A Group Assignment on Probabilistic Sampling For

Business Research Methods Course Requirement

By Section Two Group 3 Members

5-2

Group Member’s

1. Sara Jemal 2. Shimelis Birhanu 3. Setegn Addisu 4. Selamawit Wolde 5. Solomon T/Markos 6. Sisay Tufa

Course Instructor Dr. Shimelis Z.

Feb12.2017

Addis Ababa/ Ethiopia

PROBABILISTIC

SAMPLING

General Background

Sampling

Sampling Terminologies

Types Of Sampling

Types Probabilistic

Sampling

1. Simple Random Sampling

2. Systematic Random Sampling

3. Cluster Sampling

4. Stratified Sampling

5. Multi Stage Sampling

Summary on Selection

charts of Probabilistic

Sampling ?

1. Define basic sampling terminologies & Probabilistic Sampling.

2. Describe and discuss the different Probabilistic sampling

designs.

3. Discuss the factors to be taken into consideration for

determining types of Probabilistic Sampling.

4. Discuss the merit & a limitation of each Probabilistic Sampling

design.

5. To assist our colleague’s in order to utilize the concepts of

Probabilistic Sampling in there future projects Plan

Sampling method: is the process of selecting a small group to

representative the whole universe in order to:

obtain accurate info. with minimum cost, time & energy

To improve accuracy of such estimates

Probability sampling : Every item of the universe has an

equal chance/ Same probabilities of being included in the

sample.

Probability sampling AKA

Equal probability of selection (EPS) design.

self-weighting design.

Chance sampling or

Random sampling

Why Probability sampling Considered

we can measure the errors of estimation

we can measure the significance of results

It ensures the law of Statistical regularity

Population: collection of all elements in universe

Sample: is a subset of the population

Sampling: process used to determine the sample

Sampling units: collections of elements from the

population that cover the entire population

Sampling frame is a list of sampling units

Clusters: a group of similar things or people positioned or

occurring closely together.

Strata: an elements of homogeneous subgroups

Heterogeneous: is the state of being dissimilar.

Homogeneous: as the state of being similar.

• Achieving a representative sample • Minimizing sampling bias • Making statistical inferences • Less knowledge of universe is sufficient. • Sample representative of population

A) Advantages of Probability

Sampling

• It takes time • It is costly • Chances of selecting specific class of

samples only

B) Limitation of Probability

Sampling

1.5.1 Simple Random Sampling

The purest form of probability sampling & EPS design

It used when the population of interest is

Small, homogeneous & readily available

If our sampling frame has a periodic pattern

1 .5.1 Simple Random Sampling Cont.…..

A. Tossing a coin

B. Throwing a dice

C. Lottery method

D. Blind folded method

E. Random number table

F. Random number generation

A. SRSWR

SRS with replacement

AKA Equal probability SRS

B. SRSWOR

SRS without replacement

AKA Varying probability SRS

Types of SRS Techniques for Randomization

Step 3

Drawn the sample

Step 2

Prepare an exhaustive list (sampling frame) of all

member of the population

Step 1

Determine Types & Techniques of Randomization

1 .5.1 Simple Random Sampling Cont.…..

1. Easy to implement

2. Free from subjectivity & personal error.

3. Requires the minimum knowledge of population

4. provides appropriate data for one’s purpose.

5. can be used for inferential purpose

A. Imprecise for heterogeneous

B. It does not use the knowledge about the population

C. Cannot ensure the representativeness

D. Time taking & costly procedures

E. Its inferential accuracy depends upon the size of the sample

F. Can be disruptive to isolate members from a group

Advantages of SRS Limitation of SRS

Systematic Random Sampling:

It involves you selecting the sample at regular intervals from the

sampling

Each element has an equal probability of selection, but

combinations of elements have different probabilities.

It requires the complete information about the population.

There should be a list of information of all the individuals of the

population in any systematic way.

1.5.2 Systematic Random Sampling

When to Use Systematic Random Sampling?

1. When no list of population exists

2. When the list is roughly of random order

3. Small area/population

1.5.2 Systematic Random Sampling

1.5.2 Systematic Random Sampling Procedures

Step 4

Sample drawn by Adding Kth unit to the randomly chosen number j+k, j+2k….

Step 3

Determine the first number randomly b/n 1st # and kth

Step 2

Determine Sampling interval K (k=Population Size/ Sample size or N/n)

Step 1

Number each of the cases in your sampling frame with a unique number

1.5.2 Systematic Random Sampling Procedures Cont.….

1 .5.2 Systematic Random Sampling Cont.…..

1. Sample easy to select

2. Simple to implement

3. Sample evenly spread over

entire reference population

4. Provides a better random

distribution than SRS

5. Can be started without a

complete listing frame

6. It reduces the field cost.

7. Inferential statistics may be

used.

8. Conclusions & generalizations is

possible

9. It is not costly design

10. Required minimal expertise

knowledge

Advantages of SyRS Advantages of SyRS Cont.…

1 .5.2 Systematic Random Sampling Cont.…..

1. Not fit for periodical sample

2. Difficult to assess precision of

estimate from one survey.

3. linear trend

4. This is not free from error

5. Knowledge of population is

essential

6. Information of each individual is

essential.

7. Only for optimal size of

population

8. Population distribution should be

natural degree of randomness

9. There is a greater risk of data

manipulation

Limitation of SyRS Limitation of SyRS Cont.…

Cluster Sampling:

AKA block sampling.

A Cluster sampling is a form of sampling divided the

population in to groups or clusters.

Cluster sampling usually geographic or organizational.

Cluster sampling is an exampling of two stage sampling.

The cluster are homogeneous units.

Sampling units are groups rather than individuals.

1.5.3 Cluster Sampling

Cluster Sampling:

A sample of such cluster is then selected.

In pure cluster sampling whole cluster is sampled.

In simple multi stage cluster randomly chosen .

Types Cluster Sampling Methods:

1. One-stage sampling.

All of the elements within selected clusters are included in the sample.

2. Two-stage sampling.

A subset of elements within selected clusters are randomly selected for inclusion in the sample

1.5.3 Cluster Sampling Cont.…

1.5.3 Cluster Sampling Procedures

Step 4

Sample of such clusters is then selected & all units from the selected clusters are studied..

Step 3

Population divided into clusters of homogeneous units & geographical contiguity

Step 2

A sample of respondents within those areas is selected.

Step 1

A sample of areas is chosen

1.5.3 Cluster Sampling Cont.…

1. Most economical/ Cheaper

2. Larger sample for a similar fixed

cost

3. Less time for listing &

implementation

4. Also suitable for survey of

institutions

5. Reduced cost of personal

interviews

6. It may be a good representative

of the population.

7. It is an easy method.

8. It is practicable & highly

applicable in education.

9. Observations can be used for

inferential purpose.

10. Feasible

11. Reduced variability

Advantages of Cluster S. Advantages of Cluster S.

1.5.3 Cluster Sampling Cont.…

1. Most economical/ Cheaper

2. Larger sample for a similar fixed

cost

3. Less time for listing &

implementation

4. Also suitable for survey of

institutions

5. Reduced cost of personal

interviews

6. It may be a good representative

of the population.

7. It is an easy method.

8. It is practicable & highly

applicable in education.

9. Observations can be used for

inferential purpose.

10. Feasible

11. Reduced variability

Advantages of Cluster S. Advantages of Cluster S.

1.5.3 Cluster Sampling Cont.…

1. Cluster sampling is not free from

errors.

2. It is not comprehensive.

3. Higher sampling error

4. Biased samples

5. May not reflect the diversity of

the community.

6. Other elements in the same

cluster may share similar

characteristics.

7. Provides less information per

observation than an SRS

Limitation of Cluster S.

Limitation of Cluster S.

Stratified Sampling:

A sampling method applied to extract a representative

sample from a heterogeneous population

Adequate representation of minority subgroups of interest

can be ensured by stratification & varying sampling fraction

between strata as required

Finally, since each stratum is treated as an independent

population, different sampling approaches can be applied to

different strata

1.5.4 Stratified Sampling

Post Stratification:

Stratification is sometimes introduced after the sampling

phase in a process called "post stratification“.

This approach is typically implemented due to :

Lack of prior knowledge of an appropriate stratifying

variable

when the experimenter lacks the necessary

information to create a stratifying variable during the

sampling phase.

1.5.4 Stratified Sampling Cont..

In SS Design:

1. Stratum variables are mutually exclusive (non-over lapping)

2. The population (elements) homogenous within-stratum

3. the population (elements) heterogeneous between the strata.

When we use SS:

A. Population groups may have different values for the responses of interest.

B. If we want to improve our estimation for each group separately.

C. To ensure adequate sample size for each group.

1.5.4 Stratified Sampling Cont..

1.5.4 Stratified Sampling Procedures

• The basis of common characteristic(s) of the items to be put in each stratum

• Strata are purposively formed and are usually based on past experience and personal judgment of the researcher.

Form of strata

• Simple random sampling • Systematic sampling can be used if it is considered

more appropriate in certain situations

Selection of items in each

stratum

• Proportional allocation • Optimum allocation (with equal cost) • Optimum allocation (with unequal cost)

Allocate of sample size of

each

stratum?

1.5.4 Sample Allocation for Stratified Sampling Design

• is more effective for comparing strata which have different error possibilities

• is less efficient for determining population characteristics.

Disproportionate

• It refers to the selection from each sampling unit of a sample that is proportionate to the size of the unit

• variables used as the basis of classifying categories and increased chances of being able to make comparisons between strata

Proportionate

• It refers to selecting units from each stratum • Each stratum should be in proportion to the

corresponding stratum the population.

Optimum allocation

1 .5.4 Stratified Sampling Design Cont.…..

It is a good representative of the population.

It is an improvement over the earlier technique of sampling.

It is an objective method of sampling.

Observations can be used for inferential purpose

Estimate could be made for each stratum

It have a smaller variance

Reduce survey costs.

1. It is difficult to decide the relevant

criterion for stratification.

2. Sampling frame is needed for each

stratum

3. There is a risk of generalization.

4. It is costly and time consuming

method

Advantages of SS Limitation of SS

1 .5.4 Stratification Vs. Clustering

BASIS FOR COMPARISON

STRATIFIED SAMPLING CLUSTER SAMPLING

Meaning

Population is divided into

homogeneous segments, and

then the sample is randomly

taken from the segments.

The members of the population are

selected at random, from naturally

occurring groups called 'cluster'.

Sample Randomly selected individuals

are taken from all the strata.

All the individuals are taken from

randomly selected clusters.

Selection Individually Collectively

Homogeneity Within group Between groups

Heterogeneity Between groups Within group

1 .5.4 Stratification vs. Cluster Cont.…

BASIS FOR COMPARISON

STRATIFIED SAMPLING CLUSTER SAMPLING

Cost It is costly and time

consuming method. Cheaper

Representative Sample more representative

Usually not representative of whole

population

Bifurcation Imposed by the researcher Naturally occurring groups

Objective To increase precision and

representation.

To reduce cost and improve efficiency.

1 .5.4 Stratification vs. Cluster Cont.…

Video Presentation\Multistage Sampling

final_setegn_presentation.mp4

1.5.5 Multistage Sampling

1.5.6 Summary of Presentation How We Select D/t Types of Sampling Method

1. C.R Kothari 1990, Research Methods & Techniques: Sampling Design, Second Edition, New Age International (P) Ltd., Publisher, New Delhi.55-66.

2. Dr. Prabhat Pandey and Dr. Meenu Mishra Pandey 2015, RESEARCH METHODOLOGY: TOOLS AND TECHNIQUES: Research Design: Published by Buzau, Al. Marghiloman 245 bis, 120082 , Romania.18-23.

3. GeoffreyM., David D and David F. 2005,Essential of Research Design and Methodology: Planning and Designing a Research Study, Published by John Wiley & Sons, Inc., Hoboken, New Jersey.26-65.

4. Mark Saunders,Philip Lewis and Adrian Thornhill 2009,Research Methods for Business Students: Selection Sample,Fifth edition, Published by Pearson Education Limited Edinburgh Gate Harlow, England.210-256.

5. U university of Bhojanna 2007, Research Methods for Management School of Distance Education Bharathiar University,Sampling Design, EXCEL BOOKS PRIVATE LIMITED A-45, Naraina, Phase-I,, New Delhi. 73-88.

6. Uma sekaran 2003, Research Methods for Business: Sampling, Fourth Edition, Published by John Wiley & Sons, Inc., Hoboken, New Jersey.263-276.

REFERENCE

THE END!!!