general structural equation (lisrel) models week 4 #1

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General Structural Equation (LISREL) Models Week 4 #1

Non-normal data: summary of approaches Missing data approaches: summary, review and computer examplesLongitudinal data analysis: lagged dependent variables in LISREL models

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Major approaches:

1. Transform data to normality before using in SEM software

• Can be done with any stats packages• Common transformations: log, sqrt, square

2. ADF (also called WLS [in LISREL] AGLS [EQS]) estimation

• Requires construction of asymptotic covariance matrix

• Requires large Ns

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Major approaches to non-normal data

1. Transform data to normality before using in SEM software2. ADF (also called WLS [in LISREL] AGLS [EQS]) estimation

3. Scaled test statistics (Bentler-Satorra)• also referred to as “robust test statistics”

4. Bootstrapping5. New approaches (Muthen) 6. Polychoric correlations (PM matrix)

• Require asympt. Cov. Matrix• Not suitable for small Ns

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Scaled test statistics

Generate an asymptotic covariance matrix in PRELIS as well as the usual covariance matrix

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Scaled Test Statistics

Added statistics provided when asymptotic covariance matrix specified in LISREL program

Part 2A: ML estimation but scaled chi-square statisticDA NI=14 NO=1456 CM FI=e:\classes\icpsr2004\Week3Examples\nonnormaldata\relmor1.covAC FI=e:\classes\icpsr2004\Week3Examples\nonnormaldata\relmor1.acc

…PROGRAM MATRIX SPECIFICATION LINES

ou me=mll sc nd=3 mi

Degrees of Freedom = 67 Minimum Fit Function Chi-Square = 407.134 (P = 0.0) Normal Theory Weighted Least Squares Chi-Square = 409.627 (P = 0.0) Satorra-Bentler Scaled Chi-Square = 319.088 (P = 0.0) Chi-Square Corrected for Non-Normality = 342.559 (P = 0.0)

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Scaled Test Statistics

Added statistics provided when asymptotic covariance matrix specified in LISREL program

Caution: LISREL manual suggests standard errors are “robust” se’s but in version 8.54, identical to regular ML. Use nested chi-square LR tests if needed

Degrees of Freedom = 67

Minimum Fit Function Chi-Square = 407.134 (P = 0.0)

Normal Theory Weighted Least Squares Chi-Square = 409.627 (P = 0.0)

Satorra-Bentler Scaled Chi-Square = 319.088 (P = 0.0)

Chi-Square Corrected for Non-Normality = 342.559 (P = 0.0)

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Categorical Variable Model

Joreskog: with ordinal variables, “no units of measurement.. Variances and covariances have no meaning.. the only information we have is counts of cases in each cell of a multiway contingency table.

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Bivariate normality: not testable 2x2

Issue: zero cells (skipped)

Too many zero cells: imprecise estimates

Only one non-zero cell in a row or column: estimation breaks down

(in tetrachoric, PRELIS replaces 0 with 0.5; will affect estiamtes)

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Polychoric correlation very robust to violations of underlying bivariate normality

- doctoral dissert. Ana Quiroga, 1992, Upsala)

LR chi-square very sensitive

RMSEA measure:

- no serious effects unless RMSEA >1

(PRELIS will issue warning)

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What if underlying bivariate normality does not hold approximately?

- reduce # of categories

- eliminate offending variables

- assess if conditional on covariates

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Bivariate data patterns not fitting the model

Agr St Agree Neutr Dis Dis St

Agr St 20 10 10 5 0

Agree 10 20 20 10 5

Neut 40 20 20 20 10

Dis 0 5 20 40 10

Dis St 0 5 5 10 20

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Insert if time permits: brief overview of LISREL CVM approach Subdirectory Week4Examples\

OrdinalData

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Bootstrapping

Hasn’t caught on as much as one might have thought

Sample with replacement, repeat B times, get set of values for parameters and observe the distribution across “draws”

Typically, bootstrap N = sample N

(some literature suggestinng m<n might be preferred, but n is standard)

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Bootstrapping

Notes on technique:

Yung and Bentler in Marcoulides and Schumaker, Advanced SEM (text supp.)

+ article in Br. J. Math & Stat Psych. 47: 63-84 1994

Important development: see Bollen and Stine in Long, Testing Structural Equation Models.

