# I. The Definition of Causation Cause (Part I) - Elaboration II. The Statistical Elaboration Model III. Non-quantitative Statistical Example IV. Quantitative.

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I. The Definition of Causation Cause (Part I) - Elaboration II. The Statistical Elaboration Model III. Non-quantitative Statistical Example IV. Quantitative Statistical Example Topics

I. The Definition of Causation A. Co-variation B. Over a valid time frame C. Of a non-spurious nature D. That is grounded in theory - Four Characteristics Cause (Part I) - Elaboration

II. The Statistical Elaboration Model A. Elimination B. Specification 1. Antecedent 2. Intervening Spuriousness X Y Z XY Z e.g. the effect of fire size (Z) on the relationship between # of firemen (X) and damage (Y) e.g. the effect of education (Z) on the relationship between age (X) and income (Y) Cause (Part I) - Elaboration

III. Non-quantitative Statistical Example Step 1 – Construct the zero order cross-tabulation table. The Marginal (Zero-Order) Table MFTot Rep2515 40 Dem1525 40 Tot40 80 Step 2 – Calculate the zero order measure of association. e.g. Lambda = 40/40 – 30/40 =.25 or Phi = (25-20) 2 /20 + (15-20) 2 /20 + (15-20) 2 /20 + (25-20) 2 /20 = square root of 5/80 =.25 Cause (Part I) - Elaboration

Step 3 – Construct the first order partial tables. The Marginal Table M FTot Rep2515 40 Dem1525 40 Tot40 80 Step 4 – Calculate the partial measures of association = M FTot Rep15 30 Dem15 30 Tot30 60 + M FTot Rep10 0 Dem 010 Tot10 20 Partial Table for YoungPartial Table for Old TotalYoungOld Lambda.25.001.00 Since the partials have changed from the marginal measure, one getting stronger and the other disappearing, we would say that we have specified the zero order relationship as probably intervening (i.e. we are born into a sex, grow older and as a result, join a political party). Step 5 – Form the conclusion Cause (Part I) - Elaboration

IV. Quantitative Statistical Example Step 1 – Construct the zero order Pearsons correlations (r). Assume r xy =.55 where x = divorce rates and y = suicide rates. Further, assume that unemployment rates (z) is our control variable and that r xz =.60 and r yz =.40 Step 2 – Calculate the partial correlation ( r xy.z ) ==.42 Step 3 – Draw conclusions After z ( r xy.z ) 2 =.18 Before z (r xy ) 2 =.30 Therefore, Z accounts for (.30-.18) or 12% of Y and (.12/.30) or 40% of the relationship between X&Y.55 – (.6) (.4) Cause (Part I) - Elaboration

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