Datacollection
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Datacollection

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Datacollection Datacollection Document Transcript

  • UNIT I DATA COLLECTIONPopulation • All the items that fall under the purview of research are called “Universe” or “Population”. • Algebraically represented by “N”.Sample: • It is the small unit of the population, that represents all the characteristics of the population. • The sample is selected by using several techniques; called Sampling Techniques. • Algebraically represented by “n”.Census and SurveyCensus: It is the Enquiry done on the population. i.e. it is the enquiry that covers all theitems in the population.  More complicated  Involves great deal of time, money and energy.  Can be done only by government.Example: population census.Survey: it is the study conducted on the selected few items called sample. TOPIC II SAMPLINGConcept of Sampling: • Sampling Method means selection of a limited number of items representing the population or universe for studying the characteristics of the whole population or universe. • Example: to know the IQ of the Students of age between 15-16, suppose in the class of 70 students. We conduct the study on 20 students who represent the class.Population = 70Sample = 20Essentials of Sampling • Sample should posses same characteristics as the population. • Absolute accuracy is not essential. • Regulating conditions should be same for every individual item in the sample.Advantages of Sampling • the result obtained is generally more reliable than that obtained from a complete count. • Total financial burden of a sample survey is generally less than that of complete census. • Possible to collect more detailed information in a sample survey. • Causes less damage and wastage.
  • Disadvantages: • Shortage of experts in the sampling field is a serious hurdle in the way of reliable statistics. • Sampling plan may be complicated that it requires more time, labour and money than a complete count. • Must be carefully planned and executed otherwise the results obtained may be inaccurate and misleading.Reasons for Sampling: 1. Universe Size 2. Financial Constraints 3. Sufficiency of an approximation 4. Time Constraints 5. Destructive Nature of smapling TOPIC III SAMPLING DESIGNSample DesignIt comprises of: 1. Sampling Frame 2. Selection of sampling items 3. Sample SizeTypes of Sampling Design • Probability Sampling: • Non-Probability Sampling:  Simple Random  Convenience  Stratified sampling  Judgment  Area or Cluster  Quota  Multi-stage random  Panel  Systematic  PurposiveSimple Random Sampling • Merits:  Represents universe in the better way  Desired level of precision can be achieved by increasing or decreasing the sample size.  More scientific method. • Demerits:  May not be true representative if its size is small.  Units of population should be dependent.  Needs a complete list of finite population, without.
  • Stratified Sampling • Merits:  Assures representativeness  Decreases chances of excluding units of universe.  representative character can be achieved with fewer items.  Replacement of units is easily possible.  Saves time & money • Demerits:  Requires accurate knowledge of the universe.  If stratified list is not available, it will be costly to prepare the same.  Bias or error may be made in the sample through improper stratificationCluster or Area Sampling • Merits:  If clusters are geographically defined, yield lowest field costs.  Requires listing only individuals in selected clusters.  Characteristics of clusters as well as those of population can be estimated. • Demerits:  Larger Errors for comparable size than other probability samples, and  Requires ability to assign each member of population uniquely to a cluster; inability to do so may result in duplication or omission of individuals.Multi-stage Random Sampling: • Merits:  Complete listing of universe is not required.  If sampling units are geographically defined, then, it cuts down field costs • Demerits:  Errors are likely to be larger than the other types.  Errors will increase as number of selected sampling units decrease.Sampling with probability proportional to size: • Merits:  Equivalent to simple random sampling.  It is less cumbersome.  It is less expensive. • Demerits:
  • Sequential Sampling: • It is repetitive and can give more accurate results.Judgment Sampling • Merits:  Reduces the cost of preparing sample and field work, since ultimate units can be selected so that they are close together • Demerits:  Variability and bias of estimates cannot be measured or controlled, and  Requires storing assumptions or considerable knowledge of population and sub-group selected.Quota Sampling: • Merits:  Same as in judgement sampling.  Introduces some stratification effect. • Demerits:  Introduces bias of observer’s classification of subjects and non-random selection within classes.