Matilda Valentino IPL
DATA ANALYSIS
• data are empirical material organized into a form that can be analyzed. Units of analysis are the objects/
subjects to which the properties investigated pertain.
• data analysis is a process of gathering inspecting, cleansing, transforming, and modeling data with the
goal of discovering useful information, informing conclusions and supporting decision-making
A. Primary data analysis: you collect and categorize (coding process) your own data through
interviews, official documents, experiments, surveys, etc.
B. Secondary data analysis: you use data sets that have been gathered by others and have subsequently
been deposited in databases (: existing archived collections of data).
VARIABLES
• A variable is an empirical measurement of a characteristic (example: age, number of siblings, occupation,
education, gender, party in government, etc…)
• Key features of a variable:
A. A name
B. At least two values
• A variable can vary among different categories, values, which correspond to different states/categories of
the property
• Variation of a variable may occur in two ways:
A. over time, on the same case
B. among cases, at the same time
• (example think about age: it changes over time for you, and it changes across all of you)
• A constant is a variable that does not change for all units of analysis.
• Variables can be classified according to the operations that can be carried out on them based on the logical-
mathematical characteristics of a variable. There are 3 cases of variables:
A. continuous (interval/ratio)
B. categorical
1. ordinal
2. nominal
Nominal variables
• A nominal variable is one that has two or more categories, but there is no intrinsic ordering to the
categories.
• The characteristic recorded by the variable takes on non-orderable, discrete categories.
A. Discrete: the variable takes on a range of finite categories and there are no inter-mediate categories.
B. Non-orderable: no order or hierarchy can be established.
• Classification is the process of assigning categories to each unit of analysis. A classification must be
exhaustive and mutually exclusive.
• The only relationships that we can establish among the categories of a nominal variable are those of
‘equality’ and ‘inequality’.
• (an example of nominal variable can be the religion. Some categories could be: catholic, jewish, muslim,
protestant, atheist, agnostic, other).
Ordinal variables
• An ordinal variable is similar to a categorical variable, but there is a clear ordering of the categories and
communicates relative differences between units of analysis.
• The property to be recorded assumes orderable discrete categories.
• Orderable: it exists an order, which enables us to establish not only relationships of equality and inequality
among the categories, but also relationships of order - that is ‘greater than’ (>) and ‘less than’ (<).
• In an ordinal variable, the distance between one category and the next is not known.
• Categories have the ordinal, but not the cardinal properties of numbers
• (an example of ordinal variable can be the evaluation of life satisfaction: Some categories could be: very
satisfied; fairly satisfied; not very satisfied; not at all satisfied).
Continuous variables
• A continuous variable is similar to an ordinal variable, except that the intervals between the values of the
variable are equally spaced.
• The distances between the categories can be determined because we have a reference unit (a measurement
unit or a counting unit).
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