1. Collecting data for informed decision-making
Module 3

2. Introduction
Welcome
Housekeeping
etc.

3. Overview
So far in this training programme we have only studied the handling of data, and report writing.
Key concepts of statistics, e.g.
Good tables and graphs – using Excel
Data management e.g.
Data entry and validation – using Epi-info
Some analysis methods e.g. Instat
It is now time to study aspects of collecting data
(see next slide).

4. Manual checking, editing etc.
Data management cycle
Design
questionnaire
Enumerators collect
data in the field
Design
survey
Manual checking, editing etc.
Conception
Reporting of results
Data entered
onto computer
Have covered some data analysis
- more to come in Module 4
Data
analysis
Computer data management

5. Manual checking, editing etc.
Data management cycle
Module 3
Design
questionnaire
Enumerators collect
data in the field
Design
survey
Manual checking, editing etc.
Conception
Reporting of results
Data entered
onto computer
Data
analysis
Module 4
Computer data management

6. Module content Types of study Types of data
Tables to address objectives
Questionnaire design
Practical aspect of sampling surveys
Sampling techniques
simple random sampling, stratified sampling, etc.
Integrating qualitative and quantitative approaches

7. Duration and timetable
Morning
Afternoon
Day 1
Data collection and the research process
Table design
Day 2
Questionnaire design
Introduction to sampling techniques:
Day 3
Simple random sampling
Stratified sampling
Day 4
Systematic sampling
Cluster sampling
Day 5
Multistage sampling
Integrating qualitative research into quantitative research
Day 6
Wrap up

8. Module Learning Objectives
At the end of the module, participants will be able to:
Design questionnaires to address survey objectives effectively; and identify questionnaires that fail to address survey objectives
Explain to others the benefits of random sampling.
Describe stratified, cluster and multistage sampling.
Select random samples using different sampling schemes, and advise others on how to do so.

9. Module Learning Objectives (cont.)
More experienced members of the class may also be able to:
explain the benefits of stratified sampling compared to simple random sampling
explain the difference between stratified sampling and cluster sampling
explain what multistage sampling is, and how it relates to other sampling methods

10. Prerequisites
None (except possibly Module 1).

11. Resources CAST The Green Book (Chapter 4). To the Woods
Paddy Rice Survey
A case study of objectives for investigation by observation - provided by UBOS.

12. Data collection methods
Module 3
Session 1

13. Overview (of Session)
This session sets the scene for the module as a whole, and for the next three sessions in particular.
It reviews types of studies, and types of data and data collection.
It is laying the foundations for ensuring good data collection that will address the objectives of a research study.

14. Session Learning Objectives
At the end of the session participants will be able to:
Describe a range of different data collection activities.
Explain the difference between primary and secondary data and their roles in the research process.
Recount the methods and tools that are used in collecting different types of data.

15. Introduction
Within statistical offices and other departments, research studies are carried out to answer questions and inform policy.
Most of these studies are observational studies.
(i.e. researcher does not change the environment that is being studied, but observes and measures characteristics of interest with the aim of understanding the phenomenon under study.)
Sometimes they are desk studies using secondary data to draw conclusions.

16. Types of study Examples of observational studies are:
Census, sample survey, monitoring
These are all formally designed with structured questionnaires.
Sample surveys are commonly carried out observational studies.

17. Types of study Less structured examples are:
Qualitative interviewing, focus groups, etc.
Instruments for collecting data include:
semi-structured questionnaires to ad-hoc conversations
diagrams
ranking methods
mapping techniques
etc.

18. Data sources
Primary data are data that you, or your team, collect to meet specific objectives
Data collected in a sample survey is primary data.
Sometimes called raw data or micro-data
You know its quality (checking, entering, managing, etc. all done by you.)
Secondary data are collected by others for other purposes.
Sometimes only available in summary form.

19. Getting it right!
Important that the study conveys correct message to the policy makers.
This means that for surveys, for example, the study needs to be well designed:
needs well designed instruments asking the right questions and collecting all the relevant data.
needs to ensure that the sample studied is representative of the population of interest.
(more of this later in the module).

20. Types of surveys Cross-sectional studies Longitudinal studies
involve observation of some subset of a population of items all at the same time.
Cross-sectional studies are used in most branches of science, social sciences and elsewhere.
Longitudinal studies
involves repeated observations of the same items over long periods of time, often many decades.
They allow researchers to distinguish short from long-term phenomena, such as poverty.

21. Types of surveys (cont.)
Panel studies
is a longitudinal study where a cross-sectional sample of units is selected and surveyed at usually regular intervals. The observation units may be individuals, households, organisations, etc.
Cohort studies
is a form of longitudinal study. They sample a cohort, defined as a group experiencing some event (typically birth) in a selected time period, and studying them at intervals through time.

22. Uganda Examples: Sample Survey
The 2002/2003 UNHS2 survey:
approx.50,000 individuals
9700 households
970 communities
is an example of a cross-sectional survey.
UBOS designed sampling scheme, designed questionnaire etc.

23. Part of UNHS2 questionnaire

24. Uganda Examples: Focus Group

25. Types of data Quantitative Yield per hectare (numeric)
Time to travel to primary school (numeric)
Number of bundles of straw (0,1,2,….)
Traditional cash crops
(1=coffee, 2=cotton, 3=tea,….) (categorical)
Ownership of car (yes / no) (binary)

26. Types of data Qualitative results of focus group discussion
additional comments
reasons …
etc.
Can still be classified (coded) into categories.

27. Activity - Primary and secondary data
Read Chapter 4.1 (Using Secondary Data) of the Green Book.
In groups, discuss what secondary data is, where it can be used in the research process, and advantages and disadvantages.
Report back.

28. Discussion – studies and methods and instruments
Working in small groups.
Find out what types of study others have been involved in, and what methods and instruments they have used - with examples.
Discuss pros and cons of different studies and different methods and instruments.
Report back and group discussion.