Resilience - 3 scales

Initial Analysis

1 Read, Clean, Recode, Merge

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Read, Clean, Recode, Unite
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Read files

# Read current Data
folder <- "C:/Users/Mihai/Desktop/R Notebooks/notebooks/Rezilienta-3scale"
file <- "BAZA DE DATE FINALA VARIANTA EXCEL (1).xlsx"
setwd(folder)

suppressMessages({                                   # some columns are renamed and the warning breaks pandoc
Data <- rio::import(file.path(folder, file),
                           which = "Sheet1",
                           skip = 0)
})

2 Define functions for scoring

## Define function that recodes to numeric, but watches out to coercion to not introduce NAs
colstonumeric <- function(df){
  tryCatch({
    df_num <- as.data.frame(
      lapply(df,
             function(x) { as.numeric(as.character(x))})) 
  },warning = function(stop_on_warning) {
    message("Stoped the execution of numeric conversion: ", conditionMessage(stop_on_warning))
  }) 
}

## Define function that scores only rows with less than 10% NAs (returns NA if all or above threshold percentage of rows are NA); can reverse code if vector of column indexes and min, max are provided.
ScoreLikert <- function(df, napercent = .1, tonumeric = FALSE, reversecols = NULL, min = NULL, max = NULL) {
  reverse_list <- list(reversecols = reversecols, min = min, max = max)
  reverse_check <- !sapply(reverse_list, is.null)
  
  # Recode to numeric, but watch out to coercion to not introduce NAs
  colstonumeric <- function(df){
    tryCatch({
      df_num <- as.data.frame(
                    lapply(df,
                           function(x) { as.numeric(as.character(x))})) 
      },warning = function(stop_on_warning) {
        message("Stoped the execution of numeric conversion: ", conditionMessage(stop_on_warning))
    }) 
  }
  
  if(tonumeric) df <- colstonumeric(df)
  
  if(all(reverse_check)){
    df[ ,reversecols] <- (max + min) - df[ ,reversecols]
  }else if(any(reverse_check)){
    stop("Insuficient info for reversing. Please provide: ", paste(names(reverse_list)[!reverse_check], collapse = ", "))
  }
  
  ifelse(rowSums(is.na(df)) > ncol(df) * napercent,
         NA,
         rowSums(df, na.rm = TRUE) * NA ^ (rowSums(!is.na(df)) == 0)
  )
}

3 Define fuction for correlation table

# x is a matrix containing the data
# method : correlation method. "pearson"" or "spearman"" is supported
# removeTriangle : remove upper or lower triangle
# results :  if "html" or "latex"
  # the results will be displayed in html or latex format
corstars <-function(x, method=c("pearson", "spearman"), removeTriangle=c("upper", "lower"),
                     result=c("none", "html", "latex")){
  #Compute correlation matrix
  suppressMessages({
    require(Hmisc)
    require(knitr)
  })
  
    x <- as.matrix(x)
    correlation_matrix<-rcorr(x, type=method[1])
    R <- correlation_matrix$r # Matrix of correlation coeficients
    p <- correlation_matrix$P # Matrix of p-value 
    
    ## Define notions for significance levels; spacing is important.
    mystars <- ifelse(p < .0001, "****", ifelse(p < .001, "*** ", ifelse(p < .01, "**  ", ifelse(p < .05, "*   ", "    "))))
    
    ## trunctuate the correlation matrix to two decimal
    R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]
    
    ## build a new matrix that includes the correlations with their apropriate stars
    Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
    diag(Rnew) <- paste(diag(R), " ", sep="")
    rownames(Rnew) <- colnames(x)
    colnames(Rnew) <- paste(colnames(x), "", sep="")
    
    ## remove upper triangle of correlation matrix
    if(removeTriangle[1]=="upper"){
      Rnew <- as.matrix(Rnew)
      Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
      Rnew <- as.data.frame(Rnew)
    }
    
    ## remove lower triangle of correlation matrix
    else if(removeTriangle[1]=="lower"){
      Rnew <- as.matrix(Rnew)
      Rnew[lower.tri(Rnew, diag = TRUE)] <- ""
      Rnew <- as.data.frame(Rnew)
    }
    
    ## remove last column and return the correlation matrix
    Rnew <- cbind(Rnew[1:length(Rnew)-1])
    if (result[1]=="none") return(Rnew)
    else{
      if(result[1]=="html") print(knitr::kable(Rnew, format = "html")) 
      else print(knitr::kable(Rnew, format = "latex")) 
    }
} 

