《结构方程模型第三讲ppt课件》由会员分享，可在线阅读，更多相关《结构方程模型第三讲ppt课件（35页珍藏版）》请在金锄头文库上搜索。
1、构造方程模型及其运用构造方程模型及其运用第三讲第三讲LISREL 及其运用及其运用Wang Shu JiaBusiness School University of Shenzhen26 六月 2021SEM一个引例一个引例Confirm.ls8 located at c:/lisrel/project1/confirm.ls8 Confirmatory factor analysis of hypothetical data for a class example of how to run LISREL to evaluate a measurement model.DAta NInput
2、=9 NObservations=400 MAtrix=CMLADepress1 Frustrt1 Stress1 Depress2 Frustrt2 Depress2 Interact Love QualityKM SY1.00.6 1.00.5 0.6 1.00.7 0.4 0.3 1.00.3 0.4 0.4 0.5 1.00.4 0.3 0.3 0.5 0.6 1.00.4 0.3 0.4 0.3 0.3 0.3 1.00.5 0.3 0.3 0.3 0.4 0.3 0.4 1.00.4 0.4 0.3 0.3 0.2 0.3 0.4 0.5 1.0sd2.4 2.7 1.8 2.5
3、2.6 2.0 5.1 3.8 6.1MODEL NY=6 NX=3 NE=2 NK=1 LY=FU,FI LX=FU,FR CBE=FU,FI GA=FU,FR PH=SY,FR PS=DI,FR, TE=DI,FR CTD=DI,FRLKRel_QualLEStress_1 Stress_2FREE LY(1,1) LY(2,1) LY(3,1) LY(4,2) LY(5,2)FREE LY(6,2) BE(2,1)OUtput SC EF VA MR PC PTSEMLISREL的根本构造的根本构造Comments：Everything on a line that follows ei
4、ther “! or “/* is treated as a program comment:! This line is a comment or/* This line is a comment.Long lines：If you run out of space on a line and want to continue to the next line you can put a c with at least one space in front of it and then continue.This is the first line,it is long so cI can
5、go to a second line.SEM1. Title lineUntil LISREL finds a line starting with DA (for Data), it treats all lines as a title. For key words only the first two letters are read, so DA and DATA or Data are the same.Confirm.ls8 located at c:/lisrel/project1/confirm.ls8Confirmatory factor analysis of hypot
6、hetical data for a class example of how to run LISREL to evaluate a measurement model.SEM2. DAta lineThis line begins with DA OR Data. NGroups = This indicates the number of groupsNInput = Number of input variables (indicators). NObservations = The sample size. MAtrix = CM for covariance matrix, KM
7、for correlations. You can read in a correlation matrix and standard deviations, but if you say MA = CM, LISREL will convert your matrix into a covariance matrix. e.g.DAta NInput=9 NObservations=400 MAtrix=CMSEM3. LAblesYou should label your variables. Here we are referring to indicators and not late
8、nt variables. This is done by putting LA on one line and labels on the following line(s). You are limited to 8 spaces total per label. You do not need labels (LISREL will use VAR1, VAR2, etc.). Labels make reading the output much better.LADepress1 Frustrt1 Stress1 Depress2 Frustrt2 Stress2 Interact
9、Love QualityLA“Dep I “Frust 1 “Stress 1 “Dep 2 “Frust 2 “Stress 2 Interact Love Quality(The quotes are used if there is a space in a label. )SEM4. Entering DataYou can enter raw data, a covariance matrix, a correlation matrix, standard deviations, and means. Here are three examples:If the matrix is
10、large it may be useful to have the matrix in an external file.KM SY FI=c:projectmydata.datRA FI=c:projectmydata.datEX (free format)EX (fixed format)SEM5. Selecting/Rearranging VariablesLISREL allows you to select variables and rearrange them. This is very useful if you are going to run different mod
11、els on the same data. Once you enter the data, you can select/arrange variables however you want. SEDepress1 Frustrt1 Stress1 Interact Love Per Qual /orSE1 2 3 7 8 9 /SEM6. MOdelThe MOdel line is the most complex line in the LISREL program. This describes all of the matrices. This is done by specify
12、ing the numbers that determine their dimensions and then telling LISREL about the matrix (free, fixed, symmetrical, full, etc.). The description of the matrix, e.g.,fixed at some value such as 0 or 1, is selected to describe most of the parameters. Latter, you will tell LISREL those elements that ar
13、e different (e.g., free meaning you want LISREL to estimate them).First, you need to write the eight matrices that fit your figure. For our example, the matrices are:SUMMARY*Cant be changed on Model line but elements can be free.EXAMPLEMODEL NY=6 NX=3 NE=2 NK=1 LY=FU,FI LX=FU,FR CBE=FU,FI GA=FU,FR P
