lisrel正版软件百度百科 正版软件
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关 键 词：lisrel正版软件百度百科
行 业：IT 软件 教学管理软件
发布时间：2023-02-23
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. Introduction
In practice, many multivariate data sets contain missing values. These missing values may result from nonresponses in a survey, absenteeism of participants in a longitudinal study, etc. The traditional way of dealing
with these missing data values is to use list wise deletion to generate a data set that only contains the complete
data cases. However, list wise deletion may result in a very small data set. It is a well-known fact that most
multivariate statistical methods require a large sample size, especially if the number of observed variables is
large. Consequently, alternative statistical methods for dealing with data with missing values are of interest.
Multiple Imputation (MI) and Full Information Maximum Likelihood (FIML) estimation are two popular
statistical methods for dealing with data with missing values. Both these methods are available in LISREL
(Jöreskog & Sörbom 2003). The Multiple Imputation module of LISREL implements the Expected
Maximization (EM) algorithm and the Markov Chain Monte Carlo (MCMC) method for imputing missing
values in multivariate data sets. Technical details of these methods are available in Schafer (1997) and Du
Toit & Du Toit (2001). Supplementary notes on these methods are also provided by Du Toit & Mels (2002).
In this note, the Multiple Imputation and FIML methods for data with missing values of LISREL are illustrated
by fitting a measurement model to a multivariate data set consisting of the scores of a sample of girls on six
psychological tests. This data set is described in the next section. The measurement model is described in
section 3. Thereafter, the method of Multiple Imputation is used to fit the measurement model to the data set
for girls. In section 5, the measurement model is fitted to the girls’ data by means of the FIML method
• Click on the Next button to load the Survey Design dialog box.
• Click on the Finish button to open the following text editor window for FITCHOL.PR2
These results above indicate that all the factor loadings are statistically significant at a 1% level of
significance. Consequently, the indicators for socio-economical status and attitude towards home seem to
achieve an acceptable level of construct validity.
The estimated regression weights are displayed in the following text editor window.
The results above indicate that socio-economic status is significantly correlated with GPA score, but not
with attitudes towards home and school. Attitude towards home is significantly correlated with GPA
score, but not with attitude towards school. Finally, attitude towards school is not significantly correlated
with GPA score.
The next text editor window contains the estimated error variances and squared multiple correlations for
the structural equations and the reduced form.
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