research design (part 2) siti rohaida bt mohamed zainal, phd school of management...
TRANSCRIPT
What is Sampling?
“Sampling is aprocess by whichwe study a smallpart of a populationto make judgmentsabout the entirepopulation.”
Why Sampling is Needed?
Lower cost Greater speed of data collection Greater accuracy
Factors to Consider in Sample Design
Research objectives Degree of accuracy
Resources Time frame
Knowledge oftarget population Research scope
Statistical analysis needs
The Nature of Sampling
• Population• Population element• Sampling frame• Sample • Subject• Parameter• Statistics• Sampling error
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 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
Inference Process
Population
Sample
Sample Statistics
Estimation & Hypothesis
Testing
),( spX
Parameter and Statistics: Example
“Average income of engineers in Malaysia is RM5000”
Parameter
“Average income of engineers in Penang is RM5000”
Statistic
Population
Sample
The Sampling Design Process
Define the Population
Determine the Sampling Frame
Select Sampling Techniques
Determine the Sample Size
Execute the Sampling Process
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???
Sampling Error
SAMPLE SIZE???
Sampling error is any type of bias that is attributable to mistakes in either drawing a sample ordetermining the sample size
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
Two Basic Sampling Methods
Classification of Sampling Techniques
SAMPLING TECHNIQUES
Non-ProbabilitySampling
Techniques
ProbabilitySampling
Techniques
ConvenienceSampling
JudgmentalSampling
QuotaSampling
SnowballSampling
SystematicSampling
StratifiedSampling
ClusterSampling
Double Sampling
Simple RandomSampling
NON-PROBABILITY
SAMPLING
Non-probability Samples
Reasons to use: Procedure satisfactorily meets the sampling
objectives Lower Cost Limited Time Total list population not available
Non-probability Samples
Cost
FeasibilityFeasibility
TimeTime
No need to generalize
Limited objectivesLimited
objectives
Non-probability Sampling Methods
ConvenienceConvenience
JudgmentalJudgmental
QuotaQuota
SnowballSnowball
Based on ease of accessibility
Deliberately select sample to conform to some criterion
Relevant characteristics are used to segregate the sample to improve its representativeness
Referred by current sample elements
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
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
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
Quota Sampling
Population Samplecomposition composition
Characteristic Percentage PercentageNumberPostgraduate MA 60% 60% 600 PhD 40% 40% 400
____ ____ ____100 100 1000
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.
PROBABILITY SAMPLING
Simple Random Sampling
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
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.
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:1) Identify, list, and number the elements in the
population2) Identify the skip interval 3) Identify the random start 4) Draw a sample by choosing every kth element.
* kth element is the skip interval
Advantages• Simple to design• Easier than simple
random if population frame is available
Disadvantages• Systematic biases are
possible
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 100. 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.
Systematic Random Sampling
Stratified Sampling
• Population is divided into sub-population and subjects are selected randomly.
• Homogeneity within group and heterogeneity across groups.
All Postgraduates
Masters PhD
Sample
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.
Stratified Sampling
Example:
University students can be divided into:GenderRaceSchool/departmentClass level: undergraduate and postgraduateOff campus and on-campus
Stratified Sampling
Advantages• Most efficient among all
probability designs.• Increased statistical
efficiency• Provides data to
represent subgroups
Disadvantages• Stratification must be
meaningful• Time consuming
Stratified Sampling
Cluster Sampling
• Population is divided into clusters.
• Heterogeneity within group and homogeneity across groups.
All Managers in Malaysia
Kuala Lumpur Johor
Sample
Penang
Population Element Possible Clusters in Malaysia
Malaysian adult population StatesDistrictsMetropolitan
Statistical AreaHousing AreaHouseholds
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.
Cluster Sampling
Area Sampling (example of cluster)
A cluster sampling technique applied to a population with well-defined political or geographic boundaries.
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.
Double Sampling
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.
What Is a Valid Sample?
High accuracy but low precision
High precision but low accuracy
RULE OF THUMB FOR SAMPLE SIZE:
According to ROSCOE (1975):
(1) Sample size larger than 30 and less than 500 are appropriate for most research.
(2) Where samples are to be broken into subsamples (male/female, masters/PhD etc), a minimum sample size of 30 for each category is necessary.
(3) In multivariate research, sample size should be, preferably, 10 times (or more) as large as the number of variables in the study.
LET’S RECAP
...
Nonprobability Sampling Methods
Convenience sampling relies upon convenience and access
Judgment sampling relies upon belief that participants fit characteristics
Quota sampling emphasizes representationof specific characteristics
Snowball sampling relies upon respondent referrals of others with like characteristics
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Exercise 1A 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 samplingb) a stratified random samplingc) a cluster samplingd) a systematic random sampling
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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 sampleb) a stratified random samplec) a cluster sampled) a judgmental sample
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Exercise 3The 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 sampleb) a stratified random samplec) a cluster sampled) a judgmental sample
The end
Questions?