Applied Study Conditions
R. Noah Padgett
2022-01-31
Last updated: 2022-02-16
Checks: 5 1
Knit directory: Padgett-Dissertation/
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# Load packages & utility functions
source("code/load_packages.R")
source("code/load_utility_functions.R")
# environment options
options(scipen = 999, digits=3)library(readxl)
mydata <- read_excel("data/temp_data_extracted.xlsx") %>%
filter(is.na(EXCLUSION_FLAG)==T)Journal Information
mydata %>%
select(Journal) %>%
table().
Assessment EJPA JID JPA PA PID
51 10 24 25 12 22 mydata %>%
select(`Analysis (CFA, EFA, IRT, SEM, etc.)`) %>%
table().
CFA CFA/IRT Correlation
70 8 2
EFA EFA/CFA ESEM/CFA
8 5 1
IRT LCA mediation
9 1 1
Moken Scale random-effects models Rasch
1 1 4
regression SEM
3 11 Data Conditions
f <- function(x){
c(mean(x, na.rm=T), sd(x, na.rm=T), quantile(x, c(0, 0.25, 0.5, 0.75, 1), na.rm=T))
}
sum_dat <- mydata %>%
summarise(
SS = f(`Sample Size`),
NLV = f(`Number of Latent Variables`),
NOV = f(`Number of Observed Variables`),
Min_Ind = f(`Minimum Number of Indicators per Factor`),
Avg_Ind = f(`Average Number of Indicators per Factor`),
Max_Ind = f(`Maximum Number of Indicators per Factor`),
Min_FL = f(abs(`Minimum Factor Loading`)),
Avg_FL = f(`Average Factor Loading`),
Max_FL = f(`Maximum Factor Loading`),
Min_Rel = f(`Min Reliability`),
Avg_Rel = f(`Avg Reliability`),
Max_Rel = f(`Max Reliability`)
) %>% as.data.frame()
rownames(sum_dat) <- c("avg", "sd", "min", "q1", "median", "q3", "max")
sum_dat <- t(sum_dat)
kable(sum_dat, format="html", digits=2) %>%
kable_styling(full_width = T)| avg | sd | min | q1 | median | q3 | max | |
|---|---|---|---|---|---|---|---|
| SS | 4217.78 | 23241.20 | 36.00 | 318.00 | 603.00 | 1451.00 | 263683.00 |
| NLV | 5.46 | 6.93 | 1.00 | 2.00 | 4.00 | 6.00 | 60.00 |
| NOV | 50.12 | 81.58 | 5.00 | 12.00 | 22.50 | 41.75 | 442.00 |
| Min_Ind | 6.86 | 6.40 | 1.00 | 4.00 | 5.00 | 8.00 | 40.00 |
| Avg_Ind | 8.95 | 7.00 | 2.50 | 4.66 | 6.22 | 10.93 | 40.00 |
| Max_Ind | 12.93 | 15.20 | 3.00 | 5.00 | 8.00 | 14.00 | 135.00 |
| Min_FL | 0.41 | 0.20 | 0.00 | 0.28 | 0.41 | 0.51 | 0.86 |
| Avg_FL | 0.64 | 0.13 | 0.23 | 0.56 | 0.65 | 0.72 | 0.93 |
| Max_FL | 0.85 | 0.10 | 0.49 | 0.80 | 0.87 | 0.92 | 1.08 |
| Min_Rel | 0.73 | 0.13 | 0.40 | 0.63 | 0.71 | 0.84 | 0.95 |
| Avg_Rel | 0.81 | 0.09 | 0.61 | 0.74 | 0.83 | 0.88 | 0.95 |
| Max_Rel | 0.88 | 0.08 | 0.67 | 0.83 | 0.90 | 0.94 | 0.98 |
print(xtable(sum_dat[,c(1:3,5,7)]))% latex table generated in R 4.0.5 by xtable 1.8-4 package
% Wed Feb 16 13:30:50 2022
\begin{table}[ht]
\centering
\begin{tabular}{rrrrrr}
\hline
& avg & sd & min & median & max \\
\hline
SS & 4217.78 & 23241.20 & 36.00 & 603.00 & 263683.00 \\
NLV & 5.46 & 6.93 & 1.00 & 4.00 & 60.00 \\
NOV & 50.12 & 81.58 & 5.00 & 22.50 & 442.00 \\
Min\_Ind & 6.86 & 6.40 & 1.00 & 5.00 & 40.00 \\
Avg\_Ind & 8.95 & 7.00 & 2.50 & 6.22 & 40.00 \\
Max\_Ind & 12.93 & 15.20 & 3.00 & 8.00 & 135.00 \\
Min\_FL & 0.41 & 0.20 & 0.00 & 0.41 & 0.86 \\
Avg\_FL & 0.64 & 0.13 & 0.23 & 0.65 & 0.93 \\
Max\_FL & 0.85 & 0.10 & 0.49 & 0.87 & 1.08 \\
Min\_Rel & 0.73 & 0.13 & 0.40 & 0.71 & 0.95 \\
Avg\_Rel & 0.81 & 0.09 & 0.61 & 0.83 & 0.95 \\
Max\_Rel & 0.88 & 0.08 & 0.67 & 0.90 & 0.98 \\
\hline
\end{tabular}
\end{table}mydata %>%
select(`Scale of Indicators (list)`) %>%
table().
