Journal of Student Research at Saint Francis University Volume 8 (3) Winter 2018
SPECTRUM: Journal of Student Research at Saint Francis University Volume 8 Issue 3 Table of Contents Carbon footprint analysis related to household size Michaela K. Hicks; Sara King
Effects of Surprise Attention Switching between Challenge Tasks and Correctly Recalled Lyrics Megan E. Reilly; Lauren E. White; Cassandra J. Wolowic; Marnie L. Moist
“Over and over, forever and ever”: A Child’s Search for Freedom from the American Working Class Harry J. Olafsen; Robin L. Cadwallader
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Cover: Photo by Michael Sell. Courtesy of the Environmental Action Society.
Carbon footprint analysis related to household size Michaela K. Hicks Physician Assistant Sciences Department School of Health Sciences email@example.com
It is important to measure carbon emissions, for they are vital in determining the prognosis of life on Earth. Carbon dioxide is a heat-trapping gas that can contribute to climate change if it accumulates in the earth’s atmosphere. Within nature there are many natural processes that release carbon dioxide gas. Cells produce energy through a process called cellular respiration, where glucose and oxygen is combined, and the byproduct of carbon dioxide is released into the air. Carbon dioxide is also released into the air as a byproduct of natural decomposing materials. These processes are balanced by the reabsorption of carbon monoxide by various microorganisms. This natural cycle was disrupted by the Industrial Revolution of 1750 (Environmental Protection Agency- EPA, 2017). With this economic revolution, humans released more carbon dioxide into the earth’s atmosphere than had previously been emitted. Some of the greatest sources of human waste are gasoline or diesel used in transportation and industrial processes involving chemical reactions to synthesized products such as cement, iron, steel and other chemicals (EPA, 2017). Not only do these activities potentially emit toxic levels of carbon dioxide, they also pose the risk of exhausting these natural resources. Since the Industrial Revolution, waste production from using various sources of energy has continued to rise. Many studies have been aimed at developing ways to reduce carbon emissions so that the Earth’s natural cycle of emitting and reabsorbing carbon dioxide emissions can be restored. Carbon emissions need to be estimated first, in order to be controlled. A common way to estimate carbon emissions is to measure energy use. Using this method, energy demand can be projected, which, in turn, can help to anticipate resource requirements in the near future
Sara King, Ph.D. Psychology Department School of Arts & Letters firstname.lastname@example.org
(O’Neill & Chen, 2002). Using this method also poses the crucial question as to whether lowering the population would reduce carbon emissions. Carbon Emissions and Population Scarrow and Crenshaw identified population size as a “very common theme in environmental studies. All else being constant, the number of organisms in a given ecosystem drives and regulate[s] the demands placed on the resources contained therein.” (Scarrow & Crenshaw, 2014, p. 316). The question, however, of whether or not population growth impacts the natural level of carbon emissions has not been answered conclusively by research into the matter (Liddle, 2013). O’Neill argues that population acts only as a scaling factor, so a growth in population will result in proportional growth of energy consumption and carbon emissions (O’Neill et al., 2012). Therefore, according to this argument, decreasing population numbers will directly trigger a decrease in national carbon emissions. According to Cleland, reproductive rates hold the largest implications for the final destination of the world’s population, so to decrease population, the rate of reproduction must decrease (Cleland, 2013). It is commonly assumed that implementation of reproductive regulation could decrease household sizes which would hopefully result in decreased carbon emissions. There are two schools of thought concerning this issue, however. One school suggests that increasing household size increases the number of people who are living on earth, which would result in proportional growth of energy consumption and carbon emissions. Contrarily, the second theory predicts that a larger household size forces people into a situation where a certain amount of energy use
must be shared by the individuals in the household, consequently decreasing energy use and carbon emissions per person (Fredericks, Stenner & Hobman, 2015). To study this issue further, the effects of population density and urbanization must be observed. Factors related to population growth Many factors influence population, such as reproductive rates, immigration and biomass. As noted above, increased population is often viewed as a culprit for the increased energy use and waste emissions throughout the world, a belief which has driven organizations and research to seek stabilization of population in the interest of environmental health. Population Connection is a grassroots organization which was developed in response to the threat of overpopulation, and the organization “advocates progressive action to stabilize world population at a level that can be sustained by Earth’s resources.” (Population Connection Mission Statement, n.d., para. #1) Population Connection’s mission is supported with research completed by Cleland (2013) who said, “unchanged fertility implies a population of 25 billion by the end of the century,” which has significant implications for exhaustion of the earth’s resources given the fact that the Earth’s current population is just above seven billion people (Cleland, 2013, p. 543 ). The population of the United States and Canada has doubled in the past 60 years. However, this increase in population is not due solely to fertility in the countries; migration is largely responsible. Immigrants who adopt carbon dioxide emissions and consumption habits of the U.S. will have a greater environmental impact than suggested by population numbers. If everyone had the same average body mass index as individuals in the United States, the total global human biomass would increase by 20% and there would be the equivalent of an extra 473 million people on earth. These staggering statistics necessitate future research regarding the link between increased carbon emissions and increased human biomass (Walpole et al., 2012).
4 Urbanization is distinct from population density in that it refers to the share of the population living in urban areas. This leads to changes in energy consumption through differences in “labor productivity as well as consumption of energy intensive commodities;” but urbanization does not clearly predict an increase in activities that will raise carbon emissions (O’Neill et al., 2012, p. 3). The share of the population living in urban areas does increase energy consumption due to an increased number of residences connected to an electricity grid. However, urbanization also increases the likelihood that population density will increase. Furthermore, people will be prone to share a dwelling in the interest of cost savings and, in addition, public transport will be utilized more. Shared residences and public transportation are, as noted above, two factors which can lower energy consumption and carbon emissions. Dense populations have a decreased need for personal transport, whereas smaller households require higher per capita road use, thus increasing carbon emissions (Liddle, 2004, p. 31). Ultimately, urbanization, when used as a proxy for population density, is found to have an insignificant effect on total carbon emissions, due to the factors discussed (Liddle & Lung, 2010, pp. 321-322, 339). Scarrow and Crenshaw (2014) provide support for this finding with research indicating that for every additional percentage of the population that lives in large cities, energy intensity, which measures the consumption of energy in a nation as relative to the nation’s level of development, drops by 0.4% (Scarrow & Crenshaw, 2014, p. 326). Population density has an insignificant or otherwise negative effect on total carbon emissions in urban areas, suggesting that an increased household size will have the same effect in aggregate carbon emissions. Household size and consumer behavior The psychology of the consumer also plays a role in carbon emissions. Research completed by Fredericks, Stenner and Hobman (2015) looked at socio-demographic and psychological predictors of residential energy consumption. They focused on attitudes, beliefs, and socio-demographic factors in
strictly western regions of the world that influence consumer habits. Fredericks et al. (2015) researched literature that validated or invalidated certain factors’ effects on household energy expenditure, creating an article that outlines identifiable influences on consumer behavior. Household characteristics that were analyzed included the size of the household, the type of the household, the lifestyle of the household, and the stage of the household’s family life. Their research indicates that households with more people per residence generally consumed more energy. However, energy usage per capita is lower in larger households “presumably due to the sharing of resources among multiple residents” (Fredericks et al., 2015, p. 580). Also, energy consumption peaks in households raising children, most likely due to associated housework. In other words, the size of the home and the ages of its inhabitants may skew the results of energy expenditure. Also noted by Fredericks et al. (2015) was the trend in which three to four person households report a greater history of past conservation efforts and less expected difficulty in future conservation. Therefore, larger households reflect less energy use per capita in addition to increased conservation efforts, creating two conditions which work towards the good of the environment. Larger households reflect less in-home energy consumption per person; in fact, three person households consume a third less energy per person in residential and transportation activities, and two-person households consume 17% less energy per person than single person households (O’Neill & Chen, 2002, p. 68). Supporting the research by Fredericks et al. (2015), Cole and Neumayer completed research using the STIRPAT model in eighty-six mixed developed and developing countries over a period of twenty-four years that looked at different factors which influence carbon emissions. They concluded that higher household size is associated with lower carbon emissions (Cole & Neumayer, 2004, pp. 7, 9). The STIRPAT model is more sensitive to the contribution of population differences to carbon emissions (O’Neill et al., 2012). Research indicates that larger households may actually reduce energy
5 expenditure per person, and this poses a fascinating question for research. Further research is needed to test the validity of the assumption that stabilizing or halting population growth will help conserve the earth’s resources. It is possible that population size may not be as influential on environmental health as effective resource distribution. While decreasing population size may decrease the carbon footprint according to the UN Population Projection scenario, this decrease might not be sizeable enough to reach the carbon footprint target sustainable for earth (O’Neill, et al., 2012). That is, the population density of these areas may be less influential than the methods used to distribute resources and share energy sources. The resources that would be distributed are modeled in the carbon footprint calculated for households, and how much carbon footprint is left by a household indicates the ability of the household to share a set number of resources among the residents. This research investigates this question of efficacious energy consumption on a small scale level by measuring the differences in the carbon footprints left by either a larger or a smaller household, and how the household carbon foot print is proportional to the carbon footprint left by an individual in either a larger or smaller household. If larger households and more urbanized regions decrease aggregate carbon footprint per person, population control may not be the most effective method in preventing climate change; because if the current trend continues, lower population numbers will decrease household sizes. Specifically, my research measures the carbon footprint of households of differing sizes, which is, in effect, measuring the waste produced through the household’s use of energy. Measuring the waste of a household allows one to estimate the household’s energy use. The less energy used by a household, the lower the household’s overall carbon footprint. This hypothesis follows the school of thought that assumes that larger households will leave a lower carbon footprint per person because a limited amount of energy must be shared by more people in a larger household. Past studies have focused on energy use of a household, however, not specifically the household’s carbon footprint. Measuring the
carbon footprint of households covers a range of consumer behaviors and helps to provide a broader perspective in how a household is consuming energy. The population chosen for this study is also unique in that it is alumni from a small private Catholic institution which places an emphasis on Earth sustainability in its curriculum. Method Participants. For my survey’s population, the alumni of a small private Catholic University in Central Pennsylvania, was chosen. This population was a convenient sample of people who were homeowners and who could answer the questions on the survey. The only personal identifying question which was asked in the survey was the respondent’s zip code (see Appendix 1). The Alumni Office of Saint Francis University emailed the URL link of the survey to the alumni directly so that I was not given access to any personal email addresses of the alumni. The Alumni Office sent the email out to an unknown number of alumni, 806 of whom responded. Out of the 806 responses, 100 numbers were randomly chosen using an online random number generator. These 100 random numbers that were generated corresponded with the number of the participants of the survey whose responses were to be used. Not all 806 responses were chosen for inclusion in this study due to time constraints and the lengthy process of compiling the respondent’s answers into the Environmental Protection Agency’s (EPA) website in order to calculate their carbon footprint. One hundred responses were chosen because it remained a statistically significant sample of the population surveyed. There was no criteria to be eligible for completing the survey. Unfortunately, the return rate is unknown because the alumni department did not disclose the number of people who received the mailing. Additionally, the Alumni Office did not disclose the classes and majors of the alumni that were emailed. However, I received personal email responses from both Liberal Arts majors and Physician Assistant Science majors, some of who were graduated as early as the 1960s.
