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In this project, you will need to identify a research question that is answerable using one of the NHIS (National Health Interview Survey) data sets

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

Data Analysis Project – Introduction

In this project, you will need to identify a research question that is answerable using one of the NHIS (National Health Interview Survey) data sets. The contents for these data sets and SAS code for compiling the data sets are provided for you in CANVAS.

 

I have provided two NHIS data sets: (1) the SAMADULT, basic health information from a representative US sample from 2017 and (2) a cancer related data set (actually CANCERXX with PERSONSX_2015). (Note that the latter one is from an earlier date and contains questions not asked in other years of the survey. You can find more information about this data set at https://www.cdc.gov/nchs/nhis/index.htm.

 

There are a number stages for this project: the proposal, submission of written methods ad measures, and the final report.

 

It is strongly recommended that you work on the project starting the first day of class and gradually throughout the semester. To help you do this, there are periodic submission required that will guide you through various stages of the project. The time and effort needed to complete the project mean that you will not be able to complete each component over just a couple of days!

 

1) Project Plan – 3 points (NO late submissions allowed !!!!!! These will be graded according to the quality of your work. You will LOSE POINTS if you do not follow the very explicit instructions given for this Project Plan.)

2) Submission of Written Summary of Methods and Measures – 1 point (submit as assignment and bring paper copies to class for discussion)

3) Discussion Boards (DBs): The first DB of the class will ask you to submit your research question and variables for feedback from your peers. You may choose (or not if you don’t think the feedback has merit) to use some of this feedback to adjust your research question, confounders, or categories for your variables. In later DBs, you may be given the option to use the variables from your project in preliminary analyses.

4) Final Report – 12 points

 

Detailed explanations of the components required for the project proposal and for the final report are included in separate documents.

 

SPECIAL NOTE: Although the NHIS data collection uses a complex sampling scheme, I am expecting that you WILL NOT appropriately weight your analysis, as such weighting is beyond the scope of this class. (The data was collected using stratified, multistage sampling. As such all peer reviewed publications involving this data must identify the primary sampling unit and use appropriate weighting schemes. We are merely using this data as an exercise in applying categorical data analytic methods. If you do a project that you think might eventually be publishable, I can work with you after the conclusion of the class to figure out the appropriate SAS coding to include weighting.)

 

Before starting on a draft, you will need to examine the variables in the NHIS data set you choose and determine your research question. Please note that a research question includes three components: a study population, response, and predictor of interest (I will ask you to restrict it to one). You will be

 

asked to fill out a project proposal form in which you will identify one ordinal response, one predictor of interest, and the study population, as well as potential confounders (e.g., age, gender, etc.) from the data set. You will be asked to identify these variables by the NHIS SAS names and give summaries of these variables. The response variable must be an ordinal variable or you will not receive full credit for the project plan or the project! The ordinal variable may be constructed based on a continuous variable. For example, you might take BMI and using clinically established cut-offs divide the population into: underweight, normal, overweight, obese, morbidly obese. Alternatively, you might take blood sugar and divide into normal, pre-diabetic, diabetic or a similar division with blood pressure or cholesterol. Besides using clinically established cut-offs, you could also use percentiles (e.g., 25th, 50th, 75th) to divide the values of a continuous variable into ordinal categories.

 

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