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Collecting data for informed decision-making

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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.

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