What stratification variable(s) would you use for each of the following situations:
a An e-mail survey of students at your university to estimate the total
amount of money student spend on textbooks in a term
b A sample of high schools in New York City to estimate what percentage
of high schools offer one or more classes in computer programming
c A sample of public libraries in Washington DC to study the availability
of computer resources, and the per capita expenditure
a) For an e-mail survey of students at your university to estimate the total amount of money students spend on textbooks in a term, a potential stratification variable could be the academic year of the students (e.g. freshmen, sophomores, juniors, seniors). This variable would help divide the sample into different groups based on their study level, which could potentially affect their spending habits on textbooks.
b) For a sample of high schools in New York City to estimate what percentage of high schools offer one or more classes in computer programming, a potential stratification variable could be the school district. Different school districts may have varying budgets, educational priorities, or access to resources, which could influence the availability of computer programming classes.
c) For a sample of public libraries in Washington DC to study the availability of computer resources and per capita expenditure, a potential stratification variable could be the location of the library, such as the different neighborhoods or wards within the city. This variable could help ensure a representation of libraries from various areas within Washington DC, which might have different levels of funding, population density, or community needs.
a In this situation, a possible stratification variable could be the academic major of the students. This would allow for stratifying the sample by different disciplines and accounting for potential variations in textbook expenses.
b For this situation, a suitable stratification variable could be the boroughs of New York City. Stratifying by borough would ensure representation from different geographical areas of the city and account for any variations in the availability of computer programming classes across different neighborhoods.
c In this case, a potential stratification variable could be the population size of the neighborhoods where the public libraries are located. This would allow for stratifying the sample by areas with different levels of population density, which might have an impact on the availability of computer resources and per capita expenditure.
To determine the appropriate stratification variables for each situation, we need to identify the characteristics or factors that are relevant to the research question and may influence the population being studied. Here are some potential stratification variables for each situation:
a) An e-mail survey of students at your university to estimate the total amount of money students spend on textbooks in a term:
- Major/field of study: Different majors may have different textbook requirements and costs. Stratifying by major can provide insights into the spending patterns within different academic disciplines.
- Year of study: Freshmen, sophomores, juniors, and seniors may have varying textbook needs and expenses.
- Residency status: Students living on campus versus off-campus or commuting students may have different spending habits.
b) A sample of high schools in New York City to estimate what percentage of high schools offer one or more classes in computer programming:
- School district: Stratifying by different school districts within New York City can help identify variations in the availability of computer programming classes across different areas.
- School size: Larger schools may have more resources and be more likely to offer computer programming courses compared to smaller schools.
- School type: Public, private, or charter schools may have different priorities and resources, influencing their offering of computer programming classes.
c) A sample of public libraries in Washington DC to study the availability of computer resources and per capita expenditure:
- Location: Stratifying by different neighborhoods or regions within Washington DC can reveal disparities in computer resource availability among different communities.
- Library size: Large libraries may have more comprehensive computer resources compared to smaller libraries with limited space.
- Funding source or budget: Stratifying based on the libraries' funding sources or budget levels can help assess the correlation between financial resources and computer resource availability.
It is important to note that the selection of stratification variables may vary depending on the specific research question, available data, and knowledge of the population being studied. A thorough understanding of the target population and its characteristics is crucial when choosing appropriate stratification variables.