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Unit 2: Research Design

is based on other criteria that are related to the measurement (e.g., racist actions should be Notes
related to responses to racist attitude scales); third, construct validity is based on logical
relationships between variables (e.g., marital satisfaction measurements should correlate with
measurements of marital fidelity); finally, content validity refers to the degree to which a
measure covers all the meanings of a concept (e.g., racism as all kinds of racism, against
women, ethnic groups, etc.).

Notes Reliability is all in all an easier requirement, while on validity we are never sure.
Also the tension between reliability and validity, often there is a trade-off between
the two (e.g., compare in-depth interviewing with questionnaire surveys).
3. Operationalization

Operationalization is the specification of specific measures for concepts in a research (the
determination of indicators). Some guidelines— be clear about the range of variation you want
included (e.g., income, age), the amount of precision you want, and about the dimensions of
a concept you see relevant.
In addition, every variable should have two qualities:— (1) exhaustive: all the relevant
attributes of a variable must be included (e.g., the magical ‘other’ category is best not too
big), and (2) attributes should be mutually exclusive (e.g., whether a person is unemployed
or employed is not exclusive, since some people can be part-time employed and part-time
unemployed).
Variables are (1) nominal, when there attributes indicate different, mutually exclusive and
fully exhausted qualities (e.g., sex: male or female); (2) ordinal, when the attributes can also
be ranked in an order (e.g., type of education); (3) interval, when the distance between attributes
in an order is precise and meaningful (e.g., IQ test); and (4) ratio, when, in addition, these
attributes have a true zero-point (e.g., age). Note that variables do usually not in and by
themselves indicate whether they are nominal, ordinal, etc., or that you can convert them from
one type to another (e.g., dummy-variables, from nominal to metric).
Finally, note that you can use one or multiple indicators for a variable; sometimes even, a
composite measurement is necessary.

4. Indexes, Scales and Typologies
There are commonalities between indexes and scales; they both typically involve ordinal variables,
and they are both composite measures of variables.

An index is constructed by accumulating scores assigned to individual attributes. The requirements
of scales are— face validity (each item should measure the same attribute), unidimensionality
(only one dimension should be represented by the composite measure). Then you consider
all the bivariate relationships between the items in the scale, the relationship should be high,
but not perfect.
A scale is constructed by accumulating scores assigned to patterns of attributes. The advantage
is that it gives an indication of the ordinal nature of the different items, one item is in a sense
included in the other (higher ranked).
A typology is a break-down of a variable into two or more. As dependent variables this is a
difficult thing, since any one cell in the typology can be under-represented (it’s best then to
undertake a new analysis, making sure each cell is well represented).

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