4 Scoring

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Recode and Score
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## 


Data$Totala_check <- ScoreLikert(Data[, sprintf("a%d", 1:25)], tonumeric = TRUE)
Data$Totalb_check <- ScoreLikert(Data[, sprintf("b%d", 1:33)], tonumeric = TRUE)
Data$Totalc_check <- ScoreLikert(Data[, sprintf("c%d", 1:25)], tonumeric = TRUE)

all.equal(Data$Totala_check, Data$Totala)
all.equal(Data$Totalb_check, Data$Totalb)
all.equal(Data$Totalc_check, Data$Totalc)
Data$aRCompPers <- ScoreLikert(Data[, c("a1", "a2", "a3", "a4", "a5", "a6", "a9", "a10", "a13", "a14", "a15", "a17", "a18", "a19", "a20", "a23", "a24")], 
                                    tonumeric = TRUE)
Data$aRAccept <- ScoreLikert(Data[, c("a7", "a8", "a11", "a12", "a16", "a21", "a22", "a25")], tonumeric = TRUE)

Data$bPS <- ScoreLikert(Data[, c("b1", "b2", "b3", "b4", "b5", "b6")], tonumeric = TRUE)
Data$bPV <- ScoreLikert(Data[, c("b7", "b8", "b9", "b10")], tonumeric = TRUE)
Data$bCS <- ScoreLikert(Data[, c("b12", "b13", "b14", "b15", "b16")], tonumeric = TRUE)        # exclude b11 because no variance
Data$pSS <- ScoreLikert(Data[, c("b17", "b18", "b19", "b20", "b21", "b22")], tonumeric = TRUE)
Data$bCF <- ScoreLikert(Data[, c("b23", "b24", "b25", "b26", "b27", "b28", "b29")], tonumeric = TRUE)
Data$bRS <- ScoreLikert(Data[, c("b30", "b31", "b32", "b33")], tonumeric = TRUE)

Data$cCP <- ScoreLikert(Data[, c("c24", "c12", "c11", "c25", "c10", "c23", "c17", "c16")], tonumeric = TRUE)
Data$cII <- ScoreLikert(Data[, c("c20", "c18", "c15", "c6", "c7", "c19", "c14")], tonumeric = TRUE)
Data$cAP <- ScoreLikert(Data[, c("c1", "c4", "c5", "c2", "c8")], tonumeric = TRUE)
Data$cC <- ScoreLikert(Data[, c("c22", "c13", "c21")], tonumeric = TRUE)
Data$cIS <- ScoreLikert(Data[, c("c3", "c9")], tonumeric = TRUE)

5 Descriptives

Data %>%
  dplyr::select(Totala, Totalb, Totalc) %>%
  rstatix::get_summary_stats(type = "full") %>%
  kableExtra::kbl(caption = "Descriptives", digits = 2) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")

Descriptives

variable	n	min	max	median	q1	q3	iqr	mad	mean	sd	se	ci
Totala	108	97.00	167.00	138.00	129.00	148.00	19.00	13.34	138.19	13.54	1.30	2.58
Totalb	108	94.11	214.11	169.61	156.11	183.36	27.25	20.02	169.08	23.10	2.22	4.41
Totalc	108	66.00	121.00	100.00	92.00	105.00	13.00	10.38	98.10	10.87	1.05	2.07

6 Correlations

Data %>%
  dplyr::select(Totala, Totalb, Totalc) %>%
  PerformanceAnalytics::chart.Correlation()

Data %>%
  dplyr::select(Totala, Totalb, Totalc) %>%
  corstars(., result = "none") %>%
  kableExtra::kbl(caption = "Inter-scale correlations", digits = 2) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")

package 㤼㸱knitr㤼㸲 was built under R version 3.6.3

Inter-scale correlations

	Totala	Totalb
Totala
Totalb	0.64****
Totalc	0.78****	0.58****

# corstars(Data[, c(103:118)], result = "none")

Data[, c(103:118)] %>%
  corstars(., result = "none") %>%
  kableExtra::kbl(caption = "Inter-scale correlations", digits = 2) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")

