1. Mgt 540 Research Methods Sampling Issues

2. Basic Research Progress
Explorative - Descriptive
(Qualitative)
Framework / Domain
extant knowledge for reference
Research Design
Data collection / presentation
Data analysis
Emergent themes
Relationship to extant knowledge?
Tie to lit review, other research
Findings?
Possible hypothesis?
Hypothesis Testing
(Quantative)
Framework / Domain
Foundation
Conceptual framework
Hypothesis presentation
Research Design
Data collection / presentation
Data analysis
Confirm/disconfirm
Findings?
Possible additional hypotheses?

3. Research Design Flowchart
FROM CHAPTER 11
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

4. Sampling
Why sample?
Budget restrictions
Time constraints
Inaccessibility of some population members
Sufficient accuracy, reliability with good sample
Larger sample required for more heterogeneous population
Randomly chosen sample is fair in the sense that every member of the population has an equal chance of being chosen

5. Sampling issues (terms)
Selection of sufficient number of items or elements so that the properties of the sample (statistic) could be generalized to the population (parameter)
Population Frame
Listing of population elements
Population
Entire group of interest to researcher (people, things, events)
Sample
Subgroup of the population
Subject
Single member of a sample
Element
Single member of the population

6. Sampling precision Precision Degree of sampling error
Measured by the standard error of the estimate
See page 286 and
Statistical tables, beginning on page 432

7. Sampling  = Sample Mean S = Std. Deviation μ= Population Mean
FIGURE 11.1
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

8. Questions for determining sample
Relevant target population?
Exact parameters of interest?
Kind of sampling frame available?
Sample size needed (for desired level of confidence)?
Cost relating to sampling design?
Time available to collect data from sample?
FIGURE 11.2
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

9. Sampling Frame
The empirical representation of the theoretical universe of interest
In theory may be the entire population
But, for example
Not all own telephones (for a telephone survey)
Some may be homeless (for a mail survey)

10. Sampling Unit Compare to the desired unit of analysis Individuals
Dyads
Work groups, teams
Companies
Industries
Markets

11. Sampling Issues
11B
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

12. Probability & Non Probability Sampling
11C
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

13. Simple Random Sampling
Most representative for most purposes
Disadvantages
Cumbersome and tedious
Entire listing of all elements in the desired population are usually not available
Very expensive
Not the most efficient design

14. Complex probability sampling(s)
Systematic
Stratified random sampling
Cluster sampling
Area sampling
Double sampling

15. Systematic sampling
Every nth element is sampled, starting from a randomly chosen element
11F
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

16. Stratified random sampling
Number of mutually exclusive sub-populations or strata
e.g. university students divided into juniors, seniors, etc.
Homogeneity within stratum and heterogeneity between strata
Statistical efficiency greater in stratified samples
Sub-groups can be analyzed
Different methods of analysis can be used for different sub-groups
11G
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

17. Stratified random sample
11H
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

18. Stratified random sample
TABLE 11.1
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

19. 11I
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

20. 11J
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

21. Non-Probability Sampling
Convenience samples
the researcher’s convenience – unrestricted
Purposive samples
Judgment sampling – expert selection of respondents
Quota sampling – ensuring representation of certain groups, individuals
Snowball sampling – initially selected respondents (by probability or not) refer later ones

22. 11K
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

23. 11L
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

24. Precision
11M
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

25. Sampling considerations
What is the relevant population?
What type of sample should be drawn?
What sampling frame should be used?
What are the parameters of interest?
How much accuracy and precision are desired?
What is the sample size needed?
What are the sampling costs?
11N
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

26. Sample size considerations
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

27. Sampling Efficiency Using n = sample size, S = standard error
Efficiency is achieved when:
Keeping n constant,
you achieve a smaller S
Reduce n
keep the same level of S
11P
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

28. FIGURE 11.3
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

29. Precision vs. Confidence
More Precision
Less Confidence
More Confidence
Less Precision
FIGURE 11.4
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

30. Pg. 294
TABLE 11.3
Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

31. Relevance of sample size Refer back to diagram on page 175
Purpose of Research?
Exploratory
Discovery
Hypothesis testing?
Types of investigation?
Differences?
Correlations?
Causality?
Unit of analysis?
Data Collection method?
Qualitative?
Quantitative?
Measurement / Measures?

32. Sampling exercises “What kinds of sampling designs for….”
A study to get a quick idea of the medical acceptability of a new aspirin substitute which cannot be dispensed over the counter without prescription.
Purposive judgment sampling

33. Sampling exercises “What kinds of sampling designs for….”
A study involving a sample of 325 students in a university where 2,000 students are enrolled.
A systematic sampling design (using a university listing of students
An investigation of the career salience of professionals in the fields of medicine, engineering, business, and law.
A stratified random sampling with stratification along profession, gender, age, etc.

34. Sampling exercises “What kinds of sampling designs for….”
The generalizability of the attitudes of blue collar workers from a sample of 184, to the total population of 350 blue collar workers in the entire factory of a particular company.
Simple random sampling (because of the high importance attached to generalizability

35. Sampling exercise (problem)
You want to estimate the production days that would be lost during the next three months by sampling the vacation intentions of a few employees. You randomly select 36 employees in the organization and find that the average number of days they intend taking off is 16 during the coming three Summer months, with a standard deviation of seven (7) days. Based on these sample statistics, you want to estimate at a 99 percent confidence level, the days that will be lost due to the entire population of workers taking vacation time during the next three months, so that the plant manager knows how much temporary help he should plan on hiring during the summer months in order for work to proceed smoothly.

36. Exercise calculation Solution: μ =  ± z S S = S/√n = 7/6 = 1.167
= 16 ± 3.01
= to 19.01
 = Sample Mean
μ = Population Mean
S = Std. Deviation
S = Standard Error

37. If there are 100 employees in the organization expected to take vacation during Summer, then, the most optimistic estimation of the days lost through vacation time during the summer would be (13 x 100 =) 1m300 days and the most pessimistic would be (19 x 100 =) 1900 days. This would mean that temporary help would be needed anywhere between 1,300 and 1,900 days worth of labor for production to proceed smoothly.
To narrow the gap – (increase precision) requires sacrificing confidence – choose your risk.