TRC ListView

TRC’s default view is the “ListView”, which looks like a digital clipboard. Names are organized by street.   There are also other views, such as the common map view.

See http://bit.ly/1U1sgjJ  for a youtube demo.

The view looks like this:

trc-list-view

In the ListView, each row is a voter. Voters are grouped by address so that you can easily identify all voters at a single household when you’re at the door.  You can collapse voters by streets for easy managing.  The grouping is zebra striped (yellow /  white + grey) for easy-reading. The stripe color is purely ascetic.

When you edit cells, they start off red, and then turn green when they’re uploaded to the server.

trc-list-view-detailed

This provides immediate feedback on upload. Cells are colored red when you edit and green when their result is uploaded to the cloud, so you get immediate piece of mind that your changes are safely saved.

TRC supports collaborative editing: when multiple canvassers are walking the same precinct, other canvasser updates show up on your sheet in realtime.

 

Standard columns:

The default view in TRC includes some standard columns:

  • Voter address, name, age, gender – this is commonly from public Secretary of State Voter Database (VRDB)
  • History – this is a percentage of likeliness to vote in the upcoming election. This is similar to the “how many of the last  4 elections did they vote in”.  It’s computed via a predictive model and measurably more reliable than just a “4 score”.
  • Party – this is a party identification.
  • Supporter – Does the voter support your campaign? This likely correlates with party, but is very important in tracking cross-over voters and non-partisan races.
  • Comments – – this lets a canvasser provide free-form notes.

See Pinned vs. Floating values  for more details on where the data comes from.  

Targeted Voters

TRC will bold the “targeted” voters to help canvassers prioritize who to visit. For example, a precinct may have 500 people but you only have time to talk to 50 voters. The “targeting” helps prioritize which ones to talk to. TRC uses a default targeting algorithm, or you can supply your own targets, or you can coordinate with Voter-Science to use analytics to get a special target list for your campaign.

We continue to show all the voters because if you happen to talk to a non-targeted voter, we want to make it easy to record that information. (Knowing who the opponents are makes it easier to guess who our guys are).

Get-Out-The-Vote

Washington State is vote-by-mail. That means that people are casting their votes 3 weeks before the election. During these ballot chase periods, names will also be crossed off as people have voted.   Voter-Science collects the matchbacks from the county auditors.  See Prepping for the ‘16 General for more details.

Sharing with TRC

You first receive access to TRC via a “canvass code” or a secret URL. Normally, this arrives in an email to you.

The code is an 8-12 digit secret code that uniquely identifies you. It’s effectively a password. You can enter this in the starting webpage or on the phone apps.

For convenience, the code can also be directly embedded in a URL like this so that a single click takes you straight to your sheet:

https://canvas.voter-science.com/home/sheet?code=SECRET

Don’t share this url with anybody!

 

Facebook login

For larger areas, such as an entire Legislative District, TRC may also prompt you for a Facebook login. We use Facebook because:

  1. It’s convenient. Nobody wants to remember yet another password.
  2. Facebook is a secure system and has advanced security features like two-factor-authentication.
  3. We want the extra security when handling larger amounts of data.

For smaller areas, such as individual precincts with just a few hundred voters, you don’t need a Facebook login.

If you’re logged in with Facebook, then TRC can also remember the list of codes you’ve visited and will display them at the homepage, at: https://canvas.voter-science.com

Sharing sections of your sheet with new volunteers

Suppose you’re granted a code for an entire city with 10,000 voters.

You can share out individual precincts with volunteers. On the main screen, you’ll see a list of the precincts and a “share this” button:

trc-precinct-list

 

When you hit the “Share this…” button, you can enter an email address and short personal message.

trc-share-dialog

 

And then the volunteer will get an email with their own canvas code (just for their precinct) and their own url link. They can receive the email on their smartphone and open the app directly from there and be ready to go.

This code will be different than your original code. Unique codes let the system identify different users.

 

Who can I share with?

You could share a single precinct with multiple people and each would get their own unique code. This lets you identify each individual.

TRC does not charge per-user, so you are encouraged to share with as many people as you need.

Revoking codes

You can also revoke codes that you’ve shared out. For example, you may do this if you typed in the wrong email address.

Geek stuff

What a code is really doing is giving you access to a sandbox. You can share  a single sandbox with multiple people and use the per-user code to track which user is accessing the sandbox. By default, a legislative district split into precincts means that each precinct is a child sandbox.

Analyzing the WA Political Spectrum

It’s no secret that our political system has become increasingly polarized in recent years.  In fact, Pew Research regularly publishes studies on the topic and the situation is perhaps more grim than you might think.  Looking back over the past 60 years, Congress has never been so politically divided, and the result is D.C. gridlock.

So how bad is the situation here in Washington State?

After serving two terms now in the House of Representatives and being elected to caucus leadership, I have my opinions.  There’s certainly evidence to support the assertion that the Legislature is doing much better than Congress, but the partisan divide is alive and well in Olympia.  Rather than rely on anecdotal evidence, I was inspired by what Pew and others have done and set out to quantify the problem.

This slideshow requires JavaScript.

