1. RESEARCH DESIGN (PART 2) Siti Rohaida Bt Mohamed Zainal, PhD
School of Management

2. What is Sampling? “Sampling is a process by which we study a small
part of a population
to make judgments
about the entire
population.”
Example: Go to food store, taste only a slice of the orange to test its sweetness.

3. Why Sampling is Needed? Lower cost Greater speed of data collection
Greater accuracy 3

4. Factors to Consider in Sample Design
Research objectives
Degree of accuracy
Resources
Time frame
Knowledge of
target population
Research scope
Statistical analysis needs

5. The Nature of Sampling Population Population element Sampling frame
Sample
Subject
Parameter
Statistics
Sampling error
The basic idea of sampling is that by selecting some of the elements in a population, we may draw conclusions about the entire population.
A population is the total collection of elements about which we wish to make some inferences.
A population element is the individual participant or object on which the measurement is taken. It is the unit of study. It may be a person but it could also be any object of interest.
A sampling frame is the listing of all population elements from which the sample will be drawn.
A sample is a part of the population from which we actually collect information which is used to draw conclusions about the whole population.
A subject is a single member of the sample.
A census is a count of all the elements in a population-every member of the population has data collected from them.
Parameter – characteristics of the population
Statistics – characteristics of the sample 5

6. The Nature of Sampling
Population - total collection of elements about which we wish to make some inferences.
Population element - the individual participant or object on which the measurement is taken--the unit of study.
Sampling frame - the listing of population elements from which the sample will be drawn—i.e., master lists, directories etc
The basic idea of sampling is that by selecting some of the elements in a population, we may draw conclusions about the entire population.
A population is the total collection of elements about which we wish to make some inferences.
A population element is the individual participant or object on which the measurement is taken. It is the unit of study. It may be a person but it could also be any object of interest.
A sampling frame is the listing of all population elements from which the sample will be drawn.
A sample is a part of the population from which we actually collect information which is used to draw conclusions about the whole population.
A subject is a single member of the sample.
A census is a count of all the elements in a population-every member of the population has data collected from them.
Parameter – characteristics of the population
Statistics – characteristics of the sample 6

7. The Nature of Sampling
Sample - a part of the population from which we actually collect information which is used to draw conclusions about the whole population
Subject - a single member of the sample
Parameter - characteristics of the population
Statistics - characteristics of the sample
Sampling error: any error in a survey that occurs because of the sample
The basic idea of sampling is that by selecting some of the elements in a population, we may draw conclusions about the entire population.
A population is the total collection of elements about which we wish to make some inferences.
A population element is the individual participant or object on which the measurement is taken. It is the unit of study. It may be a person but it could also be any object of interest.
A sampling frame is the listing of all population elements from which the sample will be drawn.
A sample is a part of the population from which we actually collect information which is used to draw conclusions about the whole population.
A subject is a single member of the sample.
A census is a count of all the elements in a population-every member of the population has data collected from them.
Parameter – characteristics of the population
Statistics – characteristics of the sample 7

8. Estimation & Hypothesis Testing
Inference Process
Population
Sample
Sample Statistics
Estimation & Hypothesis Testing
STATISTIC IS AN ELEMENT OR CHARACTERISTIC OF A SAMPLE USED TO MAKE INFERENCES ABOUT THE POPULATION PARAMETERS 8

9. Parameter and Statistics: Example
“Average income of engineers in Malaysia is RM5000”
Parameter
“Average income of engineers in Penang is RM5000”
Statistic
Population
Sample

10. The Sampling Design Process
Define the Population
Determine the Sampling Frame
Select Sampling Techniques
Determine the Sample Size
Execute the Sampling Process
Example: Go to food store, taste only a slice of the orange to test its sweetness.

