| POL 600 Advanced
Research Methods Homework |
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Simple
Regression Modeling & & Research Proposal (due 11am of Jan.
28): (1)
describe your data; (2) select 2 variables to construct a simple
regression model: Y=ß0+ß1X, (3) use OLS to
estimate ß0 and ß1 and ó2; (4)
use Analysis of Variance to perform the following hypothesis test:
NH: Y=ß0 vs. AH: Y=ß0+ß1X; (5)
produce scatter plots of Y against all your potential predictors
(independent variables); (6) use all your predictors to create a
multiple regression model: Y=ß0+ß1X+... ßnXn
; (7) use OLS to estimate ß0 ... ßn
and ó2; (8) write your first draft of the research
proposal.
Suggestions on your writing the first research
report: (a) a description of theories supporting your hypotheses and
a list of some reference articles/books; (b) a clear stating of your
research goal (population, sample, hypotheses, ...); (c) a
discussion of your selection of
variables and why (a table listing all your variables AND
their definitions / values is needed); (d) descriptive statistics of
all your variables (this place allows us to
look into St Dev of your key variables to ensure enough variances);
(e) simple regression and relate it to your key hypothesis (see if
the simplest method can satisfy your research goal). Usually NOT, so
we go to
the next step; (f) hypothesis testing and your first evaluation of
the research question; (g) try various plots AND connect your plots
to your reasons of selecting variables & models; (h) estimation
of your initial multiple regression model and relate it to your
research question AND compare to your simple regression model; (i)
some discussion of your current results to open your next chapter.
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First
Diagnostics of Your Model (Due 11am of Feb. 11): Use
the learned methods to detect outliers, non-constant variance and
non-normality. Correct the problems if detected. Then, re-estimate
your multiple regression model.
When
write your research report #2, please provide answers to the
following questions: (a) Are you satisfied with your results from
your last homework? (provide a short summary of your results also)
(b) Do your results from first assignment support your hypotheses or
theories so far? (c) Do all the assumptions of a regression model
make sense to your theories or data? (d) What are your reasons in
choosing your correction methods? (For example, why do you decide to
delete all the outliers? Any studies of your outliers?) (e) After
corrected the detected problems, do your results (OLS estimates)
become more meaningful to your research goal?
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Discrete
Independent Variables & Variable Selection (Due 11am on Feb 25): Use
the learned methods to detect non-linearity and collinearity
problems. Correct the problems if detected. Also use the learned
dummy variable methods
to handle your use of discrete variables if any. Finalize your
selection of variables to improve your model. Make sure you use
the varying intercepts and varying slopes to interpret your results.
- Generalized
Linear Regression (Due 11am of March 10):
Use
GLIM models to fit your data and improve your modeling.
Recode your dependent variable into 1 or 0 scale and perform a
logistic regression. Carefully compare your new results to your
previous results obtained from estimating the linear regression
model. Specifically, use table to present the results and compare
Wald test to T test, likelihood ratio test to F-test, pseudo R
square to R square. Importantly, explain the B coefficient
differences from
these two models. Tell your readers if this comparison gives you
anything interesting.
Suggested
format for
writing your report: (1) summarize the results from your last
report, (2) provide justification for your recoding the dependent
variable into 0 or 1 scale: (a) your dependent variable is a
categorical one, (b) you want to know why certain units fall into
the top 5 or top 10 group, (3) use tables to summarize your logistic
regression results and explain them, (4) use tables to summarize your
comparison of the logistic results vs. linear model results, (5)
tell if this comparison helped your research. Is this logistic
regression model an improvement over your linear regression model OR
a good complement to your linear regression model?
- Model
Validation &
Missing Values (Due
11am of March 31):
Use
the learned methods to handle missing values in your dataset. Re-estimate your final
model and perform all the necessary diagnostics. Split your sample
and conduct a cross validation of your final model. This is the time
for you to revisit all your diagnostics methods used as a way of
searching for your FINAL model.
Suggested format
for your writing: (1) summarize all the decisions you have made, (2)
summarize the results from your last report, (3) report your work of
studying and handling missing values, (4) compare your results
before and after taking care of missing values, (5) report your
model validation results and summarize its meaning to your
research.
- Final
Diagnostics and Non-OLS Estimations (Due 11am of April 14):
Try Non-OLS estimation and Non-linear modeling to improve your
final model and final results. Use tables to present your results.
- Final
Presentation (Due after class on April 28)
Use
PowerPoint to create
six slides to form your presentation:
n
n1)
research question (hypothesis, population, variables)
n2)
Findings – confirm or reject hypothesis
n3)
More Findings – relationship, your model, Bs, tests, C.I.
n4)
Why tests, Cis, Bs are meaningful? Diagnostics
n5)
How good is your model – validation results
n6)
Conclusion – modeling strategies & great finding
- Final Research Paper Due 3pm
of May 7
Your Paper Should
Include: nIntroduction
(introducing your question)
nLiterature
Review (why your question is good)
nOperationalization
(concepts -> variables & data)
nResearch
Design (specification of your regression equation, diagnostics)
nResults
(presenting results with tables & graphs)
nDiscussion
(relating results to question & hypotheses)
nConclusion
(confirm or reject hypotheses)
BACK
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