Wrap-up and Course Review Info 271B

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

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

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

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

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

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

Sampling Sampling Designs Bias and Error

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

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.

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

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).

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

Correlation

t-test and t-distribution

Chi-Square distribution and test

F-test and ANOVA

OLS Regression

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

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