6 Materials. To determine the effect of household size on carbon emissions, I received permission to use the EPA’s (2016) carbon footprint calculator for my research. Permission was granted by Roy Seneca (EPA Media Contact for Region 3) on the telephone in October 2015. The carbon footprint calculator is a tool available on the EPA’s website which allows homeowners to calculate their own carbon footprint by entering the type of heating source they use, the amount they spend on electricity and heating, whether they recycle, and more (see Appendix 1). The participant’s total carbon footprint is measured in annual pounds of carbon emission. I took the questions from the EPA’s calculator and created a survey, and used the responses from the survey to directly calculate the participant’s carbon footprint, with the help of a research assistant. This method was chosen in order to reduce error that may result from respondents reporting their own carbon footprint. The items not included in the survey from the EPA’s carbon footprint calculator were challenge questions which asked what respondents were willing to change in their lifestyle habits. I also added an additional free-response question to the survey which asked, “If the world were overpopulated by the year 2030, what is one lifestyle modification you would be willing to make to preserve the environment?” This additional question was added to investigate differences in responses between larger and smaller households. Once the survey was created, it was approved by the International Board of Review (IRB) of Saint Francis University, then entered into an online survey instrument, Survey Monkey (2016), and emailed as a link. Design and Procedure. Once the alumni had responded to the survey, their answers were saved in the Survey Monkey account. To analyze the responses, the answers were coded into an Excel spread sheet. The size of the participant’s household size was defined as “small” if it had fewer than four household members, the household was coded and if the participant had four or more household members, the household size was defined as “large.”
Four members in the household became the measurement because the average size of a household in America is 2.58 people according to the United States Census Bureau (2012). That number is rounded up to three people comprising the average household size. Therefore, a participant with a household size of four or more people is higher than average. Figure 1 illustrates the trend in household sizes from 1940 to 2016.
Figure 1. Changes in Household Size (1940-2016)
The respondent reported their actual household size as part of the survey. The household’s total carbon footprint was divided by the reported household size to calculate an adjusted carbon footprint, which is an estimation of the carbon footprint of an individual in the household. This calculation was completed for the purpose of the study, to compare the carbon footprint left by individuals in either larger or smaller households. The total carbon footprint of the respondent was calculated using the EPA’s online carbon footprint calculator. Since the survey questions were modeled after those asked on the EPA’s site, the participant’s answers were plugged directly into the calculator to determine the household’s total carbon footprint. Each carbon footprint was calculated individually with the help of a research assistant. Results An alpha was set at 0.5 for all of the findings. All significant results reported have a p value of <.01.
7 The total carbon footprint and adjusted carbon footprint score was positively skewed, so a Mann Whitney U test was used to account for the skewed data. Upon running the Mann Whitney test of the household’s adjusted carbon footprint, a result with a p value of .001 was obtained. The adjusted carbon footprint level was obtained by dividing the total carbon footprint of a household by the number of household members to estimate the average carbon emissions produced per person. The adjusted carbon footprint was measured in relation to household size. The Mann Whitney U Test calculated the median adjusted carbon footprint comparing larger households (four or more members) to smaller households (fewer than four members). Sixteen out of the one hundred participants were not used in the study because they did not answer the survey questions adequately to allow for an accurate measurement of their carbon footprint. Without an accurate measurement of their carbon footprint, the average carbon footprint created per household member could not be calculated. Therefore, the number of participants used was eighty-four when total carbon footprint and adjusted carbon footprint were compared. The number of participants with a smaller household size was greater than the number of participants with a larger household size. The median adjusted carbon footprint of smaller households (four or fewer members) is 21,901, with a standard error of skewness of 0.295 (See Appendix D). The median adjusted carbon footprint of larger households is 12,787 with a standard error of skewness of 0.524 (see Table 1). The adjusted carbon footprint of larger households (Mdn= 12,782, SEK= 0.52) is significantly lower than smaller households (Mdn= 21; 901; SEK= 0.29), (U= 318.00; p = 0.001). Household size Median Carbon Footprint Score
Table 1. Effect of Household size on Adjusted Carbon Footprint.
Spearman’s rho correlation between population size of the respondent’s given zip code and the total household carbon footprint was also assessed (refer to Table 2). The correlation between the total household carbon footprint and population size of the respondent’s zip code was insignificant (r (82) = -0.12, p = 0.296). Measure
2. Total carbon footprint
3. Adjusted carbon footprint
respondents provided was used. For both larger and smaller households, free response one was the most common answer given, expressing that the participant was more likely to limit car use for more environmentally friendly transportation or limit the number of vehicles being used. A higher percentage of smaller households reported a willingness to recycle compared to larger households. Tables 3a3b summarizes free response trends.
Table 2. Correlation between Adjusted Carbon Footprint/ Total Carbon Footprint and Population Size
Additionally, a correlational study between the household’s adjusted carbon footprint and respondent’s zip code population size was considered. I used respondent’s zip code to research the population of the region where the participant lived using the government’s “Fact Finder” feature of the U.S. census website (United States Census Bureau, 2010). The population of the zip code provided was calculated using the results from the 2010 Census. Using the population of the respondent’s area of residence, a correlation was run to determine the relationship between population size and the carbon footprint/ adjusted carbon footprint produced by the households (refer to Table 2). The correlation between the household’s adjusted carbon footprint and population size was insignificant (r (82) = -0.05, p= 0.642). The last question of the survey was a free response question which asked what lifestyle modification participants would be willing to make if the world became overpopulated by the year 2030. The free response questions were analyzed for general trends among larger and smaller households. Thirteen themes were identified among the answers, illustrated in Tables 3a-3b. The themes and household sizes were analyzed for trends such as whether larger or smaller households gave a more common answer. Though the question only asked for one lifestyle modification, some participants provided up to six; only the first answer that
1- Limit car use, use more Ecofriendly Transportation (ex- train, bike, walk, Carpool, etc) or reduce the number of vehicles in use
0- Respondent skipped the question, or offered a rant which did not answer the question
3- Recycle/ Use less disposable products 14 (paper products, water bottles etc)
2- Change power source/ use less electricity
6- Live in a smaller house
8- “None” or an answer to the effect of “I’ll be too old/ dead for it to matter”
4- Conserve water/ food (includes bathing less, and eating less meat)
7- Limiting number of children (includes employing more methods of birth control)
5- Share home
11- Current gardening modifications/ Start a garden to grow own food
Table 3a. Trends in Free Response Answers for larger households (<4 per house)
1- Limit car use, use more Ecofriendly Transportation (ex- train, bike, walk, Carpool, etc) or reduce the number of vehicles in use
0- Respondent skipped the question, or offered a rant which did not answer the question
2- Change power source/ use less electricity
3- Recycle/ Use less disposable products (paper products, water bottles etc)
11- Current gardening modifications/ Start a garden to grow own food
Table 3b. Trends in Free Response Answers for larger households (>4 per house)
Discussion The findings support my hypothesis that the adjusted carbon footprint (the average carbon footprint of each household member) was lower in larger households as opposed to smaller households. This finding supports the theory that larger households may not necessarily be detrimental to the environment, in terms of carbon emissions, if larger households decrease the carbon footprint left by each person. As families are choosing to have fewer children and household size is decreasing, the trend may continue that carbon emissions rise because smaller households have a higher annual adjusted carbon footprint than larger households. The trend of decreasing household sizes may not be entirely beneficial for the environment if a situation is created in which the annual carbon footprint is increased per person in smaller households. This could potentially affect the aggregate carbon emissions for a region if smaller household members have increased annual carbon footprints. The correlation between total and adjusted carbon footprint of a household with the population size of the household’s zip code was insignificant. This finding rules out the importance of population size of the respondent’s region as a factor related to the household’s total or adjusted carbon footprint.
Because Fredericks et al. (2015) noticed that larger households reported a greater history of conservation measures, I explored whether there were any trends in the changes households were willing to make in smaller versus larger households. Both larger and smaller households were most willing to limit car use; however, larger households did not express as much willingness to recycle as did smaller households. This could be due to a variety of reasons, such as convenience and recycling practices of the respondent’s county. However, smaller households’ increased willingness to recycle testifies to their contribution to environmental health. Limitations There are factors which may have influenced the outcome of the survey. The adjusted carbon footprint reflects an estimation of individual carbon footprint left by members of a household, and does give an absolute value. Additionally, the numbers used were self-reported by participants who responded to an email. Only participants who were confident that their total carbon footprint would be low may have taken the survey. There is also the possibility that respondents were deceptive in their responses to avoid the stigma of having a high carbon footprint or being seen as one who harms the environment. Many participants expressed their opinion, in the free-response portion of the survey, that this survey was driven by liberal ideology which they disagreed or agreed with. Respondents’ assumption of the nature of the survey, which in reality was purely academic, may have impacted their honesty in answering the survey questions. Several respondents also expressed frustration that a wood burning stove or fireplace was not an option listed as a source of heat in the winter. This option was not listed because it was not part of the EPA’s Carbon Footprint Calculator formula; however, it remains a potentially limiting factor as it may impact the total carbon emissions of households. Additionally, a smaller sample size of one hundred out of eight hundred participants had to be randomly selected in the interest of time for data processing. Out of the one hundred participants randomly
selected, sixteen were not able to be used because they answered the questions incorrectly. The data population was also skewed because there was a disproportionately higher number of smaller households compared to larger households. This population sample follows general U.S. population trends, even though the alumni surveyed graduated from a small Catholic university, so the skewedness is not surprising (see Figures 1 and 2).
Figure 2. Birth rates (1925-2009) (Sutton PD, Hamilton BE, Mathews TJ, 2011)
Conclusions and Future Study Findings of this project do not support popular belief that the smaller households of smaller families are more beneficial for the environment in terms of total carbon emitted. However, the findings of the project are supported by Cole and Neumayer (2004) who found that in a twenty-four yearlong study, according to the STIRPAT model, in 86 mixed developing and developed countries, larger household size was correlated with lower carbon emissions (Cole and Neumayer, 2004, pp. 7, 9). In addition, Fredericks et. al (2015) found that energy use per capita decreased with larger households (Fredericks et al., 2015, p. 580). This study is distinguished from Cole and Neumayerâ€™s (2004) findings in that it measured the householdâ€™s carbon footprint based on a variety of self-reported household consumer behaviors. This approach allows a closer look at individual household carbon footprint based on human behaviors, whereas Cole and Neumayer (2004) pulled their data from national
10 averages and Fredericks et al. (2015) gathered data from a comprehensive literature review. Birth rates in the United States have decreased dramatically according to recent data published by the Center for Disease Control (CDC) (Sutton, PD, Hamilton, BE, Matthews, TJ, 2011). Additionally, the US Census has found household sizes have decreased dramatically (2016) which positively correlates with decreased birth rates in the United States (refer to table 2). However, this growth in small household size is not correlated with improved carbon emissions or environmental health in the United States. These findings challenge the efficacy of implementing birth control or regulating population in the interest of environmental health. Increased resources are being used by small household members as opposed to large household members. Decreasing the number of household members, certainly if a sole focus, may not be the most productive way to aid the environment. Instead, education on resource allocation may be more helpful in solving the environmental crisis. Larger households share a limited number of resources among residents; this same model could be employed on a national scale with proper consumer education. This may include helping households to adopt news ways to conserve. As evidenced by the free-response questions, both household types were more willing to limit car use, but larger households did not express as much willingness to recycle as smaller households. This finding could be a focus in future education endeavors to encourage households of all sizes to recycle. There is room, and need, for research to be done on the most effective ways of implementing resource allocation programs. These programs may include modeling, education, or initiatives to help consumers learn how to share resources. Taking the focus off of regulating the population, and placing more emphasis on regulating resources within a population, may be more beneficial for the health of our generation and generations to come.
