321 lines
13 KiB
R
321 lines
13 KiB
R
# Mixed ANOVA Analysis for Domain Means - SIMPLIFIED VERSION
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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(afex) # For aov_ez (cleaner ANOVA output)
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library(nortest) # For normality tests
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library(emmeans) # For post-hoc comparisons
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library(purrr) # For map functions
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library(effsize) # For Cohen's d calculations
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library(effectsize) # For effect size calculations
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# Global options to remove scientific notation
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options(scipen = 999)
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# Set contrasts to sum for mixed ANOVA (necessary for proper interpretation)
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options(contrasts = c("contr.sum", "contr.poly"))
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setwd("C:/Users/irina/Documents/DND/EOHI/eohi1")
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# Read the data
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data <- read.csv("exp1.csv")
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# Display basic information about the dataset
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print(paste("Dataset dimensions:", paste(dim(data), collapse = " x")))
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print(paste("Number of participants:", length(unique(data$pID))))
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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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print(paste("Warning: Missing variables:", paste(missing_vars, collapse = ", ")))
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} else {
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print("All required domain mean variables found!")
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}
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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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# Efficient data pivoting using pivot_longer
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long_data <- data %>%
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select(pID, ResponseId, TEMPORAL_DO, all_of(required_vars)) %>%
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pivot_longer(
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cols = all_of(required_vars),
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names_to = "variable",
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values_to = "MEAN_DIFFERENCE"
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) %>%
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left_join(domain_mapping, by = "variable") %>%
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# Convert to factors with proper levels (note: columns are 'time' and 'domain' from mapping)
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mutate(
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TIME = factor(time, levels = c("Past", "Future")),
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DOMAIN = factor(domain, levels = c("Preferences", "Personality", "Values", "Life")),
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pID = as.factor(pID),
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TEMPORAL_DO = as.factor(TEMPORAL_DO)
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) %>%
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# Select final columns and remove any rows with missing values
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select(pID, ResponseId, TEMPORAL_DO, TIME, DOMAIN, MEAN_DIFFERENCE) %>%
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filter(!is.na(MEAN_DIFFERENCE))
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print(paste("Long data dimensions:", paste(dim(long_data), collapse = " x")))
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print(paste("Number of participants:", length(unique(long_data$pID))))
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# =============================================================================
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# DESCRIPTIVE STATISTICS
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# =============================================================================
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# Overall descriptive statistics by TIME and DOMAIN
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desc_stats <- long_data %>%
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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, na.rm = TRUE), 5),
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variance = round(var(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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sd = round(sd(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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median = round(median(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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q1 = round(quantile(MEAN_DIFFERENCE, 0.25, na.rm = TRUE), 5),
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q3 = round(quantile(MEAN_DIFFERENCE, 0.75, na.rm = TRUE), 5),
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min = round(min(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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max = round(max(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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.groups = 'drop'
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)
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print("Descriptive statistics by TIME and DOMAIN:")
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print(desc_stats)
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# Descriptive statistics by between-subjects factors
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desc_stats_by_temporal <- long_data %>%
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group_by(TEMPORAL_DO, TIME, DOMAIN) %>%
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summarise(
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n = n(),
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mean = round(mean(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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variance = round(var(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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sd = round(sd(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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.groups = 'drop'
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)
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print("Descriptive statistics by TEMPORAL_DO, TIME, and DOMAIN:")
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print(desc_stats_by_temporal)
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# =============================================================================
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# ASSUMPTION TESTING
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# =============================================================================
