581 lines
19 KiB
R
581 lines
19 KiB
R
# Mixed ANOVA Analysis for Domain Means
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# EOHI Experiment Data Analysis - Domain Level Analysis
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# Variables: NPast_mean_pref, NPast_mean_pers, NPast_mean_val, NPast_mean_life
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# NFut_mean_pref, NFut_mean_pers, NFut_mean_val, NFut_mean_life
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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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library(ggplot2) # For plotting
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library(emmeans) # For post-hoc comparisons
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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$pID)), "\n")
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# Check experimental conditions
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cat("\nExperimental conditions:\n")
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print(table(data$GROUP, data$TEMPORAL_DO, data$ITEM_DO))
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# Check what domain mean columns are available
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cat("\nChecking available domain mean columns:\n")
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domain_mean_cols <- colnames(data)[grepl("mean_(pref|pers|val|life)", colnames(data))]
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print(domain_mean_cols)
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# Verify the specific variables we need
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required_vars <- c("NPast_mean_pref", "NPast_mean_pers", "NPast_mean_val", "NPast_mean_life",
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"NFut_mean_pref", "NFut_mean_pers", "NFut_mean_val", "NFut_mean_life")
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missing_vars <- required_vars[!required_vars %in% colnames(data)]
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if (length(missing_vars) > 0) {
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cat("Warning: Missing variables:", paste(missing_vars, collapse = ", "), "\n")
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} else {
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cat("All required domain mean variables found!\n")
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}
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# =============================================================================
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# STEP 1: DATA PIVOTING TO LONG FORMAT
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# =============================================================================
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cat("STEP 1: DATA PIVOTING TO LONG FORMAT\n")
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# Define domain mapping
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domain_mapping <- data.frame(
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variable = c("NPast_mean_pref", "NPast_mean_pers", "NPast_mean_val", "NPast_mean_life",
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"NFut_mean_pref", "NFut_mean_pers", "NFut_mean_val", "NFut_mean_life"),
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time = c(rep("Past", 4), rep("Future", 4)),
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domain = rep(c("Preferences", "Personality", "Values", "Life"), 2),
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stringsAsFactors = FALSE
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)
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cat("Domain mapping:\n")
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print(domain_mapping)
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# Function to pivot data to long format
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pivot_domain_means <- function(data, domain_mapping) {
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long_data <- data.frame()
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for (i in 1:nrow(domain_mapping)) {
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var_name <- domain_mapping$variable[i]
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time_level <- domain_mapping$time[i]
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domain_level <- domain_mapping$domain[i]
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# Check if variable exists
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if (!var_name %in% colnames(data)) {
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cat("Warning: Variable", var_name, "not found in data\n")
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next
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}
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# Create subset for this variable
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subset_data <- data[, c("pID", "ResponseId", "GROUP", "TEMPORAL_DO", "ITEM_DO", var_name)]
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subset_data$TIME <- time_level
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subset_data$DOMAIN <- domain_level
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subset_data$MEAN_DIFFERENCE <- subset_data[[var_name]]
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subset_data[[var_name]] <- NULL # Remove original column
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# Add to long data
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long_data <- rbind(long_data, subset_data)
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}
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# Convert to factors with proper levels
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long_data$TIME <- factor(long_data$TIME, levels = c("Past", "Future"))
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long_data$DOMAIN <- factor(long_data$DOMAIN, levels = c("Preferences", "Personality", "Values", "Life"))
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long_data$pID <- as.factor(long_data$pID)
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long_data$GROUP <- as.factor(long_data$GROUP)
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long_data$TEMPORAL_DO <- as.factor(long_data$TEMPORAL_DO)
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long_data$ITEM_DO <- as.factor(long_data$ITEM_DO)
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return(long_data)
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}
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# Pivot data to long format
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cat("\nPivoting data to long format...\n")
