# sim_score_data: Simulation of multivariate score data In holland: Statistics for Holland's Theory of Vocational Choice

## Description

This function will simulate Person (raw)-scores for an arbitrary number of dimensions (latent variables), assessed with any type of questionnaire given the maximum and minimum raw score for each dimension.

## Usage

 ```1 2 3 4 5 6 7 8``` ```sim_score_data( n = 1000, cormat, min.score = 0, max.score = 40, data.frame = FALSE, ... ) ```

## Arguments

 `n` integer giving the number of cases (observations) in the data to simulate. `cormat` a correlation matrix describing the associations between the dimensions – for Hollnd's theory, typical a 6 x 6 matrix with named columns and rows with `c("R","I","A","S","E","C")`. `min.score` numeric (possibly vector with max length == ncol(cormat) – will be recycled) with numeric value(s) defining the minimum raw scores per dimension `max.score` numeric (possibly vector with max length == ncol(cormat) – will be recycled) with numeric value(s) defining the maximum raw scores per dimension. `data.frame` logical whether to return a `data.frame` or a `matrix` `...` additional parameters passed through to `rmvnorm`.

## Details

For Hollnd's theory, six dimensions (`c("R","I","A","S","E","C")`) are assumed being assessed with an questionnaire with 10 questions per dimension with each question having five response categories which are scored from '0' to '4' – thus min. raw score is 0 and max. rax score is 40 for each of the six dimension respectively.

## Value

a `data.frame` with simulated raw scores.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```# get an RIASEC correlation matrix data(AIST_2005_F_1270) # simulate raw scores with minimum = 0 and maximum = 40 a<-sim_score_data(n=1000,cormat=AIST_2005_F_1270) apply(a, 2, range) apply(a, 2, mean) apply(a, 2, sd) # simulate raw scores with minimum = 10 and maximum = 50 b<-sim_score_data(n=1000,cormat=AIST_2005_F_1270,min.score=10,max.score=50) apply(b, 2, range) apply(b, 2, mean) apply(b, 2, sd) # simulate norm scores (range between 70 and 130) c<-sim_score_data(n=1000,cormat=AIST_2005_M_1226,min.score=70,max.score=130) apply(c, 2, range) apply(c, 2, mean) apply(c, 2, sd) ```

holland documentation built on Sept. 5, 2021, 5:08 p.m.