11. Define the Target Population
Important factors in determining the sample size:
the importance of the decision
the nature of the research
the number of variables
the nature of the analysis
sample sizes used in similar studies
resource constraints-time and cost
SAMPLE SIZE??? 11

12. Sampling Error Sampling error is any type of bias
that is attributable to mistakes
in either drawing a sample or
determining the sample size
SAMPLE SIZE??? 12

13. Two Basic Sampling Methods
Probability samples: ones in which members of the population have a known chance (probability) of being selected into the sample
Non-probability samples: instances in which the chances (probability) of selecting members from the population into the sample are unknown
The basic idea of sampling is that by selecting some of the elements in a population, we may draw conclusions about the entire population.
A population is the total collection of elements about which we wish to make some inferences.
A population element is the individual participant or object on which the measurement is taken. It is the unit of study. It may be a person but it could also be any object of interest.
A sampling frame is the listing of all population elements from which the sample will be drawn.
A sample is a part of the population from which we actually collect information which is used to draw conclusions about the whole population.
A subject is a single member of the sample.
A census is a count of all the elements in a population-every member of the population has data collected from them.
Parameter – characteristics of the population
Statistics – characteristics of the sample 13

14. Classification of Sampling Techniques
Non-Probability
Sampling Techniques
Probability
Sampling Techniques
Convenience
Sampling
Judgmental
Quota
Snowball
The basic idea of sampling is that by selecting some of the elements in a population, we may draw conclusions about the entire population.
A population is the total collection of elements about which we wish to make some inferences.
A population element is the individual participant or object on which the measurement is taken. It is the unit of study. It may be a person but it could also be any object of interest.
A sampling frame is the listing of all population elements from which the sample will be drawn.
A sample is a part of the population from which we actually collect information which is used to draw conclusions about the whole population.
A subject is a single member of the sample.
A census is a count of all the elements in a population-every member of the population has data collected from them.
Parameter – characteristics of the population
Statistics – characteristics of the sample
Simple Random
Sampling
Double Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling 14

15. NON-PROBABILITY SAMPLING
With a subjective approach like nonprobability sampling, the probability of selecting population elements is unknown. There is a greater opportunity for bias to enter the sample and distort findings. We cannot estimate any range within which to expect the population parameter. Despite these disadvantages, there are practical reasons to use nonprobability samples.
When the research does not require generalization to a population parameter, then there is no need to ensure that the sample fully reflects the population. The researcher may have limited objectives such as those in exploratory research. It is less expensive to use nonprobability sampling. It also requires less time. Finally, a list may not be available. 15

16. Non-probability Samples
Reasons to use:
Procedure satisfactorily meets the sampling objectives
Lower Cost
Limited Time
Total list population not available
With a subjective approach like nonprobability sampling, the probability of selecting population elements is unknown. There is a greater opportunity for bias to enter the sample and distort findings. We cannot estimate any range within which to expect the population parameter. Despite these disadvantages, there are practical reasons to use nonprobability samples.
When the research does not require generalization to a population parameter, then there is no need to ensure that the sample fully reflects the population. The researcher may have limited objectives such as those in exploratory research. It is less expensive to use nonprobability sampling. It also requires less time. Finally, a list may not be available. 16

17. Non-probability Samples
No need to generalize
Feasibility
Limited objectives
With a subjective approach like nonprobability sampling, the probability of selecting population elements is unknown. There is a greater opportunity for bias to enter the sample and distort findings. We cannot estimate any range within which to expect the population parameter. Despite these disadvantages, there are practical reasons to use nonprobability samples.
When the research does not require generalization to a population parameter, then there is no need to ensure that the sample fully reflects the population. The researcher may have limited objectives such as those in exploratory research. It is less expensive to use nonprobability sampling. It also requires less time. Finally, a list may not be available.
Time
Cost 17

18. Non-probability Sampling Methods
Convenience
Based on ease of accessibility
Deliberately select sample to conform to some criterion
Judgmental
Relevant characteristics are used to segregate the sample to improve its representativeness
Quota
Convenience samples are nonprobability samples where the element selection is based on ease of accessibility. They are the least reliable but cheapest and easiest to conduct. Examples include informal pools of friends and neighbors, people responding to an advertised invitation, and “on the street” interviews.
Judgment sampling is purposive sampling where the researcher arbitrarily selects sample units to conform to some criterion. This is appropriate for the early stages of an exploratory study.
Quota sampling is also a type of purposive sampling. In this type, relevant characteristics are used to stratify the sample which should improve its representativeness. The logic behind quota sampling is that certain relevant characteristics describe the dimensions of the population. In most quota samples, researchers specify more than one control dimension. Each dimension should have a distribution in the population that can be estimated and be pertinent to the topic studied.
Snowball sampling means that subsequent participants are referred by the current sample elements. This is useful when respondents are difficult to identify and best located through referral networks. It is also used frequently in qualitative studies.
Snowball
Referred by current sample elements 18

