library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── 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 errorslibrary(plspm)##
## Attaching package: 'plspm'
##
## The following object is masked from 'package:ggplot2':
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## alphalibrary(car)## Loading required package: carData
##
## Attaching package: 'car'
##
## The following object is masked from 'package:dplyr':
##
## recode
##
## The following object is masked from 'package:purrr':
##
## some# read data from the web
df_articulo_12 <- read.csv("https://docs.google.com/spreadsheets/d/107LWHUEJf0R1q1NpLYlwSIHlKRK22zhxfZ2RWtxtKow/export?format=csv&gid=783998258", header = TRUE, sep = ",", stringsAsFactors = FALSE)
df_articulo_12_standardized <- data.frame(
impact_sc = scale(df_articulo_12$impact),
size_sc = scale(df_articulo_12$Size),
tie_strength_sc = scale(df_articulo_12$tie_strength),
networking_intensity_sc = scale(df_articulo_12$networking_intensity),
structural_holes_sc = scale(df_articulo_12$structural_holes),
CDI_sc = scale(df_articulo_12$CDI),
tenure_sc = scale(df_articulo_12$tenure)
)# Inner model
inner_model <- matrix(c(
0, 0, 0, 0, # research_impact
1, 0, 0, 0, # social_capital
1, 0, 0, 0, # relational_capital
1, 0, 0, 0 # cognitive_capital
), nrow = 4, ncol = 4, byrow = TRUE)
# Outer model
outer_model <- list(
research_impact = c("impact"),
social_capital = c("Size", "structural_holes"),
relational_capital = c("tie_strength", "networking_intensity"),
cognitive_capital = c("CDI", "tenure")
)
# Run the PLS-PM analysis
plspm_model <- plspm(df_articulo_12, inner_model, outer_model, modes = c("B", "A", "A", "A"))
# Get the summary of the results
summary(plspm_model)## PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)
##
## ----------------------------------------------------------
## MODEL SPECIFICATION
## 1 Number of Cases 294
## 2 Latent Variables 4
## 3 Manifest Variables 7
## 4 Scale of Data Standardized Data
## 5 Non-Metric PLS FALSE
## 6 Weighting Scheme centroid
## 7 Tolerance Crit 1e-06
## 8 Max Num Iters 100
## 9 Convergence Iters 3
## 10 Bootstrapping FALSE
## 11 Bootstrap samples NULL
##
## ----------------------------------------------------------
## BLOCKS DEFINITION
## Block Type Size Mode
## 1 LV1 Exogenous 1 B
## 2 LV2 Endogenous 2 A
## 3 LV3 Endogenous 2 A
## 4 LV4 Endogenous 2 A
##
## ----------------------------------------------------------
## BLOCKS UNIDIMENSIONALITY
## Mode MVs C.alpha DG.rho eig.1st eig.2nd
## LV1 B 1 1.000 1.000 1.00 0.000
## LV2 A 2 0.000 0.000 1.57 0.426
## LV3 A 2 0.660 0.855 1.49 0.507
## LV4 A 2 0.051 0.678 1.03 0.974
##
## ----------------------------------------------------------
## OUTER MODEL
## weight loading communality redundancy
## LV1
## 1 impact 1.000 1.000 1.000 0.0000
## LV2
## 2 Size 0.768 -0.961 0.924 0.1379
## 2 structural_holes -0.336 0.777 0.604 0.0902
