124 lines
5.1 KiB
R
124 lines
5.1 KiB
R
setwd("C:/Users/irina/Documents/DND/EOHI/eohi2")
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options(scipen = 999)
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df <- read.csv("eohi2.csv")
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library(psych)
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library(dplyr)
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library(knitr)
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# Your named variable sets (replace df with your actual dataframe name)
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present_pref_vars <- c("present_pref_read", "present_pref_music", "present_pref_tv", "present_pref_nap", "present_pref_travel")
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past_5_pref_vars <- c("past_5_pref_read", "past_5_pref_music", "past_5_pref_TV", "past_5_pref_nap", "past_5_pref_travel")
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past_10_pref_vars <- c("past_10_pref_read", "past_10_pref_music", "past_10_pref_TV", "past_10_pref_nap", "past_10_pref_travel")
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fut_5_pref_vars <- c("fut_5_pref_read", "fut_5_pref_music", "fut_5_pref_TV", "fut_5_pref_nap", "fut_5_pref_travel")
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fut_10_pref_vars <- c("fut_10_pref_read", "fut_10_pref_music", "fut_10_pref_TV", "fut_10_pref_nap", "fut_10_pref_travel")
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present_pers_vars <- c("present_pers_extravert", "present_pers_critical", "present_pers_dependable", "present_pers_anxious", "present_pers_complex")
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past_5_pers_vars <- c("past_5_pers_extravert", "past_5_pers_critical", "past_5_pers_dependable", "past_5_pers_anxious", "past_5_pers_complex")
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past_10_pers_vars <- c("past_10_pers_extravert", "past_10_pers_critical", "past_10_pers_dependable", "past_10_pers_anxious", "past_10_pers_complex")
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fut_5_pers_vars <- c("fut_5_pers_extravert", "fut_5_pers_critical", "fut_5_pers_dependable", "fut_5_pers_anxious", "fut_5_pers_complex")
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fut_10_pers_vars <- c("fut_10_pers_extravert", "fut_10_pers_critical", "fut_10_pers_dependable", "fut_10_pers_anxious", "fut_10_pers_complex")
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present_val_vars <- c("present_val_obey", "present_val_trad", "present_val_opinion", "present_val_performance", "present_val_justice")
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past_5_val_vars <- c("past_5_val_obey", "past_5_val_trad", "past_5_val_opinion", "past_5_val_performance", "past_5_val_justice")
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past_10_val_vars <- c("past_10_val_obey", "past_10_val_trad", "past_10_val_opinion", "past_10_val_performance", "past_10_val_justice")
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fut_5_val_vars <- c("fut_5_val_obey", "fut_5_val_trad", "fut_5_val_opinion", "fut_5_val_performance", "fut_5_val_justice")
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fut_10_val_vars <- c("fut_10_val_obey", "fut_10_val_trad", "fut_10_val_opinion", "fut_10_val_performance", "fut_10_val_justice")
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all_scales <- list(
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"Present_Preferences" = present_pref_vars,
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"Past5_Preferences" = past_5_pref_vars,
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"Past10_Preferences" = past_10_pref_vars,
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"Fut5_Preferences" = fut_5_pref_vars,
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"Fut10_Preferences" = fut_10_pref_vars,
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"Present_Personality" = present_pers_vars,
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"Past5_Personality" = past_5_pers_vars,
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"Past10_Personality" = past_10_pers_vars,
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"Fut5_Personality" = fut_5_pers_vars,
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"Fut10_Personality" = fut_10_pers_vars,
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"Present_Values" = present_val_vars,
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"Past5_Values" = past_5_val_vars,
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"Past10_Values" = past_10_val_vars,
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"Fut5_Values" = fut_5_val_vars,
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"Fut10_Values" = fut_10_val_vars
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)
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# Reliability analysis loop
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alpha_results <- list()
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for(scale_name in names(all_scales)) {
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vars <- all_scales[[scale_name]]
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scale_data <- df %>% select(all_of(vars))
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# Only run if there is more than one column present
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if(ncol(scale_data) > 1) {
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alpha_out <- psych::alpha(scale_data, check.keys = TRUE)
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alpha_results[[scale_name]] <- alpha_out
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# Print full output for diagnostics
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cat("\n----------", scale_name, "----------\n")
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print(alpha_out$total)
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print(alpha_out$item.stats)
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print(alpha_out$alpha.drop)
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}
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}
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# Create detailed alpha if dropped table
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alpha_dropped_tbl <- data.frame(
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Scale = character(),
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Item = character(),
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AlphaIfDropped = numeric(),
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stringsAsFactors = FALSE
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)
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for(scale_name in names(all_scales)) {
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vars <- all_scales[[scale_name]]
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scale_data <- df %>% select(all_of(vars))
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if(ncol(scale_data) > 1) {
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alpha_out <- alpha_results[[scale_name]]
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# Extract alpha if dropped for each item
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for(i in 1:length(vars)) {
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item_name <- vars[i]
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alpha_dropped <- alpha_out$alpha.drop$raw_alpha[i]
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alpha_dropped_tbl <- rbind(alpha_dropped_tbl, data.frame(
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Scale = scale_name,
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Item = item_name,
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AlphaIfDropped = round(alpha_dropped, 4),
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stringsAsFactors = FALSE
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))
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}
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}
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}
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# Check for reversed items
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cat("\n=== REVERSED ITEMS CHECK ===\n")
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for(scale_name in names(all_scales)) {
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vars <- all_scales[[scale_name]]
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scale_data <- df %>% select(all_of(vars))
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if(ncol(scale_data) > 1) {
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alpha_out <- alpha_results[[scale_name]]
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# Check if any items were reversed by looking at the keys
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if(!is.null(alpha_out$keys)) {
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keys_df <- as.data.frame(alpha_out$keys)
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reversed_items <- rownames(keys_df)[keys_df[,1] < 0]
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if(length(reversed_items) > 0) {
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cat(scale_name, "reversed items:", paste(reversed_items, collapse = ", "), "\n")
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} else {
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cat(scale_name, ": No items reversed\n")
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}
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} else {
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cat(scale_name, ": No keys available\n")
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}
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}
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}
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# Export tables to CSV
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#write.csv(summary_tbl, "reliability_summary_table.csv", row.names = FALSE)
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#write.csv(alpha_dropped_tbl, "alpha_if_dropped_table.csv", row.names = FALSE)
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