library(readr)
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(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
options(scipen=999)
# Set working directory
setwd("/Users/melissalagunas/Desktop/Lab/DISSERTATION")
# Read datasets
quant_df <- read_csv("quant_data.csv")
## New names:
## Rows: 230 Columns: 18
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (18): ...1, age, gender, race, US_born, sexual_orientation, employment_s...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
qual_df <- read_csv("qual_df.csv")
## New names:
## Rows: 187 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (3): year_education, SS_13_TEXT, PD dbl (1): ...1
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
jobs_df <- read_csv("job_classifications_results.csv")
## Rows: 173 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): job_title, soc_code, soc_description
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Add participant ID to each dataset
quant_df <- quant_df %>%
mutate(Participant_ID = row_number())
qual_df <- qual_df %>%
mutate(Participant_ID = row_number())
jobs_df <- jobs_df %>%
mutate(Participant_ID = row_number())
# Merge datasets sequentially using left_join
merged_df <- quant_df %>%
left_join(qual_df, by = "Participant_ID") %>%
left_join(jobs_df, by = "Participant_ID")
# Print dimensions of all datasets to verify merge
cat("Dimensions of datasets:\n")
## Dimensions of datasets:
cat("Quantitative data:", dim(quant_df), "\n")
## Quantitative data: 230 19
cat("Qualitative data:", dim(qual_df), "\n")
## Qualitative data: 187 5
cat("Jobs data:", dim(jobs_df), "\n")
## Jobs data: 173 4
cat("Merged data:", dim(merged_df), "\n")
## Merged data: 230 26
# Optional: Check for any duplicate column names
duplicate_cols <- names(merged_df)[duplicated(names(merged_df))]
if(length(duplicate_cols) > 0) {
warning("Duplicate column names found: ", paste(duplicate_cols, collapse = ", "))
}
# First create the new grouped fields mapping from SOC codes
soc_group_mapping <- c(
# STEM & Technical (Group 1)
'15-0000' = 1, # Computer and Mathematical
'19-0000' = 1, # Life, Physical, and Social Science
'17-0000' = 1, # Architecture and Engineering
# Education & Community Services (Group 2)
'25-0000' = 2, # Educational Instruction and Library
'21-0000' = 2, # Community and Social Service
# Healthcare & Medical (Group 3)
'29-0000' = 3, # Healthcare Practitioners and Technical
# Business & Management (Group 4)
'13-0000' = 4, # Business and Financial Operations
'11-0000' = 4, # Management
# Other Professional Services (Group 5)
'51-0000' = 5, # Production
'53-0000' = 5, # Transportation and Material Moving
'27-0000' = 5, # Arts, Design, Entertainment, Sports, Media
'23-0000' = 5, # Legal
'37-0000' = 5, # Building/Grounds Maintenance
'45-0000' = 5, # Farming, Fishing, Forestry
'35-0000' = 5, # Food Preparation/Serving
'49-0000' = 5 # Installation, Maintenance, Repair
)
# Create new column with mapped values
merged_df$occupation_group <- soc_group_mapping[merged_df$soc_code]
# Add descriptive labels for the groups
merged_df$occupation_group_label <- factor(merged_df$occupation_group,
levels = 1:5,
labels = c("STEM & Technical",
"Education & Community Services",
"Healthcare & Medical",
"Business & Management",
"Other Professional Services"))
# Verify the new groupings
table(merged_df$occupation_group_label, useNA = "ifany")
##
## STEM & Technical Education & Community Services
## 56 41
## Healthcare & Medical Business & Management
## 26 34
## Other Professional Services <NA>
## 16 57
# Check structure
str(merged_df)
