1. Topic – 05
Part I Collecting primary data & credibility of research finding A- (i) Questionnaires (ii) Interviews (iii) Observations B- Credibility of research finding Part II Sampling techniques (Probability versus Non-probability sampling)

2. Part I Research interview
Definition
‘An interview is a purposeful discussion between two or more people’
Kahn and Cannell (1957)
Types of interview/Three major types
Structured interview
Semi-structured interview
Unstructured/In-depth interview

3. Structured & semi-structured interview
Data collection technique in which an interviewer physically meets the respondent, reads them the same set of questions in a predetermined order, and records his or her response, usually with pre-coded answers.
Semi-structured interview:
Wide-ranging category of interview in which the interviewer commences with a set of interview themes but is prepared to vary the order in which questions are asked and to ask new questions in the context of the research situation. The responses/information/ data are recorded by note-taking, or perhaps by tape-recording the conversation.

4. Unstructured/in-depth interview
Loosely structured and informally conducted interview that may commence with one or more themes to explore with participants but without a predetermined list of questions to work through. These are used to explore in depth a general area in which one is interested; these are therefore also known as in-depth interview. The interviewee is given the opportunity to talk freely about events, behavior and beliefs in relation to the topic area.
(See Box 9.3 for further details)

5. Approaches to questioning
(in semi-structured and unstructured interviews)
Open questions
The use of open questions allows participants to define and describe a situation or event as they wish.
Example: How has corporate strategy changed over the past five years?
Probing questions
Questions used to further explore responses that are of significance to the research topic.
Example: What external factors caused the corporate strategy to change?
Specific and closed questions
These types of question may be used to obtain specific information or to confirm a fact or opinion.
Example:
How many people responded to the customer survey? (Specific question)
Did i hear you say that the new warehouse opened on 25 march?
(close question requiring ‘yes’ or ‘No’ answer)

6. Observation as a data collection method
‘Observation involves the systematic observation, recording, description, analysis and interpretation of people’s behaviour’
Observation: The two major types
Participant observation: Observation in which the researcher attempts to participate fully in the lives and activities of the research subjects and thus becomes a member of the subjects’ group
Structured observation: Structured observation is concerned with the frequency of events. It is characterized by a high level of predetermined structure and quantitative analysis.

7. Structured observation
Data collection and analysis
Choosing an ‘off the shelf’ coding schedule
Box 8.1 provides a checklist of questions to ask when choosing an ‘Off the Shell’ coding schedule.
Designing your own coding schedule
Guidelines for developing your own coding
schedule is available in Table 8.1
Combining both types of schedule
A specimen for recording sheet for
observing behaviour in groups is
provided in Figure 8.2

8. Credibility of Research Findings Important considerations
Reliability?
Validity?
Generalizability?

9. Can we measure reliability? Yes, using computer
Reliability can be assessed by posing three questions:
Will the measure yield the same results on other occasions?
Will similar observations be reached by other observers?
Is the measure/instrument stable and consistent across time, space and researcher in yielding findings?
Can we measure reliability? Yes, using computer
4-Threats to reliability
(i) Subject/participant error
(ii) Subject/participant bias
(iii) Observer error and
(iv) Observer bias

10. Validity
Whether the findings are really about what they appear to be about.
Validity depends upon:
*History (same history or not),
*Testing (if respondents know they
are being tested),
* Mortality (participants’ dropping
out),
* Maturation (tiring up), and
* Ambiguity (about causal direction).

11. Generalizability Logic leaps and false assumptions
The extent to which research results
are generalizable.
Logic leaps and false assumptions
Research design is based on a flow of logic
and number of assumptions,
which must stand to closest scrutiny

12. (A: Semi-structured & unstructured interviews)
Data Quality Issues
(A: Semi-structured & unstructured interviews)
Lack of standardization in the use of semi-structured and unstructured/in-depth interview cases may lead to raise concerns, related to:
Reliability:
Whether alternative researchers would reveal similar information/results; concerns about reliability relate to the issues of bias.
Forms of bias:
Interviewer bias: comments, tone, or non-verbal behaviour of interviewer may create bias in the way that interviewees respond to questions being asked; or researcher may demonstrate bias in interpreting responses; or interviewer remains unable to develop trust of interviewees, and they provide limited information.
Interviewee or response bias: interviewees may feel information is sensitive; he/she is not empowered to deliver information related to organization; interview is too-time consuming.

13. (Semi-structured & unstructured interviews)
Data Quality Issues
(Semi-structured & unstructured interviews)
Generalizability:
It may not be possible to make generalizations about the entire population where results are based on a small and unrepresentative number of cases.
Validity:
High level of validity is possible to the extent to which the researcher gains access to their participants’ knowledge and experience and because of the interaction between researcher and interviewee which allows meanings to be probed, topics to be covered from a variety of angles and questions made clear to the respondents.

