Niat mencari bantuan psikologis: stigma, literasi kesehatan mental, dan SEM berbasis kovarian

# Contoh kode R: Covariance-Based SEM (lavaan)
suppressPackageStartupMessages({
  library(ggplot2)
  library(patchwork)
  library(lavaan)
})
set.seed(42)
# ---- Gaya grafik ----
pal <- c("#1f6feb", "#e8590c", "#2f9e44", "#ae3ec9", "#f59f00")
warna <- list(bg = "#f4f6fa", panel = "#ffffff", teks = "#1f2933", redup = "#5f6b78", grid = "#e3e8ee", bar = "#1d3557")
tata <- 1
theme_set(theme_minimal(base_size = 11) + theme(legend.position = "bottom"))

# ---- Data sintetis: 12 butir Likert 1-5 dari tiga konstruk laten ----
n <- 200
a <- runif(1, 0.4, 0.7)                                         # jalur X1 -> X2 (mediator)
b <- runif(1, 0.3, 0.55)                                        # jalur X2 -> Y
cp <- runif(1, 0.1, 0.35)                                       # jalur langsung X1 -> Y
e_x1 <- rnorm(n)
e_x2 <- a * e_x1 + rnorm(n, 0, sqrt(1 - a^2))
e_y <- as.numeric(scale(cp * e_x1 + b * e_x2 + rnorm(n, 0, runif(1, 0.55, 0.75))))
# butir Likert: muatan acak 0.65-0.9, ambang diacak agar rata-rata butir sekitar 3.4-3.8
likert <- function(eta, k) {
  sapply(seq_len(k), function(j) {
    l <- runif(1, 0.65, 0.9)
    s <- l * eta + rnorm(length(eta), 0, sqrt(1 - l^2))
    findInterval(s, sort(c(-2, -1.1, -0.2, 0.9) + runif(1, -0.3, 0.2))) + 1
  })
}
# butir Y ke-4 sedikit ikut dipengaruhi X1 (muatan silang kecil) agar kecocokan model realistis, tidak sempurna
d <- data.frame(likert(e_x1, 4), likert(e_x2, 4), likert(e_y, 3),
                likert(as.numeric(scale(e_y + runif(1, 0.25, 0.5) * e_x1)), 1))
names(d) <- c(paste0("mental_illness_stigma", "_", 1:4), paste0("mental_health_literacy", "_", 1:4), paste0("help_seeking_intention", "_", 1:4))
write.csv(d, "data_contoh.csv", row.names = FALSE)

# ---- Spesifikasi model: pengukuran (=~), struktural (~), efek tidak langsung (:=) ----
model <- paste0(
  "mental_illness_stigma =~ ", paste(names(d)[1:4], collapse = " + "), "\n",
  "mental_health_literacy =~ ", paste(names(d)[5:8], collapse = " + "), "\n",
  "help_seeking_intention =~ ", paste(names(d)[9:12], collapse = " + "), "\n",
  "mental_health_literacy ~ a*mental_illness_stigma\n",
  "help_seeking_intention ~ b*mental_health_literacy + c*mental_illness_stigma\n",
  "tidak_langsung := a*b\n",
  "total := c + a*b\n")
cat(model)
fit <- sem(model, data = d, estimator = "MLR")                  # ML robust (data Likert diperlakukan kontinu)
print(summary(fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE))

# ---- Indeks kecocokan model (versi robust, cadangan versi biasa) ----
fm <- fitMeasures(fit, c("cfi.robust", "tli.robust", "rmsea.robust", "srmr", "cfi", "tli", "rmsea"))
pilih <- function(r, s) if (is.finite(fm[[r]])) fm[[r]] else fm[[s]]
idx <- c(CFI = pilih("cfi.robust", "cfi"), TLI = pilih("tli.robust", "tli"),
         RMSEA = pilih("rmsea.robust", "rmsea"), SRMR = fm[["srmr"]])
print(round(idx, 3))

# ---- Koefisien terstandar ----
ss <- standardizedSolution(fit)
jalur <- ss[ss$op == "~", ]
ambil <- function(lhs, rhs) jalur[jalur$lhs == lhs & jalur$rhs == rhs, ]
j_a <- ambil("mental_health_literacy", "mental_illness_stigma"); j_b <- ambil("help_seeking_intention", "mental_health_literacy"); j_c <- ambil("help_seeking_intention", "mental_illness_stigma")
tl <- ss[ss$op == ":=" & ss$lhs == "tidak_langsung", ]
muat <- ss[ss$op == "=~", ]
r2 <- lavInspect(fit, "rsquare")
print(jalur[, c("lhs", "rhs", "est.std", "se", "pvalue")]); print(tl)


