the concept of sampling

Lecturer: Leang Sokdina

វិទ្យាស្ថា នសហប្រតិរតតិការអនតរជាតិ កម្ពុជាCambodia International Cooperation Institute

Faculty of Arts, Humanities and Languages

Year III, Semester II

2014-2015

Academic Writing

Group 10’s Members

1. Ms. Rith ChanLeaphea

2. Ms. Thea Sievhong

3. Mr. Keo Vichet

ContentI. The Concept of Sampling

II. The concept of sampling in qualitative research

III.Sampling Terminology

IV.Principles of Sampling

V. Factors affecting the inferences drawn from a sample

VI.Aims in Selecting Sample

VII.Type of Sampling

VIII.Random/probability sampling designs

IX. Methods of drawing a random sample

X. Different Systems of drawing a random sample

XI.Specific random/probability sampling designs

XII.Non-random/non-probability sampling designs

XIII.Mixed sampling designs

XIV.The calculation of sample size

Conclusion

Content

I. The Concept of Sampling

Sampling is the process of selecting a few (a

sample) from a bigger group to become the basic for

estimating the prevalence of an unknown piece of

information.

Sampling is thus a trade-off certain gains and losses.

I. The Concept of Sampling (con.)

Sample

II. The concept of sampling in

qualitative research

In qualitative research Issue of sampling has:

little significance

Does not quantify or determine the extent of diversity

Explore diversity—saturation point

Saturation point is a subjective judgment we

researcher decide

III. Sampling Terminology

The main aim of sampling terminology is to find out the

average of something in particular place.

In this process there are a number of aspects:

Population or study population

Sample

Sample size

Sampling design or strategy

Sampling unit/element

III. Sampling Terminology (con.)

Sampling frame

Sample statistics

Population parameters/mean

Saturation point

IV. Principles of Sampling

The theory of sampling is guided by three principles:

1st Principle: in a majority of cases of sampling there

will be a difference between the sample statistics and

the true population mean, which is attributable to the

selection of the units in the sample.

Sample Sample Average

(statistics sample)

Population

mean/parameter

Difference

1 19.0 21.5 -2.5

2 20.5 21.5 -1.5

3 21.5 21.5 0.0

4 21.5 21.5 0.0

5 22.5 21.5 +1.5

6 24.0 21.5 +2.5

Example of 1st Principle (select two units from sample)

Suppose there are four individuals: A(18ys), B(20ys),

C(23ys) and D(25)

Population mean = (18+20+23+25)/4 = 21.5

IV. Principles of Sampling (con.)

2nd Principle: the greater the sample size, the more

accurate will be the estimate of the true population

mean.

3rd Principle: the greater the difference in variable

under study in a population for a given sample size, the

greater will be the difference the sample statistics and

the true population mean.

Example of 2nd Principle (select three units from sample)

Sample Sample Average

(statistics sample)

Population

mean/parameter

Difference

1 20.33 21.5 -1.17

2 21.00 21.5 -0.5

3 22.00 21.5 +0.5

4 22.67 21.5 +1.17

Suppose there are four individuals: A(18ys), B(20ys),

C(23ys) and D(25)

Population mean = (18+20+23+25)/4 = 21.5

V. Factors affecting the inferences drawn

from a sample

The above principles suggest that two factors may

influence the degree of certainty:

The size of sample—findings based upon larger

sample have more certainty than those based on

smaller one.

The extent of variation in the sampling population—

the greater the variation in the study population with

respect to the characteristics under study for a given

sample size, the greater will be uncertainty.

VI. Aims in Selecting Sample

The aims in selecting a sample are to:

Achieve maximum precision in your estimates within a given sample size;

Avoid bias in the selection of your sample

Bias in the selection of a sample can occur if:

Sampling is done by a non-random method

The sampling frame

A section of a sampling population is impossible to find or refuses to cooperate

VII. Type of Sampling

The various sampling strategies can be categorized as

follows:

Random/probability sampling designs

Non-random/Non-probability sampling designs

Mixed sampling designs

VIII. Random/probability sampling designs

For a sampling design to be called a random or

probability sample, it is imperative that each

element in the population has an equal and

independent change of selection in the sample.

