Methods · Latent variable & mixture models · Growth models

Latent Growth Modeling (LGM)

Latent growth modeling (LGM) is a structural equation modeling approach that estimates individual trajectories over repeated measurements as latent intercept and slope factors and their variation across people.

잠재성장모형(LGM)은 반복 측정 자료에서 개인의 변화 궤적을 잠재 절편과 기울기 요인으로 추정하고 그 개인 간 차이를 분석하는 구조방정식모형 접근이다.

How do I model individual change over time across several measurement waves?

여러 시점에 걸친 개인의 시간에 따른 변화를 어떻게 모형화하나요?

Latent growth modeling treats each person's starting level and rate of change as latent factors estimated from three or more repeated measurements. It fits longitudinal data where you want the average trajectory, individual differences in change and predictors of those differences.

When to use Latent Growth Modeling (LGM)

When to use something else

Assumptions and requirements

What the analysis reports

How to report it

The linear growth model fit the data well, χ²([df]) = [value], CFI = [value], RMSEA = [value]; the mean slope was [value], p = [value], with significant variance in slopes, [value], p = [value].

Related methods

Run Latent Growth Modeling (LGM) on Quorant

Data you need
a table (CSV or Excel) with item columns (e.g. questionnaire items)
Price
600 credits for one analysis up to 10,000 rows (complexity level 4); larger or wider data costs more. Optional methodologist review: 2400 credits.
Checking
Python results are cross-checked against R reference outputs in every deployment
Before you pay
A free pre-analysis diagnosis checks your data and gives an exact quote; nothing is deducted until you approve it.

Method ID Q44001 · Reviewed 2026-10-25