SEM DVANCED C. MEASUREMENT NVARIANCE · Structural Equation Modeling, 14, 464–504. C26 Sequence...

REX B KLINE CONCORDIA

C. MEASUREMENT INVARIANCE

SEM ADVANCED

1 model

≥ 2 data sets

simultaneous

≥ 2 populations

≥ 2 occasions

≥ 2 methods

unstandardized

constrain to equal

fit to data

fit, yes

conclude, equal

no? release

Levels

Dimensional

Configural

Weak (Metric)

Strong (Scalar)

Strict (Error)

Dimensional

Configural

Same no. factors, match

No other constraints

Different scoring systems

Assumes configural

Equal pattern coefficients

Same scoring system

Strong

Assumes weak

Equal intercepts, thresholds

Same response level

Strict

Assumes strong

Same error variance

Identical measurement

DeShon, R. P. (2004). Measures are not invariant across

groups without error variance homogeneity.

Psychology Science, 46, 137–149.

Wu, A. D., Li, Z., & Zumbo, B. D. (2007). Decoding the

meaning of factorial Invariance and updating the

practice of multi-group confirmatory factor analysis: A

demonstration with TIMSS data. Practical Assessment

Research & Evaluation, 12(3). Retrieved from

http://pareonline.net/pdf/v12n3.pdf

Partial invariance (1)

Configural retained

Weak: Some, not all

DIF (1)

Different relative meaning

Extreme response sets

Ryder, A. G., Yang J., Zhu, X., Yao, S., Yi, J.,

Heine, S. J., & Bagby, R. M. (2009). The

cultural shaping of depression: Somatic

symptoms in China, psychological

symptoms in North America? Journal of

Abnormal Psychology, 117, 300–313.

Cheung, G. W., & Rensvold, R. B. (2000).

Assessing extreme and acquiescence

response sets in cross-cultural research

using structural equations modeling.

Journal of Cross-Cultural Psychology, 31,

187–212.

Configural retained

Strong: Some, not all

DIF (2)

Differential additive response

Cultural, gender, procedural

Gregorich, S. E. (2006). Do self-report

instruments allow meaningful

comparisons across diverse population

groups? Testing measurement invariance

using the confirmatory factor analysis

framework. Medical Care, 44 (Suppl. 3),

S78–S94.

Low power, n < 400

Very large n . . .

CFI, Δ ≤ .01 rule

Chen, F. F. (2007). Sensitivity of goodness of

fit indexes to lack of measurement

invariance. Structural Equation Modeling,

14, 464–504.

Sequence (1)

Configural → Weak → Strong → Strict

Free baseline approach

Model trimming

Sequence (2)

Strict → . . . ?

Constrained baseline approach

Model building

Millsap, R. E., & Olivera-Aguilar, M. (2012).

Investigating measurement invariance

using confirmatory factor analysis. In R. H.

Hoyle (Ed.), Handbook of structural

equation modeling (pp. 380–392). New

York: Guilford.

Example

Hispanic with teenagers

English, n1 = 193

Spanish, n2 = 257

English

Item fc1 fc4 fc5 fc6 fc9 M SD

fc1 — .381 .599 .416 .601 2.280 .887

fc4 .227 — .393 .445 .404 1.518 .925

fc5 .400 .322 — .476 .661 2.052 .972

fc6 .324 .330 .354 — .519 1.689 1.014

fc9 .473 .370 .486 .540 — 1.684 .901

Spanish M 2.113 1.175 1.708 1.366 1.319

SD 1.034 .597 .836 .785 .701

Note. English-speaking (above diagonal; n1 = 193), Spanish-speaking

(below diagonal; n2 = 257).

Conflict 1

Little, T. D., Slegers, D. W., & Card, N. A.

(2006). A non-arbitrary method of

identifying and scaling latent variables in

SEM and MACS models. Structural

Equation Modeling, 13, 59–72.

