1. Future directions for DDI
Steven McEachern and Jared Lyle
AAPOR DDI panel
May 2017

2. What have we seen today Discovering data (CLOSER)
Managing data and metadata (MIDUS)
Integrating and harmonising metadata (BLS)
Depositing data, transparency (Roper)
So what else could you do?

3. Re-using questions
Questions can be reused, at the moment you can just download the information and import into a questionnaire design tool or save to a PDF for reference

4. Re-using questions – with DDI-Lifecycle
Behind the scenes, there is the ability for say a questionnaire design tool to capture this question
What language it is in
The code list being used
Response cardinality
& within the blob of information self-provenance where it came from
Persistent URN

5. Creating new instruments
European Social Survey
QDDT – NSD Norway
INSEE France
Pogues

6. European Social Survey (ESS)
Questionnaire
Module
Concept
Question
Response
Domain
Instrument
Workflow in the
European Social Survey (ESS)
NSD©2016
NSD©2016

7. QDDT conceptual model, DDI 3.2 based
StatementItem
ControlConstruct
Instrument
QuestionAsInInstrument
Study
Universe
Concept
QuestionItem
QuestionGrid
ResponseDomain
CodeDomain
CategoryDomain
ExternalAid
DateTimeDomain
Instruction
ScaleDomain
TextDomain
IASSIST, Bergen, May 31 – June 3
NumericDomain

9. INSEE - General Architecture
Questionnaire
design
Persistence
Conversion & translation
Transformation
Questionnaire
publication
Pogues
ENO
RMéS
Pogues (back-office)
Metadata supply
Developed with Pogues
Coltrane development already in production
Development in progress

10. Questionnaire design - Guidelines

11. Same source, multiple outputs
Roper Center
Data deposit
“Automated” TI reporting
Data Archives
Codebook/data dictionary creation

12. Roper Center - Metadata
Sample: National adult including an oversample of year olds
Sample Notes: This study contains sampling using landline telephones and cellular phones
Sample Size: 1,821
Response Rate: Landline=AAPOR RR3: 8.7 percent, Cell=AAPOR RR3: 8.6 percent
So let’s take a look again at that report from Pew.
This is what Roper’s database of metadata for archived studies looks like now. It contains the necessary information in an organized and standardized form, but there’s a fair amount of information in each field. To make this conform with DDI standards, we have to introduce greater granularity in the metadata
Estimated Sample Error: +/- 2.5 percentage points at the 95 percent confidence level

13. DDI Standards Sample Size: 1,821
Geographical Location (GeographicLocation): US
Universe (StudyUnitUniverseRef): Adult population
Sample1 (SourceDescription) : All
(Source Type) Adults
Sample2 (SourceDescription) : Oversample
(Source Type) Age 18-33
Sample: National adult including an oversample of year olds
Sample Notes: This study contains sampling using landline telephones and cellular phones
Sample Size: 1,821
Response Rate: Landline=AAPOR RR3: 8.7 percent, Cell=AAPOR RR3: 8.6 percent
Estimated Sample Error: +/- 2.5 percentage points at the 95 percent confidence level
Sample Size1 (NumberofResponses): 1,821 Sample1, Mode1 and 2: Telephone interviews/landline and Telephone interviews/cell phone Sample Size2 (NumberofResponses): 481
Sample1, Mode1: Telephone interviews/landline Sample Size3 (NumberofResponses): 1,125
Sample1, Mode2: Telephone interviews/cell phone
Sample Size4 (NumberofResponses): 215
Sample2, Mode2: Screened cell phone (18-33 oversample)
DDI does help to drive that push toward greater granularity – as does the AAPOR Transparency Initiative by encouraging survey research organizations to provide more information about methods. But again, we could achieve that without adopting DDI. But by adopting DDI standards, we now have a way of identifying each piece of metadata we are including that is consistent with other organizations and that expedites machine readability.
We’re currently in betatesting for a data processing system that reads and writes to DDI. This processing system will be used internally, but there’s an external component as well, which will allow data providers to submit data. I’m going to show you some of what that looks like.
Mode1 (ModeOfCollection): Telephone interviews/landline
Response Rate 1 (SpecificResponseRate): 8.7%
Response Rate 1 Definition (Description): AAPOR RR3
Mode2 (ModeOfCollection): Telephone interviews/cell phone
Response Rate 2 (SpecificResponseRate): 8.6% Response Rate 2 (Description): AAPOR RR3
Margin of error (SamplingError): +/- 2.5 (percentage points)

14. Depositor information
Sponsor & survey organization
First page of the deposit provides basic information about the depositor and the organizations involved in the survey – which also gives essential provenance information for the archive.
Grant funding
Submit single survey or a group or series

15. DDI planning
Manifesto for long-term data infrastructure in the social sciences
Currently in development from the DDI Alliance
Looking at metadata and data usage requirements across the data lifecycle

16. Example: Element Registry and Survey Design

17. Discussion
What kinds of metadata and data management problems are you trying to solve?
What would be the next step for you to implement DDI into your own processes?
How can DDI and the DDI Alliance help?