1. SAMPLING DESIGN AND PROCEDURE
Lecture 9
SAMPLING DESIGN AND PROCEDURE

2. Population and Sample Population
The entire group that the researcher wishes to investigate
Element
A single member of the population

3. Population and Sample Population (Sampling) Frame
A listing of all the elements in the population from which the sample is drawn
Sample
A subset of the population
Subject
A single member of the sample

4. CENSUS
INVESTIGATION OF ALL INDIVIDUAL ELEMENTS THAT MAKE UP A POPULATION

5. When Is A Census Appropriate?
Necessary
Feasible
The advantages of sampling over census studies are less compelling when the population is small and the variability within the population is high. Two conditions are appropriate for a census study. A census is feasible when the population is small and necessary when the elements are quite different from each other.

6. TARGET POPULATION RELEVANT POPULATION OPERATIONALLY DEFINE
COMIC BOOK READER?

7. Availability of elements
Why Sample?
Availability of elements
Lower cost
Sampling
provides
This slide lists the reasons researchers use a sample rather than a census.
Greater speed
Greater accuracy

8. SAMPLING FRAME A LIST OF ELEMENTS FROM WHICH THE SAMPLE MAY BE DRAWN
WORKING POPULATION
MAILING LISTS - DATA BASE MARKETERS
SAMPLING FRAME ERROR

9. Sampling
Process of selecting a sufficient number of elements from the population
Reasons for Sampling: practicality (time and resources), destructive sampling
Need for a representative sample

10. SAMPLING UNITS GROUP SELECTED FOR THE SAMPLE
PRIMARY SAMPLING UNITS (PSU)
SECONDARY SAMPLING UNITS
TERTIARY SAMPLING UNITS

11. TWO MAJOR CATEGORIES OF SAMPLING
PROBABILITY SAMPLING
KNOWN, NONZERO PROBABLITY FOR EVERY ELEMENT
NONPROBABLITY SAMPLING
PROBABLITY OF SELECTING ANY PARTICULAR MEMBER IS UNKNOWN

12. Probability and Nonprobability Sampling
Elements in the population have known chance of being chosen
Used when the representativeness of the sample is of importance
Nonprobability Sampling
The elements do not have a known or predetermined chance of being selected as subjects

13. Probability Sampling Unrestricted/Simple Random Sampling
Every element in the population has a known and equal chance of being selected as a subject
Has the least bias and offers the most generalizability
Restricted/Complex Probability Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling (USM, UM, etc)
Area Sampling
Double Sampling (USM and then grad students)

14. PROBABLITY SAMPLING SIMPLE RANDOM SAMPLE SYSTEMATIC SAMPLE
STRATIFIED SAMPLE
CLUSTER SAMPLE
MULTISTAGE AREA SAMPLE

15. SIMPLE RANDOM SAMPLING
a sampling procedure that ensures that each element in the population will have an equal chance of being included in the sample

16. Simple Random Advantages Easy to implement with random dialing
Disadvantages
Requires list of population elements
Time consuming
Uses larger sample sizes
Produces larger errors
High cost
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 15-4 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.

17. SYSTEMATIC SAMPLING A simple process
every nth name from the list will be drawn

18. Systematic Advantages Simple to design Easier than simple random
Easy to determine sampling distribution of mean or proportion
Disadvantages
Periodicity within population may skew sample and results
Trends in list may bias results
Moderate cost
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.

19. STRATIFIED SAMPLING Probability sample
Subsamples are drawn within different strata
Each stratum is more or less equal on some characteristic
Do not confuse with quota sample

20. Stratified Advantages Control of sample size in strata
Increased statistical efficiency
Provides data to represent and analyze subgroups
Enables use of different methods in strata
Disadvantages
Increased error will result if subgroups are selected at different rates
Especially expensive if strata on population must be created
High cost
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.

21. CLUSTER SAMPLING
The purpose of cluster sampling is to sample economically while retaining the characteristics of a probability sample.
The primary sampling unit is no longer the individual element in the population.
The primary sampling unit is a larger cluster of elements located in proximity to one another.

22. EXAMPLES OF CLUSTERS Population Element Possible Clusters in Malaysia
Malaysian adult population States
Districts
Metropolitan Statistical Area
Census tracts
Blocks
Households

23. EXAMPLES OF CLUSTERS Population Element Possible Clusters in Malaysia
College seniors Colleges
Manufacturing firms Districts
Metropolitan Statistical Areas
Localities
Plants

24. EXAMPLES OF CLUSTERS Population Element Possible Clusters in Malaysia
Airline travelers Airports
Planes
Sports fans Football stadia
Basketball arenas
Baseball parks

25. Cluster
Advantages
Provides an unbiased estimate of population parameters if properly done
Economically more efficient than simple random
Lowest cost per sample
Easy to do without list
Disadvantages
Often lower statistical efficiency due to subgroups being homogeneous rather than heterogeneous
Moderate cost
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 15-5 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?

