Take-Home Midterm Exam: Introductory Psychological Statistics
Esly Larios
02 April, 2025
Replace “Your Name” with your actual name.
Instructions
Please complete this exam on your own. Include your R code, interpretations, and answers within this document.
Part 1: Types of Data and Measurement Errors
Question 1: Data Types in Psychological Research
Read Chapter 2 (Types of Data Psychologists Collect) and answer the following:
- Describe the key differences between nominal, ordinal, interval, and ratio data. Provide one example of each from psychological research.
Nominal data divides variables into labeled categories. These groupings do not necessarily have any order. For example, in a study examining different personality traits, for instance, introversion, extroversion, neuroticism, these are nominal data due to the fact they are distinct categories without any ranking, nominal categories cannot be ranked in a logical order. Ordinal data, on the other hand, classifies variables into categories that can be ranked. For example, a survey with responses on a scale from “strongly disagree” to “strongly agree”; These categories are ordered but the differences between ranks are probably not equal. Interval data is measured along a numerical scale, has consistent intervals between values but has no true zero point. For example, a research measuring intelligence using IQ scores. The differences between a score of 100 and 110 is the same between 110 and 120, but a score of 0 does not mean one has no intelligence.Ratio data, like interval, is measured along a numerical scale that has equal distances between values but, ratio data does have a true zero point, meaning it is not possible to have negative values in ratio data.
- For each of the following variables, identify the appropriate level
of measurement (nominal, ordinal, interval, or ratio) and explain your
reasoning:
- Scores on a depression inventory (0-63)
- Response time in milliseconds
- Likert scale ratings of agreement (1-7)
- Diagnostic categories (e.g., ADHD, anxiety disorder, no diagnosis)
- Age in years
Scores on a depression inventory, the appropriate level of measurement is interval. Scores are numerical and have consistent intervals, but there is no true zero, which indicates the absence of depression. Response time in milliseconds, the appropriate level of measurement is ratio. Response times are measured on a scale with a true zero point, no response time. Likert scale ratings of agreement, the ratings have a natural order but the intervals between ratings may not be equal, the appropriate level of measurement is ordinal. Diagnostic categories, for example anxiety disorder, ADHD, these categories are labels without any order or quantitative value. Age in years, the appropriate level of measurement is on a scale with a true zero point.
Question 2: Measurement Error
Referring to Chapter 3 (Measurement Errors in Psychological Research):
- Explain the difference between random and systematic error, providing an example of each in the context of a memory experiment.
Systematic errors are consistent biases, leading to inaccurate results. For example, if instructions are misunderstood or not clear, all participants might complete the task incorrectly, leading to systematically inaccurate results. Random errors are unpredictable variations, affecting the reliability of measurements. An example of this could be individual differences in memory capacity among participants. Some may have better memory retention due to age, health, etc.
- How might measurement error affect the validity of a study examining the relationship between stress and academic performance? What steps could researchers take to minimize these errors?
Measurement error can affect the validity of a study by introducing inaccuracies that provide misleading the true relationship between variables. Researchers can potentially conduct multiple methods of data collecting such as self-report measures, performance indicators can further provide a better understanding of the assessments in regards to stress and academic performance.
Part 2: Descriptive Statistics and Basic Probability
Question 3: Descriptive Analysis
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*:
- Calculate the mean, median, standard deviation, minimum, and maximum for reaction time and accuracy, grouped by condition (hint: use the psych package).
# Your code here
reaction_times <-c(271, 288, 377, 303, 306, 385)
# Calculate mean
mean(reaction_times)## [1] 321.6667## [1] 304.5## [1] 2273.467## [1] 47.68088- Using dplyr and piping, create a new variable
anxiety_changethat represents the difference between pre and post anxiety scores (pre minus post). Then calculate the mean anxiety change for each condition.