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tryCatch({
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long_data <- pivot_domain_means(data, domain_mapping)
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cat("Data pivoting completed successfully.\n")
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}, error = function(e) {
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cat("Error in data pivoting:", e$message, "\n")
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stop("Cannot proceed without proper data structure")
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})
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cat("Long format data dimensions:", dim(long_data), "\n")
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cat("Unique participants:", length(unique(long_data$pID)), "\n")
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cat("TIME levels:", paste(levels(long_data$TIME), collapse = ", "), "\n")
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cat("DOMAIN levels:", paste(levels(long_data$DOMAIN), collapse = ", "), "\n")
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# Check data types
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cat("\nData types check:\n")
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cat("TIME is factor:", is.factor(long_data$TIME), "\n")
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cat("DOMAIN is factor:", is.factor(long_data$DOMAIN), "\n")
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cat("pID is factor:", is.factor(long_data$pID), "\n")
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cat("MEAN_DIFFERENCE is numeric:", is.numeric(long_data$MEAN_DIFFERENCE), "\n")
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# Display structure and sample
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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(utils::head(long_data, 10))
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# Show example data for one participant
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cat("\nExample: Participant 1 across all domains and times:\n")
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participant_1_data <- long_data[long_data$pID == 1, c("pID", "GROUP", "TEMPORAL_DO", "ITEM_DO", "TIME", "DOMAIN", "MEAN_DIFFERENCE")]
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print(participant_1_data)
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# =============================================================================
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# STEP 2: ASSUMPTION CHECKING
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# =============================================================================
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# 2.1 Check for missing values
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cat("\n2.1 Missing Values Check:\n")
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missing_summary <- long_data %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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n_total = n(),
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n_missing = sum(is.na(MEAN_DIFFERENCE)),
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pct_missing = round(100 * n_missing / n_total, 2),
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.groups = 'drop'
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)
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cat("Missing values by TIME and DOMAIN:\n")
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print(missing_summary)
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# Remove missing values
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long_data_clean <- long_data[!is.na(long_data$MEAN_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("\n2.2 Outlier Detection:\n")
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outlier_summary <- long_data_clean %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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n = n(),
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mean = mean(MEAN_DIFFERENCE),
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sd = sd(MEAN_DIFFERENCE),
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q1 = quantile(MEAN_DIFFERENCE, 0.25),
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q3 = quantile(MEAN_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(MEAN_DIFFERENCE < lower_bound | MEAN_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("\n2.3 Normality Tests:\n")
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normality_results <- long_data_clean %>%
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group_by(TIME, DOMAIN) %>%
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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(MEAN_DIFFERENCE)$p.value,
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NA),
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anderson_p = ifelse(n >= 7,
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ad.test(MEAN_DIFFERENCE)$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("\n2.4 Homogeneity of Variance Tests:\n")
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# Test homogeneity across TIME within each DOMAIN
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homogeneity_time <- long_data_clean %>%
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group_by(DOMAIN) %>%
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summarise(
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levene_p = leveneTest(MEAN_DIFFERENCE ~ TIME)$`Pr(>F)`[1],
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homogeneous = levene_p > 0.05,
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.groups = 'drop'
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)
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cat("Homogeneity of variance across TIME within each DOMAIN:\n")
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print(homogeneity_time)
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# Test homogeneity across DOMAIN within each TIME
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homogeneity_domain <- long_data_clean %>%
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group_by(TIME) %>%
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summarise(
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levene_p = leveneTest(MEAN_DIFFERENCE ~ DOMAIN)$`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 across DOMAIN within each TIME:\n")
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print(homogeneity_domain)
