The Effect of the General Election on the New York City Mayoral Democratic Primary Voter Turnout

by Tayba Aziz, Emilia Decaudin, and Katlyn Palmatier

Background

Voter turnout is based on a variety of factors including morale, ties to the community, peer pressure, ease of registration, etc. (Davenport, 2010; Highton, 2000; Stockemer, 2017b) New York City is unique because the population is composed of people of all backgrounds and socioeconomic statuses. This study will analyze voting data to determine what, if any, effects general presidential elections have on voter turnout in New York City Democratic mayoral primaries.

There are also two different types of elections that this study will analyze, general and primary. A general election is an election in which eligible voters across all parties can participate. Any U.S. citizen who is registered to vote can vote in the United States general presidential elections. U.S. citizens can register to vote if they’re above the age 18. Those convicted of a felony cannot vote while serving time. A primary election is an election in which voters within a party select a candidate to represent their party in a general election. New York State has closed primaries, meaning that only voters who are registered to a specific party can vote in primaries. Another way of categorizing elections is if there is an incumbent or open seat. An incumbent is an official who already holds office. If an official who is holding office does not, or cannot, run for another election, the election is open seat.

Research Question

Did the results of the 2016 General Election affect voter turnout in the 2017 Primary Election in NYC?

Hypothesis

The unusual results of the 2016 General Election led to a higher voter turnout in the 2017 Primary Election than the voter turnouts of previous primaries (1997-2013) that followed a General Election.

Methods

We will compare the turnout of NYC Democratic mayoral primary elections from 1997 to 2017 with the turnout of each previous year’s general elections, from 1996 to 2016, and determine if there is A. a relationship between the turnout in general elections with Democratic turnout in subsequent primary elections and B. if the 2016 Presidential general election affected turnout in the 2017 Democratic mayoral primary election to an unusual degree compared to previous years.

There are two main methods for measuring voter turnout. The first method measures “voter turnout as the percentage of registered voters who actually turn out [or vote]”. (Stockemer, 2017a) The second method measures “voter turnout as the percentage of a country’s voting age population that cast their ballot on Election Day”. (Stockemer, 2017a) This study will use the first method. We will be using the former method.

We will collect registration and turnout date from the New York City and New York State Board of Elections’ websites, as well as from ourcampaigns.com for data too old to be available on the previous two sites.

We will create a regression line using this data, and then analyze it with a t-test to determine if there is any significant difference between turnout in the sampled years.

References

Davenport, T. (2010). Public Accountability and Political Participation: Effects of a Face-to-Face Feedback Intervention on Voter Turnout of Public Housing Residents. Political Behavior, 32(3), 337–368. https://doi.org/10.1007/s11109-010-9109-x

Highton, B. (2000). Residential Mobility, Community Mobility, and Electoral Participation. Political Behavior, 22(2), 109–120.

Stockemer, D. (2017a). Electoral Participation: How to Measure Voter Turnout? Social Indicators Research, 133(3), 943–962. https://doi.org/10.1007/s11205-016-1410-6

Stockemer, D. (2017b). What Affects Voter Turnout? A Review Article/Meta-Analysis of Aggregate Research. Government and Opposition, 52(4), 698–722. https://doi.org/10.1017/gov.2016.30

Background

The growth rates of plants are commonly known to be dependent on fundamental factors such as soil, sunlight, temperature, and water. Studies have been performed in the past to determine how different factors, such as soil water potential, have affected the growth rates of plants (Kang and Wan, 2005). Although Kang and Wan have found that soil water potential treatments do not significantly affect the growth or developments of radish seeds, the question remains as to how modified types of water affect such plants. The salinity of water is one major factor that has been found to affect the nutrient intake of radish seeds (Van Hooijdonk, 1999). With moderate salinity levels, nutrient intake can be increased while instigating plant growth. Carbon dioxide also plays an important role for plant development. A study on tomatoes has shown that irrigated carbonated water has achieved greater yields (Novero et al., 1991). Because radishes require very few resources and time to grow, they serve as excellent subjects to test how different water types can affect their germinations.

