TraMineR (version 2.2-13)

mvad: Example data set: Transition from school to work

Description

The data comes from a study by McVicar and Anyadike-Danes on transition from school to work. The data consist of static background characteristics and a time series sequence of 72 monthly labour market activities for each of 712 individuals in a cohort survey. The individuals were followed up from July 1993 to June 1999. The monthly states are recorded in columns 15 (Jul.93) to 86 (Jun.99).

States are:

employment	(EM)
FE	further education (FE)
HE	higher education (HE)
joblessness	(JL)
school	(SC)
training	(TR)

The data set contains also ids (id) and sample weights (weight) as well as the following binary covariates:

male
catholic
Belfast, N.Eastern, Southern, S.Eastern, Western (location of school, one of five Education and Library Board areas in Northern Ireland)
Grammar (type of secondary education, 1=grammar school)
funemp (father's employment status at time of survey, 1=father unemployed)
gcse5eq (qualifications gained by the end of compulsory education, 1=5+ GCSEs at grades A-C, or equivalent)
fmpr (SOC code of father's current or most recent job, 1=SOC1 (professional, managerial or related))
livboth (living arrangements at time of first sweep of survey (June 1995), 1=living with both parents)

Usage

data(mvad)

Format

A data frame containing 712 rows, 72 state variables, 1 id variable and 13 covariates.

Author

Gilbert Ritschard and Alexis Gabadinho

References

McVicar, Duncan and Anyadike-Danes, Michael (2002). Predicting Successful and Unsuccessful Transitions from School to Work by Using Sequence Methods, Journal of the Royal Statistical Society. Series A (Statistics in Society), 165, 2, pp. 317--334.

Examples

data(mvad)
mvad.alph <- c("employment", "FE", "HE", "joblessness", 
    "school", "training")
mvad.labels <- c("employment", "further education", 
    "higher education", "joblessness", "school", "training")
mvad.shortlab <- c("EM", "FE", "HE", "JL", "SC", "TR")

mvad.seq <- seqdef(mvad[1:6,17:86], alphabet = mvad.alph, 
    states = mvad.shortlab, labels = mvad.labels, 
    weights=mvad[1:6,]$weights, xtstep = 6)

print(mvad.seq, format="SPS")