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Introduction to Sampling : Censuses vs. Sample Surveys

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Presentation on theme: "Introduction to Sampling : Censuses vs. Sample Surveys"— Presentation transcript:

1 Introduction to Sampling : Censuses vs. Sample Surveys
Module 3 Session 4

2 Session Objectives Distinguish between censuses and sample surveys
Demonstrate the linkages between censuses and surveys Discuss the challenges of conducting censuses and large scale surveys in Uganda Distinguish between random and non random samples Identify the types and/or sources of errors in censuses and surveys Discuss how errors can be minimised in censuses and surveys

3 Reminder of Definitions
Population: totality of all units of interest Sample: part/subset of the population Censuses: inquiries that cover the whole population eg. Uganda Population and Housing Census, CIS, EMIS, HMIS, LOGICS, etc Sample surveys are inquiries that cover part/subset of the population eg. UDHS, UNHS, NSDS, etc Sampling Frame: list of distinct and distinguishable units in the population of interest; beginning step in almost all random sampling schemes, e.g. numbers written on households before the census night

4 Other Definitions Defacto census- covers all persons found within the borders of a particular territory/country at a particular point in time-census night Dejure census-tallies people according to their regular or legal residence

5 Sampling Frames Sources Administrative records-eg Construct your own
Hospital records Birth and Death Registers LC lists Voters’ register School registers etc Construct your own

6 Disadvantages of various sources of sampling frames
Administrative records may not be up to date Constructing your own may be too costly especially in large scale surveys

7 Role of censuses in Uganda
Provide benchmark data for monitoring, planning and policy formulation eg we need data for UPE monitoring, poverty monitoring Election monitoring Resource allocation

8 Role of censuses in Uganda (cont.)
Provide small area statistics - basic data disaggregated to the lowest administrative unit e.g we use census data to know the number of people in each village, sub county and district for planning purposes Show the actual status of the various indicators Health indicators-mortality, disease prevalence Fertility trends, population growth rate

9 Linkages between censuses and sample surveys
Sample surveys can be used as a substitute for censuses Sample surveys can be used to supplement census data Sample surveys can be used to pretest census materials, procedures and methods Censuses are used as a basis for surveys conducted between censuses Sample surveys can be used to monitor census results

10 Challenges of Conducting Censuses and Large Scale Sample Surveys
Challenges of Surveys and Censuses Mubiru James.ppt

11 Types of Samples There two types of samples:
Random and Non random samples Random samples are those whose composition is not influenced by the sampler Non Random samples are those whose composition is influenced by the sampler

12 Advantages of Random Samples
Objective and hence inferences based on them are reliable

13 Disadvantages of Random Samples
Costly to select Need skilled manpower to get a random sample For some surveys, random sampling may not be the best because the sample may not provide the required data.

14 Advantages of Non Random Samples
Easy and cheap to select since selection and substitution can be done at will Since they are done at will, the data needed can be easily obtained

15 Disadvantages of Non Random Samples
Subjective and hence inferences based on them are biased Sampling errors can not be estimated

16 Types of Errors There are two types of errors, namely: Sampling errors
Non sampling errors

17 Sampling Errors/Biases
Sampling errors are absent in censuses Their causes include: Use of defective sampling frame Use of defective sampling procedures Use of an estimation method that does not correspond to the sampling design

18 Non Sampling Errors Non sampling errors occur both in censuses and sample surveys but are more pronounced in censuses

19 Sources of Non sampling Errors
Defective sampling frames resulting into coverage errors Under coverage Over coverage Conceptual problems Physical environment Inadequacy of enumerators and supervisors

20 Sources continued Language problems – translation
Problems of measurement Response problems Non response problems Poor cartographic work Poorly designed questionnaires/instruments Poorly trained enumerators/supervisors Unqualified enumerators/supervisors

21 How Errors can be Minimised
Supervision Training Use of the appropriate estimation method Publicity of the survey Testing the survey instruments

22 Sampling in the Research Process
Problem Objectives Hypotheses Methodology Data Sources Target population Census or sample? If sample? What is the sampling design?

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