Testing non-nested structural equation models
Psychological Methods 21 (2016) 151-163 In this paper, we apply Vuong's (1989) likelihood ratio tests of non-nested models to the comparison of non-nested structural equation models. Similar tests have been previously applied in SEM contexts (especially to mixture models), though the non-standa...
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| creator | Merkle, Edgar C You, Dongjun Preacher, Kristopher J |
| description | Psychological Methods 21 (2016) 151-163 In this paper, we apply Vuong's (1989) likelihood ratio tests of non-nested
models to the comparison of non-nested structural equation models. Similar
tests have been previously applied in SEM contexts (especially to mixture
models), though the non-standard output required to conduct the tests has
limited their previous use and study. We review the theory underlying the tests
and show how they can be used to construct interval estimates for differences
in non-nested information criteria. Through both simulation and application, we
then study the tests' performance in non-mixture SEMs and describe their
general implementation via free R packages. The tests offer researchers a
useful tool for non-nested SEM comparison, with barriers to test implementation
now removed. |
| doi_str_mv | 10.48550/arxiv.1402.6720 |
| format | Article |
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models to the comparison of non-nested structural equation models. Similar
tests have been previously applied in SEM contexts (especially to mixture
models), though the non-standard output required to conduct the tests has
limited their previous use and study. We review the theory underlying the tests
and show how they can be used to construct interval estimates for differences
in non-nested information criteria. Through both simulation and application, we
then study the tests' performance in non-mixture SEMs and describe their
general implementation via free R packages. The tests offer researchers a
useful tool for non-nested SEM comparison, with barriers to test implementation
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models to the comparison of non-nested structural equation models. Similar
tests have been previously applied in SEM contexts (especially to mixture
models), though the non-standard output required to conduct the tests has
limited their previous use and study. We review the theory underlying the tests
and show how they can be used to construct interval estimates for differences
in non-nested information criteria. Through both simulation and application, we
then study the tests' performance in non-mixture SEMs and describe their
general implementation via free R packages. The tests offer researchers a
useful tool for non-nested SEM comparison, with barriers to test implementation
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models to the comparison of non-nested structural equation models. Similar
tests have been previously applied in SEM contexts (especially to mixture
models), though the non-standard output required to conduct the tests has
limited their previous use and study. We review the theory underlying the tests
and show how they can be used to construct interval estimates for differences
in non-nested information criteria. Through both simulation and application, we
then study the tests' performance in non-mixture SEMs and describe their
general implementation via free R packages. The tests offer researchers a
useful tool for non-nested SEM comparison, with barriers to test implementation
now removed.</abstract><doi>10.48550/arxiv.1402.6720</doi><oa>free_for_read</oa></addata></record> |
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| title | Testing non-nested structural equation models |
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