Please enable JavaScript.

Coggle requires JavaScript to display documents.

CHAPTER 8 SAMPLING, CHAPTER 9 SURVEY RESEARCH - Coggle Diagram

- - - - Sampling techniques can be grouped into two broad categories:
        
        Probability sampling is a technique in which every unit in the population has a chance (non-zero
        probability) of being selected in the sample, and this chance can be accurately determined.
        
        Cluster sampling. If you have a population dispersed over a wide geographic region, it may
        not be feasible to conduct a simple random sampling of the entire population.
        
        In such case,
        it
        may be reasonable to divide the population into ‘clusters’—usually along geographic
        boundaries—randomly sample a few clusters, and measure all units within that cluster.
        
        Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within
        one population based on a specific criterion.
        
        Matched-pairs sampling techniques are often an ideal way of understanding bipolar
        differences between different subgroups within a given population.
        
        Multi-stage sampling. The probability sampling techniques described previously are all
        examples of single-stage sampling techniques.
        
        Depending on your sampling needs, you may
        combine these single-stage techniques to conduct multi-stage sampling.
        
        Stratified sampling. In stratified sampling, the sampling frame is divided into homogeneous
        and non-overlapping subgroups (called ‘strata’), and a simple random sample is drawn within
        each subgroup.
        
        Note that the non-proportional approach is particularly effective in representing small
        subgroups, such as large-sized firms, and is not necessarily less representative of the population
        compared to the proportional approach, as long as the findings of the non-proportional approach
        are weighted in accordance to a subgroup’s proportion in the overall population.
        
        Systematic sampling. In this technique, the sampling frame is ordered according to some
        criteria and elements selected at regular intervals through that ordered list.
        
        This process will ensure that there is no over-representation of large or small firms
        in your sample, but rather that firms of all sizes are generally uniformly represented, as it is in your
        sampling frame. In other words, the sample is representative of the population, at least on the basis
        of the sorting criterion.
        
        Simple random sampling. In this technique, all possible subsets of a population (more
        accurately, of a sampling frame) are given an equal probability of being selected
        
        This is the
        simplest of all probability sampling techniques, however, the simplicity is also the strength of this
        technique. Because the sampling frame is not subdivided or partitioned, the sample is unbiased
        and the inferences are most generalisable amongst all probability sampling techniques.
        
        Non-probability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined.
        
        Expert sampling. This is a technique where respondents are chosen in a non-random manner
        based on their expertise on the phenomenon being studied.
        
        The advantage of this approach is that
        since experts tend to be more familiar with the subject matter than non-experts, opinions from
        a sample of experts are more credible than a sample that includes both experts and non-experts,
        although the findings are still not generalisable to the overall population at large.
        
        Convenience sampling. Also called accidental or opportunity sampling, this is a technique in
        which a sample is drawn from that part of the population that is close to hand, readily available,
        or convenient.
        
        This type of sampling is most useful for pilot testing, where the goal is
        instrument testing or measurement validation rather than obtaining generalisable inferences.
        
        In proportional quota sampling, the proportion
        of respondents in each subgroup should match that of the population.
        
        Quota sampling. In this technique, the population is segmented into mutually exclusive
        subgroups (just as in stratified sampling), and then a non-random set of observations is chosen
        from each subgroup to meet a predefined quota.
        
        Non-proportional quota sampling is less restrictive in that you do not have to achieve a
        proportional representation, but perhaps meet a minimum size in each subgroup.
        
        Snowball sampling. In snowball sampling, you start by identifying a few respondents that
        match the criteria for inclusion in your study, and then ask them to recommend others they
        know who also meet your selection criteria.
        
        Although this method
        hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach
        populations or when no sampling frame is available.
    - - This is an accessible
        section of the target population—usually a list with contact information—from where a sample
        can be drawn.
      - Note that sampling frames may not entirely be representative of the population at large, and if
        so, inferences derived by such a sample may not be generalisable to the population.
    - - A population can be defined as all people or items (unit
        of analysis) with the characteristics that one wishes to study.
        
        The unit of analysis may
        be a person, group, organisation, country, object, or any other entity that you wish to
        draw scientific inferences about. Sometimes the population is obvious.
  - - - In other words, a response
        is a measurement value provided by a sampled unit.
        
        Responses from different respondents to the same
        item or observation can be graphed into a frequency distribution based on their frequency of
        occurrences.
        
        For a large number of responses in a sample, this frequency distribution tends to
        resemble a bell-shaped curve called a normal distribution, which can be used to estimate overall
        characteristics of the entire sample, such as sample mean (average of all observations in a sample)
        or standard deviation (variability or spread of observations in a sample).
        
        These sample estimates
        are called sample statistics (a ‘statistic’ is a value that is estimated from observed data).
        
        If a sample is truly representative of the population, then the estimated sample statistics should
        be identical to the corresponding theoretical population parameters. How do we know if the
        sample statistics are at least reasonably close to the population parameters? Here, we need to
        understand the concept of a sampling distribution.
        
        The variability or spread of a sample statistic in a sampling
        distribution (i.e., the standard deviation of a sampling statistic) is called its standard error.
        
