SAMPLING DESIGNPROBABILITY SAMPLING

NON-PROBABILITY SAMPLING

Muhammad Bilal, R.No.06Ali Hussnain syed R.No18Abbas Ali R.No.31

A population is the set of data of all possible measurements (or observations) of individuals or items.

E.g.. the heights of all students in a junior college, the lengths of life of all the light bulbs produced by a manufacturer.

A sample is a set of data chosen from a population and is a subset of the population.

A sampling unit is an individual member of a sample.

Definition of Sampling:

Measuring a small portion of something and then making a general statement about the whole thing.

Process of selecting a number of units for a study in such a way that the units represent the larger group from which they are selected.

Why We Need Sampling (Purposes and Advantages of Sampling)

Sampling makes possible the study of a large, (different characteristics) population.

Sampling is for economy

Sampling is for speed.

Sampling is for accuracy.

Sampling saves the sources of data from being all consumed.

SAMPLING DESIGN

1. What is the target population?- Target population is the aggregation of elements (members of the population) from which the sample is actually selected.

2. What are the parameters of interest?- Parameters are summary description of a given variable in a population.

3. What is the sampling frame?- Sampling frame is the list of elements from which the sample is actually drawn. Complete and correct list of population members only.

4. What is the appropriate sampling method? - Probability or Non-Probability sampling method

SAMPLING DESIGN5. What size sample is needed?

There are no fixed rules in determining the size of a sample needed. There are guidelines that should be observed in determining the size of a sample.

When the population is more or less homogeneous and only the typical, normal, or average is desired to be known, a smaller sample is enough. However, if differences are desired to be known, a larger sample is needed.

When the population is more or less heterogeneous and only the typical, normal or average is desired to be known a larger sample is needed. However, if only their differences are desired to be known, a smaller sample is sufficient.

SAMPLING DESIGN

The size of a sample varies inversely as the size of the population. A larger proportion is required of a smaller population and a smaller proportion may do for a bigger population.

For a greater accuracy and reliability of results, a greater sample is desirable.

In biological and chemical experiments, the use of few persons is more desirable to determine the reactions of humans.

When subjects are likely to be destroyed during experiment, it is more feasible to use non-humans.

General Types of Sampling

1. Probability sampling

2. Non-probability sampling

PROBABILITY SAMPLING

The sample is a proportion (a certain percent) of the population and such sample is selected from the population by means of some systematic way in which every element of the population has a chance of being included in the sample.

Randomization is a feature of the selection process rather than an assumption about the structure of the population.

More complex, time consuming and more costly

Non-probability sampling

The sample is not a proportion of the population and

there is no system in selecting the sample. The selection depends upon the situation.

No assurance is given that each item has a chance of being included as a sample

There is an assumption that there is an even distribution of characteristics within the population, believing that any sample would be representative.

TYPES OF PROBABILITY SAMPLING

A. PURE RANDOM SAMPLING This type of sampling is one in which every one in

the population of the inquiry has an equal chance of being selected to be included in the sample.

Also called the lottery or raffle type of sampling.

This may be used if the population has no differentiated levels, sections, or classes.

Done with or without replacement

PURE RANDOM SAMPLING

main advantage of this technique of sampling is that, it is easy to understand and it is easy to apply too.

disadvantage is that, it is hard to use with too large a population because of the difficulty encountered in writing the names of the persons involved.

B. SYSTEMATIC SAMPLING

A technique of sampling in which every name (old system of counting off) in a list may be selected to be included in a sample.

Also called as interval sampling, there is a gap or interval, between each selected unit in the sample.

Used when the subjects or respondents in the study are arrayed or arranged in some systematic or logical manner such as alphabetical arrangement and geographical placement from north to south.

SYSTEMATIC SAMPLING Main advantage is that it is more convenient, faster,

and more economical

Disadvantage is that the sample becomes biased if the persons in the list belong to a class by themselves whereas the investigation requires that all sectors of the population are to be involved.

C. STRATIFIED SAMPLING

The process of selecting randomly, samples from the different strata of the population used in the study.

Advantage is that it contributes much to the representative of the sample

D. CLUSTER SAMPLING Also called as multistage cluster sampling

Used when the population is so big or the geographical area of the research is so large.

Advantage : efficiency

Disadvantage: reduced accuracy or representativeness, on the account of the fact that in every stage there is a sampling error.

TYPES OF NON-PROBABILITY SAMPLING

A. ACCIDENTAL SAMPLING /CONVENIENCE SAMPLING

No system of selection but only those whom the researcher or interviewer meet by chance are included in the sample.

Process of picking out people in the most convenient and fastest way to immediately get their reactions to a certain hot and controversial issue.

ACCIDENTAL / CONVENIENCE SAMPLING

Not representative of target population because sample are selected if they can be accessed easily and conveniently.

Advantage : easy to use

Disadvantage: bias is present

It could deliver accurate results when the population is homogeneous.

B. PURPOSIVE SAMPLING

The respondents are chosen on the basis of their knowledge of the information desired.

TYPES OF PURPOSIVE SAMPLING

1. QUOTA SAMPLING Specified number of persons of certain types are included in

the sample.

Advantage over accidental sampling is that many sectors of the population are represented. But its representativeness is doubtful because there is no proportional representation and there are no guidelines in the selection of the respondents.

PURPOSIVE SAMPLING

2. JUDGEMENT SAMPLING

Sample is taken based on certain judgements about the overall population.

Critical issue: objectivity “how much can judgement be relied upon to arrive at a typical sample?”

Advantage: reduced cost and time involved in acquiring the sample

Acknowledgment

We are very thankful to:

Sir. Dr. Iftikhar Hussain