## 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 19.93397## 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
## 7 7 323.0458 69.51247 Female Control 29.50392
## 8 8 236.7469 90.84614 Male Control 22.02049
## 9 9 NA 86.23854 Female Experimental 32.81579
## 10 10 277.7169 87.15942 Female Control 22.00335
## 11 11 NA 88.79639 Female Experimental 33.42169
## 12 12 317.9907 79.97677 Male Experimental 16.60658
## 13 13 320.0386 81.66793 Male Experimental 14.91876
## 14 14 305.5341 74.81425 Female Control 50.92832
## 15 15 272.2079 74.28209 Female Experimental 21.66514
## 16 16 NA 88.03529 Female Control 27.38582
## 17 17 324.8925 89.48210 Female Experimental 30.09256
## 18 18 201.6691 85.53004 Male Control 21.12975
## 19 19 335.0678 94.22267 Female Control 29.13490
## 20 20 276.3604 105.50085 Male Control 27.95172
## 21 21 246.6088 80.08969 Female Control 23.27696
## 22 22 289.1013 61.90831 Male Control 25.52234
## 23 23 248.6998 95.05739 Male Control 24.72746
## 24 24 263.5554 77.90799 Male Experimental 42.02762
## 25 25 268.7480 78.11991 Female Control 19.06931
## 26 26 215.6653 95.25571 Female Experimental 16.23203
## 27 27 341.8894 82.15227 Male Control 25.30231
## 28 28 307.6687 72.79282 Male Control 27.48385
## 29 29 243.0932 86.81303 Female Control 28.49219
## 30 30 362.6907 NA Male Control 21.33308
## 31 31 321.3232 85.05764 Male Experimental 16.49339
## 32 32 285.2464 88.85280 Female Experimental 35.10548
## 33 33 344.7563 81.29340 Female Control 22.20280
## 34 34 343.9067 91.44377 Male Control 18.07590
## 35 35 341.0791 82.79513 Female Control 23.10976
## 36 36 334.4320 88.31782 Female Experimental 23.42259
## 37 37 327.6959 95.96839 Female Experimental 33.87936
## 38 38 296.9044 89.35181 Female Experimental 25.67790
## 39 39 284.7019 81.74068 Female Control 31.03243
## 40 40 280.9764 96.48808 Male Experimental 21.00566
## 41 41 265.2647 94.93504 Male Control 26.71556
## 42 42 289.6041 90.48397 Female Control 22.40251
## 43 43 236.7302 NA Male Control 25.75667
## 44 44 408.4478 78.72094 Female Control 17.83709
## 45 45 360.3981 98.60652 Male Control 14.51359
## 46 46 243.8446 78.99740 Male Experimental 40.97771
## 47 47 279.8558 106.87333 Male Experimental 29.80567
## 48 48 276.6672 100.32611 Female Experimental 14.98983
## 49 49 338.9983 82.64300 Female Control 20.11067
## 50 50 295.8315 74.73579 Female Control 15.51616
## anxiety_post anxiety_change
## 1 29.053117 2.24879426
## 2 19.215099 11.93723893
## 3 20.453056 7.20456483
## 4 13.751994 3.18099329
## 5 17.847362 6.19701754
## 6 19.933968 2.82286978
## 7 24.342317 5.16159899
## 8 17.758982 4.26150823
## 9 19.863065 12.95272240
## 10 22.069157 -0.06580401
## 11 25.063956 8.35773571
## 12 7.875522 8.73106229
## 13 3.221330 11.69742764
## 14 45.327922 5.60039736
## 15 16.642661 5.02247855
## 16 21.290659 6.09516212
## 17 23.416047 6.67651035
## 18 21.642810 -0.51305479
## 19 26.912456 2.22244027
## 20 24.773302 3.17841445
## 21 18.586930 4.69002601
## 22 20.597288 4.92505594
## 23 20.358843 4.36861886
## 24 31.904850 10.12276506
## 25 14.370025 4.69928609
## 26 8.052780 8.17924981
## 27 21.952702 3.34960540
## 28 24.334744 3.14910235
## 29 24.635854 3.85633353
## 30 18.283727 3.04934997
## 31 2.627509 13.86588190
## 32 27.376440 7.72904122
## 33 18.430744 3.77205314
## 34 15.607200 2.46869675
## 35 19.873474 3.23628902
## 36 19.373641 4.04895160
## 37 26.428138 7.45122383
## 38 16.420951 9.25694721
## 39 28.470531 2.56189924
## 40 15.350273 5.65539054
## 41 21.378795 5.33676775
## 42 17.294151 5.10836205
## 43 20.466142 5.29052622
## 44 15.992029 1.84506400
## 45 7.508622 7.00496546
## 46 27.270622 13.70708547
## 47 22.108595 7.69707534
## 48 11.069351 3.92047789
## 49 17.068705 3.04196717
## 50 10.016330 5.49982914## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 5.64 3.3 5.07 5.3 2.86 -0.51 13.87 14.38 0.79 0.19 0.47Write your answer(s) here It looks like the mean between pre anxiety scores and post anxiety scores is 5.64 when you subtract anxiety_pre and anxiety_post.
Question 4: Probability Calculations
Using the concepts from Chapter 4 (Descriptive Statistics and Basic Probability in Psychological Research):
- If reaction times in a cognitive task are normally distributed with
a mean of 350ms and a standard deviation of 75ms:
- What is the probability that a randomly selected participant will have a reaction time greater than 450ms?