Inter-scale correlations

	Totala_check	Totalb_check	Totalc_check	aRCompPers	aRAccept	bPS	bPV	bCS	pSS	bCF	bRS	cCP	cII	cAP	cC
Totala_check
Totalb_check	0.64****
Totalc_check	0.78****	0.58****
aRCompPers	0.91****	0.59****	0.74****
aRAccept	0.81****	0.49****	0.58****	0.49****
bPS	0.57****	0.82****	0.52****	0.57****	0.39****
bPV	0.38****	0.56****	0.31**	0.41****	0.21*	0.52****
bCS	0.55****	0.89****	0.49****	0.49****	0.45****	0.63****	0.39****
pSS	0.58****	0.88****	0.55****	0.55****	0.43****	0.73****	0.48****	0.74****
bCF	0.57****	0.87****	0.51****	0.51****	0.47****	0.60****	0.36***	0.78****	0.65****
bRS	0.42****	0.81****	0.37****	0.35***	0.38****	0.55****	0.37****	0.66****	0.62****	0.69****
cCP	0.72****	0.48****	0.86****	0.73****	0.47****	0.46****	0.25*	0.39****	0.49****	0.39****	0.33***
cII	0.60****	0.36***	0.81****	0.55****	0.48****	0.30**	0.27**	0.29**	0.34***	0.36***	0.21*	0.54****
cAP	0.63****	0.49****	0.84****	0.59****	0.47****	0.48****	0.26**	0.42****	0.44****	0.42****	0.34***	0.68****	0.59****
cC	0.59****	0.54****	0.71****	0.54****	0.46****	0.52****	0.19	0.47****	0.55****	0.47****	0.32***	0.58****	0.41****	0.60****
cIS	0.25**	0.34***	0.37****	0.20*	0.25**	0.17	0.14	0.38****	0.28**	0.35***	0.25**	0.13	0.22*	0.19*	0.21*

Data_p <- 
  Data[, c(1, 103:118)] %>%
  dplyr::rename(id = Numar) %>%
  select(id, dplyr::everything())

##
max_a <-
  Data_p %>%
    dplyr::slice_max(Totala_check, n = 5)
max_a %>%   
  kableExtra::kbl(caption = "Max a", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")

Max a

id	Totala_check	Totalb_check	Totalc_check	aRCompPers	aRAccept	bPS	bPV	bCS	pSS	bCF	bRS	cCP	cII	cAP	cC	cIS
101	167	214	121	117	50	36	22	35	42	49	28	40	35	25	15	6
30	165	201	119	115	50	37	20	31	40	43	28	40	32	22	15	10
64	164	180	103	114	50	34	19	28	31	46	20	35	27	19	15	7
29	157	185	109	112	45	35	21	29	33	41	24	36	31	25	12	5
40	157	195	114	111	46	36	22	29	35	44	27	38	30	22	15	9
80	157	190	113	111	46	33	20	28	37	43	27	38	32	23	14	6

max_a <- max_a %>% dplyr::pull(id)

min_a <-
Data_p %>%
  dplyr::slice_min(Totala_check, n = 5)
min_a %>% 
  kableExtra::kbl(caption = "Min a", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")

Min a

id	Totala_check	Totalb_check	Totalc_check	aRCompPers	aRAccept	bPS	bPV	bCS	pSS	bCF	bRS	cCP	cII	cAP	cC	cIS
6	97	105	68	77	20	17	17	14	20	24	11	24	20	13	7	4
12	100	164	66	77	23	36	18	21	34	35	18	20	16	13	11	6
5	104	148	78	81	23	26	21	22	24	31	22	28	15	18	11	6
79	107	119	78	85	22	26	14	17	17	29	14	22	20	19	14	3
24	114	154	79	79	35	27	13	25	26	37	24	24	22	15	9	9
26	114	125	78	81	33	21	17	20	15	32	18	27	19	15	9	8

min_a <- min_a %>% dplyr::pull(id)
##

##
max_b <-
Data_p %>%
  dplyr::slice_max(Totalb_check, n = 5)
max_b %>%
  kableExtra::kbl(caption = "Max b", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")

Max b

id	Totala_check	Totalb_check	Totalc_check	aRCompPers	aRAccept	bPS	bPV	bCS	pSS	bCF	bRS	cCP	cII	cAP	cC	cIS
101	167	214	121	117	50	36	22	35	42	49	28	40	35	25	15	6
35	129	211	105	99	30	40	22	33	40	46	28	34	25	22	14	10
104	148	211	108	105	43	37	22	32	42	48	28	36	29	23	14	6
43	149	207	115	109	40	39	19	32	40	47	28	36	33	24	14	8
51	151	207	111	111	40	36	22	33	40	49	25	36	30	22	14	9

max_b <- max_b %>% dplyr::pull(id)

min_b <-
Data_p %>%
  dplyr::slice_min(Totalb_check, n = 5)
min_b %>% 
  kableExtra::kbl(caption = "Min b", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")

