In this Part, pretend that a California non-profit organization wishes to determine what factors will be the strongest predictors of online voting implementation success. They want to convince the California Legislature to begin the process of making all voting in the state online. They think that your research will be a critical part of convincing the legislature to act. They need results within the year so that they can begin the process, which they hope will lead to full implementation prior to the 2028 presidential primary election.
The organization has hired you to design and conduct this research study using a regression-based (ordinary least squares or partial least squares) approach. The organization has put together a “model” of online voting implementation, based on their own reading and discussion. It currently has 16 possible predictor variables (e.g., the trustworthiness of the technology) and one outcome variables (confidence in the results). [You don't have the whole", which ought to have ended in " model. You can answer this question knowing that there are 16 predictors, without knowing what they are."
Since none of the organization’s members are scientists, you may doubt the model, but your charge is not to create a theory of online voting implementation, it is to assess what the relative strength of the predictors is in a regression sense for predicting the one outcome variable. If you don’t find empirical support for predictors, they are likely to be excluded in future policy discussions.
The organization will provide you with adequate resources to conduct your research, which you must complete within one year. They assume that California will have automatic, universal voter registration in place no later than 2024, so eligibility to vote will not be an issue in your research.
Use the outline below as a guide to answer your question. Be sure to provide details, explain your choices, and justify each decision. You are expected to answer in terms of the readings, such as Field (2018) and Hair et al. (2017).
Outline
1. What research question(s) will you answer?
2. What must you do prior to collecting data?
3. How will you collect the data?
4. What will you do with the data prior to running your analyses?
5. How will you run the analyses?
6. What key statistics will be reported and why?
7. How will you interpret the results of your analyses?
8. What are the most likely results that you might find?
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