- What is the probability that a participant will have a reaction time between 300ms and 400ms?
## [1] 0.9087888## [1] 0.6562962Write your answer(s) here The probability that a randomly chosen participant will have a reaction time greater than 450ms is roughly 9.08% and the probability that a randomly chosen participant will have a reaction time between 300ms and 400ms is roughly 65.63%.
Part 3: Data Cleaning and Manipulation
Question 5: Data Cleaning with dplyr
Using the dataset created in Part 2, perform the following data cleaning and manipulation tasks:
- Remove all rows with missing values and create a new dataset called
clean_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
## 7 7 323.0458 69.51247 Female Control 29.50392
## 8 8 236.7469 90.84614 Male Control 22.02049
## 10 10 277.7169 87.15942 Female Control 22.00335
## 12 12 317.9907 79.97677 Male Experimental 16.60658
## 13 13 320.0386 81.66793 Male Experimental 14.91876
## 14 14 305.5341 74.81425 Female Control 50.92832
## 15 15 272.2079 74.28209 Female Experimental 21.66514
## 17 17 324.8925 89.48210 Female Experimental 30.09256
## 18 18 201.6691 85.53004 Male Control 21.12975
## 19 19 335.0678 94.22267 Female Control 29.13490
## 20 20 276.3604 105.50085 Male Control 27.95172
## 21 21 246.6088 80.08969 Female Control 23.27696
## 22 22 289.1013 61.90831 Male Control 25.52234
## 23 23 248.6998 95.05739 Male Control 24.72746
## 24 24 263.5554 77.90799 Male Experimental 42.02762
## 25 25 268.7480 78.11991 Female Control 19.06931
## 26 26 215.6653 95.25571 Female Experimental 16.23203
## 27 27 341.8894 82.15227 Male Control 25.30231
## 28 28 307.6687 72.79282 Male Control 27.48385
## 29 29 243.0932 86.81303 Female Control 28.49219
## 31 31 321.3232 85.05764 Male Experimental 16.49339
## 32 32 285.2464 88.85280 Female Experimental 35.10548
## 33 33 344.7563 81.29340 Female Control 22.20280
## 34 34 343.9067 91.44377 Male Control 18.07590
## 35 35 341.0791 82.79513 Female Control 23.10976
## 36 36 334.4320 88.31782 Female Experimental 23.42259
## 37 37 327.6959 95.96839 Female Experimental 33.87936
## 38 38 296.9044 89.35181 Female Experimental 25.67790
## 39 39 284.7019 81.74068 Female Control 31.03243
## 40 40 280.9764 96.48808 Male Experimental 21.00566
## 41 41 265.2647 94.93504 Male Control 26.71556
## 42 42 289.6041 90.48397 Female Control 22.40251
## 44 44 408.4478 78.72094 Female Control 17.83709
## 45 45 360.3981 98.60652 Male Control 14.51359
## 46 46 243.8446 78.99740 Male Experimental 40.97771
## 47 47 279.8558 106.87333 Male Experimental 29.80567
## 48 48 276.6672 100.32611 Female Experimental 14.98983
## 49 49 338.9983 82.64300 Female Control 20.11067
## 50 50 295.8315 74.73579 Female Control 15.51616
## anxiety_post anxiety_change
## 1 29.053117 2.24879426
## 2 19.215099 11.93723893
## 3 20.453056 7.20456483
## 4 13.751994 3.18099329
## 5 17.847362 6.19701754
## 6 19.933968 2.82286978
## 7 24.342317 5.16159899
## 8 17.758982 4.26150823
## 10 22.069157 -0.06580401
## 12 7.875522 8.73106229
## 13 3.221330 11.69742764
## 14 45.327922 5.60039736
## 15 16.642661 5.02247855
## 17 23.416047 6.67651035
## 18 21.642810 -0.51305479
## 19 26.912456 2.22244027
## 20 24.773302 3.17841445
## 21 18.586930 4.69002601
## 22 20.597288 4.92505594
## 23 20.358843 4.36861886
## 24 31.904850 10.12276506
## 25 14.370025 4.69928609
## 26 8.052780 8.17924981
## 27 21.952702 3.34960540
## 28 24.334744 3.14910235
## 29 24.635854 3.85633353
## 31 2.627509 13.86588190
## 32 27.376440 7.72904122
## 33 18.430744 3.77205314
## 34 15.607200 2.46869675
## 35 19.873474 3.23628902
## 36 19.373641 4.04895160
## 37 26.428138 7.45122383
## 38 16.420951 9.25694721
## 39 28.470531 2.56189924
## 40 15.350273 5.65539054
## 41 21.378795 5.33676775
## 42 17.294151 5.10836205
## 44 15.992029 1.84506400
## 45 7.508622 7.00496546
## 46 27.270622 13.70708547
## 47 22.108595 7.69707534
## 48 11.069351 3.92047789
## 49 17.068705 3.04196717
## 50 10.016330 5.49982914- Create a new variable
performance_categorythat categorizes participants based on their accuracy:- “High” if accuracy is greater than or equal to 90