Min b

id	Totala_check	Totalb_check	Totalc_check	aRCompPers	aRAccept	bPS	bPV	bCS	pSS	bCF	bRS	cCP	cII	cAP	cC	cIS
63	128	94	102	96	32	27	19	5	16	19	6	32	31	21	12	6
6	97	105	68	77	20	17	17	14	20	24	11	24	20	13	7	4
81	127	117	92	85	42	26	17	13	15	27	17	29	25	20	12	6
79	107	119	78	85	22	26	14	17	17	29	14	22	20	19	14	3
26	114	125	78	81	33	21	17	20	15	32	18	27	19	15	9	8

min_b <- min_b %>% dplyr::pull(id)
##

##
max_c <-
Data_p %>%
  dplyr::slice_max(Totalc_check, n = 5)
max_c %>% 
  kableExtra::kbl(caption = "Max c", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")

Max c

id	Totala_check	Totalb_check	Totalc_check	aRCompPers	aRAccept	bPS	bPV	bCS	pSS	bCF	bRS	cCP	cII	cAP	cC	cIS
101	167	214	121	117	50	36	22	35	42	49	28	40	35	25	15	6
30	165	201	119	115	50	37	20	31	40	43	28	40	32	22	15	10
96	155	205	118	111	44	41	20	31	37	48	26	38	31	25	15	9
43	149	207	115	109	40	39	19	32	40	47	28	36	33	24	14	8
53	147	186	115	111	36	33	19	30	36	40	26	37	30	24	14	10

max_c <- max_c %>% dplyr::pull(id)

min_c <-
Data_p %>%
  dplyr::slice_min(Totalc_check, n = 5)
min_c %>% 
  kableExtra::kbl(caption = "Min c", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")

Min c

id	Totala_check	Totalb_check	Totalc_check	aRCompPers	aRAccept	bPS	bPV	bCS	pSS	bCF	bRS	cCP	cII	cAP	cC	cIS
12	100	164	66	77	23	36	18	21	34	35	18	20	16	13	11	6
6	97	105	68	77	20	17	17	14	20	24	11	24	20	13	7	4
65	133	141	76	99	34	29	16	21	23	30	20	31	17	16	10	2
5	104	148	78	81	23	26	21	22	24	31	22	28	15	18	11	6
26	114	125	78	81	33	21	17	20	15	32	18	27	19	15	9	8
79	107	119	78	85	22	26	14	17	17	29	14	22	20	19	14	3
90	120	132	78	85	35	25	18	19	24	27	17	29	18	17	8	6

min_c <- min_c %>% dplyr::pull(id)
##

mins <- unique(c(min_a, min_b, min_c))
maxs <- unique(c(max_a, max_b, max_c))

Data_p$min_max <- dplyr::case_when(Data_p$id %in% maxs ~ "red",
                                   Data_p$id %in% mins ~ "green",
                                   TRUE ~ "darkgrey")
                                    
Data_p_long <-
  Data_p %>%
  dplyr::select(id, min_max, Totala_check, Totalb_check, Totalc_check) %>%
  tidyr::pivot_longer(cols = c(Totala_check, Totalb_check, Totalc_check), names_to = "variable", values_to = "value")

Data_p_long %>%
  ggplot(aes(x = variable, y = value)) +
    geom_violin() +
    geom_point(size = 1, alpha = 0.6, colour = Data_p_long$min_max) +
    geom_line(aes(group = id), linetype = "11", colour = Data_p_long$min_max) +
    scale_colour_identity() + 
    theme_classic()

–>

6.1 Top and bottom values

Data_p <- 
  Data[, c(1, 103:118)] %>%
  dplyr::rename(id = Numar) %>%
  select(id, dplyr::everything())

##
max_a <-
  Data_p %>%
    dplyr::slice_max(Totala_check, n = 5)
max_a %>%   
  kableExtra::kbl(caption = "Max a", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")
max_a <- max_a %>% dplyr::pull(id)

min_a <-
Data_p %>%
  dplyr::slice_min(Totala_check, n = 5)
min_a %>% 
  kableExtra::kbl(caption = "Min a", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")
min_a <- min_a %>% dplyr::pull(id)
##

