For this exercise, please try to reproduce the results from Study 1 of the associated paper (Joel, Teper, & MacDonald, 2014). The PDF of the paper is included in the same folder as this Rmd file.
In study 1, 150 introductory psychology students were randomly assigned to a “real” or a “hypothetical” condition. In the real condition, participants believed that they would have a real opportuniy to connect with potential romantic partners. In the hypothetical condition, participants simply imagined that they are on a date. All participants were required to select their favorite profile and answer whether they were willing to exchange contact information.
Below is the specific result you will attempt to reproduce (quoted directly from the results section of Study 1):
We next tested our primary hypothesis that participants would be more reluctant to reject the unattractive date when they believed the situation to be real rather than hypothetical. Only 10 of the 61 participants in the hypothetical condition chose to exchange contact information with the unattractive potential date (16%). In contrast, 26 of the 71 participants in the real condition chose to exchange contact information (37%). A chi-square test of independence indicated that participants were significantly less likely to reject the unattractive potential date in the real condition compared with the hypothetical condition, X^2(1, N = 132) = 6.77, p = .009.
library(tidyverse) # for data munging
library(knitr) # for kable table formating
library(haven) # import and export 'SPSS', 'Stata' and 'SAS' Files
library(readxl) # import excel files
# #optional packages:
# library(broom)
# library(labelled)# converts SPSS's labelled to R's factor # Just Study 1
d <- read_sav('data/Empathy Gap Study 1 data.sav')colnames(d)## [1] "ID" "attachment1"
## [3] "attachment2" "attachment3"
## [5] "attachment4" "attachment5"
## [7] "attachment6" "attachment7"
## [9] "attachment8" "attachment9"
## [11] "attachment10" "attachment11"
## [13] "attachment12" "attachment13"
## [15] "attachment14" "attachment15"
## [17] "attachment16" "attachment17"
## [19] "attachment18" "attachment19"
## [21] "attachment20" "attachment21"
## [23] "attachment22" "attachment23"
## [25] "attachment24" "attachment25"
## [27] "attachment26" "attachment27"
## [29] "attachment28" "attachment29"
## [31] "attachment30" "attachment31"
## [33] "attachment32" "attachment33"
## [35] "attachment34" "attachment35"
## [37] "attachment36" "FOBA1"
## [39] "FOBA2" "FOBA3"
## [41] "FOBA4" "FOBA5"
## [43] "FOBA6" "empathy1"
## [45] "empathy2" "empathy3"
## [47] "empathy4" "empathy5"
## [49] "empathy6" "empathy7"
## [51] "empathy8" "empathy9"
## [53] "empathy10" "empathy11"
## [55] "empathy12" "empathy13"
## [57] "empathy14" "empathy15"
## [59] "empathy16" "empathy17"
## [61] "empathy18" "empathy19"
## [63] "empathy20" "empathy21"
## [65] "empathy22" "empathy23"
## [67] "empathy24" "empathy25"
## [69] "empathy26" "empathy27"
## [71] "empathy28" "age"
## [73] "livedincanada" "orientation"
## [75] "inrel" "longterm"
## [77] "dating" "shortterm"
## [79] "intimate" "otheropen"
## [81] "drink" "children"
## [83] "responseq1" "responseq2"
## [85] "responseq3" "responseq4"
## [87] "reasontrue1" "motives1"
## [89] "reasontrue2" "motives2"
## [91] "reasontrue3" "motives3"
## [93] "reasontrue4" "motives4"
## [95] "reasontrue5" "motives5"
## [97] "reasontrue6" "motives6"
## [99] "reasontrue7" "motives7"
## [101] "reasontrue8" "motives8"
## [103] "suspicious" "selfattractive"
## [105] "otherattractive" "EmpathyPTtot"
## [107] "EmpathyFStot" "EmpathyECtot"
## [109] "EmpathyPDtot" "fobstot"
## [111] "attachmentavoidance" "attachmentanxiety"
## [113] "stateguilttot" "stateempathytot"
## [115] "excitementtot" "compatibilitytot"
## [117] "very_otherfocused" "less_otherfocused"
## [119] "gender" "genderXcondition"
## [121] "REQUIRED_VARIABLES_START_BELOW" "condition"
## [123] "exchangeinfo" "otherfocused_motives"
## [125] "selffocused_motives"data = d %>% select(c("condition", "exchangeinfo"))
head(data)## # A tibble: 6 × 2
## condition exchangeinfo
## <dbl+lbl> <dbl+lbl>
## 1 1 [real] 1 [yes]
## 2 1 [real] 2 [no]
## 3 1 [real] 2 [no]
## 4 0 [hypothetical] 2 [no]
## 5 0 [hypothetical] 1 [yes]
## 6 0 [hypothetical] 1 [yes]Only 10 of the 61 participants in the hypothetical condition chose to exchange contact information with the unattractive potential date (16%). In contrast, 26 of the 71 participants in the real condition chose to exchange contact information (37%).
