4. Sample and Sampling Process

7/28/2019 4. Sample and Sampling Process

1/37

ByMrs. Nidhi SagarAssociate Prof.(OBG)DMC&H,College of Nursing,Ldh


2/37

$ Why not study everyone?


3/37

Researchers work with Samples rather thanwith populations because it is more economical& efficient to do so.

Samples are practical means of collecting data. Researcher have neither the time nor the

resources to study all members of population.


4/37

Economical.

Improved quality of data.

Quick study results.

Precision and accuracy of data.


5/37

POPULATION

Target population

Accessible population

SAMPLE SAMPLING

ELEMENTS

STRATA SAMPLING BIAS


6/37

SAMPLING FRAME

SAMPLING ERROR

SAMPLING PLAN


7/37

Population is the aggregation of all the units inwhich a researcher is interested and to which

the results of a research are to be generalized.


8/37

TARGET POPULATION:-It consists of total no. ofpeople or objects which are meeting thedesignated set of criteria.

ACCESSIBLE POPULATION :-It is the aggregateof cases that conform to designated criteriaand are also accessible as subjects of study.


9/37

Representative unit of a target population ,which is to be worked upon by researchersduring their study.


10/37

Sampling is the process of selectinga portion of the population that

represent the entire population.


11/37

The entities that make up the samples &populations are called elements.

These are the most basic unit aboutwhich/whom information is collected.


12/37

Populations consist of subpopulations called strata.

Strata are mutually exclusivesegments of a population based on aspecified characteristics.


13/37

It is the distortion that arises when a sample isnot representative of the population from whichit was drawn.

Sampling bias is affected by the homogeneity

of the population


14/37

SAMPLING FRAME

It is the list of all elements or subjects in thepopulation from which the sample is drawn

SAMPLING ERRORFluctuations in the values of the statistics ofcharacteristics from one sample to another


15/37

SAMPLING PLANThe formal plan specifying a sampling

method , a sample size and theprocedure of selecting the subjects.


16/37

Population Target

poplation

Accessible

populationsample

Subjects


17/37

Identifying and defining the target population

Specifying the sampling unit

Describing the accessible population andEnsuring sampling frame

Specifying the sampling plan

Selecting a desired representativesample

Specifying sample selection methods

Determining the sample size


18/37

PROBABILITYType I

NON PROBABILITYType II


19/37

1.Simple random 1.Purposive

2. Stratified random 2.Convenient

3. Systematic random 3.Consecutive

4.Cluster /multistage 4.Quota5. Sequential sampling 5.Snow ball sampling

TYPES OF SAMPLING TECHNIQUE

Probability sampling techniqueNon probability sampling

technique


20/37

A probability sampling scheme is one inwhich every unit in the population has a chance(greater than zero) of being selected in the

sample, and this probability can be accuratelydetermined.

The combination of these traits makes itpossible to produce unbiased estimates of

population totals, by weighing sampled unitsaccording to their probability of selection.


21/37


22/37


23/37

Systematic sampling relies onarranging the target populationaccording to some ordering schemeand then selecting elements at

regular intervals through thatordered list.

It involves a random start and thenproceeds with the selection of every

kth element from then onwards. E.g.select every 10th name from thetelephone directory.


24/37

In a very large population, random selection ofgeographic cluster(e.g. Asia or Arab) and thenrandom selection of subjects from these

clusters. Population is divided in to groups, usually

geographic or organizational.

Possibility of high sampling error &chances ofleast representative sample due to over orunder represented cluster.


25/37

Nonprobability sampling is any samplingmethod where some elements of the

population have no chance of selection

(these are sometimes referred to as 'out ofcoverage'/'undercovered'), or where theprobability of selection can't be accuratelydetermined.

It involves the selection of elements based onassumptions regarding the population ofinterest, which forms the criteria for selection.


26/37

The 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 specifiedproportion. For example, an interviewer may be

told to sample 200 females and 300 malesbetween the age of 45 and 60.

The selection of the sample is non-random. Forexample interviewers might be tempted to

interview those who look most helpful. The problem is that these samples may be biased

because not everyone gets a chance of selection.


27/37

The Researcher might decide purposively toselect the widest possible variety ofrespondents or might choose subjects who are

judged to be typical of the population inquestion or particularly knowledgeable aboutthe issues under study.

It is used for assessing the typical-ness of theselected subjects.


28/37

It involves the sample being drawn from thatpart of the population which is close to hand.

That is, a sample population selected becauseit is readily available and convenient.

For example, if the interviewer was to conductsuch a survey at a shopping centre early in themorning on a given day, the people that he/shecould interview would be limited to those given

there at that given time, which would notrepresent the views of other members of societyin such an area, if the survey was to beconducted at different times of day and severaltimes per week.


29/37

It is also known as NETWORK SAMPLING.

In this the early sample members are asked torefer other people who meet the studys eligibility

criteria. It is most often used when the population consist

of people with specific traits who might bedifficult to identify by ordinary means.(like HIVpatients or Prostitutes population)


30/37

Linear

Exponential nondiscriminative

Exponentialdiscriminative


31/37

Picks up all the available subjects who are

meeting the preset inclusion and exclusioncriteria.

Used for continuously changing populatione.g. hospital patients.


32/37

Resources available: A large sample may ensureprecision , but it may prove to be costly .so before carrying out astudy with a large sample , the researcher need to decide if theyhave sufficient resources.

Nature of study:The sample size depends upon the type ofstudy to be carried out .

Sampling methods used: Smaller but efficientlyselected samples prove to be far better than badly select largesamples.


33/37

Homogeneity: If the population is

homogeneous, than a small sample may besufficient.

Effect size: if the relationship between theindependent and dependent variables is strong, then asmall sample will be sufficient.

Degree of accuracy desired from the estimate:precision is the limit if tolerable error exist in thesample estimates.


34/37

Degree of confidence: higher the degree of

confidence, the larger is the sample sizerequired.

Cooperation and attrition:if the data is collected

from multiple points and there is less chance ofcommunication of the researcher with the sample dueto long time gap there is possibility of attrition.

Sub group analysis: if the hypothesis are to betested not only for the population, but also for specificsub groups, then the small sample size should be largeenough to make the generalization of the results

possible to those subgroups too.


35/37

Sampling errors

Lack of sample representativeness

Difficulty in estimation of sample size

Lack of knowledge about the sampling process

Lack of resources

Lack of cooperation


36/37

Lack of existing appropriate sampling framesfor larger population

Callous approach of the researcher towards

sampling process


37/37

THANKS

4. Sample and Sampling Process

Documents

Transcript of 4. Sample and Sampling Process