Appendix 1 1) How many people are in your household? 2) Please enter your zip code. 3) What is your householdâ€™s primary heating source in the winter? (choose one) a) Natural Gas b) Electricity c) Fuel Oil d) Propane 4) What is your average monthly bill, or an estimate of you average monthly bill, which you spend on the use of natural gas in the winter? (Leave blank if you do not spend any money per month for the use of natural gas) 5) What is your average monthly bill, or an estimate of you average monthly bill, which you spend on the use of propane in the winter? (Leave blank if you do not spend any money per month for the use of propane) 6) What is your average monthly bill, or an estimate of you average monthly bill, which you spend on the use of electricity in the winter? (Leave blank if you do not spend any money per month for the use of electricity) 7) What is your average monthly bill, or an estimate of you average monthly bill, which you spend on the use of fuel oil in the winter? (Leave blank if you do not spend any money per month for the use of fuel oil) 8) How many vehicles does your household have? (choose one) a) 1 b) 2 c) 3 d) 4 e) 5 9) Do you perform regular maintenance checks on your vehicles, including, keeping then engine properly tuned, keeping the tires properly inflated and using the manufacturerâ€™s recommended grade of oil? a) Do not do b) Already do 10) Please fill out the information below for each one of the vehicles you own. You do not need to enter information for all 5 vehicles. To help you estimate, the national average number of miles of gas per gallon is 21.4 miles per gallon. a) Vehicle oneWhat is an estimate of the number of miles you drive per year on this vehicle? What is an estimate of the number of miles you can drive per gallon of gas in this vehicle? b) Vehicle twoWhat is an estimate of the number of miles you drive per year on this vehicle? What is an estimate of the number of miles you can drive per gallon of gas in this vehicle? c) Vehicle threeWhat is an estimate of the number of miles you drive per year on this vehicle? What is an estimate of the number of miles you can drive per gallon of gas in this vehicle? d) Vehicle fourWhat is an estimate of the number of miles you drive per year on this vehicle? What is an estimate of the number of miles you can drive per gallon of gas in this vehicle?
11 e) Vehicle 5What is an estimate of the number of miles you drive per year on this vehicle? What is an estimate of the number of miles you can drive per gallon of gas in this vehicle? 11) Do you recycle aluminum in your household? o Yes o No 12) Do you recycle steel cans in your household? o Yes o No 13) Do you recycle plastic in your household? o Yes o No 14) Do you recycle glass in your household? o Yes o No 15) Do you recycle newspaper in your household? o Yes o No 16) Do you recycle magazines in your household? o Yes o No 17.) If the world become overpopulated by the year 2030, what is one lifestyle modification you would be willing to make in order to preserve the environment? (There are no right or wrong answers.)
Works Cited Cleland, J. (2013, May 25). World Population Growth; Past, Present and Futiure. Springer Science and Business Media, 55, 543-554. doi:10.1007/s10640-013-9675-6 Cole, M. A., & Neumayer, E. (2004, September). Examining the Impact of Demographic Factors On Air Pollution. Springer Science and Business Media, 26(1), 5-21. Retrieved 2016 Environmental Protection Agency. (n.d.). Carbon Dioxide Emissions. Environmental Protection Agency. (2016). Carbon footprint calculator [Data file]. Retrieved from: https://www3.epa.gov/carbon-footprint-calculator/
Figure HH-6. Average Population per Household and Family: 1940 to Present [Graph] U.S. Census Bureau (2016). Retrieved March, 28, 2017 from: https://www.census.gov/hhes/families/data/households.html
Fredericks, E. R., Stenner, K., & Hobman, E. V. (2015, January 15). The Socio-Demographic and Psychological Predictors of Residential Energy Consumption: A Comprehensive Review. Energies, 8, 573-609. doi:10.3390/en8010573 Liddle, B. (2004, September). Demographic Dynamics and Per Capita Environmental Impact: Using Panel regressions and Household Decompisitions to Examine Population and Transport. Springer Science + Business, 26(1), 23-39. Retrieved 2016 from Proquest database
Liddle, B. (2013, October 31). Impact of population, age, structure, and urbanization on carbon emissions/ energy consumption: evidence from macro-level, cross-country analyses. Springer Science + Business Media, 35, 286-304. doi:10.1007/s11111-013-0198-4 Liddle, B., & Lung, S. (2010, February 2). Age-structure, urbanization, and climate change in developed countries: revisiting STIRPAT for disaggregated population and consumption-related environmental impacts. Springer Science + Business Media, 31, 317-343. doi:10.1007/s11111-010-0101-5 O'Neill, B. C., & Chen, B. S. (2002). Demograhic Determinants of Household Energy use in the United States. Population and Development Review, 28, 53-58. Retrieved November 5, 2016, from JSTOR database O'Neill, B. C., Liddle, B., Jiang, L., Smith, K. R., Pachauri, S., Dalton, M., & Fuchs, R. (2012, July 10). Demographic change and carbon dioxide emissions. The Lancet. doi:10.1016/S0140-6736(12)60958-1 Environmental Protection Agency. Overview of Greenhouse Gases. (2017, February 14). Retrieved March 28, 2016 from: https://www.epa.gov/ghgemissions/overview-greenhousegases#carbon-dioxide
Population Connection. (n.d.). Mission Statement. Retrieved from: http://www.populationconnection.org/us/statement-policy/ Scarrow, R. M., & Crenshaw, E.M. (2014, September 24). The ecology of energy use: using the POET model to analyze consumption and intensity across nations 19702000. Apringer + Science and Business Media, 36, 311330. doi:10.1007/s11111-014-0220-5
12 Sutton, P.D., Hamilton, B.E.,& Mathews TJ. Recent decline in births in the United States, 2007â€“2009 [Graph] (2011). Retrieved March, 28, 2017 from: https://www.cdc.gov/nchs/products/databriefs/db60.htm
United States Census Bureau. (2010) American fact finder. Retrieved March, 28, 2017 from: https://factfinder.census.gov/faces/nav/jsf/pages/community_facts .xhtml
United States Census Bureau. (2012). Households and Families: 2010 [PDF file]. Retrieved March, 28, 2017 from: https://www.census.gov/prod/cen2010/briefs/c2010br14.pdf
Walpole, S. C., Prieto-Merino, D., Edwards, P., Cleland, J., Stevens, G., & Roberts, I. (2012, June 18). The weight of nations: an estimation of adult human biomass. BioMed Central Public Health, 12, 1-6. Retrieved March, 28, 2017, from http://biomedcentral.com/1471-2458/12/439
Michaela Hicks ('17, B.S., Health Science; MPAS â€™18) is pursuing her Masters of Physician Assistant Sciences degree at Saint Francis University. She earned a minor in Psychology, was a member of the honors program, and worked as a lifeguard on campus. She is doing a year of clinical rotations across the United States.
Effects of Surprise Attention Switching between Challenge Tasks and Correctly Recalled Lyrics [Research conducted for PSYC 202 (Research Methods and Statistics II)] Megan E. Reilly Occupational Therapy Department School of Health Sciences email@example.com
Lauren E. White Occupational Therapy Department School of Health Sciences firstname.lastname@example.org
Cassandra J. Wolowic Occupational Therapy Department School of Health Sciences email@example.com
Marnie L. Moist, Ph.D. Psychology Department School of Arts & Letters firstname.lastname@example.org
The problem we examined was how people were able to successfully switch between tasks surprisingly. We tested 30 college students of both genders of any race around the age of 20. The two tasks at hand were a visual and an auditory tasks. Participants completed either a hard task, (i.e. Sudoku puzzle), an easy task, a (i.e. coloring Sudoku), or no task at all. Periodically, they were interrupted by unexpectedly being asked to correctly recall lyrics from the previous song played. Results showed that the harder the task, the harder it was to recall lyrics. We had non-significant results when we covaried out song familiarity with not enough power to detect a large effect size. However, when rendering the non-significant covariate and using a nonparametric analysis to adjust for significant results is p=0.048. Thus, college students have a more difficult time switching attention between sensory modalities as cognitive demands increase. We tested was how people were able to successfully switch between tasks surprisingly, or surprise attention switching. More often tends to be the ability to multi-task, which Schneider, Eliez, Birr, Menghetti, Debbané, and Van der Linden (2016). This kind of testing is defined as multiple goal-directed activities to be coordinated and achieved simultaneously. As busy as people are today, it is common that multitasking abilities are up to par, which requires divided attention. Divided attention is similar to multitasking in which two tasks are occurring simultaneously, such as texting and walking (Bordens & Abbot, 2014). This multitasking can, however, be problematic in situations such as texting and driving. Dividing attention between two heavy attention-demanding activities often leads one of those activities to be neglected. In this scenario, an increase of car accidents is likely to occur if the attention is centered
more on texting rather than driving. However, our interest for this study was not multitasking. We were interested in surprise attention switching. Simply, we tested people’s abilities to switch between two separate tasks, one auditory and one visual. The visual task varies in degree of challenge. Our goal of this study was to see if people could easily switch between a visual and an auditory task, depending on the challenge of the visual task. In our study, our focus was surprise attention switching. Surprise attention switching is the concept of redirecting one’s internal focus and processing from one entity to another. Thus, an individual is alternating quickly and at short notice between two different tasks. In our study, the two different entities were known as alternating blocks. For each alternating block, a switching cost occurred. A switching cost is the difference in time that it takes for a person to alternate blocks. The
blocks used in our study were auditory and visual tasks. When tasks are switched, it is more difficult for people to correctly attend to the block or task at hand (Smith & Kosslyn, 2007). The two entities used in this study were visual and auditory tasks. A visual task is a type of challenge that requires the participantâ€™s eyesight. For this visual task, one must also be able to use their visual memory, which is the recall of a component about an object that is visible to the eye. This visual task caused participants to process information and be able to formulate the answers needed to complete the task. This required them to use their critical thinking skills. The Critical Thinking Community (n.d.) that uses critical thinking skills based upon a presentation given by Scriven and Paul in the 1980s. Critical thinking skills are the intellectually disciplined processes of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information. This was used only in the hard challenge. Critical thinking is an ongoing form of working memory; it is building in working memory. A Sudoku puzzle was used as a hard challenge that imployed the critical thinking skills of analysis and synthesis. A coloring task given to the easy group was simply a number-to-color translation task. Meanwhile, the participants also needed to complete an auditory task, which is a challenge that is received by and perceived from their sense of hearing. Both tasks focus on sensory modality. Sensory modalities are distinct ways that external information is perceived by human sensory systems. Auditory memory is a type of recall of information, which is given to the participant verbally. Auditory and visual memory both have the potential to require the people to use long term and working memory. In both visual and auditory memory, the participant must be able to process that information. The recall and processing of both auditory and visual tasks and memory, along with other information, is known as the working memory. The term working memory is also used interchangeably with short-term memory, which is the storage of and processing of information that is readily available for fifteen to thirty seconds on average. In summary, working memory is similar to
14 using a post-it note. On the post-it, notes are information about the visual and auditory task, and that information is known as visual and auditory memory. The post-it notes are there as a reminder until they are thrown away after a temporary period of time, which again, involves only short-term memory. In our study, we tried to tease apart working memory from long-term memory, which is why we asked participants about song familiarity. We wanted to later remove statistically the influence of previous song lyric knowledge that could be stored in long-term memory. The storage of lyrics in long-term memory would affect the goal of the study, the ability to switch between two sensory modalities that used working memory. Gehring, Toglia, and Kimble (1976) focused on recognition memory between words and pictures at a variety of retention lengths. The researchers were exploring the ability to obtain information concerning pictorial and verbal recognition memory over a longer period. They also were trying to determine whether qualitative changes in memory occur at long retention intervals. During the experiment, a group of females signed up and completed two pre-scheduled experimental sessions. These sessions each had their own interval of three months, one month, one week, one day, one hour, and ten minutes. During the study, an item from the two lists was presented for 5 seconds, and then they had 8 seconds to respond to each item. The task was to classify every item as one of the six types: repeated words, verbal synonyms, study word- test picture items, repeated pictures, pictorial synonyms, study picture- test word item, or a verbal/pictorial filler. The task was completed by checking the proper column. In one instance a picture of a safety pin was given. During the test, the participant was supposed to mark the safety pin as a repeated picture. The people who wrote the study measured this repetition, by a recognition test and the calculation of separate measures of detectability and bias. Some of the results they discovered were that recognition memory was superior for pictures at all intervals tested. This also seems to reflect a difference in initial encoding because the superiority of pictorial memory appears at the shortest interval