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# =============================================================================
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# STEP 3: DESCRIPTIVE STATISTICS
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# =============================================================================
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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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# Overall descriptive statistics
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desc_stats <- long_data_clean %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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n = n(),
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mean = round(mean(MEAN_DIFFERENCE), 5),
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sd = round(sd(MEAN_DIFFERENCE), 5),
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median = round(median(MEAN_DIFFERENCE), 5),
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q1 = round(quantile(MEAN_DIFFERENCE, 0.25), 5),
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q3 = round(quantile(MEAN_DIFFERENCE, 0.75), 5),
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min = round(min(MEAN_DIFFERENCE), 5),
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max = round(max(MEAN_DIFFERENCE), 5),
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.groups = 'drop'
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)
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cat("Descriptive statistics by TIME and DOMAIN:\n")
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print(desc_stats)
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# Descriptive statistics by between-subjects factors
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desc_stats_by_group <- long_data_clean %>%
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group_by(GROUP, TIME, DOMAIN) %>%
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summarise(
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n = n(),
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mean = round(mean(MEAN_DIFFERENCE), 5),
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sd = round(sd(MEAN_DIFFERENCE), 5),
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.groups = 'drop'
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)
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cat("\nDescriptive statistics by GROUP, TIME, and DOMAIN:\n")
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print(desc_stats_by_group)
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# =============================================================================
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# STEP 4: MIXED ANOVA ANALYSES
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# =============================================================================
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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 Main Mixed ANOVA
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cat("\n4.1 Main Mixed ANOVA:\n")
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cat("Within-subjects factors: TIME, DOMAIN\n")
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cat("Between-subjects factors: GROUP, TEMPORAL_DO, ITEM_DO\n")
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cat(paste(rep("-", 50), collapse = ""), "\n")
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tryCatch({
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main_anova <- ezANOVA(
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data = long_data_clean,
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dv = MEAN_DIFFERENCE,
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wid = pID,
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within = c(TIME, DOMAIN),
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between = c(GROUP, TEMPORAL_DO, ITEM_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("Main ANOVA Results:\n")
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print(main_anova)
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# Check sphericity
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if (!is.null(main_anova$`Mauchly's Test for Sphericity`)) {
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cat("\nSphericity test results:\n")
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print(main_anova$`Mauchly's Test for Sphericity`)
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}
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}, error = function(e) {
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cat("Error in main ANOVA:", e$message, "\n")
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# Try simpler model without all between-subjects factors
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cat("Attempting simpler model with only GROUP as between-subjects factor...\n")
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tryCatch({
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simple_anova <- ezANOVA(
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data = long_data_clean,
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dv = MEAN_DIFFERENCE,
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wid = pID,
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within = c(TIME, DOMAIN),
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between = GROUP,
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type = 3,
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detailed = TRUE,
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return_aov = TRUE
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)
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cat("Simplified ANOVA Results:\n")
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print(simple_anova)
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main_anova <<- simple_anova
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}, error = function(e2) {
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cat("Simplified ANOVA also failed:", e2$message, "\n")
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})
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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 in levels(long_data_clean$DOMAIN)) {
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cat("\nAnalyzing domain:", domain, "\n")
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domain_data <- long_data_clean[long_data_clean$DOMAIN == domain, ]
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tryCatch({
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domain_anova <- ezANOVA(
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data = domain_data,
