399 lines
14 KiB
R
399 lines
14 KiB
R
# Mixed ANOVA Analysis for Past vs Future Differences
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# EOHI Experiment Data Analysis
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# Load required libraries
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library(tidyverse)
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library(ez)
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library(car)
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library(nortest) # For normality tests
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# Read the data
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data <- read.csv("eohi1/exp1.csv")
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# Display basic information about the dataset
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cat("Dataset dimensions:", dim(data), "\n")
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cat("Number of participants:", length(unique(data$pID)), "\n")
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# Check experimental conditions
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cat("\nExperimental conditions:\n")
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table(data$GROUP, data$TASK_DO, data$TEMPORAL_DO)
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# Check what columns are actually available
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cat("\nChecking available columns with NPast and NFut prefixes:\n")
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npast_cols <- colnames(data)[grepl("^NPast", colnames(data))]
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nfut_cols <- colnames(data)[grepl("^NFut", colnames(data))]
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cat("NPast columns found:\n")
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print(npast_cols)
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cat("\nNFut columns found:\n")
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print(nfut_cols)
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cat("\nTotal NPast columns:", length(npast_cols), "\n")
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cat("Total NFut columns:", length(nfut_cols), "\n")
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# STEP 1: PROPER DATA RESHAPING
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# Define domains with their categories
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domain_info <- data.frame(
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domain = c("pref_read", "pref_music", "pref_tv", "pref_nap", "pref_travel",
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"pers_extravert", "pers_critical", "pers_dependable", "pers_anxious", "pers_complex",
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"val_obey", "val_trad", "val_opinion", "val_performance", "val_justice",
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"life_ideal", "life_excellent", "life_satisfied", "life_important", "life_change"),
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domain_type = c(rep("Preferences", 5),
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rep("Personality", 5),
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rep("Values", 5),
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rep("Life_Satisfaction", 5)),
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stringsAsFactors = FALSE
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)
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# Display domain_info
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cat("\nDomain Information:\n")
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print(domain_info)
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cat("\nDomain type summary:\n")
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print(table(domain_info$domain_type))
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# Function to reshape ALL domains at once with domain information
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reshape_all_domains <- function(data, domain_info) {
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all_long_data <- data.frame()
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for (i in 1:nrow(domain_info)) {
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domain_name <- domain_info$domain[i]
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domain_type <- domain_info$domain_type[i]
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past_col <- paste0("NPastDiff_", domain_name)
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fut_col <- paste0("NFutDiff_", domain_name)
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# Check if columns exist
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if (!(past_col %in% colnames(data)) || !(fut_col %in% colnames(data))) {
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cat("Warning: Columns", past_col, "or", fut_col, "not found\n")
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next
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}
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# Create long format data for this domain - using base R approach to avoid issues
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# Past data
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past_data <- data[, c("pID", "ResponseId", "GROUP", "TASK_DO", "TEMPORAL_DO", "ITEM_DO", "COC_DO",
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"demo_sex", "demo_age_1", "AOT_total", "CRT_correct", past_col)]
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past_data$TimePerspective <- "Past"
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past_data$Difference <- past_data[[past_col]]
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past_data$Domain_Type <- domain_type
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past_data$Domain_Item <- domain_name
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past_data[[past_col]] <- NULL # Remove the original column
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# Future data
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fut_data <- data[, c("pID", "ResponseId", "GROUP", "TASK_DO", "TEMPORAL_DO", "ITEM_DO", "COC_DO",
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"demo_sex", "demo_age_1", "AOT_total", "CRT_correct", fut_col)]
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fut_data$TimePerspective <- "Future"
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fut_data$Difference <- fut_data[[fut_col]]
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fut_data$Domain_Type <- domain_type
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fut_data$Domain_Item <- domain_name
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fut_data[[fut_col]] <- NULL # Remove the original column
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# Combine past and future data for this domain
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domain_long_data <- rbind(past_data, fut_data)
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all_long_data <- rbind(all_long_data, domain_long_data)
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}
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# Convert to factors with proper levels