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Bootstrapping in AMOS

Under analysis options, Bootstrapping tab

Iterations Method 0 Method 1 Method 2

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 7 0

7 0 99 0

8 0 206 0

9 0 114 0

10 0 46 0

11 0 18 0

12 0 7 0

13 0 2 0

14 0 0 0

15 0 1 0

16 0 0 0

17 0 0 0

18 0 0 0

19 0 0 0

Total 0 500 0

0 bootstrap samples were unused because of a singular covariance matrix.0 bootstrap samples were unused because a solution was not found.500 usable bootstrap samples were obtained.

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|--------------------

207.235 |*

224.559 |

241.883 |*

259.207 |***

276.531 |*******

293.855 |**********

311.179 |*******************

N = 500 328.503 |****************

Mean = 325.643 345.827 |*************

S. e. = 1.627 363.151 |********

380.476 |*****

397.800 |***

415.124 |*

432.448 |

449.772 |*

|--------------------

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Parameter SE SE-SE Mean Bias SE-Bias

Relig <--- V368 .029 .001 .060 -.001 .001

Relig <--- V363 .032 .001 .042 .001 .001

Relig <--- V356 .032 .001 .082 -.002 .001

Relig <--- V355 .028 .001 -.094 -.003 .001

Relig <--- V353 .030 .001 .126 .000 .001

Env2 <--- Relig .044 .001 .131 -.003 .002

Env1 <--- Relig .043 .001 -.084 .002 .002

Env1 <--- V368 .035 .001 -.010 -.002 .002

Env2 <--- V368 .036 .001 .070 .000 .002

Env2 <--- V363 .039 .001 .063 .000 .002

Env1 <--- V363 .038 .001 -.111 .000 .002

Env1 <--- V356 .038 .001 -.145 .002 .002

Env2 <--- V356 .040 .001 .227 -.002 .002

Env1 <--- V355 .034 .001 .005 .001 .002

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Missing Data

The major approaches we discussed last class:EM algorithm to “replace” case values

and estimate Σ, zNearest neighbor imputationFIML

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The “mechanics” of working with missing data in PRELIS/LISREL

Nearest Neighbor:

In PRELIS syntax:IM (V356 SEX ) (V147 V176 V355) VR=.5 XN or XL

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The “mechanics” of working with missing data in PRELIS/LISREL

The “matching variables” should have relatively few missing cases (for a given case, imputation will fail if any of the matching variables is missing). Matching variables may include variables in the “imputed variables” list (though if any of these variables has a large number of missing cases, this would not be a good idea).

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PRELIS imputation

Can save results of imputation in raw data file

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Imputation

It is even possible to then re-run PRELIS and do other imputations. (Although not advised, a variable that has been imputed can now be used as a “matching variable”. It is also possible to make another attempt at imputation for the same variable using different “matching variables”).

(would need to read in raw data file back into PRELIS)

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Sample listing (IM)

SAMPLE listing: Case 13 imputed with value 7 (Variance Ratio = 0.000), NM= 1 Case 14 not imputed because of Variance Ratio = 0.939 (NM= 2) Case 21 not imputed because of missing values for matching variablesNumber of Missing Values per Variable After Imputation

V9 V147 V151 V175 V176 V304 V305 V307 -------- -------- -------- -------- -------- -------- -------- -------- 16 13 54 38 9 21 35 56 V308 V309 V310 V355 V356 SEX OCC1 OCC2 -------- -------- -------- -------- -------- -------- -------- -------- 32 37 36 29 62 13 0 0 OCC3 OCC4 OCC5 -------- -------- -------- 0 0 0Distribution of Missing Values Total Sample Size = 1839 Number of Missing Values 0 1 2 3 4 5 6 7 8 9 Number of Cases 1584 162 50 17 10 5 7 2 1 1

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EM algorithm: PRELIS

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EM algorithm: PRELIS

syntax:

!PRELIS SYNTAX: Can be edited

SY='G:\Missing\USA5.PSF'

SE 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

EM CC = 0.00001 IT = 200

OU MA=CM SM=emcovar1.cov RA=usa6.psf AC=emcovar1.acm XT XM

------------------------------- EM Algoritm for missing Data:

------------------------------- Number of different missing-value patterns= 80

Convergence of EM-algorithm in 4 iterations -2 Ln(L) = 98714.48572

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Multiple Group ApproachAllison Soc. Methods&Res. 1987Bollen, p. 374 (uses old LISREL matrix notation)

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Multiple Group Approach

Note: 13 elements of matrix have “pseudo” values

- 13 df

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Multiple group approach

Disadvantage:

- Works only with a relatively small number of missing patterns

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Other missing data option:FIML estimationLISREL PROGRAM FOR SEXUAL MORALITY AND RELIGIOSITY EXAMPLE DA NI=19 NO=1839 MA=CM RA FI='G:\MISSING\USA1.PSF' -------------------------------- EM Algorithm for missing Data: -------------------------------- Number of different missing-value patterns= 80 Convergence of EM-algorithm in 5 iterations -2 Ln(L) = 98714.48567 Percentage missing values= 1.81 Note: The Covariances and/or Means to be analyzed are estimated by the EM procedure and are only used to obtain starting values for the FIML procedure SE V9 V151 V175 V176 V147 V304 V305 V307 V308 V309 V310 V355 V356 SEX/ MO NY=11 NE=2 LY=FU,FI PS=SY TE=SY BE=FU,FI NX=3 NK=3 LX=ID C PH=SY,FR TD=ZE GA=FU,FR VA 1.0 LY 5 1 LY 8 2 FR LY 1 1 LY 2 1 LY 3 1 LY 4 1 FR LY 11 2 LY 7 2 LY 6 2 LY 9 2 LY 10 2 FR BE 2 1 OU ME=ML MI SC ND=4

LISREL IMPLEMENTATION

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FIML GAMMA

V355 V356 SEX -------- -------- -------- ETA 1 -0.0137 0.0604 0.4172 (0.0024) (0.0202) (0.0828) -5.7192 2.9812 5.0358 ETA 2 -0.0066 0.1583 -0.3198 (0.0025) (0.0215) (0.0871) -2.6128 7.3654 -3.6705 GAMMA -- regular ML, listwise

AGE EDUC SEX -------- -------- -------- ETA 1 -0.0130 0.0732 0.4257 (0.0025) (0.0205) (0.0904) -5.2198 3.5626 4.7098 ETA 2 -0.0076 0.1562 -0.3112 (0.0028) (0.0227) (0.0970) -2.7180 6.8715 -3.2087

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FIML (also referred to as “direct ML”)

Available in AMOS and in LISREL AMOS implementation fairly easy to

use (check off means and intercepts, input data with missing cases and … voila!)

LISREL implementation a bit more difficult: must input raw data from PRELIS into LISREL

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FIML

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FIML

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FIML

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(INSERT PRELIS/LISREL DEMO HERE)

EM covariance matrix Nearest neighbour imputation FIML

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EM algorithm: in SAS

PROC MI

Example: religiosity/morality problem.

/Week4Examples/MissingData/SAS

SASMIProc1.sas

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SAS MI procedure

libname in1 'e:\classes\icpsr2005\Week4Examples\MissingData2\SAS';

data one; set in1.wvssub3a;

proc mi; em outem=in1.cov; var

V9 V151 V175 V176 V147 V304 V305 V307 V308 V309 V310 v355 v356 SEX; run;

proc calis data=in1.cov cov mod;

[calis procedure specifications]

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SAS MI procedure

Data Set WORK.ONE Method MCMC Multiple Imputation Chain Single Chain Initial Estimates for MCMC EM Posterior Mode Start Starting Value Prior Jeffreys Number of Imputations 5 Number of Burn-in Iterations 200 Number of Iterations 100 Seed for random number generator 1254

Missing Data Patterns

Group V9 V151 V175 V176 V147 V304 V305 V307 V308 V309 V310 V355 V356 SEX Freq

1 X X X X X X X X X X X X X X 1456 2 X X X X X X X X X X X X . X 173 3 X X X X X X X X X X X . X X 10

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SAS MI procedure

Missing Data Patterns

Group V9 V151 V175 V176 V147 V304 V305 V307 V308 V309 V310 V355 V356 SEX Freq

4 X X X X X X X X X X X . . X 10 5 X X X X X X X X X X . X X X 5 6 X X X X X X X X X . X X X X 9 7 X X X X X X X X X . X X . X 1 8 X X X X X X X X X . . X X X 2 9 X X X X X X X X . X X X X X 3 10 X X X X X X X X . X . X X X 1 11 X X X X X X X . X X X X X X 13 12 X X X X X X X . X X X X . X 2 13 X X X X X X X . X X X . . X 1 14 X X X X X X X . X X . X X X 3 15 X X X X X X X . X X . . X X 1 16 X X X X X X X . X . X X X X 1 17 X X X X X X X . X . X X . X 1

