#clear R Environment and set the seed
rm(list=ls())
set.seed(2000)
#load dplyr package for data manipulation, ggplot2 for visualization, and writexl to write results into excel
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
library(writexl)
## Warning: package 'writexl' was built under R version 4.4.3
#load lavaan and semTools packages to perform confirmatory factor analysis and measure composite reliability
library(lavaan)
## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.
library(semTools)
##
## ###############################################################################
## This is semTools 0.5-6
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
#load robustbase and car packages to perform robust multiple linear regression
library(robustbase)
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
#load nnet package to perform multinominal regression
library(nnet)
## Warning: package 'nnet' was built under R version 4.4.3
#load the ppcor package to calculate the partial spearman's rank correlation
library(ppcor)
## Loading required package: MASS
## Warning: package 'MASS' was built under R version 4.4.3
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
#load the data file
data <- as.data.frame(read.csv("sim_input.csv"))
#Convert coded dichotomous columns into labelled factors
coded_cols <- c(14:19)
data[,coded_cols] <- lapply(data[,coded_cols],
function(x) factor(x, levels = c(0, 1), labels = c("No", "Yes"))
)
#Data cleaning
cols_to_change <- c(88, 90, 91)
data[,cols_to_change] <- lapply(data[,cols_to_change],
function(x) {
x[x == "0_1"] <- "0"
x[x == "0_2"] <- "0"
x[x == "1_2"] <- "1"
x[x == "2_2"] <- "2"
return(x)
}
)
#Subset data based on device ID
cat("No. of responses: ",
nrow(data),
"\n"
)
## No. of responses: 10000
data_unique <- data[!duplicated(data$deviceid),]
cat("No. of unique responses: ",
nrow(data_unique),
"\n"
)
## No. of unique responses: 9700
#Keep responses from undergraduate students
data_under <- data_unique[data_unique$undergrad_now == "Yes", ]
cat("No. of undergraduate participants: ",
nrow(data_under),
"\n"
)
## No. of undergraduate participants: 8306
#Exclude non-smokers
sample <- data_under[data_under$smoke_stat == "smoker" | data_under$smoke_stat == "ex_smoker", ]
cat("No. of responses following exclusion of non-smokers: ",
nrow(sample),
"\n"
)
## No. of responses following exclusion of non-smokers: 6196
#Keep responses from attentive participants
sample$attention <- ifelse(
(!is.na(sample$attention_check_a) & sample$attention_check_a == 3) |
(!is.na(sample$attention_check_b) & sample$attention_check_b == 6),
"Yes", "No"
)
data_attentive <- sample[sample$attention == "Yes", ]
cat("No. of attentive participants: ",
nrow(data_attentive),
"\n"
)
## No. of attentive participants: 5544
#Keep responses with sufficient response time
data_attentive$response_threshold <- apply(data_attentive,
1, function(row) (sum(!is.na(row))*2)-12 #12 is subtracted for ID and multiple-response variables
)
data_cleaned <- data_attentive[data_attentive$response_time > data_attentive$response_threshold, ]
cat("No. of responses following exclusion based on response time: ",
nrow(data_cleaned),
"\n"
)
## No. of responses following exclusion based on response time: 5457
#Convert behavioral rating scales into factors for data summarization
scale_cols <- c(22:42, 44:99)
data_cleaned[,scale_cols] <- lapply(data_cleaned[,scale_cols],
function(x) factor(x)
)
#Create a function to summarize each variable
summarize_variable <- function(var) {
if (is.numeric(var)) {
q <- quantile(var,
probs = c(0.25, 0.5, 0.75),
na.rm = TRUE
)
return(sprintf("Median: %.1f (Q1: %.1f, Q3: %.1f)", q[2], q[1], q[3]))
} else {
tbl <- table(var)
prop <- prop.table(tbl)
return(paste0(
names(tbl), ": ", as.integer(tbl), " (", round(100 * prop, 1), "%)",
collapse = "; "
)
)
}
}
#Report summary statistics
exc_var <- c(1, 3, 13)
summary_df <- data.frame(Characteristic = names(data_cleaned[,-exc_var]),
Distribution = sapply(data_cleaned[,-exc_var], summarize_variable),
row.names = NULL
)
summary_df
## Characteristic
## 1 response_time
## 2 age
## 3 sex
## 4 smoke_stat
## 5 ethnicity
## 6 provience
## 7 marital_stat
## 8 residence
## 9 undergrad_now
## 10 speciality
## 11 smoke_now.cigarettes
## 12 smoke_now.e_cigarettes
## 13 smoke_now.hookah
## 14 smoke_past.cigarettes
## 15 smoke_past.e_cigarettes
## 16 smoke_past.hookah
## 17 smoke_most_a
## 18 time_since_quitting
## 19 hc1
## 20 sc1
## 21 ir1
## 22 sp1
## 23 hc2
## 24 sc2
## 25 ir2
## 26 sp2
## 27 hc3
## 28 sc3
## 29 ir3
## 30 sp3
## 31 hc4
## 32 attention_check_a
## 33 sc4
## 34 ir4
## 35 sp4
## 36 hc5
## 37 sc5
## 38 ir5
## 39 sp5
## 40 smoke_most_b
## 41 auto1
## 42 loc1
## 43 tol1
## 44 craving1
## 45 taste1
## 46 cog_e1
## 47 w_control1
## 48 cue1
## 49 affect1
## 50 auto2
## 51 attach1
## 52 cue2
## 53 cog_e2
## 54 auto3
## 55 taste2
## 56 loc2
## 57 craving2
## 58 social1
## 59 w_control2
## 60 taste3
## 61 loc3
## 62 attach2
## 63 craving3
## 64 cue3
## 65 auto4
## 66 attach3
## 67 social2
## 68 attention_check_b
## 69 tol2
## 70 craving4
## 71 social3
## 72 tol3
## 73 cog_e3
## 74 affect2
## 75 w_control3
## 76 loc4
## 77 tol4
## 78 affect3
## 79 cigg_f1
## 80 cigg_f2
## 81 cigg_f3
## 82 cigg_f4
## 83 cigg_f5
## 84 cigg_f6
## 85 ecigg_f1
## 86 ecigg_f2
## 87 ecigg_f3
## 88 ecigg_f4
## 89 ecigg_f5
## 90 ecigg_f6
## 91 h_f1
## 92 h_f2
## 93 h_f3
## 94 h_f4
## 95 h_f5
## 96 h_f6
## 97 attention
## 98 response_threshold
## Distribution
## 1 Median: 350.0 (Q1: 338.0, Q3: 361.0)
## 2 Median: 21.0 (Q1: 19.0, Q3: 23.0)
## 3 Female: 2752 (50.4%); Male: 2705 (49.6%)
## 4 ex_smoker: 2373 (43.5%); smoker: 3084 (56.5%)
## 5 Arab: 2172 (39.8%); Kurd: 2184 (40%); Other: 236 (4.3%); Turkman: 865 (15.9%)
## 6 Baghdad: 1940 (35.6%); Erbil: 1299 (23.8%); Karbala: 1682 (30.8%); Kirkuk: 536 (9.8%)
## 7 Divorced: 445 (8.2%); Married: 1080 (19.8%); Single: 3818 (70%); Widowed: 114 (2.1%)
## 8 Rural: 1087 (19.9%); Urban: 4370 (80.1%)
## 9 Yes: 5457 (100%)
## 10 Business: 1045 (19.1%); Engineering: 1081 (19.8%); Medicine: 1090 (20%); Other: 1160 (21.3%); Sciences: 1081 (19.8%)
## 11 No: 3844 (70.4%); Yes: 1613 (29.6%)
## 12 No: 4367 (80%); Yes: 1090 (20%)
## 13 No: 4911 (90%); Yes: 546 (10%)
## 14 No: 3286 (60.2%); Yes: 2171 (39.8%)
## 15 No: 3826 (70.1%); Yes: 1631 (29.9%)
## 16 No: 4390 (80.4%); Yes: 1067 (19.6%)
## 17 cigarettes: 2130 (39%); e_cigarettes: 1683 (30.8%); hookah: 1115 (20.4%); none: 529 (9.7%)
## 18 <6 months: 2189 (40.1%); => 1 year: 1312 (24%); 6 months to 1 year: 1956 (35.8%)
## 19 0: 413 (7.6%); 1: 1462 (26.8%); 2: 2116 (38.8%); 3: 1220 (22.4%); 4: 246 (4.5%)
## 20 0: 343 (6.3%); 1: 1394 (25.5%); 2: 2174 (39.8%); 3: 1267 (23.2%); 4: 279 (5.1%)
## 21 0: 653 (12%); 1: 1846 (33.8%); 2: 2093 (38.4%); 3: 761 (13.9%); 4: 104 (1.9%)
## 22 0: 504 (9.2%); 1: 1658 (30.4%); 2: 2107 (38.6%); 3: 1009 (18.5%); 4: 179 (3.3%)
## 23 0: 370 (6.8%); 1: 1318 (24.2%); 2: 2139 (39.2%); 3: 1277 (23.4%); 4: 353 (6.5%)
## 24 0: 297 (5.4%); 1: 1225 (22.4%); 2: 2081 (38.1%); 3: 1460 (26.8%); 4: 394 (7.2%)
## 25 0: 597 (10.9%); 1: 1739 (31.9%); 2: 2075 (38%); 3: 903 (16.5%); 4: 143 (2.6%)
## 26 0: 589 (10.8%); 1: 1708 (31.3%); 2: 2092 (38.3%); 3: 901 (16.5%); 4: 167 (3.1%)
## 27 0: 495 (9.1%); 1: 1682 (30.8%); 2: 2194 (40.2%); 3: 933 (17.1%); 4: 153 (2.8%)
## 28 0: 383 (7%); 1: 1542 (28.3%); 2: 2275 (41.7%); 3: 1067 (19.6%); 4: 190 (3.5%)
## 29 0: 551 (10.1%); 1: 1639 (30%); 2: 2130 (39%); 3: 957 (17.5%); 4: 180 (3.3%)
## 30 0: 494 (9.1%); 1: 1542 (28.3%); 2: 2104 (38.6%); 3: 1098 (20.1%); 4: 219 (4%)
## 31 0: 470 (8.6%); 1: 1587 (29.1%); 2: 2163 (39.6%); 3: 1047 (19.2%); 4: 190 (3.5%)
## 32 1: 253 (4.6%); 2: 213 (3.9%); 3: 4052 (74.3%); 4: 240 (4.4%); 5: 235 (4.3%); 6: 231 (4.2%); 7: 233 (4.3%)
## 33 0: 311 (5.7%); 1: 1360 (24.9%); 2: 2091 (38.3%); 3: 1354 (24.8%); 4: 341 (6.2%)
## 34 0: 670 (12.3%); 1: 1879 (34.4%); 2: 2080 (38.1%); 3: 731 (13.4%); 4: 97 (1.8%)
## 35 0: 514 (9.4%); 1: 1701 (31.2%); 2: 2068 (37.9%); 3: 996 (18.3%); 4: 178 (3.3%)
## 36 0: 407 (7.5%); 1: 1371 (25.1%); 2: 2107 (38.6%); 3: 1280 (23.5%); 4: 292 (5.4%)
## 37 0: 356 (6.5%); 1: 1448 (26.5%); 2: 2204 (40.4%); 3: 1206 (22.1%); 4: 243 (4.5%)
## 38 0: 600 (11%); 1: 1762 (32.3%); 2: 2145 (39.3%); 3: 849 (15.6%); 4: 101 (1.9%)
## 39 0: 546 (10%); 1: 1779 (32.6%); 2: 2114 (38.7%); 3: 906 (16.6%); 4: 112 (2.1%)
## 40 cigarettes: 2262 (41.5%); e_cigarettes: 1580 (29%); hookah: 1094 (20%); none: 521 (9.5%)
## 41 1: 18 (0.3%); 2: 255 (4.7%); 3: 1172 (21.5%); 4: 2197 (40.3%); 5: 1418 (26%); 6: 374 (6.9%); 7: 23 (0.4%)
## 42 1: 20 (0.4%); 2: 306 (5.6%); 3: 1296 (23.7%); 4: 2128 (39%); 5: 1364 (25%); 6: 312 (5.7%); 7: 31 (0.6%)
## 43 1: 19 (0.3%); 2: 198 (3.6%); 3: 1102 (20.2%); 4: 2097 (38.4%); 5: 1576 (28.9%); 6: 409 (7.5%); 7: 56 (1%)
## 44 1: 23 (0.4%); 2: 270 (4.9%); 3: 1260 (23.1%); 4: 2143 (39.3%); 5: 1403 (25.7%); 6: 339 (6.2%); 7: 19 (0.3%)
## 45 1: 37 (0.7%); 2: 327 (6%); 3: 1400 (25.7%); 4: 2145 (39.3%); 5: 1220 (22.4%); 6: 306 (5.6%); 7: 22 (0.4%)
## 46 1: 6 (0.1%); 2: 205 (3.8%); 3: 1039 (19%); 4: 2116 (38.8%); 5: 1626 (29.8%); 6: 425 (7.8%); 7: 40 (0.7%)
## 47 1: 41 (0.8%); 2: 316 (5.8%); 3: 1399 (25.6%); 4: 2161 (39.6%); 5: 1216 (22.3%); 6: 299 (5.5%); 7: 25 (0.5%)
## 48 1: 27 (0.5%); 2: 283 (5.2%); 3: 1164 (21.3%); 4: 2120 (38.8%); 5: 1441 (26.4%); 6: 382 (7%); 7: 40 (0.7%)
## 49 1: 19 (0.3%); 2: 227 (4.2%); 3: 1156 (21.2%); 4: 2241 (41.1%); 5: 1461 (26.8%); 6: 330 (6%); 7: 23 (0.4%)
## 50 1: 14 (0.3%); 2: 251 (4.6%); 3: 1241 (22.7%); 4: 2183 (40%); 5: 1403 (25.7%); 6: 333 (6.1%); 7: 32 (0.6%)
## 51 1: 23 (0.4%); 2: 257 (4.7%); 3: 1301 (23.8%); 4: 2168 (39.7%); 5: 1364 (25%); 6: 323 (5.9%); 7: 21 (0.4%)
## 52 1: 17 (0.3%); 2: 238 (4.4%); 3: 1178 (21.6%); 4: 2159 (39.6%); 5: 1458 (26.7%); 6: 364 (6.7%); 7: 43 (0.8%)
## 53 1: 22 (0.4%); 2: 216 (4%); 3: 1093 (20%); 4: 2053 (37.6%); 5: 1599 (29.3%); 6: 430 (7.9%); 7: 44 (0.8%)
## 54 1: 21 (0.4%); 2: 260 (4.8%); 3: 1167 (21.4%); 4: 2172 (39.8%); 5: 1468 (26.9%); 6: 332 (6.1%); 7: 37 (0.7%)
## 55 1: 27 (0.5%); 2: 347 (6.4%); 3: 1358 (24.9%); 4: 2170 (39.8%); 5: 1255 (23%); 6: 282 (5.2%); 7: 18 (0.3%)
## 56 1: 15 (0.3%); 2: 285 (5.2%); 3: 1312 (24%); 4: 2154 (39.5%); 5: 1363 (25%); 6: 306 (5.6%); 7: 22 (0.4%)
## 57 1: 17 (0.3%); 2: 262 (4.8%); 3: 1296 (23.7%); 4: 2189 (40.1%); 5: 1370 (25.1%); 6: 299 (5.5%); 7: 24 (0.4%)
## 58 1: 22 (0.4%); 2: 251 (4.6%); 3: 1238 (22.7%); 4: 2163 (39.6%); 5: 1416 (25.9%); 6: 345 (6.3%); 7: 22 (0.4%)
## 59 1: 35 (0.6%); 2: 302 (5.5%); 3: 1422 (26.1%); 4: 2095 (38.4%); 5: 1274 (23.3%); 6: 298 (5.5%); 7: 31 (0.6%)
## 60 1: 25 (0.5%); 2: 315 (5.8%); 3: 1360 (24.9%); 4: 2184 (40%); 5: 1280 (23.5%); 6: 268 (4.9%); 7: 25 (0.5%)
## 61 1: 18 (0.3%); 2: 255 (4.7%); 3: 1318 (24.2%); 4: 2173 (39.8%); 5: 1343 (24.6%); 6: 323 (5.9%); 7: 27 (0.5%)
## 62 1: 30 (0.5%); 2: 269 (4.9%); 3: 1226 (22.5%); 4: 2189 (40.1%); 5: 1358 (24.9%); 6: 348 (6.4%); 7: 37 (0.7%)
## 63 1: 18 (0.3%); 2: 263 (4.8%); 3: 1252 (22.9%); 4: 2163 (39.6%); 5: 1408 (25.8%); 6: 324 (5.9%); 7: 29 (0.5%)
## 64 1: 21 (0.4%); 2: 274 (5%); 3: 1208 (22.1%); 4: 2064 (37.8%); 5: 1455 (26.7%); 6: 398 (7.3%); 7: 37 (0.7%)
## 65 1: 16 (0.3%); 2: 264 (4.8%); 3: 1183 (21.7%); 4: 2198 (40.3%); 5: 1400 (25.7%); 6: 358 (6.6%); 7: 38 (0.7%)
## 66 1: 23 (0.4%); 2: 261 (4.8%); 3: 1283 (23.5%); 4: 2213 (40.6%); 5: 1323 (24.2%); 6: 325 (6%); 7: 29 (0.5%)
## 67 1: 13 (0.2%); 2: 268 (4.9%); 3: 1185 (21.7%); 4: 2212 (40.5%); 5: 1411 (25.9%); 6: 340 (6.2%); 7: 28 (0.5%)
## 68 1: 236 (4.3%); 2: 198 (3.6%); 3: 213 (3.9%); 4: 217 (4%); 5: 206 (3.8%); 6: 4169 (76.4%); 7: 218 (4%)
## 69 1: 22 (0.4%); 2: 189 (3.5%); 3: 1044 (19.1%); 4: 2106 (38.6%); 5: 1622 (29.7%); 6: 427 (7.8%); 7: 47 (0.9%)
## 70 1: 22 (0.4%); 2: 252 (4.6%); 3: 1267 (23.2%); 4: 2168 (39.7%); 5: 1382 (25.3%); 6: 336 (6.2%); 7: 30 (0.5%)
## 71 1: 22 (0.4%); 2: 246 (4.5%); 3: 1261 (23.1%); 4: 2154 (39.5%); 5: 1407 (25.8%); 6: 333 (6.1%); 7: 34 (0.6%)
## 72 1: 16 (0.3%); 2: 199 (3.6%); 3: 1058 (19.4%); 4: 2188 (40.1%); 5: 1516 (27.8%); 6: 426 (7.8%); 7: 54 (1%)
## 73 1: 21 (0.4%); 2: 201 (3.7%); 3: 1047 (19.2%); 4: 2141 (39.2%); 5: 1549 (28.4%); 6: 443 (8.1%); 7: 55 (1%)
## 74 1: 25 (0.5%); 2: 210 (3.8%); 3: 1156 (21.2%); 4: 2188 (40.1%); 5: 1490 (27.3%); 6: 356 (6.5%); 7: 32 (0.6%)
## 75 1: 32 (0.6%); 2: 335 (6.1%); 3: 1334 (24.4%); 4: 2144 (39.3%); 5: 1310 (24%); 6: 279 (5.1%); 7: 23 (0.4%)
## 76 1: 20 (0.4%); 2: 296 (5.4%); 3: 1310 (24%); 4: 2152 (39.4%); 5: 1363 (25%); 6: 278 (5.1%); 7: 38 (0.7%)
## 77 1: 13 (0.2%); 2: 243 (4.5%); 3: 1021 (18.7%); 4: 2163 (39.6%); 5: 1544 (28.3%); 6: 431 (7.9%); 7: 42 (0.8%)
## 78 1: 18 (0.3%); 2: 267 (4.9%); 3: 1178 (21.6%); 4: 2120 (38.8%); 5: 1496 (27.4%); 6: 345 (6.3%); 7: 33 (0.6%)
## 79 0: 1632 (29.9%); 1: 1618 (29.6%); 2: 1075 (19.7%); 3: 1132 (20.7%)
## 80 0: 3332 (61.1%); 1: 2125 (38.9%)
## 81 0: 2786 (51.1%); 1: 2671 (48.9%)
## 82 0: 2210 (40.5%); 1: 1662 (30.5%); 2: 1028 (18.8%); 3: 557 (10.2%)
## 83 0: 3799 (69.6%); 1: 1658 (30.4%)
## 84 0: 4385 (80.4%); 1: 1072 (19.6%)
## 85 0: 3012 (55.2%); 1: 1374 (25.2%); 2: 1071 (19.6%)
## 86 0: 3317 (60.8%); 1: 2140 (39.2%)
## 87 0: 2973 (54.5%); 1: 1368 (25.1%); 2: 1116 (20.5%)
## 88 0: 3033 (55.6%); 1: 1363 (25%); 2: 1061 (19.4%)
## 89 0: 2185 (40%); 1: 1604 (29.4%); 2: 1127 (20.7%); 3: 541 (9.9%)
## 90 0: 4327 (79.3%); 1: 1130 (20.7%)
## 91 0: 2745 (50.3%); 1: 1634 (29.9%); 2: 824 (15.1%); 3: 254 (4.7%)
## 92 0: 4379 (80.2%); 1: 1078 (19.8%)
## 93 0: 3788 (69.4%); 1: 1669 (30.6%)
## 94 0: 3298 (60.4%); 1: 1316 (24.1%); 2: 569 (10.4%); 3: 274 (5%)
## 95 0: 4901 (89.8%); 1: 556 (10.2%)
## 96 0: 4620 (84.7%); 1: 837 (15.3%)
## 97 Yes: 5457 (100%)
## 98 Median: 188.0 (Q1: 188.0, Q3: 188.0)
#Return behavioral scales into numeric variables for analysis
data_cleaned[, scale_cols] <- lapply(data_cleaned[, scale_cols], function(x) {
as.numeric(as.character(x))
})
#Calculate the item-item correlation matrix for the WISDM-37 and write into an excel file
wisdm37 <- setdiff(44:81, 71)
wisdm37_cor <- as.data.frame(cor(data_cleaned[,wisdm37],
method = "spearman")
)
write_xlsx(wisdm37_cor,
"WISDM-37 item-item correlation matrix.xlsx"
)
#Calculate the item-item correlation matrix for the RFQ and write into an excel file
rfq <- setdiff(22:42, 35)
rfq_cor <- as.data.frame(cor(data_cleaned[,rfq],
method = "spearman")
)
write_xlsx(rfq_cor,
"RFQ item-item correlation matrix.xlsx"
)
#Confirmatory factor analysis for the WISDM-37 questionnaire:
#Based on continuous variables:
#Model 1: 1-factor solution
cont_mod1 <-'f =~ auto1 + auto2 + auto3 + auto4 +
loc1 + loc2 + loc3 + loc4 +
tol1 + tol2 + tol3 + tol4 +
craving1 + craving2 + craving3 + craving4 +
taste1 + taste2 + taste3 +
cog_e1 + cog_e2 + cog_e3 +
w_control1 + w_control2 + w_control3 +
cue1 + cue2 + cue3 +
affect1 + affect2 + affect3 +
attach1 + attach2 + attach3 +
social1 + social2 +social3
'
cont_mod1_fit <- cfa(cont_mod1,
data = data_cleaned,
estimator = "MLR"
)
summary(cont_mod1_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 39 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 74
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 10301.810 10342.332
## Degrees of freedom 629 629
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.996
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 25978.585 25963.681
## Degrees of freedom 666 666
## P-value 0.000 0.000
## Scaling correction factor 1.001
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.618 0.616
## Tucker-Lewis Index (TLI) 0.595 0.593
##
## Robust Comparative Fit Index (CFI) 0.618
## Robust Tucker-Lewis Index (TLI) 0.595
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -279425.984 -279425.984
## Scaling correction factor 1.029
## for the MLR correction
## Loglikelihood unrestricted model (H1) -274275.079 -274275.079
## Scaling correction factor 1.000
## for the MLR correction
##
## Akaike (AIC) 558999.969 558999.969
## Bayesian (BIC) 559488.713 559488.713
## Sample-size adjusted Bayesian (SABIC) 559253.564 559253.564
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.053 0.053
## 90 Percent confidence interval - lower 0.052 0.052
## 90 Percent confidence interval - upper 0.054 0.054
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.053
## 90 Percent confidence interval - lower 0.052
## 90 Percent confidence interval - upper 0.054
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.057 0.057
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f =~
## auto1 1.000 0.346 0.347
## auto2 0.940 0.048 19.405 0.000 0.325 0.328
## auto3 0.993 0.050 19.719 0.000 0.343 0.343
## auto4 1.016 0.049 20.640 0.000 0.352 0.350
## loc1 1.003 0.057 17.475 0.000 0.347 0.343
## loc2 0.930 0.054 17.077 0.000 0.322 0.324
## loc3 0.955 0.057 16.795 0.000 0.330 0.333
## loc4 0.978 0.058 16.895 0.000 0.338 0.337
## tol1 1.019 0.057 17.883 0.000 0.352 0.348
## tol2 0.991 0.056 17.794 0.000 0.343 0.341
## tol3 1.032 0.058 17.746 0.000 0.357 0.355
## tol4 1.015 0.058 17.643 0.000 0.351 0.347
## craving1 0.969 0.056 17.264 0.000 0.335 0.335
## craving2 1.038 0.059 17.711 0.000 0.359 0.365
## craving3 1.000 0.057 17.689 0.000 0.346 0.347
## craving4 0.928 0.055 16.945 0.000 0.321 0.321
## taste1 1.034 0.071 14.566 0.000 0.358 0.351
## taste2 1.047 0.072 14.518 0.000 0.362 0.360
## taste3 1.010 0.070 14.494 0.000 0.349 0.351
## cog_e1 1.004 0.070 14.413 0.000 0.347 0.350
## cog_e2 0.997 0.071 13.993 0.000 0.345 0.338
## cog_e3 1.009 0.072 14.028 0.000 0.349 0.343
## w_control1 1.007 0.071 14.242 0.000 0.348 0.342
## w_control2 1.076 0.073 14.744 0.000 0.372 0.365
## w_control3 0.980 0.070 13.984 0.000 0.339 0.335
## cue1 1.074 0.073 14.619 0.000 0.371 0.360
## cue2 1.020 0.069 14.734 0.000 0.353 0.351
## cue3 1.110 0.076 14.519 0.000 0.384 0.372
## affect1 0.947 0.065 14.589 0.000 0.328 0.336
## affect2 0.973 0.068 14.306 0.000 0.336 0.340
## affect3 0.916 0.066 13.937 0.000 0.317 0.315
## attach1 0.955 0.066 14.491 0.000 0.330 0.333
## attach2 1.046 0.071 14.682 0.000 0.362 0.356
## attach3 0.923 0.068 13.638 0.000 0.319 0.321
## social1 1.005 0.070 14.394 0.000 0.348 0.349
## social2 1.002 0.069 14.585 0.000 0.347 0.350
## social3 0.972 0.068 14.210 0.000 0.336 0.336
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .auto1 0.875 0.017 50.799 0.000 0.875 0.880
## .auto2 0.878 0.017 50.966 0.000 0.878 0.893
## .auto3 0.886 0.018 49.572 0.000 0.886 0.883
## .auto4 0.887 0.018 50.343 0.000 0.887 0.878
## .loc1 0.904 0.018 51.009 0.000 0.904 0.883
## .loc2 0.881 0.017 52.041 0.000 0.881 0.895
## .loc3 0.875 0.017 50.165 0.000 0.875 0.889
## .loc4 0.892 0.018 50.160 0.000 0.892 0.886
## .tol1 0.901 0.018 49.275 0.000 0.901 0.879
## .tol2 0.894 0.018 49.539 0.000 0.894 0.884
## .tol3 0.883 0.018 49.919 0.000 0.883 0.874
## .tol4 0.899 0.018 49.946 0.000 0.899 0.879
## .craving1 0.889 0.017 51.398 0.000 0.889 0.888
## .craving2 0.838 0.016 51.419 0.000 0.838 0.867
## .craving3 0.872 0.018 49.627 0.000 0.872 0.879
## .craving4 0.896 0.018 50.877 0.000 0.896 0.897
## .taste1 0.911 0.018 49.716 0.000 0.911 0.877
## .taste2 0.881 0.017 51.837 0.000 0.881 0.871
## .taste3 0.871 0.017 50.356 0.000 0.871 0.877
## .cog_e1 0.863 0.017 51.078 0.000 0.863 0.877
## .cog_e2 0.923 0.019 49.766 0.000 0.923 0.886
## .cog_e3 0.912 0.018 49.297 0.000 0.912 0.882
## .w_control1 0.915 0.019 49.163 0.000 0.915 0.883
## .w_control2 0.903 0.018 50.924 0.000 0.903 0.867
## .w_control3 0.910 0.018 50.874 0.000 0.910 0.888
## .cue1 0.929 0.019 49.275 0.000 0.929 0.871
## .cue2 0.886 0.018 49.970 0.000 0.886 0.877
## .cue3 0.919 0.018 50.605 0.000 0.919 0.862
## .affect1 0.841 0.017 50.726 0.000 0.841 0.887
## .affect2 0.867 0.017 50.226 0.000 0.867 0.885
## .affect3 0.913 0.018 50.844 0.000 0.913 0.901
## .attach1 0.876 0.017 51.333 0.000 0.876 0.889
## .attach2 0.902 0.018 49.527 0.000 0.902 0.873
## .attach3 0.890 0.017 50.877 0.000 0.890 0.897
## .social1 0.870 0.017 50.953 0.000 0.870 0.878
## .social2 0.863 0.017 51.217 0.000 0.863 0.878
## .social3 0.888 0.017 50.872 0.000 0.888 0.887
## f 0.120 0.011 10.586 0.000 1.000 1.000
#Model 2: 11-factor solution
cont_mod2 <-'#Primary dependence motives
auto =~ auto1 + auto2 + auto3 + auto4
loc =~ loc1 + loc2 + loc3 + loc4
tol =~ tol1 + tol2 + tol3 + tol4
craving =~ craving1 + craving2 + craving3 + craving4
#Secondary dependence motives
taste =~ taste1 + taste2 + taste3
cog_e =~ cog_e1 + cog_e2 + cog_e3
w_control =~ w_control1 + w_control2 + w_control3
cue =~ cue1 + cue2 + cue3
affect =~ affect1 + affect2 + affect3
attach =~ attach1 + attach2 + attach3
social =~ social1 + social2 +social3
'
cont_mod2_fit <- cfa(cont_mod2,
data = data_cleaned,
estimator = "MLR"
)
summary(cont_mod2_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 84 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 129
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 531.570 531.350
## Degrees of freedom 574 574
## P-value (Chi-square) 0.897 0.898
## Scaling correction factor 1.000
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 25978.585 25963.681
## Degrees of freedom 666 666
## P-value 0.000 0.000
## Scaling correction factor 1.001
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.002 1.002
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.002
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -274540.864 -274540.864
## Scaling correction factor 0.996
## for the MLR correction
## Loglikelihood unrestricted model (H1) -274275.079 -274275.079
## Scaling correction factor 1.000
## for the MLR correction
##
## Akaike (AIC) 549339.728 549339.728
## Bayesian (BIC) 550191.729 550191.729
## Sample-size adjusted Bayesian (SABIC) 549781.807 549781.807
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.002 0.002
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.002
## P-value H_0: Robust RMSEA <= 0.050 1.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.010 0.010
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## auto =~