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all_long_data$TimePerspective <- factor(all_long_data$TimePerspective, levels = c("Past", "Future"))
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all_long_data$Domain_Type <- factor(all_long_data$Domain_Type, levels = c("Preferences", "Personality", "Values", "Life_Satisfaction"))
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all_long_data$Domain_Item <- factor(all_long_data$Domain_Item, levels = c("pref_read", "pref_music", "pref_tv", "pref_nap", "pref_travel",
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"pers_extravert", "pers_critical", "pers_dependable", "pers_anxious", "pers_complex",
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"val_obey", "val_trad", "val_opinion", "val_performance", "val_justice",
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"life_ideal", "life_excellent", "life_satisfied", "life_important", "life_change"))
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all_long_data$pID <- as.factor(all_long_data$pID)
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return(all_long_data)
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}
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# Reshape all data to long format
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cat("\nReshaping data to long format...\n")
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long_data <- reshape_all_domains(data, domain_info)
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cat("Long format data dimensions:", dim(long_data), "\n")
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cat("Unique domains:", length(unique(long_data$Domain_Item)), "\n")
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cat("Domain types:", paste(unique(long_data$Domain_Type), collapse = ", "), "\n")
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cat("Domain type counts:\n")
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print(table(long_data$Domain_Type))
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# Display structure and sample of long_data
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cat("\nLong data structure:\n")
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str(long_data)
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cat("\nFirst 10 rows of long_data:\n")
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print(head(long_data, 10))
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cat("\nColumn names:\n")
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print(colnames(long_data))
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# Show factor levels for domain variables
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cat("\nDomain_Type factor levels:\n")
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print(levels(long_data$Domain_Type))
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cat("\nDomain_Item factor levels:\n")
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print(levels(long_data$Domain_Item))
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cat("\nTimePerspective factor levels:\n")
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print(levels(long_data$TimePerspective))
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# Show a sample with actual names instead of numbers
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cat("\nSample data with actual names (first 6 rows):\n")
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sample_data <- long_data[1:6, c("pID", "GROUP", "TASK_DO", "TEMPORAL_DO", "Domain_Type", "Domain_Item", "TimePerspective", "Difference")]
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print(sample_data)
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# Show a better example - one participant across multiple domains
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cat("\nExample: Participant 1 across multiple domains (first 10 rows):\n")
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participant_1_data <- long_data[long_data$pID == 1, c("pID", "GROUP", "TASK_DO", "TEMPORAL_DO", "Domain_Type", "Domain_Item", "TimePerspective", "Difference")]
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print(participant_1_data)
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# Show structure explanation
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cat("\nLong format explanation:\n")
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cat("- Each participant appears", length(unique(long_data$Domain_Item)) * 2, "times total\n")
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cat("- (", length(unique(long_data$Domain_Item)), "domains × 2 time perspectives)\n")
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cat("- Total rows per participant:", length(unique(long_data$Domain_Item)) * 2, "\n")
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cat("- Total participants:", length(unique(long_data$pID)), "\n")
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cat("- Expected total rows:", length(unique(long_data$pID)) * length(unique(long_data$Domain_Item)) * 2, "\n")
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cat("- Actual total rows:", nrow(long_data), "\n")
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# STEP 2: ASSUMPTION CHECKING
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cat("STEP 2: CHECKING ASSUMPTIONS\n")
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head(long_data)
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# 2.1 Check for missing values
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missing_summary <- long_data %>%
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group_by(Domain_Type, Domain_Item, TimePerspective) %>%
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summarise(
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n_total = n(),
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n_missing = sum(is.na(Difference)),
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pct_missing = round(100 * n_missing / n_total, 2),
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.groups = 'drop'
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)
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cat("\nMissing values by domain and time perspective:\n")
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print(missing_summary)
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# Remove missing values
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long_data_clean <- long_data[!is.na(long_data$Difference), ]
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cat("\nData after removing missing values:", dim(long_data_clean), "\n")
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# 2.2 Outlier detection
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cat("\nChecking for outliers...\n")
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outlier_summary <- long_data_clean %>%
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group_by(Domain_Type, Domain_Item, TimePerspective) %>%
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summarise(
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n = n(),
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mean = mean(Difference),
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sd = sd(Difference),
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q1 = quantile(Difference, 0.25),
