Post on 26-Jan-2016
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CENSUS AND SAMPLE SURVEYAll items in any field of inquiry
constitute a ‘Universe’ or ‘Population.’ A complete
enumeration of all items in the ‘population’ is known as a census inquiry. It can be presumed that in such an inquiry, when all items are covered, no element of chance is
left and highest accuracy is obtained.
But in practice this may not be true. Even the slightest element of bias in such an inquiry will get larger and larger as the number of observation increases. Moreover, there is no way of checking the element of bias or its extent except through a resurvey or use of sample checks. Besides, this type of inquiry involves a great deal of time, money and energy
Sampling is the process of selecting a small number of elements from a larger defined target group of elements such that the information gathered from the small group will allow judgments to be made about the larger groups
Population
Element
Defined target population
Sampling unit
Sampling frame
Sampling error is any type of bias that is attributable to mistakes in either drawing a sample ordetermining the sample size
1. Define the Population of Interest2. Identify a Sampling Frame (if possible)3. Select a Sampling Method4. Determine Sample Size5. Execute the Sampling Plan
Population of interest is entirely dependent on Management Problem, Research Problems, and Research Design.
Some Bases for Defining Population:◦ Geographic Area◦ Demographics◦ Usage/Lifestyle◦ Awareness
A list of population elements (people, companies, houses, cities, etc.) from which units to be sampled can be selected.
Difficult to get an accurate list. Sample frame error occurs when certain
elements of the population are accidentally omitted or not included on the list.
See Survey Sampling International for some good exampleshttp://www.surveysampling.com/
CHARACTERISTICS OF A GOOD SAMPLE DESIGN
(a) Sample design must result in a truly representative sample.
(b) Sample design must be such which results in a small sampling error.
(c) Sample design must be viable in the context of funds available for the research study.
(d) Sample design must be such so that systematic bias can be controlled in a better way.
(e) Sample should be such that the results of the sample study can be applied, in general, for the universe with a reasonable level of confidence.
Probability sampling
Nonprobability sampling
Probability Simple random
sampling Systematic
random sampling Stratified random
sampling Cluster sampling
Nonprobability Convenience
sampling Judgment
sampling Quota sampling Snowball sampling
Simple random sampling is a method ofprobability sampling in which
every unit has an equal nonzero chance of being selected
Systematic random sampling is a method of
probability sampling in which the defined
target population is ordered and the sample is selected
according to position using a skip interval
1: Obtain a list of units that contains an acceptable frame of the target population
2: Determine the number of units in the list and the desired sample size
3: Compute the skip interval 4: Determine a random start point 5: Beginning at the start point, select the
units by choosing each unit that corresponds to the skip interval
Stratified random sampling is a method of
probability sampling in which the population is divided
into different subgroups and samplesare selected from each
1: Divide the target population into homogeneous subgroups or strata
2: Draw random samples from each stratum
3: Combine the samples from each stratum into a single sample of the target population
Convenience sampling relies upon convenience and access
Judgment sampling relies upon belief that participants fit characteristics
Quota sampling emphasizes representationof specific characteristics
Snowball sampling relies upon respondent referrals of others with like characteristics
Research objectives Degree of accuracy
Resources Time frame
Knowledge oftarget population Research scope
Statistical analysis needs
How many completed questionnaires do we need to have a representative sample?
Generally the larger the better, but that takes more time and money.
Answer depends on:◦ How different or dispersed the population is.◦ Desired level of confidence.◦ Desired degree of accuracy.
Common Methods:◦ Budget/time available◦ Executive decision◦ Statistical methods◦ Historical data/guidelines
See Table
Variability of the population characteristic under investigation
Level of confidence desired in the estimate
Degree of precision desired in estimating the population characteristic
The confidence interval The confidence interval (also called
margin of error) is the plus-or-minus figure usually reported in newspaper or television opinion poll results. For example, if you use a confidence interval of 4 and 47% percent of your sample picks an answer you can be "sure" that if you had asked the question of the entire relevant population between 43% (47-4) and 51% (47+4) would have picked that answer
The confidence level The confidence level tells you how sure
you can be. It is expressed as a percentage and represents how often the true percentage of the population who would pick an answer lies within the confidence interval. The 95% confidence level means you can be 95% certain; the 99% confidence level means you can be 99% certain. Most researchers use the 95% confidence level.
When you put the confidence level and the confidence interval together, you can say that you are 95% sure that the true percentage of the population is between 43% and 51%. The wider the confidence interval you are willing to accept, the more certain you can be that the whole population answers would be within that range.
When estimating a population meann = (Z2
B,CL)(σ2/e2)
When estimates of a population proportion are of concern
n = (Z2B,CL)([P x Q]/e2)
For a simple sample size calculator, click here:http://www.surveysystem.com/sscalc.htm
Probability Sampling and Sample Sizes