Research Question

How do different water types affect radish seed germination?

Hypothesis

We predict that carbonated water will have the greatest positive effect on the germination rates of the radish seeds. This is due to the fact that common ingredients in carbonated water include minerals such as potassium sulphate and sodium bicarbonate. Most importantly, carbon dioxide has been added to the water, which is essential for plant growth.

Method

Our method revolves around the following materials:

• Radish seeds
• Paper towels
• Distilled water
• Tap water
• Salt water
• Carbonated water
• Mineral water

The radish seeds will be divided into 5 groups of 10 seeds each, to test the different water types we will be using: distilled, tap, salt, carbonated, and mineral. To begin, distilled water will be used as the control group. 10 radish seeds will be spread out on a flat paper towel. Afterwards, the paper towel will be watered with distilled water until completely soaked. The seeds will be left alone and watered every 2 days.

This process will be repeated for each of the other groups, watering the paper towels with their respective water types, for the next 2 weeks. Each of the groups will be placed on the window sill of a room in The Towers to allow the seeds to grow with sunlight. After 2 weeks, the effect of each water type on the seeds will be determined by the measurements in length of their germinated roots.

Proposed Stats Analysis

Using the average lengths of the seeds’ roots for each respective group, the data will be analyzed using an ANOVA. If there is a significant statistical difference present within the data, t-tests will be performed to compare every group with one another to determine which group or groups have had the greatest effects on the germination rates of the seeds.

Presentation

Our poster board will include a summary of background information needed to know about the growth rates of radish seeds and the different water types that we used for the experiment. Our data will also be presented through histograms and tables showing the ANOVA/t-tests.

References

Kang, Y., & Wan, S. 2005. Effect of soil water potential on radish (Raphanus sativus L.) growth and water use under drip irrigation. Scientia Horticulturae, 106: 275-292. Retrieved from

http://www.sciencedirect.com/science/article/pii/S030442380500124X

Novero, R., D. H. Smith, F. D. Moore, J. F. Shanahan, and R. d’Andria. 1991. Field-Grown Tomato Response to Carbonated Water Application. Agron. J. 83:911-916. doi:10.2134/agronj1991.00021962008300050026x.

Oliva, A., Lahoz, E., Contillo, R., & Aliotta, G. (2002). Effects of Ruta graveolens leaves on soil characteristics and on seed germination and early seedling growth of four crop species. Annals of Applied Biology, 141(1), 87-91.

Van Hooijdonk, M.  1999. “Effects of salinity on growth, water use, and nutrient use in

radish (Raphanus sativus L)”. Plant and Soil. 215: 57-64.

Okumura, T., Muramoto, Y., & Shimizu, N. (2012). Influence of DC electric field on growth of daikon radish (Raphanus sativus). Dielectrics and Electrical Insulation, IEEE Transactions on, 19(6), 2237-2241.

Names: Ann-Renee Rubia, Manuel Sojan, Samah Islam

Background

One of the reasons the new SAT was introduced in March 2016 was to introduce more equity for students of diverse economic backgrounds/household incomes.  Many previous studies have indicated that there was a positive correlation between preparedness for the SAT and household income, due to paid SAT coaching, access to more resources, and better funded schools.  We wanted to test if the new SAT is in fact more accommodating for students of various socioeconomic statuses.  We will achieve this by anonymously surveying freshman students at CCNY, who have taken the new SAT and the old SAT, and asking what their household income is. Then we’ll compare their household income to the scores of their old and new SAT scores.  

Research question

Does household income have an effect on the scores of the old and new SAT?

Hypothesis

Because it is very difficult and unlikely to completely eliminate correlation between SAT scores and income, there should still be a correlation between the new SAT and income level.  However, based on the goals and objectives of the introduction of the new SAT in 2016, there should be a lesser correlation between SAT scores and income on the new exam versus the older one.  

