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Regression analysis: a constructive critique

By: Material type: TextTextSeries: Advanced quantitative techniques in the social sciences series, Vol. 11Publication details: 2004 Sage Publications Thousand OaksDescription: xix, 259 pISBN:
  • 9780761929048
Subject(s): DDC classification:
  • 519.536 B3R3
Summary: Regression is often applied to questions for which it is ill equipped to answer. As a formal matter, conventional regression analysis does nothing more than produce from a data set a collection of conditional means and conditional variances. The problem, though, is that researchers typically want more: they want tests, confidence intervals and the ability to make causal claims. However, these capabilities require information external to that data themselves, and too often that information makes implausible demands on how nature is supposed to function. Convenience samples are treated as if they are random samples. Causal status is given to predictors that cannot be manipulated. Disturbance terms are assumed to behave not as nature might produce them, but as required by the model. (http://www.sagepub.com/books/Book226138?siteId=sage-us&prodTypes=Books&q=9780761929048&pageTitle=productsSearch)
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Item type Current library Item location Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 28-B / Slot 1426 (0 Floor, East Wing) General Stacks 519.536 B3R3 (Browse shelf(Opens below)) Available 174828

Regression is often applied to questions for which it is ill equipped to answer. As a formal matter, conventional regression analysis does nothing more than produce from a data set a collection of conditional means and conditional variances. The problem, though, is that researchers typically want more: they want tests, confidence intervals and the ability to make causal claims. However, these capabilities require information external to that data themselves, and too often that information makes implausible demands on how nature is supposed to function. Convenience samples are treated as if they are random samples. Causal status is given to predictors that cannot be manipulated. Disturbance terms are assumed to behave not as nature might produce them, but as required by the model. (http://www.sagepub.com/books/Book226138?siteId=sage-us&prodTypes=Books&q=9780761929048&pageTitle=productsSearch)

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