14、H=SY,FR PS=DI,FR,TE=DI,FR CTD=DI,FRORMODEL NY=6 NX=3 NE=2 NK=1 LX=FU,FR BE=FU,FI CPS=DI,FRSEM7. LK and LE linesThese lines provide labels for the latent variables. LKRel_QualLEStress_1 Stress_2SEMThese lines free parameters that should be free but were misspecified as fixed in the MODEL line and the
15、y fix parameters that were misspecified as free in the MODEL line. At the end of the FR and FI lines, Lisrel should be able to reproduce the eight matrices you wrote to describe the figure. In our example we dont have to change LX at all since it is already full and free. We have to free the lambdas
16、 in LY, and the beta in BE. We do not have to change anything from free to fixed.FREE LY(1,1) LY(2,1) LY(3,1) LY(4,2) LY(5,2) LY(6,2)FREE BE(2,1)8. FRee and FIxed linesSEM9. OUputSS -Prints standardized structural model (beta, gamma, psi)SC -Prints standardized output where everything is standardize
17、dEF -Prints total and indirect effects, their standard errors, and their t-values (z-scores). MR -Prints variances and covariances of latent variables with each other and with indicators. This also gives you the residual analysis and Q-plot FS -Prints factor scores regression. PT -This prints some t
18、echnical output such as the value ofthe fitting function for each iteration. PC -Prints correlations of parameter estimates. This can be very long. If you have 200 parameter estimates, this matrix will have 40,000 elements. This can be a useful way of diagnosing identification problems and multicoli
19、nearity. When two parameter estimates are highly correlated, say r .8, then LISREL is having trouble estimating them. ND=x This gives you x decimal places, the default is 2. AD=off This turns off an admissibility check. LISREL stops at 20 iterations if it thinks a matrix is problematic. You should t
20、urn this off if you fix any of the error variances in TE or TD to zero.OUtput SC EF VA MR PC PTConfirm.ls8 located at c:/lisrel/project1/confirm.ls8 Confirmatory factor analysis of hypothetical data for a class example of how to run LISREL to evaluate a measurement model.DAta NInput=9 NObservations=
21、400 MAtrix=CMLADepress1 Frustrt1 Stress1 Depress2 Frustrt2 Depress2 Interact Love QualityKM SY1.00.6 1.00.5 0.6 1.00.7 0.4 0.3 1.00.3 0.4 0.4 0.5 1.00.4 0.3 0.3 0.5 0.6 1.00.4 0.3 0.4 0.3 0.3 0.3 1.00.5 0.3 0.3 0.3 0.4 0.3 0.4 1.00.4 0.4 0.3 0.3 0.2 0.3 0.4 0.5 1.0sd2.4 2.7 1.8 2.5 2.6 2.0 5.1 3.8 6
22、.1MODEL NY=6 NX=3 NE=2 NK=1 LY=FU,FI LX=FU,FR CBE=FU,FI GA=FU,FR PH=SY,FR PS=DI,FR, TE=DI,FR CTD=DI,FRLKRel_QualLEStress_1 Stress_2FREE LY(1,1) LY(2,1) LY(3,1) LY(4,2) LY(5,2)FREE LY(6,2) BE(2,1)OUtput SC EF VA MR PC PTSEMEXAMPLE-Longitudinal ModelsIn this model, Q7 (Quantitative ability at grade 7)
23、and V7(Verbal Ability at grade 7) are latent exogenous variables and we assume they are correlated.丈量模型丈量模型 构造模型 We need to do all eight matrices. This includes(a) LAMBDA-X (loadings for Xi),(b) THETA-DELTA (errors for Xi),(c) LAMBDA-Y (loadings for Yi),(d) THETA-EPSILON (errors for Yi), and(e) PHI
24、(covariance of exogenous KSI variables).for the measurement model, (f) BETA (endogenous ETAs endogenous ETAs),(g) GAMMA (exogenous KSIsendogenous ETAs), and(h) PSI (for ZETAunexplained variance, residuals)SEMSEMSEMProgramSEM2_longitudinal.ls8Verbal and Quantitative Ability In Grades 7 and 9.Model: G
25、A = DI, PS = DI and TD and TE uncorrelatedDA NI=12 NO=383LAMATH7 SCI7 SS7 READ7 SCATV7 SCATQ7 MATH9 SCI9 SS9 READ9 SCATV9 SCATQ9SEMKM sy1.6498 1.6809 .7362 1.6959 .7025 .7570 1.6868 .7120 .7844 .8287 1.7053 .5971 .6433 .6088 .6096 1.7364 .6085 .6528 .6574 .6533 .6953 1.6419 .7339 .7434 .6794 .6995 .
26、5606 .6686 1.6719 .6905 .7462 .7239 .7227 .5994 .7055 .7391 1.6400 .6346 .7178 .7724 .7512 .5693 .6644 .6934 .6927 1.6837 .7155 .7557 .7909 .8830 .5998 .6911 .7355 .7400 .7879 1.6511 .5164 .5445 .5445 .5621 .7145 .7521 .5774 .6065 .5903 .5918 1SD11.4008 9.2213 13.0459 14.6407 11.6230 12.420411.6608
27、11.4428 13.3248 12.1527 11.7089 14.5348SEMATH9 SCATQ9 SCI9 SS9 READ9 SCATV9 MATH7 SCATQ7 SCI7 SS7READ7 SCATV7MO NX=6 NY=6 NK=2 NE=2LEQ9 V9LKQ7 V7FI GA 1 2 GA 2 1FR LY 1 1 LY 3 1 LY 4 1 LY 3 2 LY 4 2 ly 5 2FR LX 1 1 LX 3 1 LX 4 1 LX 3 2 LX 4 2 lx 5 2Value 1.0 ly 2 1 ly 6 2 lx 2 1 lx 6 2Path DiagramOU sc MI ad=offAny Questions?