Reference group

Conflict 1

Marker variable

Conflict 1

Effects coding (1)

Conflict

λ2 λ3 λ4 λ5

1 2 3 4 5 1

1 2 3 4 55

2 1 3 4 55

3 1 2 4 55

4 1 2 3 55

5 1 2 3 45

Effects coding (2)

Conflict

τ1 τ2 τ3 τ4 τ5

1 2 3 4 5 0

1 2 3 4 50

2 1 3 4 50

3 1 2 4 50

4 1 2 3 50

5 1 2 3 40

Models

1. Configural (M1)

2. M1 with error correlation (M2)

3. M2 with full metric (M3)

4. M3 with full error (M4)

5. M3 with full scalar (M5)

6. M3 with partial scalar (M6)

Model 1 (No)

Model 2 (Yes)

Model 3 (Yes)

Model 4 (No)

Model 5 (No)

Model 6 (Yes)

2Mχ dfM

2Dχ dfD Comparison RMSEA 90% CI

1 (N) 15.36 10 — — .049 0 – .095

2 (Y) 7.85 9 7.51** 1 2 vs. 1 0 0 – .068

3 (Y) 13.36 13 5.51* 4 3 vs. 2 .011 0 – .068

4 (N) 87.57** 18 74.21** 5 4 vs. 3 .131 .104 – .159

5 (N) 22.72 17 9.36 4 5 vs. 3 .039 0 – .076

6 (Y) 13.46 15 .10 2 6 vs. 3 0 0 – .057

Model 1

Observations

v (v + 3)/2 × no. groups

5(8)/2 × 2 = 40

Conflict 1

Parameters

Variances: (5 + 1) × 2 = 12

Loadings: (4) × 2 = 8

Intercepts = (4) × 2 = 8

Means = 2

dfM = 40 − 30 = 10

Conflict 1

LISREL “classic”

Sorry, SIMPLIS

Mplus defaults

1st indicator, loading = 1

Other loadings free but equal

Intercepts free but equal

Mplus defaults

Factor, error vars. & covs. free

1st group, factor mean = 0

Otherwise free and unequal

title:

dillman data

model 1

configural invariance

file is dillman.dat;

type is means std corr;

nobservations = 193 257;

ngroups = 2;

! group 1 is english, group 2 is spanish

variable:

names = fc1 fc4 fc5 fc6 fc9;

analysis:

type = general; estimator = ml;

model:

! names group 1 factor loadings:

Conflict by fc1* (g1_load1)

fc4 (g1_load2)

fc5 (g1_load3)

fc6 (g1_load4)

fc9 (g1_load5);

! names group 1 intercepts:

[fc1] (g1_int1);

[fc4] (g1_int2);

[fc5] (g1_int3);

[fc6] (g1_int4);

[fc9] (g1_int5);

model g1:

! group 1 factor mean is free parameter:

[Conflict];

model g2:

! names group 2 factor loadings

! separate loadings estimated in group 2:

fc4 (g2_load2)

fc5 (g2_load3)

fc6 (g2_load4)

fc9 (g2_load5);

! separate intercepts estimated in group 2:

[fc1] (g2_int1);

[fc4] (g2_int2);

[fc5] (g2_int3);

[fc6] (g2_int4);

[fc9] (g2_int5);

! separate factor mean, variance in group 2:

Conflict;

[Conflict];

! by default, measurement errors are freely

! estimated in each group

model constraint:

! effects-coding method for scaling

! and identifying factor

! average loading constrained to 1.0 and

! average intercept constrained to 0

! group 1:

g1_load1 = 5 - g1_load2 - g1_load3 - g1_load4 - g1_load5;

g1_int1 = -g1_int2 - g1_int3 - g1_int4 - g1_int5;

! group 2:

output: samp stdyx res;

THE MODEL ESTIMATION TERMINATED NORMALLY

TESTS OF MODEL FIT

Chi-Square Test of Model Fit

Value 15.363

Degrees of Freedom 10

P-Value 0.1194

Chi-Square Contributions From Each Group

G1 8.991

G2 6.372

CFI/TLI

CFI 0.991

TLI 0.983

RMSEA (Root Mean Square Error Of Approximation)

Estimate 0.049

90 Percent C.I. 0.000 0.095

SRMR (Standardized Root Mean Square Residual)

