Organizing & Reporting Data: An Intro

1. Organizing & Reporting Data: An Intro Statistical analysis works with data sets A collection of data values on some variables recorded on a number cases (records) For example, the student data from last week:

2. Organizing & Reporting Data (cont.): Structure of most data sets = “rectangular Columns = Variables Rows = Cases Cells = individual values

3. Managing Data: Basic Tasks NOTE: Reliance on Codebook for Data Set Specify information about variables in the data set Indicate Variable Names & Labels Indicate Variable Values (codes) & Value Labels Indicates “missing values” Can Modify Overall Arrangement of Data Set Sorting Change the order of the cases in the file Selecting  identify a subset of cases to work on Transforming  modify the values of a variable

4. Organizing & Reporting Data (cont.): Where do the data values come from? Raw Data: recorded from responses, record, or observations In their (more-or-less) original form Some coding (or editing) operations usually involved Usually coded into numerical values (for ease of use) Transformed Data: modified from original values Computed values (e.g., rates, %, sums, “imputations”) Recoded values (into more correct or meaningful or useful values) Created Data: values are “made up” Simulated values Demonstration values

5. Managing Data: Basic Tasks Transforming Data: Variable Transformations Computing new variables from prior ones Index = Q1 + Q2 + Q3 + Q4 Utility = probability * outcome Recode Variable by changing its values Change missing values (“blanks”) to “0” Recode Variable into a New Variable Age (yrs)  Child (1-11); Juvenile (12-17); Adult (18-over) Age (yrs)  10-19 yrs; 20-29 yrs; 30-39 yrs; 40-49 yrs; 50-59 yrs; 60-69 yrs; 70-79 yrs; 80-89 yrs; 90-99 yrs.

6. Computed Data: Some Useful forms Rates – numbers divided by populations Ratios – one number divided by another Indexes– new variable = a sum (or other combination) of multiple prior variables Rescaled Data– a raw score modified by some mathematical function (e.g., logarithm) Standardized scores– Rescaled to standard units  e.g., Z-scores

7. Recoded Data: Some Useful forms Collapsed (& abbreviated) scores Grouped scores – recoding a numeric variable into a discrete (numeric or ordinal) variable Uniform (or fixed-width) groupings  widths of groups are all the same [Note the standard rules for forming grouped variables] Non-uniform (variable or flexible) groupings  widths of groups are not all the same Normed groupings  grouped by proportions of cases  e.g., percentiles, quartiles, median-splits [a special form of non-uniform grouping]

8. How to recode variables in SPSS? Use the Transform option on the top menu bar to change the data (see Appendix B in Kirkpatrick/Feeney for details) Compute  allows for computing a new variable from prior variables Recode  allows for modifying how a variable is coded ‘Into same variables’ (change original variable) ‘Into different variables’ (create new variable with different codes & leave original variable as is)

9. Representing Data Distributions: In statistics, we are working with a collection of many data points  Our focus is on the distribution of the whole set of points Three forms of presentation for summarizing distributions of data points: Tabular tables and lists of numbers Graphical  pictures, shapes, and lines (in charts, graphs, and diagrams) Verbal  words and phrases

10. Tabular Presentations: Basic Formats Data Listing: simple inventory of points in the data set Ordered Data Listing: Inventory of data sorted into groups or arranged in increasing or decreasing order Frequency Table:summary showing each value and the number of cases having that value (most relevant for discrete variables) Percentage Table: table with percentages of total cases given rather than (or in addition to) numerical counts Cumulative Percentage Table:reporting percentages of total cases which have that specific value or lower. Cross-Tab Table:a “bivariate” frequency distribution of the values of one variable across the values of another variable

11. Cross-Tabulations (cont.) What are the parts of a cross-tab? Cells Rows and columns Marginals Grand total How to set up a cross-tab? Which variables are in the rows and columns? Use Percentages or Frequencies? How to percentage a cross-tab?

12. Representing Distributions Graphically: Basic Formats • Pie Charts • Bar Charts • Vertical or Horizontal • Simple or Grouped • Stacked • Histograms • Line Charts • Frequency polygons • Time (Trend) plots • Relationship plots

13. Representing Distributions Graphically: Basic Formats • Other Charts ( to be dealt with later): • Box Plots (aka “Box-and-Whiskers”) • Scatter Plots