        Confidence interval is the estimated probability that a population
        parameter lies within a specific interval of sample statistic values.
        
        Populations
        also have means and standard deviations that could be obtained if we could sample the entire
        population. However, since the entire population can never be sampled, population characteristics
        are always unknown, and are called population parameters (and not ‘statistic’ because they are
        not statistically estimated from data).
        
        Sample statistics may differ from population parameters if
        the sample is not perfectly representative of the population. The difference between the two is
        called sampling error.
- - - - Questions may be unstructured or structured. Unstructured questions ask respondents to
        provide a response in their own words, while structured questions ask respondents to select
        an answer from a given set of choices.
        
        Subjects’ responses to individual questions (items) on
        a structured questionnaire may be aggregated into a composite scale or index for statistical
        analysis.
    - - Response formats. Survey questions may be structured or unstructured.
        
        Responses to
        structured questions are captured using one of the following response formats:
        
        Dichotomous response, where respondents are asked to select one of two possible choices, such
        as true/false, yes/no, or agree/disagree.
        
        Interval-level response, where respondents are presented with a 5-point or 7-point Likert
        scale, semantic differential scale, or Guttman scale.
        
        Nominal response, where respondents are presented with more than two unordered options,
        such as: What is your industry of employment?: manufacturing / consumer services / retail /
        education / healthcare / tourism and hospitality / other
        
        Ordinal response, where respondents have more than two ordered options, such as: What is
        your highest level of education?: high school / bachelor’s degree / postgraduate degree.
        
        Continuous response, where respondents enter a continuous (ratio-scaled) value with a
        meaningful zero point, such as their age or tenure in a firm. These responses generally tend to
        be of the fill-in-the blanks type.
      - Question content and wording. Responses obtained in survey research are very sensitive to the
        types of questions asked. Poorly framed or ambiguous questions will likely result in meaningless
        responses with very little value.
        
        Is the question clear and understandable?: Survey questions should be stated in very simple
        language, preferably in active voice, and without complicated words or jargon that may not
        be understood by a typical respondent.
        
        Is the question ambiguous?: Survey questions should not use words or expressions that may be
        interpreted differently by different respondents (e.g., words like ‘any’ or ‘just’).
        
        Does the question have biased or value-laden words?: Bias refers to any property of a question that encourages subjects to answer in a certain way.
        
        Is the question double-barrelled?: Double-barrelled questions are those that can have multiple
        answers.
        
        Is the question too general?: Sometimes, questions that are too general may not accurately
        convey respondents’ perceptions.
        
        Is the question too detailed?: Avoid unnecessarily detailed questions that serve no specific
        research purpose.
        
        Is the question presumptuous?: If you ask, ‘What do you see as the benefits of a tax cut?’, you
        are presuming that the respondent sees the tax cut as beneficial.
        
        Is the question imaginary?: A popular question in many television game shows is, ‘If you win a
        million dollars on this show, how will you spend it?’.
        
        Do respondents have the information needed to correctly answer the question?: Oftentimes, we
        assume that subjects have the necessary information to answer a question, when in reality,
        they do not.
      - :Question sequencing. In general, questions should flow logically from one to the next.
      - Other golden rules. Do unto your respondents what you would have them do unto you. Be
        attentive and appreciative of respondents’ time, attention, trust, and confidentiality of personal
        information.
  - - - Prepare for the interview: Since the interviewer is in the forefront of the data collection effort,
        the quality of data collected depends heavily on how well the interviewer is trained to do the
        job.
      - Observe quality of response: The interviewer is in the best position to judge the quality of
        information collected, and may supplement responses obtained using personal observations of
        gestures or body language as appropriate.
      - Clarify any confusion or concerns: Interviewers must be able to think on their feet and address
        unanticipated concerns or objections raised by respondents to the respondents’ satisfaction.
      - Motivate respondents: Respondents often feed off the motivation of the interviewer.
      - Locate and enlist the co-operation of respondents: Particularly in personal, in-home surveys,
        the interviewer must locate specific addresses, and work around respondents’ schedules at
        sometimes undesirable times such as during weekends.
    - - If the respondent gives a
        brief cursory answer, the interviewer should probe the respondent to elicit a more thoughtful,
        thorough response. Some useful probing techniques are:
        
        The silent probe: Just pausing and waiting without going into the next question may suggest
        to respondents that the interviewer is waiting for more detailed response.
        
        Overt encouragement: An occasional ‘uh-huh’ or ‘okay’ may encourage the respondent to go
        into greater details. However, the interviewer must not express approval or disapproval of what
        the respondent says.
        
        Ask for elaboration: Such as, ‘Can you elaborate on that?’ or ‘A minute ago, you were talking
        about an experience you had in high school. Can you tell me more about that?’.
        
        Reflection: The interviewer can try the psychotherapist’s trick of repeating what the
        respondent said.
  - - - Several strategies may be employed to improve
        response rates:
        
        Follow-up requests
        
        Respondent-friendly questionnaire
        
        Relevance of content
        
        Incentives
        
        Confidentiality and privacy
        
        Interviewer training
        
        Advance notification
        
        Non-monetary incentives
        
        Endorsement