14. Overcoming Data Quality Issues
(Semi-structured & unstructured interviews)
The following points help avoid sources of bias
Your own preparation and readiness for the interview
The level of information supplied to the interviewee
The appropriateness of your appearance at the interview
The nature of opening comments made when interview commences
Your approach to questioning
The impact of your behaviour during the course of interview
Your ability to demonstrate attentive listening skills
Your scope to test understanding
Your approach to recording information

15. (B) Structured observation
Threats to validity and reliability
Subject error
Time error
Observer effects and strategies to overcome this:
– habituation and
– minimal interaction

16. Part - II
Sampling techniques Probability sampling? Versus Non-probability sampling?

17. Non-probability sampling
and
Non-probability sampling
Probability samples: ones in which members of the population have a known chance (probability) of being selected
Non-probability samples: instances in which the chances (probability) of selecting members from the population are unknown
Probability sampling requires a sampling frame, and when a sampling frame is not possible, non-probability sampling is used
Sampling frame: Sampling frame is a complete list of all the cases in the population, from which sample will be drawn (Recall we have already discussed sampling frame error).
Where no suitable list exists, researcher will have to compile his/her own sampling frame.
It is important to ensure that sampling frame is unbiased, current and accurate

18. Two Major Sampling techniques
Probability (representative) and Non-probability (Judgemental)
Source: Saunders et al. (2009)
Figure 7.2 Sampling techniques

19. SIMPLE RANDOM SAMPLING
Simple random sampling: the probability of being selected is “known and equal” for all members of the population
Blind Draw Method (e.g. putting names “placed in a hat” and then drawn randomly)
Random Numbers Method (all items in the sampling frame given numbers, numbers then drawn using table or computer program)
Advantages:
Known and equal chance of selection
Easy method when there is an electronic database
Disadvantages: (Overcome with electronic database)
Complete accounting of population needed
Cumbersome to provide unique designations to every population member

20. Systematic Sampling
Systematic sampling: way to select a probability-based sample from a directory or list. This method is at times more efficient than simple random sampling; here sampling interval is used
Sampling interval (SI) = population list size (N) divided by a predetermined sample size (n)
How to draw:
1) Calculate SI,
2) Select a number between 1 and SI randomly,
3) Go to this number as the starting point and the item on the list here is the first in the sample,
4) Add SI to the position number of this item and the new position will be the second sampled item,
5) continue this process until desired sample size is reached.

21. Cluster Sampling
Method by which the population is divided into groups (clusters), any of which can be considered a representative sample.
These clusters are mini-populations and therefore are heterogeneous.
Once clusters are established, a random draw is done to select one (or more) cluster(s) to represent the population.
Area (next slide) and systematic sampling (discussed earlier) are two common methods.

22. Cluster Sampling – Area Method
Divide the geo area into sectors (sub-areas) and give them names/numbers, determine how many sectors are to be sampled (typically a judgment call), randomly select these sub-areas. Do either a census or a systematic draw within each area.
To determine the total geo area estimate, add the counts in the sub-areas together and multiply this number by the ratio of the total number of sub-areas divided by number of sub-areas.

23. Stratified Sampling Method
The population is separated into homogeneous groups/segments/strata and a sample is taken from each. The results are then combined to get the picture of the total population. This method is used when the population distribution of items is skewed. It allows us to draw a more representative sample. Hence, if there are more of certain type of items in the population, the sample will have more of this type; and if there are fewer of another type, there will be fewer of that type, in the sample.

24. Non- probability sampling techniques
(Judgemental sampling)
Quota sampling
Purposive sampling
Snowball sampling
Self-selection sampling
Convenience sampling

25. selection compared to others
Judgment samples
Samples that require a judgment or an “educated guess” on the part of the researcher as to who should represent the population.
Also, “judges” (informed individuals) may be asked to suggest who should be in the sample.
Subjectivity enters in here, and certain members of the population will have a smaller, little or no chance of
selection compared to others

26. Referral and Quota Sampling Methods
Referral samples (snowball samples): samples which require respondents to provide the names of additional respondents
Members of the population who are less known, disliked, or whose opinions conflict with the respondent have a low probability of being selected.
Quota samples: samples that set a specific number of certain types of individuals to be interviewed
Often used to ensure that convenience samples will have desired proportion of different respondent classes

27. Convenience Sampling Method
Convenience samples: samples drawn at the convenience of the interviewer. People tend to make the selection at familiar locations and to choose respondents who are like themselves.
Error occurs:
1) in the form of members of the population who are infrequent or nonusers of that location, and
2) who are not typical in the population
3) who are disliked