An Analysis of Longitudinal Data in Human Development Study: With a Special Focus on Latent Growth Model
AUTHOR : 신택수
INFORMATION : page. 1~28 / 2014 Vol.21 No.3
ABSTRACT
This study explained characteristics of longitudinal data and analytic tools. It also described how various
modeling techniques can be applied to longitudinal study. Among the several longitudinal data methods,
the study focused on latent growth modeling (LGM) based analytic approaches. LGM is modeled as a
function of an underlying growth process. It also explores effects of specific factors on individual variation
in the growth characteristics. In the unconditional analysis, the growth trajectory of mathematics
achievement was followed by nonlinear shape (i.e., concave shape). For the analysis of the conditional
model, gender differences were found in terms of both initial status and growth. Although female students
reported lower initial scores, the growth rate was significantly faster in females than in males. Additionally,
low SES students repeatedly reported lower scores across years. In the school level, although significant
differences were found on the initial status, the initial status and the growth were not significantly related,
suggesting school gap sustained. Lastly, reading ability would have a positive influence on mathematic
achievement and the proper number of latent classes was deemed to be 4. Other pertaining issues were
also discussed.
Ⅰ. 서 론
Ⅱ. 종단연구의 특성
Ⅲ. 종단자료 분석방법
Ⅳ. 연구방법
Ⅴ. 잠재성장모형을 이용한 종단자료 분석 결과 및 해석
Ⅵ. 논의 및 결론
참고문헌