19. Convenience Sampling
Convenience sampling – sample is selected base on ease of accessibility.
Normally use in the early stage of exploratory study
Often, respondents are selected because they happen to be in the right place at the right time.
use of students, and members of social organizations
mall intercept interviews without qualifying the respondents
“people on the street” interviews
pool of friends and contacts
With a subjective approach like nonprobability sampling, the probability of selecting population elements is unknown. There is a greater opportunity for bias to enter the sample and distort findings. We cannot estimate any range within which to expect the population parameter. Despite these disadvantages, there are practical reasons to use nonprobability samples.
When the research does not require generalization to a population parameter, then there is no need to ensure that the sample fully reflects the population. The researcher may have limited objectives such as those in exploratory research. It is less expensive to use nonprobability sampling. It also requires less time. Finally, a list may not be available. 19

20. Judgmental Sampling
Judgmental sampling is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher or those conform to some criterion of interest.
Useful when looking for information that only a few “experts” can provide.
Example:
Academic expertise
Purchase engineers selected in industrial marketing research
Expert witnesses used in court
With a subjective approach like nonprobability sampling, the probability of selecting population elements is unknown. There is a greater opportunity for bias to enter the sample and distort findings. We cannot estimate any range within which to expect the population parameter. Despite these disadvantages, there are practical reasons to use nonprobability samples.
When the research does not require generalization to a population parameter, then there is no need to ensure that the sample fully reflects the population. The researcher may have limited objectives such as those in exploratory research. It is less expensive to use nonprobability sampling. It also requires less time. Finally, a list may not be available. 20

21. Quota Sampling
Quota sampling – relevant characteristics are used to stratify the sample.
The first stage consists of developing categories of population elements.
In the second stage, sample elements are selected based on convenience or judgment.
Example: gender, religion, ethnicity, etc
With a subjective approach like nonprobability sampling, the probability of selecting population elements is unknown. There is a greater opportunity for bias to enter the sample and distort findings. We cannot estimate any range within which to expect the population parameter. Despite these disadvantages, there are practical reasons to use nonprobability samples.
When the research does not require generalization to a population parameter, then there is no need to ensure that the sample fully reflects the population. The researcher may have limited objectives such as those in exploratory research. It is less expensive to use nonprobability sampling. It also requires less time. Finally, a list may not be available. 21

22. Quota Sampling Population Sample composition composition
Characteristic Percentage Percentage Number Postgraduate MA 60% 60% PhD 40% 40% ____ ____ ____
With a subjective approach like nonprobability sampling, the probability of selecting population elements is unknown. There is a greater opportunity for bias to enter the sample and distort findings. We cannot estimate any range within which to expect the population parameter. Despite these disadvantages, there are practical reasons to use nonprobability samples.
When the research does not require generalization to a population parameter, then there is no need to ensure that the sample fully reflects the population. The researcher may have limited objectives such as those in exploratory research. It is less expensive to use nonprobability sampling. It also requires less time. Finally, a list may not be available. 22

23. Snowball Sampling
In snowball sampling, an initial group of respondents is selected, usually at random.
After being interviewed, these respondents are asked to identify others who belong to the target population of interest.
Subsequent respondents are selected based on the referrals.
With a subjective approach like nonprobability sampling, the probability of selecting population elements is unknown. There is a greater opportunity for bias to enter the sample and distort findings. We cannot estimate any range within which to expect the population parameter. Despite these disadvantages, there are practical reasons to use nonprobability samples.
When the research does not require generalization to a population parameter, then there is no need to ensure that the sample fully reflects the population. The researcher may have limited objectives such as those in exploratory research. It is less expensive to use nonprobability sampling. It also requires less time. Finally, a list may not be available. 23

