Closely reading the codebook of the dataset that you have chosen will enable you to avoid making many mistakes with your research strategy and interpretation of your results.

Some of the datasets include nominal and ordinal variables. For each one, you’ll need to choose one category to function as the reference category and create dummy variables based on the other categories. Make sure you interpret the coefficients on the dummy variables correctly. Don’t include the original nominal or ordinal variables in your regression analysis.

Some of the nominal variables consist of only two categories (for example, “female” equals 1 for women and 0 for men). Those variables can be used in the format given. It is not necessary to create more dummy variables based on those variables.

Make sure that you understand the difference between a dependent variable and an independent variable. Your independent variables should be factors that could conceivably influence the dependent variable. If your dependent variable ends up in your table along with the independent variables and with its own coefficient and p-value, then you have done something wrong.

Make sure that you include all of the independent variables provided in the dataset that could conceivably influence your dependent variable.

**Project 5 Instructions**

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Tips

Ø Closely reading the codebook of the dataset that you have chosen will enable you to avoid making many mistakes with your research strategy and interpretation of your results.

Ø Some of the datasets include nominal and ordinal variables. For each one, you’ll need to choose one category to function as the reference category and create dummy variables based on the other categories. Make sure you interpret the coefficients on the dummy variables correctly. Don’t include the original nominal or ordinal variables in your regression analysis.

Ø Some of the nominal variables consist of only two categories (for example, “female” equals 1 for women and 0 for men). Those variables can be used in the format given. It is not necessary to create more dummy variables based on those variables.

Ø Make sure that you understand the difference between a dependent variable and an independent variable. Your independent variables should be factors that could conceivably influence the dependent variable. If your dependent variable ends up in your table along with the independent variables and with its own coefficient and p-value, then you have done something wrong.

Ø Make sure that you include all of the independent variables provided in the dataset that could conceivably influence your dependent variable.

The purpose of this assignment is to give students an opportunity to develop research questions and pursue those questions with multiple regression analysis. Students will also be expected to present and discuss the results in a clear and engaging manner. Students will select one of the datasets provided in Project 5 folder on Blackboard. A description of each dataset __is provided__ separately.

__Outline of ____Your____ Report__

Each report should be between 5 and 8 pages, double-spaced, with a font of 12, and margins of 1 inch. __You__ should pose one research question. __You__ will be expected to conduct at least one multiple regression analysis. In some cases, it might be necessary to conduct more than one multiple regression analysis in order __to fully explore your research question__. __Your__ proposal should start with an introductory section that begins with a statement of the issue to which __your__ research question is related. __This__ should not be a boring sentence like: “This is a research study that examines the effects of education spending on student’s performance on standardized tests.” Try starting with an interesting fact that grabs the reader’s attention. Imagine the first few lines spoken by a narrator like Morgan Freeman for a documentary. Hold __yourself__ to that standard. Discuss why __your__ research might be interesting from a policy, management, or social science standpoint. __Your__ introduction should proceed with a brief description of __your__ report and conclude with an outline of __your__ findings.

The next section should provide a description of the data and a table of descriptive statistics for __your__ dependent and independent variables. The next section should be a description of __your__ multiple regression model. Which independent variables are __you__ __including__? Why __are you including__ them? Do __you__ expect their effects to be positive or negative? Why? A full literature review on __your__ topic __is not required__ for this assignment. However, __you__ might have an easier time __providing the justification for__ the independent variables that __you__ have chosen and what __you__ expect their effects to be if __you__ do at least a partial literature review.

The next section is a discussion of __your__ regression results. Assess the statistical significance of each variable. If a variable is statistically significant, discuss whether it is positive or negative. Did __you__ get the effect that __you__ expected? If not, why is it different? Discuss the magnitude of the effect. __You__ do not have to discuss the magnitude of a coefficient that is not statistically significant. If the coefficient on a particular variable is not statistically significant, then __you__ can __just__ say that it is not statistically-significant and move on to the next one. __You__ should have professional-looking tables for descriptive statistics and regression results. They should look something like the tables from __your__ projects and practice exercises. End the report with a conclusion that reiterates __your__ research topic, briefly describes __your__ findings and how __your__ analysis could perhaps be improved or extended. The regression report examples on Blackboard should give you some ideas on how to structure this project.

__ __

__Tips__

- Closely reading the codebook of the dataset that you have chosen will enable you to avoid making many mistakes with your research strategy and interpretation of your results.
- Some of the datasets include nominal and ordinal variables. For each one, you’ll need to choose one category to function as the reference category and create dummy variables based on the other categories. Make sure you interpret the coefficients on the dummy variables correctly. Don’t include the original nominal or ordinal variables in your regression analysis.
- Some of the nominal variables consist of only two categories (for example, “female” equals 1 for women and 0 for men). Those variables can be used in the format given. It is not necessary to create more dummy variables based on those variables.
- Make sure that you understand the difference between a dependent variable and an independent variable. Your independent variables should be factors that could conceivably influence the dependent variable. If your dependent variable ends up in your table along with the independent variables and with its own coefficient and p-value, then you have done something wrong.
- Make sure that you include all of the independent variables provided in the dataset that could conceivably influence your dependent variable.

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