tested. The difference in favor of pictures diminishes only slightly, if at all, over 3 months, unlike verbal stimuli which became â€œoldâ€?. As the participants marked on the familiarity of the pictures and words, the words produced fewer instances of recognition. KĂźbler, Murphy, Kaufman, Stein, and Garavan (2003) focused on attention switching between verbal and visuospatial items stored in working memory. The researchers were testing to see if switching between recollections in working memory was a form of executive function. Executive functioning was additionally analyzed in this study by determining of where working memory stimulated various parts of the brain. In this study, there were twenty-five subjects used, all females, ranging in age between seventeen to thirty-five years. In the first task, participants needed to count the number of red and blue squares present during the study and to remember that number in order to record the answer at the end. Additionally in the first task, the subjects used a computer system to switch squares that appeared on the screen one at a time. A blue one followed some red squares; this was considered a switch. Some red squares were followed by red again, which was defined as a nonswitch. Switch and non-switch times were later analyzed for switching costs. Subsequently, the second task was a visuospatial task, where participants used a blue or red dot along with blue or red arrows. If a dot was present on the screen, the participant needed to move that dot to the corresponding color of the arrow. For example, a red dot needed to be moved to the right side of the screen if the red arrow was on the right. The third task focused on attention switching because the two previously mentioned tasks. This third task was given at random times during the trial. The participant would need to use attention switching to solve the task presented on the screen. The results showed the researchers first that there was a time switch cost between the verbal and visuospatial tasks. This supports the conclusion attentional limitations exist due to the idea that subjects only had access to one working memory modality at a time. Thus, the switching between tasks can be seen as surprise attention switching. The results of this
15 study supported the conclusion that the attentional switching between modalities is difficult because of the delay in reaction time. That delay causes inaccuracies and confusion. Overall results support that switching between sensory modalities is difficult. Lawo and Koch (2015) based their research on auditory attention switching. The purpose of their research was to examine the role of different response mappings concerning auditory attention switching in a series of experiments. Response mapping, the way the response was given by participants, changed between experiments. The cue stimulus interval (CSI) was either 100 or 1000 ms and the stimulus was transitioned by either switching the stimulus or repeating a previous one. In the third experiment, with twenty participants, the response mapping technique was either an abstract verbal or abstract manual response. Abstract simply meant that the subject, with his or her own thinking, formulated the answer. Verbal meant that the subject gave their response aloud and manual meant the response was written on paper. Response times for the stimuli was measured. The auditory attention switch was evident in all the experiments by dichotic listening in headphones, switching between a male or female speaker. The participants switched between response types as the experiments went on along with switching between listening and response mapping. The results of the experiments showed that there was a better performance with the longer CSI, suggesting that the participants can perform better with more time to think. Results from experiment three were compared to experiments one and two. In experiment one, the shadowing response method (filling in an already provided answer option) showed better results than a verbal response. This verbal response was based on two possible answers to choose from. These various response methods can be measured as easy or hard, with the shadowing response being easier than the verbal response. In experiment two, both response methods had a nonsignificant difference, which were direct verbal and abstract verbal. Direct verbal implies that the answer was aloud, but selected from setup answer options. In experiment three, the response mapping methods
also had a non-significant difference in answer accuracy although this experiment type had higher error rates overall. This information may be relevant if it is applied to other research because it appears that switching attention to give responses is capable of being done successfully, regardless of the type of response given. Rubinstein, Meyer, and Evans (2001) proposed that task switching entails at least two functionally distinct stages of executive control, goal shifting and rule activation. These are separable from the basic perceptual-motor and cognitive processes used for performing individual tasks. They performed four experiments in total. During each experiment, repetitive-task blocks of trials were completed for each of two tasks. In the first and last experiment, participants rapidly classified visual patterns of geometric objects with respect to classification tasks. Rule complexity was manipulated by having participants apply unidimensional or bi-dimensional classification rules. In the two middle experiments, participants solved arithmetic problems with alternative numerical combination rules. For the arithmetic tasks, rule complexity was manipulated by requiring math operations like division, subtraction, multiplication, and addition. Visual task cues were presented during some trial blocks but not others. The combined manipulations across the experiments allowed them to check for expected patterns of additive and interactive factor effects on mean reaction times (RTâ€™s) and switching-time costs, which provided diagnostic indicators of temporally separate processing stages. They showed that, as the model predicted, executive control and task processes could be empirically dissociated and affected separately by different factors. More specifically, executive control entailed at least two component stages, goal shifting and rule activation, whose mean durations depend respectively on task cuing and rule complexity. Regarding rule activation, switchingtime costs may be asymmetric in ways related to the familiarity of individual tasks between which participants must switch. All of these strongly support the modelâ€™s basic assumptions about the
16 nature of executive mental control for task switching. All of the above studies focused on a type of either memory or behavior response changes as they studied attention switching. The one by Gehrring et al. (1976), focused in on stimulus recognition memory, which tied into our study because we had participants listen to the song lyrics, requiring them to recognize the words within the song. We were determining the results of subjectâ€™s ability to switch between a visual and an auditory task, again two types of sensory modalities. We kept the responses for the visual and auditory task the same by having participants write all their answers. Similar, to the above studies, we randomly switched the two sensory tasks and tested their ability to switch. The main difference in the other four studies was the focus on their attention switching through different aspects of one or two types of sensory modalities. Ours was unique in this sense because of having a visual and auditory switch. Gehring et al (1976) focused on recognizing words and pictures, one sensory modality. In addition, in the article by Lawo and Koch (2015), they focused their attention between auditory sensory modalities, via listening in headphones and response mapping. Our hard task was much like their abstract, meaning that the participants had to develop the answers on their own. By comparison, our easy task was more like a shadow or copy of what was previously given to them. On the other hand, the other two articles by Rubinstein et al. (2001) and Kubler et al. (2003) focused on the attention switching of two sensory modalities. Kubler et al (2003) focused attention switching between writing the amount of colored squares and visuospatial by telling where the square was. These were both moderately easy tasks. Rubinstein et al. (2001) switched between geometric patterns and solving arithmetic equations. This demonstrated switching between an easy and a hard visual task. These articles suggested that because of the capacity and ability of the brain, switching between two sensory tasks, in our case music playing and the harder challenge, would be difficult (Duncan, Martens, & Ward, 1997). The cognitive ability required for a Sudoku puzzle is greater than
that needed for the tasks seen in previous research, making our hard task significantly more challenging. Our problem under investigation in the current experiment was to test people’s ability to successfully switch between two types of sensory modality tasks. The process of diverting one’s attention to one task to one’s attention to another is called surprise attention switching. In order to solve our problem, we used two sensory modalities for the subjects to switch between. For each of the three task groups, there was music playing in the background, which was loud enough to be heard by all participants. Randomly, for each of the three groups during visual task completion, we would have them stop and answer two questions about the most recent song played. The three groups would then go back to the verbal tasks after one minute and fifteen seconds of focusing on the “auditory pop quiz.” The random auditory pop quizzes continued throughout the rest of the study. The participants signed up for a session, which was randomly assigned to a visual task. The task group were visual hard--playing Sudoku, easy--coloring in already filled out numbers of a Sudoku to match the instructions, and no visual task at all, our control group. To test to see which group was the most efficient in both tasks, we created a points system based on the number of lyrics recalled in the auditory pop quiz and the number of correct colorings or number fill-ins from the easy and hard challenges. We scored the number of correct lyrics recalled, and we scored the neatness and accuracy of the coloring task in order to determine the overall score. The purpose of the combined scoring was to keep the participants focused on all tasks. Specifically, the coloring task scoring was used as a manipulation to motivate the participants to work hard. After controlling for the self-rated song familiarity, we predicted there would be some mean difference in the total number of recalled lyrics between the hard task group, the easy task group, and the no task group. The hard challenge we believed would be the hardest to switch between the two sensory tasks, leading to the most impaired overall lyric recall. We believed this because by having performed the hard
17 challenge, the participant was using critical thinking skills and was in the midst of a deep thought process. It would be harder to shut the thought process off to begin performing lyric recall, and therefore, performing worse on the lyric recall because they were not paying enough attention to the song. In addition, the participants may have lost their train of thought when they were distracted by music. When they went back to the hard challenge (Sudoku puzzle), they had to figure out what numbers go where again. By having performed the easy challenge, they would have had to use less brainpower and could focus more on the music. Then they would have had an easier time switching between the two tasks. During Kubler et al (2003), the researchers found that there was a time switch cost between the two sensory modalities used. Thus, the switch costs supported that the conclusion that people only had access to one mode of working memory to recall information provided by sensory modalities. When people switched away from one sensory, for example verbal, to another, for example auditory, this switch caused suppression of an older sensory item. Therefore, when switched from working on a visual sensory item, a Sudoku, to an auditory verbal task, the recall of lyrics and vice versa, increased suppression of old information should occur. Methods Participants. In the study, there was a total of 30 participants: 22 females and 8 males. Participants in the convenient sample, were students in psychology classes. We asked the teachers to offer extra credit to those who came to the study and to offer another extra credit option for the students who do not wish to participate. We also gave the student with score of the overall study a $10.00 Sheetz gift card as an incentive to make sure they paid attention to the lyrics and completed the challenges to the best of their abilities. We invited 72 students to participate and 30 accepted, for approximately a 41.67% response rate. . The mean age of each group was twenty years. There were 5 freshman, 6 sophomores, 8 juniors, and 11 seniors. The mean level of education was sophomore status.
