Sampling Ppt

PRESENTED BY:NEHA (50310)HEENA (50265)NEETU(50308)PRAGYA(50322)SURAJ GABBI (50382)

SAMPLING

DEFINITON

Sampling  is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Acceptance sampling is used to determine if a production lot of material meets the governing specifications. Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population.

SAMPLING PROCESS

• Defining the population of concern

• Specifying a sampling frame, a set of items or events possible to measure

• Specifying a sampling method for selecting items or events from the frame

• Determining the sample size• Implementing the sampling plan• Sampling and data collecting• Data which can be selected

The sampling process

comprises several stages

SAMPLING METHODS

• PROBABLITY SAMPLING• NON-PROBABLITY

SAMPLING

PROBABLITY

SAMPLING

SIMPLE RANDO

M

STRATIFIED

CLUSTER

SYSTEMATIC

SIMPLE RANDOM SAMPLING

A simple random sample is a sample selected in such a way that

every possible sample of the same size is equally likely to be chosen.

STRATIFIED RANDOM SAMPLING

A stratified random sample is obtained by separating the population into mutually exclusive sets, or strata,

and then drawing simple random samples from each stratum.

CLUSTER SAMPLING

A cluster sample is a simple random sample of groups or clusters of

elements (vs. a simple random sample of individual objects).This method is useful when it is difficult or costly to

develop a complete list of the population members or when the population elements are widely

dispersed geographically.

SYSTEMATIC SAMPLING

Systematic sampling is a statistical method involving the selection of elements from an ordered sampling frame . The most common form of systematic sampling is an equal-probability method. In this approach,

progression through the list is treated circularly, with a return to the top once the

end of the list is passed. The sampling starts by selecting an element from the list at

random and then every element within an intervalin the frame is selected.

NON PROBABLITY SAMPLING

CONVENIENCE

QUOTA

SNOWBALL

JUDGEMENT

CONVENIENCE SAMPLING

It involves the sample being drawn from that part of the population which is close to hand. That is, a sample population selected because it is readily available and convenient.

The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough.

JUDGEMENTAL SAMPLING

It is selected based on the opinion of an expert.

Results obtained from a judgment sample are subject to some degree of bias, due to the frame and population not being identical.

The frame is a list of all the units, items, people, etc., that define the population to be studied.

SNOWBALL SAMPLING

It is where existing study subjects recruit future subjects from among their acquaintances.

Thus the sample group appears to grow like a rolling snowball (similarly to breadth-first search (BFS) in computer science).

As the sample builds up, enough data is gathered to be useful for research.

QUOTA SAMPLING

It is a method for selecting survey participants.

In quota sampling, a population is first segmented into mutually exclusive sub-groups, just as in stratified sampling.

Then judgment is used to select the subjects or units from each segment based on a specified proportion.

OTHER PROBABILTY SAMPLING TECHNIQUES

SEQUENTIAL SAMPLING A sampling plan in which an undetermined number

of samples are tested one by one, accumulating the results until a decision can be made.

DOUBLE SAMPLING Two-phase sampling. In First Phase, a sample is

selected and some information is collected from all the elements in sample. In second phase, a subsample is drawn from the original sample and additional information is obtained from the elements in the sub-sample.

ONLINE SAMPLING TECHNIQUES

The sampling techniques commonly used on the internet may be classified as Online Intercepts (RANDOM & NON-

RANDOM).

STATISTICAL APPROACH TO DETERMINE SAMPLE SIZE

• The Confidence Interval Approach

The confidence interval approach to sample size determination is based on the construction of confidence intervals around the sample means or proportions

using the standard error formula

95% Confidence Interval

XL

_XU

_X_

0.475 0.475

Sample Size Determination: Means

This approach used to construct a confidence interval can be

adapted to determine the sample size that will result in a

desired confidence interval

PROBLEM

A study is to be performed to determine a certain parameter in a community. From a previous study a sd of 46 was obtained.

If a sample error of up to 4 is to be accepted. How many subjects should be included in this study at 99% level of confidence