAGE_POS: the right-side vs. left-side position on a page, 1 = right, 0 = left;
5. PIC_FIX: fixation count of the pictorial element (0, 1, 2, 3, ...);
6. PIC_SIZE: surface size of the pictorial element, in inches2;
7. RECALL_ACCU: whether a respondent accurately recalls the brand name, 1= yes, 0 = no;
8. RECALL_TIME: the time it takes a respondent to answer the brand recall question, in seconds.
$ bpndata: 'data.frame': 3080 obs. of 8 variables:
... $ RESPONDENT_ID: int 1 1 1 1 1 1 1 1 1 1 ...
... $ AD_ID : int 1 2 3 4 5 6 7 8 9 10 ...
... $ PAGE_NUM : int 2 5 6 11 13 14 17 18 21 22 ...
... $ PAGE_POS : int 0 1 0 1 1 0 1 0 1 0 ...
... $ PIC_FIX : int 0 2 1 1 1 2 0 3 3 8 ...
... $ PIC_SIZE : num 74.2 52.6 77.6 71.4 52.4 ...
... $ RECALL_ACCU : int 0 0 0 0 0 0 1 1 0 0 ...
... $ RECALL_TIME : num 2.56 1.04 2.76 2.8 2.28 2.32 2.04 2.04 2.48 0.6 ...
References
Wedel, M. and Pieters, R. (Autumn, 2000). Eye Fixations on Advertisements and Memory for Brands: A Model and Findings, Marketing Science, Vol. 19, No. 4, pp. 297-312
Examples
data(bpndata)
# within-subjects model using the dependent variable : PIC_FIX
library(rstan)
model <- BANOVA.model('Poisson')
stanmodel <- BANOVA.build(model)
res0 <- BANOVA.run(PIC_FIX ~ PIC_SIZE + PAGE_NUM + PAGE_POS, ~1,
fit = stanmodel, data = bpndata, id = 'RESPONDENT_ID',
iter = 200, thin = 1, chains = 2)
res0
# or
res1 <- BANOVA.Poisson(PIC_FIX ~ PIC_SIZE + PAGE_NUM
+ PAGE_POS, ~1, bpndata, bpndata$RESPONDENT_ID, burnin = 1000, sample = 1000, thin = 1)
res1
# within-subjects model using the dependent variable : RECALL_ACCU
model_bern <- BANOVA.model('Bernoulli')
stanmodel_bern <- BANOVA.build(model_bern)
res2 <- BANOVA.run(RECALL_ACCU ~ RECALL_TIME + PAGE_NUM + PAGE_POS, ~1,
fit = stanmodel_bern, data = bpndata, id = 'RESPONDENT_ID',
iter = 200, thin = 1, chains = 2)
res2
# or
res3 <- BANOVA.Bernoulli(RECALL_ACCU ~ RECALL_TIME + PAGE_NUM
+ PAGE_POS, ~1, bpndata, bpndata$RESPONDENT_ID, burnin = 1000, sample = 1000, thin = 1)
res3