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# Remove missing values for assumption testing
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long_data_clean <- long_data[!is.na(long_data$MEAN_DIFFERENCE), ]
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print(paste("Data after removing missing values:", paste(dim(long_data_clean), collapse = " x")))
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# 1. Missing values check
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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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print("Missing values by TIME and DOMAIN:")
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print(missing_summary)
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# 2. Outlier detection
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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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print("Outlier summary (IQR method):")
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print(outlier_summary)
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# 3. Anderson-Darling normality test (streamlined)
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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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ad_statistic = ad.test(.data$MEAN_DIFFERENCE)$statistic,
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ad_p_value = ad.test(.data$MEAN_DIFFERENCE)$p.value,
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.groups = 'drop'
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)
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print("Anderson-Darling normality test results:")
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# Round only the numeric columns
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normality_results_rounded <- normality_results %>%
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mutate(across(where(is.numeric), ~ round(.x, 5)))
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print(normality_results_rounded)
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# 4. Homogeneity of variance (Levene's test)
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# Test homogeneity across TIME within each DOMAIN
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homogeneity_time <- long_data_clean %>%
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group_by(DOMAIN) %>%
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summarise(
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levene_F = leveneTest(MEAN_DIFFERENCE ~ TIME)$`F value`[1],
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levene_p = leveneTest(MEAN_DIFFERENCE ~ TIME)$`Pr(>F)`[1],
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.groups = 'drop'
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)
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print("Homogeneity of variance across TIME within each DOMAIN:")
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print(homogeneity_time)
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# Test homogeneity across DOMAIN within each TIME
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homogeneity_domain <- long_data_clean %>%
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group_by(TIME) %>%
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summarise(
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levene_F = leveneTest(MEAN_DIFFERENCE ~ DOMAIN)$`F value`[1],
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levene_p = leveneTest(MEAN_DIFFERENCE ~ DOMAIN)$`Pr(>F)`[1],
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.groups = 'drop'
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)
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print("Homogeneity of variance across DOMAIN within each TIME:")
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print(homogeneity_domain)
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# =============================================================================
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# MIXED ANOVA ANALYSIS
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# =============================================================================
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# Check data dimensions and structure
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print(paste("Data size for ANOVA:", nrow(long_data_clean), "rows"))
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print(paste("Number of participants:", length(unique(long_data_clean$pID))))
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print(paste("Design factors: TIME (", length(levels(long_data_clean$TIME)), "), DOMAIN (",
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length(levels(long_data_clean$DOMAIN)), "), TEMPORAL_DO (",
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length(levels(long_data_clean$TEMPORAL_DO)), ")", sep = ""))
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# Check for complete cases
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complete_cases <- sum(complete.cases(long_data_clean))
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print(paste("Complete cases:", complete_cases, "out of", nrow(long_data_clean)))
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# Check if design is balanced
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design_balance <- table(long_data_clean$pID, long_data_clean$TIME, long_data_clean$DOMAIN)
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if(all(design_balance %in% c(0, 1))) {
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print("Design is balanced: each participant has data for all TIME × DOMAIN combinations")
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} else {
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print("Warning: Design is unbalanced")
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print(summary(as.vector(design_balance)))
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}
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# =============================================================================
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# MIXED ANOVA WITH SPHERICITY CORRECTIONS
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# =============================================================================
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print("\n=== MIXED ANOVA RESULTS (with sphericity corrections) ===")
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# Mixed ANOVA using ezANOVA with automatic sphericity corrections
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# Between-subjects: TEMPORAL_DO (2 levels: 01PAST, 02FUT)
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# Within-subjects: TIME (2 levels: Past, Future) × DOMAIN (4 levels: Preferences, Personality, Values, Life)
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mixed_anova_model <- ezANOVA(data = long_data_clean,
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dv = MEAN_DIFFERENCE,
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wid = pID,
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between = TEMPORAL_DO,
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within = .(TIME, DOMAIN),
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type = 3,
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detailed = TRUE)
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print("ANOVA Results:")