# reproduce the above results here
condition_2 <- data %>%
filter(condition == 0)
hyp_condition <- condition_2 %>%
nrow()
condition_2_exchange <- condition_2 %>%
filter(exchangeinfo == 1)
hyp_condition_exchange <- condition_2_exchange %>%
nrow()
message <- sprintf("%s of the %s participants in the real condition wanted to exchange contact info", hyp_condition_exchange, hyp_condition)
print(message)## [1] "10 of the 61 participants in the real condition wanted to exchange contact info"condition_1 <- data %>%
filter(condition == 1)
real_condition <- condition_1 %>%
nrow()
condition_1_exchange <- condition_1 %>%
filter(exchangeinfo == 1)
real_condition_exchange <- condition_1_exchange %>%
nrow()
message <- sprintf("%s of the %s participants in the hypothetical condition wanted to exchange contact info", real_condition_exchange, real_condition)
print(message)## [1] "26 of the 71 participants in the hypothetical condition wanted to exchange contact info"A chi-square test of independence indicated that participants were significantly less likely to reject the unattractive potential date in the real condition compared with the hypothetical condition, X^2(1, N = 132) = 6.77, p = .009.
Hint: if you are using the function chisq.test(), make sure to set the continuity correction to false (“correct = FALSE”) since sample size is greater than 20.
# reproduce the above results here
condition_1_no_exchange <- condition_1 %>%
filter(exchangeinfo == 2)
real_condition_no_exchange <- condition_1_no_exchange %>%
nrow()
condition_2_no_exchange <- condition_2 %>%
filter(exchangeinfo == 2)
hyp_condition_no_exchange <- condition_2_no_exchange %>%
nrow()
x <- matrix(c(real_condition_exchange, real_condition_no_exchange,
hyp_condition_exchange, hyp_condition_no_exchange), ncol = 2)
chisq.test(x, correct=FALSE)##
## Pearson's Chi-squared test
##
## data: x
## X-squared = 6.7674, df = 1, p-value = 0.009284Were you able to reproduce the results you attempted to reproduce? If not, what part(s) were you unable to reproduce?
Yes, I was able to reproduce the results that I attempted to reproduce.
How difficult was it to reproduce your results?
It was not extremely difficult to reproduce the results.
What aspects made it difficult? What aspects made it easy?
It was difficult since the column values were not labelled. For example, for condition, it was unclear what a value of 0 or 1 corresponded to. It was only by matching the outputs to the stated results in the paper that I was able to guess what they corresponded to; instead having these values as strings or including a README file with mappings would be better. Similarly, the values for exchanging the information were not easy to follow. Given that this is a boolean variable, coding the response to 0 or 1 rather than 1 or 2 would be more intuitive. Overall, however, it was not difficult as all of the information was provided and the column names mapped on to the measure in a straightforward manner.