This review examines published secondary analyses of National Longitudinal Transition Study 2 (NLTS2) data, with a primary focus upon statistical objectives, paradigms, inferences, and methods. Its primary purpose was to determine which statistical techniques have been common in secondary analyses of NLTS2 data. The review begins with an examination of past knowledge about statistical trends in special education research and follows with a summary of results. Researchers most often used logistic regression analysis or tests of group mean differences. Null hypothesis testing was the primary inferential approach and few researchers used multilevel modeling to take into account the longitudinal design of the NLTS2 data set. By learning about past approaches to analyzing NLTS2 data, future researchers may explore alternative statistical procedures in secondary analysis of the NLTS2 data set to inform the knowledge base.
AllisonP. D. (1999). Logistic regression using SAS: Theory and application. Cary, NC: SAS Institute.
2.
CohenJ. (1968). Multiple regression as a general data-analytic system. Psychological Bulletin, 70, 462–443.
3.
CohenJ. (1988). Statistical power analysis for the behavioral sciences. New York, NY: Lawrence Erlbaum.
4.
EisnhartM.DeHannR. L. (2005). Doctoral preparation of scientifically based education researchers. Educational Researcher, 34, 3–10.
5.
ElmoreP. B.WoehlkeP. L. (1996, April). Research methods employed in American Education Journal, Education Researcher, and Review of Educational Research from 1978 to 1995 (ERIC Document Reproduction Service NO ED 397122). Paper presented at the annual meeting of the American Educational Research Association, New York.
6.
FieldA. (2005). Discovering statistics using SPSS. New York, NY: SAGE.
7.
GillJ. (2002). Bayesian methods: A social and behavioral sciences approach. Washington, DC: Chapman & Hall/CRC.
8.
GlassG. V. (1976). Primary, secondary, and meta-analysis. Educational Researcher, 5, 3–8.
9.
GrigalM.HartD.MiglioreA. (2011). Comparing the transition planning, postsecondary education, and employment outcomes of students with intellectual and other disabilities. Career Developments and Transition for Exceptional Individuals, 34, 4–17.
10.
KellyS. M. (2011). The use of assistive technology by high school students with visual impairments: A second 1ook at the current problem. Journal of Visual Impairment & Blindness, 105, 235–239.
11.
KruschkeJ. K. (2011). Doing Bayesian data analysis: A tutorial with R and BUGS. Burlington, MA: Academic Press.
12.
LambardM.Snyder-DuchJ.BrackenC. C. (2002). Content analysis is mass communication: Assessment and reporting of intercoder reliabiity. Human Communication Research, 28, 587–604.
13.
LessenE.DudzinskiM.KarshK.Van AckerR. (1989). A survey of ten years of academic intervention research with learning disabled students: Implications for research and practice. Learning Disabilities Focus, 4, 106–122.
14.
LynchS. M. (2007). Introduction to applied Bayesian statistics and estimation for social scientists. New York, NY: Springer.
15.
MastropieriM. A.BerkeleyS.McDuffieK. A.MarshakL.ConnersN.DiamondC. M.. . . Cuenca-SanchezY. (2009). What is published in the field of special education? An analysis of 11 prominent journals. Exceptional Children, 76, 95–109.
MilsomA.GlanvilleJ. L. (2010). Factors mediating the relationship between social skills and academic grades in a sample of students diagnosed with learning disabilities or emotional disturbance. Remedial and Special Education, 31, 241–251.
OdomS. L.BrantlingerE.GerstenR.HornerR.ThompsonB.HarrisK. (2005). Research in special education: Scientific methods and evidence-based practices. Exceptional Children, 71, 137–148.
20.
PedhazurE. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). New York, NY: Wadsworth.
21.
RaudenbushS. W.BrykA. S. (2002). Hierarchical Linear Models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: SAGE.
22.
SchneiderB.CarnoyM.KilpatrickJ.SchmidtW. H.ShavelsonR. J. (2007). Estimating causal effects: Using experimental and observational designs. Washington, DC: American Educational Research Association.
23.
ScruggsT. E.MastropieriM. A. (1985). The first decade of the journal Behavioral Disorders: A quantitative evaluation. Behavioral Disorders, 11, 52–59.
24.
SingerJ.WilletJ. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press.
25.
SkidmoreS. T.ThompsonB. (2010). Statistical techniques used in published articles: A historical review of reviews. Educational and Psychological Measurement, 70, 777–795.
TestD. W.MazzottiV. L.FowlerA. L.KorteringC. H.KohlerP. H. (2009). Evidence-based secondary transition predictors for improving post-school outcomes for students with disabilities. Career Developments and Transition for Exceptional Individuals, 32, 160–181. doi:10.1177/0885728809346960
29.
WagnerM.KutashK.DuchnowksiA. J.EpsteinM. H. (2005). The special education elementary longitudinal study and the national longitudinal transition study: Study designs and implications for children and youth with emotional disturbance. Journal of Emotional and Behavioral Disorders, 13, 25–41.
30.
WagnerM.NewmanL.CametoR.LevineP. (2005). Changes over time in the early postschool outcomes of youth with disabilities: A report of findings from the National Longitudinal Transition Study (NLTS) and the National Longitudinal Transition Study–2 (NLTS2). Menlo Park, CA: SRI International.
