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Data management in the field Ari Haukijärvi 2nd EHES training seminar.

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Presentation on theme: "Data management in the field Ari Haukijärvi 2nd EHES training seminar."— Presentation transcript:

1 Data management in the field Ari Haukijärvi 2nd EHES training seminar

2 Division of tasks and preparation of the fieldwork The Central Office - Overall data management preparation and coordination during the fieldwork Arranging computer equipment and networks for the fieldwork teams (if computerized data collection is used): Planning the structure of computer network(s) and equipment Establishing and customizing the network(s) Installing the survey software Planning of software updates during the fieldwork Testing Training, responsibilities, and support of the fieldwork teams: In putting up the network (if needed) In using the data recording equipment and software Issuing responsibilities to team members Providing instruction manuals for the fieldwork Arranging helpdesk during the fieldwork

3 Division of tasks and preparation of the fieldwork The Central Office Coordinating the fieldwork data management Database(s) maintained in the Central office can be used to monitor and control the fieldwork: Scheduling appointments Creating invitations, lists of appointment schedules, calendars, etc. Storing fieldwork inventory data Generating feedback Receiving and processing the survey data

4 Division of tasks and preparation of the fieldwork The Fieldwork teams Setting up the equipment and network connections (if needed) at the fieldwork site Basic technical knowledge in using equipment and software for data collection Responsibilities in backing up and transferring the data Named person(s) or all team members?

5 Collecting the survey data Data from Questionnaires and interviews Physical measurements Laboratory tests and samples … are recorded on paper forms on workstations, from which data files are copied to a local (fieldwork team) network’s server directly to the local (fieldwork team) network's server directly to the central survey database (in the Central Office)

6 Example 1 (System architecture of Health 2000 Survey)

7 Example 2 (MAAMU)

8 Data error checking Routine checking procedures: Visual checking of key items at examination site In computer-assisted data collection automatic built-in software checks at examination site Extensive checking for all computerized data (in the Central Office) … to ensure: complete data records no errors no lost or mixed-up records

9 Data management in the field - Key issues Identification Data confidentiality and security Backing up the data

10 Identification Follow-up of participants through the survey process to make sure that correct data corresponds to each subject, and to monitor in which phase of the survey each subject is by means of unique subject ID code (Serial number): used to identify the subject throughout the survey barcodes (referencing to the subject ID) in survey forms in laboratory samples

11 Data confidentiality and security Paper forms Not to be left alone for unauthorized to see or get lost Clear responsibilities in collecting and storing Sealed containers (also during shipments) No personal information included > subject ID code Data in electronic format Locking workstations and server computers when not used Safe place for equipment when not used Encrypted hard disks Encrypted data transfers Data should not be archived in personal directories or local computers' hard drives The personal data of survey subjects should be encrypted and available only to persons who have authorized access

12 Backing up the data The data need to be backed up regularly on external storage device (e.g. external hard drive, optical disk, another computer, etc.) separate from the original data source ’Remote’ backup: Data transfer to the Central Office Logging the data transfers

13 Data correction/documentation Changes to the data According to the rules for data verification and correction, specified in the national manual By trained persons Need to be documented: Version of data What was changed Reason for the change Time of the change The original raw data is backed up and unchanged


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