Background

The Stroop effect is one of the most robust findings in psychological science. In a standard visual Stroop task, participants are presented a sequence of stimuli in the form of colourful words and are instructed to respond by naming the colour in which the given stimulus is presented. One stimulus-response pair is called a trial.

Study design

The present study investigated the visual Stroop effect. Participants were allocated into one of two conditions, either “congruent” or “incongruent”. Each participants was then presented with two blocks of trials. The first block – the “non-colour-word” block – the stimuli presented were not colour words. The task was to name the colour in which the stimuli are displayed as quickly and accurately as possible. The task looks something like this:

The second block – the “colour-word” block – differed based on the condition (group) to which the given participant was allocated. In the “congruent” condition, the stimuli were colour-words and the colour in which they were displayed agreed with the meaning of the word. Again, the task was to name the colour as quickly and accurately as possible. This is what the condition looks like:

In the “incongruent” condition, the colour in which the words were displayed was not the same as the meaning of the stimuli. The task was the same. This is what it looks like:

Reaction time on each trial was recorded in milliseconds as was whether or not participant’s response was correct. If no response was recorded within 3 seconds, an incorrect response was recorded and the next trial was presented.

You were called upon to analyse the data and decide whether or not there is a difference between reaction times on congruent vs incongruent trials. You should only consider correct responses on the colour-word trials, excluding any improbably fast responses (less than 200ms).

Data

The dataset is available at https://and.netlify.app/datasets/stroop.csv

Codebook

Tasks

Drawing on the information above, complete the following tasks:

Setting up

Task 1

Create a new R Markdown document in your week_06 project folder. Use a combination of code chunks with R code and body text to complete the remaining tasks.

library(dplyr)
library(magrittr)
library(readr)
library(ggplot2)
# all of the above can be replaced with just
# library(tidyverse)

library(kableExtra)

Hypotheses

Task 2

Formulate the statistical null and alternative hypotheses.

\[H_0:\ ...\]

\[H_1:\ ...\]

Data inspection

Task 3

Read in the data.

stroop <- readr::read_csv("https://and.netlify.app/datasets/stroop.csv")

Task 4

Familiarise yourself with the data set.

Task 4.1

How many variables are there?

ncol(stroop)

[1] 6

Task 4.2

Data from how many participants are included in the dataset?

stroop %>%
  dplyr::group_by(id) %>%
  dplyr::tally() %>%
  nrow()

[1] 116

table(stroop$id) %>% length()

[1] 116

unique(stroop$id) %>% length()

[1] 116

Task 4.3

How many trials did each participant complete per block?

stroop %>%
  dplyr::group_by(id, block) %>%
  dplyr::tally()

# A tibble: 232 × 3
# Groups:   id [116]
      id block     n
   <dbl> <dbl> <int>
 1     1     0    40
 2     1     1    40
 3     2     0    40
 4     2     1    40
 5     3     0    40
 6     3     1    40
 7     4     0    40
 8     4     1    40
 9     5     0    40
10     5     1    40
# … with 222 more rows

## two more lines and you don't have to browse through the output
stroop %>%
  dplyr::group_by(id, block) %>%
  dplyr::tally() %>%
  dplyr::ungroup() %>%
  dplyr::summarise(unique_n = unique(n))

# A tibble: 1 × 1
  unique_n
     <int>
1       40

Task 4.4

What proportion of trials were responded to incorrectly (including non-responses)?

n_total <- nrow(stroop)
(n_total - sum(stroop$correct)) / n_total

[1] 0.0455819

Task 4.5

Assuming a trial automatically ends after 3 seconds, how many trials recorded no response?

stroop %>%
  dplyr::filter(rt == 3000) %>%
  dplyr::tally()

# A tibble: 1 × 1
      n
  <int>
1   219

Data cleaning and wrangling

Task 5

Convert the block and group variables into factors and give them labels according to the codebook above.

stroop <- stroop %>%
  dplyr::mutate(block = factor(block, labels = c("Non-colour", "Colour")),
                group = factor(group, labels = c("Congruent", "Incongruent")))

Task 6

Remove data you don’t need but make a note of how many observations you’re removing and for what reasons.

