Page 162 - Data Science class 11
P. 162
The suggestions on improving survey response rate include personalising questionnaires by, for example,
addressing them to a particular group of respondents rather than to some generic recipient; enhancing the
questionnaire’s credibility by providing details about the qualifications, contact, etc. for the researcher, and
perhaps partnering or collaborating with renowned universities, hospitals, relevant organizations; sending out
pre-questionnaire notices and post-questionnaire reminders to prospective respondents.
A low rate of response may introduce nonresponse bias into a study’s findings. This is the major concern with the
response rate. If the respondents to a questionnaire are those with strong opinions or those with prejudice, we may
well find that our findings don’t at all represent how things are in reality or, at the very least, we are limited in the
claims we can make about patterns found in our data. While high return rates are certainly ideal, a recent body of
research shows that concern over response rates may be overblown (Langer, 2003). Several studies have shown that
low response rates did not make much difference in findings or in sample representativeness (Curtin, Presser, &
Singer, 2000; Keeter, Kennedy, Dimock, Best, & Craighill, 2006; Merkle & Edelman, 2002). For now, the jury may still be
out on what makes an ideal response rate and on whether, or to what extent, researchers should be concerned about
response rates. Nevertheless, there is no harm in expecting or aiming for a high response rate.
4.4 SaMpLing
A population is the entire group that you want to draw conclusions about. A sample is the specific group that you will
collect data from. The size of the sample is always less than the total size of the population. In research, a population
doesn’t always refer to people.
Most statisticians agree that the minimum sample size to get any kind of meaningful result is 100. If your population
is less than 100, then you really need to survey all of them.
There are two main types of sampling methods:
• Probability sampling: It involves random selection, allowing you to make strong statistical inferences about the
whole group.
• Non-probability sampling: It involves non-random selection based on convenience or other criteria, allowing you
to easily collect data.
Population
Sample
Statistical populations are used to observe behaviours, trends, and patterns in the way individuals in a defined group
interact with the world around them, allowing statisticians to draw conclusions about the characteristics of the subjects
of study, although these subjects are most often humans, animals, and plants.
4.4.1 Sampling techniques
There are numerous ways of getting a sample, let us learn about the most commonly used ones.
Simple Random Sampling
Simple random sample is a sample which is a subset of the population the researcher surveyed. We cannot consider
the whole population because it is too large. So, a simple random sample can be best described as a sample in which
the elements are chosen by chance.
160 Touchpad Data Science-XI