# ---- Grafik ----
singkat <- function(s, w = 18) {                                # nama konstruk pendek untuk grafik
  k <- strsplit(s, " ")[[1]]
  if (nchar(s) > w) k <- k[!tolower(k) %in% c("of", "to", "the", "and", "for", "in", "on", "with")]
  i <- length(k)
  while (nchar(paste(k, collapse = " ")) > w && i >= 1) {
    if (nchar(k[i]) > 5) k[i] <- paste0(substr(k[i], 1, 4), ".")
    i <- i - 1
  }
  substr(paste(k, collapse = " "), 1, w)
}
nm_k <- make.unique(unname(sapply(c("mental_illness_stigma", "mental_health_literacy", "help_seeking_intention"), singkat)), sep = " ")
bintang <- function(p) ifelse(p < 0.001, "***", ifelse(p < 0.01, "**", ifelse(p < 0.05, "*", " ns")))
elips <- function(x0, y0, rx = 0.74, ry = 0.3, id = 1) {
  t_ <- seq(0, 2 * pi, length.out = 60)
  data.frame(x = x0 + rx * cos(t_), y = y0 + ry * sin(t_), id = id)
}
pos <- data.frame(x = c(0, 1.6, 3.2), y = c(0, 1.25, 0), lab = nm_k)
de <- do.call(rbind, lapply(1:3, function(i) elips(pos$x[i], pos$y[i], id = i)))
# kotak indikator: X1 di kiri bawah, X2 di atas, Y di kanan bawah
ind <- data.frame(x = c(-0.9, -0.3, 0.3, 0.9, 0.7, 1.3, 1.9, 2.5, 2.3, 2.9, 3.5, 4.1),
                  y = rep(c(-0.95, 2.35, -0.95), each = 4), grup = rep(1:3, each = 4),
                  lab = paste0(rep(c("X1.", "M.", "Y."), each = 4), rep(1:4, 3)))
ind$xl <- pos$x[ind$grup]; ind$yl <- pos$y[ind$grup] + ifelse(ind$grup == 2, 0.3, -0.3)
st <- data.frame(x = c(0.45, 2.15, 0.78), y = c(0.3, 0.95, 0), xend = c(1.05, 2.75, 2.42), yend = c(1.0, 0.3, 0),
                 lab = sprintf("%.2f%s", c(j_a$est.std, j_b$est.std, j_c$est.std), bintang(c(j_a$pvalue, j_b$pvalue, j_c$pvalue))),
                 lx = c(0.55, 2.65, 1.6), ly = c(0.78, 0.78, -0.14))
p1 <- ggplot() +
  geom_segment(data = ind, aes(xl, yl, xend = x, yend = y + ifelse(grup == 2, -0.15, 0.15)), colour = warna$redup,
               linewidth = 0.4, arrow = arrow(length = unit(0.07, "inches"), type = "closed")) +
  geom_tile(data = ind, aes(x, y), width = 0.5, height = 0.3, fill = warna$panel, colour = warna$redup, linewidth = 0.4) +
  geom_text(data = ind, aes(x, y, label = lab), colour = warna$teks, size = 2.6) +
  geom_segment(data = st, aes(x, y, xend = xend, yend = yend), colour = warna$teks, linewidth = 0.9,
               arrow = arrow(length = unit(0.13, "inches"), type = "closed")) +
  geom_text(data = st, aes(lx, ly, label = lab), colour = warna$teks, fontface = "bold", size = 3.3) +
  geom_polygon(data = de, aes(x, y, group = id, fill = factor(id)), show.legend = FALSE) +
  scale_fill_manual(values = pal[1:3]) +
  geom_text(data = pos, aes(x, y, label = lab), colour = warna$panel, fontface = "bold", size = 3) +
  annotate("text", x = 1.6, y = -1.38, size = 3, colour = warna$teks,
           label = sprintf("Std. indirect = %.3f%s   R2 = %.2f", tl$est.std, bintang(tl$pvalue), r2[["help_seeking_intention"]])) +
  coord_cartesian(xlim = c(-1.25, 4.45), ylim = c(-1.5, 2.6)) +
  theme_void() + theme(plot.background = element_rect(fill = warna$panel, colour = NA),
                       plot.title = element_text(face = "bold", colour = warna$teks, margin = margin(4, 0, 4, 6))) +
  labs(title = "Standardised structural model (MLR)")
df <- data.frame(indeks = factor(names(idx), levels = names(idx)), nilai = unname(idx),
                 ambang = c(0.9, 0.9, 0.08, 0.08), arah = c("Higher is better", "Higher is better", "Lower is better", "Lower is better"))
p2 <- ggplot(df, aes(indeks, nilai, fill = arah)) +
  geom_col(width = 0.6) +
  geom_errorbar(aes(ymin = ambang, ymax = ambang), width = 0.8, linetype = "dashed", colour = warna$teks, linewidth = 0.6) +
  geom_text(aes(label = sprintf("%.3f", nilai)), vjust = -0.4, colour = warna$teks, size = 3) +
  facet_wrap(~arah, scales = "free") +
  scale_fill_manual(values = pal[4:5], guide = "none") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15))) +
  labs(title = "Fit indices vs cut-offs (dashed)", x = NULL, y = "Value")
muat$konstruk <- factor(nm_k[match(muat$lhs, c("mental_illness_stigma", "mental_health_literacy", "help_seeking_intention"))], levels = nm_k)
muat$butir <- factor(ind$lab, levels = rev(ind$lab))
p3 <- ggplot(muat, aes(est.std, butir, colour = konstruk)) +
  geom_vline(xintercept = 0.7, linetype = "dashed", colour = warna$redup) +
  geom_segment(aes(x = 0, xend = est.std, yend = butir), linewidth = 0.9) +
  geom_point(size = 2.6) +
  geom_text(aes(label = sprintf("%.2f", est.std)), hjust = -0.35, size = 2.6, colour = warna$teks) +
  scale_colour_manual(values = pal[1:3], name = NULL) +
  scale_x_continuous(limits = c(0, 1.1), breaks = seq(0, 1, 0.25)) +
  labs(title = "Standardised loadings", x = "Loading", y = NULL)
p_hasil <- if (tata == 1) p1 / (p2 | p3) + plot_layout(heights = c(1.15, 1)) else p1 | (p2 / p3)

print(p_hasil)