Managing

1
Managing Representing Data Basic
Considerations

• The basic material for statistical analysis is a
  data set
• A collection of data values on some variable(s)
  recorded on a sample of data points (cases)
• Where do the data values come from?
• Coded from measurements, records, or observations
  (the raw data)
• Computed from other variables (derived data
  such as rates, ratios, and indexes)
• Recoded from prior values into more useful form
  (transformed data)

2
Managing Data Basic Tasks

• Sorting and Selecting the data set
• Sorting ? Change the order of the cases in the
  file
• Selecting ? identify a subset of cases to worth
  on
• Use the Data pull-down menu in SPSS (menu bar)
• Where do the data values come from?
• Coded from measurements, records, or observations
  (the raw data)
• Computed from other variables (derived data
  such as rates, ratios, and indexes)
• Recoded from prior values into more useful form
  (transformed data)

3
Recoded Data Some Very 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
• Variable (or flexible) groupings ? widths of
  groups are not all the same
• Standardized scores
• Rescaled to standard units ? e.g., Z-scores
• Grouped by proportions of cases ? e.g.,
  percentiles

4
How to recode variables in SPSS?

• Use the Transform option on the top menu bar to
  change the data
• Compute ? allows for computing a new variable
  from prior variables
• Recode ? allows for modifying how the variable is
  coded
• Into same variables ? directly modifies the
  original variable codes
• Into different variables ? creates new variable
  with altered codes (leaves original variable as
  is)

5
Representing Data Distributions

• Because in statistics, we are working with a
  collection of multiple data points and values,
  the focus is on the distribution of the data,
  rather than on single data points
• i.e., how are the values of the variable spread
  or distributed over the elements of the sample
• Two main forms of presentation
• Tabular ? tables of numbers and values
• Graphical ? charts, graphs, and diagrams of
  distributions of data values

6
Tabular Presentations Univariate

• Data Listing
• Simple inventory of data values in the data set
• Ordered Data Listing
• Inventory of data sorted into groups and arranged
  in increasing or decreasing order
• Frequency Table
• Table showing each value and the number of cases
  having that value (most relevant for discrete
  variables)
• Percentage Table
• Frequency table with percentages of total cases
  given rather (or in addition to) than numerical
  counts
• Cumulative Percentage Table
• Percentage table with percentages of total cases
  having that value or lower.

7
Tabular Presentations Bivariate (2
variables in the same table)

• Cross-Tabulations
• Frequency (and percentage) distribution of values
  of one variable across the values of another
  variable
• May also be described or viewed as
• Conditional frequency distributions i.e.,
  distributions of one variable conditional on
  simultaneous values of another variable
• Bivariate frequency distributions

8
Cross-Tabulations (cont.)

• What are the parts of a cross-tab?
• Cells, rows, columns, margins, and grand total
• How to set up a cross-tab?
• Which variables are in the rows and columns?
• Independent variable generally in the columns
• Variable with most values in the rows (so that
  the cross-tab will fit more easily on one page)
• Use Percentages or Frequencies?
• How to percentage a cross-tab?
• Percentage in the direction of the independent
  variable

9
Representing Distributions Graphically Basic
Formats

• Pie Charts
• Bar Charts
• Vertical or Horizontal
• Simple or Grouped
• Histograms
• Line Charts
• Frequency polygons
• Time (Trend) plots
• Relationship plots
• Other Charts (later) (box plots scatter plots)