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dv = MEAN_DIFFERENCE,
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wid = pID,
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within = TIME,
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between = c(GROUP, TEMPORAL_DO, ITEM_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, ":\n")
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print(domain_anova)
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domain_results[[domain]] <- domain_anova
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}, error = function(e) {
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cat("Error in ANOVA for", domain, ":", e$message, "\n")
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# Fallback to simpler analysis
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cat("Attempting simpler repeated measures ANOVA...\n")
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tryCatch({
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simple_anova <- ezANOVA(
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data = domain_data,
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dv = MEAN_DIFFERENCE,
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wid = pID,
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within = TIME,
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between = GROUP,
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type = 3,
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detailed = TRUE
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)
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print(simple_anova)
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domain_results[[domain]] <- simple_anova
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}, error = function(e2) {
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cat("Simple ANOVA also failed:", e2$message, "\n")
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})
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})
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}
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# 4.3 Time-specific analyses
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cat("\n4.3 Time-specific Mixed ANOVAs:\n")
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cat(paste(rep("-", 50), collapse = ""), "\n")
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time_results <- list()
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for (time in levels(long_data_clean$TIME)) {
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cat("\nAnalyzing time:", time, "\n")
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time_data <- long_data_clean[long_data_clean$TIME == time, ]
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tryCatch({
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time_anova <- ezANOVA(
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data = time_data,
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dv = MEAN_DIFFERENCE,
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wid = pID,
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within = DOMAIN,
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between = c(GROUP, TEMPORAL_DO, ITEM_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", time, ":\n")
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print(time_anova)
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time_results[[time]] <- time_anova
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}, error = function(e) {
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cat("Error in ANOVA for", time, ":", e$message, "\n")
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# Fallback to simpler analysis
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cat("Attempting simpler repeated measures ANOVA...\n")
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tryCatch({
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simple_anova <- ezANOVA(
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data = time_data,
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dv = MEAN_DIFFERENCE,
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wid = pID,
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within = DOMAIN,
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between = GROUP,
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type = 3,
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detailed = TRUE
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)
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print(simple_anova)
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time_results[[time]] <- simple_anova
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}, error = function(e2) {
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cat("Simple ANOVA also failed:", e2$message, "\n")
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})
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})
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}
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# =============================================================================
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# STEP 5: POST-HOC ANALYSES
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# =============================================================================
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cat("\n", paste(rep("=", 80), collapse = ""), "\n")
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cat("STEP 5: POST-HOC ANALYSES\n")
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cat(paste(rep("=", 80), collapse = ""), "\n")
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# 5.1 Pairwise comparisons for significant effects
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if (exists("main_anova") && !is.null(main_anova)) {
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cat("\n5.1 Post-hoc comparisons for main effects:\n")
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# Check for significant main effects and interactions
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anova_table <- main_anova$ANOVA
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if ("TIME" %in% anova_table$Effect && anova_table$p[anova_table$Effect == "TIME"] < 0.05) {
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cat("Significant TIME main effect found. Computing pairwise comparisons...\n")
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# Simple paired t-tests for TIME effect
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past_means <- long_data_clean$MEAN_DIFFERENCE[long_data_clean$TIME == "Past"]
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future_means <- long_data_clean$MEAN_DIFFERENCE[long_data_clean$TIME == "Future"]
