TANF Data Collaborative (TDC) 2.0 – Formative Data Collection

11/22/24, 10:47 AM

TDC 2.0 TA Topics - Interest Inventory

TDC 2.0 TA Topics - Interest Inventory
During the TDC 2.0 project, we will be offering technical assistance on different data analytics and equity topics. Together
we want to deepen our understanding of what it takes to create, lead and sustain an organizational culture that values
using data to learn and continuously improve equity and outcomes.
We would like each site staff member to tell us which topics are of most interest using this Microsoft Form. The form
should take about 20 minutes to complete. We will use your responses to develop content for future training and
technical assistance activities such as webinars, tools, or coaching sessions. Thank you for your time and attention. 
Please rank each topic on a scale from 1 to 5:
5: Very interested in this topic
4: Moderately interested in this topic
3: Neutral
2: Not very interested in this topic
1: Not at all interested in this topic

PAPERWORK REDUCTION ACT OF 1995 (Public Law 104-13) STATEMENT OF PUBLIC BURDEN:
The purpose of this information collection is to design and tailor the TANF Data Collaborative (TDC) 2.0
technical assistance program. Public reporting burden for this collection of information is estimated to average 20 min
per respondent, including the time for reviewing instructions, gathering and maintaining the data needed, and reviewing
the collection of information. This is a voluntary collection of information. An agency may not conduct or sponsor, and a
person is not required to respond to, a collection of information subject to the requirements of the Paperwork Reduction
Act of 1995, unless it displays a currently valid OMB control number. The OMB # is 0970-0531 and the expiration date is
9/30/2025. If you have any comments on this collection of information, please contact Melissa
Wavelet (melissa.wavelet@mdrc.org)  

* Required

1. Data quality checking and cleaning - Part 1 *
Learning objectives include: how to identify data quality errors, how to prevent errors, and the effects of data
quality issues on analysis as well as policy implications.
1

2

3

4

Very uninterested

5

Very interested

2. Data quality checking and cleaning - Part 2 *
Learning objectives include how to QC data, how to use R markdown, the importance of documentation, and the
importance of sustainable coding practices.
1

Very uninterested

2

3

4

5

Very interested

https://forms.office.com/Pages/DesignPageV2.aspx?prevorigin=NeoPortalPage&origin=NeoPortalPage&subpage=design&id=nCeUMY-9lECuc7VFE7…

1/5

11/22/24, 10:47 AM

TDC 2.0 TA Topics - Interest Inventory

3. Data manipulation/record linkage *
Learning objectives include: best practices for linking data from different sources and getting data into the right
format for analysis. 
1

2

3

4

Very uninterested

5

Very interested

4. Documenting data *
Learning objectives include: Defining data documentation and why is it important, understanding what barriers
prevent agencies from doing data documentation, and learning strategies, tools or systems that can make it
easier.
1

2

3

4

Very uninterested

5

Very interested

5. Increasing transparency in data collection, manipulation, and analysis *
Learning objectives include: how to use data documentation to increase transparency and trust, and how to
document limitations of analyses & data that may mask inequities.
1

2

3

4

Very uninterested

5

Very interested

6. Data Programming in R *
Learning objectives include: how to do common descriptive and inferential statistical analysis in R (building off of
what was learned in the Applied Data Analytics (ADA) course)
1

2

3

4

Very uninterested

5

Very interested

7. Statistics 101 *
Learning objectives include: basics of statistical inference including the rationale and application of hypothesis
testing, what p-values are, and the role of sample size, variation, and effect sizes in statistical inference.
1

2

3

4

Very uninterested

5

Very interested

8. Analyzing Trajectories of TANF Families *
Learning objectives include: considerations for successfully answering questions about the trajectories of families
receiving TANF; and important concepts relevant to analyzing trajectories, such as cohort definition, structuring
analytic data, and measuring and visualizing patterns of TANF and earnings receipt.
1

Very uninterested

2

3

4

5

Very interested

https://forms.office.com/Pages/DesignPageV2.aspx?prevorigin=NeoPortalPage&origin=NeoPortalPage&subpage=design&id=nCeUMY-9lECuc7VFE7…

2/5

11/22/24, 10:47 AM

TDC 2.0 TA Topics - Interest Inventory

9. Visualizing and Clustering Trajectories of TANF Families *
Learning objectives include: defining cohorts and TANF trajectories, different approaches to visualizing and
grouping trajectories (discrete sequence analysis plots, alluvium and Sankey plots, clustered time series analyses),
and how these different methods may be applied to draw out patterns in trajectories and identify the prevalence
of certain experiences.
1