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1 Sample Size
p <- mydata %>%
mutate(`Sample Size` = ifelse(`Sample Size`>5000, 5000, `Sample Size`))%>%
ggplot(aes(x=`Sample Size`))+
geom_histogram(aes(y = ..density..), binwidth = 200)+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 500, color="red")+
geom_vline(xintercept = 2500, color="red")+
scale_x_continuous(breaks=seq(0,5000,500),limits = c(0,5000))+
labs(y=NULL,x=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)
pWarning: Removed 1 rows containing non-finite values (stat_bin).Warning: Removed 1 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).
ggsave(filename = "fig/study0_sample_size.pdf",plot=p,width = 4, height=3,units="in")Warning: Removed 1 rows containing non-finite values (stat_bin).Warning: Removed 1 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).ggsave(filename = "fig/study0_sample_size.png",plot=p,width = 4, height=3,units="in")Warning: Removed 1 rows containing non-finite values (stat_bin).Warning: Removed 1 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).ggsave(filename = "fig/study0_sample_size.eps",plot=p,width = 4, height=3,units="in")Warning: Removed 1 rows containing non-finite values (stat_bin).Warning: Removed 1 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).Warning in grid.Call.graphics(C_polygon, x$x, x$y, index): semi-transparency is
not supported on this device: reported only once per pageIndicators
p<-mydata %>%
ggplot(aes(x=`Average Number of Indicators per Factor`))+
geom_histogram(binwidth=1,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 5, color="red")+
geom_vline(xintercept = 10, color="red")+
geom_vline(xintercept = 20, color="red")+
scale_x_continuous(breaks=seq(0,40, 5),limits = c(0,45))+
labs(y=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)
pWarning: Removed 4 rows containing non-finite values (stat_bin).Warning: Removed 4 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).
ggsave(filename = "fig/study0_indicators.pdf",plot=p,width = 4, height=3,units="in")Warning: Removed 4 rows containing non-finite values (stat_bin).Warning: Removed 4 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).ggsave(filename = "fig/study0_indicators.png",plot=p,width = 4, height=3,units="in")Warning: Removed 4 rows containing non-finite values (stat_bin).Warning: Removed 4 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).ggsave(filename = "fig/study0_indicators.eps",plot=p,width = 4, height=3,units="in")Warning: Removed 4 rows containing non-finite values (stat_bin).Warning: Removed 4 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).Warning in grid.Call.graphics(C_polygon, x$x, x$y, index): semi-transparency is
not supported on this device: reported only once per pagemydata %>%
ggplot(aes(x=`Minimum Number of Indicators per Factor`))+
geom_histogram(binwidth=1,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 5, color="red")+
geom_vline(xintercept = 10, color="red")+
geom_vline(xintercept = 20, color="red")+
scale_x_continuous(breaks=seq(0,40, 5),limits = c(0,45))+
labs(y=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)Warning: Removed 5 rows containing non-finite values (stat_bin).Warning: Removed 5 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).
mydata %>%
ggplot(aes(x=`Maximum Number of Indicators per Factor`))+
geom_histogram(binwidth=1,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 5, color="red")+
geom_vline(xintercept = 10, color="red")+
geom_vline(xintercept = 20, color="red")+
scale_x_continuous(breaks=seq(0,40, 5),limits = c(0,45))+
labs(y=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)Warning: Removed 8 rows containing non-finite values (stat_bin).Warning: Removed 8 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).
Factor Loadings
mydata %>%
ggplot(aes(x=`Minimum Factor Loading`))+
#geom_histogram(binwidth=1,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 0.9, color="red")+
labs(y=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)Warning: Removed 49 rows containing non-finite values (stat_density).
mydata %>%
ggplot(aes(x=`Average Factor Loading`))+
#geom_histogram(binwidth=1,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 0.9, color="red")+
labs(y=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)Warning: Removed 49 rows containing non-finite values (stat_density).
mydata %>%
ggplot(aes(x=`Maximum Factor Loading`))+
#geom_histogram(binwidth=1,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 0.9, color="red")+
labs(y=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)Warning: Removed 49 rows containing non-finite values (stat_density).
