RM Sampling

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