##
max_b <-
Data_p %>%
  dplyr::slice_max(Totalb_check, n = 5)
max_b %>%
  kableExtra::kbl(caption = "Max b", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")
max_b <- max_b %>% dplyr::pull(id)

min_b <-
Data_p %>%
  dplyr::slice_min(Totalb_check, n = 5)
min_b %>% 
  kableExtra::kbl(caption = "Min b", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")
min_b <- min_b %>% dplyr::pull(id)
##

##
max_c <-
Data_p %>%
  dplyr::slice_max(Totalc_check, n = 5)
max_c %>% 
  kableExtra::kbl(caption = "Max c", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")
max_c <- max_c %>% dplyr::pull(id)

min_c <-
Data_p %>%
  dplyr::slice_min(Totalc_check, n = 5)
min_c %>% 
  kableExtra::kbl(caption = "Min c", digits = 0) %>%
  kableExtra::kable_classic(full_width = F, html_font = "Times New Roman")
min_c <- min_c %>% dplyr::pull(id)
##

mins <- unique(c(min_a, min_b, min_c))
maxs <- unique(c(max_a, max_b, max_c))

Data_p$min_max <- dplyr::case_when(Data_p$id %in% maxs ~ "red",
                                   Data_p$id %in% mins ~ "green",
                                   TRUE ~ "darkgrey")
                                    
Data_p_long <-
  Data_p %>%
  dplyr::select(id, min_max, Totala_check, Totalb_check, Totalc_check) %>%
  tidyr::pivot_longer(cols = c(Totala_check, Totalb_check, Totalc_check), names_to = "variable", values_to = "value")

Data_p_long %>%
  ggplot(aes(x = variable, y = value)) +
    geom_violin() +
    geom_point(size = 1, alpha = 0.6, colour = Data_p_long$min_max) +
    geom_line(aes(group = id), linetype = "11", colour = Data_p_long$min_max) +
    scale_colour_identity() + 
    theme_classic()

7 CFA

7.1 Define function

fast_cfa_func <- function(data, model, lav_estimator = "ML", ordered_vars = FALSE){
  
  if(ordered_vars){
    modelvars <- lavaan::lavNames(model)              # ! for some reason list of names in ordered needs to match order of columns in dataframe, no problem here
    data <- as.data.frame(lapply(data[, modelvars], ordered))             # specially for binary
  }else{
    modelvars <- NULL
  }                                                             # estimator = "ML" // ordered = names(data), estimator = "WLSMV"
  cfa <- lavaan::cfa(model = model, data = data, std.lv = TRUE, estimator = lav_estimator, ordered = modelvars)   
  summary(cfa, fit.measure = TRUE, standardized = TRUE) %>% print()
  fcfa <- lavaan::fitMeasures(cfa, c("nfi", "tli", "cfi","rmsea", "rmsea.ci.lower", "rmsea.ci.upper", "srmr"))
  fcfa %>% print()
  semPlot::semPaths(cfa, "std", curvePivot = TRUE, thresholds = FALSE, layout="tree2") # grafic
  text(1.2,1.1,labels=paste0('TLI=', round(fcfa["tli"], 3), '\n', 
                           'CFI=',  round(fcfa["cfi"], 3), '\n',
                           'NFI=', round(fcfa["nfi"], 3), '\n',
                           'RMSEA=', round(fcfa["rmsea"], 3), '\n',
                           '95%CI=', round(fcfa["rmsea.ci.lower"], 3), '-', round(fcfa["rmsea.ci.upper"], 3), '\n',
                           'SRMR=', round(fcfa["srmr"], 3)  )   )
  print(head(lavaan::modificationindices(cfa)[order(lavaan::modificationindices(cfa)$mi, decreasing = TRUE), ], 30)) %>% print()
  semTools::reliability(cfa) %>% print()
}

7.2 CFA Wagnild & Young

model_WY <- '
RCompPers =~  a1 + a2 + a3 + a4 + a5 + a6 + a9 + a10 + a13 + a14 + a15 + a17 + a18 + a19 + a20 + a23 + a24
RAccept =~  a7 + a8 + a11 + a12 + a16 + a21 + a22 + a25
'

model_WY_1f <- '
F =~ a1 + a2 + a3 + a4 + a5 + a6 + a7 + a8 + a9 + a10 + a11 + a12 + a13 + a14 + a15 + a16 + a17 + a18 + a19 + a20 + a21 + a22 + a23 + a24 + a25
'

fast_cfa_func(data = Data, model = model_WY_1f)