## tibble [230 × 28] (S3: tbl_df/tbl/data.frame)
## $ ...1.x : num [1:230] 1 2 3 4 5 6 7 8 9 10 ...
## $ age : num [1:230] NA 21 25 29 NA NA 40 27 60 40 ...
## $ gender : num [1:230] 1 2 2 1 1 2 1 2 1 5 ...
## $ race : num [1:230] 1 2 2 1 1 6 6 9 1 5 ...
## $ US_born : num [1:230] 1 1 1 1 1 1 1 1 1 1 ...
## $ sexual_orientation : num [1:230] 5 5 5 2 5 9 5 5 5 5 ...
## $ employment_status : num [1:230] 1 2 1 2 1 1 1 1 1 1 ...
## $ education : num [1:230] 6 4 4 4 6 5 1 3 6 3 ...
## $ religion : num [1:230] 4 5 14 4 10 5 5 10 4 4 ...
## $ income : num [1:230] 9 1 1 2 8 5 5 9 8 2 ...
## $ fam_income : num [1:230] 7 2 2 2 8 3 3 6 3 2 ...
## $ OC_AVG : num [1:230] 3.5 4 NA 3 NA NA NA 3.5 2.5 3.5 ...
## $ SBS_AVG : num [1:230] 3.14 4.43 3.57 2.57 3.14 3.86 3.86 3.57 2.86 4.57 ...
## $ SS_AVG : num [1:230] 4 5.5 5.75 3.08 4 4.82 5.92 5.27 6.42 5.92 ...
## $ BRS_AVG : num [1:230] 3.33 3.33 2.83 NA 2 3.6 2.5 NA 3.67 2.83 ...
## $ PSNQ_AVG : num [1:230] NA 5.9 5.33 2.9 4.8 5.6 5.9 5.3 4.3 6.6 ...
## $ PF_AVG : num [1:230] 3.88 6 5.12 3 3.62 6 6.12 4.88 5.5 7 ...
## $ CS_AVG : num [1:230] 3.25 NA 4.2 2.4 3.2 4 4.4 3.8 4.4 4.8 ...
## $ Participant_ID : int [1:230] 1 2 3 4 5 6 7 8 9 10 ...
## $ ...1.y : num [1:230] 1 2 3 4 5 6 7 8 9 10 ...
## $ year_education : chr [1:230] "2017" "2022" "2022" "2022" ...
## $ SS_13_TEXT : chr [1:230] "Mother and Friend" "Uncle(Family Member)" "Friend" "A friend who also is a mother-figure to me." ...
## $ PD : chr [1:230] "Associate Attorney at large international law firm." "The Information Technology (IT) profession involves designing, developing, managing, and maintaining computer s"| __truncated__ "Health education" "Education - I work as a college advisor for high school students and support them throughout the college application process." ...
## $ job_title : chr [1:230] "Attorney" "IT/Database Administrator" "Health Education" "College Advisor" ...
## $ soc_code : chr [1:230] "23-0000" "15-0000" "25-0000" "25-0000" ...
## $ soc_description : chr [1:230] "Legal Occupations" "Computer and Mathematical Occupations" "Educational Instruction and Library Occupations" "Educational Instruction and Library Occupations" ...
## $ occupation_group : Named num [1:230] 5 1 2 2 1 2 1 1 2 5 ...
## ..- attr(*, "names")= chr [1:230] "23-0000" "15-0000" "25-0000" "25-0000" ...
## $ occupation_group_label: Factor w/ 5 levels "STEM & Technical",..: 5 1 2 2 1 2 1 1 2 5 ...
## ..- attr(*, "names")= chr [1:230] "23-0000" "15-0000" "25-0000" "25-0000" ...
# Optional: Create a visualization of the new groupings
library(ggplot2)
ggplot(merged_df, aes(x = occupation_group_label)) +
geom_bar(fill = "steelblue") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Distribution of Regrouped Occupational Fields",
x = "Occupational Field",
y = "Count")
dat <- merged_df[, c("occupation_group", "OC_AVG", "SS_AVG", "PSNQ_AVG", "BRS_AVG", "SBS_AVG", "PF_AVG", "CS_AVG")]
CORRELATION
# Correlation
library(dplyr)
library(apaTables)
library(Hmisc)
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:dplyr':
##
## src, summarize
## The following objects are masked from 'package:base':
##
## format.pval, units
# Subset your quant_dfa to include only the columns of interest
subset_df <- merged_df %>%
select(OC_AVG, SS_AVG, PSNQ_AVG, BRS_AVG, SBS_AVG, PF_AVG, CS_AVG)
# Compute and save the correlation table
cor_df <- apa.cor.table(subset_df,
table.number = 1,
show.sig.stars = TRUE,
landscape = TRUE,
filename = "correlation_table.doc")
# Ensure the table was created successfully
if (file.exists("correlation_table.doc")) {
cat("Correlation table successfully saved as 'correlation_table.doc'.\n")
} else {
cat("Error: Correlation table was not saved.\n")