For our study, we asked that people who had been diagnosed with learning, memory, and/or attention disabilities not participate in the study. Additionally, we asked that people who had been diagnosed with any type of visual or hearing impairment or hand disability or injury not participate as well. Only undergraduate college students from Saint Francis University who were in the psychology classes whose professors agreed to allow extra credit opportunity were asked to be in a study. Materials. First, we read our recruitment script to the psychology classes taught by the professors willing to give their students extra credit (see Appendix 1). Those who did not want to participate in our study were directed to talk to their professor after class for an alternative extra credit option. Then, we left the class with a sign-up sheet to fill in their Saint Francis University email addresses under the session that they wished to participate in. Upon arriving for the testing, the participants signed their email next to a number that would later identify their score so that the highest score of the tasks could be later awarded with a gift card. The consent form adapted from one obtained by Stanford University (2003) which we handed out. The numbered task packets that correlated with the number by their email were distributed face down with the consent form. The participants were told to not turn over the packets until instructed. A self-selected medley of choruses from songs of the 1960s, 1970s, and 1980s, were played while the participants completed a visual task (see Appendix 2). Songs from the 1960s, 1970s, and 1980s were used to decrease familiarity. The decreased familiarity would encourage greater reliance on working memory for switching attention. Twenty-four songs were used in the study with a combined range time of twelve to seventeen minutes. Each songâ€™s chorus would be played for approximately 15 to 45 seconds. The length of each song depended on how long the chorus played for. The music was created on a Lenovo ThinkPad laptop Yoga 260. The software used was iTunes 184.108.40.206., (2016). To project the music, a Logitech S-120
18 speaker system was plugged into the laptop. The laptop was turned to sound level 100 and the speakers were turned to sound level 10. That was the greatest volume that could be achieved on both devices. While the music was playing, the people in the session were given a specific visual task and instructions. The control group was given a piece of paper with a number on it (see Appendix 3). This was given to keep all tasks in relation to a visual number and to prevent their minds from wandering too far and looking around the room. The easy challenge task was coloring in the bubble numbers of a completed Sudoku puzzle answer key according to a self-created number-color code chart (May, 2013) (see Appendix 4). We provided the 8 crayons necessary for the task, out of the 24 boxed Cra-Z-Art Crayons, manufactured by Cra-Z-Art Co.. The CraZ-Art crayons were all sufficiently sharpened for each session, and we reused over again for each task session. The colors used were red, orange, yellow, green, blue, violet, bubble gum pink, tidal wave, wildberry, black, and sandy beach. Finally, a different session was given a hard challenge to complete. The hard challenge was a difficult level Sudoku puzzle to be completed (May, 2013) (see Appendix 5). The random self-created pop quizzes were given at various points during the music medley; this was selected by using Table 1- Random Digits (Rand Corporation, 1995). The pop quizzes were given only after the music was turned off temporarily, which occurred anywhere from 15 seconds to 45 seconds within each song play. The participants were given the pop quiz randomly after songs in order to prevent them from knowing or guessing at which individual song the stop would occur. There were six pop quizzes randomly during the selfselected medley of twenty-four songs. The pop quiz consisted of two questions (see Appendix 6). The first question asked on the auditory pop quiz was â€œCan you recall any possible lyrics?â€? The relevance of this survey question was to score their ability to successfully switch attention from the verbal to auditory task, aside from checking to see how much they processed the last song that was played. The
second question asked on the pop-quiz was on a scale of 1-7, what is your familiarity with this song? (1- not familiar to 7- very familiar). The function of this scale was to eliminate the covariate of long-term memory melody recognition. Some people may have remembered the song from a previous experience. The function of the three different verbal tasks to complete while focusing on the auditory task was to determine the capability at which participants can surprise switch attention while engaged in different complexity levels of thinking. The demographics survey was completed at the end of the study before participants left the room (see Appendix 7). Design and Procedure. In the conducted study, there were three potential visual challenge tasks. The visual challenge tasks were viewed as no challenge, easy, and hard challenges, which were used as variables to be compared. The hard challenge was filling in the correct Sudoku. The easy challenge was coloring in the bubble Sudoku completed puzzle cells in correspondence with the appropriate instructions. The no challenge needed to stare at a number on a sheet of paper all while listening to the music. Meanwhile, the auditory popquiz was a method used to determine our participantâ€™s ability at surprise attention switch. The mean number of lyrics recalled in the auditory popquiz during the allowed amount of time, one minute and fifteen seconds, was the measure. In the study, there were three different ways to gain information. The demographics survey and the auditory pop quiz were two forms of surveys used. Additionally, the study used a test, the no challenge, easy, and hard challenge and the opened ended question on the auditory survey. Finally, the last item was a stimulus set. In this set the song lyrics were being played in the background well the participants are giving a verbal challenge or no challenge. We used convenient sampling to gather participants. The study was a between-subjects, randomized design. Random assignment was required in order to assign the different sessions to a condition. A Table 1- Random Digits was used to decide which session
19 was which type of visual task. Random selection of the precise timing was needed to facilitate surprise lyric recall for the auditory pop quiz. The auditory stimuli songs were put into order using random assignment. Randomization of the songs from the different decades was used, so that no participant group had to listen to only one genre of music. Random selection was also used to determine when quizzes were given, which followed some completed song stimuli. The quizzes were given after the full chorus was played of song 3, song 6, song 12, song 13, song 22, and song 24. Each quiz had two questions were was asked in the same order. We asked for lyric recall first because we did not want the initial question of familiarity to cause them to forget the lyrics. We began each session by having participants enter and put their Saint Francis University school emails next to the random number on a sign in sheet. We announced: Please enter quietly and put your three letter and three-digit Saint Francis University email next to the next number on the list. The number will match the number on your packet, which will be used to determine the prizewinner. The prizewinnerâ€™s name will be mentioned in the email. All score sheets and materials will be destroyed following the conclusion of this study.
After all participants were seated, and they listened carefully as the consent form and instructions were read. We gave the instructions for each session. For the hard task, the instructions went like so: We ask you to complete the Sudoku puzzle that is front of you. Do not start yet. Instructions are provided at the top of the sheet please read now. While you are completing the Sudoku, we ask that you additionally pay attention to the music playing. At various surprise points during the study, we will ask you to take a pop quiz based on what you can recall about the lyrics of the song you most previously heard. Both tasks are being graded so please try your best on both the puzzle and lyric recall task. For the number of lyrics recalled correctly, you will earn one point for each word. For the amount of correct numbers
placed in the Sudoku puzzle, you will also earn one point per table cell completely completed. Use the pencils provided. Please remain quiet during the entire study.
If the session was an easy task, the instructions went as so: We ask you to complete the coloring task in front of you. Do not start yet. Instructions are provided at the top of the sheet please read now. Use the crayons provided. At this time, please begin the coloring task and the music will begin. While you are completing the coloring task, we ask that you additionally pay attention to the music playing. At various points, we will ask you to take a pop quiz based on what you can recall about the lyric of the song you most previously heard. Both tasks are being graded so please try your best on both the puzzle and lyric recall task. For the number of lyrics recalled correctly, you will earn one point for each word. For the amount of correctly colored in boxes in the completed Sudoku puzzle per cell, you will earn one point. The directions are on the sheet by what number to be colored and the background. For example, one is red and since it is odd, the background is colored black. Please try to color inside the lines and as neatly as possible. Please remain quiet during the entire study.
Finally, for the no task group, the instructions went as so: Please silently focus on the music while keeping your eyes focused on the number provided on the sheet. We will give you one minute and fifteen seconds to complete each pop quiz item. The quiz is being graded so please try your best on the lyric recall task. The number of recalled lyrics correctly, one point for each word. Use the pencils provided. Please remain quiet during the entire study.
We let the participants focus on the challenge task at hand until we stopped them to complete the pop quiz. The instructions go as so for both the hard and easy challenges: “Please stop the number task. We now ask you to direct your attention to the pop quiz. Please fill out the two questions to the best of your ability. Please start.” For the no challenge, the
20 pop quiz instructions went as so: “We now ask you to direct your attention to the pop quiz. Please fill out the two questions to the best of your ability. Please start.” For each session, after each one minute and fifteen seconds, we asked them to stop the quiz and return to their hard, easy, or no challenge. At the end of the study, we asked them to turn to the last page in their packet and to fill out the demographic survey to the best of their abilities. Then we thanked our participants and reminded them that all participants were emailed through their Saint Francis University Google email (Google, 2017) regarding the $10 Sheetz gift card winner and the results of the study (see Appendix 9). Participants’ email lists were destroyed following the completion of the study in order to protect anonymity. From the participants’ view, the total time to complete all parts of the study took approximately forty-five minutes. The testing portion of the study took thirty minutes. Scoring. We scored the number of lyrics recalled correctly, so for every word recalled of the previous song that was played, one point was awarded whether they were in the correct order or not. Common words were included. We also awarded points for each correct Sudoku number completed within the hard challenge. One point for each correct solved number. For the easy task, we gave a half a point for each neat and correctly colored box, and a half a point for the neat and correctly color-bynumber inside. “Neat” meant zero coloring outside the lines. Then we totaled the number of points from each section, so the total of correctly remembered lyrics, and either correctly solved Sudoku puzzle or colored in Sudoku puzzle, and added them together for each person. This was done for a total cognitive performance score to determine the gift card winner to serve as a manipulation check later that the hard task (M= 10.50, SD= 5.73) was actually more cognitively demanding than the easy task (M= 57.38, SD= 20.34). We had an unequal variance independent samples t-test for the task scores of the easy versus the hard challenge task groups. We used total points earned on the cognitive visual task to ensure paying
attention to the visual task at hand. Thus, the statistical analysis ran verifies that the hard task was truly more difficult than the easy t(7.75)= -6.35, p= 0.00. Results Our main hypothesis had a set priori alpha level for all conclusions. Our means were based on subject means. It was necessary to drop two participants from our study because their demographics survey claimed they had memory or attention disabilities. To determine scores and statistical analysis of the hard, easy, or control task groups, we used a oneway ANCOVA statistical analysis to test lyric recall after controlling the covariance of song familiarity. There is some mean difference in the total number of recalled lyrics between the populations completing the hard task, the population of the easy task, and the population completing the no task group. Because we only tested a total of 30 college students— 12 for the hard task group, 8 for the easy task group, and 10 for the no task group—we did not have large enough power to have a significant effect size. We would need a large group size of 21 participants in order to have sufficient power. When we were running this type of statistical analysis, we discovered our results were not significant F(3, 25)= 2.00, p= 0.14. There was a general increase in the mean number of lyrics recalled as the type of task got easier with the song familiarity covaried out. Therefore, the hard task group had the lowest lyric recall followed by the no task group. The easy task group had the highest lyric recall. The effect of the type of task on the number of recalled lyrics with the factor of song familiarity covaried out can be seen in Table 1. Our mean song familiarity ratings were additionally not significant, F(1, 25)= 0.88, p= .36. The one-way ANCOVA test results showed our data to be insignificant; however, after running the Kruskal Wallis, significant results were achieved, observed x2 (2)= 6.08, p=0.048. We ran the Kruskal Wallis test because our hard task data was positively skewed and our covariate of song familiarity was not
21 significant. Table 2 includes the effects of type of task median. Type of task Mean # of Lyrics Recalled
Table 1. Mean Lyrics Recalled as a function of Type of Task with Song Familiarity Covaried Out. [Covariates are evaluated at the following value: MeanFam= 2.74.] Type of task Mean # of Lyrics Recalled
Table 2. Mean Recalled as a function of Type of Task.