- “Medium” if accuracy is between 70 and 90
- “Low” if accuracy is less than 70
# Your code here
clean_data <- data %>%
mutate(performance_category = case_when(accuracy >= 90 ~ "High", accuracy >= 70 & accuracy < 90 ~ "Medium", accuracy < 70 ~ "Low"))
print(clean_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
## 7 7 323.0458 69.51247 Female Control 29.50392
## 8 8 236.7469 90.84614 Male Control 22.02049
## 9 9 NA 86.23854 Female Experimental 32.81579
## 10 10 277.7169 87.15942 Female Control 22.00335
## 11 11 NA 88.79639 Female Experimental 33.42169
## 12 12 317.9907 79.97677 Male Experimental 16.60658
## 13 13 320.0386 81.66793 Male Experimental 14.91876
## 14 14 305.5341 74.81425 Female Control 50.92832
## 15 15 272.2079 74.28209 Female Experimental 21.66514
## 16 16 NA 88.03529 Female Control 27.38582
## 17 17 324.8925 89.48210 Female Experimental 30.09256
## 18 18 201.6691 85.53004 Male Control 21.12975
## 19 19 335.0678 94.22267 Female Control 29.13490
## 20 20 276.3604 105.50085 Male Control 27.95172
## 21 21 246.6088 80.08969 Female Control 23.27696
## 22 22 289.1013 61.90831 Male Control 25.52234
## 23 23 248.6998 95.05739 Male Control 24.72746
## 24 24 263.5554 77.90799 Male Experimental 42.02762
## 25 25 268.7480 78.11991 Female Control 19.06931
## 26 26 215.6653 95.25571 Female Experimental 16.23203
## 27 27 341.8894 82.15227 Male Control 25.30231
## 28 28 307.6687 72.79282 Male Control 27.48385
## 29 29 243.0932 86.81303 Female Control 28.49219
## 30 30 362.6907 NA Male Control 21.33308
## 31 31 321.3232 85.05764 Male Experimental 16.49339
## 32 32 285.2464 88.85280 Female Experimental 35.10548
## 33 33 344.7563 81.29340 Female Control 22.20280
## 34 34 343.9067 91.44377 Male Control 18.07590
## 35 35 341.0791 82.79513 Female Control 23.10976
## 36 36 334.4320 88.31782 Female Experimental 23.42259
## 37 37 327.6959 95.96839 Female Experimental 33.87936
## 38 38 296.9044 89.35181 Female Experimental 25.67790
## 39 39 284.7019 81.74068 Female Control 31.03243
## 40 40 280.9764 96.48808 Male Experimental 21.00566
## 41 41 265.2647 94.93504 Male Control 26.71556
## 42 42 289.6041 90.48397 Female Control 22.40251
## 43 43 236.7302 NA Male Control 25.75667
## 44 44 408.4478 78.72094 Female Control 17.83709
## 45 45 360.3981 98.60652 Male Control 14.51359
## 46 46 243.8446 78.99740 Male Experimental 40.97771
## 47 47 279.8558 106.87333 Male Experimental 29.80567
## 48 48 276.6672 100.32611 Female Experimental 14.98983
## 49 49 338.9983 82.64300 Female Control 20.11067
## 50 50 295.8315 74.73579 Female Control 15.51616
## anxiety_post anxiety_change performance_category
## 1 29.053117 2.24879426 Medium
## 2 19.215099 11.93723893 Medium
## 3 20.453056 7.20456483 Medium
## 4 13.751994 3.18099329 High
## 5 17.847362 6.19701754 Medium
## 6 19.933968 2.82286978 High
## 7 24.342317 5.16159899 Low
## 8 17.758982 4.26150823 High
## 9 19.863065 12.95272240 Medium
## 10 22.069157 -0.06580401 Medium
## 11 25.063956 8.35773571 Medium
## 12 7.875522 8.73106229 Medium
## 13 3.221330 11.69742764 Medium
## 14 45.327922 5.60039736 Medium
## 15 16.642661 5.02247855 Medium
## 16 21.290659 6.09516212 Medium
## 17 23.416047 6.67651035 Medium
## 18 21.642810 -0.51305479 Medium
## 19 26.912456 2.22244027 High
## 20 24.773302 3.17841445 High
## 21 18.586930 4.69002601 Medium
## 22 20.597288 4.92505594 Low
## 23 20.358843 4.36861886 High
## 24 31.904850 10.12276506 Medium
## 25 14.370025 4.69928609 Medium
## 26 8.052780 8.17924981 High
## 27 21.952702 3.34960540 Medium
## 28 24.334744 3.14910235 Medium
## 29 24.635854 3.85633353 Medium
## 30 18.283727 3.04934997 <NA>
## 31 2.627509 13.86588190 Medium
## 32 27.376440 7.72904122 Medium
## 33 18.430744 3.77205314 Medium
## 34 15.607200 2.46869675 High
## 35 19.873474 3.23628902 Medium
## 36 19.373641 