}
## Correlation table successfully saved as 'correlation_table.doc'.
# Print the correlation table object to check contents
print(cor_df)
##
##
## Table 1
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. OC_AVG 3.68 0.90
##
## 2. SS_AVG 5.39 0.82 .26**
## [.13, .38]
##
## 3. PSNQ_AVG 5.63 0.79 .23** .72**
## [.11, .35] [.66, .78]
##
## 4. BRS_AVG 3.31 0.64 -.00 .20** .20**
## [-.13, .13] [.07, .32] [.07, .32]
##
## 5. SBS_AVG 3.75 0.66 .28** .73** .72** .19**
## [.16, .40] [.67, .79] [.65, .77] [.06, .31]
##
## 6. PF_AVG 5.80 0.87 .23** .72** .80** .36**
## [.10, .35] [.65, .77] [.75, .84] [.25, .47]
##
## 7. CS_AVG 4.08 0.65 .17* .51** .69** .09
## [.04, .29] [.40, .60] [.62, .75] [-.04, .22]
##
## 5 6
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .74**
## [.68, .80]
##
## .49** .64**
## [.38, .58] [.55, .71]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
##
# Get p-values using rcorr from Hmisc
corr_matrix <- rcorr(as.matrix(subset_df))
# Print correlation coefficients
cat("\nCorrelation Coefficients:\n")
##
## Correlation Coefficients:
print(corr_matrix$r)
## OC_AVG SS_AVG PSNQ_AVG BRS_AVG SBS_AVG PF_AVG
## OC_AVG 1.0000000000 0.2572256 0.2339663 -0.0009919913 0.2826210 0.2299285
## SS_AVG 0.2572255684 1.0000000 0.7235918 0.2010254704 0.7331107 0.7165600
## PSNQ_AVG 0.2339662571 0.7235918 1.0000000 0.1964278942 0.7150035 0.7980969
## BRS_AVG -0.0009919913 0.2010255 0.1964279 1.0000000000 0.1852365 0.3641014
## SBS_AVG 0.2826210042 0.7331107 0.7150035 0.1852364814 1.0000000 0.7430219
## PF_AVG 0.2299285281 0.7165600 0.7980969 0.3641013971 0.7430219 1.0000000
## CS_AVG 0.1701073431 0.5071161 0.6909374 0.0906436961 0.4867393 0.6375525
## CS_AVG
## OC_AVG 0.1701073
## SS_AVG 0.5071161
## PSNQ_AVG 0.6909374
## BRS_AVG 0.0906437
## SBS_AVG 0.4867393
## PF_AVG 0.6375525
## CS_AVG 1.0000000
# Print p-values
cat("\nP-values:\n")
##
## P-values:
print(corr_matrix$P)
## OC_AVG SS_AVG PSNQ_AVG BRS_AVG
## OC_AVG NA 0.0000917145853218670482 0.0004013479 0.98822069466786
## SS_AVG 0.00009171459 NA 0.0000000000 0.00228894743622
## PSNQ_AVG 0.00040134788 0.0000000000000000000000 NA 0.00295716365666
## BRS_AVG 0.98822069467 0.0022889474362208783731 0.0029571637 NA
## SBS_AVG 0.00001607417 0.0000000000000000000000 0.0000000000 0.00501668401152
## PF_AVG 0.00049358283 0.0000000000000000000000 0.0000000000 0.00000001485944
## CS_AVG 0.01058665470 0.0000000000000002220446 0.0000000000 0.17352986726366
## SBS_AVG PF_AVG CS_AVG
## OC_AVG 0.000016074169621127510 0.00049358283231 0.0105866547001400856942
## SS_AVG 0.000000000000000000000 0.00000000000000 0.0000000000000002220446
## PSNQ_AVG 0.000000000000000000000 0.00000000000000 0.0000000000000000000000
## BRS_AVG 0.005016684011523553366 0.00000001485944 0.1735298672636571559735
## SBS_AVG NA 0.00000000000000 0.0000000000000048849813
## PF_AVG 0.000000000000000000000 NA 0.0000000000000000000000
## CS_AVG 0.000000000000004884981 0.00000000000000 NA
MAIN ANALYSES
One Way ANOVA
RQ1a - How does the type of percieved social support accessible to
first-generation professionals in their workplace vary based on their
professional discipline?