26. Stratified and Cluster Sampling
Population divided into few subgroups
Homogeneity within subgroups
Heterogeneity between subgroups
Choice of elements from within each subgroup
Cluster
Population divided into many subgroups
Heterogeneity within subgroups
Homogeneity between subgroups
Random choice of subgroups

27. Double
Advantages
May reduce costs if first stage results in enough data to stratify or cluster the population
Disadvantages
Increased costs if discriminately used
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.

28. Nonprobability Samples
No need to generalize
Feasibility
Limited objectives
Issues
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

29. Nonprobability Sampling Methods
Convenience
Judgment
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

30. NONPROBABLITY SAMPLING
CONVENIENCE
JUDGMENT
QUOTA
SNOWBALL

31. Nonprobability Sampling
Convenience Sampling
Based on availability, e.g. students in a classroom
Purposive Sampling
Specific targets, because they posses the desired info
Judgement sampling
Quota sampling

32. CONVENIENCE SAMPLING also called haphazard or accidental sampling
the sampling procedure of obtaining the people or units that are most conveniently available

33. QUOTA SAMPLING
ensures that the various subgroups in a population are represented on pertinent sample characteristics
to the exact extent that the investigators desire
it should not be confused with stratified sampling

34. JUDGMENT SAMPLING also called purposive sampling
an experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member

35. SNOWBALL SAMPLING a variety of procedures
initial respondents are selected by probability methods
additional respondents are obtained from information provided by the initial respondents

36. Area Sampling
Area sampling is a cluster sampling technique applied to a population with well-defined political or geographic boundaries. It is a low-cost and frequently used method.

37. Sample Size Factors Determining Sample Size Homogeneity of population
Level of confidence
Precision
Cost, Time and Resources

38. Larger Sample Sizes When Population variance Number of subgroups
Desired precision
When
The greater the dispersion or variance within the population, the larger the sample must be to provide estimation precision.
The greater the desired precision of the estimate, the larger the sample must be.
The narrower or smaller the error range, the larger the sample must be.
The higher the confidence level in the estimate, the larger the sample must be.
The greater the number of subgroups of interest within a sample, the greater the sample size must be, as each subgroup must meet minimum sample size requirements.
Cost considerations influence decisions about the size and type of sample and the data collection methods.
Confidence level
Small error range

39. Roscoe’s Rule of Thumb
>30 and <500 appropriate for most research
Not less than 30 for each sub-sample
In multivariate analysis, 10 times or more the number of variables
Simple experiment with tight controls, quite sufficient

40. WHAT IS THE APPROPRIATE SAMPLE DESIGN
DEGREE OF ACCURACY
RESOURCES
TIME
ADVANCED KNOWLEDGE OF THE POPULATION
NATIONAL VERSUS LOCAL
NEED FOR STATISTICAL ANALYSIS

41. What Is A Good Sample? Accurate Precise
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.
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.

42. AFTER THE SAMPLE DESIGN IS SELECTED
DETERMINE SAMPLE SIZE
SELECT ACTUAL SAMPLE UNITS
CONDUCT FIELDWORK

43. SYSTEMATIC ERRORS NONSAMPLING ERRORS UNREPRESENTATIVE SAMPLE RESULTS
NOT DUE TO CHANCE
DUE TO STUDY DESIGN OR IMPERFECTIONS IN EXECUTION

44. ERRORS ASSOCIATED WITH SAMPLING
SAMPLING FRAME ERROR
RANDOM SAMPLING ERROR
NONRESPONSE ERROR

45. RANDOM SAMPLING ERROR
THE DIFFERENCE BETWEEN THE SAMPLE RESULTS AND THE RESULT OF A CENSUS CONDUCTED USING IDENTICAL PROCEDURES
STATISTICAL FLUCTUATION DUE TO CHANCE VARIATIONS

46. Stages in the Selection of a Sample
Define the target population
Select a sampling frame
Determine if a probability or
nonprobability sampling method
will be chosen
Plan procedure for selecting
sampling units
Determine sample size
Select actual sampling units
Conduct fieldwork

47. End of lesson