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During the last forty-five years, the LISREL model, methods and software have become synonymous with structural equation modeling (SEM). Today, however, LISREL is no longer limited to SEM. LISREL 10 includes the -bit statistical applications LISREL, PRELIS, MULTILEV, SURVEYGLIM and MAPGLIM.
LISREL不仅能处理结构方程模型,还能用于其他统计应用中:
LISREL:结构方程模型
PRELIS:数据处理与基本统计分析
MULTILEV:分层线性和非线性建模
SURVEYGLIM:广义线性模型
MAPGLIM:多级数据的广义线性建模
LISREL可以用来处理
标准结构方程模型
多级结构方程模型
处理数据类型包括
分类和连续变量的完整和不完整的复杂调查数据
分类变量和连续变量的完全不完全随机样本数据
RELIS
数据处理
数据转换
数据生成
计算矩阵
计算样本矩的渐近协方差矩阵
归责的匹配
多重估算
多元线性回归分析
Logistic回归
单变量多元删失回归
The term "ordinal" is applied to variables where the response measure of interest is measured in a series of ordered categories. Examples of such variables include Likert scales and psychiatric ratings of severity. Nominal and ordinal outcome models can be seen as generalizations of the binary outcome model. The ordinal model becomes important when the outcome variable is not dichotomous, or not truly continuous. If an ordinal outcome is analyzed within a continuous model, such a model can yield predicted values outside the range of the ordinal variable. As with binary data, some transformation or link function becomes necessary to prevent this from happening. The continuous model can also yield correlated residuals and regressors when applied to ordinal outcomes because the continuous model does not take the ceiling and floor effects of the ordinal outcome into account. This can then result in biased estimates of regression coefficients, and is most critical when the ordinal variable in question is highly skewed.
Note that the –9.000 entries represent missing values.
The Measurement Model
We consider data with missing values on six psychological tests. These six psychological tests are
theoretically constructed to measure the visual perception and verbal ability of seventh- and eighth-grade
children. A path diagram for the corresponding measurement model for verbal perception and verbal ability is
shown in Figure 1.
Figure 1: A path diagram for a measurement model of Visual Perception and Verbal
Ability
These results indicate that socio-economic status, attitudes towards school and GPA score explains
approximately 21 percent of the variation in self-esteem around peers while approximately 31 percent of
the variation in work ethics is accounted for by socio-economic status, attitudes towards school and GPA
score and self-esteem around peers.
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