Last Updated: November 5, 2009
This review is intended to give you a general idea of
what may appear on the exam. The exam will cover material from the assigned
readings and material that was presented in the lecture.
I. KKV (Chs 4 and 5)
Ch4 Determining What
to Observe
Conditional
independence
Homogeneity
assumption
Constant effect
Indeterminate
research designs
More inferences than observations
Multicollinearity
The Limits of random
selection
Selection bias
Selection on the dependent variable
(Figure 4.1)
Selection on an explanatory variable
(Figure 4.1)
Intentional selection
of observations
Selecting observations on the
explanatory variable
Selecting a range of values of the
dependent variable
Selecting observations so the key
causal variable is constant
Selecting observations so the
dependent variable is constant
Ch5 Understanding
What to Avoid
Measurement error
Systematic measurement error
Nonsystematic measurement error
Nonsystematic measurement error in
the dependent variable (Figure 5.1)
Nonsystematic measurement error in
the independent variable (Fiure 5.2)
Excluding relevant
variables: Bias
Gauging the bias from omitted
variables
Variable is correlated
with the included explanatory variable and has an effect on the dependent
variable
Including irrelevant
variables: Inefficiency
Endogeneity
II. Test of
Significance
One sample case- z (computation)
One sample case- t (computation,
SPSS)
Chi
square test (computation, SPSS)
t-test for
Pearson’s correlation (computation)
III. Measures of
Association
A. Nominal and
ordinal variables
Crosstabs
Know how to produce a
cross-tabulation manually
Know how to read a cross-tabulation
(both bivariate and multivariate)
Know how to control for a third
variable (Nominal and ordinal variables only)
Tests of statistical significance and measures of association (SPSS)
Know how to write a hypothesis
Know how to test a hypothesis
Statistical significance
Strength of association
Know how to
calculate Lambda manually
Direction of association
Substantive
interpretation
Bivariate
association
Lambda, Cramer’s V, Tau b, Tau c (SPSS,
interpretation)
Controlling for a third variable (where
the third variable is a nominal or ordinal variable)
Lambda, Cramer’s V, Tau b, Tau c (SPSS,
interpretation)
B. I/R variables
Pearson’s r (Computation including testing a hypothesis, Excel, SPSS,
interpretation)
Bivariate
regression (SPSS, interpretation)
Multiple regression
(SPSS, interpretation)
The following will be provided.
I. Formulae
Z:
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t:

Chi square:
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Lambda:
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Pearson’s r

II. The z, t, and chi
square tables and the following table
|
Measurement Types |
Statistics |
|
Two nominal variables |
Lambda/Cramer’s V |
|
One nominal one ordinal variables |
Cramer’s V |
|
Two ordinal variables |
(Not square) |
|
Gamma |
|
|
I/R variables |
(Pearson’s corr. Coeff) |
|
Regression: R2 |