2

3

4

Very uninterested

5

Very interested

10. Research Methods A/B Testing *
Learning objectives include: an overview of A/B testing, the major decisions and considerations associated with
this method, and how A/B testing can be applied to agency work.
1

2

3

4

Very uninterested

5

Very interested

11. Predictive analytics *
Learning objectives include: what is predictive analytics, what kinds of questions can predictive analytics be used
to answer, and how can agencies begin using predictive analytics.
1

2

3

4

Very uninterested

5

Very interested

12. Translating Analyses and Findings into Visualization, Memos, and Reports *
Learning objectives include: guidance and considerations as sites prepare for their stakeholder briefing, final
report, and presentation. It will include examples of deliverables.
1

2

3

4

Very uninterested

5

Very interested

13. Supporting a data-informed organizational culture: Part 1 *
Learning objectives include: defining a data-informed culture, assessing & reflecting on your agency's current
culture, and imagining what you want your future culture to be.
1

2

3

4

Very uninterested

5

Very interested

14. Supporting a data-informed organizational culture: Part 2 *
Learning objectives include: learning about strategies for supporting data-informed cultures, learning what would
need to change or happen to get to your future culture, and planning how to make those changes.
1

Very uninterested

2

3

4

5

Very interested

https://forms.office.com/Pages/DesignPageV2.aspx?prevorigin=NeoPortalPage&origin=NeoPortalPage&subpage=design&id=nCeUMY-9lECuc7VFE7…

3/5

11/22/24, 10:47 AM

TDC 2.0 TA Topics - Interest Inventory

15. Exchanging knowledge between frontline staff and central data staff *
Learning objectives include: understanding learning opportunities & challenges that come from close
collaboration between policy/program & technical staff
1

2

3

4

Very uninterested

5

Very interested

16. Leading through fear and resistance to equity framing/race explicit analysis *
Learning objectives include: Defining (what does it look like when staff, managers, or leaders resist using or
making changes that the data indicates?), Understanding (how can you identify the roots of fear and resistance?),
and Doing (what are strategies for addressing fear and resistance?)
1

2

3

4

Very uninterested

5

Very interested

17. Documenting historical/policy context behind the data *
Learning objectives include: Understanding (how do policy histories shape the data we collect and disparities we
see?), Assessing & reflecting (what policy histories could our team explore to better contextualize our analysis &
findings?), and Doing or Planning (what are effective strategies for documenting this context for other data
users?).
1

2

3

4

Very uninterested

5

Very interested

18. Mitigating cognitive biases when interpreting findings *
Learning objectives include: defining cognitive biases, understanding common cognitive biases, and learning
strategies for protecting against biases when interpreting data analyses or findings.
1

2

3

4

Very uninterested

5

Very interested

19. Interpreting findings with an equity lens *
Learning objectives include the best practices of: Focusing on systemic inequities and issues rather than personal
responsibility, Challenging norms that focus on deficits rather than strengths (strengths-based interpretation),
and Relying on those working within the systems we study & those with lived experience for guidance in
interpretation
1

2

3

4

Very uninterested

5

Very interested

20. Communicating with a focus on equity *
Learning objectives include: What is strengths-based language and communication & how do we use it, and
Communicating results with a focus on systemic and historical inequities.
1

Very uninterested

2

3

4

5

Very interested

https://forms.office.com/Pages/DesignPageV2.aspx?prevorigin=NeoPortalPage&origin=NeoPortalPage&subpage=design&id=nCeUMY-9lECuc7VFE7…

4/5

11/22/24, 10:47 AM

TDC 2.0 TA Topics - Interest Inventory

21. Increasing publication of state data analyses *
Learning objectives include: Understanding (what are the barriers that prevent you from publishing your work?),
and Imagining (what are effective incentives for overcoming those barriers?).
1

2

Very uninterested

3

4

5

Very interested

22. Are there particular topics among those listed above that are unclear?
Please explain

23. Are there topics not reflected here that you would like us to consider developing?

24. Please list your name and/or agency *

https://forms.office.com/Pages/DesignPageV2.aspx?prevorigin=NeoPortalPage&origin=NeoPortalPage&subpage=design&id=nCeUMY-9lECuc7VFE7…

5/5