Discussion Our hypothesis was partially supported; originally, there was no evidence to support that the hard task would cause the individual difficulty when switching between visual and auditory tasks while covarying out song familiarity. However, after running a Kruskal-Wallis test our study was now supported by giving significant evidence, x2 (2)= 6.08, p=0.048. The results confirmed that the hard task did show more impaired recall compared to both easy and no task groups while switching between tasks. Drawing from our conclusions, while listening to music and simultaneously completing a hard task, limited capacity within working memory impairs our ability to remember the music lyrics. Alternatively, results could suggest that while performing a hard task, by listening to music, a person’s ability to complete the task at hand to the best of their ability was inhibited. There were a handful of flaws that we realized as our study progressed that may have influenced our results. Overall, we had a small number of participants in each condition. The easy task condition had the least number of participants with only eight participants. Following the review of demographic surveys, two contestants not included
in the final eight needed to have their data removed from the study. The reason behind the removal of their data was that we asked participants to not participate if they had learning or attention disabilities and two participants answered that they did have a disability of some kind. Another flaw that affected our research was during the experimentation process. Many people were confused by our instructions, which resulted in a delay at beginning of the experiment due to questions, pauses during the experiment to answer questions, and response sheets that clearly demonstrated confusion. This flaw was most evident in the first experimental session, which led us to go off script a few times in later sessions in order to clarify instructions and eliminate this flaw in later sessions. The crayons served as a flaw during the easy task sessions at times. The crayons continuously rolled off the slightly tilted desks in the experiment rooms, which could have distracted and frustrated participants. The last main flaw was with the music selection. The final song, titled “Heart of Glass” by Blondie, has lyrics that are very difficult to understand. This song was one of the pop quiz songs and several participants wrote that the words were too hard to understand, even if they were familiar with the song. The study conducted by Lawo and Koch (2015) has striking similarities to this research. The hard, Sudoku, and easy, coloring numbers, tasks in our research can be compared to various tasks seen in the previous research by Lawo and Koch. The hard task in this research, which was the Sudoku puzzle, can cause a higher level of thinking, which can be compared to the abstract response mappings described in the previous research. The easy task does not leave room for independent thinking, similar to the tasks in Lawo and Koch’s research. Lawo and Koch’s research had the participant shade in the desired answer or were to give answers based on a list of options. This made our data inconsistent with that gathered by Lawo and Koch because when it came to experiments where tasks were abstract versus non-abstract rather than written or given verbally as
22 the main difference, no significant results were achieved. The study conducted by Kübler et al. (2003) supported the trend of our main hypothesis. These researchers supported the notion that switching between two sensory modalities at random times is proven to be difficult because of the amount of switch cost. The results demonstrated that the hard task did show more impaired recall compared to both easy and no groups while switching between tasks. In the published study, the researchers used a verbal and visuospatial task while ours was auditory and verbal. The more involved the researchers’ tasks became between the visuals of red and blue squares and the spatial dynamic, the longer participants took to switch and successfully complete the task. Similarly, in our conducted study, the harder task groups performed poorly on the lyric recall task in comparison to the easy or control group task. The hard group participants performed poorly because the study involved more attention to more difficult and attention focused sensory modalities. It was simpler for the no task group and the easy task group to focus on the auditory and visual task because the lack of attention needed for working memory functions. The study conducted by Gehring, et al. (1976) was much different from our own study. They presented that familiarity of pictures was greater than familiarity of words over a period. Our study found that the familiarity results produced nothing significant in regard to the study. The study also conducted by Rubinstein, et al. (2001) does not support our study. They require different components to determine the speed of processing and ability to switch between goal setting and rule activation. We showed that while listening to music while performing a hard task can potentially inhibit a person’s ability to recall the music lyrics. They also will have trouble switching back over to the challenge at hand. Unlike other studies, these were done with two different sensory tasks. Our study showed that when participants listened to familiar music as they completed an easy task, there was a greater ability to recall the information.. In real life,
this study can be applied to a classroom setting. If a person were trying to do homework for another class while listening to a professor’s lecture, the person would not be able to pay good attention to the professor talking. If they wanted to do something while listening to a professor’s lecture, it would have to be a simpler task like fidgeting with a fidget cube or pencil, or doodling on the side of their notes. For future research, the study could be angled to focus on listening to a lecture rather than music. This new variable would fit in with the real-life applications as previously mentioned and could further support those ideas. The provided feedback for participants who tend to lose focus during class would mathematically show them how much information they are missing. It would also eliminate the problems faced with trying to create a list of music that would not be so familiar that participants already had the lyrics memorized. The study could also implement written answers that are multiple choice rather than the critical-thinking answers the lyric recall required with its “freerecall” format. This type of testing could test the ability of recalling the information even if they cannot recall freely what the background “noise” (music or lecture) was.
23 Appendix 2 Songs: 1.
Appendix 1 Recruitment Sheet Hello! We are student researchers from the research methods and statistics 202 class taught by Dr. Moist. Our names are Megan Reilly, Lauren White and Cassie Wolowic. We are here because your psychology professor has agreed to allow an extra credit opportunity to help us recruit participants in our research. If you do not choose to participate, your professor will offer an alternative extra credit assignment. This can be discussed with your professor individually. If you are interested, please sign up for one of the six available dates using only your email address. For confidentiality, reasons we do not want to see your names. Your email address will be temporarily linked to a participant number because the top scorer overall participation will win a Sheetz gift card worth $10.00. Please consider the alternative and avoid our research if you have any of the following: memory, learning, or attention disabilities, visual (including color blindness) or hearing impairments, or a disability or injury of your hands. Participation is voluntary! We will greatly appreciate your time, which should only be approximately 30-45 minutes. Thank You!
Summer of ‘69 Artist: Bryan Adams Year: 1984 The Gambler Artist: Kenny Rogers Year: 1978 Rose Colored Glasses Artist: John Conlee Year: 1978 Walk This Way Artist: Aerosmith Year: 1975 Here I Go Again Artist: Whitesnake Year: 1982 Take this Job an’ Shove It Artist: Johnny Paycheck Year: 1974 Simon Says Artist: 1910 fruitgum Company Year: 1968 Brother Louie Artist: Stories Year: 1973 Yummy Yummy Yummy Artist: Ohio Express Year: 1968 Safety Dance Artist: Men without Hats Year: 1982 Misunderstanding Artist: Genesis Year: 1980 The Boys of Summer Artist: Don Henley Year: 1984 You Can’t Hurry Love Artist: Phil Collins Year: 1982 Kiss on My List Artist: Daryl Hall and John Oates Year: 1980 Rocket Man Artist: Elton John Year: 1972 Mr. Roboto Artist: Styx Year: 1983 Locomotion Artist: Grand Funk Railroad Year: 1974
18. Can’t Get enough of your Love Baby Artist: Barry White Year: 1974 19. Just The Way You Are Artist: Billy Joel Year: 1977 20. Hold the Line Artist: Toto Year: 1978 21. Who Loves You Artist: Frankie Vally and the Four Seasons Year: 1975 22. We Got the Beat Artist: The Go-Go’s Year: 1981 23. Dreamweaver Artist: Gary Wright Year: 1975 24. Heart of Glass Artist: Blondie Year: 1978
Appendix 3 Directions: During the time of the entire study, please keep
your eyes focused on the number below. Do not look around the room, do not close your eyes, and do not look anywhere else. Please keep your eyes focused on the number below.
24 Appendix 4 Directions: Please color the numbers by the code below:
1= red: 2=orange: 3=yellow: 4=green: 5=blue: 6=violet: 7=pink: 8=blue green: 9=violet blue. Then color the boxes around the numbers: odd numbers=black and even numbers=gray
2 3 4 6 5 1 8 7 9
1 5 8 9 7 3 4 6 2
6 7 9 2 4 8 3 1 5
3 1 2 4 6 5 9 8 7
4 8 5 7 1 9 2 3 6
7 9 6 8 3 2 5 4 1
9 6 3 1 2 4 7 5 8
5 2 1 3 8 7 6 9 4
8 4 7 5 9 6 1 2 3
6 2 9 7 8 5 4 1 3
5 3 4 1 6 9 8 2 7
1 7 8 3 4 2 9 5 6
4 1 7 2 5 3 6 9 8
3 5 2 6 9 8 1 7 4
8 9 6 4 7 1 5 3 2
7 4 5 9 2 6 3 8 1
9 6 3 8 1 7 2 4 5
2 8 1 5 3 4 7 6 9
Directions: The goal of Sudoku is to fill in a 9×9 grid with
Directions: Please complete each question to the best of your
digits so that each column, row, and 3×3 section contain the numbers between 1 to 9. At the beginning of the game, the 9×9 grid will have some of the squares filled in. Your job is to use logic to fill in the missing digits and complete the grid. Don’t forget, a move is incorrect if: ● Any row contains more than one of the same number from 1 to 9 ● Any column contains more than one of the same number from 1 to 9 ● Any 3×3 grid contains more than one of the same number from 1 to 9
ability. For the first question, please circle the best option for you. For the second question, please fill in answers on subsequent lines.
6 5 7
1 3 9 3 8 1
5 9 8 3
2 6 5
2 8 7
7 5 3 4 3
2 1. Can you recall any possible lyrics? 2. On a scale of 1-7, what is your familiarity with this song? 1 2 3 4 5 6 7 Not familiar Very familiar 3 1. Can you recall any possible lyrics? 2. On a scale of 1-7, what is your familiarity with this song? 1 2 3 4 5 6 7 Not familiar Very familiar
1 1. Can you recall any possible lyrics? 2. On a scale of 1-7, what is your familiarity with this song? 1 2 3 4 5 6 7 Not familiar Very familiar
4 1. Can you recall any possible lyrics? 2. On a scale of 1-7, what is your familiarity with this song? 1 2 3 4 5 6 7 Not familiar Very familiar 5 1. Can you recall any possible lyrics? 2. On a scale of 1-7, what is your familiarity with this song? 1 2 3 4 5 6 7 Not familiar Very familiar 6 1. Can you recall any possible lyrics? 2. On a scale of 1-7, what is your familiarity with this song? 1 2 3 4 5 6 7 Not familiar Very familiar
Appendix 7 Instructions. Please circle the response that best describes you for each question. In the blank spaces, please write in any answers that are applicable to you.