# combine for plot
p<-mydata %>%
select(`Minimum Factor Loading`, `Average Factor Loading`, `Maximum Factor Loading`)%>%
pivot_longer(
cols=everything(),
names_to="type",
values_to="value"
)%>%
ggplot(aes(x=value))+
geom_histogram(binwidth=0.1,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 0.9, color="red")+
scale_x_continuous(breaks=seq(0,1, .25),limits = c(-0.1,1.1))+
labs(y=NULL,x=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)
pWarning: Removed 149 rows containing non-finite values (stat_bin).Warning: Removed 149 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).
ggsave(filename = "fig/study0_factor_loadings.pdf",plot=p,width = 4, height=3,units="in")Warning: Removed 149 rows containing non-finite values (stat_bin).Warning: Removed 149 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).ggsave(filename = "fig/study0_factor_loadings.png",plot=p,width = 4, height=3,units="in")Warning: Removed 149 rows containing non-finite values (stat_bin).Warning: Removed 149 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).ggsave(filename = "fig/study0_factor_loadings.eps",plot=p,width = 4, height=3,units="in")Warning: Removed 149 rows containing non-finite values (stat_bin).Warning: Removed 149 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).Warning in grid.Call.graphics(C_polygon, x$x, x$y, index): semi-transparency is
not supported on this device: reported only once per pageReliability
mydata %>%
ggplot(aes(x=`Min Reliability`))+
#geom_histogram(binwidth=1,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 0.80, color="red")+
geom_vline(xintercept = 0.89, color="red")+
geom_vline(xintercept = 0.94, color="red")+
labs(y=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)Warning: Removed 23 rows containing non-finite values (stat_density).
mydata %>%
ggplot(aes(x=`Avg Reliability`))+
#geom_histogram(binwidth=1,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 0.80, color="red")+
geom_vline(xintercept = 0.89, color="red")+
geom_vline(xintercept = 0.94, color="red")+
labs(y=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)Warning: Removed 23 rows containing non-finite values (stat_density).
mydata %>%
ggplot(aes(x=`Max Reliability`))+
#geom_histogram(binwidth=1,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 0.80, color="red")+
geom_vline(xintercept = 0.89, color="red")+
geom_vline(xintercept = 0.94, color="red")+
labs(y=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)Warning: Removed 23 rows containing non-finite values (stat_density).
# combine for plot
p<-mydata %>%
select(`Min Reliability`, `Avg Reliability`, `Max Reliability`)%>%
pivot_longer(
cols=everything(),
names_to="type",
values_to="value"
)%>%
ggplot(aes(x=value))+
geom_histogram(binwidth=0.05,aes(y = ..density..))+
geom_density(adjust=2, fill="grey", alpha=0.5)+
geom_vline(xintercept = 0.80, color="red")+
geom_vline(xintercept = 0.89, color="red")+
geom_vline(xintercept = 0.94, color="red")+
scale_x_continuous(breaks=seq(0,1, .25),limits = c(0,1))+
labs(y=NULL,x=NULL)+
theme_classic()+
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y =element_blank()
)
pWarning: Removed 69 rows containing non-finite values (stat_bin).Warning: Removed 69 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).
ggsave(filename = "fig/study0_reliability.pdf",plot=p,width = 4, height=3,units="in")Warning: Removed 69 rows containing non-finite values (stat_bin).Warning: Removed 69 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).ggsave(filename = "fig/study0_reliability.png",plot=p,width = 4, height=3,units="in")Warning: Removed 69 rows containing non-finite values (stat_bin).Warning: Removed 69 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).ggsave(filename = "fig/study0_reliability.eps",plot=p,width = 4, height=3,units="in")Warning: Removed 69 rows containing non-finite values (stat_bin).Warning: Removed 69 rows containing non-finite values (stat_density).Warning: Removed 2 rows containing missing values (geom_bar).Warning in grid.Call.graphics(C_polygon, x$x, x$y, index): semi-transparency is
not supported on this device: reported only once per pagesum(mydata$`Method of Computing Reliability` == "omega", na.rm=T)/nrow(mydata)[1] 0.16Response Categories
mydata %>%
select(`Scale of Indicators (list)`) %>%
table().
