LCA and LTA 목차 정리

Ref : Latent Class and Latent Transition Analysis with Applications in the Social, Behavioral and Health Sciences.

 

<주요 내용 목차>

 

(PART 1 FUNDAMENTALS)

 

2. The Latent Class model(하위 보충)

 

3. The relation between the latent variable and its indicators

 

4. Parameter estimation and model selection

  4.2 Maximum Likihood estimation

     4.2.1 모형 parameters 추정

     4.2.2 Options for treatment of individual parameters: Parameter restrictions

     4.2.3 Missing data and estimation

  4.3 모형 적합과 모형 선택

     4.3.1 Absolute model  fit

     4.3.2 The likelihood-ratio statistic G^2 and 자유도

     4.3.3 Relative model fit

     4.3.4 Cross-validation

  4.4 Finding the ML solution

     4.4.1 Overview of model identification issues

     4.4.2 Visualizing identification, underidentification, and unidentification

     4.4.3 Identification and information

     4.4.4 How to find the ML solution

     4.4.5 Label switching

     4.4.6 User-provided starting values

4.5 Empirical example of using many starting values(하위항목 생략)

4.6 Empirical example of selecting the number of latent classes(하위항목 생략)

4.7 More about parameter restrictions(하위항목 생략)

4.8 Standard errors

 

(PART 2 ADVANCED LCA)

 

5. Multiple-group LCA

  5.3 Multiple-group LCA: Model and notaion

  5.4 Computing the number of parameters estimated

  5.5 Expressing group differences in the LCA model

  5.6 Measurement invariance

  5.7 Establishing whether the number of latent classes is identical across groups(하위 생략)

  5.8 Establishing invariance of item-response probabilities across groups

  5.9 Interpretation when measurement invariance does not hold

  5.10 Strategies when measurement invariance does not hold

  5.11 Siginificant diffrences and important differences

  5.12 Testing equivalence of latent class prevalences across groups

 

6. LCA with Covariates

  6.3 Preparing to conduct LCA with covariates

  6.4 LCA with covariates: Model and notation

  6.5 Hypothesis testing in LCA with covariates

  6.6 Interpretation of the intercepts and regression coefficients

  6.7 Empirical examples of LCA with a single covariate

  6.8 Empirical example of multiple covariates and interaction terms

  6.9 Multiple-group LCA with covariates: Model and notation

  6.10 Grouping variable or covariate?

     6.10.1 How the multiple-group and covariate models are different

     6.10.2 When the multiple-group and covariate models are mathematically equivalent

  6.11 Use of Bayesian prior to stabilize estimation

  6.12 Binomial logistic regression

  

(PART 3 LATENT CLASS MODELS FOR LONGITUDINAL DATA)

 

7. RMLCA and LTA

  7.2 RMLCA

     7.2.1 Adding a grouping variable

     7.2.2 RMLCA and growth mixture modeling

   7.3 LTA

      7.3.3 Estimation and assessing model fit

      7.3.4 Model fit in the adolescent delinquency example

   7.4 LTA model parameters

      7.4.1 Latent status prevalences

      7.4.2 Item-response probabilities

      7.4.3 Transition probabilities

  7.5 LTA:Model and notation

     7.5.1 Fundamental expression

  7.6 Degress of freedom associated with latent transition models

  7.9 Interpreting what a latent transition model reveals about change

  7.10 Parameter restrictions in LTA

  7.11 Testing the hypothesis of measurement invariance across times

  7.12 Testing hypotheses about change between times

  7.13 Relation between RMLCA and LTA

  7.14 Invariance of the transition probability matrix

 

8. Multiple-Group LTA and LTA with Covariates

  8.2 LTA with grouping variable

  8.3 Multiple-group LTA: Model and notation

  8.4 Computing the number of parameters estimated in multiple-group latent transition models

  8.5 Hypothesis tests concerning group differences: General considerations

  8.6 Overall hypothesis tests about group differences in LTA

  8.7 Testing the hypothesis of equality of latent status prevalences

  8.8 Testing the hypothesis of equality of transition probabilities

  8.9 Incorporating covariates in LTA

  8.10 LTA with covariate: Model and notation

  8.11 Hypothesis testing in LTA with covariates

  8.12 Including both a grouping variable and a covaiate in LTA

  8.13 Binomial logistic regression

  8.14 The relation between multiple-group LTA and LTA with a covariate

  

 

#Acronyms(줄임말)

AIC            Akaike information criterion

BIC            Bayesian information criterion

DA              Data augmentation

EM              expectaion-maximization

GPA            grade-point average

LCA             latent class analysis

LTA              latent transition analysis

MAR            missing at random

MCAR          missing completely at random

MNAR          missing not at random

ML               maximum likelihood

NLSY           National Longitudinal Survey of Youth

RMLCA        repeated-mesures latent class analysis

STI              sexually transmitted infection