Independent and dependent variables

Independent variables: Family income

Dependent Variables: Old and new sat score

Data collection
A survey will be sent out to students from the Macaulay Honors program and Honors program at The City College of New York inquiring of their household incomes and their results on the old and new SAT. This will include whether they chose to take the essay portion of the exam (for those who took the new examination) as well as if they took both the new and old SAT.

Survey questions
Household income?

Have you taken both the old and new SAT?

How did you score on the old SAT?

How did you score on the new SAT?

Did you take the optional essay portion of the new SAT?

Data Analysis and Presentation

To analyze the raw data, an ANOVA test will be conducted between the different income levels for both the old and the new SAT exam. For the old exam, there should be a statistical difference between the different income brackets and SAT scores. However, for the new SAT, there should be no statistical difference. The results of the ANOVA will be tabulated and displayed on the poster board (this will be labeled as Table 1). A linear regression analysis will also be conducted, and a graph showing the correlation (if any) between income level and SAT scores will be plotted (for both SAT exams). There should be a weaker correlation between SAT scores and income level for the new exam. The scatter plot will be labeled as Figure 1. In addition to the ANOVA and the scatter plot, the three main measures of central tendency—mean, median, and mode—will also be calculated and evaluated.

Citations

Reed, B. (2015, June 3). The New SAT: Everything You Need to Know. Retrieved October 01,  2017, from http://time.com/3905719/the-new-sat/

12. Z. (2014, October 7). SAT Scores and Income Inequality: How Wealthier Kids Rank Higher . Retrieved October 1, 2017, from              https://www.supremecourt.gov/opinions/URLs_Cited/OT2015/14-981/14-981-12.pdf

Dixon-Román, Ezekiel J., et al. “Race, Poverty and SAT Scores: Modeling the Influences of Family Income on Black and White High School Students’ SAT Performance.” Teachers College Record, vol. 115, no. 4, Apr. 2013, pp. 1-33. EBSCOhost,

ccny-proxy1.libr.ccny.cuny.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=87024881&site=ehost-live.

Hoover, E. (2016, March 18). What students said right after taking the new SAT. The Chronicle of Higher Education, 62(27), A6. Retrieved from http://go.galegroup.com/ps/i.do?p=AONE&sw=w&u=cuny_ccny&v=2.1&it=r&id=GALE%7CA448900503&asid=29a16508705ea8846cd4c564441e6cb7

Hoover, E. (2014, March 14). Plans for New SAT Spark Mixed Reviews. The Chronicle of Higher Education, 60(26). Retrieved from

http://go.galegroup.com/ps/i.do?p=AONE&sw=w&u=cuny_ccny&v=2.1&it=r&id=GALE%7CA364442620&asid=b86279ecd60f0e085cccac11d0e8dbfe

Lin, Y., Clough, P. J., Welch, J., & Papageorgiou, K. A. (2017). Individual differences in mental toughness associate with academic performance and income. Personality and Individual Differences, 113, 178+. Retrieved from http://go.galegroup.com/ps/i.do?p=AONE&sw=w&u=cuny_ccny&v=2.1&it=r&id=GALE%7CA489037234&asid=263d8fac0693e9e5e42949aaf07e2a26

Graham, LaConda T., “Factors that Impact Performance on the Scholastic Aptitude Test (SAT) between Urban High School Seniors and their Parents.” (2008). Counselor Education Master’s Theses. Paper 41.

http://digitalcommons.brockport.edu/cgi/viewcontent.cgi?article=1040&context=edc_theses

Introduction:

The education system in America is largely based on examinations which leads adolescents to sacrifice sleep in favor of studying. The lack of sleep led students to have more academic problems as they advanced in their education (Gillen-O’Neel, Huynh & Fuligni, 2013; Estes, 1985). Many studies have found that sleep does have an effect on academic performance. It was found that better sleep quality led to the achievement of an individual’s scholarly goals (Flueckiger, Lieb, Meyer, & Mata, 2014). In our study, we aim to investigate the relationship between study habits of City College students and their grades. Based on the studies we have examined, we have able to deduce that good study and sleep habits yield positive academic outcomes (Kerdijk, Cohen, Mulder, Muntinghe, & Tio, 2015). The study habits that we are examining are the Spacing Effect and “cramming.” Many students tend to pull all-nighters or study a significant amount of the course material the night before the exam, which is known as cramming, while others choose to study the same material over a spaced out period of time (the spacing effect) (Thacher, 2008). This study aims to discover whether or not there is a relationship between study habits that affect sleep levels and the resulting examination scores.

References

Estes, Thomas H., and Herbert C. Richards. “Habits of Study and Test Performance.” Journal of Reading Behavior, vol. 17, no. 1, 1985, pp. 1–13., doi:10.1080/10862968509547527.

Flueckiger, L., Lieb, R., Meyer, A. H., & Mata, J. (2014). How Health Behaviors Relate to Academic Performance via Affect: An Intensive Longitudinal Study. Plos ONE, 9(10), 1-10. doi:10.1371/journal.pone.0111080

Gillen-O’Neel, C., Huynh, V. W. and Fuligni, A. J. (2013), To Study or to Sleep? The Academic Costs of Extra Studying at the Expense of Sleep. Child Development, 84: 133–142. doi:10.1111/j.1467-8624.2012.01834.x\

Kerdijk, W., Cohen-Schotanus, J., Mulder, B. F., Muntinghe, F. H., & Tio, R. A. (2015). Cumulative versus end-of-course assessment: effects on self-study time and test performance. Medical Education, 49(7), 709-716

Thacher, P. V. (2008). University Students and the “All Nighter”: Correlates and Patterns of Students’ Engagement in a Single Night of Total Sleep Deprivation. Behavioral Sleep Medicine, 6(1), 16-31. doi:10.1080/15402000701796114

Research Question:

Do study habits that affect sleep levels influence test scores of City College students?

Methods:

To gather substantial data, we are conducting surveys of 60 students in various locations across the City College campus (NAC Building/Courtyard, Shepard Hall, Compton-Goethals Hall, Marshak Cafe). We plan on gathering data after midterms so our subjects will have exam scores fresh in their minds. Our survey consists of the following questions:  

• What year are you in? (List years)
• What is your major? (Short Answer)
• What midterms are you taking this semester? (Short Answer)
• How many classes are you taking? (Multiple Choice)
• Rank what order you prioritize your classes for studying. (Subject)
• How many hours did you study? (Intervals in hours)
• Did you space out your studying or did you study the night before? (2 Options)
• How much sleep did you get the night before the examination? (Intervals in hours)
• How many hours of sleep do you get on an average night? (Intervals in hours)
• What were your examination Scores ? (Letter Grades or did not take)

We have created a Google Form (https://goo.gl/forms/jMQW6MZFx4L8Rg0l1) that we plan on distributing the second week of November (November 6 – November 10).

We plan on examining the correlation (if any) within the following variable sets :

• Year vs. Hours of studying (Bar Graph)
• Year vs. Hours of Sleep (Bar Graph)
• Hours of Sleep vs. Test scores (Linear Regression)
• Cramming vs. Average Test Score, Spaced out studying vs. Average Test Score (Bar Graph, T-test)

To examine our data, we are going to create bar graphs for our ordinal data. For our ratio data, we are going to create a scatterplot and then perform a Linear Regression test to examine the correlation between Hours of Sleep and Test Scores. Finally, we are going to create a bar graph that analyzes the average test score received when students practiced the study method of cramming the night before and the average test score when the students practiced the study method of spacing. Using this data, we will perform a t-test to determine whether there is a significant difference between study methods and the average test score received.