• Only include data from colour-word trials.
• Remove data from
  • trials with incorrect responses
  • non-response trials
  • trials responded to too quickly (< 200ms).

n_non_response <- stroop %>%
  dplyr::filter(rt == 3000) %>%
  nrow()

n_incorrect <- stroop %>%
  dplyr::filter(correct == 0 & rt < 3000) %>%
  nrow()

n_too_fast <- stroop %>%
  dplyr::filter(rt < 200) %>%
  nrow()

# remove unwanted data
stroop_clean <- stroop %>%
  dplyr::filter(
    correct == 1 & # only correct responses (rt = 3000 is automatically incorrect)
      rt >= 200 & # <200 is too fast
      block == "Colour") # only colour-word block

Task 7

Create a tibble with mean reaction time per participant.

stroop_clean <- stroop_clean %>%
  dplyr::group_by(id, block, group) %>%
  dplyr::summarise(m_rt = mean(rt))

Descriptives

Task 8

Visualise the distribution of mean reaction times per participants for each of the two groups.

stroop_clean %>%
  dplyr::rename(Condition = group) %>%
  ggplot2::ggplot(aes(x = Condition, y = m_rt, colour = Condition, fill = Condition)) +
  geom_violin(
    trim = FALSE,
    scale = "count",
    alpha = .3,
    color = NA
  ) +
  geom_boxplot(
    width = .1,
    fill="white", alpha = .8
  ) +
  geom_point(
    position = position_jitter(.05),
    alpha=.4) +
  labs(y = "Mean reaction time per participant (ms)") +
  scale_fill_manual(values = c("#52006f", "#009fa7")) +
  scale_colour_manual(values = c("#52006f", "#009fa7")) +
  theme_bw() +
  theme(legend.position = c(.85, .85),
        legend.background = element_blank())

Figure 1: Distribution of participants’ average reaction time (in ms) on valid colour-word trials by condition

Task 9

Create a well-formatted table of the following descriptive statistics per group:

• mean reaction time
• standard deciation of reaction time
• minimum and maximum of RT
• number of participants

desc_tib <- stroop_clean %>%
    dplyr::group_by(group) %>%
    dplyr::summarise(
      mean_rt = mean(m_rt),
      sd_rt = sd(m_rt),
      min = min(m_rt),
      max = max(m_rt),
      n = dplyr::n(),
      # we're going to need SE for plot of results
      se = sd_rt / sqrt(n)
    )
desc_tib %>%
  dplyr::select(-se) %>% # we don't need std. errors in our descriptive table
  kableExtra::kbl(
    digits = 1, # measurement is in grammes so we can just round to whole numbers
    col.names = c("Condition", "*M*", "*SD*", "Minimum", "Maximum", "*N*"),
    caption = "*Descriptive statistics of participants' average reaction time (in ms) on valid colour-word trials by condition.*") %>% 
  kableExtra::kable_classic(full_width = FALSE)

The table should have informative column names and a caption.

Analysis

Task 10

Create an appropriate statistical model to test your hypothesis.

m1 <- t.test(m_rt ~ group, stroop_clean)
m1

    Welch Two Sample t-test

data:  m_rt by group
t = -6.5694, df = 78.329, p-value = 5.059e-09
alternative hypothesis: true difference in means between group Congruent and group Incongruent is not equal to 0
95 percent confidence interval:
 -453.7564 -242.7083
sample estimates:
  mean in group Congruent mean in group Incongruent 
                 600.2761                  948.5085 

Task 11

Report on any excluded data. How many observations did you exclude and for what reason?

Task 12

Report the results in accordance with the APA guidelines.

Task 13

Visualise the results in a publication-quality errorbar plot.

• Plot should have a title and clearly labelled axes.
• It should be clear what the plot shows and what the errorbars represent.

desc_tib %>%
  dplyr::rename(Condition = group) %>%
  ggplot2::ggplot(aes(x = Condition, y = mean_rt)) +
  geom_errorbar(
    aes(
      ymin = mean_rt - 2 * se,
      ymax = mean_rt + 2 * se
    ),
    width = .05) +
  geom_point(
    shape = 23,
    size = 3,
    fill = "steelblue"
  ) +
  scale_y_continuous(
    name = "Mean reaction time (ms)",
    limits = c(500, 1250) # custom limits to Y-axis
  ) +
  theme_bw()

Figure 2: Plot of mean reaction times on valid trials (±2×SE) by condition

Task 14

What do you conclude about your hypothesis based on your analysis?

Output

Task 15

Knit your .Rmd file into a formatted HTML document. Make sure it looks good and that there are no markdown rendering issues.

Well done!

If you successfully completed all the tasks, you’re ready to do the TAP.