Research Methods –STA630 VU
© Copyright Virtual University of Pakistan 101
Lesson 30
DATA TRANSFROMATION
Data transformation is the process of changing data from their original form to a format that is more
suitable to perform a data analysis that will achieve the research objectives. Researchers often modify
thee values of a scalar data or create new variables. For example many researchers believe that response
bias will be less if interviewers ask consumers for their year of birth rather than their age, even though
the objective of the data analysis is to investigate respondents’ age in years. This does not present a
problem for thee research analyst, because a simple data transformation is possible. The raw data coded
at birth year can be easily transformed to age by subtracting the birth year from thee current year.
Collapsing or combining categories of a variable is a common data transformation that reduces the
number of categories. For example five categories of Likert scale response categories to a question may
be combined like: the “strongly agree” and the “agree” response categories are combined. The
“strongly disagree” and the “disagree” response categories are combined into a single category. The
result is the collapsing of the five-category scale down to three.
Creating new variables by re-specifying the data numeric or logical transformations is another important
data transformation. For example, Likert summated scale reflect the combination of scores (raw data)
from various attitudinal statements. The summative score for an attitude scale with three statements is
calculated as follows:
Summative Score = Variable 1 + Variable 2 + Variable 3
This calculation can be accomplished by using simple arithmetic or by programming a computer with a
data transformation equation that creates the new variable “summative score.”
The researchers have created numerous different scales and indexes to measure social phenomenon. For
example scales and indexes have been developed to measure the degree of formalization in bureaucratic
organization, the prestige of occupations, the adjustment of people in marriage, the intensity of group
interaction, thee level of social activity in a community, and thee level of socio-economic development
of a nation.
Keep it in mind that every social phenomenon can be measured. Some constructs can be measured
directly and produce precise numerical values (e.g. family income). Other constructs require the use of
surrogates or proxies that indirectly measure a variable (e.g. job satisfaction). Second, a lot can be
learned from measures used by other researchers. We are fortunate to have the work of thousands of
researchers to draw on. It is not always necessary to start from a scratch. We can use a past scale or
index, or we can modify it for our own purposes. The process of creating measures for a construct
evolves over time. Measurement is an ongoing process with constant change; new concepts are
developed, theoretical definitions are refined, and scales or indexes that measure old or new constructs
are improved.
Indexes and Scales
Scales and indexes are often used interchangeably. One researcher’s scale is another’s index. Both
produce ordinal- or interval- level measures of variable. To add to thee confusion, scale and index
techniques can be combined in one measure. Scales and indexes give a researcher more information
about variables and make it possible to assess thee quality of measurement. Scales and indexes increase
reliability and validity, and they aid in data reduction; that is condense and simplify the information
that is collected.
A scale is a measure in which the researcher captures the intensity, direction, level, or potency of a
variable construct. It arranges responses or observation on a continuum. A scale can use single
indicator or multiple indicators. Most are at thee ordinal level of measurement.
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