## auto1 1.000 0.547 0.548
## auto2 0.968 0.038 25.183 0.000 0.529 0.534
## auto3 0.969 0.038 25.180 0.000 0.529 0.528
## auto4 0.971 0.040 24.516 0.000 0.531 0.528
## loc =~
## loc1 1.000 0.542 0.536
## loc2 0.941 0.038 24.702 0.000 0.510 0.514
## loc3 0.978 0.039 25.257 0.000 0.530 0.534
## loc4 0.965 0.039 24.553 0.000 0.523 0.521
## tol =~
## tol1 1.000 0.548 0.542
## tol2 0.952 0.038 24.973 0.000 0.522 0.519
## tol3 1.004 0.040 25.205 0.000 0.550 0.548
## tol4 1.011 0.040 25.429 0.000 0.554 0.548
## craving =~
## craving1 1.000 0.506 0.506
## craving2 1.031 0.042 24.267 0.000 0.522 0.531
## craving3 1.069 0.044 24.077 0.000 0.541 0.544
## craving4 0.987 0.043 23.175 0.000 0.500 0.500
## taste =~
## taste1 1.000 0.546 0.536
## taste2 1.017 0.044 23.117 0.000 0.556 0.552
## taste3 0.975 0.041 23.616 0.000 0.533 0.535
## cog_e =~
## cog_e1 1.000 0.532 0.537
## cog_e2 1.018 0.043 23.560 0.000 0.542 0.531
## cog_e3 1.006 0.045 22.164 0.000 0.535 0.527
## w_control =~
## w_control1 1.000 0.555 0.545
## w_control2 1.046 0.044 23.753 0.000 0.581 0.569
## w_control3 0.968 0.040 23.913 0.000 0.537 0.531
## cue =~
## cue1 1.000 0.580 0.562
## cue2 0.940 0.037 25.735 0.000 0.545 0.542
## cue3 1.004 0.042 24.048 0.000 0.582 0.564
## affect =~
## affect1 1.000 0.495 0.508
## affect2 1.073 0.051 21.011 0.000 0.531 0.536
## affect3 1.011 0.047 21.665 0.000 0.500 0.497
## attach =~
## attach1 1.000 0.516 0.519
## attach2 1.095 0.048 22.759 0.000 0.564 0.555
## attach3 0.995 0.045 22.005 0.000 0.513 0.515
## social =~
## social1 1.000 0.517 0.519
## social2 1.008 0.046 21.672 0.000 0.521 0.525
## social3 0.987 0.045 21.803 0.000 0.511 0.510
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## auto ~~
## loc 0.160 0.009 18.224 0.000 0.539 0.539
## tol 0.160 0.009 18.086 0.000 0.534 0.534
## craving 0.154 0.009 17.802 0.000 0.557 0.557
## taste 0.078 0.008 10.235 0.000 0.260 0.260
## cog_e 0.067 0.007 9.231 0.000 0.230 0.230
## w_control 0.074 0.008 9.851 0.000 0.245 0.245
## cue 0.089 0.008 10.818 0.000 0.281 0.281
## affect 0.070 0.007 9.500 0.000 0.260 0.260
## attach 0.073 0.007 9.823 0.000 0.258 0.258
## social 0.076 0.007 10.323 0.000 0.269 0.269
## loc ~~
## tol 0.170 0.009 18.689 0.000 0.573 0.573
## craving 0.158 0.009 17.910 0.000 0.577 0.577
## taste 0.070 0.007 9.525 0.000 0.235 0.235
## cog_e 0.074 0.007 10.104 0.000 0.255 0.255
## w_control 0.067 0.007 9.280 0.000 0.224 0.224
## cue 0.076 0.008 9.738 0.000 0.241 0.241
## affect 0.067 0.007 9.365 0.000 0.251 0.251
## attach 0.070 0.007 9.805 0.000 0.250 0.250
## social 0.072 0.007 9.867 0.000 0.256 0.256
## tol ~~
## craving 0.162 0.009 18.525 0.000 0.583 0.583
## taste 0.075 0.008 9.825 0.000 0.250 0.250
## cog_e 0.074 0.007 9.925 0.000 0.253 0.253
## w_control 0.078 0.008 10.314 0.000 0.255 0.255
## cue 0.072 0.008 9.330 0.000 0.225 0.225
## affect 0.067 0.007 9.324 0.000 0.246 0.246
## attach 0.069 0.007 9.725 0.000 0.244 0.244
## social 0.082 0.007 11.160 0.000 0.288 0.288
## craving ~~
## taste 0.075 0.007 10.144 0.000 0.270 0.270
## cog_e 0.069 0.007 9.724 0.000 0.258 0.258
## w_control 0.070 0.007 9.912 0.000 0.249 0.249
## cue 0.076 0.008 10.098 0.000 0.259 0.259
## affect 0.063 0.007 9.373 0.000 0.254 0.254
## attach 0.070 0.007 10.061 0.000 0.269 0.269
## social 0.073 0.007 10.232 0.000 0.279 0.279
## taste ~~
## cog_e 0.151 0.009 16.442 0.000 0.519 0.519
## w_control 0.146 0.009 15.876 0.000 0.481 0.481
## cue 0.159 0.009 17.291 0.000 0.502 0.502
## affect 0.137 0.009 15.592 0.000 0.506 0.506
## attach 0.135 0.009 15.778 0.000 0.480 0.480
## social 0.148 0.009 15.802 0.000 0.523 0.523
## cog_e ~~
## w_control 0.147 0.009 16.239 0.000 0.497 0.497
## cue 0.151 0.009 16.148 0.000 0.490 0.490
## affect 0.131 0.009 15.265 0.000 0.496 0.496
## attach 0.135 0.009 15.281 0.000 0.491 0.491
## social 0.140 0.009 15.772 0.000 0.510 0.510
## w_control ~~
## cue 0.156 0.009 16.763 0.000 0.483 0.483
## affect 0.138 0.009 15.413 0.000 0.502 0.502
## attach 0.139 0.009 15.946 0.000 0.486 0.486
## social 0.135 0.009 15.031 0.000 0.470 0.470
## cue ~~
## affect 0.146 0.009 15.936 0.000 0.509 0.509
## attach 0.142 0.009 15.366 0.000 0.475 0.475
## social 0.154 0.010 16.156 0.000 0.514 0.514
## affect ~~
## attach 0.117 0.008 14.551 0.000 0.459 0.459
## social 0.129 0.009 14.806 0.000 0.503 0.503
## attach ~~
## social 0.133 0.009 15.340 0.000 0.499 0.499
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .auto1 0.696 0.016 42.270 0.000 0.696 0.700
## .auto2 0.704 0.016 42.687 0.000 0.704 0.715
## .auto3 0.724 0.017 41.533 0.000 0.724 0.721
## .auto4 0.729 0.018 41.395 0.000 0.729 0.722
## .loc1 0.730 0.017 42.836 0.000 0.730 0.713
## .loc2 0.724 0.017 43.443 0.000 0.724 0.735
## .loc3 0.703 0.017 42.150 0.000 0.703 0.714
## .loc4 0.733 0.017 43.098 0.000 0.733 0.728
## .tol1 0.725 0.017 42.656 0.000 0.725 0.707
## .tol2 0.738 0.017 43.698 0.000 0.738 0.730
## .tol3 0.707 0.017 41.787 0.000 0.707 0.700
## .tol4 0.715 0.017 42.646 0.000 0.715 0.699
## .craving1 0.745 0.017 43.770 0.000 0.745 0.744
## .craving2 0.694 0.016 43.050 0.000 0.694 0.718
## .craving3 0.699 0.017 42.031 0.000 0.699 0.705
## .craving4 0.749 0.017 44.774 0.000 0.749 0.750
## .taste1 0.741 0.018 40.262 0.000 0.741 0.713
## .taste2 0.703 0.018 38.949 0.000 0.703 0.695
## .taste3 0.709 0.017 40.726 0.000 0.709 0.714
## .cog_e1 0.700 0.018 39.701 0.000 0.700 0.712
## .cog_e2 0.749 0.019 39.891 0.000 0.749 0.718
## .cog_e3 0.747 0.019 40.224 0.000 0.747 0.723
## .w_control1 0.729 0.019 38.881 0.000 0.729 0.703
## .w_control2 0.705 0.018 38.443 0.000 0.705 0.676
## .w_control3 0.736 0.018 40.336 0.000 0.736 0.719
## .cue1 0.730 0.019 38.860 0.000 0.730 0.684
## .cue2 0.714 0.018 39.720 0.000 0.714 0.706
## .cue3 0.727 0.019 39.220 0.000 0.727 0.682
## .affect1 0.703 0.018 39.681 0.000 0.703 0.742
## .affect2 0.698 0.018 38.442 0.000 0.698 0.712
## .affect3 0.763 0.018 41.460 0.000 0.763 0.753
## .attach1 0.719 0.018 40.914 0.000 0.719 0.730
## .attach2 0.715 0.019 37.869 0.000 0.715 0.692
## .attach3 0.729 0.018 40.218 0.000 0.729 0.735
## .social1 0.724 0.018 40.489 0.000 0.724 0.730
## .social2 0.712 0.018 39.457 0.000 0.712 0.724
## .social3 0.741 0.018 41.441 0.000 0.741 0.740
## auto 0.299 0.017 17.258 0.000 1.000 1.000
## loc 0.294 0.017 17.159 0.000 1.000 1.000
## tol 0.301 0.017 17.284 0.000 1.000 1.000
## craving 0.256 0.016 16.069 0.000 1.000 1.000
## taste 0.298 0.018 16.324 0.000 1.000 1.000
## cog_e 0.283 0.017 16.367 0.000 1.000 1.000
## w_control 0.308 0.019 16.575 0.000 1.000 1.000
## cue 0.336 0.019 17.502 0.000 1.000 1.000
## affect 0.245 0.016 14.984 0.000 1.000 1.000
## attach 0.266 0.017 15.469 0.000 1.000 1.000
## social 0.267 0.017 15.580 0.000 1.000 1.000
#Model 3: 2-factor solution
cont_mod3 <- 'pdm =~ auto1 + auto2 + auto3 + auto4 +
loc1 + loc2 + loc3 + loc4 +
tol1 + tol2 + tol3 + tol4 +
craving1 + craving2 + craving3 + craving4
sdm =~ taste1 + taste2 + taste3 +
cog_e1 + cog_e2 + cog_e3 +
w_control1 + w_control2 + w_control3 +
cue1 + cue2 + cue3 +
affect1 + affect2 + affect3 +
attach1 + attach2 + attach3 +
social1 + social2 +social3
'
cont_mod3_fit <- cfa(cont_mod3,
data = data_cleaned,
estimator = "MLR"
)
summary(cont_mod3_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 43 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 75
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 5270.999 5275.290
## Degrees of freedom 628 628
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.999
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 25978.585 25963.681
## Degrees of freedom 666 666
## P-value 0.000 0.000
## Scaling correction factor 1.001
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.817 0.816
## Tucker-Lewis Index (TLI) 0.805 0.805
##
## Robust Comparative Fit Index (CFI) 0.817
## Robust Tucker-Lewis Index (TLI) 0.805
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -276910.579 -276910.579
## Scaling correction factor 1.003
## for the MLR correction
## Loglikelihood unrestricted model (H1) -274275.079 -274275.079
## Scaling correction factor 1.000
## for the MLR correction
##
## Akaike (AIC) 553971.158 553971.158
## Bayesian (BIC) 554466.507 554466.507
## Sample-size adjusted Bayesian (SABIC) 554228.180 554228.180
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.037 0.037
## 90 Percent confidence interval - lower 0.036 0.036
## 90 Percent confidence interval - upper 0.038 0.038
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.037
## 90 Percent confidence interval - lower 0.036
## 90 Percent confidence interval - upper 0.038
## P-value H_0: Robust RMSEA <= 0.050 1.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.033 0.033
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pdm =~
## auto1 1.000 0.421 0.422
## auto2 0.984 0.041 23.985 0.000 0.414 0.418
## auto3 0.996 0.042 23.469 0.000 0.420 0.419
## auto4 1.010 0.042 23.922 0.000 0.425 0.423
## loc1 1.036 0.052 20.066 0.000 0.436 0.431
## loc2 0.977 0.049 19.936 0.000 0.412 0.415
## loc3 0.999 0.051 19.671 0.000 0.421 0.424
## loc4 1.008 0.052 19.409 0.000 0.424 0.423
## tol1 1.052 0.052 20.417 0.000 0.443 0.438
## tol2 1.016 0.051 19.761 0.000 0.428 0.426
## tol3 1.060 0.053 19.818 0.000 0.446 0.444
## tol4 1.078 0.053 20.406 0.000 0.454 0.449
## craving1 0.986 0.050 19.915 0.000 0.415 0.415
## craving2 1.007 0.050 20.059 0.000 0.424 0.431
## craving3 1.050 0.051 20.662 0.000 0.442 0.444
## craving4 0.966 0.049 19.651 0.000 0.407 0.407
## sdm =~
## taste1 1.000 0.412 0.404
## taste2 1.005 0.043 23.159 0.000 0.414 0.412
## taste3 0.968 0.041 23.434 0.000 0.399 0.401
## cog_e1 0.962 0.049 19.807 0.000 0.397 0.400
## cog_e2 0.962 0.049 19.682 0.000 0.397 0.389
## cog_e3 0.981 0.049 19.829 0.000 0.404 0.398
## w_control1 0.977 0.051 19.264 0.000 0.403 0.396
## w_control2 1.037 0.051 20.461 0.000 0.428 0.419
## w_control3 0.945 0.050 18.742 0.000 0.390 0.385
## cue1 1.039 0.053 19.727 0.000 0.428 0.415
## cue2 0.970 0.050 19.465 0.000 0.400 0.398
## cue3 1.081 0.054 20.141 0.000 0.446 0.432
## affect1 0.885 0.047 18.928 0.000 0.365 0.375
## affect2 0.951 0.050 18.997 0.000 0.392 0.396
## affect3 0.874 0.048 18.237 0.000 0.361 0.358
## attach1 0.895 0.047 18.872 0.000 0.369 0.372
## attach2 0.980 0.050 19.721 0.000 0.404 0.397
## attach3 0.900 0.048 18.610 0.000 0.371 0.373
## social1 0.950 0.048 19.694 0.000 0.392 0.393
## social2 0.939 0.049 19.249 0.000 0.387 0.391
## social3 0.917 0.046 19.730 0.000 0.378 0.378
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pdm ~~
## sdm 0.074 0.005 15.080 0.000 0.427 0.427
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .auto1 0.818 0.016 50.585 0.000 0.818 0.822
## .auto2 0.812 0.016 49.543 0.000 0.812 0.825
## .auto3 0.828 0.018 47.328 0.000 0.828 0.825
## .auto4 0.830 0.017 48.357 0.000 0.830 0.821
## .loc1 0.834 0.017 49.547 0.000 0.834 0.814
## .loc2 0.815 0.016 50.015 0.000 0.815 0.828
## .loc3 0.807 0.016 49.385 0.000 0.807 0.820
## .loc4 0.826 0.017 49.072 0.000 0.826 0.821
## .tol1 0.829 0.017 48.631 0.000 0.829 0.808
## .tol2 0.828 0.017 48.984 0.000 0.828 0.819
## .tol3 0.811 0.017 48.278 0.000 0.811 0.803
## .tol4 0.816 0.017 48.889 0.000 0.816 0.798
## .craving1 0.829 0.016 50.479 0.000 0.829 0.828
## .craving2 0.787 0.016 50.314 0.000 0.787 0.814
## .craving3 0.796 0.016 48.622 0.000 0.796 0.803
## .craving4 0.834 0.017 50.268 0.000 0.834 0.834
## .taste1 0.869 0.018 49.533 0.000 0.869 0.836
## .taste2 0.840 0.016 50.959 0.000 0.840 0.830
## .taste3 0.833 0.017 49.800 0.000 0.833 0.839
## .cog_e1 0.826 0.016 50.295 0.000 0.826 0.840
## .cog_e2 0.885 0.018 49.505 0.000 0.885 0.849
## .cog_e3 0.870 0.018 48.929 0.000 0.870 0.842
## .w_control1 0.874 0.018 48.740 0.000 0.874 0.844
## .w_control2 0.859 0.017 50.551 0.000 0.859 0.824
## .w_control3 0.872 0.017 49.865 0.000 0.872 0.852
## .cue1 0.883 0.018 48.979 0.000 0.883 0.828
## .cue2 0.851 0.017 49.880 0.000 0.851 0.842
## .cue3 0.868 0.017 50.071 0.000 0.868 0.814
## .affect1 0.815 0.016 49.921 0.000 0.815 0.860
## .affect2 0.826 0.017 49.736 0.000 0.826 0.843
## .affect3 0.884 0.017 50.823 0.000 0.884 0.872
## .attach1 0.849 0.017 50.864 0.000 0.849 0.862
## .attach2 0.870 0.018 49.081 0.000 0.870 0.842
## .attach3 0.854 0.017 50.160 0.000 0.854 0.861
## .social1 0.838 0.017 50.235 0.000 0.838 0.845
## .social2 0.833 0.017 49.815 0.000 0.833 0.847
## .social3 0.859 0.017 50.585 0.000 0.859 0.857
## pdm 0.177 0.013 13.801 0.000 1.000 1.000
## sdm 0.170 0.012 13.769 0.000 1.000 1.000
#Model 4: 2-order solution
cont_mod4 <- '#1st order
auto =~ auto1 + auto2 + auto3 + auto4
loc =~ loc1 + loc2 + loc3 + loc4
tol =~ tol1 + tol2 + tol3 + tol4
craving =~ craving1 + craving2 + craving3 + craving4
taste =~ taste1 + taste2 + taste3
cog_e =~ cog_e1 + cog_e2 + cog_e3
w_control =~ w_control1 + w_control2 + w_control3
cue =~ cue1 + cue2 + cue3
affect =~ affect1 + affect2 + affect3
attach =~ attach1 + attach2 + attach3
social =~ social1 + social2 +social3
#2nd order
pdm =~ auto + loc + tol + craving
sdm =~ taste + cog_e + w_control + cue + affect + attach + social
'
cont_mod4_fit <- cfa(cont_mod4,
data = data_cleaned,
estimator = "MLR"
)
summary(cont_mod4_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 63 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 86
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 557.072 556.735
## Degrees of freedom 617 617
## P-value (Chi-square) 0.960 0.960
## Scaling correction factor 1.001
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 25978.585 25963.681
## Degrees of freedom 666 666
## P-value 0.000 0.000
## Scaling correction factor 1.001
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.003 1.003
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.003
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -274553.615 -274553.615
## Scaling correction factor 0.992
## for the MLR correction
## Loglikelihood unrestricted model (H1) -274275.079 -274275.079
## Scaling correction factor 1.000
## for the MLR correction
##
## Akaike (AIC) 549279.231 549279.231
## Bayesian (BIC) 549847.231 549847.231
## Sample-size adjusted Bayesian (SABIC) 549573.950 549573.950
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: Robust RMSEA <= 0.050 1.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.011 0.011
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## auto =~
## auto1 1.000 0.547 0.548
## auto2 0.969 0.038 25.205 0.000 0.530 0.534
## auto3 0.969 0.038 25.192 0.000 0.530 0.529
## auto4 0.969 0.039 24.571 0.000 0.530 0.527
## loc =~
## loc1 1.000 0.542 0.536
## loc2 0.941 0.038 24.716 0.000 0.510 0.514
## loc3 0.977 0.039 25.304 0.000 0.530 0.534
## loc4 0.965 0.039 24.558 0.000 0.523 0.522
## tol =~
## tol1 1.000 0.549 0.542
## tol2 0.951 0.038 25.038 0.000 0.522 0.519
## tol3 1.003 0.040 25.243 0.000 0.550 0.548
## tol4 1.011 0.040 25.501 0.000 0.554 0.548
## craving =~
## craving1 1.000 0.506 0.506
## craving2 1.032 0.042 24.370 0.000 0.522 0.531
## craving3 1.071 0.044 24.173 0.000 0.542 0.544
## craving4 0.988 0.042 23.273 0.000 0.500 0.500
## taste =~
## taste1 1.000 0.546 0.536
## taste2 1.017 0.044 23.223 0.000 0.555 0.552
## taste3 0.977 0.041 23.701 0.000 0.534 0.536
## cog_e =~
## cog_e1 1.000 0.533 0.537
## cog_e2 1.018 0.043 23.536 0.000 0.542 0.531
## cog_e3 1.004 0.045 22.245 0.000 0.535 0.526
## w_control =~
## w_control1 1.000 0.555 0.545
## w_control2 1.046 0.044 23.818 0.000 0.581 0.569
## w_control3 0.968 0.040 23.918 0.000 0.537 0.531
## cue =~
## cue1 1.000 0.579 0.561
## cue2 0.941 0.036 25.803 0.000 0.545 0.542
## cue3 1.006 0.042 24.110 0.000 0.583 0.564
## affect =~
## affect1 1.000 0.495 0.508
## affect2 1.073 0.051 21.098 0.000 0.531 0.536
## affect3 1.011 0.047 21.729 0.000 0.500 0.497
## attach =~
## attach1 1.000 0.515 0.519
## attach2 1.095 0.048 22.920 0.000 0.564 0.555
## attach3 0.996 0.045 22.168 0.000 0.513 0.515
## social =~
## social1 1.000 0.518 0.520
## social2 1.007 0.046 21.664 0.000 0.521 0.526
## social3 0.985 0.045 21.815 0.000 0.510 0.509
## pdm =~
## auto 1.000 0.722 0.722
## loc 1.028 0.051 20.311 0.000 0.748 0.748
## tol 1.046 0.050 20.819 0.000 0.752 0.752
## craving 0.991 0.050 19.951 0.000 0.773 0.773
## sdm =~
## taste 1.000 0.714 0.714
## cog_e 0.971 0.050 19.563 0.000 0.711 0.711
## w_control 0.978 0.051 19.198 0.000 0.687 0.687
## cue 1.046 0.053 19.606 0.000 0.703 0.703
## affect 0.895 0.048 18.560 0.000 0.705 0.705
## attach 0.903 0.048 18.742 0.000 0.683 0.683
## social 0.958 0.051 18.909 0.000 0.721 0.721
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pdm ~~
## sdm 0.074 0.005 15.044 0.000 0.482 0.482
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .auto1 0.696 0.016 42.310 0.000 0.696 0.700
## .auto2 0.703 0.016 42.653 0.000 0.703 0.715
## .auto3 0.724 0.017 41.492 0.000 0.724 0.721
## .auto4 0.730 0.018 41.509 0.000 0.730 0.722
## .loc1 0.730 0.017 42.859 0.000 0.730 0.713
## .loc2 0.724 0.017 43.490 0.000 0.724 0.736
## .loc3 0.704 0.017 42.136 0.000 0.704 0.715
## .loc4 0.732 0.017 43.079 0.000 0.732 0.728
## .tol1 0.724 0.017 42.699 0.000 0.724 0.707
## .tol2 0.739 0.017 43.699 0.000 0.739 0.731
## .tol3 0.707 0.017 41.720 0.000 0.707 0.700
## .tol4 0.715 0.017 42.646 0.000 0.715 0.699
## .craving1 0.745 0.017 43.901 0.000 0.745 0.744
## .craving2 0.694 0.016 43.112 0.000 0.694 0.718
## .craving3 0.699 0.017 42.085 0.000 0.699 0.704
## .craving4 0.749 0.017 44.787 0.000 0.749 0.750
## .taste1 0.741 0.018 40.350 0.000 0.741 0.713
## .taste2 0.704 0.018 39.096 0.000 0.704 0.696
## .taste3 0.708 0.017 40.790 0.000 0.708 0.713
## .cog_e1 0.700 0.018 39.692 0.000 0.700 0.712
## .cog_e2 0.748 0.019 39.822 0.000 0.748 0.718
## .cog_e3 0.747 0.019 40.305 0.000 0.747 0.723
## .w_control1 0.729 0.019 38.938 0.000 0.729 0.703
## .w_control2 0.705 0.018 38.469 0.000 0.705 0.676
## .w_control3 0.736 0.018 40.306 0.000 0.736 0.718
## .cue1 0.731 0.019 38.991 0.000 0.731 0.685
## .cue2 0.714 0.018 39.806 0.000 0.714 0.706
## .cue3 0.727 0.019 39.263 0.000 0.727 0.681
## .affect1 0.703 0.018 39.750 0.000 0.703 0.742
## .affect2 0.698 0.018 38.479 0.000 0.698 0.712
## .affect3 0.763 0.018 41.496 0.000 0.763 0.753
## .attach1 0.719 0.018 41.041 0.000 0.719 0.730
## .attach2 0.714 0.019 37.889 0.000 0.714 0.692
## .attach3 0.729 0.018 40.272 0.000 0.729 0.735
## .social1 0.723 0.018 40.428 0.000 0.723 0.730
## .social2 0.711 0.018 39.444 0.000 0.711 0.724
## .social3 0.741 0.018 41.423 0.000 0.741 0.740
## .auto 0.143 0.011 13.026 0.000 0.479 0.479
## .loc 0.129 0.010 12.490 0.000 0.440 0.440
## .tol 0.131 0.010 12.521 0.000 0.434 0.434
## .craving 0.103 0.009 11.340 0.000 0.402 0.402
## .taste 0.146 0.012 12.514 0.000 0.490 0.490
## .cog_e 0.140 0.011 12.301 0.000 0.495 0.495
## .w_control 0.163 0.013 12.966 0.000 0.528 0.528
## .cue 0.170 0.013 12.931 0.000 0.505 0.505
## .affect 0.123 0.010 11.857 0.000 0.503 0.503
## .attach 0.142 0.011 12.513 0.000 0.533 0.533
## .social 0.129 0.011 11.999 0.000 0.480 0.480
## pdm 0.156 0.011 14.017 0.000 1.000 1.000
## sdm 0.152 0.011 13.277 0.000 1.000 1.000
#Based on ordinal variables:
items <- c("auto1", "auto2", "auto3", "auto4",
"loc1", "loc2", "loc3", "loc4",
"tol1", "tol2", "tol3", "tol4",
"craving1", "craving2", "craving3", "craving4",
"taste1", "taste2", "taste3",
"cog_e1", "cog_e2", "cog_e3",
"w_control1", "w_control2", "w_control3",
"cue1", "cue2", "cue3",
"affect1", "affect2", "affect3",
"attach1", "attach2", "attach3",
"social1", "social2", "social3"
)
#Model 1: 1-factor solution
ord_mod1 <- 'f =~ auto1 + auto2 + auto3 + auto4 +
loc1 + loc2 + loc3 + loc4 +
tol1 + tol2 + tol3 + tol4 +
craving1 + craving2 + craving3 + craving4 +
taste1 + taste2 + taste3 +
cog_e1 + cog_e2 + cog_e3 +
w_control1 + w_control2 + w_control3 +
cue1 + cue2 + cue3 +
affect1 + affect2 + affect3 +
attach1 + attach2 + attach3 +
social1 + social2 +social3
'
ord_mod1_fit <- cfa(ord_mod1,
data = data_cleaned,
estimator = "WLSMV",
ordered = items
)
summary(ord_mod1_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 29 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 259
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 13988.601 14539.247
## Degrees of freedom 629 629
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.969
## Shift parameter 108.836
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 69804.877 42644.851
## Degrees of freedom 666 666
## P-value 0.000 0.000
## Scaling correction factor 1.647
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.807 0.669
## Tucker-Lewis Index (TLI) 0.795 0.649
##
## Robust Comparative Fit Index (CFI) 0.605
## Robust Tucker-Lewis Index (TLI) 0.582
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.062 0.064
## 90 Percent confidence interval - lower 0.061 0.063
## 90 Percent confidence interval - upper 0.063 0.065
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.058
## 90 Percent confidence interval - lower 0.057
## 90 Percent confidence interval - upper 0.059
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.062 0.062
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f =~
## auto1 1.000 0.370 0.370
## auto2 0.948 0.045 21.057 0.000 0.351 0.351
## auto3 0.987 0.047 21.161 0.000 0.365 0.365
## auto4 1.009 0.046 22.043 0.000 0.373 0.373
## loc1 0.993 0.049 20.184 0.000 0.367 0.367
## loc2 0.942 0.048 19.683 0.000 0.349 0.349
## loc3 0.965 0.050 19.461 0.000 0.357 0.357
## loc4 0.974 0.050 19.526 0.000 0.361 0.361
## tol1 0.998 0.048 20.611 0.000 0.370 0.370
## tol2 0.975 0.048 20.377 0.000 0.361 0.361
## tol3 1.020 0.049 20.719 0.000 0.378 0.378
## tol4 1.000 0.049 20.326 0.000 0.370 0.370
## craving1 0.957 0.048 19.794 0.000 0.354 0.354
## craving2 1.043 0.051 20.607 0.000 0.386 0.386
## craving3 0.994 0.049 20.151 0.000 0.368 0.368
## craving4 0.919 0.048 19.265 0.000 0.340 0.340
## taste1 0.989 0.051 19.456 0.000 0.366 0.366
## taste2 1.019 0.052 19.760 0.000 0.377 0.377
## taste3 0.993 0.051 19.508 0.000 0.368 0.368
## cog_e1 0.992 0.051 19.328 0.000 0.367 0.367
## cog_e2 0.955 0.051 18.675 0.000 0.353 0.353
## cog_e3 0.969 0.051 18.942 0.000 0.359 0.359
## w_control1 0.972 0.051 19.232 0.000 0.360 0.360
## w_control2 1.033 0.051 20.343 0.000 0.382 0.382
## w_control3 0.949 0.051 18.759 0.000 0.351 0.351
## cue1 1.024 0.052 19.787 0.000 0.379 0.379
## cue2 0.998 0.050 19.935 0.000 0.369 0.369
## cue3 1.055 0.053 20.043 0.000 0.390 0.390
## affect1 0.951 0.049 19.257 0.000 0.352 0.352
## affect2 0.958 0.050 19.288 0.000 0.354 0.354
## affect3 0.885 0.049 18.078 0.000 0.328 0.328
## attach1 0.939 0.049 19.067 0.000 0.348 0.348
## attach2 1.009 0.051 19.655 0.000 0.373 0.373
## attach3 0.909 0.049 18.369 0.000 0.337 0.337
## social1 0.989 0.052 19.172 0.000 0.366 0.366
## social2 0.991 0.051 19.367 0.000 0.367 0.367
## social3 0.951 0.051 18.763 0.000 0.352 0.352
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## auto1|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## auto1|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## auto1|t3 -0.629 0.018 -34.456 0.000 -0.629 -0.629
