# Class_20 - Multiple Contingency-Table Analysis A...

• Notes
• 50

This preview shows pages 1–14. Sign up to view the full content.

Multiple Contingency-Table Analysis

This preview has intentionally blurred sections. Sign up to view the full version.

A. Philosophical Introduction We are now in position to begin dealing with cause and effect, that is, causality. Let's take a look at what we are saying and what we are NOT saying when we describe something as the cause (X) of some effect (Y): X → Y There is nothing mystical, metaphysical, or superhuman about this. We are simply playing a game, one with rules created by human beings.

This preview has intentionally blurred sections. Sign up to view the full version.

To label one variable the cause of another variable is nothing more than to have gathered the evidence required by these rules in order to impress other human beings that the labels "cause" and "effect" are being properly used. All we have done is satisfied the rules of the game sufficiently to be granted by others the right to use these labels. What are the rules? There are three of them.

This preview has intentionally blurred sections. Sign up to view the full version.

B. Criteria for Evaluating Causality The three criteria (rules) that you must demonstrate to be allowed to label some X the cause of some Y are: Covariation That is, the independent variable (X) and the dependent variable (Y) must covary (i.e., must NOT be statistically independent).
Remember statistical independence? It can look like this, . . . ========================================== Years of Formal Annual Salary Education (in \$1,000) ( X ) ( Y ) ------------------------------------------------------------------------- 10 35 16 35 21 35 -------------------------------------------------------------------------

This preview has intentionally blurred sections. Sign up to view the full version.

. . . or it can look like this. (“A constant cannot explain a variable.”) ========================================== Years of Formal Annual Salary Education (in \$1,000) ( X ) ( Y ) ------------------------------------------------------------------------- 16 25 16 85 16 45 -------------------------------------------------------------------------
Temporal priority The proposed cause (X) MUST precede in time the proposed effect (Y). X → Y t 1 t 2 Nonspuriousness NO variables OTHER THAN the proposed cause (X) could have produced the proposed effect (Y).

This preview has intentionally blurred sections. Sign up to view the full version.

Before going any further, two qualifiers must be noted: Monocausal —sounds like a search for THE ONE cause Deterministic —seems to say that the presence of the cause GUARANTEES the production of the effect
Control Group Test Group t 1 t 2 R R Y ij Y ij

This preview has intentionally blurred sections. Sign up to view the full version.

The principal weapon that the controlled experiment possesses is physical control: covariation : established with a t-test or analysis of variance at the end of the experiment; time order : no problem; physically manipulate the treatment (X), so we know the temporal sequence; nonspuriousness : control all potentially spurious variables through both random selection (R 1 ) and random assignment (R 2 ) to groups (test and control) and through control of the physical environment during the experiment; at the end of the experiment, change could ONLY have been caused by the ONE THING that varied, the treatment— present in the test group, absent in the control group.
Statistical control is the next best thing in non- experimental settings: covariation : use a statistical measure of association (like λ ) AND a significance test ( χ 2 ); time order : can be a problem; research design, measurement, and logic (especially in the case of demographic variables) are ways of establishing; nonspuriousness : this is the real issue; usually have no physical control over subjects in field research strategy: homogenize samples with respect to categories of control variables; need to both know and be able to measure potentially spurious variables in order to do this.

This preview has intentionally blurred sections. Sign up to view the full version.

This is the end of the preview. Sign up to access the rest of the document.
• Fall '07
• Velez
• Statistics, Statistical hypothesis testing, Statistical significance, Fisher's exact test, Statistical dependence

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern