Modeling College and Career Readiness for Adolescents With and Without Disabilities: A Bifactor Approach

Abstract

Nonacademic skills related to college and career readiness (CCR) have become more prevalent in the literature as proposed conceptual models and frameworks, yet little empirical research exists in their support. We employed latent variable modeling to empirically test a previously proposed six-domain framework of CCR for adolescents with and without disabilities. Results support four specific factors of CCR: Academic Engagement, Critical Learning Processes, Mind-Set, and Transition Knowledge. Using a bifactor model, we confirmed one general factor (CCR) and one specific factor (Transition Knowledge), established measurement invariance on the basis of disability, and found latent mean differences between these groups; students without disabilities had greater overall CCR and transition knowledge. Findings support the use of a CCR measurement model with two potential factor scores in future research and practice and may inform efforts to measure CCR nonacademic skills.

Get full access to this article

View all access options for this article.

References

ACT, Inc. (2012). The condition of college and career readiness. Retrieved from www.act.org/readiness/2012

Bentler

P. M.

(1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238. doi:10.1037/0033-2909.107.2.238

Bentler

P. M.

(1995). EQS structural equations program manual. Encino, CA: Multivariate Software.

Bohanon

Gilman

Parker

Arnell

Sortino

(2016). Using school improvement and implementation science to integrate multi-tiered systems of support in secondary schools. Australasian Journal of Special Education, 1–18. doi:10.1017/jse.2016.8

Brown

R. S.

Conley

D. T.

(2007). Comparing state high school assessments to standards for success in entry-level university courses. Educational Assessment, 12, 137–160. doi:10.1080/10627190701232811

Brown

T. A.

(2015). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.

Camara

W. J.

(2013). College persistence, graduation, and remediation (Research Notes No. RN-19). New York, NY: College Entrance Examination Board.

Cheung

G. W.

Rensvold

R. B.

(2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255.

College and Career Readiness and Success Center. (2014). The college and career readiness and success organizer. Washington, DC: American Institutes for Research. Retrieved from www.ccrscenter.org/ccrs-landscape/ccrs-organizer

10.

Conley

D. T.

(2010). College and career ready: Helping all students succeed beyond high school. San Francisco, CA: Jossey-Bass.

11.

Cronbach

L. J.

(1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.

12.

Ding

Velicer

W. F.

Harlow

L. L.

(1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal, 2, 119–143. doi:10.1080/10705519509540000

13.

Doren

Murray

Gau

(2014). Salient predictors of school dropout among secondary students with learning disabilities. Learning Disabilities Research & Practice, 29, 150–159. doi:10.1111/ldrp.12044

14.

Duckworth

A. L.

Peterson

Matthews

M. D.

Kelly

D. R.

(2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92, 1087–1101. doi:10.1037/0022–3514.92.6.1087

15.

Farrington

C. A.

Roderick

Allensworth

Nagaoka

Keyes

T. S.

Johnson

D. W.

Beechum

N. O.

(2012). Teaching adolescents to become learners: The role of noncognitive factors in shaping school performance. A critical literature review. Chicago, IL: University of Chicago Consortium on Chicago School Research.

16.

Freeman

Simonsen

McCoach

D. B.

Sugai

Lombardi

Horner

(2016). Relationship between school-wide positive behavior interventions and supports and academic, attendance, and behavior outcomes in high schools. Journal of Positive Behavior Interventions, 18, 41–51. doi:10.1177/1098300715580992

17.

Gregg

(2007). Underserved and unprepared: Postsecondary learning disabilities. Learning Disabilities Research & Practice, 22, 219–228. doi:10.1111/j.1540-5826.2007.00250.x

18.

Holzinger

K. J.

Swineford

(1937). The bi-factor method. Psychometrika, 2, 41–54. doi:10.1007/BF02287965

19.

L. T.

Bentler

P. M.

(1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453. doi:10.1037/1082-989X.3.4.424

20.

L. T.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. doi:10.1080/10705519909540118

21.

Individuals with Disabilities Education Act, 20 U.S.C. §§ 1400 et seq (2006 & Supp. V. 2011).

22.

Jöreskog

K. G.

(1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409–426. doi:10.1007/bf02291366

23.

Kline

R. B.

(2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.

24.

Little

T. D.

Cunningham

W. A.

Shahar

Widaman

K. F.

(2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9, 151–173. doi:10.1207/S15328007SEM0902_1

25.

Lombardi

A. R.

Conley

D. T.

Seburn

Downs

A. M.

(2013). College and career readiness assessment: Validation of the key cognitive strategies framework. Assessment for Effective Intervention, 38, 163–171. doi:10.1177/1534508412448668

26.

Lombardi

A. R.

Kowitt

Staples

(2015). Correlates of college readiness and critical thinking for secondary students with and without disabilities. Career Development and Transition for Exceptional Individuals, 38, 142–151. doi:10.1177/2165143414534888

27.