## auto1|t4 0.433 0.018 24.647 0.000 0.433 0.433
## auto1|t5 1.456 0.025 57.251 0.000 1.456 1.456
## auto1|t6 2.634 0.071 37.289 0.000 2.634 2.634
## auto2|t1 -2.799 0.086 -32.466 0.000 -2.799 -2.799
## auto2|t2 -1.659 0.029 -57.441 0.000 -1.659 -1.659
## auto2|t3 -0.595 0.018 -32.855 0.000 -0.595 -0.595
## auto2|t4 0.457 0.018 25.903 0.000 0.457 0.457
## auto2|t5 1.499 0.026 57.470 0.000 1.499 1.499
## auto2|t6 2.520 0.062 40.621 0.000 2.520 2.520
## auto3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## auto3|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## auto3|t3 -0.627 0.018 -34.378 0.000 -0.627 -0.627
## auto3|t4 0.422 0.018 24.058 0.000 0.422 0.422
## auto3|t5 1.494 0.026 57.448 0.000 1.494 1.494
## auto3|t6 2.469 0.059 42.099 0.000 2.469 2.469
## auto4|t1 -2.755 0.082 -33.734 0.000 -2.755 -2.755
## auto4|t2 -1.632 0.028 -57.534 0.000 -1.632 -1.632
## auto4|t3 -0.619 0.018 -33.985 0.000 -0.619 -0.619
## auto4|t4 0.442 0.018 25.155 0.000 0.442 0.442
## auto4|t5 1.457 0.025 57.259 0.000 1.457 1.457
## auto4|t6 2.459 0.058 42.371 0.000 2.459 2.459
## loc1|t1 -2.681 0.075 -35.903 0.000 -2.681 -2.681
## loc1|t2 -1.557 0.027 -57.607 0.000 -1.557 -1.557
## loc1|t3 -0.532 0.018 -29.790 0.000 -0.532 -0.532
## loc1|t4 0.488 0.018 27.530 0.000 0.488 0.488
## loc1|t5 1.531 0.027 57.567 0.000 1.531 1.531
## loc1|t6 2.531 0.063 40.298 0.000 2.531 2.531
## loc2|t1 -2.776 0.084 -33.118 0.000 -2.776 -2.776
## loc2|t2 -1.598 0.028 -57.602 0.000 -1.598 -1.598
## loc2|t3 -0.538 0.018 -30.055 0.000 -0.538 -0.538
## loc2|t4 0.496 0.018 27.956 0.000 0.496 0.496
## loc2|t5 1.554 0.027 57.604 0.000 1.554 1.554
## loc2|t6 2.649 0.072 36.846 0.000 2.649 2.649
## loc3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## loc3|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## loc3|t3 -0.549 0.018 -30.611 0.000 -0.549 -0.549
## loc3|t4 0.495 0.018 27.903 0.000 0.495 0.495
## loc3|t5 1.521 0.026 57.541 0.000 1.521 1.521
## loc3|t6 2.579 0.066 38.899 0.000 2.579 2.579
## loc4|t1 -2.681 0.075 -35.903 0.000 -2.681 -2.681
## loc4|t2 -1.573 0.027 -57.615 0.000 -1.573 -1.573
## loc4|t3 -0.530 0.018 -29.684 0.000 -0.530 -0.530
## loc4|t4 0.502 0.018 28.276 0.000 0.502 0.502
## loc4|t5 1.573 0.027 57.615 0.000 1.573 1.573
## loc4|t6 2.459 0.058 42.371 0.000 2.459 2.459
## tol1|t1 -2.699 0.076 -35.400 0.000 -2.699 -2.699
## tol1|t2 -1.753 0.031 -56.843 0.000 -1.753 -1.753
## tol1|t3 -0.701 0.019 -37.734 0.000 -0.701 -0.701
## tol1|t4 0.321 0.017 18.580 0.000 0.321 0.321
## tol1|t5 1.371 0.024 56.540 0.000 1.371 1.371
## tol1|t6 2.317 0.050 46.286 0.000 2.317 2.317
## tol2|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## tol2|t2 -1.766 0.031 -56.729 0.000 -1.766 -1.766
## tol2|t3 -0.739 0.019 -39.381 0.000 -0.739 -0.739
## tol2|t4 0.295 0.017 17.098 0.000 0.295 0.295
## tol2|t5 1.360 0.024 56.426 0.000 1.360 1.360
## tol2|t6 2.382 0.053 44.529 0.000 2.382 2.382
## tol3|t1 -2.755 0.082 -33.734 0.000 -2.755 -2.755
## tol3|t2 -1.758 0.031 -56.806 0.000 -1.758 -1.758
## tol3|t3 -0.728 0.019 -38.919 0.000 -0.728 -0.728
## tol3|t4 0.343 0.017 19.791 0.000 0.343 0.343
## tol3|t5 1.353 0.024 56.347 0.000 1.353 1.353
## tol3|t6 2.330 0.051 45.924 0.000 2.330 2.330
## tol4|t1 -2.823 0.089 -31.773 0.000 -2.823 -2.823
## tol4|t2 -1.676 0.029 -57.366 0.000 -1.676 -1.676
## tol4|t3 -0.726 0.019 -38.816 0.000 -0.726 -0.726
## tol4|t4 0.333 0.017 19.226 0.000 0.333 0.333
## tol4|t5 1.362 0.024 56.439 0.000 1.362 1.362
## tol4|t6 2.423 0.056 43.389 0.000 2.423 2.423
## craving1|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## craving1|t2 -1.610 0.028 -57.585 0.000 -1.610 -1.610
## craving1|t3 -0.569 0.018 -31.616 0.000 -0.569 -0.569
## craving1|t4 0.460 0.018 26.090 0.000 0.460 0.460
## craving1|t5 1.509 0.026 57.506 0.000 1.509 1.509
## craving1|t6 2.699 0.076 35.400 0.000 2.699 2.699
## craving2|t1 -2.735 0.080 -34.318 0.000 -2.735 -2.735
## craving2|t2 -1.634 0.028 -57.529 0.000 -1.634 -1.634
## craving2|t3 -0.557 0.018 -31.034 0.000 -0.557 -0.557
## craving2|t4 0.495 0.018 27.903 0.000 0.495 0.495
## craving2|t5 1.562 0.027 57.611 0.000 1.562 1.562
## craving2|t6 2.620 0.069 37.714 0.000 2.620 2.620
## craving3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## craving3|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## craving3|t3 -0.580 0.018 -32.144 0.000 -0.580 -0.580
## craving3|t4 0.460 0.018 26.090 0.000 0.460 0.460
## craving3|t5 1.517 0.026 57.529 0.000 1.517 1.517
## craving3|t6 2.555 0.064 39.622 0.000 2.555 2.555
## craving4|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## craving4|t2 -1.643 0.029 -57.501 0.000 -1.643 -1.643
## craving4|t3 -0.576 0.018 -31.933 0.000 -0.576 -0.576
## craving4|t4 0.467 0.018 26.437 0.000 0.467 0.467
## craving4|t5 1.498 0.026 57.465 0.000 1.498 1.498
## craving4|t6 2.543 0.064 39.965 0.000 2.543 2.543
## taste1|t1 -2.469 0.059 -42.099 0.000 -2.469 -2.469
## taste1|t2 -1.501 0.026 -57.476 0.000 -1.501 -1.501
## taste1|t3 -0.459 0.018 -26.010 0.000 -0.459 -0.459
## taste1|t4 0.572 0.018 31.748 0.000 0.572 0.572
## taste1|t5 1.554 0.027 57.604 0.000 1.554 1.554
## taste1|t6 2.649 0.072 36.846 0.000 2.649 2.649
## taste2|t1 -2.579 0.066 -38.899 0.000 -2.579 -2.579
## taste2|t2 -1.487 0.026 -57.417 0.000 -1.487 -1.487
## taste2|t3 -0.475 0.018 -26.864 0.000 -0.475 -0.475
## taste2|t4 0.568 0.018 31.563 0.000 0.568 0.568
## taste2|t5 1.598 0.028 57.602 0.000 1.598 1.598
## taste2|t6 2.717 0.078 34.872 0.000 2.717 2.717
## taste3|t1 -2.606 0.068 -38.124 0.000 -2.606 -2.606
## taste3|t2 -1.536 0.027 -57.576 0.000 -1.536 -1.536
## taste3|t3 -0.492 0.018 -27.717 0.000 -0.492 -0.492
## taste3|t4 0.558 0.018 31.087 0.000 0.558 0.558
## taste3|t5 1.610 0.028 57.585 0.000 1.610 1.610
## taste3|t6 2.606 0.068 38.124 0.000 2.606 2.606
## cog_e1|t1 -3.062 0.122 -25.068 0.000 -3.062 -3.062
## cog_e1|t2 -1.766 0.031 -56.729 0.000 -1.766 -1.766
## cog_e1|t3 -0.742 0.019 -39.509 0.000 -0.742 -0.742
## cog_e1|t4 0.297 0.017 17.233 0.000 0.297 0.297
## cog_e1|t5 1.371 0.024 56.540 0.000 1.371 1.371
## cog_e1|t6 2.441 0.057 42.893 0.000 2.441 2.441
## cog_e2|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## cog_e2|t2 -1.710 0.030 -57.168 0.000 -1.710 -1.710
## cog_e2|t3 -0.694 0.019 -37.424 0.000 -0.694 -0.694
## cog_e2|t4 0.306 0.017 17.718 0.000 0.306 0.306
## cog_e2|t5 1.360 0.024 56.426 0.000 1.360 1.360
## cog_e2|t6 2.406 0.055 43.861 0.000 2.406 2.406
## cog_e3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## cog_e3|t2 -1.743 0.031 -56.931 0.000 -1.743 -1.743
## cog_e3|t3 -0.730 0.019 -39.022 0.000 -0.730 -0.730
## cog_e3|t4 0.318 0.017 18.418 0.000 0.318 0.318
## cog_e3|t5 1.333 0.024 56.100 0.000 1.333 1.333
## cog_e3|t6 2.323 0.050 46.107 0.000 2.323 2.323
## w_control1|t1 -2.432 0.056 -43.144 0.000 -2.432 -2.432
## w_control1|t2 -1.511 0.026 -57.511 0.000 -1.511 -1.511
## w_control1|t3 -0.463 0.018 -26.224 0.000 -0.463 -0.463
## w_control1|t4 0.576 0.018 31.959 0.000 0.576 0.576
## w_control1|t5 1.560 0.027 57.610 0.000 1.560 1.560
## w_control1|t6 2.606 0.068 38.124 0.000 2.606 2.606
## w_control2|t1 -2.489 0.060 -41.533 0.000 -2.489 -2.489
## w_control2|t2 -1.540 0.027 -57.585 0.000 -1.540 -1.540
## w_control2|t3 -0.461 0.018 -26.143 0.000 -0.461 -0.461
## w_control2|t4 0.542 0.018 30.293 0.000 0.542 0.542
## w_control2|t5 1.552 0.027 57.602 0.000 1.552 1.552
## w_control2|t6 2.531 0.063 40.298 0.000 2.531 2.531
## w_control3|t1 -2.520 0.062 -40.621 0.000 -2.520 -2.520
## w_control3|t2 -1.497 0.026 -57.459 0.000 -1.497 -1.497
## w_control3|t3 -0.491 0.018 -27.690 0.000 -0.491 -0.491
## w_control3|t4 0.538 0.018 30.055 0.000 0.538 0.538
## w_control3|t5 1.595 0.028 57.605 0.000 1.595 1.595
## w_control3|t6 2.634 0.071 37.289 0.000 2.634 2.634
## cue1|t1 -2.579 0.066 -38.899 0.000 -2.579 -2.579
## cue1|t2 -1.582 0.027 -57.614 0.000 -1.582 -1.582
## cue1|t3 -0.612 0.018 -33.696 0.000 -0.612 -0.612
## cue1|t4 0.409 0.017 23.361 0.000 0.409 0.409
## cue1|t5 1.423 0.025 57.025 0.000 1.423 1.423
## cue1|t6 2.441 0.057 42.893 0.000 2.441 2.441
## cue2|t1 -2.735 0.080 -34.318 0.000 -2.735 -2.735
## cue2|t2 -1.677 0.029 -57.357 0.000 -1.677 -1.677
## cue2|t3 -0.635 0.018 -34.770 0.000 -0.635 -0.635
## cue2|t4 0.408 0.017 23.308 0.000 0.408 0.408
## cue2|t5 1.442 0.025 57.166 0.000 1.442 1.442
## cue2|t6 2.414 0.055 43.628 0.000 2.414 2.414
## cue3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## cue3|t2 -1.607 0.028 -57.590 0.000 -1.607 -1.607
## cue3|t3 -0.596 0.018 -32.934 0.000 -0.596 -0.596
## cue3|t4 0.395 0.017 22.638 0.000 0.395 0.395
## cue3|t5 1.407 0.025 56.890 0.000 1.407 1.407
## cue3|t6 2.469 0.059 42.099 0.000 2.469 2.469
## affect1|t1 -2.699 0.076 -35.400 0.000 -2.699 -2.699
## affect1|t2 -1.695 0.030 -57.265 0.000 -1.695 -1.695
## affect1|t3 -0.653 0.018 -35.580 0.000 -0.653 -0.653
## affect1|t4 0.433 0.018 24.673 0.000 0.433 0.433
## affect1|t5 1.517 0.026 57.529 0.000 1.517 1.517
## affect1|t6 2.634 0.071 37.289 0.000 2.634 2.634
## affect2|t1 -2.606 0.068 -38.124 0.000 -2.606 -2.606
## affect2|t2 -1.716 0.030 -57.128 0.000 -1.716 -1.716
## affect2|t3 -0.659 0.018 -35.867 0.000 -0.659 -0.659
## affect2|t4 0.401 0.017 22.959 0.000 0.401 0.401
## affect2|t5 1.468 0.026 57.321 0.000 1.468 1.468
## affect2|t6 2.520 0.062 40.621 0.000 2.520 2.520
## affect3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## affect3|t2 -1.624 0.028 -57.557 0.000 -1.624 -1.624
## affect3|t3 -0.619 0.018 -33.985 0.000 -0.619 -0.619
## affect3|t4 0.403 0.017 23.067 0.000 0.403 0.403
## affect3|t5 1.481 0.026 57.392 0.000 1.481 1.481
## affect3|t6 2.509 0.061 40.934 0.000 2.509 2.509
## attach1|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## attach1|t2 -1.632 0.028 -57.534 0.000 -1.632 -1.632
## attach1|t3 -0.554 0.018 -30.876 0.000 -0.554 -0.554
## attach1|t4 0.487 0.018 27.504 0.000 0.487 0.487
## attach1|t5 1.530 0.027 57.564 0.000 1.530 1.530
## attach1|t6 2.665 0.073 36.385 0.000 2.665 2.665
## attach2|t1 -2.543 0.064 -39.965 0.000 -2.543 -2.543
## attach2|t2 -1.600 0.028 -57.600 0.000 -1.600 -1.600
## attach2|t3 -0.584 0.018 -32.355 0.000 -0.584 -0.584
## attach2|t4 0.469 0.018 26.571 0.000 0.469 0.469
## attach2|t5 1.472 0.026 57.343 0.000 1.472 1.472
## attach2|t6 2.469 0.059 42.099 0.000 2.469 2.469
## attach3|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## attach3|t2 -1.625 0.028 -57.552 0.000 -1.625 -1.625
## attach3|t3 -0.562 0.018 -31.246 0.000 -0.562 -0.562
## attach3|t4 0.503 0.018 28.329 0.000 0.503 0.503
## attach3|t5 1.515 0.026 57.524 0.000 1.515 1.515
## attach3|t6 2.555 0.064 39.622 0.000 2.555 2.555
## social1|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## social1|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## social1|t3 -0.592 0.018 -32.723 0.000 -0.592 -0.592
## social1|t4 0.449 0.018 25.502 0.000 0.449 0.449
## social1|t5 1.497 0.026 57.459 0.000 1.497 1.497
## social1|t6 2.649 0.072 36.846 0.000 2.649 2.649
## social2|t1 -2.823 0.089 -31.773 0.000 -2.823 -2.823
## social2|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## social2|t3 -0.617 0.018 -33.906 0.000 -0.617 -0.617
## social2|t4 0.451 0.018 25.609 0.000 0.451 0.451
## social2|t5 1.495 0.026 57.453 0.000 1.495 1.495
## social2|t6 2.567 0.065 39.266 0.000 2.567 2.567
## social3|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## social3|t2 -1.654 0.029 -57.463 0.000 -1.654 -1.654
## social3|t3 -0.582 0.018 -32.249 0.000 -0.582 -0.582
## social3|t4 0.454 0.018 25.743 0.000 0.454 0.454
## social3|t5 1.497 0.026 57.459 0.000 1.497 1.497
## social3|t6 2.499 0.061 41.238 0.000 2.499 2.499
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .auto1 0.863 0.863 0.863
## .auto2 0.877 0.877 0.877
## .auto3 0.866 0.866 0.866
## .auto4 0.861 0.861 0.861
## .loc1 0.865 0.865 0.865
## .loc2 0.878 0.878 0.878
## .loc3 0.872 0.872 0.872
## .loc4 0.870 0.870 0.870
## .tol1 0.863 0.863 0.863
## .tol2 0.870 0.870 0.870
## .tol3 0.857 0.857 0.857
## .tol4 0.863 0.863 0.863
## .craving1 0.875 0.875 0.875
## .craving2 0.851 0.851 0.851
## .craving3 0.865 0.865 0.865
## .craving4 0.884 0.884 0.884
## .taste1 0.866 0.866 0.866
## .taste2 0.858 0.858 0.858
## .taste3 0.865 0.865 0.865
## .cog_e1 0.865 0.865 0.865
## .cog_e2 0.875 0.875 0.875
## .cog_e3 0.871 0.871 0.871
## .w_control1 0.871 0.871 0.871
## .w_control2 0.854 0.854 0.854
## .w_control3 0.877 0.877 0.877
## .cue1 0.856 0.856 0.856
## .cue2 0.863 0.863 0.863
## .cue3 0.848 0.848 0.848
## .affect1 0.876 0.876 0.876
## .affect2 0.874 0.874 0.874
## .affect3 0.893 0.893 0.893
## .attach1 0.879 0.879 0.879
## .attach2 0.861 0.861 0.861
## .attach3 0.887 0.887 0.887
## .social1 0.866 0.866 0.866
## .social2 0.866 0.866 0.866
## .social3 0.876 0.876 0.876
## f 0.137 0.010 13.586 0.000 1.000 1.000
#Model 2: 11-factor solution
ord_mod2 <- '#Primary dependence motives
auto =~ auto1 + auto2 + auto3 + auto4
loc =~ loc1 + loc2 + loc3 + loc4
tol =~ tol1 + tol2 + tol3 + tol4
craving =~ craving1 + craving2 + craving3 + craving4
#Secondary dependence motives
taste =~ taste1 + taste2 + taste3
cog_e =~ cog_e1 + cog_e2 + cog_e3
w_control =~ w_control1 + w_control2 + w_control3
cue =~ cue1 + cue2 + cue3
affect =~ affect1 + affect2 + affect3
attach =~ attach1 + attach2 + attach3
social =~ social1 + social2 +social3
'
ord_mod2_fit <- cfa(ord_mod2,
data = data_cleaned,
estimator = "WLSMV",
ordered = items
)
summary(ord_mod2_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 57 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 314
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 391.724 535.940
## Degrees of freedom 574 574
## P-value (Chi-square) 1.000 0.871
## Scaling correction factor 0.856
## Shift parameter 78.553
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 69804.877 42644.851
## Degrees of freedom 666 666
## P-value 0.000 0.000
## Scaling correction factor 1.647
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.003 1.001
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.002
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.002
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.003
## P-value H_0: Robust RMSEA <= 0.050 1.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.011 0.011
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## auto =~
## auto1 1.000 0.568 0.568
## auto2 0.964 0.038 25.600 0.000 0.547 0.547
## auto3 0.979 0.039 25.113 0.000 0.556 0.556
## auto4 0.989 0.038 25.920 0.000 0.562 0.562
## loc =~
## loc1 1.000 0.561 0.561
## loc2 0.956 0.038 25.209 0.000 0.536 0.536
## loc3 0.985 0.038 26.048 0.000 0.553 0.553
## loc4 0.980 0.038 25.548 0.000 0.550 0.550
## tol =~
## tol1 1.000 0.563 0.563
## tol2 0.970 0.038 25.537 0.000 0.546 0.546
## tol3 1.017 0.039 26.282 0.000 0.573 0.573
## tol4 1.012 0.039 25.952 0.000 0.570 0.570
## craving =~
## craving1 1.000 0.530 0.530
## craving2 1.071 0.042 25.477 0.000 0.567 0.567
## craving3 1.061 0.043 24.946 0.000 0.562 0.562
## craving4 0.973 0.040 24.031 0.000 0.515 0.515
## taste =~
## taste1 1.000 0.560 0.560
## taste2 1.028 0.043 24.090 0.000 0.576 0.576
## taste3 0.998 0.041 24.213 0.000 0.559 0.559
## cog_e =~
## cog_e1 1.000 0.564 0.564
## cog_e2 0.968 0.042 23.285 0.000 0.546 0.546
## cog_e3 0.982 0.042 23.228 0.000 0.554 0.554
## w_control =~
## w_control1 1.000 0.565 0.565
## w_control2 1.063 0.044 24.350 0.000 0.600 0.600
## w_control3 0.975 0.041 23.488 0.000 0.550 0.550
## cue =~
## cue1 1.000 0.580 0.580
## cue2 0.968 0.038 25.422 0.000 0.561 0.561
## cue3 1.031 0.041 25.260 0.000 0.598 0.598
## affect =~
## affect1 1.000 0.541 0.541
## affect2 1.034 0.046 22.570 0.000 0.559 0.559
## affect3 0.945 0.043 21.753 0.000 0.511 0.511
## attach =~
## attach1 1.000 0.544 0.544
## attach2 1.073 0.047 22.990 0.000 0.584 0.584
## attach3 0.981 0.044 22.079 0.000 0.533 0.533
## social =~
## social1 1.000 0.548 0.548
## social2 1.001 0.044 22.909 0.000 0.548 0.548
## social3 0.965 0.042 22.801 0.000 0.528 0.528
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## auto ~~
## loc 0.172 0.009 19.435 0.000 0.541 0.541
## tol 0.171 0.009 19.285 0.000 0.534 0.534
## craving 0.168 0.009 19.139 0.000 0.559 0.559
## taste 0.083 0.008 10.551 0.000 0.261 0.261
## cog_e 0.074 0.008 9.452 0.000 0.230 0.230
## w_control 0.079 0.008 10.088 0.000 0.245 0.245
## cue 0.092 0.008 11.110 0.000 0.280 0.280
## affect 0.080 0.008 10.245 0.000 0.260 0.260
## attach 0.080 0.008 10.332 0.000 0.258 0.258
## social 0.084 0.008 10.763 0.000 0.270 0.270
## loc ~~
## tol 0.181 0.009 20.135 0.000 0.573 0.573
## craving 0.172 0.009 19.511 0.000 0.578 0.578
## taste 0.074 0.008 9.703 0.000 0.235 0.235
## cog_e 0.081 0.008 10.236 0.000 0.256 0.256
## w_control 0.071 0.008 9.399 0.000 0.225 0.225
## cue 0.079 0.008 9.894 0.000 0.242 0.242
## affect 0.076 0.008 10.121 0.000 0.252 0.252
## attach 0.076 0.008 9.948 0.000 0.249 0.249
## social 0.079 0.008 10.072 0.000 0.256 0.256
## tol ~~
## craving 0.174 0.009 19.905 0.000 0.583 0.583
## taste 0.079 0.008 10.160 0.000 0.251 0.251
## cog_e 0.080 0.008 10.373 0.000 0.252 0.252
## w_control 0.081 0.008 10.625 0.000 0.255 0.255
## cue 0.074 0.008 9.525 0.000 0.225 0.225
## affect 0.075 0.008 9.863 0.000 0.246 0.246
## attach 0.075 0.008 9.904 0.000 0.245 0.245
## social 0.089 0.008 11.288 0.000 0.289 0.289
## craving ~~
## taste 0.080 0.007 10.806 0.000 0.271 0.271
## cog_e 0.077 0.008 10.198 0.000 0.258 0.258
## w_control 0.074 0.007 10.054 0.000 0.248 0.248
## cue 0.080 0.008 10.331 0.000 0.260 0.260
## affect 0.073 0.007 9.908 0.000 0.254 0.254
## attach 0.077 0.007 10.490 0.000 0.269 0.269
## social 0.081 0.008 10.724 0.000 0.280 0.280
## taste ~~
## cog_e 0.164 0.009 17.488 0.000 0.519 0.519
## w_control 0.152 0.009 16.619 0.000 0.481 0.481
## cue 0.163 0.009 17.781 0.000 0.502 0.502
## affect 0.153 0.009 16.658 0.000 0.506 0.506
## attach 0.146 0.009 16.286 0.000 0.479 0.479
## social 0.160 0.009 17.014 0.000 0.523 0.523
## cog_e ~~
## w_control 0.158 0.009 17.151 0.000 0.497 0.497
## cue 0.160 0.009 17.226 0.000 0.490 0.490
## affect 0.151 0.009 16.223 0.000 0.495 0.495
## attach 0.151 0.009 16.522 0.000 0.492 0.492
## social 0.158 0.009 16.895 0.000 0.512 0.512
## w_control ~~
## cue 0.158 0.009 16.947 0.000 0.483 0.483
## affect 0.153 0.009 16.524 0.000 0.501 0.501
## attach 0.149 0.009 16.404 0.000 0.486 0.486
## social 0.145 0.009 15.845 0.000 0.470 0.470
## cue ~~
## affect 0.160 0.009 16.938 0.000 0.509 0.509
## attach 0.150 0.009 16.299 0.000 0.475 0.475
## social 0.163 0.009 17.489 0.000 0.514 0.514
## affect ~~
## attach 0.135 0.009 15.447 0.000 0.459 0.459
## social 0.149 0.009 16.085 0.000 0.503 0.503
## attach ~~
## social 0.149 0.009 16.409 0.000 0.499 0.499
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## auto1|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## auto1|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## auto1|t3 -0.629 0.018 -34.456 0.000 -0.629 -0.629