Value 0.026

RESIDUAL OUTPUT

ESTIMATED MODEL AND RESIDUALS (OBSERVED - ESTIMATED) FOR G1

Model Estimated Covariances/Correlations/Residual Correlations

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

FC1 0.783

FC4 0.312 0.851

FC5 0.499 0.373 0.940

FC6 0.406 0.304 0.487 1.023

FC9 0.477 0.357 0.572 0.465 0.808

Residuals for Covariances/Correlations/Residual Correlations

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

FC1 0.000

FC4 -0.001 0.000

FC5 0.014 -0.022 0.000

FC6 -0.034 0.112 -0.020 0.000

FC9 0.001 -0.022 0.004 0.007 0.000

Standardized Residuals (z-scores) for Covariances/Correlations/Residual Corr

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

FC1 0.014

FC4 -0.019 0.000

FC5 0.843 -0.924 0.000

FC6 -1.365 2.466 -0.941 0.000

FC9 0.055 -1.151 0.396 0.324 999.000

Model Estimated Covariances/Correlations/Residual Correlations

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

FC1 1.065

FC4 0.164 0.355

FC5 0.298 0.141 0.696

FC6 0.293 0.139 0.252 0.614

FC9 0.338 0.160 0.292 0.287 0.489

Residuals for Covariances/Correlations/Residual Correlations

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

FC1 0.000

FC4 -0.024 0.000

FC5 0.047 0.019 0.000

FC6 -0.031 0.015 -0.021 0.000

FC9 0.003 -0.006 -0.008 0.009 0.000

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

FC1 999.000

FC4 -1.011 0.000

FC5 1.548 0.978 0.000

FC6 -1.301 0.884 -1.195 0.000

FC9 0.305 -0.880 -1.265 1.183 0.000

PAGE : 6 EQS

TITLE: Group 1: English

MULTIPLE POPULATION ANALYSIS, INFORMATION IN GROUP 1

MAXIMUM LIKELIHOOD SOLUTION (NORMAL DISTRIBUTION THEORY)

LARGEST STANDARDIZED RESIDUALS:

NO. PARAMETER ESTIMATE NO. PARAMETER ESTIMATE

--- --------- -------- --- --------- --------

1 V4, V2 .119 11 V4, V4 .000

2 V4, V1 -.038 12 V2, V2 .000

3 V5, V2 -.026 13 V3, V3 .000

4 V3, V2 -.024 14 V5, V5 .000

5 V4, V3 -.020 15 V999,V4 .000

6 V3, V1 .017 16 V999,V5 .000

7 V5, V4 .007 17 V999,V2 .000

8 V5, V3 .005 18 V999,V3 .000

9 V5, V1 .001 19 V999,V1 .000

10 V2, V1 -.001 20 V1, V1 .000

PAGE : 12 EQS

TITLE: Group 2: Spanish

--- --------- -------- --- --------- --------

1 V3, V1 .054 11 V5, V5 .000

2 V2, V1 -.039 12 V4, V4 .000

3 V3, V2 .038 13 V3, V3 .000

4 V4, V1 -.038 14 V2, V2 .000

5 V4, V2 .033 15 V999,V5 .000

6 V4, V3 -.032 16 V999,V4 .000

7 V5, V4 .017 17 V999,V3 .000

8 V5, V2 -.015 18 V999,V2 .000

9 V5, V3 -.014 19 V999,V1 .000

10 V5, V1 .005 20 V1, V1 .000

Model 2 (Yes)

title:

dillman-carpentier

model 2

configural invariance with error correlation

group 1, fc4 and fc6

model g1:

! group 1 factor mean is free parameter:

[Conflict];

! ** new to model 2 **

! error covariance group 1 only:

fc4 with fc6;