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q3 = quantile(Difference, 0.75),
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iqr = q3 - q1,
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lower_bound = q1 - 1.5 * iqr,
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upper_bound = q3 + 1.5 * iqr,
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n_outliers = sum(Difference < lower_bound | Difference > upper_bound),
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.groups = 'drop'
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)
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cat("Outlier summary (IQR method):\n")
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print(outlier_summary)
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# 2.3 Normality tests
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cat("\nTesting normality...\n")
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normality_results <- long_data_clean %>%
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group_by(Domain_Type, Domain_Item, TimePerspective) %>%
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summarise(
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n = n(),
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shapiro_p = ifelse(n >= 3 & n <= 5000,
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shapiro.test(Difference)$p.value,
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NA),
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anderson_p = ifelse(n >= 7,
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ad.test(Difference)$p.value,
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NA),
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.groups = 'drop'
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) %>%
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mutate(
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shapiro_normal = shapiro_p > 0.05,
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anderson_normal = anderson_p > 0.05,
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overall_normal = case_when(
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!is.na(shapiro_p) & !is.na(anderson_p) ~ shapiro_normal & anderson_normal,
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!is.na(shapiro_p) ~ shapiro_normal,
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!is.na(anderson_p) ~ anderson_normal,
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TRUE ~ NA
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)
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)
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cat("Normality test results:\n")
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print(normality_results)
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# 2.4 Homogeneity of variance (Levene's test)
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cat("\nTesting homogeneity of variance...\n")
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homogeneity_results <- long_data_clean %>%
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group_by(Domain_Type, Domain_Item) %>%
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summarise(
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levene_p = leveneTest(Difference ~ TimePerspective)$`Pr(>F)`[1],
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homogeneous = levene_p > 0.05,
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.groups = 'drop'
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)
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cat("Homogeneity of variance results:\n")
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print(homogeneity_results)
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# STEP 3: DESCRIPTIVE STATISTICS
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cat("\n", paste(rep("=", 80), collapse = ""), "\n")
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cat("STEP 3: DESCRIPTIVE STATISTICS\n")
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cat(paste(rep("=", 80), collapse = ""), "\n")
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desc_stats <- long_data_clean %>%
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group_by(Domain_Type, Domain_Item, TimePerspective) %>%
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summarise(
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n = n(),
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mean = mean(Difference),
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sd = sd(Difference),
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median = median(Difference),
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q1 = quantile(Difference, 0.25),
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q3 = quantile(Difference, 0.75),
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min = min(Difference),
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max = max(Difference),
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.groups = 'drop'
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)
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cat("Descriptive statistics:\n")
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print(desc_stats)
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# STEP 4: MIXED ANOVA ANALYSES
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cat("\n", paste(rep("=", 80), collapse = ""), "\n")
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cat("STEP 4: MIXED ANOVA ANALYSES\n")
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cat(paste(rep("=", 80), collapse = ""), "\n")
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# 4.1 Overall analysis across all domains
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cat("\n4.1 Overall Mixed ANOVA (all domains combined):\n")
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cat(paste(rep("-", 50), collapse = ""), "\n")
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tryCatch({
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overall_anova <- ezANOVA(
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data = long_data_clean,
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dv = Difference,
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wid = pID,
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within = c(TimePerspective, Domain_Type),
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between = c(GROUP, TASK_DO),
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type = 3,
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detailed = TRUE,
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return_aov = TRUE
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)
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cat("Overall ANOVA Results:\n")
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print(overall_anova)
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# Check sphericity
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if (!is.null(overall_anova$`Mauchly's Test for Sphericity`)) {
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cat("\nSphericity test results:\n")
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print(overall_anova$`Mauchly's Test for Sphericity`)
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}