## auto1|t4 0.433 0.018 24.647 0.000 0.433 0.433
## auto1|t5 1.456 0.025 57.251 0.000 1.456 1.456
## auto1|t6 2.634 0.071 37.289 0.000 2.634 2.634
## auto2|t1 -2.799 0.086 -32.466 0.000 -2.799 -2.799
## auto2|t2 -1.659 0.029 -57.441 0.000 -1.659 -1.659
## auto2|t3 -0.595 0.018 -32.855 0.000 -0.595 -0.595
## auto2|t4 0.457 0.018 25.903 0.000 0.457 0.457
## auto2|t5 1.499 0.026 57.470 0.000 1.499 1.499
## auto2|t6 2.520 0.062 40.621 0.000 2.520 2.520
## auto3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## auto3|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## auto3|t3 -0.627 0.018 -34.378 0.000 -0.627 -0.627
## auto3|t4 0.422 0.018 24.058 0.000 0.422 0.422
## auto3|t5 1.494 0.026 57.448 0.000 1.494 1.494
## auto3|t6 2.469 0.059 42.099 0.000 2.469 2.469
## auto4|t1 -2.755 0.082 -33.734 0.000 -2.755 -2.755
## auto4|t2 -1.632 0.028 -57.534 0.000 -1.632 -1.632
## auto4|t3 -0.619 0.018 -33.985 0.000 -0.619 -0.619
## auto4|t4 0.442 0.018 25.155 0.000 0.442 0.442
## auto4|t5 1.457 0.025 57.259 0.000 1.457 1.457
## auto4|t6 2.459 0.058 42.371 0.000 2.459 2.459
## loc1|t1 -2.681 0.075 -35.903 0.000 -2.681 -2.681
## loc1|t2 -1.557 0.027 -57.607 0.000 -1.557 -1.557
## loc1|t3 -0.532 0.018 -29.790 0.000 -0.532 -0.532
## loc1|t4 0.488 0.018 27.530 0.000 0.488 0.488
## loc1|t5 1.531 0.027 57.567 0.000 1.531 1.531
## loc1|t6 2.531 0.063 40.298 0.000 2.531 2.531
## loc2|t1 -2.776 0.084 -33.118 0.000 -2.776 -2.776
## loc2|t2 -1.598 0.028 -57.602 0.000 -1.598 -1.598
## loc2|t3 -0.538 0.018 -30.055 0.000 -0.538 -0.538
## loc2|t4 0.496 0.018 27.956 0.000 0.496 0.496
## loc2|t5 1.554 0.027 57.604 0.000 1.554 1.554
## loc2|t6 2.649 0.072 36.846 0.000 2.649 2.649
## loc3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## loc3|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## loc3|t3 -0.549 0.018 -30.611 0.000 -0.549 -0.549
## loc3|t4 0.495 0.018 27.903 0.000 0.495 0.495
## loc3|t5 1.521 0.026 57.541 0.000 1.521 1.521
## loc3|t6 2.579 0.066 38.899 0.000 2.579 2.579
## loc4|t1 -2.681 0.075 -35.903 0.000 -2.681 -2.681
## loc4|t2 -1.573 0.027 -57.615 0.000 -1.573 -1.573
## loc4|t3 -0.530 0.018 -29.684 0.000 -0.530 -0.530
## loc4|t4 0.502 0.018 28.276 0.000 0.502 0.502
## loc4|t5 1.573 0.027 57.615 0.000 1.573 1.573
## loc4|t6 2.459 0.058 42.371 0.000 2.459 2.459
## tol1|t1 -2.699 0.076 -35.400 0.000 -2.699 -2.699
## tol1|t2 -1.753 0.031 -56.843 0.000 -1.753 -1.753
## tol1|t3 -0.701 0.019 -37.734 0.000 -0.701 -0.701
## tol1|t4 0.321 0.017 18.580 0.000 0.321 0.321
## tol1|t5 1.371 0.024 56.540 0.000 1.371 1.371
## tol1|t6 2.317 0.050 46.286 0.000 2.317 2.317
## tol2|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## tol2|t2 -1.766 0.031 -56.729 0.000 -1.766 -1.766
## tol2|t3 -0.739 0.019 -39.381 0.000 -0.739 -0.739
## tol2|t4 0.295 0.017 17.098 0.000 0.295 0.295
## tol2|t5 1.360 0.024 56.426 0.000 1.360 1.360
## tol2|t6 2.382 0.053 44.529 0.000 2.382 2.382
## tol3|t1 -2.755 0.082 -33.734 0.000 -2.755 -2.755
## tol3|t2 -1.758 0.031 -56.806 0.000 -1.758 -1.758
## tol3|t3 -0.728 0.019 -38.919 0.000 -0.728 -0.728
## tol3|t4 0.343 0.017 19.791 0.000 0.343 0.343
## tol3|t5 1.353 0.024 56.347 0.000 1.353 1.353
## tol3|t6 2.330 0.051 45.924 0.000 2.330 2.330
## tol4|t1 -2.823 0.089 -31.773 0.000 -2.823 -2.823
## tol4|t2 -1.676 0.029 -57.366 0.000 -1.676 -1.676
## tol4|t3 -0.726 0.019 -38.816 0.000 -0.726 -0.726
## tol4|t4 0.333 0.017 19.226 0.000 0.333 0.333
## tol4|t5 1.362 0.024 56.439 0.000 1.362 1.362
## tol4|t6 2.423 0.056 43.389 0.000 2.423 2.423
## craving1|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## craving1|t2 -1.610 0.028 -57.585 0.000 -1.610 -1.610
## craving1|t3 -0.569 0.018 -31.616 0.000 -0.569 -0.569
## craving1|t4 0.460 0.018 26.090 0.000 0.460 0.460
## craving1|t5 1.509 0.026 57.506 0.000 1.509 1.509
## craving1|t6 2.699 0.076 35.400 0.000 2.699 2.699
## craving2|t1 -2.735 0.080 -34.318 0.000 -2.735 -2.735
## craving2|t2 -1.634 0.028 -57.529 0.000 -1.634 -1.634
## craving2|t3 -0.557 0.018 -31.034 0.000 -0.557 -0.557
## craving2|t4 0.495 0.018 27.903 0.000 0.495 0.495
## craving2|t5 1.562 0.027 57.611 0.000 1.562 1.562
## craving2|t6 2.620 0.069 37.714 0.000 2.620 2.620
## craving3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## craving3|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## craving3|t3 -0.580 0.018 -32.144 0.000 -0.580 -0.580
## craving3|t4 0.460 0.018 26.090 0.000 0.460 0.460
## craving3|t5 1.517 0.026 57.529 0.000 1.517 1.517
## craving3|t6 2.555 0.064 39.622 0.000 2.555 2.555
## craving4|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## craving4|t2 -1.643 0.029 -57.501 0.000 -1.643 -1.643
## craving4|t3 -0.576 0.018 -31.933 0.000 -0.576 -0.576
## craving4|t4 0.467 0.018 26.437 0.000 0.467 0.467
## craving4|t5 1.498 0.026 57.465 0.000 1.498 1.498
## craving4|t6 2.543 0.064 39.965 0.000 2.543 2.543
## taste1|t1 -2.469 0.059 -42.099 0.000 -2.469 -2.469
## taste1|t2 -1.501 0.026 -57.476 0.000 -1.501 -1.501
## taste1|t3 -0.459 0.018 -26.010 0.000 -0.459 -0.459
## taste1|t4 0.572 0.018 31.748 0.000 0.572 0.572
## taste1|t5 1.554 0.027 57.604 0.000 1.554 1.554
## taste1|t6 2.649 0.072 36.846 0.000 2.649 2.649
## taste2|t1 -2.579 0.066 -38.899 0.000 -2.579 -2.579
## taste2|t2 -1.487 0.026 -57.417 0.000 -1.487 -1.487
## taste2|t3 -0.475 0.018 -26.864 0.000 -0.475 -0.475
## taste2|t4 0.568 0.018 31.563 0.000 0.568 0.568
## taste2|t5 1.598 0.028 57.602 0.000 1.598 1.598
## taste2|t6 2.717 0.078 34.872 0.000 2.717 2.717
## taste3|t1 -2.606 0.068 -38.124 0.000 -2.606 -2.606
## taste3|t2 -1.536 0.027 -57.576 0.000 -1.536 -1.536
## taste3|t3 -0.492 0.018 -27.717 0.000 -0.492 -0.492
## taste3|t4 0.558 0.018 31.087 0.000 0.558 0.558
## taste3|t5 1.610 0.028 57.585 0.000 1.610 1.610
## taste3|t6 2.606 0.068 38.124 0.000 2.606 2.606
## cog_e1|t1 -3.062 0.122 -25.068 0.000 -3.062 -3.062
## cog_e1|t2 -1.766 0.031 -56.729 0.000 -1.766 -1.766
## cog_e1|t3 -0.742 0.019 -39.509 0.000 -0.742 -0.742
## cog_e1|t4 0.297 0.017 17.233 0.000 0.297 0.297
## cog_e1|t5 1.371 0.024 56.540 0.000 1.371 1.371
## cog_e1|t6 2.441 0.057 42.893 0.000 2.441 2.441
## cog_e2|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## cog_e2|t2 -1.710 0.030 -57.168 0.000 -1.710 -1.710
## cog_e2|t3 -0.694 0.019 -37.424 0.000 -0.694 -0.694
## cog_e2|t4 0.306 0.017 17.718 0.000 0.306 0.306
## cog_e2|t5 1.360 0.024 56.426 0.000 1.360 1.360
## cog_e2|t6 2.406 0.055 43.861 0.000 2.406 2.406
## cog_e3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## cog_e3|t2 -1.743 0.031 -56.931 0.000 -1.743 -1.743
## cog_e3|t3 -0.730 0.019 -39.022 0.000 -0.730 -0.730
## cog_e3|t4 0.318 0.017 18.418 0.000 0.318 0.318
## cog_e3|t5 1.333 0.024 56.100 0.000 1.333 1.333
## cog_e3|t6 2.323 0.050 46.107 0.000 2.323 2.323
## w_control1|t1 -2.432 0.056 -43.144 0.000 -2.432 -2.432
## w_control1|t2 -1.511 0.026 -57.511 0.000 -1.511 -1.511
## w_control1|t3 -0.463 0.018 -26.224 0.000 -0.463 -0.463
## w_control1|t4 0.576 0.018 31.959 0.000 0.576 0.576
## w_control1|t5 1.560 0.027 57.610 0.000 1.560 1.560
## w_control1|t6 2.606 0.068 38.124 0.000 2.606 2.606
## w_control2|t1 -2.489 0.060 -41.533 0.000 -2.489 -2.489
## w_control2|t2 -1.540 0.027 -57.585 0.000 -1.540 -1.540
## w_control2|t3 -0.461 0.018 -26.143 0.000 -0.461 -0.461
## w_control2|t4 0.542 0.018 30.293 0.000 0.542 0.542
## w_control2|t5 1.552 0.027 57.602 0.000 1.552 1.552
## w_control2|t6 2.531 0.063 40.298 0.000 2.531 2.531
## w_control3|t1 -2.520 0.062 -40.621 0.000 -2.520 -2.520
## w_control3|t2 -1.497 0.026 -57.459 0.000 -1.497 -1.497
## w_control3|t3 -0.491 0.018 -27.690 0.000 -0.491 -0.491
## w_control3|t4 0.538 0.018 30.055 0.000 0.538 0.538
## w_control3|t5 1.595 0.028 57.605 0.000 1.595 1.595
## w_control3|t6 2.634 0.071 37.289 0.000 2.634 2.634
## cue1|t1 -2.579 0.066 -38.899 0.000 -2.579 -2.579
## cue1|t2 -1.582 0.027 -57.614 0.000 -1.582 -1.582
## cue1|t3 -0.612 0.018 -33.696 0.000 -0.612 -0.612
## cue1|t4 0.409 0.017 23.361 0.000 0.409 0.409
## cue1|t5 1.423 0.025 57.025 0.000 1.423 1.423
## cue1|t6 2.441 0.057 42.893 0.000 2.441 2.441
## cue2|t1 -2.735 0.080 -34.318 0.000 -2.735 -2.735
## cue2|t2 -1.677 0.029 -57.357 0.000 -1.677 -1.677
## cue2|t3 -0.635 0.018 -34.770 0.000 -0.635 -0.635
## cue2|t4 0.408 0.017 23.308 0.000 0.408 0.408
## cue2|t5 1.442 0.025 57.166 0.000 1.442 1.442
## cue2|t6 2.414 0.055 43.628 0.000 2.414 2.414
## cue3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## cue3|t2 -1.607 0.028 -57.590 0.000 -1.607 -1.607
## cue3|t3 -0.596 0.018 -32.934 0.000 -0.596 -0.596
## cue3|t4 0.395 0.017 22.638 0.000 0.395 0.395
## cue3|t5 1.407 0.025 56.890 0.000 1.407 1.407
## cue3|t6 2.469 0.059 42.099 0.000 2.469 2.469
## affect1|t1 -2.699 0.076 -35.400 0.000 -2.699 -2.699
## affect1|t2 -1.695 0.030 -57.265 0.000 -1.695 -1.695
## affect1|t3 -0.653 0.018 -35.580 0.000 -0.653 -0.653
## affect1|t4 0.433 0.018 24.673 0.000 0.433 0.433
## affect1|t5 1.517 0.026 57.529 0.000 1.517 1.517
## affect1|t6 2.634 0.071 37.289 0.000 2.634 2.634
## affect2|t1 -2.606 0.068 -38.124 0.000 -2.606 -2.606
## affect2|t2 -1.716 0.030 -57.128 0.000 -1.716 -1.716
## affect2|t3 -0.659 0.018 -35.867 0.000 -0.659 -0.659
## affect2|t4 0.401 0.017 22.959 0.000 0.401 0.401
## affect2|t5 1.468 0.026 57.321 0.000 1.468 1.468
## affect2|t6 2.520 0.062 40.621 0.000 2.520 2.520
## affect3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## affect3|t2 -1.624 0.028 -57.557 0.000 -1.624 -1.624
## affect3|t3 -0.619 0.018 -33.985 0.000 -0.619 -0.619
## affect3|t4 0.403 0.017 23.067 0.000 0.403 0.403
## affect3|t5 1.481 0.026 57.392 0.000 1.481 1.481
## affect3|t6 2.509 0.061 40.934 0.000 2.509 2.509
## attach1|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## attach1|t2 -1.632 0.028 -57.534 0.000 -1.632 -1.632
## attach1|t3 -0.554 0.018 -30.876 0.000 -0.554 -0.554
## attach1|t4 0.487 0.018 27.504 0.000 0.487 0.487
## attach1|t5 1.530 0.027 57.564 0.000 1.530 1.530
## attach1|t6 2.665 0.073 36.385 0.000 2.665 2.665
## attach2|t1 -2.543 0.064 -39.965 0.000 -2.543 -2.543
## attach2|t2 -1.600 0.028 -57.600 0.000 -1.600 -1.600
## attach2|t3 -0.584 0.018 -32.355 0.000 -0.584 -0.584
## attach2|t4 0.469 0.018 26.571 0.000 0.469 0.469
## attach2|t5 1.472 0.026 57.343 0.000 1.472 1.472
## attach2|t6 2.469 0.059 42.099 0.000 2.469 2.469
## attach3|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## attach3|t2 -1.625 0.028 -57.552 0.000 -1.625 -1.625
## attach3|t3 -0.562 0.018 -31.246 0.000 -0.562 -0.562
## attach3|t4 0.503 0.018 28.329 0.000 0.503 0.503
## attach3|t5 1.515 0.026 57.524 0.000 1.515 1.515
## attach3|t6 2.555 0.064 39.622 0.000 2.555 2.555
## social1|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## social1|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## social1|t3 -0.592 0.018 -32.723 0.000 -0.592 -0.592
## social1|t4 0.449 0.018 25.502 0.000 0.449 0.449
## social1|t5 1.497 0.026 57.459 0.000 1.497 1.497
## social1|t6 2.649 0.072 36.846 0.000 2.649 2.649
## social2|t1 -2.823 0.089 -31.773 0.000 -2.823 -2.823
## social2|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## social2|t3 -0.617 0.018 -33.906 0.000 -0.617 -0.617
## social2|t4 0.451 0.018 25.609 0.000 0.451 0.451
## social2|t5 1.495 0.026 57.453 0.000 1.495 1.495
## social2|t6 2.567 0.065 39.266 0.000 2.567 2.567
## social3|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## social3|t2 -1.654 0.029 -57.463 0.000 -1.654 -1.654
## social3|t3 -0.582 0.018 -32.249 0.000 -0.582 -0.582
## social3|t4 0.454 0.018 25.743 0.000 0.454 0.454
## social3|t5 1.497 0.026 57.459 0.000 1.497 1.497
## social3|t6 2.499 0.061 41.238 0.000 2.499 2.499
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .auto1 0.678 0.678 0.678
## .auto2 0.700 0.700 0.700
## .auto3 0.691 0.691 0.691
## .auto4 0.685 0.685 0.685
## .loc1 0.685 0.685 0.685
## .loc2 0.713 0.713 0.713
## .loc3 0.695 0.695 0.695
## .loc4 0.698 0.698 0.698
## .tol1 0.683 0.683 0.683
## .tol2 0.702 0.702 0.702
## .tol3 0.672 0.672 0.672
## .tol4 0.675 0.675 0.675
## .craving1 0.720 0.720 0.720
## .craving2 0.679 0.679 0.679
## .craving3 0.684 0.684 0.684
## .craving4 0.735 0.735 0.735
## .taste1 0.686 0.686 0.686
## .taste2 0.668 0.668 0.668
## .taste3 0.687 0.687 0.687
## .cog_e1 0.682 0.682 0.682
## .cog_e2 0.702 0.702 0.702
## .cog_e3 0.693 0.693 0.693
## .w_control1 0.681 0.681 0.681
## .w_control2 0.640 0.640 0.640
## .w_control3 0.697 0.697 0.697
## .cue1 0.664 0.664 0.664
## .cue2 0.685 0.685 0.685
## .cue3 0.642 0.642 0.642
## .affect1 0.708 0.708 0.708
## .affect2 0.687 0.687 0.687
## .affect3 0.739 0.739 0.739
## .attach1 0.704 0.704 0.704
## .attach2 0.659 0.659 0.659
## .attach3 0.716 0.716 0.716
## .social1 0.700 0.700 0.700
## .social2 0.699 0.699 0.699
## .social3 0.721 0.721 0.721
## auto 0.322 0.017 18.821 0.000 1.000 1.000
## loc 0.315 0.017 18.981 0.000 1.000 1.000
## tol 0.317 0.017 18.781 0.000 1.000 1.000
## craving 0.280 0.016 17.307 0.000 1.000 1.000
## taste 0.314 0.018 17.641 0.000 1.000 1.000
## cog_e 0.318 0.018 17.585 0.000 1.000 1.000
## w_control 0.319 0.018 17.679 0.000 1.000 1.000
## cue 0.336 0.018 18.908 0.000 1.000 1.000
## affect 0.292 0.018 16.476 0.000 1.000 1.000
## attach 0.296 0.018 16.426 0.000 1.000 1.000
## social 0.300 0.018 16.949 0.000 1.000 1.000
#Model 3: 2-factor solution
ord_mod3 <- 'pdm =~ auto1 + auto2 + auto3 + auto4 +
loc1 + loc2 + loc3 + loc4 +
tol1 + tol2 + tol3 + tol4 +
craving1 + craving2 + craving3 + craving4
sdm =~ taste1 + taste2 + taste3 +
cog_e1 + cog_e2 + cog_e3 +
w_control1 + w_control2 + w_control3 +
cue1 + cue2 + cue3 +
affect1 + affect2 + affect3 +
attach1 + attach2 + attach3 +
social1 + social2 +social3
'
ord_mod3_fit <- cfa(ord_mod3,
data = data_cleaned,
estimator = "WLSMV",
ordered = items
)
summary(ord_mod3_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 30 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 260
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 4898.764 5024.751
## Degrees of freedom 628 628
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.997
## Shift parameter 113.620
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 69804.877 42644.851
## Degrees of freedom 666 666
## P-value 0.000 0.000
## Scaling correction factor 1.647
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.938 0.895
## Tucker-Lewis Index (TLI) 0.934 0.889
##
## Robust Comparative Fit Index (CFI) 0.806
## Robust Tucker-Lewis Index (TLI) 0.794
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.035 0.036
## 90 Percent confidence interval - lower 0.034 0.035
## 90 Percent confidence interval - upper 0.036 0.037
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.041
## 90 Percent confidence interval - lower 0.040
## 90 Percent confidence interval - upper 0.042
## P-value H_0: Robust RMSEA <= 0.050 1.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.036 0.036
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pdm =~
## auto1 1.000 0.453 0.453
## auto2 0.968 0.039 24.911 0.000 0.439 0.439
## auto3 0.984 0.040 24.444 0.000 0.446 0.446
## auto4 0.998 0.039 25.329 0.000 0.452 0.452
## loc1 1.000 0.042 23.894 0.000 0.454 0.454
## loc2 0.956 0.041 23.413 0.000 0.434 0.434
## loc3 0.980 0.042 23.390 0.000 0.445 0.445
## loc4 0.979 0.042 23.142 0.000 0.444 0.444
## tol1 1.007 0.041 24.446 0.000 0.457 0.457
## tol2 0.982 0.041 23.834 0.000 0.445 0.445
## tol3 1.025 0.042 24.202 0.000 0.465 0.465
## tol4 1.027 0.042 24.270 0.000 0.465 0.465
## craving1 0.961 0.041 23.316 0.000 0.436 0.436
## craving2 1.024 0.042 24.092 0.000 0.464 0.464
## craving3 1.018 0.042 24.103 0.000 0.461 0.461
## craving4 0.935 0.041 22.800 0.000 0.424 0.424
## sdm =~
## taste1 1.000 0.422 0.422
## taste2 1.024 0.042 24.133 0.000 0.432 0.432
## taste3 0.998 0.041 24.141 0.000 0.421 0.421
## cog_e1 0.996 0.045 22.044 0.000 0.420 0.420
## cog_e2 0.961 0.044 21.805 0.000 0.406 0.406
## cog_e3 0.979 0.044 22.156 0.000 0.413 0.413
## w_control1 0.983 0.045 21.733 0.000 0.415 0.415
## w_control2 1.042 0.045 23.305 0.000 0.440 0.440
## w_control3 0.957 0.045 21.053 0.000 0.404 0.404
## cue1 1.031 0.046 22.623 0.000 0.435 0.435
## cue2 0.999 0.045 22.186 0.000 0.421 0.421
## cue3 1.065 0.047 22.905 0.000 0.450 0.450
## affect1 0.946 0.044 21.624 0.000 0.399 0.399
## affect2 0.977 0.045 21.719 0.000 0.412 0.412
## affect3 0.891 0.044 20.360 0.000 0.376 0.376
## attach1 0.935 0.043 21.585 0.000 0.394 0.394
## attach2 1.000 0.044 22.604 0.000 0.422 0.422
## attach3 0.920 0.044 20.950 0.000 0.388 0.388
## social1 0.984 0.045 22.072 0.000 0.415 0.415
## social2 0.981 0.045 21.688 0.000 0.414 0.414
## social3 0.945 0.043 21.996 0.000 0.399 0.399
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pdm ~~
## sdm 0.081 0.005 16.786 0.000 0.422 0.422
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## auto1|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## auto1|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## auto1|t3 -0.629 0.018 -34.456 0.000 -0.629 -0.629
## auto1|t4 0.433 0.018 24.647 0.000 0.433 0.433
## auto1|t5 1.456 0.025 57.251 0.000 1.456 1.456
## auto1|t6 2.634 0.071 37.289 0.000 2.634 2.634
## auto2|t1 -2.799 0.086 -32.466 0.000 -2.799 -2.799
## auto2|t2 -1.659 0.029 -57.441 0.000 -1.659 -1.659
## auto2|t3 -0.595 0.018 -32.855 0.000 -0.595 -0.595
## auto2|t4 0.457 0.018 25.903 0.000 0.457 0.457
## auto2|t5 1.499 0.026 57.470 0.000 1.499 1.499
## auto2|t6 2.520 0.062 40.621 0.000 2.520 2.520
## auto3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## auto3|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## auto3|t3 -0.627 0.018 -34.378 0.000 -0.627 -0.627
## auto3|t4 0.422 0.018 24.058 0.000 0.422 0.422
## auto3|t5 1.494 0.026 57.448 0.000 1.494 1.494
## auto3|t6 2.469 0.059 42.099 0.000 2.469 2.469
## auto4|t1 -2.755 0.082 -33.734 0.000 -2.755 -2.755
## auto4|t2 -1.632 0.028 -57.534 0.000 -1.632 -1.632
## auto4|t3 -0.619 0.018 -33.985 0.000 -0.619 -0.619
## auto4|t4 0.442 0.018 25.155 0.000 0.442 0.442
## auto4|t5 1.457 0.025 57.259 0.000 1.457 1.457
## auto4|t6 2.459 0.058 42.371 0.000 2.459 2.459
## loc1|t1 -2.681 0.075 -35.903 0.000 -2.681 -2.681
## loc1|t2 -1.557 0.027 -57.607 0.000 -1.557 -1.557
## loc1|t3 -0.532 0.018 -29.790 0.000 -0.532 -0.532
## loc1|t4 0.488 0.018 27.530 0.000 0.488 0.488
## loc1|t5 1.531 0.027 57.567 0.000 1.531 1.531
## loc1|t6 2.531 0.063 40.298 0.000 2.531 2.531
## loc2|t1 -2.776 0.084 -33.118 0.000 -2.776 -2.776
## loc2|t2 -1.598 0.028 -57.602 0.000 -1.598 -1.598
## loc2|t3 -0.538 0.018 -30.055 0.000 -0.538 -0.538
## loc2|t4 0.496 0.018 27.956 0.000 0.496 0.496
## loc2|t5 1.554 0.027 57.604 0.000 1.554 1.554
## loc2|t6 2.649 0.072 36.846 0.000 2.649 2.649
## loc3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## loc3|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## loc3|t3 -0.549 0.018 -30.611 0.000 -0.549 -0.549
## loc3|t4 0.495 0.018 27.903 0.000 0.495 0.495
## loc3|t5 1.521 0.026 57.541 0.000 1.521 1.521
## loc3|t6 2.579 0.066 38.899 0.000 2.579 2.579
## loc4|t1 -2.681 0.075 -35.903 0.000 -2.681 -2.681
## loc4|t2 -1.573 0.027 -57.615 0.000 -1.573 -1.573
## loc4|t3 -0.530 0.018 -29.684 0.000 -0.530 -0.530
## loc4|t4 0.502 0.018 28.276 0.000 0.502 0.502
## loc4|t5 1.573 0.027 57.615 0.000 1.573 1.573
## loc4|t6 2.459 0.058 42.371 0.000 2.459 2.459
## tol1|t1 -2.699 0.076 -35.400 0.000 -2.699 -2.699
## tol1|t2 -1.753 0.031 -56.843 0.000 -1.753 -1.753
## tol1|t3 -0.701 0.019 -37.734 0.000 -0.701 -0.701
## tol1|t4 0.321 0.017 18.580 0.000 0.321 0.321
## tol1|t5 1.371 0.024 56.540 0.000 1.371 1.371
## tol1|t6 2.317 0.050 46.286 0.000 2.317 2.317
## tol2|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## tol2|t2 -1.766 0.031 -56.729 0.000 -1.766 -1.766
## tol2|t3 -0.739 0.019 -39.381 0.000 -0.739 -0.739
## tol2|t4 0.295 0.017 17.098 0.000 0.295 0.295
## tol2|t5 1.360 0.024 56.426 0.000 1.360 1.360
## tol2|t6 2.382 0.053 44.529 0.000 2.382 2.382
## tol3|t1 -2.755 0.082 -33.734 0.000 -2.755 -2.755
## tol3|t2 -1.758 0.031 -56.806 0.000 -1.758 -1.758
## tol3|t3 -0.728 0.019 -38.919 0.000 -0.728 -0.728
## tol3|t4 0.343 0.017 19.791 0.000 0.343 0.343
## tol3|t5 1.353 0.024 56.347 0.000 1.353 1.353
## tol3|t6 2.330 0.051 45.924 0.000 2.330 2.330
## tol4|t1 -2.823 0.089 -31.773 0.000 -2.823 -2.823
## tol4|t2 -1.676 0.029 -57.366 0.000 -1.676 -1.676
## tol4|t3 -0.726 0.019 -38.816 0.000 -0.726 -0.726
## tol4|t4 0.333 0.017 19.226 0.000 0.333 0.333
## tol4|t5 1.362 0.024 56.439 0.000 1.362 1.362
## tol4|t6 2.423 0.056 43.389 0.000 2.423 2.423
## craving1|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## craving1|t2 -1.610 0.028 -57.585 0.000 -1.610 -1.610
## craving1|t3 -0.569 0.018 -31.616 0.000 -0.569 -0.569
## craving1|t4 0.460 0.018 26.090 0.000 0.460 0.460
## craving1|t5 1.509 0.026 57.506 0.000 1.509 1.509
## craving1|t6 2.699 0.076 35.400 0.000 2.699 2.699
## craving2|t1 -2.735 0.080 -34.318 0.000 -2.735 -2.735
## craving2|t2 -1.634 0.028 -57.529 0.000 -1.634 -1.634
## craving2|t3 -0.557 0.018 -31.034 0.000 -0.557 -0.557
## craving2|t4 0.495 0.018 27.903 0.000 0.495 0.495
## craving2|t5 1.562 0.027 57.611 0.000 1.562 1.562
## craving2|t6 2.620 0.069 37.714 0.000 2.620 2.620
## craving3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## craving3|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## craving3|t3 -0.580 0.018 -32.144 0.000 -0.580 -0.580
## craving3|t4 0.460 0.018 26.090 0.000 0.460 0.460
## craving3|t5 1.517 0.026 57.529 0.000 1.517 1.517
## craving3|t6 2.555 0.064 39.622 0.000 2.555 2.555
## craving4|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## craving4|t2 -1.643 0.029 -57.501 0.000 -1.643 -1.643
## craving4|t3 -0.576 0.018 -31.933 0.000 -0.576 -0.576
## craving4|t4 0.467 0.018 26.437 0.000 0.467 0.467
## craving4|t5 1.498 0.026 57.465 0.000 1.498 1.498
## craving4|t6 2.543 0.064 39.965 0.000 2.543 2.543
## taste1|t1 -2.469 0.059 -42.099 0.000 -2.469 -2.469
## taste1|t2 -1.501 0.026 -57.476 0.000 -1.501 -1.501
## taste1|t3 -0.459 0.018 -26.010 0.000 -0.459 -0.459
## taste1|t4 0.572 0.018 31.748 0.000 0.572 0.572
## taste1|t5 1.554 0.027 57.604 0.000 1.554 1.554
## taste1|t6 2.649 0.072 36.846 0.000 2.649 2.649
## taste2|t1 -2.579 0.066 -38.899 0.000 -2.579 -2.579
## taste2|t2 -1.487 0.026 -57.417 0.000 -1.487 -1.487
## taste2|t3 -0.475 0.018 -26.864 0.000 -0.475 -0.475
## taste2|t4 0.568 0.018 31.563 0.000 0.568 0.568
## taste2|t5 1.598 0.028 57.602 0.000 1.598 1.598
## taste2|t6 2.717 0.078 34.872 0.000 2.717 2.717
## taste3|t1 -2.606 0.068 -38.124 0.000 -2.606 -2.606
## taste3|t2 -1.536 0.027 -57.576 0.000 -1.536 -1.536
## taste3|t3 -0.492 0.018 -27.717 0.000 -0.492 -0.492
## taste3|t4 0.558 0.018 31.087 0.000 0.558 0.558
## taste3|t5 1.610 0.028 57.585 0.000 1.610 1.610
## taste3|t6 2.606 0.068 38.124 0.000 2.606 2.606
## cog_e1|t1 -3.062 0.122 -25.068 0.000 -3.062 -3.062
## cog_e1|t2 -1.766 0.031 -56.729 0.000 -1.766 -1.766
## cog_e1|t3 -0.742 0.019 -39.509 0.000 -0.742 -0.742
## cog_e1|t4 0.297 0.017 17.233 0.000 0.297 0.297
## cog_e1|t5 1.371 0.024 56.540 0.000 1.371 1.371
## cog_e1|t6 2.441 0.057 42.893 0.000 2.441 2.441
## cog_e2|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## cog_e2|t2 -1.710 0.030 -57.168 0.000 -1.710 -1.710
## cog_e2|t3 -0.694 0.019 -37.424 0.000 -0.694 -0.694
## cog_e2|t4 0.306 0.017 17.718 0.000 0.306 0.306
## cog_e2|t5 1.360 0.024 56.426 0.000 1.360 1.360
## cog_e2|t6 2.406 0.055 43.861 0.000 2.406 2.406
## cog_e3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## cog_e3|t2 -1.743 0.031 -56.931 0.000 -1.743 -1.743
## cog_e3|t3 -0.730 0.019 -39.022 0.000 -0.730 -0.730
## cog_e3|t4 0.318 0.017 18.418 0.000 0.318 0.318
## cog_e3|t5 1.333 0.024 56.100 0.000 1.333 1.333
## cog_e3|t6 2.323 0.050 46.107 0.000 2.323 2.323
## w_control1|t1 -2.432 0.056 -43.144 0.000 -2.432 -2.432
## w_control1|t2 -1.511 0.026 -57.511 0.000 -1.511 -1.511
## w_control1|t3 -0.463 0.018 -26.224 0.000 -0.463 -0.463
## w_control1|t4 0.576 0.018 31.959 0.000 0.576 0.576
## w_control1|t5 1.560 0.027 57.610 0.000 1.560 1.560
## w_control1|t6 2.606 0.068 38.124 0.000 2.606 2.606
## w_control2|t1 -2.489 0.060 -41.533 0.000 -2.489 -2.489
## w_control2|t2 -1.540 0.027 -57.585 0.000 -1.540 -1.540
## w_control2|t3 -0.461 0.018 -26.143 0.000 -0.461 -0.461
## w_control2|t4 0.542 0.018 30.293 0.000 0.542 0.542
## w_control2|t5 1.552 0.027 57.602 0.000 1.552 1.552
## w_control2|t6 2.531 0.063 40.298 0.000 2.531 2.531
## w_control3|t1 -2.520 0.062 -40.621 0.000 -2.520 -2.520
## w_control3|t2 -1.497 0.026 -57.459 0.000 -1.497 -1.497
## w_control3|t3 -0.491 0.018 -27.690 0.000 -0.491 -0.491
## w_control3|t4 0.538 0.018 30.055 0.000 0.538 0.538
## w_control3|t5 1.595 0.028 57.605 0.000 1.595 1.595
## w_control3|t6 2.634 0.071 37.289 0.000 2.634 2.634
## cue1|t1 -2.579 0.066 -38.899 0.000 -2.579 -2.579
## cue1|t2 -1.582 0.027 -57.614 0.000 -1.582 -1.582
## cue1|t3 -0.612 0.018 -33.696 0.000 -0.612 -0.612
## cue1|t4 0.409 0.017 23.361 0.000 0.409 0.409
## cue1|t5 1.423 0.025 57.025 0.000 1.423 1.423
## cue1|t6 2.441 0.057 42.893 0.000 2.441 2.441
## cue2|t1 -2.735 0.080 -34.318 0.000 -2.735 -2.735
## cue2|t2 -1.677 0.029 -57.357 0.000 -1.677 -1.677
## cue2|t3 -0.635 0.018 -34.770 0.000 -0.635 -0.635
## cue2|t4 0.408 0.017 23.308 0.000 0.408 0.408
## cue2|t5 1.442 0.025 57.166 0.000 1.442 1.442
## cue2|t6 2.414 0.055 43.628 0.000 2.414 2.414
## cue3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## cue3|t2 -1.607 0.028 -57.590 0.000 -1.607 -1.607
## cue3|t3 -0.596 0.018 -32.934 0.000 -0.596 -0.596
## cue3|t4 0.395 0.017 22.638 0.000 0.395 0.395
## cue3|t5 1.407 0.025 56.890 0.000 1.407 1.407
## cue3|t6 2.469 0.059 42.099 0.000 2.469 2.469
## affect1|t1 -2.699 0.076 -35.400 0.000 -2.699 -2.699
## affect1|t2 -1.695 0.030 -57.265 0.000 -1.695 -1.695
## affect1|t3 -0.653 0.018 -35.580 0.000 -0.653 -0.653
## affect1|t4 0.433 0.018 24.673 0.000 0.433 0.433
## affect1|t5 1.517 0.026 57.529 0.000 1.517 1.517
## affect1|t6 2.634 0.071 37.289 0.000 2.634 2.634
## affect2|t1 -2.606 0.068 -38.124 0.000 -2.606 -2.606
## affect2|t2 -1.716 0.030 -57.128 0.000 -1.716 -1.716
## affect2|t3 -0.659 0.018 -35.867 0.000 -0.659 -0.659
## affect2|t4 0.401 0.017 22.959 0.000 0.401 0.401
## affect2|t5 1.468 0.026 57.321 0.000 1.468 1.468
## affect2|t6 2.520 0.062 40.621 0.000 2.520 2.520
## affect3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## affect3|t2 -1.624 0.028 -57.557 0.000 -1.624 -1.624
## affect3|t3 -0.619 0.018 -33.985 0.000 -0.619 -0.619
## affect3|t4 0.403 0.017 23.067 0.000 0.403 0.403
## affect3|t5 1.481 0.026 57.392 0.000 1.481 1.481
## affect3|t6 2.509 0.061 40.934 0.000 2.509 2.509
## attach1|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## attach1|t2 -1.632 0.028 -57.534 0.000 -1.632 -1.632
## attach1|t3 -0.554 0.018 -30.876 0.000 -0.554 -0.554
## attach1|t4 0.487 0.018 27.504 0.000 0.487 0.487
## attach1|t5 1.530 0.027 57.564 0.000 1.530 1.530
## attach1|t6 2.665 0.073 36.385 0.000 2.665 2.665
## attach2|t1 -2.543 0.064 -39.965 0.000 -2.543 -2.543
## attach2|t2 -1.600 0.028 -57.600 0.000 -1.600 -1.600
## attach2|t3 -0.584 0.018 -32.355 0.000 -0.584 -0.584
## attach2|t4 0.469 0.018 26.571 0.000 0.469 0.469
## attach2|t5 1.472 0.026 57.343 0.000 1.472 1.472
## attach2|t6 2.469 0.059 42.099 0.000 2.469 2.469
## attach3|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## attach3|t2 -1.625 0.028 -57.552 0.000 -1.625 -1.625
## attach3|t3 -0.562 0.018 -31.246 0.000 -0.562 -0.562
## attach3|t4 0.503 0.018 28.329 0.000 0.503 0.503
## attach3|t5 1.515 0.026 57.524 0.000 1.515 1.515
## attach3|t6 2.555 0.064 39.622 0.000 2.555 2.555
## social1|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## social1|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## social1|t3 -0.592 0.018 -32.723 0.000 -0.592 -0.592
## social1|t4 0.449 0.018 25.502 0.000 0.449 0.449
## social1|t5 1.497 0.026 57.459 0.000 1.497 1.497
## social1|t6 2.649 0.072 36.846 0.000 2.649 2.649
## social2|t1 -2.823 0.089 -31.773 0.000 -2.823 -2.823
## social2|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## social2|t3 -0.617 0.018 -33.906 0.000 -0.617 -0.617
## social2|t4 0.451 0.018 25.609 0.000 0.451 0.451
## social2|t5 1.495 0.026 57.453 0.000 1.495 1.495
## social2|t6 2.567 0.065 39.266 0.000 2.567 2.567
## social3|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## social3|t2 -1.654 0.029 -57.463 0.000 -1.654 -1.654
## social3|t3 -0.582 0.018 -32.249 0.000 -0.582 -0.582
## social3|t4 0.454 0.018 25.743 0.000 0.454 0.454
## social3|t5 1.497 0.026 57.459 0.000 1.497 1.497
## social3|t6 2.499 0.061 41.238 0.000 2.499 2.499
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .auto1 0.794 0.794 0.794
