1. Life history data Repeated measurements of states or events
17 November 2018
On life course
Life history data Repeated measurements of states or events
Focus: state occupied
Focus: status change (events)
Link life history analysis and demographic analysis: life histories and cohorts

2. Status and status change
Status (state): particular combination of (personal) attributes e.g. employment status, educational status, residence status, family status, health status, political preference, values
Current status: set of current (personal) attributes (may include info on whether or not a given event has occurred)
Status at some prior date or age; sequence of states since reference time
Objective measures (e.g. place of residence, employment status)
Subjective measures (e.g. values, health status, partnership)
Possible combinations of attributes (states): state space

3. Status and status change
Life event: change in status (transition) e.g. entry/exit labour market, graduation, migration,marriage, infection, recovery
Occurrence
Timing
Sequence
Meaning to person and society (rites of passage)
Episode, stage, spell : period between events
Onset (clock)
Continuation (progression)
Termination/cessation

4. Age/time in discrete time (time interval)
Observational plan The measurement of age and time The age-time framework
Age/time in discrete time (time interval)

5. Time scales Measurement of time Micro-macro
Individual time (age)
Process time: time since ONSET of process
Duration in situation is often more important than chronological age
Calendar time (historical time)
Historical context (e.g. level of economic development, technology)
Operational time: 1 over rate of change
Measurement of time
Exact time at event (continuous time) [e.g. days]
Time interval (discrete time) [e.g. months, years, multi-year periods]: grouped data
Fixed time interval
Variable time interval
Micro-macro
State occupied by individual [micro] OR number of individuals in given state [macro] (state occupancy)
Event occurring to individual during specified period [micro] OR number of events occurring to members of a population during specified period [macro]: aggregate data

6. Elementary process: The Lexis diagram: lifeline Exact age/time Event
Onset
Elementary process: t
The Lexis diagram: lifeline
Age a
Exact age/time x
Time t

7. The Lexis diagram
Discrete age/time

8. Observational plan: discrete time
Period observation (age-period): QSWV
Calendar year of event and age at time of event
(age in completed years; age at last birthday)
Cohort observation (cohort-age): QSVP
Cohort of person experiencing event and age at time of event
Period-cohort observation: QRSV
Calendar year of event and cohort of person experiencing event
Cohort = age at beginning of interval (t) or end (t+1)
Age-period-cohort observation: QSV
Two cohorts
Two calendar years
Two age groups
See e.g. Willekens and Drewe (1984)

9. Data types and observations
Issues:
limited dependent variables
missing (incomplete) data: observation window

10. Measurement of life-history or biography
Complete history of state occupancies (status data)
State occupied at beginning, type and timing of event, state occupied after event
ti = timing (date) of i-th event
yi = state occupied after i-th event
 = end of observation
Sample path (Tuma and Hannan, 1984, p. 48)

11. Sources of life-history data
Census: limited
Number of children ever born
Place of residence at a prior data (e.g. 5 years ago)
Survey
Sequence of cross sectional surveys and record linkage
Prospective survey: attrition (censoring)
Continuous observation
Panel
Retrospective survey: selection bias
Register: records events as they occur (reporting)

12. Observations - data types
Level of measurement:
Individual actor (subject): micro-data
Group of actors: grouped data (aggregate data, tabulated data, contingency tables)
Status or event:
Attribute or status: attribute data (status data)
Event: event = change in status. Event data
Period or cohort:
Period: cross-section across cohorts
Cohort: follows a cohort in time

13. Observations - data types
Micro-data: data on individual respondents
Attribute or status: attribute data (status data) Cross-section: state occupancy
Occurrence of non-occurrence of event during period (Y/N) OR number of occurrences: event data
Sequence of states or sequence of (repeatable) events: longitudinal data / life history data
Individual records
Three types of data files (life history data structures):
Person-file: record per person
Episode file: record per event
Person-month file: record per month/year

14. Observations - data types
Grouped data / aggregate data: data on groups of respondents
Number of persons with given attribute or having experienced given event: count data (person count)
State occupancy
Number of events during period: count data (event count)
Tabulations / cross-classifications (by covariate class)
Micro-data and tabulated data may give same results (parameters of probability models and regression models). Key: weights.

15. Multiple observations on the same subject Repeated measurements Longitudinal data
Observations over time on a number of individuals (careers of life trajectories)
subjects are observed backward or forward in time
Retrospectively: survivors recollect the past
Prospectively
subjects are observed continuously or at discrete intervals
continuously for a given period: continuous observation
at several points in time during a given period: discrete observation (discrete intervals)

16. Multiple observations on the same subject Longitudinal data
Continuous observation: attributes and changes in attributes (events)
e.g. places of residence and migrations [movement approach]
Discrete observation: attributes at several points in time (discrete intervals)
e.g. places of residence at two, three, or more points in time [transition approach]

17. Observation window Duration in state is incompletely recorded Censoring
X Date at entry
Y Length of risk period

18. Source: M. Mills. PhD dissertation, PRC-RUG, 2000

19. Sample selection bias Effect of timing at entry: marriage at young age
Effect of length of risk period:
Long intervals more likely included
Retrospective survey: survivors only (survivors may be a selection), e.g. Micro-Census, migration

