We Played Psychologizer So You Don’t Have To

psychologizer

Psychologizer: The Board Game that’s 100% boring, and also the WORST. Simply put, the object of this game is to earn enough credits to obtain a Psychologizer degree. However, there is nothing simple (or remotely fun) about this game. To earn points, “wager players” bet an amount of credits (5, 15, or 25) while the “opinion player” selects one of four options from one of the (literally) 1,008 cards that most closely matches their belief or personal nature. If the wager players are correct, they earn class credit. If not, the player will fall back.

psychologizer two

As far as art design goes, while the line drawings are aesthetically pleasing, the pastel/ruddy maroon color scheme is not. The pieces themselves are not attractive either, consisting of a lightweight three piece token/square cap/tassel.

Each card and potential response is quite lengthy, and not enjoyable at all to read.

psycho

To demonstrate the difference in word count (and to provide evidence for how truly dull this game is), I drew 5 cards at random from Therapy: The Game (a game I believe is comparable and also a lot more fun) and and 5 cards from Psychologizer, and counted the words. The results are summarized below, accompanied with the syntax in R I used.

psycho back

The mean number of words for a randomly selected Psychologizer card M = 91.8 (SD = 36.17), while the mean word count for Therapy the game was M = 23. 6 (SD = 10.92). There were significant differences in word count with a large effect size, 𝑡(8) = 4.04, 𝑝 < .001, 𝑑 = 2.55. Clearly, Psychologizer has way too much text to read aloud comfortably for any length of time.

graph

In conclusion, if you happen to come across this game, keep walking! I would hardly even describe it as a game. In fact, I cannot fathom that anyone ever has stuck with “playing” it for any length of time. See R syntax for my chart below. 

words = c(30, 21, 39, 16, 12, 83, 132, 120, 40, 84)
game = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
comp = data.frame(words, game)
comp$game = factor(comp$game,
levels = c(1,2),
labels = c(“Therapy: The Game”, “Psychologizer”))

mean(x)
mean(y)
sd(x)
sd(y)

t.test(comp$words ~ comp$game,
paired = F,
var.equal = T)

t = 4.0363
n = length(comp$game)
Cohen = (2*t/sqrt(n))
Cohen

bar = ggplot(comp, aes(game, words))
bar +
stat_summary(fun.y = mean,
geom = “bar”,
position = “dodge”,
color = “black”,
fill = “#c5dec2”)+
stat_summary(fun.data = mean_cl_normal,
geom = “errorbar”,
position = position_dodge(width = 0.90),
width = .2)+
theme_classic()+
xlab(“Game”) +
ylab(“Mean Word Count of Question Card”)