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Issues in structural equation modeling
Hans BaumgartnerPenn State University
Issues in structural equation modeling
Common problems Incomplete information
2 statistic and degrees of freedom Misinterpretation of overall model fit
Covariance fit vs. variance fit Reflective vs. formative indicators Discriminant validity Measurement model vs. latent variable model Questionable model modification
Size of MI vs. conceptual meaningfulness Correlated errors in equations vs. directed paths
Issues in structural equation modeling
Common problems Incomplete information
2 statistic and degrees of freedom Misinterpretation of overall model fit
Covariance fit vs. variance fit Reflective vs. formative indicators Discriminant validity Measurement model vs. latent variable model Questionable model modification
Size of MI vs. conceptual meaningfulness Correlated errors in equations vs. directed paths
Issues in structural equation modeling
Misinterpretation of overall model fit
Baumgartner and Homburg (1996) showed:□ the median number of degrees of freedom in type III
models was 49 (28, 124);□ The median percentage contribution of the
measurement model to the total number of degrees of freedom was 93 (81, 97);
□ the percentage of type III models for which R2 for structural equations was reported was 45;
Issues in structural equation modeling
Common problems Incomplete information
2 statistic and degrees of freedom Misinterpretation of overall model fit
Covariance fit vs. variance fit Reflective vs. formative indicators Discriminant validity Measurement model vs. latent variable model Questionable model modification
Size of MI vs. conceptual meaningfulness Correlated errors in equations vs. directed paths
Issues in structural equation modeling
Common problems Incomplete information
2 statistic and degrees of freedom Misinterpretation of overall model fit
Covariance fit vs. variance fit Reflective vs. formative indicators Discriminant validity Measurement model vs. latent variable model Questionable model modification
Size of MI vs. conceptual meaningfulness Correlated errors in equations vs. directed paths
Issues in structural equation modeling
21
.8011
.71 .74 .64 .75 .75 .78 .70 .76
AVE ( 1 ) = .51 AVE ( 2 ) = .56
Discriminant validity
Issues in structural equation modeling
Common problems Incomplete information
2 statistic and degrees of freedom Misinterpretation of overall model fit
Covariance fit vs. variance fit Reflective vs. formative indicators Discriminant validity Measurement model vs. latent variable model Questionable model modification
Size of MI vs. conceptual meaningfulness Correlated errors in equations vs. directed paths
Issues in structural equation modeling
Desires
Perf
Exp
Descon
Expdis
SQ
Sat
Measurement model:2(38)=45.16RMSEA=.026SRMR=.016CFI=1.00TLI=1.00
Latent variable model:2(49)=151.55RMSEA=.088SRMR=.09CFI=.96TLI=.95
Desires Perf Exp Descon Expdis SQ Sat
Issues in structural equation modeling
Desires
Perf
Exp
Descon
Expdis
SQ
Sat
Measurement model:2(38)=45.16RMSEA=.026SRMR=.016CFI=1.00TLI=1.00
Latent variable model:2(49)=151.55RMSEA=.088SRMR=.09CFI=.96TLI=.95
Desires Perf Exp Descon Expdis SQ Sat
Issues in structural equation modeling
Common problems Incomplete information
2 statistic and degrees of freedom Misinterpretation of overall model fit
Covariance fit vs. variance fit Reflective vs. formative indicators Discriminant validity Measurement model vs. latent variable model Questionable model modification
Size of MI vs. conceptual meaningfulness Correlated errors in equations vs. directed paths
Issues in structural equation modeling
Desires
Perf
Exp
Descon
Expdis
SQ
Sat
?
Issues in structural equation modeling
Common problems (cont’d) Baron & Kenny and SEM Pooling data from multiple samples Assessment of measurement invariance
Issues in structural equation modeling
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2
1
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Mediation
Issues in structural equation modeling
Common problems (cont’d)
Baron & Kenny and SEM Pooling data from multiple samples Assessment of measurement invariance
Issues in structural equation modeling
Common problems (cont’d)
Baron & Kenny and SEM Pooling data from multiple samples Assessment of measurement invariance