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1 indCats <- mydata$`Scale of Indicators (list)`
ind2 <- ind3 <- ind4 <- ind5 <- ind6 <- ind7 <- indSS <- numeric(length(indCats))
for(i in 1:length(indCats)){
indi <- str_split(indCats[i], ",", simplify = T)
ind2[i] <- ifelse(any(indi %like% "dichotomous"), 1, 0)
ind3[i] <- ifelse(any(indi %like% "3pt"), 1, 0)
ind4[i] <- ifelse(any(indi %like% "4pt"), 1, 0)
ind5[i] <- ifelse(any(indi %like% "5pt"), 1, 0)
ind6[i] <- ifelse(any(indi %like% "6pt"), 1, 0)
ind7[i] <- ifelse(any(indi %like% "7pt"), 1, 0)
indSS[i] <- ifelse(any(indi %like% "sum-score"), 1, 0)
}
catDat <- as.data.frame(cbind(ind2, ind3, ind4, ind5, ind6, ind7, indSS))
catDat$multi <- ifelse(rowSums(catDat) > 1, 1, 0)
# summarizing
mean(ind2)[1] 0.153mean(ind3)[1] 0.0625mean(ind4)[1] 0.278mean(ind5)[1] 0.472mean(ind6)[1] 0.0486mean(ind7)[1] 0.167mean(indSS)[1] 0.104mean(catDat$multi)[1] 0.222
sessionInfo()R version 4.0.5 (2021-03-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] readxl_1.3.1 car_3.0-10 carData_3.0-4
[4] mvtnorm_1.1-1 LaplacesDemon_16.1.4 runjags_2.2.0-2
[7] lme4_1.1-26 Matrix_1.3-2 sirt_3.9-4
[10] R2jags_0.6-1 rjags_4-12 eRm_1.0-2
[13] diffIRT_1.5 statmod_1.4.35 xtable_1.8-4
[16] kableExtra_1.3.4 lavaan_0.6-7 polycor_0.7-10
[19] bayesplot_1.8.0 ggmcmc_1.5.1.1 coda_0.19-4
[22] data.table_1.14.0 patchwork_1.1.1 forcats_0.5.1
[25] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4
[28] readr_1.4.0 tidyr_1.1.3 tibble_3.1.0
[31] ggplot2_3.3.5 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] minqa_1.2.4 TAM_3.5-19 colorspace_2.0-0 rio_0.5.26
[5] ellipsis_0.3.1 ggridges_0.5.3 rprojroot_2.0.2 fs_1.5.0
[9] rstudioapi_0.13 farver_2.1.0 fansi_0.4.2 lubridate_1.7.10
[13] xml2_1.3.2 splines_4.0.5 mnormt_2.0.2 knitr_1.31
[17] jsonlite_1.7.2 nloptr_1.2.2.2 broom_0.7.5 dbplyr_2.1.0
[21] compiler_4.0.5 httr_1.4.2 backports_1.2.1 assertthat_0.2.1
[25] cli_2.3.1 later_1.1.0.1 htmltools_0.5.1.1 tools_4.0.5
[29] gtable_0.3.0 glue_1.4.2 Rcpp_1.0.7 cellranger_1.1.0
[33] jquerylib_0.1.3 vctrs_0.3.6 svglite_2.0.0 nlme_3.1-152
[37] psych_2.0.12 xfun_0.21 ps_1.6.0 openxlsx_4.2.3
[41] rvest_1.0.0 lifecycle_1.0.0 MASS_7.3-53.1 scales_1.1.1
[45] ragg_1.1.1 hms_1.0.0 promises_1.2.0.1 parallel_4.0.5
[49] RColorBrewer_1.1-2 curl_4.3 yaml_2.2.1 sass_0.3.1
[53] reshape_0.8.8 stringi_1.5.3 highr_0.8 zip_2.1.1
[57] boot_1.3-27 rlang_0.4.10 pkgconfig_2.0.3 systemfonts_1.0.1
[61] evaluate_0.14 lattice_0.20-41 labeling_0.4.2 tidyselect_1.1.0
[65] GGally_2.1.1 plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
[69] generics_0.1.0 DBI_1.1.1 foreign_0.8-81 pillar_1.5.1
[73] haven_2.3.1 withr_2.4.1 abind_1.4-5 modelr_0.1.8
[77] crayon_1.4.1 utf8_1.1.4 tmvnsim_1.0-2 rmarkdown_2.7
[81] grid_4.0.5 CDM_7.5-15 pbivnorm_0.6.0 git2r_0.28.0
[85] reprex_1.0.0 digest_0.6.27 webshot_0.5.2 httpuv_1.5.5
[89] textshaping_0.3.1 stats4_4.0.5 munsell_0.5.0 viridisLite_0.3.0
[93] bslib_0.2.4 R2WinBUGS_2.1-21