4 7 4 5 3 5 9
1. Gender a. Female b. Male 2. Age a. ________________________ 3. Current College Standing a. Freshman b. Sophomore c. Junior d. Senior e. Graduate
4. Academic Major a. _________________________ 5. GPA a. _________________________ 6. On a scale of 1-7 rate your current mood today 1 2 3 4 5 6 7 very sad very happy 7. How much attention did you put into listening to the song lyrics during the survey? On a scale of 1-7 rate your attention to the song lyrics 1 2 3 4 5 6 7 No/very little attention Full attention 8. On a scale of 1-7 rate your attention to the visual task 1 2 3 4 5 6 7 No/very little attention Full attention
26 Rubinstein, J.S., Meyer, D.E., & Evans, J.E. (2001). Executive Control of Cognitive Processes in Task Switching. Journal of Experimental Psychology: Human Perception and Performance, 27(4), 763-797. doi: 10.1037//0096-15220.127.116.113 Schneider, M., Eliez, S., Birr, J., Menghetti, S., Debbané, M., & Van der Linden, M. (2016). Multitasking abilities in adolescents with 22q11.2 deletion syndrome: Results from an experimental ecological paradigm. American Journal on Intellectual and Developmental Disabilities, 121(2), 151164,166,168. doi: 10.1352/1944-7558-121.2.151 Stanford University (2003). Sample Informed Consent Form. Retrieved December 02, 2016, from http://web.stanford.edu/group/ncpi/unspecified/student_assess_too lkit/pdf/sampleinformedconsent.pdf
Smith, E.E. & Kosslyn, S.M. (2007). Executive Processes In J. Mosher, Cognitive Psychology: Mind and Brain (1st ed., pp. 301-305). Upper Saddle River, NJ: Pearson.
Works Cited Bordens, K.S. & Abbott, B.B. (2014). Glossary in A. Lonn (Ed.), Research Design and Methods: A Process Approach, G-9. New York City, NY: McGraw-Hill Education. Critical Thinking Community. (n.d). Defining Critical Thinking. Retrieved January 20, 2017 from http://www.criticalthinking.org/pages/defining-criticalthinking/766.
Duncan, J, Martens, S, & Ward, R. (1997). Restricted attentional capacity within but not between sensory modalities. Nature, 387(6635), 808-810. doi: 10.1038/42947. Gehring, R.E., Toglia, M.P., & Kimble, G.A. (1976). Recognition memory for words and pictures at short and long retention intervals. Memory & Cognition, 4 (3), 256260. doi: 10.3758/BF03213172. Google. (2017). Saint Francis University Gmail. Retrieved April 25, 2017, from https://mail.google.com Kübler, A., Murphy, K., Kaufman, J., Stein, E.A., & Garavan, H. (2003). Co-ordination within and between verbal and visuospatial working memory: network modulation and anterior frontal recruitment. NeuroImage, 20(2), 12981308. doi: 10.1016/S1053-8119(03)00400-2 Lawo, V., & Koch, I. (2015). Attention and Action: The Role of Response Mappings in Auditory Attention Switching. Journal of Cognitive Psychology, 27(2), 194-206. doi: 10.1080/20445911.2014.995669 May, R. (2013). GameHouse© Sudoku. Retrieved December 02, 2016, from http://www.sudoku.com/ Rand Corporation. (1955). A Million Random Digits with 100,000 Normal Deviates, B-2. Glencoe, IL: Free Press.
Megan Reilly (’18) is an Occupational Therapy major with minors in Medical Spanish and Psychology. She is involved in SFU Admission’s Student Ambassador program as well as the Student Occupational Therapy Association. Following graduation, Megan will continue to work toward her Masters of Occupational Therapy. Lauren White (‘18) is an Occupational Therapy major with minors in psychology and American Sign Language. She is involved within the SFU community as a member of the Red Flash Marching and Pep Bands and the Student Athlete Advisory Committee (SAAC). After graduation, Lauren will finish her Masters in Occupational Therapy at SFU with hopes to one day obtain her doctorate. Cassandra Wolowic (’18) is an Occupational Therapy major with minors in Psychology and English. She has been actively involved in the SFU community being a member of the Gamma Sigma Sigma sorority and the Student Occupational Therapy Association. After graduation, Cassandra will finish her Masters in Occupational Therapy, and hopes to publish her first book.
“Over and over, forever and ever”: A Child’s Search for Freedom from the American Working Class Harry J. Olafsen Literature & Languages Department School of Arts & Letters email@example.com
For centuries, the people of the American working class have been going into mills, mines, factories, restaurants, transportation, and offices to work long hours doing tedious jobs for very little pay. Every day of every week, they work, eat, sleep, and (if they are lucky) have an hour or two for themselves and other household work. Since this life is so demanding and so grueling, working-class Americans have no other option than to be sucked into a perpetual cycle of labor. In turn, this repetition is passed onto their children, and very few workingclass individuals ever break the cycle. Due to the treacherous working conditions and meager pittances given to the workers in exchange for absurdly long hours of labor, working-class parents had to send their children into textile mills, coal mines, and factories at very young ages just to alleviate the economic hardships of their families. As a result, these children had no time to actually be children. Too early in their lives, they understood the struggle of being working class in America, and their aspirations to be something more than their illfated destiny were shattered. The continuous struggle to break the cycle of working-class oppression is reflected throughout American Literature, particularly in that written between the industrial revolution and the Great Depression. In this literature, hardworking children become even harder-working adults, a cycle that is then passed on from working-class generation to working-class generation, leaving the children in a constant search for freedom from the oppression that keeps them entrenched in working-class society for the duration of their lives. The principle of “childism” can help us to better understand the position of working-class children.
Robin L. Cadwallader, Ph.D. Literature & Languages Department School of Arts & Letters firstname.lastname@example.org
John Wall, an ethicist in the field of children’s studies, claims that “childism” is the ability “to respond more self-critically to children’s particular experiences by transforming fundamental structures of understanding and practice for all” (68). In the case of working-class children, the “particular experiences” begin with a loss of childhood as they enter the workforce. From there, the cycle of work perpetuates itself, and the children enter adulthood passing on the same beliefs about work to their own children. Wall explains the cycle in this way, extending it to more than just work: “[Children] are active participants who engage in the same moral dynamics as adults by reconstructing their moral surroundings over time” (76). In society, it seems as though children’s voices are continuously oppressed due to the idea that their moral values are not perfectly developed; however, children, as Wall argues, can be equally as active in society if given the chance to be treated as morally equal to adults. As “active participants,” he contends, children deserve “participation rights” that allow them to be heard, seen, and expressive (81). Unfortunately, working-class children have to rid themselves of these inherent rights in order to survive. Although many parents want the best for their children, work is crucial to their survival; therefore, children must work to contribute to their own survival as well as the survival of their families. In her collection of essays on childhood and children’s literature in the nineteenth and twentieth centuries, Anne Scott MacLeod makes the claim that “[c]oncepts of childhood change as societies change. . . .Yet it is culture that determines how children experience childhood” (viii). During the period being examined here, children were typically seen as
little adults; the concept of childhood that is accepted today came about later in American history. Prior to the mid-twentieth century, American working-class children were not really children—they were workers. They were not out in the streets playing ball or learning school lessons like the middle- and upper-class children. Instead, the working-class children had to replace bobbins, sift coal, and roll cigars for very little pay. In fact, middle- and upper-class adults had more time for play than working-class children. Sarah N. Cleghorn, author of the poem “The Golf Links,” observes in her 1917 poem, The golf links lie so near the mill That almost every day The laboring children can look out And see the men at play. (lines 1-4)
Perhaps because Cleghorn herself was not born into wealth, she felt more passionately about the plight of working-class children,1 for as she shows in her poem the children slave their young lives away while the upper class men, who control the meager pittances they dole out to their employees, get to lavishly play a round of golf. Like readers of the poem, the mill-children live vicariously through these men and wish for the same freedom from labor. Unfortunately, the need for them to work is imperative, for both the factories and the families, in working-class society. No matter how much people want either themselves or their children to break the workingclass cycle, it is almost impossible for them to do so. To be sure, parents want better for their children than they personally had, but there always seems to be a wrinkle in the plan. In To Make My Bread, a riveting novel about a Southern Appalachian working-class family’s struggle to live, Grace Lumpkin demonstrates the parents’ struggle to create more opportunities and better lives for their children in the quest of the McClure family. Centering a portion of her story on Emma’s and Ora’s wishes for their children’s future, Lumpkin writes, “Even if [Emma] Not much is known about Cleghorn’s life other than that she moved to Vermont to live with relatives after her mother’s death. Researchers suggest that she lived a middle-class life and saw the effects of labor on working-class individuals. Her 1
28 had to work day and night, the young ones were to get their schooling. And that was enough satisfaction” (208). Viewing education as offering freedom for the working class, these women share a dream that their children will be educated and not have to suffer the same harsh economic conditions their parents did. This desire, along with the encroachment of a saw mill into their mountain home, uproots them and sends them into the town, seeking employment in a mill whose promoters have convinced them that they are “a-going to the Promised Land” (142). Unfortunately, this is far from the truth, and many families find that they have to combine households just to survive. Even when they are so poor that they must share a house, Ora declares, “[The kids are] going t’ school. If I have t’ work my hands off they’re going t’ get schooling” (158). Motivated by hope, Emma and Ora work long hours for little pay to make sure their children, collectively, have better lives separate from the working class. While their sentiment is good, education for the children is not a long-lived reality. Because those of the working class need to do whatever they can to survive, that sadly means working-class children do not get the education they deserve. In this case, survival boils down to the idea of freedom of choice. According to this principle, a group of psychologists insists that “[p]eople who choose are happier, healthier, persevere longer, and are more productive than people who do not choose” (Stephens, Fryberg, and Markus 33). However, the working-class is trapped in the cycle of work, thus making them incapable of choosing any other sort of life. While their lives may seem productive, they are actually the repetitious performance of the same job day in and day out, while they are simultaneously trying to improve the lives of their children through freedom from the drudgery. Nicole M. Stephens, Stephanie A. Fryberg, and Hazel Rose Markus assert that “agency in working-class contexts is more likely to involve a focus on others than a focus on the individual self. Given this focus on others, the poetry, generally didactic, reflects Christian Socialist values, particularly in regard to the inequalities she witnessed. See “Sarah Norcliffe Cleghorn” for more on her life and work.
