1. Research Design, Sampling & Generalizability
Chapter 5

2. Steps in research

3. Research Design
A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure

4. Questions that need to be answered
(i) What is the study about?
(ii) Why is the study being made?
(iii) Where will the study be carried out?
(iv) What type of data is required?
(v) Where can the required data be found?
(vi) What periods of time will the study include?
(vii) What will be the sample design?
(viii) What techniques of data collection will be used?
(ix) How will the data be analysed?
(x) In what style will the report be prepared?

5. Research Design Contents
The sampling design = the method of selecting items to be observed for the given study
The observational design = the conditions under which the observations are to be made
The statistical design = the question of how many items are to be observed and how the information and data gathered are to be analysed
The operational design = the techniques by which the procedures specified in the sampling, statistical and observational designs can be carried out

6. The considerations of choosing the appropriate research design
(i) the means of obtaining information;
(ii) the availability and skills of the researcher and his staff, if any;
(iii) the objective of the problem to be studied;
(iv) the nature of the problem to be studied; and
(v) the availability of time and money for the research work.

7. Example 1: Identifying key issues in TQM’s

8. Population and samples
Inferential method is based on inferring from a sample to a population
Sample—a representative subset of the population
Population—the entire set of participants of interest
Generalizability—the ability to infer population characteristics based on the sample

9. How and Why Do Samples Work?

10. STEPS IN SAMPLE DESIGN Type of universe Sampling unit Source list
clearly define the set of objects to be studied
The population of a city, the number of workers in a factory
Sampling unit
geographical one such as state, district, village, etc
list that can present the population
Source list
also known as ‘sampling frame’ from which sample is to be drawn.

11. STEPS IN SAMPLE DESIGN Size of sample Parameters of interest
the number of items to be selected from the universe to constitute a sample
The size of sample should be optimum
An optimum sample is one which fulfills the requirements of efficiency, representativeness, reliability and flexibility.
Parameters of interest
the question of the specific population parameters which are of interest.

12. STEPS IN SAMPLE DESIGN Budgetary constraint Sampling procedure
Cost considerations
Sampling procedure
the type of sample
the technique to be used in selecting the items

13. CHARACTERISTICS OF A GOOD SAMPLE DESIGN
(a) Sample design must result in a truly representative sample.
(b) Sample design must be such which results in a small sampling error.
(c) Sample design must be viable in the context of funds available for the research study.
(d) Sample design must be such so that systematic bias can be controlled in a better way.
(e) Sample should be such that the results of the sample study can be applied, in general, for the universe with a reasonable level of confidence.

14. DIFFERENT TYPES OF SAMPLE DESIGNS

15. CHOOSING A REPRESENTATIVE SAMPLE
Probability sampling (random sampling)—the likelihood of any member of the population being selected is known
Non-probability sampling (non-random sampling)—the likelihood of any member of the population being selected is unknown

16. Non-probability sampling
Types of non-random sampling : Deliberate sampling, purposive sampling and judgement sampling
The investigator may select a sample which shall yield results favourable to his point of view
Sampling error in this type of sampling cannot be estimated and the element of bias, great or small, is always there.

17. Focusing On At A Specific Group: Four Types Of Non-Random Samples
Convenience sampling (Accidental or Haphazard) = a non-random sample in which you use an non-systematic selection method that often produces samples very unlike the population.
Quota sample = non-random sample in which you use any means to fill pre-set categories that are characteristics of the population.

18. Focusing On At A Specific Group: Four Types Of Non-Random Samples

19. Focusing On At A Specific Group: Four Types Of Non-Random Samples
Purposive (Judgmental) sampling = a non-random sample in which you use many diverse means to select units that fit very specific characteristics.
Snowball (network) sampling = a non-random sample in which selection is based on connections in a pre-existing network.

20. Probability Sampling
Simple random sampling= a sample drawn in which a a random process is used to select units from a population
Each member of the population has an equal and independent chance of being chosen
The sample should be very representative of the population
These are best to get an accurate representation of the population
But are difficult to conduct.

