<- c(1, .8, 1.2, 1.1, .7, .75, 1.1, 1, 1.15, 1.05, .75, .8)
measure <- c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2)
time <- c('a', 'a', 'a', 'b', 'b', 'b', 'a', 'a', 'a', 'b', 'b', 'b')
group <- c(1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6)
sub_id <- data.frame(sub_id = sub_id, measure = measure, time = time, test_group = group)
df
library(ggplot2)
|>
df ggplot() +
aes(x = as.factor(time), y = measure, group = sub_id, color = test_group) +
geom_point() +
geom_line() +
facet_grid(~ test_group) +
xlab("Test occasion") +
ylim(0, 1.5)
Spaghetti Plots & Causal Models
About
This page provides some additional material related to the discussion of Cuddy (2012), Carney, Cuddy, & Yap (2010), and Ranehill et al. (2015).
Presentation comments
Gilmore prefers plots that show both individual data and group trends.
Exploring logical/causal diagrams
Causal diagrams are one tool to reveal an authors’ claims and assumptions.
Causal diagram for Carney et al. (2010)
A folk psychology view might hold that feelings cause behaviors.
Here, the role of feelings on endocrine measures might not be known or clear.
Neuroscientists would argue that feelings are the outcome of physiological processes like hormone levels and nervous system activity.
And that physiological processes (hormones & nervous system activity) influence one another.
If the neuroscientific view is correct, there must be a route for behaviors (like posing) to influence feelings through physiology.