A couple of weeks ago I published my first analysis of the partisan distribution of the Washington State Legislature.  It also included a Partisan Leaderboard, calling out the members who are least and most likely to cross the aisle.

The methodology is simple:  The partisanship score for each floor vote is calculated as the percentage of Republican supporters minus the percentage of Democrat supporters, giving each a range from 100 (exclusively Republican) to -100 (exclusively Democrat) with unanimous votes scoring zero.  The member’s aggregate score is just the average of all the scores of floor votes they supported, minus the scores from those they opposed.

This approach is different than many other studies like the McCarty & Shor (2015) Measuring American Legislatures Project, which use the Political Courage Test (former National Political Awareness Test).  Theirs are based on subjective questionnaires, while our scores are based on recorded floor votes.

Now we’ve taken this analysis to the next level…

Screenshot

If you visit WhipStat.com, you’ll now find an interactive and animated version of our partisan analysis that allows you to filter by policy area, chamber and date range.  Each member is “stacked” in a histogram, allowing you to roll over the chart and see more detailed information in tooltips.  Also, the median Democrat and Republican scores are indicated as vertical lines.  (Technically, the median is more meaningful than the average for these scores, with half the members being above and half below the line.)

And finally, this updated analysis includes floor votes on amendments, so the scores may be slightly different than those previously published.

Capture

If you’d like to do further analysis, feel free to hit the Download button for a tab-delimited table of the charted data.

A few things become apparent when reviewing the results:

  • Democrats are typically over twice as partisan as Republicans, and even more so in years when there was divided control of the House and Senate.
  • The majority party will control the floor agenda, and so will exhibit more “cohesion” and be less likely to allow members to cross the aisle.
  • The Democrat-controlled House was over twice as likely than the Republican-controlled Senate to bring bills to the floor that were rejected by the opposing party.
  • Some members show willingness to regularly cross the aisle for specific policy areas that are important to them or their constituents.  Lobbyists and advocates should take note of these policy areas.

Conclusion

Our goal here is simple:  To provide some transparency into partisan behavior in our state legislature.

There’s nothing inherently wrong with being a partisan voter, and one could argue that a certain degree of caucus cohesion is necessary to be a functioning majority.  However, many constituents elected their representatives with the expectation that they would exercise independent thought and work aggressively across the aisle to get results.  What they may discover looking at this data is that some of their representatives are more loyal to their partisan ideology than they are to a process that involves compromise to find common ground.  Given the example being set in Washington D.C., I also think we should take this moment to recognize those members on both sides of the aisle with the courage to break the partisan gridlock and work in the best interests of our entire state.  If you consider yourself an independent, these members deserve your support.

 

Measuring the Trump Effect (Updated 11/15)

One of the most contentious elections in U.S. history is over and Donald J. Trump will be our next president.    Although Trump’s victory was moderately large in terms of the electoral vote, the closeness of the popular vote will prompt questions for Republicans moving forward, particularly in “blue” states such as Washington.  Just how did Trump do with Independent and soft Democrat voters in Washington state with whom Trump, though a polarizing figure, polled somewhat well since the early this year?

 

The question we wanted to answer was, “What exactly was the impact of Trump on Washington state races?”

 

Voter Science undertook a statistical modeling analysis of the early return data to assess Trump’s effect on local and statewide races.  We used data from ballot returns through Nov. 11th (with the understanding that final certified results will not be available for another several days).  Our statistical modeling utilized regression modeling using the form:

equation

Legislative district race results were modeled using Trump/Pence performance and other factors as predictors.  Performance deltas to Romney/Ryan 2012 were factored in to determine the detriment or improvement a legislative candidate received this cycle, what we’re calling the Trump Effect.

 

In general, we found that the Trump Effect amplified pre-existing voter preferences, giving Republican candidates a lift in predominantly “red” areas while dragging them down in places that would otherwise have been more likely to be closely contested.

Trump’s Impact on State Legislative Races: Tailwind or Drag?

For state legislative candidates, the Trump Effect depended largely on where you were.

The Trump Effect produced a “Trump Tailwind” for Republican legislative candidates in primarily Eastern and Southwest Washington.  The effect was particularly strong in Southwest Washington counties; Republican legislative candidates there received an average 5 to 6-point lift.

However, the Trump Effect was a net negative – “Trump Drag” – in many of Washington’s most populous counties, primarily those touching Interstate 5.  The Trump Drag was most evident in King County where the effect of the presidential ticket was to subtract an average of 5.2% from Republican legislative candidates.

Table 1: Net Impact of Trump Effect on Legislative Races (select counties updated 11/15)

updatedlegresults20161115

 

 

 

Figure 1: Net Impact of Trump Effect on Legislative Races by County

map_impact

The impact on legislative races was stark in some cases:

Table 2: Trump Effect Impact Select Races 

As of 11/10/16, Republicans running in King County have racked up four losses.  Even in the deeply red 5th Legislative District, there was a point at which all three seats were at risk, and although the two state House positions are currently leaning R in terms of returns, and the state Senate race is currently a loss but trending toward a possible late Republican win, our modeling predicted the margin should have been decisive at this point where it not for Trump Drag.