## LV3
## 3 tie_strength 0.691 0.917 0.841 0.0297
## 3 networking_intensity 0.458 0.799 0.639 0.0225
## LV4
## 4 CDI 0.845 -0.831 0.690 0.0582
## 4 tenure -0.557 0.535 0.286 0.0241
##
## ----------------------------------------------------------
## CROSSLOADINGS
## LV1 LV2 LV3 LV4
## LV1
## 1 impact 1.000 -0.3863 0.1879 0.290
## LV2
## 2 Size 0.422 -0.9614 0.0537 0.312
## 2 structural_holes -0.185 0.7774 0.1752 -0.284
## LV3
## 3 tie_strength 0.189 0.1175 0.9170 0.579
## 3 networking_intensity 0.125 -0.1388 0.7991 0.185
## LV4
## 4 CDI -0.240 0.0216 -0.7052 -0.831
## 4 tenure 0.158 -0.5695 -0.1999 0.535
##
## ----------------------------------------------------------
## INNER MODEL
## $LV2
## Estimate Std. Error t value Pr(>|t|)
## Intercept -6.63e-17 0.054 -1.23e-15 1.0e+00
## LV1 -3.86e-01 0.054 -7.16e+00 6.7e-12
##
## $LV3
## Estimate Std. Error t value Pr(>|t|)
## Intercept 1.09e-16 0.0575 1.90e-15 1.00000
## LV1 1.88e-01 0.0575 3.27e+00 0.00121
##
## $LV4
## Estimate Std. Error t value Pr(>|t|)
## Intercept 7.32e-17 0.056 1.31e-15 1.00e+00
## LV1 2.90e-01 0.056 5.19e+00 4.01e-07
##
## ----------------------------------------------------------
## CORRELATIONS BETWEEN LVs
## LV1 LV2 LV3 LV4
## LV1 1.000 -0.3863 0.1879 0.290
## LV2 -0.386 1.0000 0.0176 -0.335
## LV3 0.188 0.0176 1.0000 0.485
## LV4 0.290 -0.3353 0.4849 1.000
##
## ----------------------------------------------------------
## SUMMARY INNER MODEL
## Type R2 Block_Communality Mean_Redundancy AVE
## LV1 Exogenous 0.0000 1.000 0.0000 0.000
## LV2 Endogenous 0.1492 0.764 0.1141 0.764
## LV3 Endogenous 0.0353 0.740 0.0261 0.740
## LV4 Endogenous 0.0844 0.488 0.0412 0.488
##
## ----------------------------------------------------------
## GOODNESS-OF-FIT
## [1] 0.244
##
## ----------------------------------------------------------
## TOTAL EFFECTS
## relationships direct indirect total
## 1 LV1 -> LV2 -0.386 0 -0.386
## 2 LV1 -> LV3 0.188 0 0.188
## 3 LV1 -> LV4 0.290 0 0.290
## 4 LV2 -> LV3 0.000 0 0.000
## 5 LV2 -> LV4 0.000 0 0.000
## 6 LV3 -> LV4 0.000 0 0.000VIF
library(car)
# Create a linear model
linear_model <- lm(impact_sc ~ size_sc + structural_holes_sc + tie_strength_sc + networking_intensity_sc + CDI_sc + tenure_sc,
data = df_articulo_12_standardized)
# Calculate VIF
vif_values <- vif(linear_model)
# Print VIF values
print(vif_values)## size_sc structural_holes_sc tie_strength_sc
## 1.801420 2.011733 3.148896
## networking_intensity_sc CDI_sc tenure_sc
## 1.669127 2.583517 1.912704Correlation matrix
Correlation between variables
library(apaTables)
library(car)
# Compute VIF
vif_values <- vif(lm(impact ~ ., data = df_articulo_12))
# Create APA-style correlation table
apa_corr_table <- apaTables::apa.cor.table(df_articulo_12)
# Add VIF row to the APA-style correlation table
apa_corr_table$Table <- rbind(apa_corr_table$Table, c("VIF", sprintf("%.2f", vif_values)))
