Page 162 - Data Science class 11
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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.
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