4.04895160 Medium
## 37 26.428138 7.45122383 High
## 38 16.420951 9.25694721 Medium
## 39 28.470531 2.56189924 Medium
## 40 15.350273 5.65539054 High
## 41 21.378795 5.33676775 High
## 42 17.294151 5.10836205 High
## 43 20.466142 5.29052622 <NA>
## 44 15.992029 1.84506400 Medium
## 45 7.508622 7.00496546 High
## 46 27.270622 13.70708547 Medium
## 47 22.108595 7.69707534 High
## 48 11.069351 3.92047789 High
## 49 17.068705 3.04196717 Medium
## 50 10.016330 5.49982914 Medium- Filter the dataset to include only participants in the Experimental condition with reaction times faster than the overall mean reaction time.
# Your code here
filtered_data <-data %>%
filter(reaction_time > 311.75 & condition == "Experimental")
print(filtered_data)## participant_id reaction_time accuracy gender condition anxiety_pre
## 1 3 377.9354 84.57130 Female Experimental 27.65762
## 2 12 317.9907 79.97677 Male Experimental 16.60658
## 3 13 320.0386 81.66793 Male Experimental 14.91876
## 4 17 324.8925 89.48210 Female Experimental 30.09256
## 5 31 321.3232 85.05764 Male Experimental 16.49339
## 6 36 334.4320 88.31782 Female Experimental 23.42259
## 7 37 327.6959 95.96839 Female Experimental 33.87936
## anxiety_post anxiety_change
## 1 20.453056 7.204565
## 2 7.875522 8.731062
## 3 3.221330 11.697428
## 4 23.416047 6.676510
## 5 2.627509 13.865882
## 6 19.373641 4.048952
## 7 26.428138 7.451224Write your answer(s) here describing your data cleaning process.
Part 4: Visualization and Correlation Analysis
Question 6: Correlation Analysis with the psych Package
Using the psych package, create a correlation plot for the simulated dataset created in Part 2. Include the following steps:
- Select the numeric variables from the dataset (reaction_time, accuracy, anxiety_pre, anxiety_post, and anxiety_change if you created it).
- Use the psych package’s
corPlot()function to create a correlation plot. - Interpret the resulting plot by addressing:
- Which variables appear to be strongly correlated?
- Are there any surprising relationships?
- How might these correlations inform further research in psychology?
# 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).
numeric_data <- data %>%
select(reaction_time, accuracy, anxiety_pre, anxiety_post, anxiety_change) %>%
corPlot(upper = FALSE)
Write your answer(s) here The variables anxiety_pre and anxiety_post seem to be correlated, with a correlation of .901. This might be the only strong correlation. These might benefit further research in psychology by prompting researchers to question why these variables might be related. Correlations can help identify potential risk factors as well.
Part 5: Reflection and Application
Question 7: Reflection
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?
Write your answer(s) here 1. Is there any significant difference in the accuracy of eyewitness testimony between children and adults when recalling details of an event? In order to further the research I would recruit a group of children ages 8-13 and adults ages 18-25. I would have the participants watch a short video or an interaction between two people, then after a delayed period (24 hrs, 2 days), I would then survey participants and ask them to recall events. Challenges one might face during this research is that children are more susceptible to suggestion than adults so it would be important to not suggest or lead questions when interviewing participants.
- I am honestly still learning and continue to learn R, but I have gained an understanding of data manipulation and statistical testing. R offers a wide range of functions and packages and I think that is a big advantage because we can customize it to our specific research needs. This may also be seen as a disadvantage because R relies heavily on packages for specific analyses. Sometimes managing these installations, packages, can be complex.
Submission Instructions:
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.