Friday, March 17, 2017

McElwee and McDaniel Validate my Analysis on Hate Groups and Trump


Working on a data set and then posting it on this blog (or putting it in a presentation for work) takes a long time.  In The Nation Magazine I read an article that must have taken a long time to put together on white voters fear of diversity and their likelihood to vote for Trump.  Jason McDaniel and Sean McElwee analyzed individual level data from the Cooperative Congressional Analysis Project (CCAP) where the same individuals were surveyed annually on attitudes regarding the election.  This is called a panel survey. 

They conducted an analysis of the data from 2016 and from 2012 and 2008 for white voters attitudes towards ethnic diversity and their likelihood to vote for Trump.  This is probably a logistic generalized estimating equation model looking at changes over time in voter preference.  The model controlled for other potential confounding variables of  age, race, education, income, gender, party affiliation, and concerns about their financial well being as well as the predictor variable of interest, concern about immigration and racial resentment.  

The results presented in the graph above show that the likelihood of supporting Trump increased with increasingly negative views of ethnic diversity.  These views were not as strongly predictive of support for Romney and McCain among white voters when they ran for President.  This suggests that Trump was better at tapping into their concerns than the previous two GOP nominees.  

Their analysis does coincide with mine of hate group concentration and Trump's % of the vote at the state level.   This effect was still significant after controlling for variables like poverty and the % of the population with a bachelor's degree or higher in that state.  McElwee and McDaniel (happy St. Patty's day guys) provide validation for my method.  It also provides validation for the Southern Poverty Law Center's method for tracking hate groups by showing that the number of hate groups in a state are a reflection of negative attitudes toward diversity in that state.
**Related Posts**

Hate Groups and Trump's Vote %: Predictive effect present when education and poverty are considered

 

More Hate Groups in States Where Trump and Clinton Win (and in DC Where He Lost)   

 

Bradley Effect for Trump?

Friday, March 10, 2017

A Successful Rally for Women's Rights

Last Wednesday was a successful rally and march for International Women's Day and LGBTQ equality with a large turnout.  Afterward there was a Johnstown, PA city council meeting at the Public Safety Building to vote on two ordinances to protect LGBTQ rights for all citizens in general and for city employees in particular. You can see the turnout at this meeting at the panoramic photo I took for the meeting below.  It was a packed house with those who supported the measures out numbering opponents by 3:1.  Many in the oppose camp came in from out of town.  The council voted to table the general measure while approving the smaller measure for city employees.  Tabling a measure or an ordinance means putting off a vote for a later meeting.  They said that the reason that they did this was out of concern for actions that the Trump administration might take.

 
http://www.wearecentralpa.com/news/anti-discrimination-bill-controversy/669503237

You can see the coverage of the meeting at WTAJ-TV in Altoona at the link above and at WJAC-TV in Johnstown.  The Johnstown Tribune-Democrat coverage can be seen at the video clip above.

 http://wjactv.com/news/local/johnstown-city-council-tables-anti-discrimination-ordinance

Neither side got everything they wanted at the meeting but newcomers to the process got a good glimpse of how their city government works. The battle for change will continue.  

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Bullying & Society

Friday, March 3, 2017

Make Your Voice Heard for Gender Equality and Science on March 8 and April 22


I have no data to work on this week for the blog.  I have two new articles posted on Data Driven Journalism on odds ratios and on confidence intervals for your statistical reading pleasure.  I did have some commentary on these strange times in which we are living.

I hadn't heard of Milo Yiannopolus before I saw this article on Breitbart calling for a limit in the number of women in STEM (science, technology, engineering and mathematics) fields because they can't handle the pressure.  These views should have been discredited decades ago but apparently they still linger.  Yiannopolus cites the opinions of of a few Nobel prize winners who said that women are too delicate to complete advanced degrees in STEM fields.  As someone who has pursued in advanced degrees in science and technology, women are well represented in the biological, medical, and biostatistical fields.  Some of them handled the pressure of pursuing a doctoral degree better than I did.  According to the national science foundation, 60% of all master's degrees in science given out in 2011 were to women.  Women make up 46% of all graduates in science fields.  You can look up other numbers on women in STEM fields here.

I was tempted to say that Yiannopolus' ancient Hellenic ancestors would be very disappointed in him but then I remembered that his ancestors, for all their achievements in math, science, the arts, and democracy, were sexist snobs.  Women were not permitted to attend the quadrennial games at Mount Olympus.  When the Athenians colonized territories in southern Italy they were asked about democracy for them.  Their reply was "for us there is democracy."  They would have had no problem with his comments about 13 year old boys and older men that got him fired from Breitbart and disinvited as the keynote speaker at the CPAC conference.  He would've found a following on the Athenian Acropolis.