## .auto2 0.807 0.807 0.807
## .auto3 0.801 0.801 0.801
## .auto4 0.795 0.795 0.795
## .loc1 0.794 0.794 0.794
## .loc2 0.812 0.812 0.812
## .loc3 0.802 0.802 0.802
## .loc4 0.803 0.803 0.803
## .tol1 0.791 0.791 0.791
## .tol2 0.802 0.802 0.802
## .tol3 0.784 0.784 0.784
## .tol4 0.783 0.783 0.783
## .craving1 0.810 0.810 0.810
## .craving2 0.785 0.785 0.785
## .craving3 0.787 0.787 0.787
## .craving4 0.820 0.820 0.820
## .taste1 0.822 0.822 0.822
## .taste2 0.813 0.813 0.813
## .taste3 0.823 0.823 0.823
## .cog_e1 0.823 0.823 0.823
## .cog_e2 0.835 0.835 0.835
## .cog_e3 0.829 0.829 0.829
## .w_control1 0.828 0.828 0.828
## .w_control2 0.807 0.807 0.807
## .w_control3 0.837 0.837 0.837
## .cue1 0.811 0.811 0.811
## .cue2 0.822 0.822 0.822
## .cue3 0.798 0.798 0.798
## .affect1 0.841 0.841 0.841
## .affect2 0.830 0.830 0.830
## .affect3 0.858 0.858 0.858
## .attach1 0.844 0.844 0.844
## .attach2 0.822 0.822 0.822
## .attach3 0.849 0.849 0.849
## .social1 0.828 0.828 0.828
## .social2 0.829 0.829 0.829
## .social3 0.841 0.841 0.841
## pdm 0.206 0.012 16.506 0.000 1.000 1.000
## sdm 0.178 0.012 15.369 0.000 1.000 1.000
#Model 4: 2-order solution
ord_mod4 <- '#1st order
auto =~ auto1 + auto2 + auto3 + auto4
loc =~ loc1 + loc2 + loc3 + loc4
tol =~ tol1 + tol2 + tol3 + tol4
craving =~ craving1 + craving2 + craving3 + craving4
taste =~ taste1 + taste2 + taste3
cog_e =~ cog_e1 + cog_e2 + cog_e3
w_control =~ w_control1 + w_control2 + w_control3
cue =~ cue1 + cue2 + cue3
affect =~ affect1 + affect2 + affect3
attach =~ attach1 + attach2 + attach3
social =~ social1 + social2 +social3
#2nd order
pdm =~ auto + loc + tol + craving
sdm =~ taste + cog_e + w_control + cue + affect + attach + social
'
ord_mod4_fit <- cfa(ord_mod4,
data = data_cleaned,
estimator = "WLSMV",
ordered = items
)
summary(ord_mod4_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 49 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 271
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 437.075 552.564
## Degrees of freedom 617 617
## P-value (Chi-square) 1.000 0.970
## Scaling correction factor 0.981
## Shift parameter 107.024
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 69804.877 42644.851
## Degrees of freedom 666 666
## P-value 0.000 0.000
## Scaling correction factor 1.647
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.003 1.002
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.002
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.002
## P-value H_0: Robust RMSEA <= 0.050 1.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.011 0.011
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## auto =~
## auto1 1.000 0.568 0.568
## auto2 0.965 0.038 25.686 0.000 0.548 0.548
## auto3 0.979 0.039 25.189 0.000 0.556 0.556
## auto4 0.988 0.038 25.996 0.000 0.561 0.561
## loc =~
## loc1 1.000 0.561 0.561
## loc2 0.955 0.038 25.116 0.000 0.536 0.536
## loc3 0.985 0.038 25.944 0.000 0.552 0.552
## loc4 0.981 0.039 25.446 0.000 0.550 0.550
## tol =~
## tol1 1.000 0.563 0.563
## tol2 0.970 0.038 25.499 0.000 0.546 0.546
## tol3 1.017 0.039 26.237 0.000 0.573 0.573
## tol4 1.012 0.039 25.924 0.000 0.570 0.570
## craving =~
## craving1 1.000 0.529 0.529
## craving2 1.071 0.042 25.471 0.000 0.567 0.567
## craving3 1.062 0.043 24.942 0.000 0.562 0.562
## craving4 0.973 0.041 24.034 0.000 0.515 0.515
## taste =~
## taste1 1.000 0.560 0.560
## taste2 1.028 0.043 24.052 0.000 0.576 0.576
## taste3 0.999 0.041 24.178 0.000 0.559 0.559
## cog_e =~
## cog_e1 1.000 0.564 0.564
## cog_e2 0.968 0.042 23.217 0.000 0.546 0.546
## cog_e3 0.981 0.042 23.162 0.000 0.554 0.554
## w_control =~
## w_control1 1.000 0.565 0.565
## w_control2 1.063 0.044 24.296 0.000 0.600 0.600
## w_control3 0.975 0.042 23.429 0.000 0.550 0.550
## cue =~
## cue1 1.000 0.580 0.580
## cue2 0.968 0.038 25.362 0.000 0.561 0.561
## cue3 1.032 0.041 25.199 0.000 0.598 0.598
## affect =~
## affect1 1.000 0.541 0.541
## affect2 1.034 0.046 22.541 0.000 0.559 0.559
## affect3 0.945 0.043 21.728 0.000 0.511 0.511
## attach =~
## attach1 1.000 0.543 0.543
## attach2 1.074 0.047 23.026 0.000 0.584 0.584
## attach3 0.981 0.044 22.111 0.000 0.533 0.533
## social =~
## social1 1.000 0.548 0.548
## social2 1.001 0.044 22.961 0.000 0.548 0.548
## social3 0.964 0.042 22.852 0.000 0.528 0.528
## pdm =~
## auto 1.000 0.731 0.731
## loc 1.005 0.048 20.862 0.000 0.743 0.743
## tol 1.016 0.048 21.312 0.000 0.749 0.749
## craving 0.986 0.048 20.359 0.000 0.773 0.773
## sdm =~
## taste 1.000 0.713 0.713
## cog_e 1.002 0.050 20.133 0.000 0.708 0.708
## w_control 0.970 0.049 19.647 0.000 0.686 0.686
## cue 1.020 0.050 20.340 0.000 0.702 0.702
## affect 0.955 0.048 19.795 0.000 0.704 0.704
## attach 0.935 0.047 19.704 0.000 0.687 0.687
## social 0.997 0.050 20.003 0.000 0.727 0.727
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pdm ~~
## sdm 0.080 0.005 15.844 0.000 0.482 0.482
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## auto1|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## auto1|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## auto1|t3 -0.629 0.018 -34.456 0.000 -0.629 -0.629
## auto1|t4 0.433 0.018 24.647 0.000 0.433 0.433
## auto1|t5 1.456 0.025 57.251 0.000 1.456 1.456
## auto1|t6 2.634 0.071 37.289 0.000 2.634 2.634
## auto2|t1 -2.799 0.086 -32.466 0.000 -2.799 -2.799
## auto2|t2 -1.659 0.029 -57.441 0.000 -1.659 -1.659
## auto2|t3 -0.595 0.018 -32.855 0.000 -0.595 -0.595
## auto2|t4 0.457 0.018 25.903 0.000 0.457 0.457
## auto2|t5 1.499 0.026 57.470 0.000 1.499 1.499
## auto2|t6 2.520 0.062 40.621 0.000 2.520 2.520
## auto3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## auto3|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## auto3|t3 -0.627 0.018 -34.378 0.000 -0.627 -0.627
## auto3|t4 0.422 0.018 24.058 0.000 0.422 0.422
## auto3|t5 1.494 0.026 57.448 0.000 1.494 1.494
## auto3|t6 2.469 0.059 42.099 0.000 2.469 2.469
## auto4|t1 -2.755 0.082 -33.734 0.000 -2.755 -2.755
## auto4|t2 -1.632 0.028 -57.534 0.000 -1.632 -1.632
## auto4|t3 -0.619 0.018 -33.985 0.000 -0.619 -0.619
## auto4|t4 0.442 0.018 25.155 0.000 0.442 0.442
## auto4|t5 1.457 0.025 57.259 0.000 1.457 1.457
## auto4|t6 2.459 0.058 42.371 0.000 2.459 2.459
## loc1|t1 -2.681 0.075 -35.903 0.000 -2.681 -2.681
## loc1|t2 -1.557 0.027 -57.607 0.000 -1.557 -1.557
## loc1|t3 -0.532 0.018 -29.790 0.000 -0.532 -0.532
## loc1|t4 0.488 0.018 27.530 0.000 0.488 0.488
## loc1|t5 1.531 0.027 57.567 0.000 1.531 1.531
## loc1|t6 2.531 0.063 40.298 0.000 2.531 2.531
## loc2|t1 -2.776 0.084 -33.118 0.000 -2.776 -2.776
## loc2|t2 -1.598 0.028 -57.602 0.000 -1.598 -1.598
## loc2|t3 -0.538 0.018 -30.055 0.000 -0.538 -0.538
## loc2|t4 0.496 0.018 27.956 0.000 0.496 0.496
## loc2|t5 1.554 0.027 57.604 0.000 1.554 1.554
## loc2|t6 2.649 0.072 36.846 0.000 2.649 2.649
## loc3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## loc3|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## loc3|t3 -0.549 0.018 -30.611 0.000 -0.549 -0.549
## loc3|t4 0.495 0.018 27.903 0.000 0.495 0.495
## loc3|t5 1.521 0.026 57.541 0.000 1.521 1.521
## loc3|t6 2.579 0.066 38.899 0.000 2.579 2.579
## loc4|t1 -2.681 0.075 -35.903 0.000 -2.681 -2.681
## loc4|t2 -1.573 0.027 -57.615 0.000 -1.573 -1.573
## loc4|t3 -0.530 0.018 -29.684 0.000 -0.530 -0.530
## loc4|t4 0.502 0.018 28.276 0.000 0.502 0.502
## loc4|t5 1.573 0.027 57.615 0.000 1.573 1.573
## loc4|t6 2.459 0.058 42.371 0.000 2.459 2.459
## tol1|t1 -2.699 0.076 -35.400 0.000 -2.699 -2.699
## tol1|t2 -1.753 0.031 -56.843 0.000 -1.753 -1.753
## tol1|t3 -0.701 0.019 -37.734 0.000 -0.701 -0.701
## tol1|t4 0.321 0.017 18.580 0.000 0.321 0.321
## tol1|t5 1.371 0.024 56.540 0.000 1.371 1.371
## tol1|t6 2.317 0.050 46.286 0.000 2.317 2.317
## tol2|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## tol2|t2 -1.766 0.031 -56.729 0.000 -1.766 -1.766
## tol2|t3 -0.739 0.019 -39.381 0.000 -0.739 -0.739
## tol2|t4 0.295 0.017 17.098 0.000 0.295 0.295
## tol2|t5 1.360 0.024 56.426 0.000 1.360 1.360
## tol2|t6 2.382 0.053 44.529 0.000 2.382 2.382
## tol3|t1 -2.755 0.082 -33.734 0.000 -2.755 -2.755
## tol3|t2 -1.758 0.031 -56.806 0.000 -1.758 -1.758
## tol3|t3 -0.728 0.019 -38.919 0.000 -0.728 -0.728
## tol3|t4 0.343 0.017 19.791 0.000 0.343 0.343
## tol3|t5 1.353 0.024 56.347 0.000 1.353 1.353
## tol3|t6 2.330 0.051 45.924 0.000 2.330 2.330
## tol4|t1 -2.823 0.089 -31.773 0.000 -2.823 -2.823
## tol4|t2 -1.676 0.029 -57.366 0.000 -1.676 -1.676
## tol4|t3 -0.726 0.019 -38.816 0.000 -0.726 -0.726
## tol4|t4 0.333 0.017 19.226 0.000 0.333 0.333
## tol4|t5 1.362 0.024 56.439 0.000 1.362 1.362
## tol4|t6 2.423 0.056 43.389 0.000 2.423 2.423
## craving1|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## craving1|t2 -1.610 0.028 -57.585 0.000 -1.610 -1.610
## craving1|t3 -0.569 0.018 -31.616 0.000 -0.569 -0.569
## craving1|t4 0.460 0.018 26.090 0.000 0.460 0.460
## craving1|t5 1.509 0.026 57.506 0.000 1.509 1.509
## craving1|t6 2.699 0.076 35.400 0.000 2.699 2.699
## craving2|t1 -2.735 0.080 -34.318 0.000 -2.735 -2.735
## craving2|t2 -1.634 0.028 -57.529 0.000 -1.634 -1.634
## craving2|t3 -0.557 0.018 -31.034 0.000 -0.557 -0.557
## craving2|t4 0.495 0.018 27.903 0.000 0.495 0.495
## craving2|t5 1.562 0.027 57.611 0.000 1.562 1.562
## craving2|t6 2.620 0.069 37.714 0.000 2.620 2.620
## craving3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## craving3|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## craving3|t3 -0.580 0.018 -32.144 0.000 -0.580 -0.580
## craving3|t4 0.460 0.018 26.090 0.000 0.460 0.460
## craving3|t5 1.517 0.026 57.529 0.000 1.517 1.517
## craving3|t6 2.555 0.064 39.622 0.000 2.555 2.555
## craving4|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## craving4|t2 -1.643 0.029 -57.501 0.000 -1.643 -1.643
## craving4|t3 -0.576 0.018 -31.933 0.000 -0.576 -0.576
## craving4|t4 0.467 0.018 26.437 0.000 0.467 0.467
## craving4|t5 1.498 0.026 57.465 0.000 1.498 1.498
## craving4|t6 2.543 0.064 39.965 0.000 2.543 2.543
## taste1|t1 -2.469 0.059 -42.099 0.000 -2.469 -2.469
## taste1|t2 -1.501 0.026 -57.476 0.000 -1.501 -1.501
## taste1|t3 -0.459 0.018 -26.010 0.000 -0.459 -0.459
## taste1|t4 0.572 0.018 31.748 0.000 0.572 0.572
## taste1|t5 1.554 0.027 57.604 0.000 1.554 1.554
## taste1|t6 2.649 0.072 36.846 0.000 2.649 2.649
## taste2|t1 -2.579 0.066 -38.899 0.000 -2.579 -2.579
## taste2|t2 -1.487 0.026 -57.417 0.000 -1.487 -1.487
## taste2|t3 -0.475 0.018 -26.864 0.000 -0.475 -0.475
## taste2|t4 0.568 0.018 31.563 0.000 0.568 0.568
## taste2|t5 1.598 0.028 57.602 0.000 1.598 1.598
## taste2|t6 2.717 0.078 34.872 0.000 2.717 2.717
## taste3|t1 -2.606 0.068 -38.124 0.000 -2.606 -2.606
## taste3|t2 -1.536 0.027 -57.576 0.000 -1.536 -1.536
## taste3|t3 -0.492 0.018 -27.717 0.000 -0.492 -0.492
## taste3|t4 0.558 0.018 31.087 0.000 0.558 0.558
## taste3|t5 1.610 0.028 57.585 0.000 1.610 1.610
## taste3|t6 2.606 0.068 38.124 0.000 2.606 2.606
## cog_e1|t1 -3.062 0.122 -25.068 0.000 -3.062 -3.062
## cog_e1|t2 -1.766 0.031 -56.729 0.000 -1.766 -1.766
## cog_e1|t3 -0.742 0.019 -39.509 0.000 -0.742 -0.742
## cog_e1|t4 0.297 0.017 17.233 0.000 0.297 0.297
## cog_e1|t5 1.371 0.024 56.540 0.000 1.371 1.371
## cog_e1|t6 2.441 0.057 42.893 0.000 2.441 2.441
## cog_e2|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## cog_e2|t2 -1.710 0.030 -57.168 0.000 -1.710 -1.710
## cog_e2|t3 -0.694 0.019 -37.424 0.000 -0.694 -0.694
## cog_e2|t4 0.306 0.017 17.718 0.000 0.306 0.306
## cog_e2|t5 1.360 0.024 56.426 0.000 1.360 1.360
## cog_e2|t6 2.406 0.055 43.861 0.000 2.406 2.406
## cog_e3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## cog_e3|t2 -1.743 0.031 -56.931 0.000 -1.743 -1.743
## cog_e3|t3 -0.730 0.019 -39.022 0.000 -0.730 -0.730
## cog_e3|t4 0.318 0.017 18.418 0.000 0.318 0.318
## cog_e3|t5 1.333 0.024 56.100 0.000 1.333 1.333
## cog_e3|t6 2.323 0.050 46.107 0.000 2.323 2.323
## w_control1|t1 -2.432 0.056 -43.144 0.000 -2.432 -2.432
## w_control1|t2 -1.511 0.026 -57.511 0.000 -1.511 -1.511
## w_control1|t3 -0.463 0.018 -26.224 0.000 -0.463 -0.463
## w_control1|t4 0.576 0.018 31.959 0.000 0.576 0.576
## w_control1|t5 1.560 0.027 57.610 0.000 1.560 1.560
## w_control1|t6 2.606 0.068 38.124 0.000 2.606 2.606
## w_control2|t1 -2.489 0.060 -41.533 0.000 -2.489 -2.489
## w_control2|t2 -1.540 0.027 -57.585 0.000 -1.540 -1.540
## w_control2|t3 -0.461 0.018 -26.143 0.000 -0.461 -0.461
## w_control2|t4 0.542 0.018 30.293 0.000 0.542 0.542
## w_control2|t5 1.552 0.027 57.602 0.000 1.552 1.552
## w_control2|t6 2.531 0.063 40.298 0.000 2.531 2.531
## w_control3|t1 -2.520 0.062 -40.621 0.000 -2.520 -2.520
## w_control3|t2 -1.497 0.026 -57.459 0.000 -1.497 -1.497
## w_control3|t3 -0.491 0.018 -27.690 0.000 -0.491 -0.491
## w_control3|t4 0.538 0.018 30.055 0.000 0.538 0.538
## w_control3|t5 1.595 0.028 57.605 0.000 1.595 1.595
## w_control3|t6 2.634 0.071 37.289 0.000 2.634 2.634
## cue1|t1 -2.579 0.066 -38.899 0.000 -2.579 -2.579
## cue1|t2 -1.582 0.027 -57.614 0.000 -1.582 -1.582
## cue1|t3 -0.612 0.018 -33.696 0.000 -0.612 -0.612
## cue1|t4 0.409 0.017 23.361 0.000 0.409 0.409
## cue1|t5 1.423 0.025 57.025 0.000 1.423 1.423
## cue1|t6 2.441 0.057 42.893 0.000 2.441 2.441
## cue2|t1 -2.735 0.080 -34.318 0.000 -2.735 -2.735
## cue2|t2 -1.677 0.029 -57.357 0.000 -1.677 -1.677
## cue2|t3 -0.635 0.018 -34.770 0.000 -0.635 -0.635
## cue2|t4 0.408 0.017 23.308 0.000 0.408 0.408
## cue2|t5 1.442 0.025 57.166 0.000 1.442 1.442
## cue2|t6 2.414 0.055 43.628 0.000 2.414 2.414
## cue3|t1 -2.665 0.073 -36.385 0.000 -2.665 -2.665
## cue3|t2 -1.607 0.028 -57.590 0.000 -1.607 -1.607
## cue3|t3 -0.596 0.018 -32.934 0.000 -0.596 -0.596
## cue3|t4 0.395 0.017 22.638 0.000 0.395 0.395
## cue3|t5 1.407 0.025 56.890 0.000 1.407 1.407
## cue3|t6 2.469 0.059 42.099 0.000 2.469 2.469
## affect1|t1 -2.699 0.076 -35.400 0.000 -2.699 -2.699
## affect1|t2 -1.695 0.030 -57.265 0.000 -1.695 -1.695
## affect1|t3 -0.653 0.018 -35.580 0.000 -0.653 -0.653
## affect1|t4 0.433 0.018 24.673 0.000 0.433 0.433
## affect1|t5 1.517 0.026 57.529 0.000 1.517 1.517
## affect1|t6 2.634 0.071 37.289 0.000 2.634 2.634
## affect2|t1 -2.606 0.068 -38.124 0.000 -2.606 -2.606
## affect2|t2 -1.716 0.030 -57.128 0.000 -1.716 -1.716
## affect2|t3 -0.659 0.018 -35.867 0.000 -0.659 -0.659
## affect2|t4 0.401 0.017 22.959 0.000 0.401 0.401
## affect2|t5 1.468 0.026 57.321 0.000 1.468 1.468
## affect2|t6 2.520 0.062 40.621 0.000 2.520 2.520
## affect3|t1 -2.717 0.078 -34.872 0.000 -2.717 -2.717
## affect3|t2 -1.624 0.028 -57.557 0.000 -1.624 -1.624
## affect3|t3 -0.619 0.018 -33.985 0.000 -0.619 -0.619
## affect3|t4 0.403 0.017 23.067 0.000 0.403 0.403
## affect3|t5 1.481 0.026 57.392 0.000 1.481 1.481
## affect3|t6 2.509 0.061 40.934 0.000 2.509 2.509
## attach1|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## attach1|t2 -1.632 0.028 -57.534 0.000 -1.632 -1.632
## attach1|t3 -0.554 0.018 -30.876 0.000 -0.554 -0.554
## attach1|t4 0.487 0.018 27.504 0.000 0.487 0.487
## attach1|t5 1.530 0.027 57.564 0.000 1.530 1.530
## attach1|t6 2.665 0.073 36.385 0.000 2.665 2.665
## attach2|t1 -2.543 0.064 -39.965 0.000 -2.543 -2.543
## attach2|t2 -1.600 0.028 -57.600 0.000 -1.600 -1.600
## attach2|t3 -0.584 0.018 -32.355 0.000 -0.584 -0.584
## attach2|t4 0.469 0.018 26.571 0.000 0.469 0.469
## attach2|t5 1.472 0.026 57.343 0.000 1.472 1.472
## attach2|t6 2.469 0.059 42.099 0.000 2.469 2.469
## attach3|t1 -2.634 0.071 -37.289 0.000 -2.634 -2.634
## attach3|t2 -1.625 0.028 -57.552 0.000 -1.625 -1.625
## attach3|t3 -0.562 0.018 -31.246 0.000 -0.562 -0.562
## attach3|t4 0.503 0.018 28.329 0.000 0.503 0.503
## attach3|t5 1.515 0.026 57.524 0.000 1.515 1.515
## attach3|t6 2.555 0.064 39.622 0.000 2.555 2.555
## social1|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## social1|t2 -1.645 0.029 -57.495 0.000 -1.645 -1.645
## social1|t3 -0.592 0.018 -32.723 0.000 -0.592 -0.592
## social1|t4 0.449 0.018 25.502 0.000 0.449 0.449
## social1|t5 1.497 0.026 57.459 0.000 1.497 1.497
## social1|t6 2.649 0.072 36.846 0.000 2.649 2.649
## social2|t1 -2.823 0.089 -31.773 0.000 -2.823 -2.823
## social2|t2 -1.631 0.028 -57.539 0.000 -1.631 -1.631
## social2|t3 -0.617 0.018 -33.906 0.000 -0.617 -0.617
## social2|t4 0.451 0.018 25.609 0.000 0.451 0.451
## social2|t5 1.495 0.026 57.453 0.000 1.495 1.495
## social2|t6 2.567 0.065 39.266 0.000 2.567 2.567
## social3|t1 -2.649 0.072 -36.846 0.000 -2.649 -2.649
## social3|t2 -1.654 0.029 -57.463 0.000 -1.654 -1.654
## social3|t3 -0.582 0.018 -32.249 0.000 -0.582 -0.582
## social3|t4 0.454 0.018 25.743 0.000 0.454 0.454
## social3|t5 1.497 0.026 57.459 0.000 1.497 1.497
## social3|t6 2.499 0.061 41.238 0.000 2.499 2.499
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .auto1 0.678 0.678 0.678
## .auto2 0.700 0.700 0.700
## .auto3 0.691 0.691 0.691
## .auto4 0.685 0.685 0.685
## .loc1 0.685 0.685 0.685
## .loc2 0.713 0.713 0.713
## .loc3 0.695 0.695 0.695
## .loc4 0.697 0.697 0.697
## .tol1 0.683 0.683 0.683
## .tol2 0.702 0.702 0.702
## .tol3 0.672 0.672 0.672
## .tol4 0.675 0.675 0.675
## .craving1 0.720 0.720 0.720
## .craving2 0.679 0.679 0.679
## .craving3 0.684 0.684 0.684
## .craving4 0.734 0.734 0.734
## .taste1 0.686 0.686 0.686
## .taste2 0.669 0.669 0.669
## .taste3 0.687 0.687 0.687
## .cog_e1 0.681 0.681 0.681
## .cog_e2 0.702 0.702 0.702
## .cog_e3 0.693 0.693 0.693
## .w_control1 0.681 0.681 0.681
## .w_control2 0.640 0.640 0.640
## .w_control3 0.697 0.697 0.697
## .cue1 0.664 0.664 0.664
## .cue2 0.685 0.685 0.685
## .cue3 0.642 0.642 0.642
## .affect1 0.708 0.708 0.708
## .affect2 0.687 0.687 0.687
## .affect3 0.739 0.739 0.739
## .attach1 0.705 0.705 0.705
## .attach2 0.659 0.659 0.659
## .attach3 0.715 0.715 0.715
## .social1 0.700 0.700 0.700
## .social2 0.699 0.699 0.699
## .social3 0.721 0.721 0.721
## .auto 0.150 0.011 13.487 0.000 0.466 0.466
## .loc 0.141 0.011 12.841 0.000 0.447 0.447
## .tol 0.139 0.011 12.800 0.000 0.439 0.439
## .craving 0.113 0.010 11.122 0.000 0.402 0.402
## .taste 0.154 0.012 12.814 0.000 0.492 0.492
## .cog_e 0.159 0.013 12.566 0.000 0.498 0.498
## .w_control 0.169 0.012 13.536 0.000 0.530 0.530
## .cue 0.170 0.013 13.520 0.000 0.507 0.507
## .affect 0.147 0.012 12.080 0.000 0.504 0.504
## .attach 0.156 0.012 12.735 0.000 0.529 0.529
## .social 0.142 0.012 11.968 0.000 0.472 0.472
## pdm 0.172 0.012 14.541 0.000 1.000 1.000
## sdm 0.159 0.011 13.957 0.000 1.000 1.000
#Perform the chi-square DiffTest to assess the relative fitness:
#Relative fitness between the multidimensional solutions and the unidimensional solution
anova(ord_mod1_fit, ord_mod2_fit)
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## ord_mod2_fit 574 391.72
## ord_mod1_fit 629 13988.60 7185.2 55 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(ord_mod1_fit, ord_mod3_fit)
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## ord_mod3_fit 628 4898.8
## ord_mod1_fit 629 13988.6 1435 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(ord_mod1_fit, ord_mod4_fit)
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## ord_mod4_fit 617 437.07
## ord_mod1_fit 629 13988.60 6098.8 12 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Select the most plausible continuous and ordinal WISDM-37 models
cont_select_fit <-
ord_select_fit <-
#Explore the modification indices of the selected WISMD-37 structural model
wisdm_mi <- modificationIndices(...)
