1. Wrap-up and Course Review
Info 271B

2. Recap of Course Goals from Week 1
You will have good knowledge of common research methods used in quantitative research (surveys, experiments)
You will be able to prepare, recode and error-check numeric data
You will be able to use a general purpose statistical package to conduct statistical analyses
You will understand basic univariate statistics, bivariate statistics and linear regression

3. Recap and Review Research problems, foundations and measurement
Sampling
Experimental Designs
Survey Designs
Coding/Data Preparation
Univariate Statistics
Bivariate Statistics
Multi-variate Statistics

4. Constructs, Variables and Operationalization
What is the difference between the two?
How do you justify an operationalization?

5. Scales and Scaling
Scales are any device used to assign units of analysis to categories of a variable.

6. Experiments and Design
Active and Attribute independent variables
True Experiments
Validity
Reliability

7. Basic Concepts in Elementary Probability
Random Selection
Every possibility has equal chance of being chosen.
Independence
The probability of a response on one trial does not depend on the outcome of any other trials.
Elementary Event
Possible outcomes of a probability experiment
E.g., each coin toss
Sample Space
The complete set of elementary events
E.g., all coin tosses

8. Sampling
Sampling Designs
Bias and Error

9. Causality X Y
Covariation, Non-spurious, logical time ordering, mechanism

10. Distributions and Their Properties
Skewness in a normal distribution = 0
Kurtosis = 3 (though some texts use kurtosis excess, which is 0 for normal distribution)
A distribution is platykurtic if it is flatter than the corresponding normal curve and leptokurtic if it is more peaked than the normal curve.

11. The Standard Deviation and Standard Error
What is the difference? Spread of List versus Spread of Chance Process (or, dispersion of variable vs dispersion of sampling distribution)
Graphics: Wikipedia

12. Hypotheses H0: μ1 = μc H1: μ1 < μc H1: μ1 > μc H1: μ1 ≠ μc
“hypothesis statements contain two or more variables that are measurable or potentially measurable and that specify how the variables are related” (Kerlinger 1986)
H0: μ1 = μc
H1: μ1 < μc
H1: μ1 > μc
H1: μ1 ≠ μc
A good research question will produce one or more testable hypotheses.
Testable hypotheses predict a relationship between variables (not concepts).

13. Hypothesis Testing Test statistic Critical Values P-value
One and two-tailed tests

14. Correlation

15. t-test and t-distribution

16. Chi-Square distribution and test

17. F-test and ANOVA

18. OLS Regression

19. Test Selection and Variables
Correlation
Metric
T-test
Binary Categorical
Chi-Square
Nominal/
Ordered
ANOVA
Polytomous (3+ categories)
OLS Regression
Metric Dependent
Various

20. Course Evals
(to be returned to main office, 102 South Hall)