20. Observation window Only part of life trajectory is observed Incomplete data: Censoring
Right censoring: observation (study) is terminated before all respondents experience the event
Reasons:
Due to unrelated event
Due to competing event
Due to interruption of observation (truncation; cutoff date)
Censoring due to termination of study:
Type I censoring: study is terminated after a fixed time period
Type II censoring: study is terminated after a given number of occurrences
Censoring due to occurrence of competing event leading to attrition (e.g. death, withdrawal)

21. Observation window Only part of life trajectory is observed Incomplete data: Censoring
Left censoring: time of entry into risk set is either before observation started or during observation
Reasons: e.g. diagnosis, immigration

22. Illustration Data types applied to life-history data: the case of migration

23. Data types applied to migration
Micro-data: data on individuals or households
Status data:
Current status:
migrant status (e.g. ever migrated / never migrated in given period)
current place (region) of residence
Place of residence at two points in time: transition data (migrant data)
Time interval of fixed length: e.g. census and 5 years prior
“Where did you live 5 years ago?”
Time interval variable: e.g. census and place of birth
“Place of birth”
Place of residence at 3 or more points in time

24. Data types applied to migration
Micro-data:
Event data : migration data (movement data)
Migration during given period (yes/no): migrant status
Ever migrated?
Number of migrations (quantum)
Timing of migration (tempo)
Time scale: calendar time, age, process time (time since event-origin)
Measurement of time: exact time, time interval (discrete time, e.g. month, year)
Timing of all migrations vs timing of last migration

25. Data types applied to migration
Grouped data: data on groups of individuals or households (actors)
Status data:
Current status: number of actors (subjects) in given status
Number of actors by place of residence at two points in time: transition data (migrant data) CENSUS
Number of actors by place of residence at 3 or more points in time
Event data:
Number of events during given period POP. REGISTER

26. Examples of life-history data

27. Examples of life-history data
The Netherlands: Transition to adulthood (leaving home)
Germany: the German Life History Survey
India: The National Family and Health Survey
USA: Population by place of current residence, place of residence 5 years ago, and place of birth (census)

28. Leaving parental home in the Netherlands: micro-data

29. Leaving parental home in the Netherlands Tabulated data

30. Occurrences and exposures
Leaving parental home in the Netherlands
Occurrences and exposures
Occurrences: Number leaving home by age and sex, 1961 birth cohort: nij
Exposures: number of months living at home (includes censored observations): PMij

31. Age at leaving home, The Netherlands: Person-years file
Claartje

32. The German life history survey (GLHS)
Retrospective survey about life histories of men and women born in , , and (3 cohorts)
Data collected in
Description: Brückner and Mayer (1998). In: Giele and Elder ‘Methods of life course research’, Sage.
Blossfeld and Rohwer (1995): subsample of 201 respondents with total of 600 employment spells

33. The GLS subsample Overall average rate of job change:
Number of job episodes: 600
Number of job changes: 458 (closed intervals)
Total time spent in jobs: months
Time spent in closed intervals and open intervals
Average per episode: months (B&R, p. 86)
458/40781 = per month (B&R, p. 86)

34. CMC = YEAR*12+MONTHS
YEAR=INT[(CMC-1)/12]
MONTH=CMC-YEAR*12 OR
Exact date in years
(Framingham Study)

35. German life history study, Sample data (Blossfeld and Rohwer, 1995)
Event file
1 ID Identif. Number
2 NOJ Serial number of the job
3 TS Starting time of job
4 TF Ending time of job
5 SEX (1 =men, 2 = women)
6 TI Date of interview
7 TB Date of birth
8 T Date of entry into labor market
9 TM Date of marriage (0 if no marriage)
10 PRES Prestige score of job i
11 PRESN Prestige score of job i+1
12 EDU Highest educational attainment

36. Person file

37. Number of jobs held, by sex (201 respondents)

38. Duration of jobs Duration = endjcm - startcjm (or interview) + 1
(B&R, 95, p. 86)
Duration = endjcm - startcjm (or interview) + 1

39. National Fertility and Family Health Survey, Karnataka, 1992-93: Person file

40. National Fertility and Family Health Survey, Karnataka, 1992-93: Event file
Continued

41. Q149: HH owns agricultural land?
Q121: Ever attended school?
Q124: Can read and write?
Q216: Line number of child
Q217: Single or multiple birth
Q218: Sex of child
Q219M: Month of birth
Q219Y: Year of birth
Q219C: Date of birth (CMC)
Q220: Child still alive?

42. Age at first marriage and age at change in education: Person-years file
Educ: 0 = not in school full-time
1 = secondary eduction
2 = postsecondary education
Marriage [MS]: 0 = not married
1 = married
All age periods prior to marriage and age at marriage are included.
Source: Yamaguchi, 1991, p. 22

43. Number of persons by place of residence in 1985 and place of residence in 1990, USA

44. The life history calendar
Measure and explain the health status in a given month (current status)
…. in relation to health status in previous month (transition)
…. in relation to (present and past) nutrition status (causality)
Friedman et al., 1988)

45. Life history calendar: Bangladesh (CDHS96-97)