Programme: Master of Library & Information Science
Course: MLIS-09: Research Methodology
The
errors that arise due to the use of sampling surveys are called sampling
errors. If we draw four samples from the same population for study, the results
may not be the same for all the samples. The results of sample study may not
completely represent the total population or in other words, it can be said
that the sample is not exactly representative of the total population. The
sampling method used to draw the sample, bias of the researcher in selecting
the sample, faulty method of analysis are some of the factors that cause
sampling errors.
Sampling
errors must be reduced to the minimum so that the results and conclusions are
sufficiently representative of the population. This can be done by avoiding
bias and increasing the size of the sample. The sampling error usually
decreases with increase in sample size.
The general formula for the margin
of error for a sample proportion (if certain conditions are met) is
where
is the sample proportion, n is
the sample size, and z* is the appropriate z*-value for your
desired level of confidence (from the following table).
z*-Values for Selected (Percentage) Confidence Levels
|
|
Percentage Confidence
|
z*-Value
|
80
|
1.28
|
90
|
1.645
|
95
|
1.96
|
98
|
2.33
|
99
|
2.58
|
Steps:
Here are the steps for calculating the margin of error for a sample proportion:
1. Find the sample size, n, and the sample proportion.
(The
sample proportion - is
the number in the sample with the characteristic of interest, divided by n.)
2. Multiply the sample proportion by (1-p)
3. Divide the result by n.
4. Take the square root of the calculated value.
You
now have the standard error,
___________
/ 1-p
/ ______
\/ n
- Multiply the result by the appropriate z*-value
for the confidence level desired.
Refer
to the above table for the appropriate z*-value. If the confidence level
is 95%, the z*-value is 1.96.