opportunity to choose for oneself may not be the key to agency in working-class contexts and thus may not equal freedom” (39). According to these psychologists, working-class individuals see freedom as the chance for others to thrive rather than themselves. Therefore, it makes perfect sense that Emma and Ora would want their children to get an education, for in educating their children they are freeing them to become more than fodder for the mills, mines, and factories. Because working-class agency comes from the success of others, the parents of these children are stuck in a perpetual cycle of wishing and attempting to provide a better life for their offspring, always hoping but never achieving the freedom they seek. In Yonnondio from the Thirties, Tillie Olsen tells the story of the Holbrook family, who in Wyoming during the 1920s are practically forced to leave the country and head to the city in search of work. The theme of movement from country to city, and the destruction of family and childhood experienced in the city, in Olsen’s work is similar to that found in Lumpkin’s. Likewise, in Yonnondio, the reader gets to experience the longing Anna, similar to that of Emma and Ora, has for her children. When Chris, a family friend, passes away, his son is forced to go to work in a coal mine. Of this event, Anna announces, “[Christ] wanted the kid to be different, get an edjication [sic]” (2). Anna, like Emma and Ora, believes education would have put Chris’s son in a position to do better than his father. Anna’s concern for Chris seems to be a reflection of her despondency over the future of her own children. Feeling helpless, Anna suffers through her almost dreary existence in each place she relocates to with Jim, her husband, and the children. Unfortunately, Jim is unreliable, changing jobs quite frequently, regardless of the number of mouths he is responsible to feed. This life is grueling both physically and emotionally on Anna, which makes her become apathetic toward her own children. However, there are glimmers of hope where Anna wishes a better life for her kids. She tells Jim, “[A] man came by today and for a quarter a week if we start now, a kid gets three hundred dollars when he’s sixteen. For a sure edjication” (125). Jim immediately strikes
29 down the proposition, mainly because they are physically and financially unable to spare even a quarter per week. As this shows, even the potential for an education is unable to break the cycle because the working class never does have a penny—or a moment—to spare; every penny they earn and every effort they put forth must go into paying bills and making money. Unfortunately, some working-class individuals do not have enough agency even to contemplate better lives for their children—they are forced to work simply to survive. This creates a condition where the children develop a mental framework shaped around the need to work, since that becomes the only life they truly know. Because they begin working at an early age, “children come to enact certain kinds of selves by virtue of their everyday participation with other people in characteristic selfrelevant practices” (Wiley et al. 833). Since they are around adults and children acting like adults for the majority of the day, working-class children begin to identify themselves as extensions of their work, thus making them, in actuality, adults. One of the most adult-like children in the literature of the working class is Bub Mell, from Elizabeth Stuart Phelps’s The Silent Partner. In the narrative, Bub has to take on the responsibilities of an adult by working in the local mill at an extremely young age. Phelps describes the nine-year-old boy this way: “Bub’s little old face wears an extra shade of age and evil as he trots away to work, and he swears roundly” (9). From working in the mill, Bub has aged well beyond his earthly years, and his evil look stems from the fact that his childhood has been stripped from him. With his “sly eyes, and tobacco-yellowed skin” (9), Bub trots off for another day in the mill, just one of many children among the adults, shaping his childhood and his future for a life of unending toil. Bub represents the child who aged too quickly, and in the nineteenth and twentieth centuries, many children met with the same fate: years of labor and an early death. Sadly, Bub is chewed up in a machine when he reaches for a bit of tobacco hanging from another boy’s pocket. Thus, while “concepts of childhood change as societies change” and the idea of childhood may be different today (MacLeod viii),
the children of the mills, mines, and factories lost a bit more of their freedom when they entered their workplace each day, leaving them each night more and more unrecognizable as children. The cycle of labor and loss is perfectly represented in To Make My Bread once Emma’s children grow up without the education she tried to get them but maintaining their experiences from the mill. John, the youngest McClure child, eventually marries Zinie, and they live with Bonnie, his sister, and her children for quite some time. By this point, John and Bonnie have experienced first-hand what the loss of childhood means. John Stevens, another worker in the mill, explains the grim reality of the cycle of work to John McClure, making him think that “[h]e and Zinie would die without having really lived, and their young ones would do the same; and Bonnie growing old before his eyes would live and die, and her young ones would be mill hands like her” (Lumpkin 324). In the working class, the people live to work, and so do their children. The hopes and dreams of the children to be something more than mill workers, miners, or factory labor were almost immediately crushed the minute they were conceived. While there was hope in the parents that their children would be something more, the adults fully understood the inevitable cycle that goes “[o]ver and over, forever and ever” (325). John and Bonnie, along with the other mill workers, strive to achieve “the joy of freedom for all the poor workers” (328), a freedom would give their children a chance at actually having a childhood, and a better life than their parents had. Breaking away from a socioeconomic class is extremely difficult to accomplish. According to Alana Semuels, “It really is getting harder to move up in America. Those who make very little money in their first jobs will probably still be making very little decades later” (“Poor at 20”). In the case of the working class, the shared identity between the individuals of the group is so strong and transparent that separating oneself is almost frowned upon. Members of this class understand the hardships and woes of being part of a group of people who work their fingers to the bone for everything they have. However, this seems quite confusing when put in
30 context with the desire of working-class parents for their children to have a better life. While the parents may be pleased to see their children succeed, the class as a whole seems to shun working-class individuals who move up in the world. In response to this idea, Joan C. Williams proposes that “[t]he dream is not to become upper-middle-class, with its different food, family, and friendship patterns; the dream is to live in your own class milieu, where you feel comfortable.” While Williams is not claiming that working-class people does not want their members to succeed, she is urging that workingclass individuals can find success and comfort in the confines of their own social class. People who advance into a higher social class typically feel resentment from their original class, yet everyone is still trying to move up. Williams concludes that working-class adults and their children are “just [trying to get by] with more money,” not necessarily to accomplish a change in social status. Regardless of the time period, working-class children are always working. However, the idea of how each working-class individual manages to fit into his or her class changes over time via a shift in identity. However, Andrew McMillan proposes, “The power of writing, to be able to articulate our own experience, is the thing that allows us to feel like a citizen of everywhere.” The ability to read and write grants people the ability to have their stories and voices be heard. Literacy also greatly affects the workers’ strike in the To Make My Bread, as one of the factory workers sings, Our children they grow up unlearned No time to go to school Almost before they’ve learned to walk They learn to spin or spool. (259)
As a lifetime member of the working class, the singer realizes that the children are its backbone, and they cannot be educated because they have to work. In Lumpkin’s novel, Basil is fortunate enough to go off to study, whereas John and Bonnie only begin their studies. This is the cycle of working-class life. Each individual has a unique experience, yet none, individually or collectively, can ever break the cycle. As McMillan asserts, “[W]orking class became not a solid identity, with its own cultural
history, but a transitory phase one was meant to escape from as quickly as possible,” although this was never possible. Working-class children never truly escape from the confines of their class, for the experience of being working class remains with them. Since the working-class identity is so fluid, almost everyone can fit into this mold, including Basil. While he tries to leave his former life behind, he never really can, and everyone else in the novel is fully ingrained in the social class, fighting day after day simply to survive. Survival is the catalyst that forces working-class children to leave their studies and their childhoods behind in exchange for lives in the mills, mines, and factories. From the time of the industrial revolution until after the Great Depression, many children were met with this grim fate. The cycle of work for them was so strong, due to the pressures of the upper classes, that entire families had to feverishly work for meager pittances. The literature exploring the lives of working-class children, however, is quite scarce, but their experiences are so important for shaping what we know today of the working-class identity and experience. Not having the ability to write, many working-class individuals were unable to leave their marks. For children, the chances for this agency were further minimized. From birth, working-class children had no opportunities to be children; instead, they became miniature adults responsible for the survival of themselves and their families. Unfortunately, the cycle simply perpetuated itself from generation to generation, never ceasing to force working-class children into being laborers for life. Works Cited Cleghorn, Sarah N. “The Golf Links.” 1917. Literature, Class, and Culture, edited by Paul Lauter and Ann Fitzgerald, Longman, 2001, pp. 19. Lumpkin, Grace. To Make My Bread. 1932. M. Evans, 2014. MacLeod, Anne Scott. Preface. American Childhood: Essays on Children’s Literature of the Nineteenth and Twentieth Centuries, by MacLeod, U of Georgia P, 1994, pp. vii-x. McMillan, Andrew. “The Working Class Has Its Own Cultural Identity and We Must See It on the Page.” TheGuardian.com, 24 Feb. 2017, https://www.theguardian.com/books/2017 /feb/24/written-words-
31 are-citizens-of-everywhere-and-we-should-admit-more. Accessed 23 Aug. 2017.
Olsen, Tillie. Yonnondio: From the Thirties. 1974. Delta, 1989. Phelps, Elizabeth Stuart. “Economical.” The Silent Partner. 1871. Reprinted in The Silent Partner: A Novel and “The Tenth of January”: A Short Story, Feminist Press, 1983, pp. 203-21. “Sarah Norcliffe Cleghorn.” Almanac of Famous People. Biography in Context. Gale, 2011, http://www.gale.com/c/biography-in-context. Accessed 17 Oct. 2017.
Semuels, Alana. “Poor at 20, Poor for Life.” TheAtlantic.com, 14 Jul. 2016, https://www.theatlantic.com/business/archive/2016/07/socialmobility-america/491240/. Accessed 15 Aug. 2017.
Stephens, Nicole M., Stephanie A. Fryberg, and Hazel Rose Markus. “When Choice Does Not Equal Freedom: A Sociocultural Analysis of Agency in Working-Class American Contexts.” Social Psychological and Personality Science, vol. 2, no.1, 2011, pp. 33-41. Wall, John. “Childism: The Challenge of Childhood to Ethics and the Humanities.” The Children’s Table: Childhood Studies and the Humanities, edited by Anna Mae Duane, U of Georgia P, 2013, pp. 68-84. Wiley, Angela R., Amanda J. Rose, Lisa K. Burger, and Peggy J. Miller. “Constructing Autonomous Selves through Narrative Practices: A Comparative Study of WorkingClass and Middle-Class Families.” Child Development, vol. 69, no. 3, 1998, pp. 833-47. Williams, Joan C. “What So Many People Don’t Get About the U.S. Working Class.” HBR [Harvard Business Review], 10 Nov. 2016, https://hbr.org/2016/11/what-so-many-people-dontget-about-the-u-s-working-class. Accessed 15 Aug. 2017.
Harry Olafsen (’18) is a triple major in English, History, and Women’s Studies, with four minors in American Studies, Religious Studies, Social Responsibility, and Arts & Letters. He is the President of Iota Iota Iota (Women’s Studies) and a member of Sigma Tau Delta (English) and Phi Alpha Theta (History). On campus, Harry is a thirdyear Resident Assistant, a tutor at the Writing Center, and a work study student for Dr. Robin Cadwallader. Additionally, he is the President of the Blue Stockings Society and the History Club. After graduation, Harry has plans to attend graduate school to obtain both his Masters and Ph.D. degrees in English Literature.
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Volume 8 (3) Winter 2018