SAMPLING ERRORSPresented By : Angela Mary George S-2,MBA (Evening)

IMK,Karyavattom

OUTLINE OF PRESENTATION

SAMPLING TERMINOLOGY

NEED OF SAMPLING

SAMPLING PROCESS

SAMPLING TYPES & TECHNIQUES

SAMPLING ERRORS

SAMPLING TERMINOLOGY

Sample: Sample is a subset of population, selected in such a way that it is representative of the larger population.Population: any complete group of entities that share some common set of characteristics.Population element: an individual member of a population.Sampling frame: a list of Population from which a sample is selected.– Example: student email list,

membership list.

Sampling unit: is the unit of selection.

Sample size: the number of units or subjects sampled for inclusion in the study is called sample size.

Sampling technique: Method of selecting sampling units from sampling frame.

SAMPLING TERMINOLOGY

SAMPLING PROCESS

Sampling is the process of selecting observations (a sample) from a population to provide an adequate description and inferences of the population.

NEED OF SAMPLING

Sampling- a valid alternative to a census when : A survey of the entire population is impracticable

Budget constraints restrict data collection

Time constraints restrict data collection

Results from data collection are needed quickly

TYPES OF SAMPLINGPROBABILITY SAMPLING

(RANDOM SAMPLING)

Simple Random SamplingSystematic Random SamplingStratified Random SamplingCluster Random SamplingMultistage Random Sampling

NON-PROBABILITY SAMPLING (NON-RANDOM SAMPLING)

Quota SamplingPurposive/ Judgmental SamplingSnowball/ Network SamplingConvenience/ Grab Sampling

SAMPLE ERRORS

Error caused by the act of taking a sample

sample results to be different from the results of real population (census)

Expressed as “standard error”

We have no control over

Sample error depends upon:• Size of the sample (larger

size lesser error)• Distribution of character

of interest in population

NON - SAMPLE ERRORS (SYSTEMATIC ERRORS)

Results from some imperfect aspect of the research design such as mistakes in sample selection, sampling frame error, or non–responses.

Errors not due to chance fluctuations, but due to errors resulting from the researcher

ERRORS ASSOCIATED WITH SAMPLING

SAMPLING ERRORS

SAMPLING ERROR: is incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population.For example, if one measures the height of a thousand individuals from a country of one million, the average height of the thousand is typically not the same as the average height of all one million people in the country.

Random Sampling Error:- Random error is a pattern of errors that tend to cancel one another out so that the overall result still accurately reflects the true value. Every sample design will generate a certain amount of random errorBias Sampling Error:- Bias, on the other hand, is more serious because the pattern of errors is loaded in one direction or another and therefore do not balance each other out, producing a true distortion.

TYPES OF SAMPLING ERRORS

5 COMMON ERRORS IN THE RESEARCH PROCESS

• 1. POPULATION SPECIFICATIONExample: Packaged goods manufacturers often conduct surveys of housewives, because they are easier to contact, and it is assumed they decide what is to be purchased and also do the actual purchasing. In this situation there often is population specification error. The husband may purchase a significant share of the packaged goods, and have significant direct and indirect influence over what is bought. For this reason, excluding husbands from samples may yield results targeted to the wrong audience.

• 2. SAMPLINGExample: Suppose that we collected a random sample of 500 people from the general Indian adult population to gauge their entertainment preferences. Then, upon analysis, found it to be composed of 70% females. This sample would not be representative of the general adult population and would influence the data. The entertainment preferences of females would hold more weight, preventing accurate extrapolation to the Indian general adult population. Sampling error is affected by the homogeneity of the population being studied and sampled from and by the size of the sample

5 COMMON ERRORS IN THE RESEARCH PROCESS

• 3. SELECTIONExample: Interviewers conducting a mall intercept study have a natural tendency to select those respondents who are the most accessible and agreeable whenever there is latitude to do so. Such samples often comprise friends and associates who bear some degree of resemblance in characteristics to those of the desired population.

5 COMMON ERRORS IN THE RESEARCH PROCESS

• 4. NON-RESPONSIVEExample: In telephone surveys, some respondents are inaccessible because they are not at home for the initial call or call-backs. Others have moved or are away from home for the period of the survey. Not-at-home respondents are typically younger with no small children, and have a much higher proportion of working wives than households with someone at home. People who have moved or are away for the survey period have a higher geographic mobility than the average of the population. Thus, most surveys can anticipate errors from non-contact of respondents. Online surveys seek to avoid this error through e-mail distribution, thus eliminating not-at-home respondents.

5 COMMON ERRORS IN THE RESEARCH PROCESS

• 5. MEASUREMENTExample: A retail store would like to assess customer feedback from at-the-counter purchases. The survey is developed but fails to target those who purchase in the store. Instead, results are skewed by customers who bought items online.

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5 COMMON ERRORS IN THE RESEARCH PROCESS

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