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SAS MI procedure

Initial Parameter Estimates for EM

_TYPE_ _NAME_ V9 V151 V175 V176 V147

MEAN 1.720790 1.174790 1.414770 8.058470 3.958927


V304 V305 V307 V308 V309 V310 V355

1.876238 2.151885 3.049916 2.395683 4.001110 4.896284 46.792265


V356 SEX

7.775246 0.489396

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SAS MI procedure


_TYPE_ _NAME_ V9 V151 V175 V176 V147

COV V9 0.808388 0 0 0 0 COV V151 0 0.168983 0 0 0 COV V175 0 0 0.483982 0 0 COV V176 0 0 0 6.783348 0 COV V147 0 0 0 0 6.575298

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SAS MI procedure

EM (MLE) Parameter Estimates

_TYPE_ _NAME_ V9 V151 V175 V176 V147

MEAN 1.721840 1.180968 1.420315 8.046136 3.959583 COV V9 0.807215 0.184412 0.307067 -1.599731 1.301326 COV V151 0.184412 0.170271 0.137480 -0.626684 0.454568 COV V175 0.307067 0.137480 0.485803 -1.073616 0.753307 COV V176 -1.599731 -0.626684 -1.073616 6.805023 -3.428576 COV V147 1.301326 0.454568 0.753307 -3.428576 6.567477 COV V304 0.390792 0.165856 0.263160 -1.368173 1.069671 COV V305 0.455902 0.114129 0.249936 -1.353161 0.993579

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SAS PROC mi

Multiple Imputation Variance Information

Relative Fraction -----------------Variance----------------- Increase Missing Variable Between Within Total DF in Variance Information

V9 0.000000239 0.000439 0.000439 1834.4 0.000653 0.000653 V151 0.000002904 0.000092789 0.000096275 1120.1 0.037561 0.036832 V175 0.000002180 0.000264 0.000266 1741.7 0.009913 0.009863 V176 0.000002364 0.003710 0.003713 1834.1 0.000765 0.000764 V147 0.000025982 0.003571 0.003602 1760.1 0.008731 0.008692 V304 0.000002260 0.001621 0.001623 1830.6 0.001674 0.001672 V305 0.000034129 0.001946 0.001987 1509.7 0.021050 0.020824 V307 0.000027451 0.003995 0.004028 1767.2 0.008245 0.008211

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Sas PROC mi

SAS log:115 proc mi; em outem=in1.cov; varNOTE: This is an experimental version of the MI

procedure.116 V9 V151 V175 V176 V147 V304 V305 V307 V308

V309 V310 v355 v356 SEX; run;

NOTE: The data set IN1.COV has 15 observations and 16 variables.

NOTE: PROCEDURE MI used: real time 2.77 seconds cpu time 2.65 seconds

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CALIS (SAS)

proc calis data=in1.cov cov nobs=1836 mod; nobs= not needed if working with raw datalineqs v9 = 1.0 F1 + e1,V175 = b1 F1 + e2,V176 = b2 F1 + e3,V147 = b3 F1 + e4,V304 = 1.0 F2 + e5,V305 = b4 F2 + e6,V307 = b5 F2 + e7,V308 = b6 F2 + e8,V309 = b7 F2 + e9,V310 = b8 F2 + e10,F1 = b9 V355 + b10 V356 + b11 SEX + d1,F2 = b12 V355 + b13 V356 + b14 SEX + d2;stde1-e10 = errvar:, - special convention for more than 1 at a time (generates warning msg.)v355=vv355, v356 = vv356, sex = vsex,d1 = vd1, d2= vd2;covd1 d2 = covD1D2;run;

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SAS - CALIS The CALIS Procedure Covariance Structure Analysis: Maximum Likelihood Estimation

