56 lines
1.7 KiB
R
56 lines
1.7 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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library(irr)
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# Select the 4 variables for reliability analysis
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reliability_vars <- df[, c("ehiDGEN_5_mean", "ehiDGEN_10_mean", "ehi5_global_mean", "ehi10_global_mean")]
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# Check for missing values
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print(colSums(is.na(reliability_vars)))
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# Remove rows with any missing values for reliability analysis
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reliability_data <- reliability_vars[complete.cases(reliability_vars), ]
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print(nrow(reliability_data))
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# Cronbach's Alpha
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alpha_result <- alpha(reliability_data)
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print(alpha_result)
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# Split-half reliability
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split_half <- splitHalf(reliability_data)
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print(split_half)
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# Alpha if item dropped
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alpha_dropped <- alpha(reliability_data, check.keys = TRUE)
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print(alpha_dropped$alpha.drop)
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# Inter-item correlations
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cor_matrix <- cor(reliability_data, use = "complete.obs")
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print(round(cor_matrix, 5))
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# Descriptive statistics
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desc_stats <- describe(reliability_data)
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print(desc_stats)
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# Create a summary table
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summary_table <- data.frame(
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Variable = names(reliability_data),
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Mean = round(colMeans(reliability_data, na.rm = TRUE), 5),
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SD = round(apply(reliability_data, 2, sd, na.rm = TRUE), 5),
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Min = round(apply(reliability_data, 2, min, na.rm = TRUE), 5),
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Max = round(apply(reliability_data, 2, max, na.rm = TRUE), 5),
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Skewness = round(apply(reliability_data, 2, skew, na.rm = TRUE), 5),
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Kurtosis = round(apply(reliability_data, 2, kurtosi, na.rm = TRUE), 5)
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)
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print(summary_table)
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# Save results
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write.csv(summary_table, "reliability_summary_ehi.csv", row.names = FALSE) |