VIII. Random/probability sampling designs

(con.)

There are two main advantages of Random/Probability

samples:

As they represent the total sampling population

Some statistical tests based upon the theory of

probability can be applied only to data collected from

random samples.


Of the methods that you can adopt to select a random

sample the three most common are :

The fishbowl draw –- This method is used in some

lotteries.

Computer Program –- there are a number of

programs that can help you to select random samples.

A table of random numbers –- A table of randomly

generated number in their appendices.


The procedure for selecting a sample using a table of

random number is as follows:

Step 1: Identify the total number of element in the

study population.

Step 2: Number of each element starting from 1

Step 3: If the table or random numbers is on more than

one page, choose the starting page by a random

procedure.

A table of random numbers


Step 4: Corresponding to the number of digits to which

the total population runs, select the same number,

randomly, of columns or rows of digits from the table.

Step 5: Decide on your sample size

Step 6: Select the require number of elements for your

sample from the table.

A table of random numbers

X. Different Systems of drawing a random

sample

There are two ways of selecting a random sample:

Sampling without replacement

Sampling with replacement

XI. Specific random/probability sampling designs

There are three types:

simple random sampling(SRS)

Step 1 : Identify by number all elements or sampling units in

the population.

Step 2 : Decide on the sample size (n)

Step 3 : Select (n) using either the fishbowl draw the table of

random numbers or a computer program

Stratified random sampling

XI. Specific random/probability sampling designs

Cluster sampling: is bases on the ability of the researcher to

divide sampling population into group.

Step 1 : Identify all elements or sampling units in the sampling

population.

Step 2 : Decide upon the different strata (K) into which you want

to stratify the population.

Step 3 : Place each element into the appropriate stratum

Step 4 : Number every element in each stratum separately

Step 5 : Decide the total sample size (n)

Step 6 : Decide whether you want to select proportionate or

disproportionate stratified sampling and follow the steps

below.

XII. Non-random/non-probability sampling designs

There are four non-random designs, which are commonly used

in qualitative and quantitative:

Quota sampling is a researcher’s ease of access to the

sample population. There are advantages and disadvantages

with this design.

- advantages: you do not need any information such the

total number of element, their location…

- disadvantages: the finding cannot be generalized to the

total sampling population hence might not be truly

representative of the total sampling population.

XII. Non-random/non-probability sampling designs

Accidental sampling is also base upon convenience inaccessing the sample population. It common amongmarket research and new paper report.

Judgemental or purposive sampling is the judgment ofthe researcher as to who can provide the best informationto achieve the objectives of the study.

Snowball sampling is the process of selecting sampleusing network. To start with, a few individuals in a group ororganization are selected and the required information iscollect from them.

XIII. Mixed sampling designs

Systematic sampling design: has been classified under the mixedsapling category. It has characteristics of both random and non-random sampling design.

The procedure for selecting a systematic simple

Step 1: Prepare a list of all the elements in the studypopulation (N).

Step 2 : Decide on the sample size (n).

Step 3 : Determine the width of the interval (k) = N/n.

Step 4 : Using the SRS, select an element from the first interval(nth order)

Step 5 : Select the same order element from each subsequentinterval.

XIII. Mixed sampling designs

Systematic sampling

Sampling frame

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Sample selected

XIV. The calculation of sample size

It You can use this sample size calculator to determine

how many subjects you need to collect data from in order

to get results that reflect the target population as

precisely as needed. You can also find the level of

precision you have in an existing sample.

It depends on what you want to do with the findings and

what type of relationships you want to establish

Conclusion

References

Academic Writing, CICI.

http://www.surveysystem.com/sscalc.htm

the concept of sampling

Education