#Reconstruct the selected models following hypothesis-consistent modifications
cont_modified <-
ord_modified <-
#Calculate composite reliability for the selected continuous and ordinal WISDM-37 model
cont_rel <- compRelSEM(...)
print(cont_rel, digits = 3)
ord_rel <- compRelSEM(...)
print(ord_rel, digits = 3)
#Calculate the corrected item-total correlation for the selected WISDM-37 model
#Store items of each factors
#Calculate the Fagerström Test for Nicotine Dependence scores for each method of smoking
data_cleaned$cigg_f = rowSums(data_cleaned[,82:87])
data_cleaned$ecigg_f = rowSums(data_cleaned[,88:93])
data_cleaned$h_f = rowSums(data_cleaned[,94:99])
#Calculate the mean score for each WISDM-37 factor
data_cleaned$auto = rowMeans(data_cleaned[,c(44, 53, 57, 68)])
data_cleaned$loc = rowMeans(data_cleaned[,c(45, 59, 64, 79)])
data_cleaned$tol = rowMeans(data_cleaned[,c(46, 72, 75, 80)])
data_cleaned$craving = rowMeans(data_cleaned[,c(47, 60, 66, 73)])
data_cleaned$taste = rowMeans(data_cleaned[,c(48, 58, 63)])
data_cleaned$cog_e = rowMeans(data_cleaned[,c(49, 56, 76)])
data_cleaned$w_control = rowMeans(data_cleaned[,c(50, 62, 78)])
data_cleaned$cue = rowMeans(data_cleaned[,c(51, 55, 67)])
data_cleaned$affect = rowMeans(data_cleaned[,c(52, 77, 81)])
data_cleaned$attach = rowMeans(data_cleaned[,c(54, 65, 69)])
data_cleaned$social = rowMeans(data_cleaned[,c(61, 70, 74)])
data_cleaned$pdm = rowMeans(data_cleaned[,105:108])
data_cleaned$sdm = rowMeans(data_cleaned[,109:115])
#Robust multiple linear regression analysis:
#WISDM-37 factors -> dependence on cigarettes
wisdm37_cigg <- lmrob(cigg_f ~ auto + loc + tol + craving + taste + cog_e + w_control + cue + affect + attach + social,
data = data_cleaned,
k.max = 1000
)
cor.wisdm37.cigg <- (cor(data_cleaned[c(105:115, 102)],
method = "spearman")^2
)*100
pcor.wisdm37.cigg <- (pcor(data_cleaned[c(105:115, 102)],
method = "spearman")$estimate^2
)*100
summary(wisdm37_cigg)
##
## Call:
## lmrob(formula = cigg_f ~ auto + loc + tol + craving + taste + cog_e + w_control +
## cue + affect + attach + social, data = data_cleaned, k.max = 1000)
## \--> method = "MM"
## Residuals:
## Min 1Q Median 3Q Max
## -3.8390 -1.5427 0.2549 1.3393 6.4192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.727979 0.273533 13.629 <2e-16 ***
## auto 0.018916 0.040712 0.465 0.6422
## loc 0.010344 0.041247 0.251 0.8020
## tol -0.042284 0.040749 -1.038 0.2995
## craving 0.049164 0.042407 1.159 0.2464
## taste 0.009821 0.038548 0.255 0.7989
## cog_e -0.018704 0.038935 -0.480 0.6310
## w_control 0.051698 0.037366 1.384 0.1665
## cue -0.068716 0.036770 -1.869 0.0617 .
## affect 0.031023 0.040076 0.774 0.4389
## attach -0.017658 0.038373 -0.460 0.6454
## social -0.041091 0.038702 -1.062 0.2884
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Robust residual standard error: 1.84
## Multiple R-squared: 0.001839, Adjusted R-squared: -0.0001775
## Convergence in 9 IRWLS iterations
##
## Robustness weights:
## 28 weights are ~= 1. The remaining 5429 ones are summarized as
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1986 0.8637 0.9505 0.9202 0.9908 0.9990
## Algorithmic parameters:
## tuning.chi bb tuning.psi refine.tol
## 1.548e+00 5.000e-01 4.685e+00 1.000e-07
## rel.tol scale.tol solve.tol zero.tol
## 1.000e-07 1.000e-10 1.000e-07 1.000e-10
## eps.outlier eps.x warn.limit.reject warn.limit.meanrw
## 1.833e-05 1.213e-11 5.000e-01 5.000e-01
## nResample max.it groups n.group best.r.s
## 500 50 5 400 2
## k.fast.s k.max maxit.scale trace.lev mts
## 1 1000 200 0 1000
## compute.rd fast.s.large.n
## 0 2000
## psi subsampling cov
## "bisquare" "nonsingular" ".vcov.avar1"
## compute.outlier.stats
## "SM"
## seed : int(0)
print(cor.wisdm37.cigg)
## auto loc tol craving taste
## auto 1.000000e+02 9.872299e+00 9.77208988 10.48697587 2.044612e+00
## loc 9.872299e+00 1.000000e+02 11.21686338 11.16199459 1.708174e+00
## tol 9.772090e+00 1.121686e+01 100.00000000 11.49644288 2.036112e+00
## craving 1.048698e+01 1.116199e+01 11.49644288 100.00000000 2.200149e+00
## taste 2.044612e+00 1.708174e+00 2.03611248 2.20014868 1.000000e+02
## cog_e 1.559625e+00 2.027995e+00 1.98006231 1.94266223 7.691140e+00
## w_control 1.789944e+00 1.690201e+00 2.07520550 1.94072199 6.295567e+00
## cue 2.198697e+00 1.712706e+00 1.49259913 2.12405616 7.430507e+00
## affect 1.605646e+00 1.730705e+00 1.73828821 1.64514537 6.572715e+00
## attach 1.876114e+00 1.833911e+00 1.86008329 2.14871229 6.313243e+00
## social 1.927375e+00 2.048385e+00 2.32494424 2.13391912 7.196366e+00
## cigg_f 3.178403e-05 9.472948e-04 0.01330205 0.01537463 1.632977e-03
## cog_e w_control cue affect attach
## auto 1.559625e+00 1.78994421 2.19869674 1.605646e+00 1.876114e+00
## loc 2.027995e+00 1.69020100 1.71270646 1.730705e+00 1.833911e+00
## tol 1.980062e+00 2.07520550 1.49259913 1.738288e+00 1.860083e+00
## craving 1.942662e+00 1.94072199 2.12405616 1.645145e+00 2.148712e+00
## taste 7.691140e+00 6.29556668 7.43050667 6.572715e+00 6.313243e+00
## cog_e 1.000000e+02 6.96089490 7.13009517 6.603245e+00 6.408226e+00
## w_control 6.960895e+00 100.00000000 6.75730045 6.602734e+00 6.872604e+00
## cue 7.130095e+00 6.75730045 100.00000000 6.857935e+00 6.284347e+00
## affect 6.603245e+00 6.60273437 6.85793476 1.000000e+02 5.153372e+00
## attach 6.408226e+00 6.87260430 6.28434730 5.153372e+00 1.000000e+02
## social 6.826808e+00 6.13453474 7.05203310 6.444894e+00 6.011915e+00
## cigg_f 5.605809e-03 0.02488712 0.04522647 3.009714e-03 3.683273e-03
## social cigg_f
## auto 1.92737502 3.178403e-05
## loc 2.04838476 9.472948e-04
## tol 2.32494424 1.330205e-02
## craving 2.13391912 1.537463e-02
## taste 7.19636602 1.632977e-03
## cog_e 6.82680840 5.605809e-03
## w_control 6.13453474 2.488712e-02
## cue 7.05203310 4.522647e-02
## affect 6.44489412 3.009714e-03
## attach 6.01191490 3.683273e-03
## social 100.00000000 2.610947e-02
## cigg_f 0.02610947 1.000000e+02
print(pcor.wisdm37.cigg)
## auto loc tol craving taste
## auto 1.000000e+02 2.947550e+00 2.780742e+00 3.28926245 1.012891e-01
## loc 2.947550e+00 1.000000e+02 3.784523e+00 3.58761783 1.326891e-02
## tol 2.780742e+00 3.784523e+00 1.000000e+02 3.84666756 7.440109e-02
## craving 3.289262e+00 3.587618e+00 3.846668e+00 100.00000000 1.021976e-01
## taste 1.012891e-01 1.326891e-02 7.440109e-02 0.10219760 1.000000e+02
## cog_e 4.534375e-03 1.236099e-01 7.605839e-02 0.04606846 1.574737e+00
## w_control 5.171281e-02 2.607061e-02 1.261694e-01 0.05109352 8.431175e-01
## cue 1.963960e-01 2.861515e-02 4.936060e-04 0.10909190 1.392809e+00
## affect 3.711994e-02 7.440560e-02 5.589869e-02 0.01856168 1.114761e+00
## attach 7.557930e-02 6.264434e-02 4.835599e-02 0.11999545 1.014334e+00
## social 5.103383e-02 8.411690e-02 1.600514e-01 0.06543171 1.347774e+00
## cigg_f 4.670581e-04 7.291670e-04 2.167265e-02 0.03368838 4.968880e-06
## cog_e w_control cue affect attach
## auto 4.534375e-03 0.05171281 1.963960e-01 0.03711994 7.557930e-02
## loc 1.236099e-01 0.02607061 2.861515e-02 0.07440560 6.264434e-02
## tol 7.605839e-02 0.12616944 4.936060e-04 0.05589869 4.835599e-02
## craving 4.606846e-02 0.05109352 1.090919e-01 0.01856168 1.199954e-01
## taste 1.574737e+00 0.84311751 1.392809e+00 1.11476132 1.014334e+00
## cog_e 1.000000e+02 1.24844093 1.217241e+00 1.13322594 1.071867e+00
## w_control 1.248441e+00 100.00000000 1.140309e+00 1.27197877 1.506291e+00
## cue 1.217241e+00 1.14030924 1.000000e+02 1.30994551 1.002752e+00
## affect 1.133226e+00 1.27197877 1.309946e+00 100.00000000 5.829042e-01
## attach 1.071867e+00 1.50629074 1.002752e+00 0.58290419 1.000000e+02
## social 1.120622e+00 0.85571405 1.271819e+00 1.13973770 9.306308e-01
## cigg_f 2.468033e-03 0.05389534 5.042369e-02 0.01148714 1.580967e-03
## social cigg_f
## auto 0.05103383 4.670581e-04
## loc 0.08411690 7.291670e-04
## tol 0.16005137 2.167265e-02
## craving 0.06543171 3.368838e-02
## taste 1.34777440 4.968880e-06
## cog_e 1.12062239 2.468033e-03
## w_control 0.85571405 5.389534e-02
## cue 1.27181932 5.042369e-02
## affect 1.13973770 1.148714e-02
## attach 0.93063075 1.580967e-03
## social 100.00000000 2.290894e-02
## cigg_f 0.02290894 1.000000e+02
#WISDM-37 factors -> dependence on e-cigarettes
wisdm37_ecigg <- lmrob(ecigg_f ~ auto + loc + tol + craving + taste + cog_e + w_control + cue + affect + attach + social,
data = data_cleaned,
k.max = 1000
)
cor.wisdm37.ecigg <- (cor(data_cleaned[c(105:115, 103)],
method = "spearman")^2
)*100
pcor.wisdm37.ecigg <- (pcor(data_cleaned[c(105:115, 103)],
method = "spearman")$estimate^2
)*100
summary(wisdm37_ecigg)
##
## Call:
## lmrob(formula = ecigg_f ~ auto + loc + tol + craving + taste + cog_e + w_control +
## cue + affect + attach + social, data = data_cleaned, k.max = 1000)
## \--> method = "MM"
## Residuals:
## Min 1Q Median 3Q Max
## -3.7177 -1.4498 -0.3829 1.4558 7.4889
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7751235 0.2763993 13.658 <2e-16 ***
## auto 0.0591177 0.0405857 1.457 0.145
## loc 0.0126242 0.0429812 0.294 0.769
## tol 0.0008161 0.0414909 0.020 0.984
## craving -0.0219715 0.0425889 -0.516 0.606
## taste -0.0081781 0.0395349 -0.207 0.836
## cog_e -0.0662966 0.0396999 -1.670 0.095 .
## w_control -0.0119813 0.0384973 -0.311 0.756
## cue 0.0075851 0.0375557 0.202 0.840
## affect -0.0436664 0.0403954 -1.081 0.280
## attach -0.0292278 0.0389644 -0.750 0.453
## social 0.0345590 0.0391024 0.884 0.377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Robust residual standard error: 1.873
## Multiple R-squared: 0.00177, Adjusted R-squared: -0.0002466
## Convergence in 10 IRWLS iterations
##
## Robustness weights:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.07391 0.85280 0.94600 0.91890 0.99300 0.99840
## Algorithmic parameters:
## tuning.chi bb tuning.psi refine.tol
## 1.548e+00 5.000e-01 4.685e+00 1.000e-07
## rel.tol scale.tol solve.tol zero.tol
## 1.000e-07 1.000e-10 1.000e-07 1.000e-10
## eps.outlier eps.x warn.limit.reject warn.limit.meanrw
## 1.833e-05 1.213e-11 5.000e-01 5.000e-01
## nResample max.it groups n.group best.r.s
## 500 50 5 400 2
## k.fast.s k.max maxit.scale trace.lev mts
## 1 1000 200 0 1000
## compute.rd fast.s.large.n
## 0 2000
## psi subsampling cov
## "bisquare" "nonsingular" ".vcov.avar1"
## compute.outlier.stats
## "SM"
## seed : int(0)
print(cor.wisdm37.ecigg)
## auto loc tol craving taste
## auto 1.000000e+02 9.872299e+00 9.772090e+00 1.048698e+01 2.04461247
## loc 9.872299e+00 1.000000e+02 1.121686e+01 1.116199e+01 1.70817400
## tol 9.772090e+00 1.121686e+01 1.000000e+02 1.149644e+01 2.03611248
## craving 1.048698e+01 1.116199e+01 1.149644e+01 1.000000e+02 2.20014868
## taste 2.044612e+00 1.708174e+00 2.036112e+00 2.200149e+00 100.00000000
## cog_e 1.559625e+00 2.027995e+00 1.980062e+00 1.942662e+00 7.69114011
## w_control 1.789944e+00 1.690201e+00 2.075206e+00 1.940722e+00 6.29556668
## cue 2.198697e+00 1.712706e+00 1.492599e+00 2.124056e+00 7.43050667
## affect 1.605646e+00 1.730705e+00 1.738288e+00 1.645145e+00 6.57271530
## attach 1.876114e+00 1.833911e+00 1.860083e+00 2.148712e+00 6.31324337
## social 1.927375e+00 2.048385e+00 2.324944e+00 2.133919e+00 7.19636602
## ecigg_f 9.134188e-03 2.297808e-03 2.432842e-03 2.348026e-03 0.01559358
## cog_e w_control cue affect attach
## auto 1.55962500 1.78994421 2.198697e+00 1.60564604 1.87611439
## loc 2.02799516 1.69020100 1.712706e+00 1.73070483 1.83391068
## tol 1.98006231 2.07520550 1.492599e+00 1.73828821 1.86008329
## craving 1.94266223 1.94072199 2.124056e+00 1.64514537 2.14871229
## taste 7.69114011 6.29556668 7.430507e+00 6.57271530 6.31324337
## cog_e 100.00000000 6.96089490 7.130095e+00 6.60324527 6.40822578
## w_control 6.96089490 100.00000000 6.757300e+00 6.60273437 6.87260430
## cue 7.13009517 6.75730045 1.000000e+02 6.85793476 6.28434730
## affect 6.60324527 6.60273437 6.857935e+00 100.00000000 5.15337163
## attach 6.40822578 6.87260430 6.284347e+00 5.15337163 100.00000000
## social 6.82680840 6.13453474 7.052033e+00 6.44489412 6.01191490
## ecigg_f 0.08422186 0.03176931 7.455958e-03 0.03965147 0.02739291
## social ecigg_f
## auto 1.927375e+00 9.134188e-03
## loc 2.048385e+00 2.297808e-03
## tol 2.324944e+00 2.432842e-03
## craving 2.133919e+00 2.348026e-03
## taste 7.196366e+00 1.559358e-02
## cog_e 6.826808e+00 8.422186e-02
## w_control 6.134535e+00 3.176931e-02
## cue 7.052033e+00 7.455958e-03
## affect 6.444894e+00 3.965147e-02
## attach 6.011915e+00 2.739291e-02
## social 1.000000e+02 1.086602e-03
## ecigg_f 1.086602e-03 1.000000e+02
print(pcor.wisdm37.ecigg)
## auto loc tol craving taste
## auto 1.000000e+02 2.94409650 2.776956e+00 3.294124e+00 1.014821e-01
## loc 2.944097e+00 100.00000000 3.784883e+00 3.588275e+00 1.330982e-02
## tol 2.776956e+00 3.78488289 1.000000e+02 3.839610e+00 7.453138e-02
## craving 3.294124e+00 3.58827537 3.839610e+00 1.000000e+02 1.020871e-01
## taste 1.014821e-01 0.01330982 7.453138e-02 1.020871e-01 1.000000e+02
## cog_e 4.904099e-03 0.12467742 7.730119e-02 4.502628e-02 1.572413e+00
## w_control 5.247190e-02 0.02606924 1.242689e-01 5.280075e-02 8.429467e-01
## cue 1.958108e-01 0.02878908 3.632045e-04 1.065593e-01 1.393745e+00
## affect 3.778861e-02 0.07467445 5.555599e-02 1.888348e-02 1.113961e+00
## attach 7.608477e-02 0.06296288 4.887103e-02 1.191381e-01 1.013833e+00
## social 5.045045e-02 0.08407098 1.614628e-01 6.431042e-02 1.348562e+00
## ecigg_f 1.515208e-02 0.00414200 4.624897e-03 4.329402e-03 7.292634e-04
## cog_e w_control cue affect attach
## auto 4.904099e-03 5.247190e-02 1.958108e-01 0.03778861 7.608477e-02
## loc 1.246774e-01 2.606924e-02 2.878908e-02 0.07467445 6.296288e-02
## tol 7.730119e-02 1.242689e-01 3.632045e-04 0.05555599 4.887103e-02
## craving 4.502628e-02 5.280075e-02 1.065593e-01 0.01888348 1.191381e-01
## taste 1.572413e+00 8.429467e-01 1.393745e+00 1.11396125 1.013833e+00
## cog_e 1.000000e+02 1.241134e+00 1.220746e+00 1.12549863 1.067710e+00
## w_control 1.241134e+00 1.000000e+02 1.130683e+00 1.27559074 1.502749e+00
## cue 1.220746e+00 1.130683e+00 1.000000e+02 1.30562622 1.005335e+00
## affect 1.125499e+00 1.275591e+00 1.305626e+00 100.00000000 5.806296e-01
## attach 1.067710e+00 1.502749e+00 1.005335e+00 0.58062964 1.000000e+02
## social 1.125669e+00 8.510839e-01 1.279983e+00 1.13849212 9.331725e-01
## ecigg_f 5.250725e-02 8.368717e-03 4.244783e-04 0.01511988 7.020368e-03
## social ecigg_f
## auto 5.045045e-02 1.515208e-02
## loc 8.407098e-02 4.142000e-03
## tol 1.614628e-01 4.624897e-03
## craving 6.431042e-02 4.329402e-03
## taste 1.348562e+00 7.292634e-04
## cog_e 1.125669e+00 5.250725e-02
## w_control 8.510839e-01 8.368717e-03
## cue 1.279983e+00 4.244783e-04
## affect 1.138492e+00 1.511988e-02
## attach 9.331725e-01 7.020368e-03
## social 1.000000e+02 6.210693e-03
## ecigg_f 6.210693e-03 1.000000e+02
#WISDM-37 factors -> dependence on hookah
wisdm37_h <- lmrob(h_f ~ auto + loc + tol + craving + taste + cog_e + w_control + cue + affect + attach + social,
data = data_cleaned,
k.max = 1000
)
cor.wisdm37.h <- (cor(data_cleaned[c(105:115, 104)],
method = "spearman")^2
)*100
pcor.wisdm37.h <- (pcor(data_cleaned[c(105:115, 104)],
method = "spearman")$estimate^2
)*100
summary(wisdm37_h)
##
## Call:
## lmrob(formula = h_f ~ auto + loc + tol + craving + taste + cog_e + w_control +
## cue + affect + attach + social, data = data_cleaned, k.max = 1000)
## \--> method = "MM"
## Residuals:
## Min 1Q Median 3Q Max
## -2.20364 -1.01122 -0.02085 0.98922 7.01385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.306366 0.219113 10.526 <2e-16 ***
## auto -0.009782 0.034512 -0.283 0.777
## loc -0.011976 0.033643 -0.356 0.722
## tol -0.005931 0.034682 -0.171 0.864
## craving 0.014757 0.033713 0.438 0.662
## taste -0.010101 0.030348 -0.333 0.739
## cog_e 0.045182 0.030245 1.494 0.135
## w_control -0.016010 0.030412 -0.526 0.599
## cue -0.044126 0.030478 -1.448 0.148
## affect -0.016643 0.031201 -0.533 0.594
## attach 0.025505 0.031336 0.814 0.416
## social -0.043991 0.031104 -1.414 0.157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Robust residual standard error: 1.448
## Multiple R-squared: 0.001762, Adjusted R-squared: -0.0002551
## Convergence in 10 IRWLS iterations
##
## Robustness weights:
## 2 observations c(1863,2072) are outliers with |weight| = 0 ( < 1.8e-05);
## 1398 weights are ~= 1. The remaining 4057 ones are summarized as
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.04266 0.83410 0.95240 0.88570 0.95860 0.99900
## Algorithmic parameters:
## tuning.chi bb tuning.psi refine.tol
## 1.548e+00 5.000e-01 4.685e+00 1.000e-07
## rel.tol scale.tol solve.tol zero.tol
## 1.000e-07 1.000e-10 1.000e-07 1.000e-10
## eps.outlier eps.x warn.limit.reject warn.limit.meanrw
## 1.833e-05 1.213e-11 5.000e-01 5.000e-01
## nResample max.it groups n.group best.r.s
## 500 50 5 400 2
## k.fast.s k.max maxit.scale trace.lev mts
## 1 1000 200 0 1000
## compute.rd fast.s.large.n
## 0 2000
## psi subsampling cov
## "bisquare" "nonsingular" ".vcov.avar1"
## compute.outlier.stats
## "SM"
## seed : int(0)
print(cor.wisdm37.h)
## auto loc tol craving taste
## auto 100.00000000 9.87229910 9.772090e+00 1.048698e+01 2.0446125
## loc 9.87229910 100.00000000 1.121686e+01 1.116199e+01 1.7081740
## tol 9.77208988 11.21686338 1.000000e+02 1.149644e+01 2.0361125
## craving 10.48697587 11.16199459 1.149644e+01 1.000000e+02 2.2001487
## taste 2.04461247 1.70817400 2.036112e+00 2.200149e+00 100.0000000
## cog_e 1.55962500 2.02799516 1.980062e+00 1.942662e+00 7.6911401
## w_control 1.78994421 1.69020100 2.075206e+00 1.940722e+00 6.2955667
## cue 2.19869674 1.71270646 1.492599e+00 2.124056e+00 7.4305067
## affect 1.60564604 1.73070483 1.738288e+00 1.645145e+00 6.5727153
## attach 1.87611439 1.83391068 1.860083e+00 2.148712e+00 6.3132434
## social 1.92737502 2.04838476 2.324944e+00 2.133919e+00 7.1963660
## h_f 0.01746395 0.01624268 8.124967e-03 1.687271e-03 0.0163737
## cog_e w_control cue affect attach
## auto 1.559625e+00 1.78994421 2.19869674 1.60564604 1.876114e+00
## loc 2.027995e+00 1.69020100 1.71270646 1.73070483 1.833911e+00
## tol 1.980062e+00 2.07520550 1.49259913 1.73828821 1.860083e+00
## craving 1.942662e+00 1.94072199 2.12405616 1.64514537 2.148712e+00
## taste 7.691140e+00 6.29556668 7.43050667 6.57271530 6.313243e+00
## cog_e 1.000000e+02 6.96089490 7.13009517 6.60324527 6.408226e+00
## w_control 6.960895e+00 100.00000000 6.75730045 6.60273437 6.872604e+00
## cue 7.130095e+00 6.75730045 100.00000000 6.85793476 6.284347e+00
## affect 6.603245e+00 6.60273437 6.85793476 100.00000000 5.153372e+00
## attach 6.408226e+00 6.87260430 6.28434730 5.15337163 1.000000e+02
## social 6.826808e+00 6.13453474 7.05203310 6.44489412 6.011915e+00
## h_f 2.068663e-03 0.02723217 0.07635238 0.02105977 6.265674e-04
## social h_f
## auto 1.92737502 1.746395e-02
## loc 2.04838476 1.624268e-02
## tol 2.32494424 8.124967e-03
## craving 2.13391912 1.687271e-03
## taste 7.19636602 1.637370e-02
## cog_e 6.82680840 2.068663e-03
## w_control 6.13453474 2.723217e-02
## cue 7.05203310 7.635238e-02
## affect 6.44489412 2.105977e-02
## attach 6.01191490 6.265674e-04
## social 100.00000000 5.363631e-02
## h_f 0.05363631 1.000000e+02
print(pcor.wisdm37.h)
## auto loc tol craving taste
## auto 1.000000e+02 2.945568e+00 2.779797e+00 3.292886e+00 1.011322e-01
## loc 2.945568e+00 1.000000e+02 3.786274e+00 3.588120e+00 1.321329e-02
## tol 2.779797e+00 3.786274e+00 1.000000e+02 3.838438e+00 7.440101e-02
## craving 3.292886e+00 3.588120e+00 3.838438e+00 1.000000e+02 1.023257e-01
## taste 1.011322e-01 1.321329e-02 7.440101e-02 1.023257e-01 1.000000e+02
## cog_e 4.678008e-03 1.245310e-01 7.662432e-02 4.526121e-02 1.575879e+00
## w_control 5.170366e-02 2.568771e-02 1.237333e-01 5.329754e-02 8.428352e-01
## cue 1.947130e-01 2.831874e-02 3.717299e-04 1.071931e-01 1.391150e+00
## affect 3.707172e-02 7.404505e-02 5.512769e-02 1.919217e-02 1.114378e+00
## attach 7.582518e-02 6.297859e-02 4.868378e-02 1.192075e-01 1.014880e+00
## social 5.040716e-02 8.370679e-02 1.616493e-01 6.448113e-02 1.346466e+00
## h_f 4.310982e-03 4.728394e-03 2.880851e-04 2.978072e-03 1.266327e-03
## cog_e w_control cue affect attach
## auto 4.678008e-03 5.170366e-02 1.947130e-01 3.707172e-02 7.582518e-02
## loc 1.245310e-01 2.568771e-02 2.831874e-02 7.404505e-02 6.297859e-02
## tol 7.662432e-02 1.237333e-01 3.717299e-04 5.512769e-02 4.868378e-02
## craving 4.526121e-02 5.329754e-02 1.071931e-01 1.919217e-02 1.192075e-01
## taste 1.575879e+00 8.428352e-01 1.391150e+00 1.114378e+00 1.014880e+00
## cog_e 1.000000e+02 1.249551e+00 1.227950e+00 1.133952e+00 1.068821e+00
## w_control 1.249551e+00 1.000000e+02 1.125787e+00 1.277218e+00 1.506414e+00
## cue 1.227950e+00 1.125787e+00 1.000000e+02 1.301890e+00 1.008123e+00
## affect 1.133952e+00 1.277218e+00 1.301890e+00 1.000000e+02 5.829696e-01
## attach 1.068821e+00 1.506414e+00 1.008123e+00 5.829696e-01 1.000000e+02
## social 1.127966e+00 8.470194e-01 1.271660e+00 1.134452e+00 9.342968e-01
## h_f 3.212502e-02 7.544784e-03 4.661221e-02 3.027651e-03 6.898714e-03
## social h_f
## auto 0.05040716 4.310982e-03
## loc 0.08370679 4.728394e-03
## tol 0.16164930 2.880851e-04
## craving 0.06448113 2.978072e-03
## taste 1.34646635 1.266327e-03
## cog_e 1.12796632 3.212502e-02
## w_control 0.84701942 7.544784e-03
## cue 1.27166026 4.661221e-02
## affect 1.13445203 3.027651e-03
## attach 0.93429677 6.898714e-03
## social 100.00000000 2.589130e-02
## h_f 0.02589130 1.000000e+02
#Confirmatory factor analysis for the RFQ:
#Based on continuous variables:
#Model 1: 1-factor solution
rfq_cmod1 <-'f =~ hc1 + hc2 + hc3 + hc4 + hc5 + sc1 + sc2 + sc3 + sc4 + sc5 +
ir1 + ir2 + ir3 + ir4 + ir5 + sp1 + sp2 + sp3 + sp4 + sp5
'
rfq_cmod1_fit <- cfa(rfq_cmod1,
data = data_cleaned,
estimator = "MLR"
)
summary(rfq_cmod1_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 40
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 20841.261 21839.192
## Degrees of freedom 170 170
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.954
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 47834.419 47433.136
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.008
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.566 0.541
## Tucker-Lewis Index (TLI) 0.515 0.487
##
## Robust Comparative Fit Index (CFI) 0.566
## Robust Tucker-Lewis Index (TLI) 0.515
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -137360.398 -137360.398
## Scaling correction factor 1.133
## for the MLR correction
## Loglikelihood unrestricted model (H1) -126939.768 -126939.768
## Scaling correction factor 0.988
## for the MLR correction
##
## Akaike (AIC) 274800.796 274800.796
## Bayesian (BIC) 275064.983 275064.983
## Sample-size adjusted Bayesian (SABIC) 274937.875 274937.875
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.149 0.153
