Modelling causal error structures in longitudinal data /
The purpose of the present research was to investigate how
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| Format: | Thesis Book |
| Language: | English |
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[Place of publication not identified] :
[publisher not identified] ;
1997.
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| Online Access: | http://proxy.library.tamu.edu/login?url=http://proquest.umi.com/pqdweb?did=739891601&sid=1&Fmt=2&clientId=2945&RQT=309&VName=PQD |
| Summary: | The purpose of the present research was to investigate how well moving average and autoregressive-moving average models that specify correlated measurement errors fit longitudinal data compared to autoregressive, quasi-simplex and one-factor models. Sufficient evidence is available from theoretical and applied studies to encourage testing for stochastic models other than the predominantly used quasi-simplex models. Three criteria for successful detection of underlying structures were supported: fit, propriety, and parsimony. Sometimes fit indices alone were sufficient in ruling out models as representative of dynamic occurring in a given data set. Yet, often more than one model fit a given data well. In such cases the propriety of the solution estimated for the models first required examination. Improper solutions were recognized when fit indices or estimated parameters were out of bounds. When neither fit nor propriety were in question, competing models were eliminated as contenders on the basis of parsimony. Conclusions concerning model fit included the following: (1) when testing for one stochastic process hypothesized to be present in a longitudinal data set, testing for other processes as well prevents erroneous conclusions, because incorrect models often fit other processes well enough to be deceiving; (2) when measurement error correlations are relatively high, the fit of other models, particularly the quasi-simplex and the autoregressive model, should first be compared to the fit of a moving average model; (3) analysis of simulation data disclosed that the moving average model exhibits measurement error correlations when fit to some autoregressive data sets, although the moving average model fit indices are expected to be lower than those for an autoregressive model; and (4) the one-factor model may be a legitimate alternative to stochastic models and the assumption longitudinal data sets always contain measurement error correlations is false. In summary, when evaluating longitudinal data, all five models should be considered and compared. |
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| Item Description: | Vita. "Major Subject: Educational Psychology". |
| Physical Description: | xiv, 150 leaves ; 28 cm. Issued also on microfiche from University Microfilms Inc. |
| Bibliography: | Includes bibliographical references: pages 121-126. |