The 30th LD, where the Republican party worked so hard in 2015, has seen two star incumbents removed.  State Senator Steve Litzow in the 41st ran to a tie in the primary but lost by more than five points in the general.  Again, our predictive modeling indicated that Litzow should won handily; Trump Drag pulled roughly 3,800 votes from the R column in that race.

Statewide Races

Statewide Republican candidates suffered because of Trump Drag.  Overall losses to statewide candidates ran between 2 to 3 points and effectively made the races for Lands Commissioner and Auditor uncompetitive.  Were it not for the Trump Effect, both races would have been within 1 percentage points.

Table 3: Trump Effect Impact on Statewide Candidates

table_statewideraces

Depending on the race, the Trump Effect sliced off 50,000 to 70,000 votes from each of our key races.  Only Kim Wyman, the incumbent Secretary of State, fared better than the remaining slate of Republican candidates.

As with state legislative races, statewide candidates took the biggest hit in King County, a particularly rough Trump Effect because the county accounted for approximately two-thirds of the lost votes across all candidates.

Figure 2: Trump/Pence Performance as of Nov 12th

map_trump_results

 

Conclusions

Although many Republicans are celebrating Trump’s presidential victory, it is important to recognize this Trump Effect and what it says about the electorate here in Washington state.  A deep divide persists among Washingtonians that represents a major challenge moving forward.  How do Republicans reconnect with Independent and more conservative Democrats who have at times walked across party lines to vote for Republican candidates.  There is fertile opportunity to engage with these voters outside of the traditional social justice spectrum.  The final part of our challenge is identifying the hot button issues that these voters care about that have alignment with conservative values.

 

WA Voting Turnout Statistics

In WA state, voter turnout is at 52% [As 11/7/16].  A further breakdown shows Democrats are enjoying significant turnout advantage: Seattle turnout is at 58% whereas Yakima County turnout is 43%. Seattle is a traditional litmus test  for Democrat turnout, whereas Yakima County is a traditional litmus test for Republican turnout, especially in eastern Washington.

Low GOP turnout in eastern WA was a large factor in Rob McKenna’s 2012 loss. The above data is from the Secretary of State and County auditors. We can draw additional insights by join that with party identification scores [1].

Continue reading “WA Voting Turnout Statistics”

Late voter registration trends

Voter registration for Nov 8th, 2016 in WA  has closed and here’s a breakdown of some late registration trends.

The average age of a recently registered voters is 37, roughly 11 years younger than the average age of all voters. This trend is consistent across all 49 legislative districts.

Here’s a breakdown by legislative district, showing average ages of All voters, only voters registered 2016, and the difference between them.

Leg. District

All Voters

Registered in 2016

Difference

15

47.2

33.2

14.0

43

41.7

33.2

8.5

9

47.5

34.1

13.3

29

45.8

35.1

10.6

36

44.6

35.2

9.4

28

48.2

35.3

12.9

44

46.8

35.5

11.2

46

47.3

35.7

11.6

27

47.2

36.2

11.0

25

47.6

36.3

11.3

40

49.4

36.4

13.1

1

47.4

36.6

10.9

2

47.2

36.6

10.6

45

48.0

36.8

11.3

5

47.6

36.8

10.8

38

48.2

36.9

11.3

21

48.0

36.9

11.1

48

48.8

36.9

11.8

39

48.5

37.1

11.5

16

49.5

37.1

12.4

17

48.1

37.1

11.0

23

50.3

37.1

13.2

41

49.4

37.2

12.1

47

47.4

37.3

10.1

6

48.8

37.3

11.4

18

48.7

37.3

11.4

3

46.4

37.4

9.1

31

48.3

37.5

10.8

11

46.6

37.5

9.1

14

50.6

37.5

13.1

30

47.9

37.7

10.2

22

48.5

37.7

10.8

49

48.8

37.9

10.9

13

49.6

38.0

11.7

8

48.9

38.1

10.9

37

46.0

38.1

7.9

32

49.1

38.3

10.8

33

48.3

38.4

10.0

26

50.3

38.6

11.7

34

48.4

38.6

9.8

4

49.1

38.6

10.5

42

49.0

39.8

9.2

35

51.6

40.1

11.5

12

52.0

40.4

11.5

20

51.1

40.6

10.4

10

52.7

40.8

12.0

7

52.0

40.8

11.2

19

52.1

42.0

10.1

24

55.6

45.9

9.7

Districts average about a 1% increase from new registrations in ’16.

Here are the top 10 legislative districts by new-registration.

Leg. District

Number Voters

New Registrations

% growth

40

100,566

3,090

3.07%

43

118,302

2,516

2.13%

12

83,491

1,675

2.01%

22

108,232

2,066

1.91%

42

100,282

1,792

1.79%

13

78,080

1,389

1.78%

9

83,842

1,481

1.77%

8

89,393

1,567

1.75%

21

94,752

1,650

1.74%

3

86,858

1,511

1.74%

Here are some visualizations of the breakdown.

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clip_image006[6]


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Have a question about the VRDB? Leave a comment…