Estimating True Changes when Categorical Panel Data are Affected by Uncorrelated and Correlated Classification Errors: An Application to Unemployment Data
Conclusions about changes in categorical characteristics based on observed panel data can be incorrect when (even a small amount of) measurement error is present. Random measurement errors, referred to as independent classification errors, usually lead to over-estimation of the total amount of gross...
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| Veröffentlicht in: | Sociological methods & research 2000-11, Vol.29 (2), p.230-268 |
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| creator | BASSI, FRANCESCA HAGENAARS, JACQUES A. CROON, MARCEL A. VERMUNT, JEROEN K. |
| description | Conclusions about changes in categorical characteristics based on observed panel data can be incorrect when (even a small amount of) measurement error is present. Random measurement errors, referred to as independent classification errors, usually lead to over-estimation of the total amount of gross change, whereas systematic, correlated errors usually cause underestimation of the transitions. Furthermore, the patterns of true change may be seriously distorted by independent or systematic classification errors. Latent class models and directed log-linear analysis are excellent tools to correct for both independent and correlated measurement errors. An extensive example on labor market states taken from the Survey of Income and Program Participation panel is presented. |
| doi_str_mv | 10.1177/0049124100029002003 |
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| source | Sociological Abstracts; Periodicals Index Online; Applied Social Sciences Index & Abstracts (ASSIA); SAGE Complete A-Z List |
| subjects | Categorical Data Classification Correlation Error of Measurement Errors Estimation History, theory and methodology Labor Market Latent Structure Analysis Loglinear Analysis Longitudinal Studies Methodology Panel Data Panel studies Research methods Social change Sociology Statistical analysis Surveys Unemployment |
| title | Estimating True Changes when Categorical Panel Data are Affected by Uncorrelated and Correlated Classification Errors: An Application to Unemployment Data |
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