24. PROBABILITY SAMPLING
With a subjective approach like nonprobability sampling, the probability of selecting population elements is unknown. There is a greater opportunity for bias to enter the sample and distort findings. We cannot estimate any range within which to expect the population parameter. Despite these disadvantages, there are practical reasons to use nonprobability samples.
When the research does not require generalization to a population parameter, then there is no need to ensure that the sample fully reflects the population. The researcher may have limited objectives such as those in exploratory research. It is less expensive to use nonprobability sampling. It also requires less time. Finally, a list may not be available. 24

25. Simple Random Sampling
All elements in the population are considered and each has equal chance to be selected.
Each possible sample of a given size (n) has a known and equal probability of being the sample actually selected.
Advantages
High generalisability of the findings
Easy to implement with random number table.
Disadvantages
Requires list of population elements
Time consuming
Uses larger sample sizes
In drawing a sample with simple random sampling, each population element has an equal chance of being selected into the samples. The sample is drawn using a random number table or generator. This slide shows the advantages and disadvantages of using this method.
The probability of selection is equal to the sample size divided by the population size.
Exhibit 14-6 covers how to choose a random sample. The steps are as follows:
Assign each element within the sampling frame a unique number.
Identify a random start from the random number table.
Determine how the digits in the random number table will be assigned to the sampling frame.
Select the sample elements from the sampling frame. 25

26. Systematic Random Sampling
The sample is chosen by selecting a random starting point and then picking every kth element from the sampling frame.
To draw a systematic sample, the steps are as follows:
Identify, list, and number the elements in the population
Identify the skip interval
Identify the random start
Draw a sample by choosing every kth element.
* kth element is the skip interval
In drawing a sample with systematic sampling, an element of the population is selected at the beginning with a random start and then every Kth element is selected until the appropriate size is selected.
The kth element is the skip interval, the interval between sample elements drawn from a sample frame in systematic sampling. It is determined by dividing the population size by the sample size.
To draw a systematic sample, the steps are as follows:
Identify, list, and number the elements in the population
Identify the skip interval
Identify the random start
Draw a sample by choosing every kth entry.
To protect against subtle biases, the research can
Randomize the population before sampling,
Change the random start several times in the process, and
Replicate a selection of different samples. 26

27. Systematic Random Sampling
EXAMPLE:
There are 100,000 elements in the population and a sample of 1,000 is desired. In this case the sampling interval, k, is A random number between 1 and 100 is selected. If, for example, this number is 23, the sample consists of elements 23, 123, 223, 323, 423, 523, and so on.
In drawing a sample with systematic sampling, an element of the population is selected at the beginning with a random start and then every Kth element is selected until the appropriate size is selected.
The kth element is the skip interval, the interval between sample elements drawn from a sample frame in systematic sampling. It is determined by dividing the population size by the sample size.
To draw a systematic sample, the steps are as follows:
Identify, list, and number the elements in the population
Identify the skip interval
Identify the random start
Draw a sample by choosing every kth entry.
To protect against subtle biases, the research can
Randomize the population before sampling,
Change the random start several times in the process, and
Replicate a selection of different samples.
Advantages
Simple to design
Easier than simple random if population frame is available
Disadvantages
Systematic biases are possible 27

28. Stratified Sampling All Postgraduates Masters PhD Sample
Population is divided into sub-population and subjects are selected randomly.
Homogeneity within group and heterogeneity across groups.
In drawing a sample with stratified sampling, the population is divided into subpopulations or strata and uses simple random on each strata. Results may be weighted or combined. The cost is high.
Stratified sampling may be proportion or disproportionate. In proportionate stratified sampling, each stratum’s size is proportionate to the stratum’s share of the population.
Any stratification that departs from the proportionate relationship is disproportionate. 28

29. Stratified Sampling
A two-step process in which the population is partitioned into sub-population.
Elements are selected from each sub-population by a random procedure, usually simple random sampling.
The elements within each sub-population should be as homogeneous as possible, but the elements across sub-population should be as heterogeneous as possible.
The stratification variables should also be closely related to the characteristic of interest.