# One-way ANOVA for RQ1a
# Examining differences in perceived social support across professional disciplines
# First, let's get descriptive statistics by professional discipline
library(dplyr)
desc_stats <- dat %>%
group_by(occupation_group) %>%
summarise(
n = n(),
mean_ss = mean(SS_AVG, na.rm = TRUE),
sd_ss = sd(SS_AVG, na.rm = TRUE),
se_ss = sd_ss/sqrt(n)
)
print(desc_stats)
## # A tibble: 6 × 5
## occupation_group n mean_ss sd_ss se_ss
## <dbl> <int> <dbl> <dbl> <dbl>
## 1 1 56 5.29 0.860 0.115
## 2 2 41 5.62 0.811 0.127
## 3 3 26 5.37 0.808 0.158
## 4 4 34 5.47 0.786 0.135
## 5 5 16 5.78 0.855 0.214
## 6 NA 57 5.19 0.735 0.0973
# Check assumptions
# 1. Test for normality within each group
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:purrr':
##
## some
## The following object is masked from 'package:dplyr':
##
## recode
leveneTest(SS_AVG ~ as.factor(occupation_group), data = dat)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.1758 0.9506
## 168
# 2. Visual check of distributions
library(ggplot2)
# Box plot
ggplot(dat, aes(x = as.factor(occupation_group), y = SS_AVG)) +
geom_boxplot() +
labs(title = "Social Support by Professional Discipline",
x = "Professional Discipline Code",
y = "Social Support Score")
# Conduct one-way ANOVA
anova_result <- aov(SS_AVG ~ as.factor(occupation_group), data = dat)
summary(anova_result)
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(occupation_group) 4 4.34 1.0845 1.589 0.18
## Residuals 168 114.68 0.6826
## 57 observations deleted due to missingness
# If ANOVA is significant, conduct post-hoc tests
TukeyHSD(anova_result)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SS_AVG ~ as.factor(occupation_group), data = dat)
##
## $`as.factor(occupation_group)`
## diff lwr upr p adj
## 2-1 0.32210366 -0.1461913 0.7903986 0.3230865
## 3-1 0.07817308 -0.4625138 0.6188599 0.9946279
## 4-1 0.17889706 -0.3164475 0.6742416 0.8568890
## 5-1 0.48125000 -0.1646002 1.1271002 0.2448464
## 3-2 -0.24393058 -0.8151177 0.3272565 0.7641321
## 4-2 -0.14320660 -0.6716748 0.3852616 0.9449195
## 5-2 0.15914634 -0.5124450 0.8307377 0.9657891
## 4-3 0.10072398 -0.4928421 0.6942901 0.9900896
## 5-3 0.40307692 -0.3208547 1.1270086 0.5410042
## 5-4 0.30235294 -0.3883720 0.9930779 0.7472813
library(lavaan)
## This is lavaan 0.6-16
## lavaan is FREE software! Please report any bugs.
# Define the full SEM model
model <- '
# Direct effects from IVs to Mediators
PSNQ_AVG ~ a1 * SS_AVG + oc1 * OC_AVG # Network Quality
BRS_AVG ~ a2 * SS_AVG + oc2 * OC_AVG # Resilience
# Direct effects to DVs
SBS_AVG ~ c1 * SS_AVG + b1 * PSNQ_AVG + b2 * BRS_AVG + oc3 * OC_AVG # Belonging
PF_AVG ~ c2 * SS_AVG + b3 * PSNQ_AVG + b4 * BRS_AVG + oc4 * OC_AVG # Flourishing
CS_AVG ~ c3 * SS_AVG + b5 * PSNQ_AVG + b6 * BRS_AVG + oc5 * OC_AVG # Career Satisfaction
# Allow DVs to covary
SBS_AVG ~~ PF_AVG + CS_AVG
PF_AVG ~~ CS_AVG
# Define indirect effects through Network Quality
indirect_nq_sbs := a1 * b1 # to Belonging
indirect_nq_pf := a1 * b3 # to Flourishing
indirect_nq_cs := a1 * b5 # to Career Satisfaction
# Define indirect effects through Resilience
indirect_rs_sbs := a2 * b2 # to Belonging
indirect_rs_pf := a2 * b4 # to Flourishing
indirect_rs_cs := a2 * b6 # to Career Satisfaction
'
# Fit the model
fit <- sem(model, data = dat,
se = "bootstrap",
bootstrap = 5000)
# Get summary with standardized estimates and confidence intervals
summary(fit, standardized = TRUE, ci = TRUE)
