The job-episode data from Blossfeld and Rohwer

rrdat.1 is the data file used by nearly every example in examples/ehhnew, the command files accompanying Blossfeld and Rohwer, Techniques of Event History Modeling. This reads it and names its columns as the examples do.

Usage

tda_rrdat(file = NULL, states = c(2, 4), upward = c("gt", "ge"))

Arguments

file: path to rrdat.1. Defaults to the copy shipped with the package.
states: 2 for a single destination state, 4 to distinguish upward, lateral and downward job changes.
upward: with states = 4, how an upward move is coded: "gt" is ed2.cf's gt(PRESN/PRES - 1, 0.2), "ge" is rt1m.cf's ge(...); one episode lies on the boundary, so the two give 84/219 and 85/218 upward/lateral moves. Both use TDA's comparison tolerance.

Value

A data frame of 600 episodes.

Source

The data are 600 job episodes of 201 randomly selected respondents from the German Life History Study (GLHS), collected at the Max-Planck-Institut für Bildungsforschung, Berlin, in 1981-1983 for the birth cohorts 1929-31, 1939-41 and 1949-51 (Mayer and Brückner 1989). TDA's manual (section 3.3.3) thanks Karl Ulrich Mayer and Hans-Peter Blossfeld for providing the data set; it has shipped with TDA since 1994 as rrdat.1 and is the example throughout the manual and the book.

References

Mayer, K. U., Brückner, E. (1989). Lebensverläufe und Wohlfahrtsentwicklung. Konzeption, Design und Methodik der Erhebung von Lebensverläufen der Geburtsjahrgänge 1929-1931, 1939-1941, 1949-1951. Berlin: Max-Planck-Institut für Bildungsforschung.

Blossfeld, H.-P., Rohwer, G. (2002). Techniques of Event History Modeling. New Approaches to Causal Analysis, 2nd ed. Mahwah, NJ: Lawrence Erlbaum.

The derived variables are the ones the examples define in their nvar blocks:

TFP: duration of the episode, TFin - TStart + 1.
DES: destination state. With states = 2 it is 1 for a job change and 0 for a censored episode, which is what most of the examples use. With states = 4 the job changes are split by prestige: 1 upward, 2 lateral, 3 downward.
LFX: labour force experience before the episode, TStart - TE.
PNOJ: number of previous jobs, NOJ - 1.
COHO2, COHO3: birth cohort indicators, 1939-41 and 1949-51.

Examples

d <- tda_rrdat()
tda_ltb(Surv(TFP, DES) ~ 1, d, tp = seq(0, 500, by = 30))
#> Call: tda_ltb(formula = Surv(TFP, DES) ~ 1, data = d, tp = seq(0, 500, 
#>     by = 30))
#> 
#> Episodes: 600 
#> 
#>  start midpoint entering censored exposed events       prob
#>      0       15      600       28   586.0    223 0.38054608
#>     30       45      349       23   337.5    113 0.33481481
#>     60       75      213       15   205.5     51 0.24817518
#>     90      105      147       16   139.0     25 0.17985612
#>    120      135      106       15    98.5     24 0.24365482
#>    150      165       67        5    64.5      9 0.13953488
#>    180      195       53        9    48.5      4 0.08247423
#>    210      225       40        5    37.5      3 0.08000000
#>    240      255       32        5    29.5      0 0.00000000
#>    270      285       27        7    23.5      2 0.08510638
#> ... 5 more rows