## 90 Percent confidence interval - lower 0.148 0.151
## 90 Percent confidence interval - upper 0.151 0.155
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.149
## 90 Percent confidence interval - lower 0.148
## 90 Percent confidence interval - upper 0.151
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.133 0.133
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f =~
## hc1 1.000 0.495 0.504
## hc2 1.066 0.025 41.842 0.000 0.528 0.526
## hc3 0.940 0.024 39.256 0.000 0.465 0.495
## hc4 0.990 0.024 40.408 0.000 0.490 0.509
## hc5 1.060 0.025 41.678 0.000 0.525 0.526
## sc1 1.119 0.037 29.920 0.000 0.554 0.571
## sc2 1.151 0.038 29.915 0.000 0.570 0.572
## sc3 1.006 0.035 28.749 0.000 0.498 0.533
## sc4 1.126 0.038 29.563 0.000 0.557 0.564
## sc5 1.067 0.037 29.024 0.000 0.528 0.551
## ir1 1.007 0.073 13.761 0.000 0.498 0.534
## ir2 1.073 0.076 14.117 0.000 0.531 0.552
## ir3 1.084 0.078 13.926 0.000 0.536 0.552
## ir4 0.978 0.071 13.848 0.000 0.484 0.521
## ir5 1.011 0.074 13.730 0.000 0.500 0.536
## sp1 1.160 0.078 14.815 0.000 0.574 0.595
## sp2 1.157 0.078 14.889 0.000 0.572 0.590
## sp3 1.198 0.081 14.853 0.000 0.593 0.601
## sp4 1.134 0.077 14.679 0.000 0.561 0.580
## sp5 1.074 0.074 14.516 0.000 0.531 0.569
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hc1 0.719 0.020 36.608 0.000 0.719 0.746
## .hc2 0.727 0.021 34.435 0.000 0.727 0.723
## .hc3 0.669 0.018 37.916 0.000 0.669 0.755
## .hc4 0.686 0.019 35.552 0.000 0.686 0.741
## .hc5 0.720 0.021 34.937 0.000 0.720 0.723
## .sc1 0.635 0.019 34.246 0.000 0.635 0.674
## .sc2 0.668 0.019 34.490 0.000 0.668 0.673
## .sc3 0.626 0.017 37.498 0.000 0.626 0.716
## .sc4 0.665 0.019 34.247 0.000 0.665 0.682
## .sc5 0.639 0.017 36.749 0.000 0.639 0.696
## .ir1 0.624 0.016 38.467 0.000 0.624 0.715
## .ir2 0.642 0.017 37.706 0.000 0.642 0.695
## .ir3 0.656 0.018 36.110 0.000 0.656 0.695
## .ir4 0.629 0.016 39.597 0.000 0.629 0.729
## .ir5 0.620 0.017 36.666 0.000 0.620 0.712
## .sp1 0.603 0.018 33.055 0.000 0.603 0.647
## .sp2 0.613 0.018 33.788 0.000 0.613 0.652
## .sp3 0.622 0.019 32.090 0.000 0.622 0.639
## .sp4 0.623 0.018 35.282 0.000 0.623 0.664
## .sp5 0.590 0.017 35.313 0.000 0.590 0.676
## f 0.245 0.021 11.605 0.000 1.000 1.000
#Model 2: 4-factor solution
rfq_cmod2 <-'hc =~ hc1 + hc2 + hc3 + hc4 + hc5
sc =~ sc1 + sc2 + sc3 + sc4 + sc5
ir =~ ir1 + ir2 + ir3 + ir4 + ir5
sp =~ sp1 + sp2 + sp3 + sp4 + sp5
'
rfq_cmod2_fit <- cfa(rfq_cmod2,
data = data_cleaned,
estimator = "MLR"
)
summary(rfq_cmod2_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 46
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 186.312 185.408
## Degrees of freedom 164 164
## P-value (Chi-square) 0.112 0.121
## Scaling correction factor 1.005
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 47834.419 47433.136
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.008
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 0.999 0.999
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 0.999
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -127032.924 -127032.924
## Scaling correction factor 0.929
## for the MLR correction
## Loglikelihood unrestricted model (H1) -126939.768 -126939.768
## Scaling correction factor 0.988
## for the MLR correction
##
## Akaike (AIC) 254157.848 254157.848
## Bayesian (BIC) 254461.662 254461.662
## Sample-size adjusted Bayesian (SABIC) 254315.489 254315.489
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.005 0.005
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.008 0.008
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.005
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.008
## P-value H_0: Robust RMSEA <= 0.050 1.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.008 0.008
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hc =~
## hc1 1.000 0.726 0.740
## hc2 1.065 0.019 57.169 0.000 0.773 0.771
## hc3 0.933 0.017 53.353 0.000 0.677 0.720
## hc4 0.985 0.018 55.649 0.000 0.715 0.743
## hc5 1.045 0.018 56.860 0.000 0.759 0.761
## sc =~
## sc1 1.000 0.727 0.749
## sc2 1.032 0.018 55.855 0.000 0.750 0.753
## sc3 0.910 0.018 51.076 0.000 0.662 0.708
## sc4 1.026 0.018 56.318 0.000 0.746 0.755
## sc5 0.949 0.018 52.146 0.000 0.690 0.720
## ir =~
## ir1 1.000 0.663 0.709
## ir2 1.068 0.021 51.884 0.000 0.708 0.736
## ir3 1.090 0.021 51.448 0.000 0.722 0.744
## ir4 0.983 0.020 48.938 0.000 0.651 0.701
## ir5 1.015 0.020 50.558 0.000 0.673 0.721
## sp =~
## sp1 1.000 0.719 0.745
## sp2 0.990 0.019 52.969 0.000 0.712 0.734
## sp3 1.037 0.019 55.606 0.000 0.746 0.756
## sp4 0.980 0.018 53.010 0.000 0.705 0.728
## sp5 0.927 0.018 52.685 0.000 0.667 0.714
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hc ~~
## sc 0.313 0.010 30.885 0.000 0.593 0.593
## ir 0.140 0.008 17.528 0.000 0.291 0.291
## sp 0.172 0.009 19.275 0.000 0.330 0.330
## sc ~~
## ir 0.172 0.008 20.364 0.000 0.356 0.356
## sp 0.215 0.009 22.963 0.000 0.410 0.410
## ir ~~
## sp 0.317 0.010 31.151 0.000 0.664 0.664
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hc1 0.436 0.010 43.803 0.000 0.436 0.453
## .hc2 0.408 0.010 40.752 0.000 0.408 0.405
## .hc3 0.427 0.010 44.402 0.000 0.427 0.482
## .hc4 0.415 0.010 43.395 0.000 0.415 0.448
## .hc5 0.419 0.010 42.293 0.000 0.419 0.422
## .sc1 0.413 0.010 42.732 0.000 0.413 0.438
## .sc2 0.429 0.010 41.387 0.000 0.429 0.432
## .sc3 0.436 0.010 45.434 0.000 0.436 0.499
## .sc4 0.419 0.010 42.037 0.000 0.419 0.429
## .sc5 0.442 0.010 43.508 0.000 0.442 0.482
## .ir1 0.433 0.010 43.470 0.000 0.433 0.497
## .ir2 0.423 0.010 42.589 0.000 0.423 0.458
## .ir3 0.422 0.010 42.080 0.000 0.422 0.447
## .ir4 0.439 0.010 44.887 0.000 0.439 0.509
## .ir5 0.417 0.010 42.450 0.000 0.417 0.480
## .sp1 0.416 0.010 42.478 0.000 0.416 0.446
## .sp2 0.434 0.010 44.030 0.000 0.434 0.461
## .sp3 0.417 0.010 40.740 0.000 0.417 0.429
## .sp4 0.441 0.010 43.831 0.000 0.441 0.471
## .sp5 0.428 0.010 44.913 0.000 0.428 0.491
## hc 0.527 0.016 32.669 0.000 1.000 1.000
## sc 0.529 0.016 32.799 0.000 1.000 1.000
## ir 0.439 0.015 29.559 0.000 1.000 1.000
## sp 0.517 0.016 32.624 0.000 1.000 1.000
#Model 3: 2-factor solution
rfq_cmod3 <-'intrinsic =~ hc1 + hc2 + hc3 + hc4 + hc5 + sc1 + sc2 + sc3 + sc4 + sc5
extrinsic =~ ir1 + ir2 + ir3 + ir4 + ir5 + sp1 + sp2 + sp3 + sp4 + sp5
'
rfq_cmod3_fit <- cfa(rfq_cmod3,
data = data_cleaned,
estimator = "MLR"
)
summary(rfq_cmod3_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 41
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 9051.760 9183.355
## Degrees of freedom 169 169
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.986
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 47834.419 47433.136
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.008
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.814 0.809
## Tucker-Lewis Index (TLI) 0.790 0.785
##
## Robust Comparative Fit Index (CFI) 0.814
## Robust Tucker-Lewis Index (TLI) 0.790
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -131465.648 -131465.648
## Scaling correction factor 0.999
## for the MLR correction
## Loglikelihood unrestricted model (H1) -126939.768 -126939.768
## Scaling correction factor 0.988
## for the MLR correction
##
## Akaike (AIC) 263013.296 263013.296
## Bayesian (BIC) 263284.086 263284.086
## Sample-size adjusted Bayesian (SABIC) 263153.801 263153.801
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.098 0.099
## 90 Percent confidence interval - lower 0.096 0.097
## 90 Percent confidence interval - upper 0.100 0.101
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.098
## 90 Percent confidence interval - lower 0.096
## 90 Percent confidence interval - upper 0.100
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.068 0.068
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intrinsic =~
## hc1 1.000 0.643 0.655
## hc2 1.073 0.020 53.811 0.000 0.690 0.688
## hc3 0.931 0.019 49.934 0.000 0.599 0.636
## hc4 0.987 0.019 52.100 0.000 0.635 0.660
## hc5 1.054 0.019 54.144 0.000 0.678 0.679
## sc1 0.986 0.039 25.375 0.000 0.634 0.653
## sc2 1.011 0.040 25.157 0.000 0.650 0.652
## sc3 0.903 0.036 24.801 0.000 0.580 0.621
## sc4 1.002 0.040 24.959 0.000 0.644 0.652
## sc5 0.940 0.038 24.856 0.000 0.604 0.630
## extrinsic =~
## ir1 1.000 0.593 0.634
## ir2 1.047 0.021 48.721 0.000 0.621 0.645
## ir3 1.079 0.022 48.810 0.000 0.639 0.658
## ir4 0.963 0.021 46.189 0.000 0.571 0.614
## ir5 1.003 0.021 46.758 0.000 0.594 0.637
## sp1 1.113 0.033 33.418 0.000 0.660 0.683
## sp2 1.106 0.034 32.970 0.000 0.655 0.676
## sp3 1.149 0.035 33.017 0.000 0.681 0.690
## sp4 1.094 0.034 32.396 0.000 0.648 0.670
## sp5 1.044 0.032 32.607 0.000 0.618 0.662
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intrinsic ~~
## extrinsic 0.166 0.007 22.927 0.000 0.436 0.436
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hc1 0.550 0.014 38.210 0.000 0.550 0.571
## .hc2 0.529 0.015 34.905 0.000 0.529 0.526
## .hc3 0.527 0.013 39.231 0.000 0.527 0.595
## .hc4 0.523 0.014 37.210 0.000 0.523 0.565
## .hc5 0.536 0.015 36.216 0.000 0.536 0.539
## .sc1 0.539 0.015 36.616 0.000 0.539 0.573
## .sc2 0.570 0.016 35.912 0.000 0.570 0.575
## .sc3 0.537 0.013 40.636 0.000 0.537 0.614
## .sc4 0.560 0.016 35.983 0.000 0.560 0.574
## .sc5 0.553 0.014 39.168 0.000 0.553 0.603
## .ir1 0.522 0.012 44.152 0.000 0.522 0.598
## .ir2 0.539 0.012 43.888 0.000 0.539 0.583
## .ir3 0.535 0.012 43.557 0.000 0.535 0.567
## .ir4 0.538 0.012 45.470 0.000 0.538 0.623
## .ir5 0.516 0.012 43.269 0.000 0.516 0.594
## .sp1 0.498 0.012 43.156 0.000 0.498 0.533
## .sp2 0.511 0.012 44.225 0.000 0.511 0.544
## .sp3 0.510 0.012 41.948 0.000 0.510 0.524
## .sp4 0.517 0.012 44.835 0.000 0.517 0.552
## .sp5 0.490 0.011 45.682 0.000 0.490 0.562
## intrinsic 0.413 0.019 21.353 0.000 1.000 1.000
## extrinsic 0.351 0.015 22.811 0.000 1.000 1.000
#Model 4: 2-order solution
rfq_cmod4 <-'#1st order
hc =~ hc1 + hc2 + hc3 + hc4 + hc5
sc =~ sc1 + sc2 + sc3 + sc4 + sc5
ir =~ ir1 + ir2 + ir3 + ir4 + ir5
sp =~ sp1 + sp2 + sp3 + sp4 + sp5
#2nd order
intrinsic =~ hc + sc
extrinsic =~ ir + sp
'
rfq_cmod4_fit <- cfa(rfq_cmod4,
data = data_cleaned,
estimator = "MLR"
)
summary(rfq_cmod4_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 39 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 186.412 185.529
## Degrees of freedom 165 165
## P-value (Chi-square) 0.121 0.131
## Scaling correction factor 1.005
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 47834.419 47433.136
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.008
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 0.999 0.999
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -127032.974 -127032.974
## Scaling correction factor 0.928
## for the MLR correction
## Loglikelihood unrestricted model (H1) -126939.768 -126939.768
## Scaling correction factor 0.988
## for the MLR correction
##
## Akaike (AIC) 254155.948 254155.948
## Bayesian (BIC) 254453.158 254453.158
## Sample-size adjusted Bayesian (SABIC) 254310.162 254310.162
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.005 0.005
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.008 0.008
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.005
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.008
## P-value H_0: Robust RMSEA <= 0.050 1.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.008 0.008
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hc =~
## hc1 1.000 0.726 0.740
## hc2 1.065 0.019 57.189 0.000 0.773 0.771
## hc3 0.933 0.017 53.362 0.000 0.677 0.720
## hc4 0.985 0.018 55.654 0.000 0.715 0.743
## hc5 1.045 0.018 56.879 0.000 0.759 0.761
## sc =~
## sc1 1.000 0.727 0.749
## sc2 1.032 0.018 55.857 0.000 0.750 0.753
## sc3 0.910 0.018 51.084 0.000 0.662 0.708
## sc4 1.026 0.018 56.321 0.000 0.746 0.755
## sc5 0.949 0.018 52.159 0.000 0.690 0.720
## ir =~
## ir1 1.000 0.663 0.709
## ir2 1.068 0.021 51.891 0.000 0.708 0.736
## ir3 1.090 0.021 51.449 0.000 0.722 0.744
## ir4 0.983 0.020 48.939 0.000 0.651 0.701
## ir5 1.015 0.020 50.568 0.000 0.673 0.721
## sp =~
## sp1 1.000 0.719 0.745
## sp2 0.990 0.019 52.971 0.000 0.712 0.734
## sp3 1.037 0.019 55.606 0.000 0.746 0.756
## sp4 0.980 0.018 53.010 0.000 0.705 0.728
## sp5 0.927 0.018 52.686 0.000 0.667 0.714
## intrinsic =~
## hc 1.000 0.693 0.693
## sc 1.238 0.054 23.047 0.000 0.857 0.857
## extrinsic =~
## ir 1.000 0.761 0.761
## sp 1.246 0.050 25.059 0.000 0.873 0.873
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intrinsic ~~
## extrinsic 0.139 0.007 18.536 0.000 0.548 0.548
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hc1 0.436 0.010 43.806 0.000 0.436 0.453
## .hc2 0.408 0.010 40.752 0.000 0.408 0.405
## .hc3 0.427 0.010 44.408 0.000 0.427 0.482
## .hc4 0.415 0.010 43.395 0.000 0.415 0.448
## .hc5 0.419 0.010 42.296 0.000 0.419 0.422
## .sc1 0.413 0.010 42.732 0.000 0.413 0.438
## .sc2 0.429 0.010 41.394 0.000 0.429 0.432
## .sc3 0.436 0.010 45.433 0.000 0.436 0.499
## .sc4 0.419 0.010 42.037 0.000 0.419 0.429
## .sc5 0.442 0.010 43.517 0.000 0.442 0.482
## .ir1 0.433 0.010 43.471 0.000 0.433 0.497
## .ir2 0.423 0.010 42.593 0.000 0.423 0.458
## .ir3 0.422 0.010 42.078 0.000 0.422 0.447
## .ir4 0.439 0.010 44.887 0.000 0.439 0.509
## .ir5 0.417 0.010 42.463 0.000 0.417 0.480
## .sp1 0.416 0.010 42.478 0.000 0.416 0.446
## .sp2 0.434 0.010 44.040 0.000 0.434 0.461
## .sp3 0.417 0.010 40.741 0.000 0.417 0.429
## .sp4 0.441 0.010 43.830 0.000 0.441 0.471
## .sp5 0.428 0.010 44.914 0.000 0.428 0.491
## .hc 0.274 0.013 20.318 0.000 0.520 0.520
## .sc 0.141 0.016 8.773 0.000 0.266 0.266
## .ir 0.185 0.011 17.001 0.000 0.421 0.421
## .sp 0.123 0.015 8.439 0.000 0.237 0.237
## intrinsic 0.253 0.014 17.634 0.000 1.000 1.000
## extrinsic 0.254 0.014 18.596 0.000 1.000 1.000
#Based on ordinal variables:
rfq_items <- c("hc1", "hc2", "hc3", "hc4", "hc5",
"sc1", "sc2", "sc3", "sc4", "sc5",
"ir1", "ir2", "ir3", "ir4", "ir5",
"sp1", "sp2", "sp3", "sp4", "sp5"
)
#Model 1: 1-factor solution
rfq_omod1 <- 'f =~ hc1 + hc2 + hc3 + hc4 + hc5 + sc1 + sc2 + sc3 + sc4 + sc5 +
ir1 + ir2 + ir3 + ir4 + ir5 + sp1 + sp2 + sp3 + sp4 + sp5
'
rfq_omod1_fit <- cfa(rfq_omod1,
data = data_cleaned,
estimator = "WLSMV",
ordered = rfq_items
)
summary(rfq_omod1_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 27 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 100
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 33513.452 28795.182
## Degrees of freedom 170 170
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.167
## Shift parameter 76.854
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 204417.968 80216.076
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 2.552
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.837 0.642
## Tucker-Lewis Index (TLI) 0.818 0.600
##
## Robust Comparative Fit Index (CFI) 0.535
## Robust Tucker-Lewis Index (TLI) 0.481
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.190 0.176
## 90 Percent confidence interval - lower 0.188 0.174
## 90 Percent confidence interval - upper 0.191 0.177
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.170
## 90 Percent confidence interval - lower 0.168
## 90 Percent confidence interval - upper 0.172
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.154 0.154
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f =~
## hc1 1.000 0.606 0.606
## hc2 1.055 0.018 60.278 0.000 0.639 0.639
## hc3 0.978 0.017 56.579 0.000 0.592 0.592
## hc4 1.007 0.017 58.021 0.000 0.610 0.610
## hc5 1.046 0.017 59.993 0.000 0.633 0.633
## sc1 1.056 0.019 55.019 0.000 0.640 0.640
## sc2 1.059 0.020 54.291 0.000 0.641 0.641
## sc3 0.983 0.020 50.016 0.000 0.595 0.595
## sc4 1.053 0.019 54.793 0.000 0.638 0.638
## sc5 1.011 0.019 52.200 0.000 0.612 0.612
## ir1 0.972 0.021 46.586 0.000 0.589 0.589
## ir2 1.006 0.021 48.173 0.000 0.609 0.609
## ir3 1.014 0.020 49.784 0.000 0.614 0.614
## ir4 0.946 0.021 45.638 0.000 0.573 0.573
## ir5 0.976 0.021 45.862 0.000 0.591 0.591
## sp1 1.056 0.020 52.557 0.000 0.639 0.639
## sp2 1.038 0.020 51.967 0.000 0.628 0.628
## sp3 1.075 0.020 53.027 0.000 0.651 0.651
## sp4 1.026 0.020 50.971 0.000 0.621 0.621
## sp5 1.008 0.020 49.235 0.000 0.610 0.610
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hc1|t1 -1.435 0.025 -57.111 0.000 -1.435 -1.435
## hc1|t2 -0.403 0.017 -23.040 0.000 -0.403 -0.403
## hc1|t3 0.617 0.018 33.906 0.000 0.617 0.617
## hc1|t4 1.695 0.030 57.265 0.000 1.695 1.695
## hc2|t1 -1.492 0.026 -57.442 0.000 -1.492 -1.492
## hc2|t2 -0.498 0.018 -28.036 0.000 -0.498 -0.498
## hc2|t3 0.528 0.018 29.578 0.000 0.528 0.528
## hc2|t4 1.517 0.026 57.529 0.000 1.517 1.517
## hc3|t1 -1.336 0.024 -56.142 0.000 -1.336 -1.336
## hc3|t2 -0.256 0.017 -14.914 0.000 -0.256 -0.256
## hc3|t3 0.845 0.019 43.644 0.000 0.845 0.845
## hc3|t4 1.910 0.035 54.984 0.000 1.910 1.910
## hc4|t1 -1.365 0.024 -56.477 0.000 -1.365 -1.365
## hc4|t2 -0.314 0.017 -18.149 0.000 -0.314 -0.314
## hc4|t3 0.750 0.019 39.841 0.000 0.750 0.750
## hc4|t4 1.814 0.032 56.245 0.000 1.814 1.814
## hc5|t1 -1.442 0.025 -57.166 0.000 -1.442 -1.442
## hc5|t2 -0.451 0.018 -25.636 0.000 -0.451 -0.451
## hc5|t3 0.559 0.018 31.114 0.000 0.559 0.559
## hc5|t4 1.612 0.028 57.582 0.000 1.612 1.612
## sc1|t1 -1.531 0.027 -57.567 0.000 -1.531 -1.531
## sc1|t2 -0.472 0.018 -26.731 0.000 -0.472 -0.472
## sc1|t3 0.573 0.018 31.801 0.000 0.573 0.573
## sc1|t4 1.634 0.028 57.529 0.000 1.634 1.634
## sc2|t1 -1.603 0.028 -57.595 0.000 -1.603 -1.603
## sc2|t2 -0.586 0.018 -32.434 0.000 -0.586 -0.586
## sc2|t3 0.413 0.018 23.603 0.000 0.413 0.413
## sc2|t4 1.460 0.025 57.275 0.000 1.460 1.460
## sc3|t1 -1.474 0.026 -57.358 0.000 -1.474 -1.474
## sc3|t2 -0.378 0.017 -21.698 0.000 -0.378 -0.378
## sc3|t3 0.738 0.019 39.330 0.000 0.738 0.738
## sc3|t4 1.814 0.032 56.245 0.000 1.814 1.814
## sc4|t1 -1.581 0.027 -57.615 0.000 -1.581 -1.581
## sc4|t2 -0.507 0.018 -28.488 0.000 -0.507 -0.507
## sc4|t3 0.494 0.018 27.850 0.000 0.494 0.494
## sc4|t4 1.534 0.027 57.573 0.000 1.534 1.534
## sc5|t1 -1.512 0.026 -57.515 0.000 -1.512 -1.512
## sc5|t2 -0.438 0.018 -24.941 0.000 -0.438 -0.438
## sc5|t3 0.626 0.018 34.352 0.000 0.626 0.626
## sc5|t4 1.700 0.030 57.230 0.000 1.700 1.700
## ir1|t1 -1.177 0.022 -53.462 0.000 -1.177 -1.177
## ir1|t2 -0.106 0.017 -6.212 0.000 -0.106 -0.106
## ir1|t3 1.001 0.020 48.938 0.000 1.001 1.001
## ir1|t4 2.074 0.040 52.061 0.000 2.074 2.074
## ir2|t1 -1.230 0.023 -54.504 0.000 -1.230 -1.230
## ir2|t2 -0.181 0.017 -10.620 0.000 -0.181 -0.181
## ir2|t3 0.872 0.020 44.631 0.000 0.872 0.872
## ir2|t4 1.940 0.036 54.528 0.000 1.940 1.940
## ir3|t1 -1.276 0.023 -55.290 0.000 -1.276 -1.276
## ir3|t2 -0.250 0.017 -14.563 0.000 -0.250 -0.250
## ir3|t3 0.812 0.019 42.373 0.000 0.812 0.812
## ir3|t4 1.839 0.033 55.961 0.000 1.839 1.839
## ir4|t1 -1.161 0.022 -53.130 0.000 -1.161 -1.161
## ir4|t2 -0.083 0.017 -4.859 0.000 -0.083 -0.083
## ir4|t3 1.029 0.021 49.777 0.000 1.029 1.029
## ir4|t4 2.102 0.041 51.464 0.000 2.102 2.102
## ir5|t1 -1.227 0.023 -54.451 0.000 -1.227 -1.227
## ir5|t2 -0.169 0.017 -9.917 0.000 -0.169 -0.169
## ir5|t3 0.938 0.020 46.952 0.000 0.938 0.938
## ir5|t4 2.086 0.040 51.812 0.000 2.086 2.086
## sp1|t1 -1.326 0.024 -56.014 0.000 -1.326 -1.326
## sp1|t2 -0.263 0.017 -15.319 0.000 -0.263 -0.263
## sp1|t3 0.780 0.019 41.087 0.000 0.780 0.780
## sp1|t4 1.841 0.033 55.931 0.000 1.841 1.841
## sp2|t1 -1.238 0.023 -54.646 0.000 -1.238 -1.238
## sp2|t2 -0.200 0.017 -11.674 0.000 -0.200 -0.200
## sp2|t3 0.857 0.019 44.089 0.000 0.857 0.857
## sp2|t4 1.872 0.034 55.533 0.000 1.872 1.872
## sp3|t1 -1.338 0.024 -56.156 0.000 -1.338 -1.338
## sp3|t2 -0.324 0.017 -18.714 0.000 -0.324 -0.324
## sp3|t3 0.702 0.019 37.786 0.000 0.702 0.702
## sp3|t4 1.749 0.031 56.879 0.000 1.749 1.749
## sp4|t1 -1.315 0.024 -55.868 0.000 -1.315 -1.315
## sp4|t2 -0.238 0.017 -13.889 0.000 -0.238 -0.238
## sp4|t3 0.789 0.019 41.441 0.000 0.789 0.789
## sp4|t4 1.844 0.033 55.900 0.000 1.844 1.844
## sp5|t1 -1.281 0.023 -55.372 0.000 -1.281 -1.281
## sp5|t2 -0.186 0.017 -10.917 0.000 -0.186 -0.186
## sp5|t3 0.891 0.020 45.315 0.000 0.891 0.891
## sp5|t4 2.043 0.039 52.677 0.000 2.043 2.043
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hc1 0.633 0.633 0.633
## .hc2 0.592 0.592 0.592
## .hc3 0.649 0.649 0.649
## .hc4 0.628 0.628 0.628
## .hc5 0.599 0.599 0.599
## .sc1 0.591 0.591 0.591
## .sc2 0.589 0.589 0.589
## .sc3 0.646 0.646 0.646
## .sc4 0.594 0.594 0.594
## .sc5 0.625 0.625 0.625
## .ir1 0.654 0.654 0.654
## .ir2 0.629 0.629 0.629
## .ir3 0.623 0.623 0.623
## .ir4 0.672 0.672 0.672
## .ir5 0.651 0.651 0.651
## .sp1 0.591 0.591 0.591
## .sp2 0.605 0.605 0.605
## .sp3 0.577 0.577 0.577
## .sp4 0.614 0.614 0.614
## .sp5 0.627 0.627 0.627
## f 0.367 0.011 33.891 0.000 1.000 1.000
#Model 2: 4-factor solution
rfq_omod2 <- 'hc =~ hc1 + hc2 + hc3 + hc4 + hc5
sc =~ sc1 + sc2 + sc3 + sc4 + sc5
ir =~ ir1 + ir2 + ir3 + ir4 + ir5
sp =~ sp1 + sp2 + sp3 + sp4 + sp5
'
rfq_omod2_fit <- cfa(rfq_omod2,
data = data_cleaned,
estimator = "WLSMV",
ordered = rfq_items
)
summary(rfq_omod2_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 46 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 106
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 92.040 167.265
## Degrees of freedom 164 164
## P-value (Chi-square) 1.000 0.415
## Scaling correction factor 0.744
## Shift parameter 43.576
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 204417.968 80216.076
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 2.552
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) 0.999
## Robust Tucker-Lewis Index (TLI) 0.999
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.002
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.007
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.006
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.010
## P-value H_0: Robust RMSEA <= 0.050 1.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.009 0.009
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hc =~
## hc1 1.000 0.773 0.773
## hc2 1.046 0.014 74.555 0.000 0.808 0.808
## hc3 0.978 0.014 69.515 0.000 0.756 0.756
## hc4 1.009 0.014 72.239 0.000 0.780 0.780
## hc5 1.034 0.014 74.438 0.000 0.799 0.799
## sc =~
## sc1 1.000 0.789 0.789
## sc2 1.001 0.014 72.596 0.000 0.790 0.790
## sc3 0.943 0.014 66.507 0.000 0.744 0.744
## sc4 0.999 0.013 74.502 0.000 0.788 0.788
## sc5 0.962 0.014 68.406 0.000 0.759 0.759
## ir =~
## ir1 1.000 0.751 0.751
## ir2 1.031 0.016 66.192 0.000 0.775 0.775
## ir3 1.039 0.016 66.373 0.000 0.781 0.781
## ir4 0.979 0.016 62.311 0.000 0.736 0.736
## ir5 1.011 0.016 62.624 0.000 0.760 0.760
## sp =~
## sp1 1.000 0.781 0.781
## sp2 0.990 0.014 71.727 0.000 0.773 0.773
## sp3 1.012 0.014 72.817 0.000 0.791 0.791
## sp4 0.976 0.014 69.671 0.000 0.763 0.763
## sp5 0.962 0.014 68.461 0.000 0.751 0.751
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hc ~~
