Please complete this exam on your own. Include your R code, interpretations, and answers within this document.
Read Chapter 2 (Types of Data Psychologists Collect) and answer the following:
Referring to Chapter 3 (Measurement Errors in Psychological Research):
The code below creates a simulated dataset for a psychological experiment. Run the below code chunk without making any changes:
# Create a simulated dataset
set.seed(123) # For reproducibility
# Number of participants
n <- 50
# Create the data frame
data <- data.frame(
participant_id = 1:n,
reaction_time = rnorm(n, mean = 300, sd = 50),
accuracy = rnorm(n, mean = 85, sd = 10),
gender = sample(c("Male", "Female"), n, replace = TRUE),
condition = sample(c("Control", "Experimental"), n, replace = TRUE),
anxiety_pre = rnorm(n, mean = 25, sd = 8),
anxiety_post = NA # We'll fill this in based on condition
)
# Make the experimental condition reduce anxiety more than control
data$anxiety_post <- ifelse(
data$condition == "Experimental",
data$anxiety_pre - rnorm(n, mean = 8, sd = 3), # Larger reduction
data$anxiety_pre - rnorm(n, mean = 3, sd = 2) # Smaller reduction
)
# Ensure anxiety doesn't go below 0
data$anxiety_post <- pmax(data$anxiety_post, 0)
# Add some missing values for realism
data$reaction_time[sample(1:n, 3)] <- NA
data$accuracy[sample(1:n, 2)] <- NA
# View the first few rows of the dataset
head(data)## participant_id reaction_time accuracy gender condition anxiety_pre
## 1 1 271.9762 87.53319 Female Control 31.30191
## 2 2 288.4911 84.71453 Female Experimental 31.15234
## 3 3 377.9354 84.57130 Female Experimental 27.65762
## 4 4 303.5254 98.68602 Male Control 16.93299
## 5 5 306.4644 82.74229 Female Control 24.04438
## 6 6 385.7532 100.16471 Female Control 22.75684
## anxiety_post
## 1 29.05312
## 2 19.21510
## 3 20.45306
## 4 13.75199
## 5 17.84736
## 6 19.93397Now, perform the following computations*:
# Your code here
#Using dplyr and piping, create a new variable `anxiety_change` that represents the difference between pre and post anxiety scores (pre minus post). Then calculate the mean anxiety change for each condition.
'''{r_anxiety-change, error = TRUE, message = FALSE, warning = FALSE}
anxiety_change_stats <- data %>%
mutate(anxiety_change = anxiety_pre - anxiety_post) %>% # Create the new variable anxiety_change
group_by(condition) %>% # Group the data by condition
summarise(mean_anxiety_change = mean(anxiety_change, na.rm = TRUE)) # Calculate the mean change (ignoring NA values)
print(anxiety_change_stats)## Error in parse(text = input): <text>:4:3: unexpected INCOMPLETE_STRING
## 8: summarise(mean_anxiety_change = mean(anxiety_change, na.rm = TRUE)) # Calculate the mean change (ignoring NA values)
## 9: print(anxiety_change_stats)
## ^Write your answer(s) here
Using the concepts from Chapter 4 (Descriptive Statistics and Basic Probability in Psychological Research):
## Error in parse(text = input): <text>:2:15: unexpected symbol
## 1:
## 2: pnorm(mean=350ms
## ^Using the dataset created in Part 2, perform the following data cleaning and manipulation tasks:
clean_data.## Error in remove_outliers(missing_values): could not find function "remove_outliers"performance_category that
categorizes participants based on their accuracy:
## Error in performance_catergory(high_accuarcy): could not find function "performance_catergory"Write your answer(s) here describing your data cleaning process.
Using the psych package, create a correlation plot for the simulated dataset created in Part 2. Include the following steps:
corPlot()
function to create a correlation plot.# Your code here. Hint: first, with dplyr create a new dataset that selects only the numeric variable (reaction_time, accuracy, anxiety_pre, anxiety_post, and anxiety_change if you created it).Write your answer(s) here
Reflect on how the statistical concepts and R techniques covered in this course apply to psychological research:
Describe a specific research question in psychology that interests you. What type of data would you collect, what statistical analyses would be appropriate, and what potential measurement errors might you need to address?
How has learning R for data analysis changed your understanding of psychological statistics? What do you see as the biggest advantages and challenges of using R compared to other statistical software?
-1. I would like to research on how social media affects self esteem in young adults. I would also use despcriptive statistics and take the means, mins and maxs of test such as self esteem scores, time spent on social media etc.
-2.I think it helps see it from a very numerical perspective. I think the biggest Challenge is not being able to see the plots or data right away. However I do think it is easier to manipulate the data and you get to control more on how its presented.
Ensure to knit your document to HTML format, checking that all content is correctly displayed before submission. Publish your assignment to RPubs and submit the URL to canvas.