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HLM 多層次模式分析軟體

研究分析軟體
Research & Analysis Software

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HLM 8 多層次模式分析軟體

HLM 8為多級和縱向數據建模提供了前所未有的靈活性。HLM 7具有相同的完整陣列圖形程序和殘留文件以及計算速度，收斂的穩健性和用戶友好的界面

New in HLM 8

HLM 8 offers unprecedented flexibility in modeling multilevel and longitudinal data. With the same full array of graphical procedures and residual files along with the speed of computation, robustness of convergence, and user-friendly interface of HLM 6, HLM 7 highlights include three new procedures that handle binary, count, ordinal and multinomial (nominal) response variables as well as continuous response variables for normal-theory hierarchical linear models:

Four-level nested models:

  • Four-level nested models for cross-sectional data (for example, models for item response within students within classrooms within schools).
  • Four-level models for longitudinal data (for example items within time points within persons within neighborhoods).

Four-way cross-classified and nested mixtures:

  • Repeated measures on students who are moving across teachers within schools over time, or item responses nested within immigrants who are cross-classified by country of origin and country of destination.
  • Repeated measures on persons who are simultaneously living in a given neighborhood and attending a given school.

Hierarchical models with dependent random effects:

  • Spatially dependent neighborhood effects.
  • Social network interactions.

HLM 8 also offers new flexibility in estimating hierarchical generalized linear models through the use of Adaptive Gauss-Hermite Quadrature (AGH) and high-order Laplace approximations to maximum likelihood. The AGH approach has been shown to work very well when cluster sizes are small and variance components are large. the high-order Laplace approach requires somewhat larger cluster sizes but allows an arbitrarily large number of random effects (important when cluster sizes are large)

New HTML output that supplies elegant notation for statistical models including visually attractive tables is also now available, allowing the user to cut and paste output of interest into manuscripts.

HLM 8 manual
  • A hard copy of the HLM 7 manual is not available.
  • PDF copies of the HLM 7 manual are available via the HLM 7 Manual option on the Help menu of the full, rental, trial, and student editions of HLM 7 for Windows.

Overview of modeling options in the HLM statistical applications


Interface option

HLM2

HLM3
HLM4

HMLM

HMLM2
HCM2
HCM3

HLMHCM

Basic Settings: Distribution of outcome

Normal outcome

Y

Y
Y

Y

Y
Y
Y

Y

Bernoulli outcome

Y

Y
Y

-

-
Y
Y

Y

Poisson outcome (constant exposure)

Y

Y
Y

-

-
Y
Y

Y

Poisson outcome (variable exposure)

Y

Y
Y

-

-
Y
Y

Y

Binomial outcome

Y

Y
Y

-

-
Y
Y

Y

Multinomial outcome

Y

Y
-

-

-

  

-

Ordinal outcome

Y

Y
-

-

-

    

-

Over-dispersion

Y

Y
Y

-

-
Y
Y

Y

Basic Settings: Residual files, title and file names

Residual file at all levels

Y

Y
Y

-

-
Y
Y

Y

Title

Y

Y
Y

Y

Y
Y
Y

Y

Output filename

Y

Y
Y

Y

Y
Y
Y

Y

Graph filename

Y

Y
Y

Y

Y
Y
-

-

Basic Settings: Treatment of level-1 variance

Unrestricted

-

-
-

Y

Y
-
-

-

Skip unrestricted

-

-
-

Y

Y
-
-

-

Homogeneous

-

-
-

Y

Y
-
-

-

Heterogeneous

-

-
-

Y

Y
-
-

-

Log-linear

-

-
-

Y

Y
-
-

-

Predictor of level-1 var

-

-
-

Y

Y
-
-

-

1-st order autoregressive

-

-
-

Y

Y
-
-

-

Iteration Settings

Number of  iterations

Y

Y
Y

Y

Y
Y
Y

Y

Frequency of accelerator

Y

Y
Y

Y

Y
Y
Y

Y

% change to stop iterating

Y

Y
Y

Y

Y
Y
Y

Y

How to handle bad variance-covariance matrix

Y

Y
Y

Y

Y
Y
Y

Y

What to do when convergence not reached

Y

Y
Y

Y

Y
Y
Y

Y

Mode of acceleration

Y

Y
-

-

-
-
-

-

Estimation Settings

REML

Y

-
-

-

-
-
-

-

FML

Y

Y
Y

Y

Y
Y
Y

Y

PQL

Y

Y
-

-

-
Y
Y

-

 (HGLM)

(HGLM)

LaPlace 6

Y

Y
-

-

-
-
-

-

(HGLM)

(HGLM)

EM Laplace

Y

-
-

-

-
-
-

-

(HGLM)
Adaptive Gaussian Quadrature
Y
Y
-
-
-
-
-
-

Constraint of fixed effects

Y

Y
-

-

-
-
-

-

Heterogeneous sigma^2

Y

-
-

-

-
-
-

-

Plausible values

Y

Y
-

-

-
-
-

-

Multiple imputation

Y

Y
-

-

-
-
-

-

Latent variable regression

Y

Y
-

Y

Y
-
-

-
Design weighting
Y
Y
-
-
-
Y
Y
-

Precision weighting (v-known)

Y

Y
-

-

-
-
-

-

Level-1 deletion variables

Y

Y
Y

-

-
Y
Y

Y
Fix sigma^2 to specified value
Y
Y
Y
Y
Y
Y
Y
Y

Spatial dependence

Y

-
-

-

-
-
-

Y

Hypothesis Testing

Multivariate hypothesis tests

Y

Y
Y

Y

Y
Y
Y

Y

Deviance of models comparison

Y

Y
Y

Y

Y
Y
Y

Y

Test homogeneity of level-1 var

Y

-
-

-

-
-
-

-

Output Settings

No of OLS estimates shown

Y

-
-

-

-
-
-

-

Reduced output

Y

Y
Y

Y

Y
Y
Y

Y

Print variance-covariance matrices

Y

Y
-

Y

Y
-
-

-

Exploratory Analysis (level-2)

Y

Y
-

-

-
-
-

-

Exploratory Analysis (level-3)

-

Y
-

-

-
-
-

-

Graph Equations (model based)

Model graphs

Y

Y
Y

Y

Y
Y
-

-

Level-1 equation graphing

Y

Y
-

-

-
-
-

-

Level-1 residual box-whisker plots

Y

Y
-

-

-
-
-

-

Level-1 residual vspredicted values

Y

Y
-

-

-
-
-

-

Level-2 EB/OLS coefficient confidence intervals

Y

Y
-

-

-
-
-

-

Graph Data

line plots, scatter plots

Y

Y
-

-

-
-
-

-

box-whisker plots

Y

Y
-

-

-
-
-

-

 


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