KENYATTA UNIVERSITY
DIGITAL SCHOOL OF VIRTUAL AND OPEN LEARNING
IN COLLABORATION WITH
SCHOOL OF PURE & APPLIED SCIENCES
DEPARTMENT: MATHEMATICS AND ACTUARIAL SCIENCE
SST 308: DESIGN AND ANALYSIS OF SAMPLE SURVEY I
WRITTEN BY: Bernard N. Ngigi
VETTED BY;
SST308: DESIGN AND ANALYSIS OF SAMPLE SURVEY I 33 Credit Hours.
Prerequisite: SST 305
Purpose of the course
The aim of the course is to provide learners with knowledge in design and analysis of sample surveys.
Expected Learning Outcomes
At the end of this course, the student should be able to:a) Explain how to design and organize a survey.
b) Determine the sample size in sample surveys.
c) To estimate finite population parameters under the different sampling schemes.
Course content.
Designing and organizing sample surveys. Determination of sample size. Sampling designs: simple random
sampling, stratified random sampling, systematic sampling, cluster and double sampling. Multistage sampling.
Properties of various estimators including Ratio and Regression estimators. Errors in surveys.
Mode of Delivery
Virtual and physical class lectures, discussions, problem solving, exercises.
Instructional Materials
Whiteboard/chalkboard, overhead projector, smart board.
Assessment
Continuous Assessment: 30%
Final Exam:
70%
Core reading material
Lohr, S.L. (1999). Sampling Design and Analysis. Pacific Grove: Duxbury Press. ISBN13:9780495105275
Recommended reading materials
Cochran, W. G. (1977). Sampling Techniques; 3rd edition. New York: Wiley ISBN-047116240X
Barnett, V. (1991). Sample Surveys, Principles and Methods. London: Edward
Arnold. ISBN-13:9780470685907
Read this book.
Journals
Journal of Statistical Theory and Practice Taylor & Francis.
Journal of American Statistical Association-ASA
Journal of Official statistics.-Stefan Lundgren
Journal of Royal Statistical Society-John Wiley & Sons
LESSON ONE
Introduction
In this lesson we distinguish between two types of population surveys in determining the
absolute value of a certain characteristic of a population.
1.2
Learning Outcomes
By the end of this lesson the learner will be able to distinguish between::
1.2.1 Absolute and comparative statistical problems
1.2.2 Census and sample surveys approaches
1.2.3
Purposive and probability sampling
1.2.1 Introduction
There are two types of statistical problems; absolute and comparative problems
Absolute Problem determines the absolute value of a certain characteristic of the
population. For example obtaining the average weight of a group of individuals. This is
called randomization approach to inference.
Comparative problem compares the effects of two or more objects on a certain
characteristic of the population. For example obtaining the correlation between the
height and weight of an individual. This is called analytical approach to inference.
This course will deal with the absolute problem rather than the comparative problem.
There are two approaches to the solution of the absolute problem; Complete enumeration
(census) and sample survey approaches
Complete enumeration (census) approach
In this approach we observe the characteristic under study for each and every element of the
population. This method has some shortcomings:
i)
ii)
iii)
iv)
If the population is infinite, complete enumeration is not possible. On the other
hand , if the population is finite, the method can be difficult and tedious in terms
of multiplicity of causes like time, cost etc.
Time consuming.
Costly to carry out.
In some cases, complete enumeration is not possible. For example to test
whether the bulbs produced in a factory have met the burning hours it will
require you to destroy each and every bulb produced. That will not be possible..
Sample survey approach
This approach consists of selecting a finite subset of the population called a sample, observing
the individuals in the sample and then utilizing the sample characteristics to determine or
estimate the absolute value of the population.
Advantages of sampling over complete enumeration
i)
ii)
iii)
iv)
There is considerable saving in time and labour since only a part of the population is
under study.
Sampling results in reduction in cost in terms of money and man hours.
If testing is destructive, complete enumeration is impracticable and sampling
technique should be used.
Greater accuracy of observations.
Limitations of sample survey method.
i)
ii)
iii)
If the characteristics of every unit in the population is to be required, then sampling
won’t help
If time and money are not important factors, complete enumeration may be better.
Sampling theory requires the services of trained and qualified personnel and
sophisticated equipment for its planning, execution and analysis. In the absence of
these, the results of the sample survey are not trustworthy.
General methods of selecting the sample.
There are two main methods of selecting a sample. It should be noted from the outset that the
aim should be to choose a sample that is representative of the population. The sample should
be like the population it is representing. Hence in a given situation the method of choice should
be the one which achieves this aim. The two method are
i)
ii)
Purposive (subjective) sampling.
Probability sampling.
Purposive sampling
This type depends on the discretion and judgement of the person choosing the sample. The
sample is chosen with a definite purpose in view. It is the experimenter who decides which
sample to choose. The choosing can be biased in a way, hence the method suffers from
favouritism and nepotism depending on the beliefs and prejudices of the experimenter. This
method cannot be recommended for general use as it is biased due to the experimenter’s
subjective-ness. For example is if the investigator want to give a picture that the standard of
living has gone up, he will choose posh areas in Nairobi but if he wanted to show that it has
gone down, he will choose the slums areas.
Probability sampling
This is a scientific method of choosing a sample according to the laws of chance in which each
unit in the population has a definite pre-assigned probability of being selected in the sample. In
this course we shall consider the following probability sampling designs.
i)
ii)
iii)
iv)
v)
Simple random sampling.
Stratified simple random sampling.
Systematic sampling.
Clusters sampling.
Two stage cluster sampling.
This course will consider probability sampling methods.