30. Stratified Sampling Example: University students can be divided into:
Gender
Race
School/department
Class level: undergraduate and postgraduate
Off campus and on-campus

31. Stratified Sampling Advantages Disadvantages
Most efficient among all probability designs.
Increased statistical efficiency
Provides data to represent subgroups
Disadvantages
Stratification must be meaningful
Time consuming

32. All Managers in Malaysia
Cluster Sampling
All Managers in Malaysia
Population is divided into clusters.
Heterogeneity within group and homogeneity across groups.
Kuala Lumpur
Penang
Johor
In drawing a sample with cluster sampling, the population is divided into internally heterogeneous subgroups. Some are randomly selected for further study.
Two conditions foster the use of cluster sampling:
the need for more economic efficiency than can be provided by simple random sampling, and
2) the frequent unavailability of a practical sampling frame for individual elements.
Exhibit 14-7 provides a comparison of stratified and cluster sampling and is highlighted on the next slide.
Several questions must be answered when designing cluster samples.
How homogeneous are the resulting clusters?
Shall we seek equal-sized or unequal-sized clusters?
How large a cluster shall we take?
Shall we use a single-stage or multistage cluster?
How large a sample is needed?
Sample 32

33. Cluster Sampling Population Element Possible Clusters in Malaysia
Malaysian adult population States
Districts
Metropolitan Statistical Area
Housing Area
Households

34. Cluster Sampling
The target population is first divided into mutually exclusive clusters.
Then a random sample of clusters is selected, based on a probability sampling technique
Elements within a cluster should be as heterogeneous as possible, but clusters themselves should be as homogeneous as possible.
Ideally, each cluster should be a small-scale representation of the population.

35. Area Sampling (example of cluster)
A cluster sampling technique applied to a population with well-defined political or geographic boundaries.

36. Double Sampling
The same sample or a subset of the sample is studied twice.
Double and multiple sampling plans were invented to give a questionable lot another chance.
For example:
a structured interview might indicate that a subgroup of the respondents has more insights into a problem in the organization, then, these respondents might be approached again with additional questions.
In drawing a sample with double (sequential or multiphase) sampling, data are collected using a previously defined technique. Based on the information found, a subsample is selected for further study. 36

37. Double Sampling
In drawing a sample with double (sequential or multiphase) sampling, data are collected using a previously defined technique. Based on the information found, a subsample is selected for further study. 37

38. What Is a Valid Sample? Accurate Precise
The degree to which bias is absent from the sample. The sample is drawn properly.
The degree to which the sample selected closely represent the population.
The ultimate test of a sample design is how well it represents the characteristics of the population it purports to represent. In measurement terms, the sample must be valid. Validity of a sample depends on two considerations: accuracy and precision.
Here a sample is being taken of water, using a can suspended on a fishing line.
Accuracy is the degree to which bias is absent from the sample. When the sample is drawn properly, the measure of behavior, attitudes, or knowledge of some sample elements will be less than the measure of those same variables drawn from the population. The measure of other sample elements will be more than the population values. Variations in these sample values offset each other, resulting in a sample value that is close to the population value. For these offsetting effects to occur, there must be enough elements in the sample and they must be drawn in a way that favors neither overestimation nor underestimation. Increasing the sample size can reduce systematic variance as a cause of error. Systematic variance is a variation that causes measurements to skew in one direction or another.
Precision of estimate is the second criterion of a good sample design. The numerical descriptors that describe samples may be expected to differ from those that describe populations because of random fluctuations inherent in the sampling process. This is called sampling error and reflects the influence of chance in drawing the sample members. Sampling error is what is left after all known sources of systematic variance have been accounted for.
Precision is measured by the standard error of estimate, a type of standard deviation measurement. The smaller the standard error of the estimate, the higher is the precision of the sample. 38