In these times where the rights of transgender rights are under attack, where almost half of the US public does not believe in climate change caused by humans or in evolution, where millions of Americans wrongly believe that vaccines cause autism and where some even do not believe that the earth is a sphere.  It's time to speak up for those who do believe in these things and to reach out to those who aren't sure.  Two events are coming up where you can do just that.  

On March 8 there will be a rally and march for equal rights for transgender and other LGBT groups in Johnstown, PA at 4:45pm in Central Park.  March 8 also happens to be International Women's Day with many other events throughout the world.  The City Council in Johnstown will be considering adding transgender women to the non discrimination ordinance.  


Earth day will be on April 22.  To commemorate this, there will be a March for Science in Washington, DC and other cities throughout the US. It's about time we spoke up for the field to make it less ivory tower.  Donald Trump, Milo Yiannopolus, Jenny McCarthy, and their ilk are symptoms of a much larger disease.  

It's time to start counteracting it not with angry protests but with educating the public about what science is.  I'm personally a fan of another one of Yiannopolus' Hellenic ancestors, Socrates (sexist and snooty though he may have been).  He traveled around the Acropolis in Athens by asking a lot of unpleasant questions that needed to be asked.  By working within the frame work of what people know he was able to get people to think.  Eventually, he did endure the scorn of his fellow Yiannopolus' and was forced to choose either expulsion or drinking hemlock but he is most remembered and admired for sticking up for his beliefs.

**Related Posts**

Ivory Tower Science and the Rest of Us

Cosmos Redux?

 

The Civil War in a Larger International Context: Darwinian Edition

 

The Science of Why We Don't Believe Science | Mother Jones

 

Cause and Effect, Slip Slidin' Away 

Thursday, February 23, 2017

Hate Groups and Trump's Vote%: Predictive effect present when education and poverty are considered

When I posted last week on the correlation between the population hate group numbers and Trump's % of the vote, one friend on Facebook asked whether the result would hold up if third variables such as poverty and education were added to the model.  The figure below shows the association from last week,  I thought I would take a look at what would happen if I added educational attainment in each state (defined as the % of the population with a bachelor's degree or higher) and % of the states population in poverty were added to the regression model.  This is done to see whether these variables can account for the relationship between hate groups and Trump's % of the vote.


When poverty and education are both added to the model with hate group rate as predictors and Trump's % of the vote as an outcome, the rate of hate groups is still a significant predictor of Trump's vote.  However because the % of poverty in a state and the corresponding % of the state's population with a bachelor's degree or more are highly correlated with each other, the regression coefficients became nonsensical so the other two predictor variables must be considered in the presence of the hate group rate in separate models.  This problem is called multicollinearity by statisticians.



When education is added to the model with hate groups as a predictor, both variables are statistically significant with 58.2% of the total variability in Trump's vote accounted for.  The model with hate groups only as a predictor accounted for 20.1% of the variability which means that the % achieving a bachelors degree or higher in the state accounts for an additional 38.1%.  This means that educational attainment is a stronger effect than the concentration of hate groups.  The fact that the concentration of hate groups is still significant when education is added means that we can rule out education as an alternative explanation.  The regression equation is:


Trump % = 81.9 + 1.32*(Hate Group conc.) -1.22*(Educational Attainment)

This means that a state with 0 hate groups and 0 individuals with a bachelors degree would have an estimated 81.9% of the vote for Trump.  With every increase of one hate group per million in the state, Trump's % of the vote should increase by 1.32%.  With every 1% increase in the state's population having a bachelors degree or higher, Trumps % of the vote should decrease by 1.22% in that state.  The graph above shows the strength of the relationship between education and Trump's vote.


When the % in poverty in each state is added, both predictor variables are significant but the contribution of poverty is weaker than it is with education.  A total 26.2 of the variability is explained by both variables in the model.  This means that an additional 6.1% of the variability was accounted for with the regression equation as:

Trump % of the vote = 31.67 + 1.88*(Hate Group Concentration) + 0.88*(% in poverty)

For this model a state with zero poverty and zero hate groups should have an estimated 31.67%.  For each increase by one in the hate group concentration, Trump should have an estimated additional 1.88% of the vote while for every 1% increase in poverty Trump should expect an increase of 0.88% in his vote.

These models make it possible to rule out poverty and educational attainment as potential confounding variables in stating that the concentration of hate groups in each state predict Donald Trump's % of the vote in that state.  If hate group concentration became non-significant in the presence of either education poverty or both we could rule it out as a predictive factor but that did not happen.  I did test for the possibility of an interaction between the variables (The effect of both increasing at the same time) but did find a significant effect.
Prediction does not necessarily mean that a cause and effect relationship exists between predictor and outcome.  It means that one is more likely to find a higher % of the vote in states with a higher concentration of hate groups.  If I meant to say that a cause and effect relationship existed I would use the word cause instead of predict.  It could be that the sentiments that cause hate groups to form also motivated individuals to vote for Trump.  A lack of education and poverty can fuel these sentiments.  A lack of education was sited as the primary factor associated with white voters voting for Trump.  