TESTS OF MODEL FIT

Value 7.850

P-Value 0.5493

G1 1.478

G2 6.372

CFI/TLI

CFI 1.000

TLI 1.004

Estimate 0.000

90 Percent C.I. 0.000 0.068

Value 0.018

RESIDUAL OUTPUT

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

FC1 999.000

FC4 0.487 0.032

FC5 0.617 -0.195 999.000

FC6 -0.847 0.020 -0.321 0.010

FC9 -0.401 -0.257 -0.437 0.916 999.000

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

FC1 999.000

FC4 -1.011 0.000

FC5 1.547 0.977 0.000

FC6 -1.302 0.884 -1.195 999.000

FC9 0.305 -0.880 -1.265 1.184 999.000

PAGE : 6 EQS

--- --------- -------- --- --------- --------

1 V4, V1 -.025 11 V4, V2 .000

2 V5, V4 .021 12 V4, V4 .000

3 V2, V1 .018 13 V3, V3 .000

4 V3, V1 .011 14 V2, V2 .000

5 V4, V3 -.007 15 V1, V1 .000

6 V5, V2 -.006 16 V999,V5 .000

7 V3, V2 -.005 17 V999,V2 .000

8 V5, V1 -.005 18 V999,V4 .000

9 V5, V3 -.003 19 V999,V1 .000

10 V5, V5 .000 20 V999,V3 .000

PAGE : 14 EQS

--- --------- -------- --- --------- --------

1 V3, V1 .054 11 V5, V5 .000

2 V2, V1 -.039 12 V4, V4 .000

3 V3, V2 .038 13 V3, V3 .000

4 V4, V1 -.038 14 V2, V2 .000

5 V4, V2 .033 15 V999,V5 .000

6 V4, V3 -.032 16 V999,V4 .000

7 V5, V4 .017 17 V999,V3 .000

8 V5, V2 -.015 18 V999,V2 .000

9 V5, V3 -.014 19 V999,V1 .000

10 V5, V1 .005 20 V1, V1 .000

Model 3 (Yes)

title:

dillman-carpentier

model 3

metric invariance with error correlation

model g2:

! pairwise equality constraints imposed on loadings

! by commenting out the following group 2 syntax:

! Conflict by fc1* (g2_load1)

! fc4 (g2_load2)

! fc5 (g2_load3)

! fc6 (g2_load4)

! fc9 (g2_load5);

model constraint:

! group 1:

! group 2:

! original group 2 constraint on loadings not needed

! and is commented out:

! g2_load1 = 5 - g2_load2 - g2_load3 - g2_load4 - g2_load5;

TESTS OF MODEL FIT

Value 13.361

P-Value 0.4203

G1 3.617

G2 9.744

CFI/TLI

CFI 0.999

TLI 0.999

Estimate 0.011

90 Percent C.I. 0.000 0.068

Value 0.036

RESIDUAL OUTPUT

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

FC1 999.000

FC4 0.744 0.850

FC5 0.404 1.019 1.079

FC6 -1.764 0.565 0.028 -0.626

FC9 -3.828 0.675 0.839 -0.074 -0.274

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

FC1 1.205

FC4 -0.604 -1.958

FC5 1.356 -0.535 -2.635

FC6 0.402 0.354 -1.347 0.688

FC9 1.511 -1.508 -2.435 1.302 0.228

PAGE : 6 EQS

--- --------- -------- --- --------- --------

1 V4, V1 -.065 11 V4, V2 .021

2 V3, V2 .057 12 V3, V1 .013

3 V1, V1 -.048 13 V5, V5 -.007

4 V3, V3 .047 14 V5, V4 -.003

5 V5, V1 -.043 15 V4, V3 .001

6 V2, V1 .041 16 V999,V5 .000

7 V2, V2 .041 17 V999,V4 .000

8 V5, V2 .035 18 V999,V1 .000

9 V5, V3 .026 19 V999,V2 .000

10 V4, V4 -.023 20 V999,V3 .000

PAGE : 14 EQS

--- --------- -------- --- --------- --------

1 V5, V1 .068 11 V4, V4 .026

2 V3, V1 .067 12 V3, V2 -.022

3 V3, V3 -.057 13 V4, V1 .020

4 V1, V1 .055 14 V4, V2 .016

5 V5, V3 -.055 15 V5, V5 .006

6 V4, V3 -.052 16 V999,V3 .000

7 V5, V2 -.048 17 V999,V2 .000

8 V5, V4 .047 18 V999,V1 .000

9 V2, V2 -.035 19 V999,V4 .000

10 V2, V1 -.031 20 V999,V5 .000

Model 4 (No)

title:

dillman-carpentier

model 4

equality of error variances

model:

! names group 1 factor loadings:

fc4 (g1_load2)

fc5 (g1_load3)

fc6 (g1_load4)

fc9 (g1_load5);

! names group 1 intercepts:

[fc1] (g1_int1);

[fc4] (g1_int2);

[fc5] (g1_int3);

[fc6] (g1_int4);

[fc9] (g1_int5);

! names group 1 error variances:

fc1 (g1_err1);

fc4 (g1_err2);

fc5 (g1_err3);

fc6 (g1_err4);

fc9 (g1_err5);

model g2:

! fc4 (g2_load2)

! fc5 (g2_load3)

! fc6 (g2_load4)

! fc9 (g2_load5);

[fc1] (g2_int1);

[fc4] (g2_int2);

[fc5] (g2_int3);

[fc6] (g2_int4);

[fc9] (g2_int5);

! names group 2 error variances:

fc1 (g2_err1);

fc4 (g2_err2);

fc5 (g2_err3);

fc6 (g2_err4);

fc9 (g2_err5);

model constraint:

! group 1:

! group 2:

! constrain error variances:

g1_err1 = g2_err1;

g1_err2 = g2_err2;

g1_err3 = g2_err3;

g1_err4 = g2_err4;

g1_err5 = g2_err5;

Value 87.571

P-Value 0.0000

G1 42.617

G2 44.954

CFI/TLI

CFI 0.888

TLI 0.876

Estimate 0.131

90 Percent C.I. 0.104 0.159

Value 0.122

Model 3 (Yes)

Model 3 MODEL RESULTS

Two-Tailed

Estimate S.E. Est./S.E. P-Value

Group G1

Residual Variances

FC1 0.355 0.047 7.592 0.000

FC4 0.651 0.069 9.397 0.000

FC5 0.353 0.049 7.207 0.000

FC6 0.648 0.073 8.825 0.000

FC9 0.249 0.041 6.021 0.000

Group G2

Residual Variances

FC1 0.682 0.043 15.807 0.000

FC4 0.808 0.036 22.353 0.000

FC5 0.572 0.047 12.283 0.000

FC6 0.685 0.043 15.951 0.000

FC9 0.384 0.051 7.570 0.000

Model 5 (metric + scalar invariance)

title:

dillman-carpentier

model 5

scalar invariance

model g2:

! fc4 (g2_load2)

! fc5 (g2_load3)

! fc6 (g2_load4)

! fc9 (g2_load5);

! comment out naming of constraints in group 2

! now pairwise constrained to equal group 1 values:

![fc1] (g2_int1);

![fc4] (g2_int2);

![fc5] (g2_int3);

![fc6] (g2_int4);

![fc9] (g2_int5);

model constraint:

! group 1:

! group 2:

! g2_int1 = -g2_int2 - g2_int3 - g2_int4 - g2_int5;

