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Data from the Freiburg Complaint Checklist. The data contain all 8 items corresponding to the scale
*Tenseness* for 1847 participants of the standardization sample of the Freiburg Complaint Checklist.
Additionally, several person characteristics are available.

A data frame containing data from the Freiburg Complaint Checklist with 1847 observations.
All items refer to the scale *Tenseness* and are measured on a 5-point Likert scale where low numbers
correspond to low frequencies or low intensitites of the respective complaint and vice versa.

- Clammy_hands
Do you have clammy hands?

- Sweat_attacks
Do you have sudden attacks of sweating?

- Clumsiness
Do you notice that you behave clumsy?

- Wavering_hands
Are your hands wavering frequently, e.g. when lightning a cigarette or when holding a cup?

- Restless_hands
Do you notice that your hands are restless?

- Restless_feet
Do you notice that your feet are restless?

- Twitching_eyes
Do you notice unvoluntary twitching of your eyes?

- Twitching_mouth
Do you notice unvoluntary twitching of your mouth?

- Gender
Gender of the participant

- Household
Does participant live alone in a houshold or together with others?

- WestEast
is the participant from East Germany (former GDR) or West Germany?

- Age
Age in 15 categories, treated as continuous variable

- Abitur
Does the participant have Abitur (a-levels)?

- Income
Income in 11 categories, treated as continuous variable

ZPID (2013). PsychData of the Leibniz Institute for Psychology Information ZPID. Trier: Center for Research Data in Psychology.

Fahrenberg, J. (2010). Freiburg Complaint Checklist [Freiburger Beschwerdenliste (FBL)]. Goettingen, Hogrefe.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ```
data(tenseness)
## create a small subset of the data to speed up calculations
set.seed(1860)
tenseness <- tenseness[sample(1:nrow(tenseness), 300),]
## scale all metric variables to get comparable parameter estimates
tenseness$Age <- scale(tenseness$Age)
tenseness$Income <- scale(tenseness$Income)
## two formulas, one without and one with explanatory variables (gender and age)
f.tense0 <- as.formula(paste("cbind(",paste(names(tenseness)[1:4],collapse=","),") ~ 1"))
f.tense1 <- as.formula(paste("cbind(",paste(names(tenseness)[1:4],collapse=","),") ~ Gender + Age"))
####
## Adjacent Categories Models
####
## Multivariate adjacent categories model, without response style, without explanatory variables
m.tense0 <- multordRS(f.tense0, data = tenseness, control = ctrl.multordRS(RS = FALSE))
m.tense0
## Multivariate adjacent categories model, with response style as a random effect,
## without explanatory variables
m.tense1 <- multordRS(f.tense0, data = tenseness)
m.tense1
## Multivariate adjacent categories model, with response style as a random effect,
## without explanatory variables for response style BUT for location
m.tense2 <- multordRS(f.tense1, data = tenseness, control = ctrl.multordRS(XforRS = FALSE))
m.tense2
## Multivariate adjacent categories model, with response style as a random effect, with
## explanatory variables for location AND response style
m.tense3 <- multordRS(f.tense1, data = tenseness)
m.tense3
plot(m.tense3)
####
## Cumulative Models
####
## Multivariate cumulative model, without response style, without explanatory variables
m.tense0.cumul <- multordRS(f.tense0, data = tenseness, control =
ctrl.multordRS(RS = FALSE), model = "cumulative")
m.tense0.cumul
## Multivariate cumulative model, with response style as a random effect,
## without explanatory variables
m.tense1.cumul <- multordRS(f.tense0, data = tenseness, model = "cumulative")
m.tense1.cumul
## Multivariate cumulative model, with response style as a random effect,
## without explanatory variables for response style BUT for location
m.tense2.cumul <- multordRS(f.tense1, data = tenseness,
control = ctrl.multordRS(XforRS = FALSE), model = "cumulative")
m.tense2.cumul
## Multivariate cumulative model, with response style as a random effect,
## with explanatory variables
## for location AND response style
m.tense3.cumul <- multordRS(f.tense1, data = tenseness, model = "cumulative")
m.tense3.cumul
plot(m.tense3.cumul)
``` |

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