# Print the updated APA-style correlation table
print(apa_corr_table)##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3
## 1. impact 7.53 17.28
##
## 2. Size 9.74 11.06 .42**
## [.32, .51]
##
## 3. structural_holes 0.47 0.32 -.18** -.57**
## [-.29, -.07] [-.65, -.49]
##
## 4. Density 0.55 0.29 -.22** -.63** .97**
## [-.32, -.11] [-.69, -.55] [.96, .97]
##
## 5. tie_strength 2.57 1.87 .19** -.06 .22**
## [.08, .30] [-.17, .06] [.11, .33]
##
## 6. total_weighted 44.17 71.88 .52** .84** -.44**
## [.43, .59] [.80, .87] [-.53, -.34]
##
## 7. CDI 0.54 0.27 -.24** -.06 -.06
## [-.34, -.13] [-.17, .06] [-.18, .05]
##
## 8. tenure 16.77 15.19 .16** .47** -.61**
## [.04, .27] [.38, .56] [-.68, -.53]
##
## 9. networking_intensity 4.05 6.70 .13* .20** .05
## [.01, .24] [.09, .31] [-.06, .16]
##
## 4 5 6 7 8
##
##
##
##
##
##
##
##
##
##
##
## .22**
## [.10, .32]
##
## -.44** .21**
## [-.52, -.34] [.10, .32]
##
## -.06 -.77** -.31**
## [-.17, .06] [-.82, -.72] [-.41, -.20]
##
## -.65** -.13* .27** .03
## [-.71, -.57] [-.24, -.02] [.16, .37] [-.09, .14]
##
## .12* .49** .49** -.37** -.23**
## [.01, .23] [.40, .57] [.40, .57] [-.47, -.27] [-.34, -.12]
##
##
## 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.
## Cohens d
# Standardize the dataset
df_articulo_12_standardized_new <- as.data.frame(scale(df_articulo_12))
# Full model with all predictors
full_model_new <- lm(impact ~ Size + structural_holes + tie_strength + networking_intensity + CDI + tenure, data = df_articulo_12_standardized_new)
# Models without each predictor
model_without_size <- lm(impact ~ structural_holes + tie_strength + networking_intensity + CDI + tenure, data = df_articulo_12_standardized_new)
model_without_sh <- lm(impact ~ Size + tie_strength + networking_intensity + CDI + tenure, data = df_articulo_12_standardized_new)
model_without_ts <- lm(impact ~ Size + structural_holes + networking_intensity + CDI + tenure, data = df_articulo_12_standardized_new)
model_without_ni <- lm(impact ~ Size + structural_holes + tie_strength + CDI + tenure, data = df_articulo_12_standardized_new)
model_without_cdi <- lm(impact ~ Size + structural_holes + tie_strength + networking_intensity + tenure, data = df_articulo_12_standardized_new)
model_without_tenure <- lm(impact ~ Size + structural_holes + tie_strength + networking_intensity + CDI, data = df_articulo_12_standardized_new)
# Calculate R² for each model
r2_full_new <- summary(full_model_new)$r.squared
r2_without_size <- summary(model_without_size)$r.squared
r2_without_sh <- summary(model_without_sh)$r.squared
r2_without_ts <- summary(model_without_ts)$r.squared
r2_without_ni <- summary(model_without_ni)$r.squared
r2_without_cdi <- summary(model_without_cdi)$r.squared
r2_without_tenure <- summary(model_without_tenure)$r.squared
# Calculate Cohen's f² for each predictor
f2_size <- (r2_full_new - r2_without_size) / (1 - r2_full_new)
f2_sh <- (r2_full_new - r2_without_sh) / (1 - r2_full_new)
f2_ts <- (r2_full_new - r2_without_ts) / (1 - r2_full_new)
f2_ni <- (r2_full_new - r2_without_ni) / (1 - r2_full_new)
f2_cdi <- (r2_full_new - r2_without_cdi) / (1 - r2_full_new)
f2_tenure <- (r2_full_new - r2_without_tenure) / (1 - r2_full_new)
# Print Cohen's f² values
cat("Cohen's f² for Size: ", f2_size, "\n")## Cohen's f² for Size: 0.1753342cat("Cohen's f² for Structural_Holes: ", f2_sh, "\n")## Cohen's f² for Structural_Holes: 0.0002177353cat("Cohen's f² for Tie_Strength: ", f2_ts, "\n")## Cohen's f² for Tie_Strength: 0.01131456cat("Cohen's f² for Networking_Intensity: ", f2_ni, "\n")## Cohen's f² for Networking_Intensity: 0.01105287cat("Cohen's f² for CDI: ", f2_cdi, "\n")## Cohen's f² for CDI: 0.008076722cat("Cohen's f² for Tenure: ", f2_tenure, "\n")## Cohen's f² for Tenure: 0.003003241