## sc 0.362 0.009 39.614 0.000 0.594 0.594
## ir 0.169 0.009 18.420 0.000 0.290 0.290
## sp 0.200 0.009 21.194 0.000 0.331 0.331
## sc ~~
## ir 0.211 0.009 22.599 0.000 0.357 0.357
## sp 0.253 0.009 26.675 0.000 0.411 0.411
## ir ~~
## sp 0.391 0.009 42.538 0.000 0.666 0.666
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hc1|t1 -1.435 0.025 -57.111 0.000 -1.435 -1.435
## hc1|t2 -0.403 0.017 -23.040 0.000 -0.403 -0.403
## hc1|t3 0.617 0.018 33.906 0.000 0.617 0.617
## hc1|t4 1.695 0.030 57.265 0.000 1.695 1.695
## hc2|t1 -1.492 0.026 -57.442 0.000 -1.492 -1.492
## hc2|t2 -0.498 0.018 -28.036 0.000 -0.498 -0.498
## hc2|t3 0.528 0.018 29.578 0.000 0.528 0.528
## hc2|t4 1.517 0.026 57.529 0.000 1.517 1.517
## hc3|t1 -1.336 0.024 -56.142 0.000 -1.336 -1.336
## hc3|t2 -0.256 0.017 -14.914 0.000 -0.256 -0.256
## hc3|t3 0.845 0.019 43.644 0.000 0.845 0.845
## hc3|t4 1.910 0.035 54.984 0.000 1.910 1.910
## hc4|t1 -1.365 0.024 -56.477 0.000 -1.365 -1.365
## hc4|t2 -0.314 0.017 -18.149 0.000 -0.314 -0.314
## hc4|t3 0.750 0.019 39.841 0.000 0.750 0.750
## hc4|t4 1.814 0.032 56.245 0.000 1.814 1.814
## hc5|t1 -1.442 0.025 -57.166 0.000 -1.442 -1.442
## hc5|t2 -0.451 0.018 -25.636 0.000 -0.451 -0.451
## hc5|t3 0.559 0.018 31.114 0.000 0.559 0.559
## hc5|t4 1.612 0.028 57.582 0.000 1.612 1.612
## sc1|t1 -1.531 0.027 -57.567 0.000 -1.531 -1.531
## sc1|t2 -0.472 0.018 -26.731 0.000 -0.472 -0.472
## sc1|t3 0.573 0.018 31.801 0.000 0.573 0.573
## sc1|t4 1.634 0.028 57.529 0.000 1.634 1.634
## sc2|t1 -1.603 0.028 -57.595 0.000 -1.603 -1.603
## sc2|t2 -0.586 0.018 -32.434 0.000 -0.586 -0.586
## sc2|t3 0.413 0.018 23.603 0.000 0.413 0.413
## sc2|t4 1.460 0.025 57.275 0.000 1.460 1.460
## sc3|t1 -1.474 0.026 -57.358 0.000 -1.474 -1.474
## sc3|t2 -0.378 0.017 -21.698 0.000 -0.378 -0.378
## sc3|t3 0.738 0.019 39.330 0.000 0.738 0.738
## sc3|t4 1.814 0.032 56.245 0.000 1.814 1.814
## sc4|t1 -1.581 0.027 -57.615 0.000 -1.581 -1.581
## sc4|t2 -0.507 0.018 -28.488 0.000 -0.507 -0.507
## sc4|t3 0.494 0.018 27.850 0.000 0.494 0.494
## sc4|t4 1.534 0.027 57.573 0.000 1.534 1.534
## sc5|t1 -1.512 0.026 -57.515 0.000 -1.512 -1.512
## sc5|t2 -0.438 0.018 -24.941 0.000 -0.438 -0.438
## sc5|t3 0.626 0.018 34.352 0.000 0.626 0.626
## sc5|t4 1.700 0.030 57.230 0.000 1.700 1.700
## ir1|t1 -1.177 0.022 -53.462 0.000 -1.177 -1.177
## ir1|t2 -0.106 0.017 -6.212 0.000 -0.106 -0.106
## ir1|t3 1.001 0.020 48.938 0.000 1.001 1.001
## ir1|t4 2.074 0.040 52.061 0.000 2.074 2.074
## ir2|t1 -1.230 0.023 -54.504 0.000 -1.230 -1.230
## ir2|t2 -0.181 0.017 -10.620 0.000 -0.181 -0.181
## ir2|t3 0.872 0.020 44.631 0.000 0.872 0.872
## ir2|t4 1.940 0.036 54.528 0.000 1.940 1.940
## ir3|t1 -1.276 0.023 -55.290 0.000 -1.276 -1.276
## ir3|t2 -0.250 0.017 -14.563 0.000 -0.250 -0.250
## ir3|t3 0.812 0.019 42.373 0.000 0.812 0.812
## ir3|t4 1.839 0.033 55.961 0.000 1.839 1.839
## ir4|t1 -1.161 0.022 -53.130 0.000 -1.161 -1.161
## ir4|t2 -0.083 0.017 -4.859 0.000 -0.083 -0.083
## ir4|t3 1.029 0.021 49.777 0.000 1.029 1.029
## ir4|t4 2.102 0.041 51.464 0.000 2.102 2.102
## ir5|t1 -1.227 0.023 -54.451 0.000 -1.227 -1.227
## ir5|t2 -0.169 0.017 -9.917 0.000 -0.169 -0.169
## ir5|t3 0.938 0.020 46.952 0.000 0.938 0.938
## ir5|t4 2.086 0.040 51.812 0.000 2.086 2.086
## sp1|t1 -1.326 0.024 -56.014 0.000 -1.326 -1.326
## sp1|t2 -0.263 0.017 -15.319 0.000 -0.263 -0.263
## sp1|t3 0.780 0.019 41.087 0.000 0.780 0.780
## sp1|t4 1.841 0.033 55.931 0.000 1.841 1.841
## sp2|t1 -1.238 0.023 -54.646 0.000 -1.238 -1.238
## sp2|t2 -0.200 0.017 -11.674 0.000 -0.200 -0.200
## sp2|t3 0.857 0.019 44.089 0.000 0.857 0.857
## sp2|t4 1.872 0.034 55.533 0.000 1.872 1.872
## sp3|t1 -1.338 0.024 -56.156 0.000 -1.338 -1.338
## sp3|t2 -0.324 0.017 -18.714 0.000 -0.324 -0.324
## sp3|t3 0.702 0.019 37.786 0.000 0.702 0.702
## sp3|t4 1.749 0.031 56.879 0.000 1.749 1.749
## sp4|t1 -1.315 0.024 -55.868 0.000 -1.315 -1.315
## sp4|t2 -0.238 0.017 -13.889 0.000 -0.238 -0.238
## sp4|t3 0.789 0.019 41.441 0.000 0.789 0.789
## sp4|t4 1.844 0.033 55.900 0.000 1.844 1.844
## sp5|t1 -1.281 0.023 -55.372 0.000 -1.281 -1.281
## sp5|t2 -0.186 0.017 -10.917 0.000 -0.186 -0.186
## sp5|t3 0.891 0.020 45.315 0.000 0.891 0.891
## sp5|t4 2.043 0.039 52.677 0.000 2.043 2.043
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hc1 0.403 0.403 0.403
## .hc2 0.346 0.346 0.346
## .hc3 0.429 0.429 0.429
## .hc4 0.392 0.392 0.392
## .hc5 0.361 0.361 0.361
## .sc1 0.378 0.378 0.378
## .sc2 0.376 0.376 0.376
## .sc3 0.447 0.447 0.447
## .sc4 0.379 0.379 0.379
## .sc5 0.424 0.424 0.424
## .ir1 0.436 0.436 0.436
## .ir2 0.400 0.400 0.400
## .ir3 0.391 0.391 0.391
## .ir4 0.458 0.458 0.458
## .ir5 0.423 0.423 0.423
## .sp1 0.390 0.390 0.390
## .sp2 0.402 0.402 0.402
## .sp3 0.375 0.375 0.375
## .sp4 0.418 0.418 0.418
## .sp5 0.436 0.436 0.436
## hc 0.597 0.012 48.606 0.000 1.000 1.000
## sc 0.622 0.012 50.555 0.000 1.000 1.000
## ir 0.564 0.013 42.977 0.000 1.000 1.000
## sp 0.610 0.012 49.692 0.000 1.000 1.000
#Model 3: 2-factor solution
rfq_omod3 <- 'intrinsic =~ hc1 + hc2 + hc3 + hc4 + hc5 + sc1 + sc2 + sc3 + sc4 + sc5
extrinsic =~ ir1 + ir2 + ir3 + ir4 + ir5 + sp1 + sp2 + sp3 + sp4 + sp5
'
rfq_omod3_fit <- cfa(rfq_omod3,
data = data_cleaned,
estimator = "WLSMV",
ordered = rfq_items
)
summary(rfq_omod3_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 28 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 101
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 8788.236 8436.875
## Degrees of freedom 169 169
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.050
## Shift parameter 66.373
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 204417.968 80216.076
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 2.552
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.958 0.897
## Tucker-Lewis Index (TLI) 0.953 0.884
##
## Robust Comparative Fit Index (CFI) 0.791
## Robust Tucker-Lewis Index (TLI) 0.765
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.097 0.095
## 90 Percent confidence interval - lower 0.095 0.093
## 90 Percent confidence interval - upper 0.098 0.096
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.115
## 90 Percent confidence interval - lower 0.112
## 90 Percent confidence interval - upper 0.117
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.077 0.077
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intrinsic =~
## hc1 1.000 0.700 0.700
## hc2 1.051 0.015 70.213 0.000 0.735 0.735
## hc3 0.979 0.015 65.340 0.000 0.685 0.685
## hc4 1.006 0.015 67.797 0.000 0.704 0.704
## hc5 1.036 0.015 70.383 0.000 0.725 0.725
## sc1 1.036 0.016 64.956 0.000 0.725 0.725
## sc2 1.034 0.016 64.113 0.000 0.723 0.723
## sc3 0.976 0.016 59.269 0.000 0.682 0.682
## sc4 1.033 0.016 65.204 0.000 0.723 0.723
## sc5 0.995 0.016 61.428 0.000 0.696 0.696
## extrinsic =~
## ir1 1.000 0.684 0.684
## ir2 1.032 0.016 63.585 0.000 0.706 0.706
## ir3 1.040 0.016 63.985 0.000 0.711 0.711
## ir4 0.979 0.016 59.861 0.000 0.670 0.670
## ir5 1.010 0.017 60.036 0.000 0.691 0.691
## sp1 1.066 0.017 62.230 0.000 0.730 0.730
## sp2 1.055 0.017 61.519 0.000 0.722 0.722
## sp3 1.082 0.017 62.294 0.000 0.741 0.741
## sp4 1.043 0.017 59.948 0.000 0.714 0.714
## sp5 1.028 0.018 58.325 0.000 0.704 0.704
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intrinsic ~~
## extrinsic 0.198 0.007 27.454 0.000 0.414 0.414
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hc1|t1 -1.435 0.025 -57.111 0.000 -1.435 -1.435
## hc1|t2 -0.403 0.017 -23.040 0.000 -0.403 -0.403
## hc1|t3 0.617 0.018 33.906 0.000 0.617 0.617
## hc1|t4 1.695 0.030 57.265 0.000 1.695 1.695
## hc2|t1 -1.492 0.026 -57.442 0.000 -1.492 -1.492
## hc2|t2 -0.498 0.018 -28.036 0.000 -0.498 -0.498
## hc2|t3 0.528 0.018 29.578 0.000 0.528 0.528
## hc2|t4 1.517 0.026 57.529 0.000 1.517 1.517
## hc3|t1 -1.336 0.024 -56.142 0.000 -1.336 -1.336
## hc3|t2 -0.256 0.017 -14.914 0.000 -0.256 -0.256
## hc3|t3 0.845 0.019 43.644 0.000 0.845 0.845
## hc3|t4 1.910 0.035 54.984 0.000 1.910 1.910
## hc4|t1 -1.365 0.024 -56.477 0.000 -1.365 -1.365
## hc4|t2 -0.314 0.017 -18.149 0.000 -0.314 -0.314
## hc4|t3 0.750 0.019 39.841 0.000 0.750 0.750
## hc4|t4 1.814 0.032 56.245 0.000 1.814 1.814
## hc5|t1 -1.442 0.025 -57.166 0.000 -1.442 -1.442
## hc5|t2 -0.451 0.018 -25.636 0.000 -0.451 -0.451
## hc5|t3 0.559 0.018 31.114 0.000 0.559 0.559
## hc5|t4 1.612 0.028 57.582 0.000 1.612 1.612
## sc1|t1 -1.531 0.027 -57.567 0.000 -1.531 -1.531
## sc1|t2 -0.472 0.018 -26.731 0.000 -0.472 -0.472
## sc1|t3 0.573 0.018 31.801 0.000 0.573 0.573
## sc1|t4 1.634 0.028 57.529 0.000 1.634 1.634
## sc2|t1 -1.603 0.028 -57.595 0.000 -1.603 -1.603
## sc2|t2 -0.586 0.018 -32.434 0.000 -0.586 -0.586
## sc2|t3 0.413 0.018 23.603 0.000 0.413 0.413
## sc2|t4 1.460 0.025 57.275 0.000 1.460 1.460
## sc3|t1 -1.474 0.026 -57.358 0.000 -1.474 -1.474
## sc3|t2 -0.378 0.017 -21.698 0.000 -0.378 -0.378
## sc3|t3 0.738 0.019 39.330 0.000 0.738 0.738
## sc3|t4 1.814 0.032 56.245 0.000 1.814 1.814
## sc4|t1 -1.581 0.027 -57.615 0.000 -1.581 -1.581
## sc4|t2 -0.507 0.018 -28.488 0.000 -0.507 -0.507
## sc4|t3 0.494 0.018 27.850 0.000 0.494 0.494
## sc4|t4 1.534 0.027 57.573 0.000 1.534 1.534
## sc5|t1 -1.512 0.026 -57.515 0.000 -1.512 -1.512
## sc5|t2 -0.438 0.018 -24.941 0.000 -0.438 -0.438
## sc5|t3 0.626 0.018 34.352 0.000 0.626 0.626
## sc5|t4 1.700 0.030 57.230 0.000 1.700 1.700
## ir1|t1 -1.177 0.022 -53.462 0.000 -1.177 -1.177
## ir1|t2 -0.106 0.017 -6.212 0.000 -0.106 -0.106
## ir1|t3 1.001 0.020 48.938 0.000 1.001 1.001
## ir1|t4 2.074 0.040 52.061 0.000 2.074 2.074
## ir2|t1 -1.230 0.023 -54.504 0.000 -1.230 -1.230
## ir2|t2 -0.181 0.017 -10.620 0.000 -0.181 -0.181
## ir2|t3 0.872 0.020 44.631 0.000 0.872 0.872
## ir2|t4 1.940 0.036 54.528 0.000 1.940 1.940
## ir3|t1 -1.276 0.023 -55.290 0.000 -1.276 -1.276
## ir3|t2 -0.250 0.017 -14.563 0.000 -0.250 -0.250
## ir3|t3 0.812 0.019 42.373 0.000 0.812 0.812
## ir3|t4 1.839 0.033 55.961 0.000 1.839 1.839
## ir4|t1 -1.161 0.022 -53.130 0.000 -1.161 -1.161
## ir4|t2 -0.083 0.017 -4.859 0.000 -0.083 -0.083
## ir4|t3 1.029 0.021 49.777 0.000 1.029 1.029
## ir4|t4 2.102 0.041 51.464 0.000 2.102 2.102
## ir5|t1 -1.227 0.023 -54.451 0.000 -1.227 -1.227
## ir5|t2 -0.169 0.017 -9.917 0.000 -0.169 -0.169
## ir5|t3 0.938 0.020 46.952 0.000 0.938 0.938
## ir5|t4 2.086 0.040 51.812 0.000 2.086 2.086
## sp1|t1 -1.326 0.024 -56.014 0.000 -1.326 -1.326
## sp1|t2 -0.263 0.017 -15.319 0.000 -0.263 -0.263
## sp1|t3 0.780 0.019 41.087 0.000 0.780 0.780
## sp1|t4 1.841 0.033 55.931 0.000 1.841 1.841
## sp2|t1 -1.238 0.023 -54.646 0.000 -1.238 -1.238
## sp2|t2 -0.200 0.017 -11.674 0.000 -0.200 -0.200
## sp2|t3 0.857 0.019 44.089 0.000 0.857 0.857
## sp2|t4 1.872 0.034 55.533 0.000 1.872 1.872
## sp3|t1 -1.338 0.024 -56.156 0.000 -1.338 -1.338
## sp3|t2 -0.324 0.017 -18.714 0.000 -0.324 -0.324
## sp3|t3 0.702 0.019 37.786 0.000 0.702 0.702
## sp3|t4 1.749 0.031 56.879 0.000 1.749 1.749
## sp4|t1 -1.315 0.024 -55.868 0.000 -1.315 -1.315
## sp4|t2 -0.238 0.017 -13.889 0.000 -0.238 -0.238
## sp4|t3 0.789 0.019 41.441 0.000 0.789 0.789
## sp4|t4 1.844 0.033 55.900 0.000 1.844 1.844
## sp5|t1 -1.281 0.023 -55.372 0.000 -1.281 -1.281
## sp5|t2 -0.186 0.017 -10.917 0.000 -0.186 -0.186
## sp5|t3 0.891 0.020 45.315 0.000 0.891 0.891
## sp5|t4 2.043 0.039 52.677 0.000 2.043 2.043
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hc1 0.511 0.511 0.511
## .hc2 0.460 0.460 0.460
## .hc3 0.531 0.531 0.531
## .hc4 0.504 0.504 0.504
## .hc5 0.475 0.475 0.475
## .sc1 0.474 0.474 0.474
## .sc2 0.477 0.477 0.477
## .sc3 0.534 0.534 0.534
## .sc4 0.478 0.478 0.478
## .sc5 0.515 0.515 0.515
## .ir1 0.532 0.532 0.532
## .ir2 0.501 0.501 0.501
## .ir3 0.494 0.494 0.494
## .ir4 0.551 0.551 0.551
## .ir5 0.522 0.522 0.522
## .sp1 0.468 0.468 0.468
## .sp2 0.479 0.479 0.479
## .sp3 0.452 0.452 0.452
## .sp4 0.491 0.491 0.491
## .sp5 0.505 0.505 0.505
## intrinsic 0.489 0.012 42.375 0.000 1.000 1.000
## extrinsic 0.468 0.012 38.504 0.000 1.000 1.000
#Model 4: 2-order solution
rfq_omod4 <- '#1st order
hc =~ hc1 + hc2 + hc3 + hc4 + hc5
sc =~ sc1 + sc2 + sc3 + sc4 + sc5
ir =~ ir1 + ir2 + ir3 + ir4 + ir5
sp =~ sp1 + sp2 + sp3 + sp4 + sp5
#2nd order
intrinsic =~ hc + sc
extrinsic =~ ir + sp
'
rfq_omod4_fit <- cfa(rfq_omod4,
data = data_cleaned,
estimator = "WLSMV",
ordered = rfq_items
)
summary(rfq_omod4_fit,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-19 ended normally after 51 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 105
##
## Number of observations 5457
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 92.175 165.300
## Degrees of freedom 165 165
## P-value (Chi-square) 1.000 0.479
## Scaling correction factor 0.764
## Shift parameter 44.693
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 204417.968 80216.076
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 2.552
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) 0.999
## Robust Tucker-Lewis Index (TLI) 0.999
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.001
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.006
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.006
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.010
## P-value H_0: Robust RMSEA <= 0.050 1.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.009 0.009
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hc =~
## hc1 1.000 0.773 0.773
## hc2 1.046 0.014 74.557 0.000 0.808 0.808
## hc3 0.978 0.014 69.517 0.000 0.756 0.756
## hc4 1.009 0.014 72.244 0.000 0.780 0.780
## hc5 1.034 0.014 74.441 0.000 0.799 0.799
## sc =~
## sc1 1.000 0.789 0.789
## sc2 1.001 0.014 72.594 0.000 0.790 0.790
## sc3 0.943 0.014 66.507 0.000 0.744 0.744
## sc4 0.999 0.013 74.502 0.000 0.788 0.788
## sc5 0.962 0.014 68.402 0.000 0.759 0.759
## ir =~
## ir1 1.000 0.751 0.751
## ir2 1.031 0.016 66.194 0.000 0.775 0.775
## ir3 1.039 0.016 66.376 0.000 0.781 0.781
## ir4 0.979 0.016 62.313 0.000 0.736 0.736
## ir5 1.011 0.016 62.629 0.000 0.760 0.760
## sp =~
## sp1 1.000 0.781 0.781
## sp2 0.990 0.014 71.725 0.000 0.773 0.773
## sp3 1.012 0.014 72.815 0.000 0.791 0.791
## sp4 0.976 0.014 69.670 0.000 0.763 0.763
## sp5 0.962 0.014 68.460 0.000 0.751 0.751
## intrinsic =~
## hc 1.000 0.693 0.693
## sc 1.262 0.052 24.502 0.000 0.857 0.857
## extrinsic =~
## ir 1.000 0.762 0.762
## sp 1.193 0.044 26.941 0.000 0.874 0.874
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intrinsic ~~
## extrinsic 0.168 0.008 19.862 0.000 0.548 0.548
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hc1|t1 -1.435 0.025 -57.111 0.000 -1.435 -1.435
## hc1|t2 -0.403 0.017 -23.040 0.000 -0.403 -0.403
## hc1|t3 0.617 0.018 33.906 0.000 0.617 0.617
## hc1|t4 1.695 0.030 57.265 0.000 1.695 1.695
## hc2|t1 -1.492 0.026 -57.442 0.000 -1.492 -1.492
## hc2|t2 -0.498 0.018 -28.036 0.000 -0.498 -0.498
## hc2|t3 0.528 0.018 29.578 0.000 0.528 0.528
## hc2|t4 1.517 0.026 57.529 0.000 1.517 1.517
## hc3|t1 -1.336 0.024 -56.142 0.000 -1.336 -1.336
## hc3|t2 -0.256 0.017 -14.914 0.000 -0.256 -0.256
## hc3|t3 0.845 0.019 43.644 0.000 0.845 0.845
## hc3|t4 1.910 0.035 54.984 0.000 1.910 1.910
## hc4|t1 -1.365 0.024 -56.477 0.000 -1.365 -1.365
## hc4|t2 -0.314 0.017 -18.149 0.000 -0.314 -0.314
## hc4|t3 0.750 0.019 39.841 0.000 0.750 0.750
## hc4|t4 1.814 0.032 56.245 0.000 1.814 1.814
## hc5|t1 -1.442 0.025 -57.166 0.000 -1.442 -1.442
## hc5|t2 -0.451 0.018 -25.636 0.000 -0.451 -0.451
## hc5|t3 0.559 0.018 31.114 0.000 0.559 0.559
## hc5|t4 1.612 0.028 57.582 0.000 1.612 1.612
## sc1|t1 -1.531 0.027 -57.567 0.000 -1.531 -1.531
## sc1|t2 -0.472 0.018 -26.731 0.000 -0.472 -0.472
## sc1|t3 0.573 0.018 31.801 0.000 0.573 0.573
## sc1|t4 1.634 0.028 57.529 0.000 1.634 1.634
## sc2|t1 -1.603 0.028 -57.595 0.000 -1.603 -1.603
## sc2|t2 -0.586 0.018 -32.434 0.000 -0.586 -0.586
## sc2|t3 0.413 0.018 23.603 0.000 0.413 0.413
## sc2|t4 1.460 0.025 57.275 0.000 1.460 1.460
## sc3|t1 -1.474 0.026 -57.358 0.000 -1.474 -1.474
## sc3|t2 -0.378 0.017 -21.698 0.000 -0.378 -0.378
## sc3|t3 0.738 0.019 39.330 0.000 0.738 0.738
## sc3|t4 1.814 0.032 56.245 0.000 1.814 1.814
## sc4|t1 -1.581 0.027 -57.615 0.000 -1.581 -1.581
## sc4|t2 -0.507 0.018 -28.488 0.000 -0.507 -0.507
## sc4|t3 0.494 0.018 27.850 0.000 0.494 0.494
## sc4|t4 1.534 0.027 57.573 0.000 1.534 1.534
## sc5|t1 -1.512 0.026 -57.515 0.000 -1.512 -1.512
## sc5|t2 -0.438 0.018 -24.941 0.000 -0.438 -0.438
## sc5|t3 0.626 0.018 34.352 0.000 0.626 0.626
## sc5|t4 1.700 0.030 57.230 0.000 1.700 1.700
## ir1|t1 -1.177 0.022 -53.462 0.000 -1.177 -1.177
## ir1|t2 -0.106 0.017 -6.212 0.000 -0.106 -0.106
## ir1|t3 1.001 0.020 48.938 0.000 1.001 1.001
## ir1|t4 2.074 0.040 52.061 0.000 2.074 2.074
## ir2|t1 -1.230 0.023 -54.504 0.000 -1.230 -1.230
## ir2|t2 -0.181 0.017 -10.620 0.000 -0.181 -0.181
## ir2|t3 0.872 0.020 44.631 0.000 0.872 0.872
## ir2|t4 1.940 0.036 54.528 0.000 1.940 1.940
## ir3|t1 -1.276 0.023 -55.290 0.000 -1.276 -1.276
## ir3|t2 -0.250 0.017 -14.563 0.000 -0.250 -0.250
## ir3|t3 0.812 0.019 42.373 0.000 0.812 0.812
## ir3|t4 1.839 0.033 55.961 0.000 1.839 1.839
## ir4|t1 -1.161 0.022 -53.130 0.000 -1.161 -1.161
## ir4|t2 -0.083 0.017 -4.859 0.000 -0.083 -0.083
## ir4|t3 1.029 0.021 49.777 0.000 1.029 1.029
## ir4|t4 2.102 0.041 51.464 0.000 2.102 2.102
## ir5|t1 -1.227 0.023 -54.451 0.000 -1.227 -1.227
## ir5|t2 -0.169 0.017 -9.917 0.000 -0.169 -0.169
## ir5|t3 0.938 0.020 46.952 0.000 0.938 0.938
## ir5|t4 2.086 0.040 51.812 0.000 2.086 2.086
## sp1|t1 -1.326 0.024 -56.014 0.000 -1.326 -1.326
## sp1|t2 -0.263 0.017 -15.319 0.000 -0.263 -0.263
## sp1|t3 0.780 0.019 41.087 0.000 0.780 0.780
## sp1|t4 1.841 0.033 55.931 0.000 1.841 1.841
## sp2|t1 -1.238 0.023 -54.646 0.000 -1.238 -1.238
## sp2|t2 -0.200 0.017 -11.674 0.000 -0.200 -0.200
## sp2|t3 0.857 0.019 44.089 0.000 0.857 0.857
## sp2|t4 1.872 0.034 55.533 0.000 1.872 1.872
## sp3|t1 -1.338 0.024 -56.156 0.000 -1.338 -1.338
## sp3|t2 -0.324 0.017 -18.714 0.000 -0.324 -0.324
## sp3|t3 0.702 0.019 37.786 0.000 0.702 0.702
## sp3|t4 1.749 0.031 56.879 0.000 1.749 1.749
## sp4|t1 -1.315 0.024 -55.868 0.000 -1.315 -1.315
## sp4|t2 -0.238 0.017 -13.889 0.000 -0.238 -0.238
## sp4|t3 0.789 0.019 41.441 0.000 0.789 0.789
## sp4|t4 1.844 0.033 55.900 0.000 1.844 1.844
## sp5|t1 -1.281 0.023 -55.372 0.000 -1.281 -1.281
## sp5|t2 -0.186 0.017 -10.917 0.000 -0.186 -0.186
## sp5|t3 0.891 0.020 45.315 0.000 0.891 0.891
## sp5|t4 2.043 0.039 52.677 0.000 2.043 2.043
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hc1 0.403 0.403 0.403
## .hc2 0.346 0.346 0.346
## .hc3 0.429 0.429 0.429
## .hc4 0.392 0.392 0.392
## .hc5 0.361 0.361 0.361
## .sc1 0.378 0.378 0.378
## .sc2 0.376 0.376 0.376
## .sc3 0.447 0.447 0.447
## .sc4 0.379 0.379 0.379
## .sc5 0.424 0.424 0.424
## .ir1 0.435 0.435 0.435
## .ir2 0.400 0.400 0.400
## .ir3 0.391 0.391 0.391
## .ir4 0.458 0.458 0.458
## .ir5 0.423 0.423 0.423
## .sp1 0.390 0.390 0.390
## .sp2 0.402 0.402 0.402
## .sp3 0.375 0.375 0.375
## .sp4 0.418 0.418 0.418
## .sp5 0.436 0.436 0.436
## .hc 0.311 0.014 22.171 0.000 0.520 0.520
## .sc 0.165 0.019 8.836 0.000 0.266 0.266
## .ir 0.237 0.013 17.701 0.000 0.420 0.420
## .sp 0.144 0.017 8.491 0.000 0.236 0.236
## intrinsic 0.287 0.015 19.636 0.000 1.000 1.000
## extrinsic 0.327 0.015 21.783 0.000 1.000 1.000
#Relative fitness between the multidimensional solutions and the unidimensional solution
anova(rfq_omod1_fit, rfq_omod2_fit)
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## rfq_omod2_fit 164 92.04
## rfq_omod1_fit 170 33513.45 6401.8 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(rfq_omod1_fit, rfq_omod3_fit)
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## rfq_omod3_fit 169 8788.2
## rfq_omod1_fit 170 33513.5 2546.2 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(rfq_omod1_fit, rfq_omod4_fit)
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## rfq_omod4_fit 165 92.175
## rfq_omod1_fit 170 33513.452 5950.1 5 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Select the most plausible continuous and ordinal RFQ models
rfq_cselect_fit <-
rfq_oselect_fit <-
#Explore the modification indices of the selected WISMD-37 structural model
rfq_mi <- modificationIndices(...)
#Reconstruct the selected models following hypothesis-consistent modifications
rfq_cmodified <-
rfq_omodified <-
#Calculate composite reliability for the selected continuous and ordinal RFQ model
rfq_cont_rel <- compRelSEM(...)
print(rfq_cont_rel, digits = 3)
rfq_ord_rel <- compRelSEM(...)
print(rfq_ord_rel, digits = 3)
#Calculate the corrected item-total correlation for the selected RFQ model
#Store items of each factors
#Calculate the RFQ scores
data_cleaned$hc = rowMeans(data_cleaned[,c(22, 26, 30, 34, 39)])
data_cleaned$sc = rowMeans(data_cleaned[,c(23, 27, 31, 36, 40)])
data_cleaned$ir = rowMeans(data_cleaned[,c(24, 28, 32, 37, 41)])
data_cleaned$sp = rowMeans(data_cleaned[,c(25, 29, 33, 38, 42)])
data_cleaned$intrinsic = rowMeans(data_cleaned[,c(118, 119)])
data_cleaned$extrinsic = rowMeans(data_cleaned[,c(120, 121)])
#Robust multiple linear regression analysis:
#RFQ factors -> dependence on cigarettes
rfq_time <- multinom(time_since_quitting ~ hc + sc + ir + sp,
data = data_cleaned,
)
## # weights: 18 (10 variable)
## initial value 5995.127259
## iter 10 value 5885.425176
## final value 5874.165673
## converged
rfq_time_sum <- summary(rfq_time)
z_vals <- rfq_time_sum$coefficients / rfq_time_sum$standard.errors
p_vals <- 2 * (1 - pnorm(abs(z_vals)))
rfq_time_sum
## Call:
## multinom(formula = time_since_quitting ~ hc + sc + ir + sp, data = data_cleaned)
##
## Coefficients:
## (Intercept) hc sc ir sp
## => 1 year -0.6336889 -0.001657043 0.03124429 -0.007280025 0.04333373
## 6 months to 1 year -0.1170516 -0.006777783 -0.03977053 -0.023712100 0.07699098
##
## Std. Errors:
## (Intercept) hc sc ir sp
## => 1 year 0.1187273 0.05220082 0.05453709 0.05754741 0.05686789
## 6 months to 1 year 0.1051800 0.04652587 0.04860551 0.05130505 0.05067117
##
## Residual Deviance: 11748.33
## AIC: 11768.33
p_vals
## (Intercept) hc sc ir sp
## => 1 year 9.431715e-08 0.9746765 0.5667125 0.8993323 0.4460558
## 6 months to 1 year 2.657645e-01 0.8841758 0.4132253 0.6439532 0.1286559