Page 260 - [Uma_Sekaran]_Research_methods_for_business__a_sk(BookZZ.org)

244 DATA COLLECTION METHODS

Example 10.1 Age (years) Annual Income
Under 20 Less than $20,000
20–30 $20,000–30,000
31–40 $30,001–40,000
41–50 $40,001–50,000
51–60 $50,001–70,000
Over 60 $70,001–90,000
Over $90,000

In organizational surveys, it is advisable to gather certain demographic data
such as age, sex, educational level, job level, department, and number of years
in the organization, even if the theoretical framework does not necessitate or
include these variables. Such data will help to describe the sample characteris-
tics in the report written after data analysis. However, when there are only a few
respondents in a department, then questions likely to reveal their identity might
render them futile, objectionable, and threatening to employees. For instance, if
there is only one female in a department, then she would refrain from respond-
ing to the question on gender, because it would establish the source of the data;
this apprehension is understandable.

To sum up, certain principles of wording need to be followed while design-
ing a questionnaire. The questions asked must be appropriate for tapping the
variable. The language and wording used should be such that it is meaningful to
the employees. The form and type of questions should be geared to minimize
respondent biases. The sequencing of the questions should facilitate the smooth
progress of the responses from the start to the finish. The personal data should
be gathered with due regard to the sensitivity of the respondents’ feelings, and
with respect for privacy.

PRINCIPLES OF MEASUREMENT

Just as there are guidelines to be followed to ensure that the wording of the
questionnaire is appropriate to minimize bias, so also are there some principles
of measurement to be followed to ensure that the data collected are appropriate
to test our hypotheses. These refer to the scales and scaling techniques used in
measuring concepts, as well as the assessment of reliability and validity of the
measures used, which were all discussed in Chapter 9.
As we have seen, appropriate scales have to be used depending on the type of
data that need to be obtained. The different scaling mechanisms that help us to
anchor our scales appropriately should be properly used. Wherever possible, the
interval and ratio scales should be used in preference to nominal or ordinal scales.
Once data are obtained, the “goodness of data” is assessed through tests of valid-
ity and reliability. Validity establishes how well a technique, instrument, or process
measures a particular concept, and reliability indicates how stably and consistently
the instrument taps the variable. Finally, the data have to be obtained in a manner
that makes for easy categorization and coding, both of which are discussed later.