39. High accuracy but low precision High precision but low accuracy
What Is a Valid Sample?
The ultimate test of a sample design is how well it represents the characteristics of the population it purports to represent. In measurement terms, the sample must be valid. Validity of a sample depends on two considerations: accuracy and precision.
Here a sample is being taken of water, using a can suspended on a fishing line.
Accuracy is the degree to which bias is absent from the sample. When the sample is drawn properly, the measure of behavior, attitudes, or knowledge of some sample elements will be less than the measure of those same variables drawn from the population. The measure of other sample elements will be more than the population values. Variations in these sample values offset each other, resulting in a sample value that is close to the population value. For these offsetting effects to occur, there must be enough elements in the sample and they must be drawn in a way that favors neither overestimation nor underestimation. Increasing the sample size can reduce systematic variance as a cause of error. Systematic variance is a variation that causes measurements to skew in one direction or another.
Precision of estimate is the second criterion of a good sample design. The numerical descriptors that describe samples may be expected to differ from those that describe populations because of random fluctuations inherent in the sampling process. This is called sampling error and reflects the influence of chance in drawing the sample members. Sampling error is what is left after all known sources of systematic variance have been accounted for.
Precision is measured by the standard error of estimate, a type of standard deviation measurement. The smaller the standard error of the estimate, the higher is the precision of the sample.
High accuracy but low precision
High precision but low accuracy 39

40. RULE OF THUMB FOR SAMPLE SIZE:
According to ROSCOE (1975):
Sample size larger than 30 and less than 500 are appropriate for most research.
Where samples are to be broken into subsamples (male/female, masters/PhD etc), a minimum sample size of 30 for each category is necessary.
In multivariate research, sample size should be, preferably, 10 times (or more) as large as the number of variables in the study. 40 40

41. LET’S RECAP...
The ultimate test of a sample design is how well it represents the characteristics of the population it purports to represent. In measurement terms, the sample must be valid. Validity of a sample depends on two considerations: accuracy and precision.
Here a sample is being taken of water, using a can suspended on a fishing line.
Accuracy is the degree to which bias is absent from the sample. When the sample is drawn properly, the measure of behavior, attitudes, or knowledge of some sample elements will be less than the measure of those same variables drawn from the population. The measure of other sample elements will be more than the population values. Variations in these sample values offset each other, resulting in a sample value that is close to the population value. For these offsetting effects to occur, there must be enough elements in the sample and they must be drawn in a way that favors neither overestimation nor underestimation. Increasing the sample size can reduce systematic variance as a cause of error. Systematic variance is a variation that causes measurements to skew in one direction or another.
Precision of estimate is the second criterion of a good sample design. The numerical descriptors that describe samples may be expected to differ from those that describe populations because of random fluctuations inherent in the sampling process. This is called sampling error and reflects the influence of chance in drawing the sample members. Sampling error is what is left after all known sources of systematic variance have been accounted for.
Precision is measured by the standard error of estimate, a type of standard deviation measurement. The smaller the standard error of the estimate, the higher is the precision of the sample. 41

42. Nonprobability Sampling Methods
Convenience sampling relies
upon convenience and access
Judgment sampling relies upon belief
that participants fit characteristics
Quota sampling emphasizes representation
of specific characteristics
Snowball sampling relies upon respondent
referrals of others with like characteristics

43. Exercise 1
A researcher wants a sample of 35 households from a total population of 260 houses in Medan, Indonesia. He samples every 7th house starting from a random number of 1 to 7. He then choose houses numbered 7, 14, 21, 28 and so on. What type of sampling technique does the researcher adopt?
a)    a simple random sampling
b)    a stratified random sampling
c)    a cluster sampling
d)    a systematic random sampling 43

44. Exercise 2
A pharmaceutical company wants to trace the effects of a new drug on patients with specific health problems. It then contacts such individuals and with the group of voluntarily consenting patients, tests the drugs. What type of sampling is appropriate?
a)     a simple random sample
b)    a stratified random sample
c)     a cluster sample
d)    a judgmental sample 44

45. Exercise 3
The director of human resources of a manufacturing firm wants to offer stress management seminars to the personnel who are exposed to high levels of stress. He predicts that three groups are most prone to stress; (1) those who handle dangerous chemicals, (2) counselors who listen to problems, and (3) those who handle production line. What type of sampling is most appropriate in this case?
a)     a simple random sample
b)    a stratified random sample
c)     a cluster sample
d)    a judgmental sample 45

46. The end
Questions?