1. Exploring educational attainment across the life course using sequence analysis David Monaghan Senior Researcher Wisconsin HOPE Lab

2. Cultural image of undergraduates

3. The reality of today’s undergraduates Source: National Postsecondary Student Aid Survey, 2012 (NPSAS:12)

4. Where are older undergraduates enrolled? Source: NPSAS:12

5. Older undergrads in the four- year sector Source: NPSAS:12

6. Socioeconomic status and academic preparation

7. Competing demands

8. “Non-traditional” undergraduates 2 of every 5 undergraduates About 7 million individuals Generally from more disadvantaged backgrounds and with less robust academic preparation Often combine college attendance with one or more competing “adult” roles Roughly 2/3 first enrolled in “traditional” ages Diversification of higher education occurs through/because of diversification of patterns of enrollment Delayed entry Part-time attendance Interrupted enrollment

9. Research Questions 1)What patterns of college enrollment and attainment do individuals engage in across the life course? 2)How do patterns of enrollment relate to the other aspects of “adult” status?

10. Plan 1)The social scientific study of the life course and transition to adulthood 2)Data 3)Sequence analysis 4)Patterns of enrollment 5)Enrollment patterns and other transitions 6)Implications

11. The life course and the transition to adulthood

12. The life course framework Age structure – a set of socially defined life stages through which people proceed as they age Social age Distinct from chronological age Socially defined life-stage Set of appropriate behaviors (age norms) – age graded set of “expectations, privileges, and constraints” (Elder & Rockwell 1979) Transition between life stages is socially defined and normatively enforced infancy childhood adolescence middle adulthood young adulthood later adulthood old age

13. The transition to adulthood Move from dependency/subordination to independence/autonomy Indicated by a set of events/sub- transitions Timing of each sub-transition is normatively regulated Sequence of sub-transitions is considered to be normatively defined 1) “Family transitions” 1)Leaving household of origin 2)Marriage 3)Having children 2)“Non-family transitions” 1)Leaving schooling 2)Full-time employment

14. Normative sequencing Finish school Independent residence Full-time work Marriage Childbearing

15. Life course deviations Transitions can be deviant with regard to Timing Sequencing “Off-time” or “out of sequence” transitions are thought to result in negative consequences Normative sanctioning Being out of harmony with institutional/social context “Structures of social institutions are designed for compatibility” with normal/expected ordering/timing of events (Hogan 1978)

16. Challenges presented by non- traditional college-going Being an undergraduate is conceptualized as a non-adult, “dependent” status Qualitative evidence suggests that people generally do not consider undergraduate status to be incompatible with adulthood (Pallas 2006) Leaving school presumed to be Part of a ‘transition to adulthood’ - but people attend college well through 40s Prior to full-time work – ignores large numbers of full-time workers in college Prior to marriage/parenthood – ignores large numbers of students who are parents Assumption that education occurs in one stretch, prior to “adulthood” is massively obsolete (it may always have been) Formal education occurs throughout the life course, though the prevalence of enrollment varies by age

17. Data

18. What do you need to study education across the life course? Individual-level data on enrollment and attainment Year-by-year data Must cover 20s and 30s – 20+ years of data National Longitudinal Survey of Youth – 1979 cohort Representative sample of people ages 14-22 in US in 1978 Interviewed annually 1979-1994, biennially since (last data processed is from 2010) Very low attrition - in 2010, 76% of eligible subjects Initial sample: 12,800 respondents

19. NLSY-79 sample I use a subset of observations Respondents younger than 18 at first interview I exclude respondents who dropped out of the survey/were discontinued before their 35 th birthday Sample: 4,766 Of these, 2,528 attended college at some point I make use of year-by-year data on: College enrollment and attainment Labor force participation Marital behavior Fertility

20. Sequence Analysis

21. Typical way of analyzing longitudinal data CaseTimeYX1X2 11010.51 12010.32 13110.75 14000.89 21000.23 22110.45 24100.76 31000.56 3201 33010.23 34110.77

22. Analyzing time series Define dependent variable Model outcome as a function of a set of independent variables Presume data is generated by stochastic process Fixed-effects, random effects, event history, growth curve, etc.