Manifest Variable Equations with Estimates

V9 = 1.0000 F1 + 1.0000 e1 V175 = 0.6232*F1 + 1.0000 e2 Std Err 0.0223 b1 t Value 27.9048 V176 = -3.0284*F1 + 1.0000 e3 Std Err 0.0835 b2 t Value -36.2766 V147 = 2.2839*F1 + 1.0000 e4 Std Err 0.0822 b3 t Value 27.7987 V304 = 1.0000 F2 + 1.0000 e5 V305 = 1.0732*F2 + 1.0000 e6 Std Err 0.0671 b4 t Value 15.9949 V307 = 2.1959*F2 + 1.0000 e7 Std Err 0.1118 b5 t Value 19.6468 V308 = 1.6376*F2 + 1.0000 e8 Std Err 0.0863 b6 t Value 18.9819 V309 = 2.3768*F2 + 1.0000 e9 Std Err 0.1184 b7 t Value 20.0708 V310 = 1.9628*F2 + 1.0000 e10 Std Err 0.1037 b8 t Value 18.9346

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SAS - CALIS

Variances of Exogenous Variables

Standard Variable Parameter Estimate Error t Value

V355 vv355 314.89289 9.85535 31.95 V356 vv356 4.80150 0.14968 32.08 SEX vsex 0.24989 0.00821 30.44 e1 errvar1 0.27695 0.01365 20.29 e2 errvar2 0.27984 0.01049 26.67 e3 errvar3 1.94179 0.11086 17.52 e4 errvar4 3.80162 0.14232 26.71 e5 errvar5 2.20316 0.07811 28.21 e6 errvar6 2.68880 0.09493 28.32 e7 errvar7 3.58210 0.14913 24.02 e8 errvar8 2.60696 0.10216 25.52 e9 errvar9 3.40464 0.15093 22.56 e10 errvar10 3.81099 0.14885 25.60 d1 vd1 0.50262 0.02572 19.54 d2 vd2 0.70781 0.06601 10.72

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SAS - CALIS

Lagrange Multiplier and Wald Test Indices _PHI_ [15:15] Symmetric Matrix Univariate Tests for Constant Constraints Lagrange Multiplier or Wald Index / Probability / Approx Change of Value

V355 V356 SEX e1 e2

V355 1020.8953 0.0000 0.0000 0.1671 9.0161 . 1.0000 1.0000 0.6827 0.0027 . 0.0000 -0.0000 -0.1071 0.6931 [vv355]

V356 0.0000 1029.0776 0.0000 3.6310 0.4885 1.0000 . 1.0000 0.0567 0.4846 0.0000 . 0.0000 0.0610 -0.0197 [vv356]

SEX 0.0000 0.0000 926.8575 1.3898 0.1477 1.0000 1.0000 . 0.2384 0.7007 -0.0000 0.0000 . 0.0089 0.0026 [vsex]

e1 0.1671 3.6310 1.3898 411.7985 29.2403 0.6827 0.0567 0.2384 . 0.0000 -0.1071 0.0610 0.0089 . -0.0528 [errvar1]

e2 9.0161 0.4885 0.1477 29.2403 711.2491 0.0027 0.4846 0.7007 0.0000 . 0.6931 -0.0197 0.0026 -0.0528 . [errvar2]

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A general “what to do when” outline (see handout)

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Longitudinal data

I. Modeling of latent variable mean differences over time

II. More complicated tests (linear growth, quadratic growth, etc.)

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Applications to longitudinal data

Basic model for assessing latent variable mean change: Can run this model

on X or Y side (LISREL)

Equations:

X1 = a1 + 1.0L1 + e1

X2 = a2 + b1 L1 + e2

X3 = a3 + b2 L1 + e3

X4 = a4 + 1.0 L2 + e4

X5 = a5 + b3 L2 + e5

X6 = a6 + b4 L2 + 36

Constraints:

b1=b3 b2=b4 LX=IN

a1=a4 a2=a5 a3=a6 TX=IN

Ka1 = 0 ka2 = (to be estimated)

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Basic model for assessing latent variable mean change:

Can run this model on X or Y side (LISREL)

Equations:

X1 = a1 + 1.0L1 + e1

X2 = a2 + b1 L1 + e2

X3 = a3 + b2 L1 + e3

X4 = a4 + 1.0 L2 + e4

X5 = a5 + b3 L2 + e5

X6 = a6 + b4 L2 + 36

Constraints:

b1=b3 b2=b4 LX=IN

a1=a4 a2=a5 a3=a6 TX=IN

Ka1 = 0 ka2 = (to be estimated)

Correlated errors

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Model for assessing latent variable mean change

Ksi-1

x11

1

x2

1

x3

1

Ksi-2

x4 x5 x61

1 1 1

Ksi-3

x7 x8 x91

1 1 1

Usual parameter constraints:

TX(1)=TX(4)=TX(7)

LISREL: EQ TX 1 TX 4 TX 7

AMOS: same parameter name

0,

Ksi-1

a1

x1

0,

1

1a2

x2

0,

1a3

x3

0,

1

0,

Ksi-2

a1

x4

0,

a2

x5

0,

a3

x6

0,

1

1 1

0,

Ksi-3

a1

x7

0,

a2

x8

0,

a3

x9

0,

1

1 1 1

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Ksi-1

x11

1

x2

1

x3

1

Ksi-2

x4 x5 x61

1 1 1

Ksi-3

x7 x8 x91

1 1 1


TX(1)=TX(4)=TX(7)



KA(1) = 0

KA(2) = mean difference parameter #1


LISREL: KA=FI group 1 KA=FR groups 2,3

IN AMOS:

0,

Ksi-1

a1

x1

0,

1

1a2

x2

0,

1a3

x3

0,

1

kappa1,

Ksi-2

a1

x4

0,

a2

x5

0,

a3

x6

0,

1

1 1

kappa2,

Ksi-3

a1

x7

0,

a2

x8

0,

a3

x9

0,

1

1 1 1

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Ksi-1

x11

1

x2

1

x3

1

Ksi-2

x4 x5 x61

1 1 1

Ksi-3

x7 x8 x91

1 1 1


TX(1)=TX(4)=TX(7)



KA(1) = 0



LISREL: KA=FI group 1 KA=FR groups 2,3

Some tests:

Test for change: H0: ka1=ka2=0

Linear change model: ka2 = 2*ka1

Quadratic change model: ka2 = 4*ka1

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As a causal model:

• Beta 1 “stability coefficient”

Eta-1

1

1 1 1

Eta-2

1

1 1 1

Beta-1 1

• Stability coefficient is high if relative rankings preserved, even if there has been massive change with respect to means

• In model with AL1=0 and AL2=free, can have high Beta2,1 with a) AL(1)=AL(2) or AL(1) massively different from AL(2)

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Causal models:

Ksi-1

Ksi-2 Eta-1

gamma1,1

gamma1,2

Ksi-2 as lagged (time 1) version of eta-1

(could re-specify as an eta variable)

Temporal order in Ksi-1 Eta-1 relationship

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Causal models:

Ksi-1

Ksi-2 Eta-2

ga2,1

Eta-1

ga1,2

1

1

Cross-lagged panel coefficients

[Reduced form of model on next slide]

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Causal models:

Reciprocal effects, using lagged values to achieve model identification

Ksi-1

Ksi-2 Eta-2

Eta-1

1

1

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Causal models:

TV Use

PoliticalTrust

Pol TrustTime 2

gamma 1,1 gamma2,1

Beta 2,1

A variant

Issue: what does ga(1,1) mean given concern over causal direction?

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Lagged and contemporaneous effects

1

1

This model is underidentified

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Lagged and contemporaneous effects

Three wave model with constraints:

a

e f

b

d

c

1

1

a

b

e f

1

1

d

c

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Lagged effects model

ksi-2 eta-1 eta-2

ksi-1

Ksi-1 could be an “event”

1/0 dummy variable

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Lagged effects model

1

1

1

1

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Re-expressing parameters:GROWTH CURVE MODELS

Intercept & linear (& sometimes quadratic) terms

Exogenous variables

Alternative: HLM, subjects as level-2 observations within subjects as level-1

(mixed models: discussed elsewhere)

general structural equation (lisrel) models week 4 #1

Documents

squares chisquare

upsalalr chisquare

data approaches

lisrel modelsmajor approaches

lisrel programcaution

lisrel manual

lisrel programpart

nonnormal datatransform