Lombardi

A. R.

Rifenbark

G. G.

Freeman

(2017). Exploring grit in adolescents with and without disabilities. Manuscript submitted for publication.

28.

Lombardi

A. R.

Seburn

Conley

D. T.

(2011). Development and initial validation of a measure of academic behaviors associated with college and career readiness. Journal of Career Assessment, 19, 375–391. doi:10.1177/1069072711409345

29.

McWhirter

E. H.

Rasheed

Crothers

(2000). The effects of high school career education on social-cognitive variables. Journal of Counseling Psychology, 47, 330–341. doi:10.1037/0022-0167.47.3.330

30.

Millsap

R. E.

(2012). Statistical approaches to measurement invariance. New York, NY: Routledge.

31.

Mishkind

(2014). Definitions of college and career readiness: An analysis by state. Washington, DC: American Institutes for Research

32.

Morningstar

M. E.

Lombardi

Fowler

C. H.

Test

D. W.

(2017). A college and career readiness framework for secondary students with disabilities. Career Development and Transition for Exceptional Individuals, 40, 79–91. doi:10.1177/2165143415589926

33.

Morningstar

M. E.

Lombardi

A. R.

Test

(2017). Including college and career readiness within a multi-tiered systems of support framework. Manuscript submitted for publication.

34.

Muthén

L. K.

Muthén

B. O.

(2015). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.

35.

National Center for Education Statistics. (2012). The condition of education 2012 (Publication No. NCES 2012-045). Washington, DC: U.S. Department of Education. Retrieved from http://nces.ed.gov/fastfacts/display.asp?id=16

36.

National Center for Education Statistics. (2014). Digest of education statistics: 2014. Washington, DC: U.S. Department of Education. Retrieved from http://nces.ed.gov/programs/digest/2015menu_tables.asp

37.

Pornprasertmanit

(2014). The unified approach for model evaluation in structural equation modeling (Doctoral dissertation). University of Kansas, Lawrence.

38.

R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from www.R-project.org/

39.

Reise

S. P.

(2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47, 667–696. doi:10.1080/00273171.2012.715555

40.

Reise

S. P.

Moore

T. M.

Haviland

M. G.

(2010). Bifactor models and rotations: Exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment, 92, 544–559. doi:10.1080/00223891.2010.496477

41.

Reise

S. P.

Scheines

Widaman

K. F.

Haviland

M. G.

(2013). Multidimensionality and structural coefficient bias in structural equation modeling: A bifactor perspective. Educational and Psychological Measurement, 73, 5–26. doi:10.1177/0013164412449831

42.

Revelle

(2016). psych: Procedures for personality and psychological research. Evanston, IL: Northwestern University. Retrieved from http://CRAN.R-project.org/package=psychVersion=1.6.4

43.

Rhemtulla

Brosseau-Liard

P. E.

Savalei

(2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17, 354–373. doi:10.1037/a0029315

44.

Sanford

Newman

Wagner

Cameto

Knokey

A. M.

Shaver

(2011). The post–high school outcomes of young adults with disabilities up to 6 years after high school: Key findings from the National Longitudinal Transition Study–2 (NLTS2) (Publication No. NCSER 2011-3004). Menlo Park, CA: SRI International.

45.

Satorra

Bentler

P. M.

(2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507–514. doi:10.1007/BF02296192

46.

Shogren

K. A.

Garnier Villarreal

(2015). Developing student, family, and school constructs from NLTS2 data. The Journal of Special Education, 49, 89–103. doi:10.1177/0022466913513336

47.

Shogren

K. A.

Wehmeyer

M. L.

Lane

K. L.

(2016). Embedding interventions to promote self-determination within multi-tiered systems of supports. Exceptionality, 1–12. doi:10.1080/09362835.2015.1064421

48.

Sörbom

(1974). A general method for studying differences in factor means and factor structure between groups. British Journal of Mathematical and Statistical Psychology, 27, 229–239. doi:10.1111/j.2044-8317.1974.tb00543.x

49.

Steiger

J. H.

Lind

J. C.

(1980, May). Statistically based tests for the number of common factors. Paper presented at the Annual Spring Meeting of the Psychometric Society, Iowa City, IA.

50.

Tucker

L. R.

Lewis

(1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10. doi:10.1007/BF02291170

51.

West

M. R.

Kraft

M. A.

Finn

A. S.

Martin

R. E.

Duckworth

A. L.

Gabrieli

C. F.

Gabrieli

J. D.

(2016). Promise and paradox: Measuring students’ non-cognitive skills and the impact of schooling. Educational Evaluation and Policy Analysis, 38, 148–170. doi:10.3102/0162373715597298

52.

Zinbarg

R. E.

Yovel

Revelle

McDonald

R. P.

(2006). Estimating generalizability to a latent variable common to all of a scale’s indicators: A comparison of estimators for ωh. Applied Psychological Measurement, 30, 121–144. doi:10.1177/0146621605278814

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.09 MB