2OverviewThis session considers the role of Data Management (DM) within the Project Life CycleIt is important to plan effective DM at the project planning stageWe show how negligence in DM can results in wrong decisions being made at policy level
3Objectives To introduce the basic concepts of Data Management To identify the stakeholders in Data ManagementTo outline the stages and levels of Data DanagementTo equip participants with skills to manage their data
4Epi-Info vs MS-Access Epi-Info creates an Access Database file It is easy to learn and easy to useMS-Access is more flexible e.g. in terms of designing data entry screens (show demo)BUT: MS-Access has a steep learning curveConsider engaging an expert for more complex data structures/surveys
5What is “Data”? Data can be defined as “individual measurements” They are the individual records in a questionnaireThey are the “raw materials” in a field or laboratory research activityblockplotcobwtgrainwtcarbon117.78112.6219.91362.54328.31722.8326.631482.4124.881702.3925.461582.47
6What is “Meta-data”? So meta-data turns raw data into “information” Is data about the dataDescribes the datasetEnables effective management of the data resourcesAllows the dataset to be fully understoodIs an essential part of the data documentationSo meta-data turns raw data into “information”
7What is “Information”?Information is processed data from which conclusions can be drawnInformation is a valuable resource for decision-making and for planningIt is the results of processing, gathering, manipulating and organising data in a way that adds to the knowledge of the receiver
8What is Data Management? DM is concerned with “looking after” and processing data – it involves:Looking after field data sheetsEntering data into computer filesChecking and correcting the raw dataPreparing data for analysisDocumenting and archiving the data and meta-dataDM is the consolidation of data (and meta-data) in a way that is easy to manipulate, retrieve and maintain
9Why is DM important?Ensures data for analysis are of high quality so that conclusions are correctGood DM allows further use of the data in the future and enables efficient integration of results with other studiesGood DM leads to :Improved processing efficiencyImproved data qualityImproved meaningfulness of the data
10Data Management Problems Lack of skills – inability to use software or set up data checking proceduresMultiple copies of filesNo one with responsibility for checking dataNo clear policy on archiving or making data availableLack of documentationMultiple entry of the same dataHand pre-processing of data
11Activity 2 Case Study Discussion In small groups discuss the two examplesHave you encountered similar situations in your own workplace?
12Some steps in DM Designing field data collection sheets Collecting data with appropriate quality controlChecking raw dataData entry and organisation of computer filesBackup of filesProcessing of data for analysisChecking of processed dataArchiving data and meta-data for future use
14From Problems to Knowledge Formulate (fm) the objectivesDevelop (dv) the protocolDesign (ds) the observation unitsCollect (coll) the dataCompile (cm) data into well-structured datasetsQuery (qy) to select subsetsAnalyse (as) the dataPublish (pb) the results
16Non-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 themEditing and codingScanning – is this an option? – Budgetary implications
17Electronic Data Management Designing data entry systemData entry – including double entryData cleaning – consistency checksData security – regular backups – where are backups stored?Storage – how safe is your data?Documentation
18Documentation Documentation should be part of the project planning: File Structure – how will the data be organisedNaming conventions – for files and variablesData integrity – what checks are in placeDataset documentation – how will this be producedVariable construction – what variables will be constructed following data collection; how will these be documentedProject documentation – how will you document decisions taken on field procedures, coding, etc.
19ArchivingMany funding agencies now specify that the data be made public at the end of the projectPlans for archiving should be included in the project proposal and must be fully costedProposal should include:Schedule for data sharingFormal of final datasetDocumentation to be providedAnalytical tools to be provided if anyMode of data sharing
20Household Survey Archive Data from the Uganda National Household Survey 2002/2003 are available onlineThe online archive was created with the International Household Survey Network Microdata Management ToolkitEitherBrowse to on the web, and follow the link to Survey DocumentationOrOn the UBOS Resources DVD, browse to drive:/UBOS-UNHC2-CD-image/index.html
23Activity 4 In groups discuss the steps involved in data management Identify who commonly undertakes which task in your District OfficeWhat does each task involve?What resources – skills, equipment – are needed?How are paper records stored in your workplace?
24Building a DM StrategyGood DM is not something that will “look after itself” or evolve is left long enoughA DM strategy requires:CommitmentSkillsTimeMoney
25Data Management Plan A DM plan will include: Clearly defined roles for staffA regular backup procedureDetails of data quality checksIncluding DM on the agenda of project meetingsProcedure for upgrading softwareDetails of how archive is to be producedDetails of how the archive is to be maintainedFor example can you still read 5¼” floppy disks?!
26Roles and Responsibilities Organisers – handle raw data on a daily basis. They set up data filing systems, enter and check data and maintain data banksAnalysers – analyse and interpret data, reducing raw observations to useful informationManagers – responsible for providing an enabling environment for the first two groups and ensuring all commitments to stakeholders are met
27Hints on building a DM strategy Document current procedureSeek consensusEstablish a data management forumStandardise – use similar DM plans for all projectsObtain funding – include DM plan in project proposals and budget for it
28Activity 6In groups discuss what would be a feasible data management strategy in your workplace