21. CHOOSING A SIMPLE RANDOM SAMPLE
1. Jane
18. Steve
35. Fred
2. Bill
19. Sam
36. Mike
3. Harriet
20. Marvin
37. Doug
4. Leni
21. Ed. T.
38. Ed M.
5. Micah
22. Jerry
39. Tom
6. Sara
23. Chitra
40. Mike G.
7. Terri
24. Clenna
41. Nathan
8. Joan
25. Misty
42. Peggy
9. Jim
26. Cindy
43. Heather
10. Terrill
27. Sy
44. Debbie
11. Susie
28. Phyllis
45. Cheryl
12. Nona
29. Jerry
46. Wes
13. Doug
30. Harry
47. Genna
14. John S.
31. Dana
48. Ellie
15. Bruce A.
32. Bruce M.
49. Alex
16. Larry
33. Daphne
50. John D.
17. Bob
34. Phil
Define the population
List all members of the population
Assign numbers to each member of the population
Use criterion to select a sample
5. Kerjie and Morgan Table

22. Coming to Conclusions about Large Populations
Sampling element = a case or unit of analysis of the population that can be selected for a sample.
Universe = the broad group to whom you wish to generalize your theoretical results.
Population = a collection of elements from which you draw a sample.

23. Coming to Conclusions about Large Populations
Target population = the specific population that you used.
Sampling frame = a specific list of sampling elements in the target population.
Population parameter = any characteristic of the entire population that you estimate from a sample.

24. Coming to Conclusions about Large Populations
Sampling ratio = the ratio of the sample size to the size of the target population.

25. Coming to Conclusions about Large Populations
Why Use a Random Sample?
Random samples are most likely to produce a sample that truly represents the population.
They are purely mathematical or mechanical.
Allow calculation of probability of outcomes with great precision.
sampling ratio = the ratio of the sample size to the size of the target population.
Sampling error = the degree to which a sample deviates from a population.

26. Coming to Conclusions about Large Populations
Types of Random Samples
Simple Random Samples = sample elements selected from the frame based on a mathematically random selection procedure
most times, a proper random sample yields results that are close to the population parameter
Sampling distribution = A plot of many random samples, with a sample characteristic across the bottom and the number of samples indicated along the side.

27. Coming to Conclusions about Large Populations
Types of Random Samples
Systematic Sampling = An approximation to random sampling in which you select one in a certain number of sample elements, the number is from the sampling interval.
Sampling Interval = the size of the sample frame over the sample size, used in systematic sampling to select units.

28. Coming to Conclusions about Large Populations
Types of Random Samples
Stratified Sampling = a type of random sampling in which a random sample is draw from multiple sampling frames, each for a part of the population.
sample is to be drawn does not constitute a homogeneous group
the population is divided into several sub-populations that are individually more homogeneous than the total population
then we select items from each stratum to constitute a sample.
each stratum is more homogeneous than the total population

29. Coming to Conclusions about Large Populations

30. Coming to Conclusions about Large Populations
Types of Random Samples
Cluster (multi-stage) sampling = a multi-stage sampling method, in which clusters are randomly sampled, then a random sample of elements is taken from sampled clusters.
divide the area into a number of smaller non-overlapping areas
the ultimate sample consisting of all (or samples of) units in these small areas or clusters
reduces cost by concentrating surveys in selected clusters. But certainly it is less precise than random sampling.

31. Coming to Conclusions about Large Populations

32. Coming to Conclusions about Large Populations

33. Three Specialized Sampling Techniques
Random Digit Dialing = Computer based random sampling of telephone numbers.
Within Household Samples = Random sampling from within households.
Sampling Hidden Populations
Hidden Population = A group that is very difficult to locate and may not want to be found, and therefore, are difficult to sample.

34. Inferences from A Sample to A Population
How to Reduce Sampling Errors
the larger the sample size, the smaller the sampling error.
the greater the homogeneity (or the less the diversity), the smaller its sampling error.
How Large Should My Sample Be?
the smaller the population, the bigger the sampling ratio must be for an accurate sample.
as populations increase to over 250,000, sample size no longer needs to increase.

35. Inferences from A Sample to A Population
How to Create a Zone of Confidence
Confidence interval = a zone, above and below the estimate from a sample, within which a population parameter is likely to be.
Confidence Interval with sample size of 100, 99% confidence
48.4
55.6
52% estimate
Confidence Interval with sample size of 100, 99% confidence
50.5
53.5
52% estimate