For this exercise, please try to reproduce the results from Experiment 2 of the associated paper (de la Fuente, Santiago, Roman, Dumitrache, & Casasanto, 2014). The PDF of the paper is included in the same folder as this Rmd file.

Methods summary:

Researchers tested the question of whether temporal focus differs between Moroccan and Spanish cultures, hypothesizing that Moroccans are more past-focused, whereas Spaniards are more future-focused. Two groups of participants (\(N = 40\) Moroccan and \(N=40\) Spanish) completed a temporal-focus questionnaire that contained questions about past-focused (“PAST”) and future-focused (“FUTURE”) topics. In response to each question, participants provided a rating on a 5-point Likert scale on which lower scores indicated less agreement and higher scores indicated greater agreement. The authors then performed a mixed-design ANOVA with agreement score as the dependent variable, group (Moroccan or Spanish, between-subjects) as the fixed-effects factor, and temporal focus (past or future, within-subjects) as the random effects factor. In addition, the authors performed unpaired two-sample t-tests to determine whether there was a significant difference between the two groups in agreement scores for PAST questions, and whether there was a significant difference in scores for FUTURE questions.

Target outcomes:

Below is the specific result you will attempt to reproduce (quoted directly from the results section of Experiment 2):

According to a mixed analysis of variance (ANOVA) with group (Spanish vs. Moroccan) as a between-subjects factor and temporal focus (past vs. future) as a within-subjectS factor, temporal focus differed significantly between Spaniards and Moroccans, as indicated by a significant interaction of temporal focus and group, F(1, 78) = 19.12, p = .001, ηp2 = .20 (Fig. 2). Moroccans showed greater agreement with past-focused statements than Spaniards did, t(78) = 4.04, p = .001, and Spaniards showed greater agreement with future-focused statements than Moroccans did, t(78) = −3.32, p = .001. (de la Fuente et al., 2014, p. 1685).

Step 1: Load packages

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
library(rstatix)

# #optional packages/functions:
# library(afex) # anova functions
# library(ez) # anova functions 2
# library(scales) # for plotting
# std.err <- function(x) sd(x)/sqrt(length(x)) # standard error

Step 2: Load data

# Just Experiment 2
data_path <- 'data/DeLaFuenteEtAl_2014_RawData.xls'
d <- read_excel(data_path, sheet=3)

Step 3: Tidy data

d_tidy <- d %>%
  select(-item) %>%
  rename(agreement = "Agreement (0=complete disagreement; 5=complete agreement)")

Step 4: Run analysis

Pre-processing

d_tidy <- d_tidy %>% 
  mutate(group = factor(group,
                        levels = c("young Spaniard", "Moroccan"))) %>% 
  mutate(subscale = factor(subscale,
                        levels = c("PAST", "FUTURE")))

#this is for the anova and t tests
d_averages <- d_tidy %>% 
  group_by(group,
           participant,
           subscale) %>% 
  summarize(avg_agree = mean(agreement)) %>% 
  ungroup()

Descriptive statistics

Try to recreate Figure 2 (fig2.png, also included in the same folder as this Rmd file):

d_tidy %>% 
  group_by(group,
           subscale) %>% 
  ggplot(mapping = aes(x = group,
                       y = agreement,
                       fill = subscale)) +
  stat_summary(fun = "mean",
               geom = "bar",
               position = position_dodge(width = 0.9)) +
  stat_summary(fun.data = mean_se,
               geom = "linerange",
               position = position_dodge(width = 0.9)) +
  coord_cartesian(ylim = c(2.00, 4.00)) +
  xlab("") + 
  ylab("Rating") +
  scale_x_discrete(labels = c("Spaniards", "Moroccans")) +
  scale_fill_brewer(palette = "Set1",
                    name = "",
                    labels = c("Past-Focused Statements", "Future-Focused Statements"))

Inferential statistics

According to a mixed analysis of variance (ANOVA) with group (Spanish vs. Moroccan) as a between-subjects factor and temporal focus (past vs. future) as a within-subjects factor, temporal focus differed significantly between Spaniards and Moroccans, as indicated by a significant interaction of temporal focus and group, F(1, 78) = 19.12, p = .001, ηp2 = .20 (Fig. 2).

# reproduce the above results here

rating.aov <- d_averages %>% 
  anova_test(wid = participant,
           dv = avg_agree,
           between = group,
           within = subscale,
           type = 3,
           effect.size = 'pes')
get_anova_table(rating.aov)
## ANOVA Table (type III tests)
## 
##           Effect DFn DFd      F        p p<.05   pes
## 1          group   1  76  2.192 1.43e-01       0.028
## 2       subscale   1  76  7.979 6.00e-03     * 0.095
## 3 group:subscale   1  76 18.346 5.33e-05     * 0.194

Moroccans showed greater agreement with past-focused statements than Spaniards did, t(78) = 4.04, p = .001,

# reproduce the above results here
d_past <- d_averages %>% 
  filter(subscale == 'PAST')

t.test(d_past$avg_agree[d_past$group == 'Moroccan'],
       d_past$avg_agree[d_past$group == 'young Spaniard'])
## 
##  Welch Two Sample t-test
## 
## data:  d_past$avg_agree[d_past$group == "Moroccan"] and d_past$avg_agree[d_past$group == "young Spaniard"]
## t = 3.8562, df = 74.91, p-value = 0.0002416
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.2850812 0.8944060
## sample estimates:
## mean of x mean of y 
##  3.280886  2.691142

and Spaniards showed greater agreement with future-focused statements than Moroccans did, t(78) = −3.32, p = .001.(de la Fuente et al., 2014, p. 1685)

# reproduce the above results here
d_future<- d_averages %>% 
  filter(subscale == 'FUTURE')

t.test(d_past$avg_agree[d_future$group == 'young Spaniard'],
       d_past$avg_agree[d_future$group == 'Moroccan'])
## 
##  Welch Two Sample t-test
## 
## data:  d_past$avg_agree[d_future$group == "young Spaniard"] and d_past$avg_agree[d_future$group == "Moroccan"]
## t = -3.695, df = 73.517, p-value = 0.0004204
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.8785695 -0.2629377
## sample estimates:
## mean of x mean of y 
##  2.707955  3.278708

Step 5: Reflection

Were you able to reproduce the results you attempted to reproduce? If not, what part(s) were you unable to reproduce?

I was able to reproduce all the results, but the anova and t test statistics are not exactly the same as the original. However, I think it still counts a reproduction because the values are quite close, and have the same significance levels.

How difficult was it to reproduce your results?

It was not difficult to reproduce Figure 2, but it was pretty difficult to reproduce the anova and t test results.

What aspects made it difficult? What aspects made it easy?

I think since there are many ways you can conduct an anova in r, this made it difficult to recreate what the researchers did. I started by specifying the model and then using joint_tests() for the anova, but the degrees of freedom were very off. I then realized that I should make a dataframe of the average agreement scores for each subscale for each participant, in order to get the degrees of freedom to be accurately reflected in the statistics. It also took me a while to figure out how to make sure that subscale was being treated as a within-subjects factor and group was being treated as a between-subjects factor. What made the reproduction easy was that all the columns were clearly labeled, and it was clear what the values in the columns meant. There was also no missing data and the only tidying I had to do was renaming one column.