As with the previous posts the District of Columbia was an extreme outlier as can be seen in the data table below.  It has an extremely high concentration of hate groups, a high % in poverty, a high % with a bachelors degree or more, and a low % of the vote for Trump.  For these reasons and that it is fundamentally different from the other states it was excluded from the analysis.

State Name
Hate groups 2016
Pop 2016
Hate groups per million '16
% bachelors degree or higher
% in poverty
Trump %
US
917
323,127,513
2.84
29.8
14.7
46.7
Alabama
27
4,863,300
5.55
15.4
18.5
62.9
Alaska
0
741,894
0.00
29.7
10.4
52.9
Arizona
18
6,931,071
2.60
27.7
17.4
49.5
Arkansas
16
2,988,248
5.35
21.8
18.7
60.4
California
79
39,250,017
2.01
32.3
15.4
32.7
Colorado
16
5,540,545
2.89
39.2
11.5
44.4
Connecticut
5
3,576,452
1.40
38.3
10.6
41.2
Delaware
4
952,065
4.20
30.9
12.6
41.9
District of Columbia
21
681,170
30.83
56.7
17.7
4.1
Florida
63
20,612,439
3.06
28.4
15.8
49.1
Georgia
32
10,310,371
3.10
29.9
17.2
51.3
Hawaii
0
1,428,557
0.00
31.4
10.7
30
Idaho
12
1,683,140
7.13
26.0
14.7
59.2
Illinois
32
12,801,539
2.50
32.9
13.6
39.4
Indiana
26
6,633,053
3.92
24.9
14.4
57.2
Iowa
4
3,134,693
1.28
26.8
12.1
51.8
Kansas
7
2,907,289
2.41
31.7
12.9
57.2
Kentucky
23
4,436,974
5.18
23.3
18.3
62.5
Louisiana
14
4,681,666
2.99
23.2
19.5
58.1
Maine
3
1,331,479
2.25
30.1
13.2
45.2
Maryland
18
6,016,447
2.99
38.8
9.9
35.3
Massachusetts
12
6,811,779
1.76
41.5
11.5
33.5
Michigan
28
9,928,300
2.82
27.8
15.7
47.6
Minnesota
10
5,519,952
1.81
34.7
10.2
45.4
Mississippi
18
2,988,726
6.02
20.8
22.1
58.3
Missouri
24
6,093,000
3.94
27.8
14.8
57.1
Montana
10
1,042,520
9.59
30.6
14.4
56.5
Nebraska
5
1,907,116
2.62
30.2
12.2
60.3
Nevada
4
2,940,058
1.36
23.6
14.9
45.5
New Hampshire
6
1,334,795
4.50
35.7
8.4
47.2
New Jersey
15
8,944,469
1.68
37.6
10.8
41.8
New Mexico
2
2,081,015
0.96
26.5
19.8
40
New York
47
19,745,289
2.38
35.0
15.5
37.5
North Carolina
31
10,146,788
3.06
29.4
16.4
50.5
North Dakota
1
757,952
1.32
29.1
10.7
64.1
Ohio
35
11,614,373
3.01
26.8
14.8
52.1
Oklahoma
6
3,923,561
1.53
24.6
16
65.3
Oregon
11
4,093,465
2.69
32.2
15.2
41.1
Pennsylvania
40
12,784,227
3.13
29.7
13.1
48.8
Rhode Island
1
1,056,426
0.95
32.7
14.1
39.8
South Carolina
12
4,961,119
2.42
26.8
16.8
54.9
South Dakota
7
865,454
8.09
27.5
13.5
61.5
Tennessee
38
6,651,194
5.71
25.7
16.7
61.1
Texas
55
27,862,596
1.97
28.4
15.9
52.6
Utah
3
3,051,217
0.98
31.8
11.2
45.9
Vermont
1
624,594
1.60
36.9
10.4
32.6
Virginia
39
8,411,808
4.64
37.0
11.2
45
Washington
21
7,288,000
2.88
34.2
12.2
38.2
West Virginia
4
1,831,102
2.18
19.6
18
68.7
Wisconsin
9
5,778,708
1.56
28.4
12.1
47.9
Wyoming
2
585,501
3.42
26.2
10.6
70.1
 
**Related Posts**

2016 Hate Group Concentration Predicts Trump % of the Vote


Concentration of Hate Groups Predict Hate Crimes (if you consider DC) and Trump Vote (if you don't)


 


More Hate Groups in States Where Trump and Clinton Win (and in DC Where He Lost)

 

Income and Life Expectancy. What does it Tell Us About US?