1. Univariate Analysis The first step to analyzing data

2. Quantitative Data Analysis • Purpose • It is the examination of variables and relationship among variables using numbers. • summarized a variable • examine relationship among variables • To test hypotheses

3. The first step • To become familiar with each variable that you intend to use in your analysis. • Make sure that variable has enough cases across responses for meaningful analysis. • To reorganize the responses to an original variable to better suit the specific analysis to be undertaken. • To accomplish this must examine each variable separately. This is called Univariate analysis.

4. Types of analysis appropriate for different types of variables. • Categorical Variables • Frequency distributions • Bar graphs • Numerical Variables • Measures of central tendencies • Means, modes, medians • Measures of spread • Standard deviation • Range • histograms

5. Frequencies • The number of cases that fall into each attribute of a variable. • Categorical variables • The number cases and percent of total cases that fall into each response category of the variable. • Numerical variables • The number of cases for a variable that fall into each possible response category.

6. Examples of Categorical VariablesUsing Race of Respondent from GSS Dataset • Say we want to test the following model and hypotheses: • An we want to first just look at relationship between age and stereotypical attitudes using a table that will tell us the number of people in an age group by their racial attitude called a crosstabulation. age Interaction with blacks Negative Attitude towards blacks Race/ethnicity

7. The actual Indicators • Age asks respondents their age at the time of the interview. • Numerical • The dependent variable negative attitudes will be created from 3 categorical variables that asked respondents how most people in the group (blacks) can be characterized on each of the following characteristics: • Rich vs. poor • Hard-working vs. lazy • Violence prone vs. not violence prone • Unintelligent vs. intelligent

8. A created sample dataset

9. Create a table that groups responses together Example of a Univariate Analysis Univariate Distribution of Age

10. Univariate Distribution of Hard Working - Lazy

11. Univariate Distribution of Age, recoded into 3 groups