7.3 CFA Wagnild & Young - Short 14

model_WY_short <- '
F =~ a2 + a6 + a7 + a8 + a9 + a10 + a13 + a14 + a15 + a16 + a17 + a18 + a21 + a23
'

fast_cfa_func(data = Data, model = model_WY_short)

7.4 CFA Resilience Scale for Adults

# Analiza factoriala a scalei norvegiene RSA evidentiaza 6 factori:
# 1.Perceptia de sine (itemii 1 la 6)
# 2.Planificarea viitorului (itemii 7 la 10)
# 3.Competenta sociala (itemii 11 la 16)
# 4.Stil structurat (itemii 17 la 22)
# 5.Coeziunea familiala (itemii 23 la 29)
# 6.Resursele sociale (itemii 30 la 33)

# paste(sprintf("b%d", 1:6), collapse = " + ")
model_RSA <- '
bPS =~ b1 + b2 + b3 + b4 + b5 + b6
bPV =~ b7 + b8 + b9 + b10
bCS =~ b12 + b13 + b14 + b15 + b16          
pSS =~ b17 + b18 + b19 + b20 + b21 + b22
bCF =~ b23 + b24 + b25 + b26 + b27 + b28 + b29
bRS =~ b30 + b31 + b32 + b33
'

model_RSA_1f <- '
F =~ b1 + b2 + b3 + b4 + b5 + b6 + b7 + b8 + b9 + b10 + b12 + b13 + b14 + b15 + b16 + b17 + b18 + b19 + b20 +     # exclude b11 because no variance
     b21 + b22 + b23 + b24 + b25 + b26 + b27 + b28 + b29 + b30 + b31 + b32 + b33 
'

fast_cfa_func(data = Data, model = model_RSA_1f)

7.4.1 CFA Resilience Scale for Adults - Short 11 (German)

# the German short version already doesnt't include b11 which is missing in our data

# test_df <- Data
# test_df$b8 <- 8 - test_df$b8    # try reverse coding b8

model_RSA_short <- '
F =~ b1 + b2 + b4 + b8 + b9 + b10 + b15 + b16 + b19 + b20 + b24 
'

fast_cfa_func(data = Data, model = model_RSA_short)

7.5 CFA Connor-Davidson

# Analiza factoriala la scalei Connor-Davidson pune în evidenta 5 factori:
# -Factorul 1 – Competenta personala, standarde înalte si tenacitate
# [itemii 24, 12, 11, 25, 10, 23, 17, 16]
# -Factorul 2 – Încrederea în intuitiile sale, toleranta fata de afectele negative si fata de efectele accentuarii stresului
# [itemii 20, 18, 15, 6, 7, 19, 14]
# -Factorul 3 – Acceptarea pozitiva a schimbarilor si relatii sigure/secure
# [itemii 1, 4, 5, 2, 8]
# -Factorul 4 – Controlul
# [itemii 22, 13, 21]
# -Factorul 5 – Influentele spirituale
# [itemii 3, 9]


# paste(sprintf("c%d", c(24, 12, 11, 25, 10, 23, 17, 16)), collapse = " + ")
model_CD <- '
cCP =~ c24 + c12 + c11 + c25 + c10 + c23 + c17 + c16
cII =~ c20 + c18 + c15 + c6 + c7 + c19 + c14
cAP =~ c1 + c4 + c5 + c2 + c8
cC =~ c22 + c13 + c21
cIS =~ c3 + c9 
'

model_CD_1f <- '
F =~ c1 + c2 + c3 + c4 + c5 + c6 + c7 + c8 + c9 + c10 + c11 + c12 + c13 + c14 + c15 + c16 + c17 + c18 + c19 + c20 + c21 + c22 + c23 + c24 + c25
'

fast_cfa_func(data = Data, model = model_CD_1f)

7.5.1 CFA Connor-Davidson - Short 10

model_CD_short <- '
F =~ c1 + c4 + c6 + c7 + c8 + c11 + c14 + c16 + c17 + c19
'

fast_cfa_func(data = Data, model = model_CD_short)

8 Session Info

 

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A work by Claudiu Papasteri

claudiu.papasteri@gmail.com