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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 examples Longitudinal data analysis: lagged dependent variables in LISREL models. Major approaches:.

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general structural equation lisrel models week 4 1

General Structural Equation (LISREL) Models Week 4 #1

Non-normal data: summary of approaches

Missing data approaches: summary, review and computer examples

Longitudinal data analysis: lagged dependent variables in LISREL models

major approaches
Major approaches:
  • Transform data to normality before using in SEM software
    • Can be done with any stats packages
    • Common transformations: log, sqrt, square
  • ADF (also called WLS [in LISREL] AGLS [EQS]) estimation
    • Requires construction of asymptotic covariance matrix
    • Requires large Ns
major approaches to non normal data
Major approaches to non-normal data
  • Transform data to normality before using in SEM software
  • ADF (also called WLS [in LISREL] AGLS [EQS]) estimation
  • Scaled test statistics (Bentler-Satorra)
    • also referred to as “robust test statistics”
  • Bootstrapping
  • New approaches (Muthen)
  • Polychoric correlations (PM matrix)
    • Require asympt. Cov. Matrix
    • Not suitable for small Ns
scaled test statistics
Scaled test statistics
  • Generate an asymptotic covariance matrix in PRELIS as well as the usual covariance matrix
scaled test statistics1
Scaled Test Statistics

Added statistics provided when asymptotic covariance matrix specified in LISREL program

Part 2A: ML estimation but scaled chi-square statistic

DA NI=14 NO=1456

CM FI=e:\classes\icpsr2004\Week3Examples\nonnormaldata\relmor1.cov

AC 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)

scaled test statistics2
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)

categorical variable model
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.

categorical variable model6
Categorical Variable Model

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)

categorical variable model7
Categorical Variable Model

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)

categorical variable model8
Categorical Variable Model

What if underlying bivariate normality does not hold approximately?

- reduce # of categories

- eliminate offending variables

- assess if conditional on covariates

insert if time permits brief overview of lisrel cvm approach
Insert if time permits: brief overview of LISREL CVM approach
  • Subdirectory Week4Examples\OrdinalData
bootstrapping
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)

bootstrapping1
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.

bootstrapping in amos
Bootstrapping in AMOS
  • Under analysis options, Bootstrapping tab

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.

missing data
Missing Data
  • The major approaches we discussed last class:
    • EM algorithm to “replace” case values and estimate Σ, z
    • Nearest neighbor imputation
    • FIML
the mechanics of working with missing data in prelis lisrel
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

the mechanics of working with missing data in prelis lisrel1
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).

prelis imputation
PRELIS imputation

Can save results of imputation in raw data file

imputation
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)

sample listing im
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 variables

Number 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 0

Distribution 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

em algorithm prelis1
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

multiple group approach
Multiple Group Approach

Allison Soc. Methods&Res. 1987

Bollen, p. 374 (uses old LISREL matrix notation)

multiple group approach1
Multiple Group Approach

Note: 13 elements of matrix have “pseudo” values - 13 df

multiple group approach2
Multiple group approach

Disadvantage:

- Works only with a relatively small number of missing patterns

other missing data option fiml estimation
Other missing data option:FIML estimation

LISREL 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

slide35
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

fiml also referred to as direct ml
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
insert prelis lisrel demo here
(INSERT PRELIS/LISREL DEMO HERE)
  • EM covariance matrix
  • Nearest neighbour imputation
  • FIML
em algorithm in sas
EM algorithm: in SAS
  • PROC MI

Example: religiosity/morality problem.

/Week4Examples/MissingData/SAS

SASMIProc1.sas

sas mi procedure
SAS MI procedure

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

data one; set in1.wvssub3a;

procmi; em outem=in1.cov; var

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

proccalis data=in1.cov cov mod;

[calis procedure specifications]

sas mi procedure1
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

sas mi procedure2
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

sas mi procedure3
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

Initial Parameter Estimates for EM

V304 V305 V307 V308 V309 V310 V355

1.876238 2.151885 3.049916 2.395683 4.001110 4.896284 46.792265

Initial Parameter Estimates for EM

V356 SEX

7.775246 0.489396

sas mi procedure4
SAS MI procedure

Initial Parameter Estimates for EM

_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

sas mi procedure5
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

sas proc mi
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

sas proc mi1
Sas PROC mi

SAS log:

115 proc mi; em outem=in1.cov; var

NOTE: 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

calis sas
CALIS (SAS)

proccalis data=in1.cov cov nobs=1836 mod;  nobs= not needed if working with raw data

lineqs

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;

std

e1-e10 = errvar:, - special convention for more than 1 at a time (generates warning msg.)

v355=vv355, v356 = vv356, sex = vsex,

d1 = vd1, d2= vd2;

cov

d1 d2 = covD1D2;

run;

sas calis
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

sas calis1
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

sas calis2
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]

longitudinal data
Longitudinal data
  • Modeling of latent variable mean differences over time
  • More complicated tests (linear growth, quadratic growth, etc.)
applications to longitudinal data
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)

applications to longitudinal data1
Applications to longitudinal data

Basic model for assessing latent variable mean change:

Constraints:

b1=b3 b2=b4 LX=IN

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

Ka1 = 0 ka2 = (to be estimated)

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

Correlated errors

applications to longitudinal data2
Applications to longitudinal data

Model for assessing latent variable mean change

Usual parameter constraints:

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

LISREL: EQ TX 1 TX 4 TX 7

AMOS: same parameter name

applications to longitudinal data3
Applications to longitudinal data

Model for assessing latent variable mean change

Usual parameter constraints:

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

LISREL: EQ TX 1 TX 4 TX 7

AMOS: same parameter name

KA(1) = 0

KA(2) = mean difference parameter #1

KA(3) = mean difference parameter #2

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

IN AMOS:

applications to longitudinal data4
Applications to longitudinal data

Model for assessing latent variable mean change

Usual parameter constraints:

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

LISREL: EQ TX 1 TX 4 TX 7

AMOS: same parameter name

KA(1) = 0

KA(2) = mean difference parameter #1

KA(3) = mean difference parameter #2

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

as a causal model
As a causal model:
  • Beta 1 “stability coefficient”
  • 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)
causal models
Causal models:

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

causal models1
Causal models:

Cross-lagged panel coefficients

[Reduced form of model on next slide]

causal models2
Causal models:

Reciprocal effects, using lagged values to achieve model identification

causal models3
Causal models:

A variant

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

lagged and contemporaneous effects
Lagged and contemporaneous effects

This model is underidentified

lagged and contemporaneous effects1
Lagged and contemporaneous effects

Three wave model with constraints:

lagged effects model
Lagged effects model

Ksi-1 could be an “event”

1/0 dummy variable

re expressing parameters growth curve models
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)