Tuesday, June 9, 2009

Differentiate between the following :

Differentiate between the following :

(a) Primary data and secondary data
(b) Sampling and census
(c) Stratified sampling and cluster sampling

(a) PRIMARY AND SECONDARY DATA
The primary data are those which are collected afresh and for the first time, and thus happen to be original in character. Such data are published by authorities who themselves are responsible for collection.
The secondary data. On the other hand, are those which have already been collected by some other agency and which have already been processed.
Generally speaking secondary data are information which have previously been collected by some organization to satisfy its own need buy it is being used by the department under reference for an entirely different reason.
For example, the census figures are published every tenth year by the Registrar General of India. But the census figures are also used by demographers and other social scientists for planning and research.
Thus, the officials of the department of Registrar General will visualize the census figures as primary data. But a demographer using the same census figures to prepare a mortality table will consider them as secondary data.


(b) SAMPLING AND CENSUS
The census or consists in collecting data from each and every unit from the population. The sampling only chooses a part of the units from the population for the same study. The sampling has a number of advantages a compared to complete enumeration due to a variety of reasons.
The first obvious advantage of sampling is that it is less expensive. If we want to study the consumer reaction before launching a new product it will be much less expensive to carry out a consumer survey based on a sample rather than studying the entire population which is the potential group of customers. Although in decennial census every individual is the potential group of customers. Although in decennial census every individual is enumerated, certain aspects of the population are studied on a sample basis with a view to reduce cost.
The smaller size of the sample enables us to collect the data more quickly than to survey all the units of the population even if we are willing to spend money. This is particularly the case if the decision is time bound. An accountant may be interested to know the total inventory value quickly to prepare a periodical report like a monthly balance sheet and a profit and loss account. A detailed study on the inventory is likely to take too long to enable him to prepare the report in time.
If we want to measure the consumer prices even if the expenditure is not a hindrance. The collection of data on all the consumer items and their processing in all probability are going to take more than a month. Thus when ready, the price index will not serve any meaningful purpose.

It is possible to achieve greater accuracy by using appropriate sampling techniques than by a complete enumeration of all the units of the population. Complete enumeration may result in accuracies of the data. Consider an inspector who is visually inspector who is visually inspecting the quality of the finishing of certain machinery. After observing a large number of such items he cannot just distinguish items with defective finish from good ones. Once such inspection fatigue develops the accuracy of examining the population completely is considerably decreased. On the other hand, if a small number of items is observed the basic data will be much more accurate.
It is of course true that the conclusion about a population characteristic such as the proportion of defective items from a sample will also introduce error in the system. However, such errors, known as sampling errors, can be studied, controlled and probability statements can be made about their magnitude.
The accuracy which results due to fatigue of the inspector is known as non sampling error. It is difficult to recognize the pattern of the non sampling error and it is not possible to make any comment about its magnitude even probabilistically.

Sampling is indispensable if the enumeration is destructive. If you are interested in computing the average life of fluorescent lamps supplied in a batch the life of the entire batch cannot be examined to compute the average since this means that the entire supply will be waste. Thus, in this case there is no other alternative than to examine the life of a sample of lamps and draw an inference about the entire batch.

(C)STRATIFIED RANDOM SAMPLING AND CLUSTER SAMPLING
The sample random sampling may not always provide a representative miniature of the population. Certain segments of a population can easily be under represented when an unrestricted random sample is chosen. Hence, when considerable heterogeneity is present in the population with regard to subject mater under study, it is often a good idea to divide the population into segments or strata and select a certain number of sampling units from each stratum thus ensuring representation from all relevant segments. Thus for designing a suitable marketing strategy for a consumers durable, the population of consumers may b e divided into strata by income level and a certain number of consumers can be selected randomly from each strata. Speaking formally, the population of N units is subdivided into K sub-populations or strata the ith sub-population having Ni unit (i = 1, 2 ……..K). These sub-populations are non overlapping so that they comprise the whole population such that
N1 + N2 + ……..+ Nk = N

A simple random sample (with or without replacement ) is selected independently in each stratum, the sample size in the ith stratum being
ni (i = 1, 2……, k). Thus the total sample size is n1 = n2 = ……. = nk.

The stratification should be performed in such a manner that the strata are homogeneous within themselves with respect to the characteristic under study. On the other hand, strata should be heterogeneous between themselves. Sometimes administrative convenience is taken into consideration to stratify the population. For instance, in order to study the problems of railway commuters each railway division may be considered to be a different stratum. In rural areas, the region covering adjacent districts are likely to be homogenous with respect to socio-economic and cultural pattern. Hence they could be included in a Common strata. Distribution of consumer products may face different types of problems in rural, urban or hilly areas. These may be considered as separate stratum from the point of view of management.

Cluster Sampling : In this method of sampling a collection or a Cluster of sampling units are selected in a random manner. Then each unit of the cluster is included in the sample.
In order to motivate the use of a Cluster, we consider a survey where the sampling units are households in a rural area. If simple random sampling is used to select households they will be located over several villages. On the other hand, a village can be regarded as a Cluster of households. We select a few villages randomly and include every household in the selected villages in our sample. Such a sampling procedure will be an illustration of Cluster Sampling. It has a number of advantages over simple random sampling.
i) If the households are chosen using simple random sampling, they are likely to be distributed over several villages. Hence from administrative point of view such a selection will involve more cost, more field supervision and more traveling. On the other hand, if a selected village is completely enumerated, the cost involved will be lower and the supervision exercised will be better.
ii) If the households in the sample are distributed over several villages then a frame containing the list of households of each of these villages is necessary for proper identification and selection of the household in the sample. On the other hand, if every household in a selected village is included in the sample, no sampling frame listing the households in a village is necessary.
iii) If the type of question is of intimate nature an isolated household selected in a village is unlikely to cooperate with the investigators. On the other hand, if every household in a village is visited, a particular household after observing that his neighbours are also being interviewed are likely to offer greater cooperation and as such the quality of the basic data will be more reliable.

In the example presented in the section clusters have been formed based on geographic subdivisions. A village is a natural geographic cluster of households. A Cluster sample with clusters based on geographic subdivisions is known as area Sample and the procedure is known as area Sampling.

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