Polit. Vertrauen
2004
## Call:
## strel(data = trust04, estimates = "alpha", interval = 0.95, n.iter = 5000,
## n.burnin = 500, thin = 1, n.chains = 3, item.dropped = TRUE)
##
## Results:
## point est 95% CI lower 95% CI upper
## Bayes_alpha 0.84150 0.83547 0.84765
## freq_alpha 0.83984 0.83372 0.84577
##
## Bayesian point est is the posterior mean
## Missing data handling: using pairwise complete cases
##
## Bayesian alpha if item dropped:
## point est 95% CI lower 95% CI upper
## original 0.84150 0.83547 0.84765
## x1 0.84699 0.84063 0.85323
## x2 0.79307 0.78420 0.80175
## x3 0.77046 0.76165 0.77954
## x4 0.78682 0.77854 0.79506
##
## Frequentist point estimate if item dropped:
## alpha
## original 0.83984
## x1 0.84540
## x2 0.79122
## x3 0.76738
## x4 0.78533
Trace-Plot
Autokorrelation-Plot
2012
## Call:
## strel(data = trust12, estimates = "alpha", interval = 0.95, n.iter = 5000,
## n.burnin = 500, thin = 1, n.chains = 3, item.dropped = TRUE)
##
## Results:
## point est 95% CI lower 95% CI upper
## Bayes_alpha 0.87278 0.86795 0.87761
## freq_alpha 0.87257 0.86764 0.87735
##
## Bayesian point est is the posterior mean
## Missing data handling: using pairwise complete cases
##
## Bayesian alpha if item dropped:
## point est 95% CI lower 95% CI upper
## original 0.87278 0.86795 0.87761
## x1 0.88254 0.87769 0.88730
## x2 0.82853 0.82130 0.83555
## x3 0.81363 0.80659 0.82086
## x4 0.82190 0.81498 0.82875
##
## Frequentist point estimate if item dropped:
## alpha
## original 0.87257
## x1 0.88271
## x2 0.82769
## x3 0.81358
## x4 0.82159
Trace-Plot
Autokorrelation-Plot
2018
## Call:
## strel(data = trust18, estimates = "alpha", interval = 0.95, n.iter = 5000,
## n.burnin = 500, thin = 1, n.chains = 3, item.dropped = TRUE)
##
## Results:
## point est 95% CI lower 95% CI upper
## Bayes_alpha 0.86490 0.85955 0.87076
## freq_alpha 0.86543 0.85980 0.87086
##
## Bayesian point est is the posterior mean
## Missing data handling: using pairwise complete cases
##
## Bayesian alpha if item dropped:
## point est 95% CI lower 95% CI upper
## original 0.86490 0.85955 0.87076
## x1 0.86508 0.85925 0.87130
## x2 0.82390 0.81585 0.83161
## x3 0.80383 0.79540 0.81187
## x4 0.81472 0.80696 0.82261
##
## Frequentist point estimate if item dropped:
## alpha
## original 0.86543
## x1 0.86589
## x2 0.82439
## x3 0.80429
## x4 0.81546
Trace-Plot
Autokorrelation-Plot
Outcome
2012
## Call:
## strel(data = outcome12, estimates = "alpha", interval = 0.95,
## n.iter = 5000, n.burnin = 500, thin = 1, n.chains = 3, item.dropped = TRUE)
##
## Results:
## point est 95% CI lower 95% CI upper
## Bayes_alpha 0.71288 0.70158 0.72414
## freq_alpha 0.71327 0.70202 0.72416
##
## Bayesian point est is the posterior mean
## Missing data handling: using pairwise complete cases
##
## Bayesian alpha if item dropped:
## point est 95% CI lower 95% CI upper
## original 0.71288 0.70158 0.72414
## x1 0.73411 0.72242 0.74641
## x2 0.55441 0.53442 0.57483
## x3 0.56270 0.54370 0.58313
##
## Frequentist point estimate if item dropped:
## alpha
## original 0.71327
## x1 0.73505
## x2 0.55717
## x3 0.56033
Trace-Plot
Autokorrelation-Plot
2018
## Call:
## strel(data = outcome18, estimates = "alpha", interval = 0.95,
## n.iter = 5000, n.burnin = 500, thin = 1, n.chains = 3, item.dropped = TRUE)
##
## Results:
## point est 95% CI lower 95% CI upper
## Bayes_alpha 0.75422 0.74376 0.76430
## freq_alpha 0.75202 0.74159 0.76209
##
## Bayesian point est is the posterior mean
## Missing data handling: using pairwise complete cases
##
## Bayesian alpha if item dropped:
## point est 95% CI lower 95% CI upper
## original 0.75422 0.74376 0.76430
## x1 0.69915 0.68485 0.71436
## x2 0.65483 0.63731 0.67144
## x3 0.65771 0.64123 0.67397
##
## Frequentist point estimate if item dropped:
## alpha
## original 0.75202
## x1 0.69644
## x2 0.64957
## x3 0.65758
Trace-Plot
Autokorrelation-Plot
Demokratisches Verständnis
2012
## Call:
## strel(data = demun12, estimates = "alpha", interval = 0.95, n.iter = 5000,
## n.burnin = 500, thin = 1, n.chains = 3, item.dropped = TRUE)
##
## Results:
## point est 95% CI lower 95% CI upper
## Bayes_alpha 0.89794 0.89451 0.90146
## freq_alpha 0.89823 0.89476 0.90160
##
## Bayesian point est is the posterior mean
## Missing data handling: using pairwise complete cases
##
## Bayesian alpha if item dropped:
## point est 95% CI lower 95% CI upper
## original 0.89794 0.89451 0.90146
## x1 0.88702 0.88307 0.89092
## x2 0.89215 0.88844 0.89585
## x3 0.88432 0.88021 0.88817
## x4 0.88272 0.87863 0.88673
## x5 0.88727 0.88350 0.89132
## x6 0.88022 0.87605 0.88432
## x7 0.88566 0.88173 0.88957
## x8 0.88603 0.88217 0.88990
## x9 0.89412 0.89058 0.89788
##
## Frequentist point estimate if item dropped:
## alpha
## original 0.89823
## x1 0.88736
## x2 0.89236
## x3 0.88477
## x4 0.88342
## x5 0.88772
## x6 0.88062
## x7 0.88582
## x8 0.88588
## x9 0.89447
Trace-Plot
Autokorrelation-Plot
Copyright: 
Benedikt Philipp Kleer, 2022 (Online-Appendix zur Promotion, eingereicht am 14. September 2022, Fachbereich 03, Justus-Liebig-Universität Gießen).