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}, error = function(e) {
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cat("Error in overall ANOVA:", e$message, "\n")
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})
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# 4.2 Domain-specific analyses
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cat("\n4.2 Domain-specific Mixed ANOVAs:\n")
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cat(paste(rep("-", 50), collapse = ""), "\n")
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domain_results <- list()
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for (domain_type in unique(long_data_clean$Domain_Type)) {
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cat("\nAnalyzing domain type:", domain_type, "\n")
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domain_data <- long_data_clean[long_data_clean$Domain_Type == domain_type, ]
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tryCatch({
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domain_anova <- ezANOVA(
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data = domain_data,
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dv = Difference,
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wid = pID,
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within = c(TimePerspective, Domain_Item),
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between = c(GROUP, TASK_DO),
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type = 3,
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detailed = TRUE
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)
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cat("ANOVA results for", domain_type, ":\n")
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print(domain_anova)
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domain_results[[domain_type]] <- domain_anova
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}, error = function(e) {
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cat("Error in ANOVA for", domain_type, ":", e$message, "\n")
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# Fallback to simpler analysis
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cat("Attempting simpler repeated measures ANOVA...\n")
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tryCatch({
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simple_anova <- ezANOVA(
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data = domain_data,
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dv = Difference,
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wid = pID,
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within = TimePerspective,
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between = c(GROUP, TASK_DO),
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type = 3,
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detailed = TRUE
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)
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print(simple_anova)
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domain_results[[domain_type]] <- simple_anova
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}, error = function(e2) {
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cat("Simple ANOVA also failed:", e2$message, "\n")
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})
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})
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}
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# 4.3 Individual domain item analyses
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cat("\n4.3 Individual Domain Item Analyses:\n")
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cat(paste(rep("-", 50), collapse = ""), "\n")
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item_results <- list()
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for (domain_item in unique(long_data_clean$Domain_Item)) {
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cat("\nAnalyzing individual item:", domain_item, "\n")
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item_data <- long_data_clean[long_data_clean$Domain_Item == domain_item, ]
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tryCatch({
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item_anova <- ezANOVA(
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data = item_data,
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dv = Difference,
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wid = pID,
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within = TimePerspective,
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between = c(GROUP, TASK_DO),
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type = 3,
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detailed = TRUE
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)
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cat("ANOVA results for", domain_item, ":\n")
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print(item_anova)
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item_results[[domain_item]] <- item_anova
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}, error = function(e) {
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cat("Error in ANOVA for", domain_item, ":", e$message, "\n")
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# Fallback to paired t-test
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past_vals <- item_data$Difference[item_data$TimePerspective == "Past"]
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fut_vals <- item_data$Difference[item_data$TimePerspective == "Future"]
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if (length(past_vals) > 1 && length(fut_vals) > 1) {
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t_test <- t.test(past_vals, fut_vals, paired = TRUE)
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cat("Fallback paired t-test for", domain_item, ":\n")
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cat("t =", round(t_test$statistic, 3),
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", df =", t_test$parameter,
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", p =", round(t_test$p.value, 5), "\n")
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item_results[[domain_item]] <- t_test
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}
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})
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}
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cat("\n", paste(rep("=", 80), collapse = ""), "\n")
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cat("ANALYSIS COMPLETE!\n")
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cat(paste(rep("=", 80), collapse = ""), "\n")
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cat("Summary:\n")
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cat("- Total domains analyzed:", length(unique(long_data_clean$Domain_Item)), "\n")
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cat("- Domain types analyzed:", length(unique(long_data_clean$Domain_Type)), "\n")
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cat("- Individual item analyses completed:", length(item_results), "\n")
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