1 Data Management Module 2 Session 5. 2 Overview This session considers the role of Data Management (DM) within the Project Life Cycle It is important.

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Data Management

Module 2 Session 5

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Overview

This session considers the role of Data Management (DM) within the Project Life Cycle

It is important to plan effective DM at the project planning stage

We show how negligence in DM can results in wrong decisions being made at policy level

Objectives

To introduce the basic concepts of Data Management

To identify the stakeholders in Data Management

To outline the stages and levels of Data Danagement

To equip participants with skills to manage their data

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Epi-Info vs MS-Access

Epi-Info creates an Access Database file It is easy to learn and easy to use MS-Access is more flexible e.g. in terms of

designing data entry screens (show demo) BUT: MS-Access has a steep learning curve Consider engaging an expert for more

complex data structures/surveys

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What is “Data”?

Data can be defined as “individual measurements” They are the individual records in a questionnaire They are the “raw materials” in a field or laboratory

research activity

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block plot cobwt grainwt carbon

1 1 17.7 811 2.6

1 2 19.9 136 2.54

1 3 28.3 172 2.83

2 1 26.63 148 2.41

2 2 24.88 170 2.39

2 3 25.46 158 2.47

What is “Meta-data”?

Meta-data Is data about the data Describes the dataset Enables effective management of the data

resources Allows the dataset to be fully understood Is an essential part of the data documentation

So meta-data turns raw data into “information”6

What is “Information”?

Information is processed data from which conclusions can be drawn

Information is a valuable resource for decision-making and for planning

It is the results of processing, gathering, manipulating and organising data in a way that adds to the knowledge of the receiver

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What is Data Management?

DM is concerned with “looking after” and processing data – it involves: Looking after field data sheets Entering data into computer files Checking and correcting the raw data Preparing data for analysis Documenting and archiving the data and meta-data

DM is the consolidation of data (and meta-data) in a way that is easy to manipulate, retrieve and maintain

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Why is DM important?

Ensures data for analysis are of high quality so that conclusions are correct

Good DM allows further use of the data in the future and enables efficient integration of results with other studies

Good DM leads to : Improved processing efficiency Improved data quality Improved meaningfulness of the data

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Data Management Problems

Lack of skills – inability to use software or set up data checking procedures

Multiple copies of files No one with responsibility for checking data No clear policy on archiving or making data

available Lack of documentation Multiple entry of the same data Hand pre-processing of data

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Activity 2

Case Study Discussion In small groups discuss the two examples Have you encountered similar situations in your

own workplace?

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Some steps in DM

Designing field data collection sheets Collecting data with appropriate quality control Checking raw data Data entry and organisation of computer files Backup of files Processing of data for analysis Checking of processed data Archiving data and meta-data for future use

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Project Life Cycle

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From Problems to Knowledge

Formulate (fm) the objectives Develop (dv) the protocol Design (ds) the observation units Collect (coll) the data Compile (cm) data into well-structured datasets Query (qy) to select subsets Analyse (as) the data Publish (pb) the results

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Scope of Data Management

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Non-Electronic Data Management

Handling of questionnaires in the field (both completed and blank)

Storage of questionnaires – protection from weather, termites, etc.

Movement of the questionnaires – who has access to them

Editing and coding Scanning – is this an option? – Budgetary

implications

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Electronic Data Management

Designing data entry system Data entry – including double entry Data cleaning – consistency checks Data security – regular backups – where are

backups stored? Storage – how safe is your data? Documentation

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Documentation

Documentation should be part of the project planning: File Structure – how will the data be organised Naming conventions – for files and variables Data integrity – what checks are in place Dataset documentation – how will this be produced Variable construction – what variables will be

constructed following data collection; how will these be documented

Project documentation – how will you document decisions taken on field procedures, coding, etc.

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Archiving

Many funding agencies now specify that the data be made public at the end of the project

Plans for archiving should be included in the project proposal and must be fully costed

Proposal should include: Schedule for data sharing Formal of final dataset Documentation to be provided Analytical tools to be provided if any Mode of data sharing

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Household Survey Archive

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Data from the Uganda National Household Survey 2002/2003 are available online

The online archive was created with the International Household Survey Network Microdata Management Toolkit

Either

Browse to http://www.ubos.org/ on the web, and follow the link to Survey Documentation

Or

On the UBOS Resources DVD, browse to drive:/UBOS-UNHC2-CD-image/index.html

Online Survey Archive

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Online Frequency tables

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Activity 4

In groups discuss the steps involved in data management

Identify who commonly undertakes which task in your District Office

What does each task involve? What resources – skills, equipment – are

needed? How are paper records stored in your

workplace?23

Building a DM Strategy

Good DM is not something that will “look after itself” or evolve is left long enough

A DM strategy requires: Commitment Skills Time Money

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Data Management Plan

A DM plan will include: Clearly defined roles for staff A regular backup procedure Details of data quality checks Including DM on the agenda of project meetings Procedure for upgrading software Details of how archive is to be produced Details of how the archive is to be maintained

For example can you still read 5¼” floppy disks?!

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Roles and Responsibilities

Organisers – handle raw data on a daily basis. They set up data filing systems, enter and check data and maintain data banks

Analysers – analyse and interpret data, reducing raw observations to useful information

Managers – responsible for providing an enabling environment for the first two groups and ensuring all commitments to stakeholders are met

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Hints on building a DM strategy

Document current procedure Seek consensus Establish a data management forum Standardise – use similar DM plans for all

projects Obtain funding – include DM plan in project

proposals and budget for it

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Activity 6

In groups discuss what would be a feasible data management strategy in your workplace

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