Sampling Design

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SAMPLING DESIGN
PROBABILITY SAMPLING & NON-PROBABILITY SAMPLING

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

- The universe or population to be studied maybe too large or unlimited that it is almost impossible to reach all of them. Sampling makes possible this kind of study because in sampling only a small portion of the population maybe involved in the study, enabling the researcher to reach all through this small portion of the population.

2. Sampling is for economy.
- Research without sampling may be too costly. Sampling reduces the study population to a reasonable size that expenses are greatly reduced.

3. Sampling is for speed.
- Research without sampling might be too time consuming.

4. Sampling is for accuracy.
- If it takes too long a time to cover the whole study population, there maybe inaccuracy. The research must be finished within a reasonable period of time so that the data are still true, valid and reasonable.

5. Sampling saves the sources of data from being all consumed. - The act of gathering data may consume all the sources of information without sampling. In such a case, there is no more data to apply the conclusion to.

Disadvantages of Sampling (Defective Sampling)
1. If sampling is biased, or not well represented, or too small, the conclusion may not be valid and reliable. 2. In research, the respondents to a study must have a common characteristics which is the basis of the study. 3. If the population is very large and there are many sections and subsections, the sampling procedure becomes very complicated. 4. If the researcher does not possess the necessary skill and technical knowhow in sampling procedure.

WHAT IS A GOOD SAMPLE?
■ The sample must be valid.

■ Validity depends on 2 considerations:
1. Accuracy – bias is absent from the sample
(e.g. A company is thinking of lowering its price for its soap bar product. After making a survey in the sales of their product in a known mall in Makati they concluded that they will not cut down the price of the soap bar since there was an increased in sales compared to last year. Bias is present in this study since the company based its decision for the sales of a known mall which have consumers who can afford high price products. They did not consider the sales of their products in other area wherein they have middle class or low class consumers.) 2. Precision – sample represents the population

(e.g. Customers who visited a particular dress shop are requested to log in their phone numbers so that they will receive information for discounts and new arrivals. Management wish to study customers satisfaction for that shop. By means of interviewing thru phone they get comments and reactions of their client. Samples used are not an exact representative of the population since it is limited only to those customers who log in their phone numbers and they did not consider customers without phone numbers indicated.)

STEPS IN 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
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