Recommendations and Outlook

Models of Section 11.5

Sample size planning and a priori power analysis

#### Loading of data set

load("chapter_3.RData")

#### Activating of lavaan

library(lavaan)
library(simsem)

#### Fit correlated factor model to data from chapter 3

model.corr.fakt.mod<-
  '
# Loneliness-self factor
L_self =~ Slon1+Slon2+Slon3 

# Loneliness-parent factor
L_parent =~ Elon1+Elon2+Elon3 

# Loneliness-peer factor
L_peer =~ Plon1+Plon2+Plon3 

# Flourishing-self factor
F_self =~ Sflou1+Sflou2 

# Flourishing-parent factor
F_parent =~ Eflou1+Eflou2 

# Flourishing-peer factor
F_peer =~ Pflou1+Pflou2 
'
model.corr.fakt.fit<-sem(model.corr.fakt.mod,data=chapter_3.dat, 
                         meanstructure = TRUE, 
                         estimator= "MLR",
                         missing= "fiml")
summary(model.corr.fakt.fit, standardized=TRUE, fit.measures = TRUE, rsquare=TRUE)


#### apriori power analysis via simulation study

simulate <- sim(nRep=NULL, 
                model=model.corr.fakt.fit, 
                n=seq(100, 375, 25), 
                generate=model.corr.fakt.fit)

#### plot power
plotPower(simulate,'L_parent~~L_peer')
plotPower(simulate,'F_parent~~F_peer')

#### find power
aprior.power <- getPower(simulate)
findPower(aprior.power,'N',.8)['L_parent~~L_peer']
findPower(aprior.power,'N',.8)['F_parent~~F_peer']

MPlus code and data

Download