1. Quantitative Data Analysis • Definitions • Examples of a data set • Creating a data set • Displaying and presenting data – frequency distributions • Grouping and recoding • Visual presentations • Summary statistics, central tendency, variability

2. What do we analyze? • Variable – characteristic that varies • Data – information on variables (values) • Data set – lists variables, cases, values • Qualitative variable – discrete values, categories. • Frequencies, percentages, proportions • Quantitative variable- range of numerical values • Mean, median, range, standard deviation, etc.

3. Creating a data set • Enter into a statistical package (program) • Program does calculations and displays results • Examples: census data Data on CD (GSS 2004)http://www.d.umn.edu/~sjanssen/Intro%20to%20SPSS%20exercise.htm

4. Creating a data set • May involve coding and data entry • Coding = assigning numerical value to each value of a variable • Gender: 1= male, 2 = female • Year in school: 1= freshman, 2= sophomore, etc. • May need codes for missing data (no response, not applicable) • Large data sets come with codebooks

5. Displaying and Presenting Data • Frequency distribution – list of all possible values of a variable and the # of times each occurs • May require grouping into categories • May include percentages, cumulative frequencies, cumulative percentages

6. Displaying and Presenting Data • Ungrouped frequency distribution • Usually qualitative variables • Grouped frequency distribution • Values are combined (grouped) into categories • Use for quantitative variables • Many separate values

7. Grouping into categories • May use meaningful groupings • May use equal intervals (more common) • Equal width • Mutually exclusive • Exhaustive • Class interval = category, range of values • Midpoint = exact middle of interval • Limits = halfway to next interval

8. Summary statistics • Percent = relative frequencies; standardized units. • Cumulative frequency or percent = frequency at or below a given category (at least ordinal data required)

9. Visual Presentation of Data • Bar graph (column chart, histogram): best with fewer categories • Pie chart: good for displaying percentages; easily understood by general audience • Line graph: good for numerical variables with many values or for trend data

10. Summary statistics: central tendency • “Where is the center of the distribution?” • Mode = category with highest frequency • Median = middle category or score • Mean = average score

11. Summary Statistics: Variability • “Where are the ends of the distribution? How are cases distributed around the middle?” • Range = difference between highest and lowest scores • Standard deviation = measure of variability; involves deviations of scores from mean; most scores fall within one standard deviation above or below mean.