Data, Tables and Graphs

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Data, Tables and Graphs

• Presentation

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Types of data

• Qualitative and quantitative
• Qualitative is descriptive (nominal, categories),
  labels or words
• Quantitative involves numbers
• Data information to be analyzed

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Types of data

• Discrete and continuous
• Discrete takes on only whole number values
• Continuous can take on decimal (fractional)
  values

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Coding schemes

• Coding schemes are numbers assigned to
  characteristics of the data to be analyzed
• Best to use numeric coding schemes

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Example age, race and gender, coding scheme

• Age recorded as a two digit number
• Race
• Coded as a single digit number using a coding
  scheme
• African American 2. Hispanic 3. White
• 4. Asian 5. Other

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Example continued

• Gender
• 1. male 2. female
• Andy is a 22 year old white male
• Age 22, Race 3, Gender 1
• Coded as 2231

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Data file

• Usually rectangular
• Variable values recorded for the unit of analysis
• We will use SPSS as an example Statistical
  Package for the Social Sciences

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Data file example
ID Age Sex Race IQ Hand MS
1 22 1 3 102 1 1
2 34 2 1 110 1 2
3 60 2 1 112 1 3
4 54 1 3 92 1 2
5 39 1 1 120 2 1
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Data file

• Each row is the unit of analysis (usually a
  subject)
• Each column is a variable
• Every variable should be given a label (name)
• If it is a nominal variable, each value should
  have a value label

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Example of value label

• Unit of analysis subject
• Variable marital status
• Values might include single, married, divorced,
  widowed
• Each value should be coded as a number, and the
  label provided

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Missing value

• Data is often incompletethere will be missing
  information
• There should be a code to indicate if a piece of
  data (a variable) is missing for a particular
  subject (often 0 is used)
• Example no IQ score available, coded as a 0,
  indicated in the data file

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Simple descriptive statistics

• Frequency number of times a value occurs
• If there are 48 females and 52 males in a sample,
  f 48 for females and 52 for males
• Proportion f/N, P 48/100 for females, or .48
• Percent f/N 100

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Qualitative (nominal)

• Frequency distributions
• Tables and graphs
• Always label tables and graphs

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Table 1. Gender of Sample
Frequency Proportion Percent
Male 52 .52 52
Female 48 .48 48
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Pictorial representations

• Pie charts
• Bar charts

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Displaying two variables in a table

• Crosstabs
• Race and gender, as an example

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Quantitative data

• Tables and graphs
• Ungrouped data
• Each value is displayed
• Count each value
• Frequency number of times each value occurs

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Quantitative

• Frequency number of times each value occurs
• Cumulative frequency arrange the numbers in
  ascending (or descending), and sum the
  frequencies going down the table
• Indicates how many scores are less than a given
  score (cf)

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Quantitative tables

• Proportion, cumulative proportion
• Percent, cumulative percent

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Graphs, quantitative, ungrouped

• Histogram
• Bar graphs
• Line graphs frequency
• Cumulative

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Quantitative, grouped data

• Sometimes cumbersome to list each valuetoo many
  values
• Example agecould be 0 to 90
• Set up group intervals, i.e., 0-5, 6-10, etc.
• Rules
• 1. first and last interval should not have a 0
  frequency

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Grouped data

• Mutually exclusive and exhaustive
• All intervals should be the same width
• Important rule, not in the book when collecting
  data, do not group (collapse)information is
  lost. You can always group later

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Interval width

• No hard and fast ruleswhat seems to be most
  meaningful
• Appearance also a consideration
• As a start, use the formula, width range of
  scores (highest-lowest), divided by the number of
  intervals

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Continuous data

• If data is continuous, actually decimal values
  are possible
• Must develop a rule for handling this
• For example, use a rounding rule