23. Sequence Analysis Principally descriptive method Interest is in the sequence of states through which a unit moves over time Agnostic regarding data-generating mechanism

24. Sequence Analysis Developed in genetics (for studying sequences of DNA proteins) and computer science Imported into sociology by Andrew Abbot (Abbot 1990, 1995; Abbot & Forest 1986; Abbot & Hrycak 1990) Used in analysis of careers, and soon afterwards brought into life-course studies

25. What is a sequence? Consider a discreet set of states an individual can be in: Employment: {employed (E), unemployed (U), out of labor force (O)} Or Marital status: {never married (N), married (M), divorced/separated (D), widowed (W)}

26. What is a sequence? In each time period, an individual is in one of these states Over time, an individual moves through a succession of these states – a sequence

27. Higher education enrollment and attainment sequences Each sequence has an alphabet (set of possible states) Alphabet: Not enrolled in higher education, no BA (N) Enrolled in higher education, no BA (E) Bachelor’s degree attained (B) Each unit of time = 1 year Age range: 18-39 Sequence will be 22 units in length

28. Higher education enrollment and attainment sequences “Traditional” college-going sequence: E-E-E-E-E-B-B-B-B-B-B-B-B-B-B-B-B-B-B-B-B-B “Non-Traditional” student sequence: N-N-E-E-N-N-N-N-E-E-N-N-E-B-B-B-B-B-B-B-B-B Possible sequences: 3 22 =31,381,059,609 Separate sequences in data: 1,049 (college-going N=2528)

29. Analyzing sequences List of sequences organized – now what? Distances between sequences – which are more similar, and which more different? Measure of dissimilarity must be developed This can be accomplished through optimal matching

30. Optimal Matching Distance between two sequences (1, 2): The number of transformations needed to turn sequence 1 into sequence 2 “Transformation cost” Similar sequences require fewer transformations, lower cost

31. Finding subgroups through clustering OM results in an NxN cost matrix To find subgroups, one submits this cost matrix to hierarchical clustering

32. Sequences of college enrollment and attainment

33. All schooling sequences – state frequencies

34. All schooling sequences – state distribution

35. Probability of transition between states ->N->E->B N->0.890.100.01 E->0.320.600.07 B->0.00 1.00

36. Generating subgroups through clustering Number of cases per cluster Average transformation cost between clusters

37. Subgroup frequency plots

38. Marginal college-goers 45% of observations Moderate early college, rapidly tapering off Very light non-traditional enrollment No conversion to BA

39. Rapid completers 35% of observations Heavy early college Rapid conversion to degree Universal BA attainment by age 31

40. Lifelong students 12% of all observations Modest early college Rate of enrollment remains moderate through 20s Very low rate of BA attainment

41. Delayed completers 7% of all observations Moderate early college, tapering off slightly Conversion to BA begins in late 20s Universal attainment by late 30s

42. Background characteristics No college Marginal College-Goers Rapid Completers Lifelong Students Delayed Completers Parent has BA (%)4.112.141.317.618.8 Female (%)43.756.450.860.959.7 AFQT35.254.277.658.365.3 Latino (%)6.36.02.57.34.6 Black (%)13.913.46.015.99.1 Expected to finish college (%)14.138.280.148.347.9

43. College-going and the transition to adulthood

44. Components of transition Transition to work ◦Not employed, part-time employment, full-time employment Transition to marriage ◦Never married, married, “post-married” (divorced/separated/widowed) Transition to parenthood ◦0 kids, 1 kid, 2+ kids How do these transitions vary by enrollment pattern?

45. Transition to work Probability of transition between states -> No work->PT->FT No work ->0.720.190.09 PT->0.170.440.39 FT->0.020.080.90

47. Transition to marriage Probability of transition between states ->Never->Married->Post Never->0.930.070.00 Married->0.000.950.05 Post->0.000.100.90

49. Transition to parenthood Probability of transition between states ->0->1->2 0 ->0.930.070.00 1->0.000.870.13 2->0.00 1.00

51. Transition to adulthood and enrollment patterns Rapid completers’ patterns of transition are distinctive for all three transitions For all three transitions, non-standard groups fall in between rapid completers and those who don’t attend college But they resemble non-college individuals considerably more

52. Implications

53. Methodological limitations Sequence analysis does not lend itself well to causal inference High likelihood of reciprocal causation across individual biographies Early childbearing Stopping out

54. What do descriptive findings suggest? Heterogeneity among “non-traditional” students A distinct subset of non-traditional college-goers is likely degree-bound Even “rapid completers” group contains some “non-traditional” college-going patterns Future work will tease out proximal and distal predictors of enrollment patterns

55. What do descriptive findings suggest? Rapid completion is associated with a particular pattern of transition to adulthood This pattern involves delay of the other markers of adult status Colleges, as institutions, do not play well with other life-commitments Relatively easy to negotiate if one is primarily oriented to the student role

56. Colleges as (relatively)“greedy institutions”? Coser (1974): institutions that “make total claims on their members” and “seek exclusive and undivided loyalty” Colleges generally prefer students with flexible schedules, who can devote substantial time to coursework If you are a traditional student, the claims college makes on your life seem natural and manageable (though considerable) If you must also manage full-time work, children, and commuting, these claims can be excessive

57. The challenge of non- traditional students The proportion of college-goers combining college-going with substantial life-commitments is substantial and growing How do we redesign higher education in order to allow such students to complete degrees… Allowing that college may not be students’ only or even most important concern… While not sacrificing educational quality?