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The logic of causal order / James A. Davis.

By: Series: Sage university papers series. Quantitative applications in the social sciences ; ; no. 07-055.Publication details: Beverly Hills : Sage Publications, c1985.Description: 72 p. : ill. ; 22 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 0803925530 :
Subject(s): LOC classification:
  • HA29 .D335 1985
Summary: Social scientists routinely draw conclusions about cause and effect from their data. This book spells out the pre-statistical assumptions of multivariate research and explains in nonmathematical terms: the concepts of causal direction and system order; direct, indirect, and spurious statistical effects; signs and the sign rule; rules for introducing control variables, elaboration and explanation, "effects analysis," and path analysis. The book is not statistical in the sense of developing specific statistical tools. Rather, it explains the prestatistical assumptions required, whatever the technique. The importance of substantive knowledge about the " real world" is stressed, and the myth that causal problems can be solved by statistical calculations alone is repeatedly challenged.
Holdings
Item type Current library Home library Shelving location Call number Materials specified Status Barcode
Books Books American University in Dubai American University in Dubai Main Collection HA 29 .D335 1985 (Browse shelf(Opens below)) Copy Type:01 - Books Available 631713

Bibliography: p. 71.

Social scientists routinely draw conclusions about cause and effect from their data. This book spells out the pre-statistical assumptions of multivariate research and explains in nonmathematical terms: the concepts of causal direction and system order; direct, indirect, and spurious statistical effects; signs and the sign rule; rules for introducing control variables, elaboration and explanation, "effects analysis," and path analysis. The book is not statistical in the sense of developing specific statistical tools. Rather, it explains the prestatistical assumptions required, whatever the technique. The importance of substantive knowledge about the " real world" is stressed, and the myth that causal problems can be solved by statistical calculations alone is repeatedly challenged.

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