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if (length(past_means) == length(future_means)) {
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time_t_test <- t.test(past_means, future_means, paired = TRUE)
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cat("Paired t-test for TIME effect:\n")
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cat("t =", round(time_t_test$statistic, 5),
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", df =", time_t_test$parameter,
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", p =", round(time_t_test$p.value, 5), "\n")
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cat("Mean difference (Past - Future):", round(time_t_test$estimate, 5), "\n")
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}
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}
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if ("DOMAIN" %in% anova_table$Effect && anova_table$p[anova_table$Effect == "DOMAIN"] < 0.05) {
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cat("Significant DOMAIN main effect found.\n")
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# Pairwise comparisons between domains
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domain_means <- long_data_clean %>%
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group_by(DOMAIN) %>%
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summarise(mean_diff = mean(MEAN_DIFFERENCE), .groups = 'drop')
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cat("Domain means:\n")
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print(domain_means)
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}
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if ("TIME:DOMAIN" %in% anova_table$Effect && anova_table$p[anova_table$Effect == "TIME:DOMAIN"] < 0.05) {
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cat("Significant TIME × DOMAIN interaction found.\n")
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|
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# Simple effects analysis
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||
interaction_means <- long_data_clean %>%
|
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group_by(TIME, DOMAIN) %>%
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summarise(mean_diff = mean(MEAN_DIFFERENCE), .groups = 'drop')
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cat("TIME × DOMAIN interaction means:\n")
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print(interaction_means)
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}
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}
|
||
|
||
# =============================================================================
|
||
# STEP 6: EFFECT SIZES
|
||
# =============================================================================
|
||
|
||
cat("\n", paste(rep("=", 80), collapse = ""), "\n")
|
||
cat("STEP 6: EFFECT SIZES\n")
|
||
cat(paste(rep("=", 80), collapse = ""), "\n")
|
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|
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if (exists("main_anova") && !is.null(main_anova)) {
|
||
anova_table <- main_anova$ANOVA
|
||
|
||
# Calculate partial eta squared for each effect
|
||
anova_table$partial_eta_squared <- round(anova_table$SSn / (anova_table$SSn + anova_table$SSd), 5)
|
||
|
||
cat("Effect sizes (partial eta squared):\n")
|
||
effect_sizes <- anova_table[, c("Effect", "partial_eta_squared")]
|
||
print(effect_sizes)
|
||
}
|
||
|
||
# =============================================================================
|
||
# STEP 7: SUMMARY AND INTERPRETATION
|
||
# =============================================================================
|
||
|
||
cat("\n", paste(rep("=", 80), collapse = ""), "\n")
|
||
cat("STEP 7: SUMMARY AND INTERPRETATION\n")
|
||
cat(paste(rep("=", 80), collapse = ""), "\n")
|
||
|
||
cat("Analysis Summary:\n")
|
||
cat("- Total participants:", length(unique(long_data_clean$pID)), "\n")
|
||
cat("- Total observations:", nrow(long_data_clean), "\n")
|
||
cat("- Within-subjects factors: TIME (Past vs Future), DOMAIN (Preferences, Personality, Values, Life)\n")
|
||
cat("- Between-subjects factors: GROUP, TEMPORAL_DO, ITEM_DO\n")
|
||
cat("- Dependent variable: Mean absolute differences in domain ratings\n")
|
||
|
||
cat("\nResearch Question:\n")
|
||
cat("Do participants rate changes in domains differently from past to now vs past to future?\n")
|
||
|
||
if (exists("main_anova") && !is.null(main_anova)) {
|
||
anova_table <- main_anova$ANOVA
|
||
|
||
cat("\nKey Findings:\n")
|
||
|
||
# Check for significant effects
|
||
significant_effects <- anova_table$Effect[anova_table$p < 0.05]
|
||
|
||
if (length(significant_effects) > 0) {
|
||
cat("Significant effects found:\n")
|
||
for (effect in significant_effects) {
|
||
p_val <- anova_table$p[anova_table$Effect == effect]
|
||
cat("-", effect, "(p =", round(p_val, 5), ")\n")
|
||
}
|
||
} else {
|
||
cat("No significant effects found at α = 0.05\n")
|
||
}
|
||
|
||
# Interpret TIME effect
|
||
if ("TIME" %in% anova_table$Effect) {
|
||
time_p <- anova_table$p[anova_table$Effect == "TIME"]
|
||
if (time_p < 0.05) {
|
||
cat("\nTIME Effect: Participants show different levels of change when comparing\n")
|
||
cat("past-to-now vs past-to-future perspectives (p =", round(time_p, 5), ")\n")
|
||
} else {
|
||
cat("\nTIME Effect: No significant difference between past-to-now and past-to-future\n")
|
||
cat("perspectives (p =", round(time_p, 5), ")\n")
|
||
}
|
||
}
|
||
|
||
# Interpret DOMAIN effect
|
||
if ("DOMAIN" %in% anova_table$Effect) {
|
||
domain_p <- anova_table$p[anova_table$Effect == "DOMAIN"]
|
||
if (domain_p < 0.05) {
|
||
cat("\nDOMAIN Effect: Different domains show different levels of perceived change\n")
|
||
cat("(p =", round(domain_p, 5), ")\n")
|
||
} else {
|
||
cat("\nDOMAIN Effect: No significant differences between domains in perceived change\n")
|
||
cat("(p =", round(domain_p, 5), ")\n")
|
||
}
|
||
}
|
||
|
||
# Interpret interaction
|
||
if ("TIME:DOMAIN" %in% anova_table$Effect) {
|
||
interaction_p <- anova_table$p[anova_table$Effect == "TIME:DOMAIN"]
|
||
if (interaction_p < 0.05) {
|
||
cat("\nTIME × DOMAIN Interaction: The effect of time perspective on perceived change\n")
|
||
cat("varies across domains (p =", round(interaction_p, 5), ")\n")
|
||
} else {
|
||
cat("\nTIME × DOMAIN Interaction: No significant interaction between time perspective\n")
|
||
cat("and domain (p =", round(interaction_p, 5), ")\n")
|
||
}
|
||
}
|
||
}
|
||
|
||
cat("\n", paste(rep("=", 80), collapse = ""), "\n")
|
||
cat("ANALYSIS COMPLETE!\n")
|
||
cat(paste(rep("=", 80), collapse = ""), "\n")
|