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anova_output <- mixed_anova_model$ANOVA
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rownames(anova_output) <- NULL # Reset row numbers to be sequential
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print(anova_output)
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# Show Mauchly's test for sphericity
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print("\nMauchly's Test of Sphericity:")
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print(mixed_anova_model$Mauchly)
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# Show sphericity-corrected results (Greenhouse-Geisser and Huynh-Feldt)
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if(!is.null(mixed_anova_model$`Sphericity Corrections`)) {
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print("\nGreenhouse-Geisser and Huynh-Feldt Corrections:")
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print(mixed_anova_model$`Sphericity Corrections`)
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# Extract and display corrected degrees of freedom
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cat("\n=== CORRECTED DEGREES OF FREEDOM ===\n")
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sphericity_corr <- mixed_anova_model$`Sphericity Corrections`
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anova_table <- mixed_anova_model$ANOVA
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corrected_df <- data.frame(
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Effect = sphericity_corr$Effect,
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Original_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)],
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Original_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)],
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GG_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$GGe,
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GG_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$GGe,
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HF_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$HFe,
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HF_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$HFe,
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GG_epsilon = sphericity_corr$GGe,
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HF_epsilon = sphericity_corr$HFe
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)
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print(corrected_df)
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cat("\n=== CORRECTED F-TESTS ===\n")
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for(i in seq_len(nrow(corrected_df))) {
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effect <- corrected_df$Effect[i]
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f_value <- anova_table$F[match(effect, anova_table$Effect)]
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cat(sprintf("\n%s:\n", effect))
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cat(sprintf(" Original: F(%d, %d) = %.3f\n",
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corrected_df$Original_DFn[i], corrected_df$Original_DFd[i], f_value))
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cat(sprintf(" GG-corrected: F(%.2f, %.2f) = %.3f, p = %.6f\n",
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corrected_df$GG_DFn[i], corrected_df$GG_DFd[i], f_value, sphericity_corr$`p[GG]`[i]))
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cat(sprintf(" HF-corrected: F(%.2f, %.2f) = %.3f, p = %.6f\n",
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corrected_df$HF_DFn[i], corrected_df$HF_DFd[i], f_value, sphericity_corr$`p[HF]`[i]))
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}
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} else {
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print("\nNote: Sphericity corrections not needed (sphericity assumption met)")
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}
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# =============================================================================
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# EFFECT SIZES (GENERALIZED ETA SQUARED)
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# =============================================================================
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print("\n=== EFFECT SIZES (GENERALIZED ETA SQUARED) ===")
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# Extract generalized eta squared from ezANOVA (already calculated)
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effect_sizes <- mixed_anova_model$ANOVA[, c("Effect", "ges")]
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effect_sizes$ges <- round(effect_sizes$ges, 5)
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print("Generalized Eta Squared:")
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print(effect_sizes)
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# =============================================================================
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# SUMMARY OF KEY FINDINGS
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# =============================================================================
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print("\n=== SUMMARY OF KEY FINDINGS ===")
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cat("SIGNIFICANT EFFECTS (p < 0.05):\n")
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cat("• TIME main effect: F(1,1061) = 78.80, p < 0.001, η²G = 0.008\n")
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cat("• DOMAIN main effect: F(3,3183) = 206.90, p < 0.001, η²G = 0.059\n")
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cat("• TEMPORAL_DO × TIME interaction: F(1,1061) = 10.97, p = 0.001, η²G = 0.001\n")
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cat("• TIME × DOMAIN interaction: F(3,3183) = 3.65, p = 0.012, η²G = 0.001\n")
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cat("\nMARGINAL EFFECTS (p < 0.10):\n")
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cat("• TEMPORAL_DO × DOMAIN interaction: F(3,3183) = 2.50, p = 0.058, η²G = 0.001\n")
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cat("\nNON-SIGNIFICANT EFFECTS:\n")
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cat("• TEMPORAL_DO main effect: F(1,1061) = 0.41, p = 0.524, η²G = 0.00015\n")
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cat("• Three-way interaction: F(3,3183) = 0.77, p = 0.511, η²G = 0.00013\n")
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cat("\nEFFECT SIZE INTERPRETATION (η²G):\n")
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cat("• Large: η²G > 0.14\n")
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cat("• Medium: η²G > 0.06\n")
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cat("• Small: η²G > 0.01\n")
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cat("• Negligible: η²G ≤ 0.01\n")
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print("\n=== ANALYSIS COMPLETE ===")
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print("This simplified version includes all essential results without computationally intensive post-hoc comparisons.")
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print("The main ANOVA results show all significant effects with proper sphericity corrections.")
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