## lavaan 0.6.16 ended normally after 21 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 24
##
## Used Total
## Number of observations 222 230
##
## Model Test User Model:
##
## Test statistic 1.299
## Degrees of freedom 1
## P-value (Chi-square) 0.254
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 5000
## Number of successful bootstrap draws 5000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## PSNQ_AVG ~
## SS_AVG (a1) 0.676 0.055 12.381 0.000 0.565 0.779
## OC_AVG (oc1) 0.044 0.049 0.911 0.362 -0.046 0.143
## BRS_AVG ~
## SS_AVG (a2) 0.170 0.055 3.065 0.002 0.062 0.280
## OC_AVG (oc2) -0.039 0.049 -0.782 0.434 -0.131 0.060
## SBS_AVG ~
## SS_AVG (c1) 0.364 0.066 5.539 0.000 0.242 0.500
## PSNQ_AVG (b1) 0.322 0.068 4.753 0.000 0.185 0.447
## BRS_AVG (b2) 0.023 0.041 0.549 0.583 -0.060 0.102
## OC_AVG (oc3) 0.060 0.028 2.108 0.035 0.002 0.112
## PF_AVG ~
## SS_AVG (c2) 0.260 0.064 4.062 0.000 0.135 0.387
## PSNQ_AVG (b3) 0.608 0.074 8.204 0.000 0.465 0.752
## BRS_AVG (b4) 0.256 0.057 4.487 0.000 0.150 0.370
## OC_AVG (oc4) 0.030 0.038 0.779 0.436 -0.049 0.102
## CS_AVG ~
## SS_AVG (c3) -0.001 0.076 -0.010 0.992 -0.148 0.149
## PSNQ_AVG (b5) 0.567 0.074 7.677 0.000 0.428 0.716
## BRS_AVG (b6) -0.047 0.058 -0.800 0.424 -0.170 0.061
## OC_AVG (oc5) 0.005 0.045 0.111 0.912 -0.086 0.091
## Std.lv Std.all
##
## 0.676 0.699
## 0.044 0.052
##
## 0.170 0.212
## -0.039 -0.054
##
## 0.364 0.439
## 0.322 0.376
## 0.023 0.022
## 0.060 0.081
##
## 0.260 0.252
## 0.608 0.569
## 0.256 0.199
## 0.030 0.033
##
## -0.001 -0.001
## 0.567 0.684
## -0.047 -0.047
## 0.005 0.007
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .SBS_AVG ~~
## .PF_AVG 0.063 0.012 5.367 0.000 0.039 0.084
## .CS_AVG -0.004 0.014 -0.264 0.791 -0.030 0.023
## .PF_AVG ~~
## .CS_AVG 0.049 0.017 2.899 0.004 0.015 0.082
## Std.lv Std.all
##
## 0.063 0.336
## -0.004 -0.018
##
## 0.049 0.230
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .PSNQ_AVG 0.295 0.034 8.597 0.000 0.229 0.363
## .BRS_AVG 0.396 0.038 10.432 0.000 0.319 0.468
## .SBS_AVG 0.170 0.017 10.266 0.000 0.135 0.201
## .PF_AVG 0.208 0.027 7.708 0.000 0.152 0.258
## .CS_AVG 0.222 0.024 9.321 0.000 0.171 0.263
## Std.lv Std.all
## 0.295 0.491
## 0.396 0.958
## 0.170 0.386
## 0.208 0.304
## 0.222 0.537
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## indirct_nq_sbs 0.218 0.046 4.709 0.000 0.127 0.308
## indirect_nq_pf 0.411 0.064 6.425 0.000 0.297 0.544
## indirect_nq_cs 0.384 0.066 5.838 0.000 0.266 0.520
## indirct_rs_sbs 0.004 0.007 0.527 0.598 -0.011 0.019
## indirect_rs_pf 0.043 0.018 2.379 0.017 0.013 0.086
## indirect_rs_cs -0.008 0.010 -0.777 0.437 -0.030 0.012
## Std.lv Std.all
## 0.218 0.262
## 0.411 0.398
## 0.384 0.478
## 0.004 0.005
## 0.043 0.042
## -0.008 -0.010
# Get fit indices
fitMeasures(fit)
## npar fmin chisq
## 24.000 0.003 1.299
## df pvalue baseline.chisq
## 1.000 0.254 825.442
## baseline.df baseline.pvalue cfi
## 20.000 0.000 1.000
## tli nnfi rfi
## 0.993 0.993 0.969
## nfi pnfi ifi
## 0.998 0.050 1.000
## rni logl unrestricted.logl
## 1.000 -777.470 -776.821
## aic bic ntotal
## 1602.941 1684.605 222.000
## bic2 rmsea rmsea.ci.lower
## 1608.547 0.037 0.000
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.187 0.900 0.376
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.469 0.080
## rmr rmr_nomean srmr
## 0.007 0.007 0.014
## srmr_bentler srmr_bentler_nomean crmr
## 0.014 0.014 0.016
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.016 0.014 0.014
## cn_05 cn_01 gfi
## 657.508 1134.909 0.999
## agfi pgfi mfi
## 0.958 0.036 0.999
## ecvi
## 0.222
# Optional: Create a path diagram
library(semPlot)
semPaths(fit, "std", layout = "tree")