TESTS OF MODEL FIT

Value 22.717

P-Value 0.1587

G1 7.779

G2 14.938

CFI/TLI

CFI 0.991

TLI 0.989

Estimate 0.039

90 Percent C.I. 0.000 0.076

Value 0.046

RESIDUAL OUTPUT

Model Estimated Means/Intercepts/Thresholds

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

2.334 1.410 2.052 1.664 1.676

Residuals for Means/Intercepts/Thresholds

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

-0.054 0.108 0.000 0.025 0.008

Standardized Residuals (z-scores) for Means/Intercepts/Thresholds

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

-2.418 2.272 -0.005 0.603 0.437

Model Estimated Means/Intercepts/Thresholds

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

2.028 1.209 1.708 1.367 1.323

Residuals for Means/Intercepts/Thresholds

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

0.085 -0.034 0.000 -0.001 -0.004

Standardized Residuals (z-scores) for Means/Intercepts/Thresholds

FC1 FC4 FC5 FC6 FC9

________ ________ ________ ________ ________

2.060 -2.788 0.007 -0.040 -0.380

PAGE : 6 EQS

--- --------- -------- --- --------- --------

1 V999,V2 .117 11 V1, V1 -.021

2 V999,V1 -.061 12 V5, V3 .016

3 V4, V1 -.054 13 V5, V5 -.015

4 V3, V3 .042 14 V5, V4 -.015

5 V3, V2 .035 15 V999,V5 .009

6 V2, V1 .032 16 V5, V2 .008

7 V4, V4 -.032 17 V4, V3 -.007

8 V3, V1 .031 18 V2, V2 .006

9 V5, V1 -.027 19 V4, V2 -.001

10 V999,V4 .024 20 V999,V3 .000

PAGE : 14 EQS

--- --------- -------- --- --------- --------

1 V5, V1 .088 11 V5, V4 .047

2 V3, V1 .085 12 V3, V2 -.037

3 V999,V1 .082 13 V4, V1 .034

4 V5, V2 -.068 14 V2, V1 -.031

5 V1, V1 .062 15 V4, V4 .027

6 V2, V2 -.061 16 V5, V5 .007

7 V999,V2 -.056 17 V999,V5 -.006

8 V3, V3 -.052 18 V999,V4 -.001

9 V5, V3 -.050 19 V999,V3 .000

10 V4, V3 -.049 20 V4, V2 .000

Model 6 (metric + partial scalar)

title:

dillman-carpentier

model 6

partial scalar invariance

model g2:

! fc4 (g2_load2)

! fc5 (g2_load3)

! fc6 (g2_load4)

! fc9 (g2_load5);

! comment out naming of constraints in group 2

! for only 3 indicators:

[fc1] (g2_int1);

[fc4] (g2_int2);

![fc5] (g2_int3);

![fc6] (g2_int4);

![fc9] (g2_int5);

TESTS OF MODEL FIT

Value 13.462

P-Value 0.5667

G1 3.808

G2 9.654

CFI/TLI

CFI 1.000

TLI 1.003

Estimate 0.000

90 Percent C.I. 0.000 0.057

Value 0.036

MODEL RESULTS

Two-Tailed

Group G1

CONFLICT BY

FC1 1.062 0.059 18.033 0.000

FC4 0.635 0.055 11.570 0.000

FC5 1.144 0.053 21.441 0.000

FC6 0.988 0.056 17.706 0.000

FC9 1.171 0.049 23.847 0.000

FC4 WITH

FC6 0.139 0.052 2.657 0.008

Group G2

CONFLICT BY

FC1 1.062 0.059 18.033 0.000

FC4 0.635 0.055 11.570 0.000

FC5 1.144 0.053 21.441 0.000

FC6 0.988 0.056 17.706 0.000

FC9 1.171 0.049 23.847 0.000

MODEL RESULTS

Two-Tailed

Group G1

Residual Variances

FC1 0.355 0.047 7.592 0.000

FC4 0.651 0.069 9.395 0.000

FC5 0.354 0.049 7.262 0.000

FC6 0.648 0.073 8.821 0.000

FC9 0.249 0.041 6.040 0.000

Group G2

Residual Variances

FC1 0.741 0.071 10.438 0.000

FC4 0.273 0.026 10.400 0.000

FC5 0.427 0.045 9.429 0.000

FC6 0.370 0.038 9.666 0.000

FC9 0.165 0.027 6.173 0.000

MODEL RESULTS

Two-Tailed

Group G1

CONFLICT 1.843 0.051 36.087 0.000

Intercepts

FC1 0.323 0.115 2.797 0.005

FC4 0.346 0.113 3.062 0.002

FC5 -0.051 0.095 -0.531 0.595

FC6 -0.144 0.097 -1.476 0.140

FC9 -0.474 0.087 -5.427 0.000

Group G2

CONFLICT 1.532 0.039 39.431 0.000

Intercepts

FC1 0.486 0.105 4.641 0.000

FC4 0.202 0.089 2.263 0.024

FC5 -0.051 0.095 -0.531 0.595

FC6 -0.144 0.097 -1.476 0.140

FC9 -0.474 0.087 -5.427 0.000

MODEL RESULTS

Two-Tailed

Group G1

CONFLICT 1.843 0.051 36.087 0.000

Variances

CONFLICT 0.412 0.051 8.151 0.000

Group G2

CONFLICT 1.532 0.039 39.431 0.000

Variances

CONFLICT 0.235 0.027 8.759 0.000

1.843 1.532 .311.48

.642.412

1.843 1.532 .311.64

.484.235

full metric

partial scalar (3/5)

error, none tally

SEM DVANCED C. MEASUREMENT NVARIANCE · Structural Equation Modeling, 14, 464–504. C26 Sequence...

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