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# Multiple Sample Models - PowerPoint PPT Presentation

Multiple Sample Models. James G. Anderson, Ph.D. Purdue University. Rationale of Multiple Sample SEMs. Do estimates of model parameters vary across groups? Another way of asking this question is: Does group membership moderate the relationships specified in the model?.

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### Multiple Sample Models

James G. Anderson, Ph.D.

Purdue University

Rationale of Multiple Sample SEMs

• Do estimates of model parameters vary across groups?

• Another way of asking this question is: Does group membership moderate the relationships specified in the model?

• Use for analysis of cross-sectional, longitudinal, experimental and quasi-experimental data and to test for measurement variance.

• This procedure allows the investigator to:

• Estimate separately the parameters for multiple samples

• Test whether specified parameters are equivalent across these groups.

• Test whether there are group mean differences for the indicator variables and/or for the structural equations

• Estimate the parameters of the model with no constraints (i.e., allow the parameters to differ among groups)

• Compute chi-square as a measure of fit.

• Re-estimate the parameters of the model after imposing cross-group equality constraints on parameters

• Determine the chi square difference is significant

• If the relative fit of the constrained model is significantly worse than that of the unconstrained model, then individual path coefficients should be compared across samples.

Lyman, DR., Moff, HT, Stouthamer-Loeber,M. (1993). “Explaining the Relation Between IQ and Delinquency: Class, Race, Test Motivation or Self-Control.” Journal of Abnormal Psychology, 102, 187-196.

• Covariance matrices for White (n=214) and African American (n=181) male adolescents

• Total observations: n=395

• Degrees of Freedom: 2 * 5(6) = 30

2

• 7 parameters constrained to be equal

• Variances and covariances allowed to vary between groups.

• Χ2 = 11.68

df = 7

NS

• Χ2/df =1.67

• NFI = 0.96

• NNFI = 0.95

• MI values estimate the amount by which the overall chi square value would decrease if the associated parameters were estimated separately in each group.

• Statistical significance of a modification index indicates a group difference on that parameter

• For example, there is a statistically significant difference on the Achievement to Delinquency path and the Social Class to Achievement path.

• Path coefficients were estimated separately for each sample

• Standardized values can only be used for comparisons within a group.

• Unstandardized values are used for comparisons between or across groups.

• In both samples, Verbal Ability has a significant effect on Achievement.

• Verbal Ability is the only significant predictor of Delinquency in the White sample.

• Achievement is the only significant predictor of Delinquency in the African-American sample.

• Conclusion: Among male adolescents, school has a larger role in preventing the development of delinquency for African-Americans that for Whites

• Test for measurement invariance, whether a set of indicators assesses the same latent variables in different groups.

• Examine a test for construct bias, whether a test measures something different in one group than in another.

• Estimate the parameters of the model with no constraints (i.e., allow the factor loadings and error variances to differ among groups).

• Compute chi square as a measure of fit.

• Re-estimate the parameters of the model after imposing cross-group equality constraints parameters

• Determine if the chi square difference is significant

• If the relative fit of the constrained model is significantly worse than that of the unconstrained model, then individual factor loadings should be compared across samples to determine the extent of partial measurement invariance..

Werts, CE, Rock, DA, Linn, RL and Joreskog, KG. (1976). “A Comparison of Correlations, Variances, Covariances and Regression Weights With or Without Measurement Errors.” Psychology Bulletin, 83, 52-56.

• Covariance matrices are for two samples (n1=865 and n2=900) of candidates who took the SAT in January 1971.

• Total observations: n=1765

• Degrees of Freedom: 2 * 4(5) = 20

2

• The error variances differ between the two groups.

• Parameters for the two groups

• Factor Correlations Equal

• Error Variances Equal

Model Fit

Chi Square = 34.89

df= 11

p < 0.00011

• Parameters for the two groups

• Factor Correlations Equal

• Error Variances Equal

Model Fit

Chi Square = 29.67

df= 7

p < 0.00011

• Parameters for the two groups

• Factor Correlations Equal

• Error Variances Unequal

Model Fit

Chi Square = 4.03

df= 11

p < 0.26

Chi Square difference = 29-67-4.03 =25.03

df difference = 7-3=4

• Parameters for the two groups

• Factor Correlations Equal

• Error Variances Unequal

Model Fit

Chi Square = 10.87

df= 7

p < 0.14

Chi Square difference = 34.89-10.87 =24.01

df difference = 11-7=4