The document discusses a health and nutrition survey of U.S. children and adults conducted since 1971. It provides an Excel file with health data on 1,576 youths from the 2012 survey, including height, weight, gender, race/ethnicity and age. Students are asked to analyze whether BMI varies between these factors and with age, with options like t-tests or ANOVA cited. Comparing methods is encouraged, with the goal of interpreting results to address public health.

1. UOP MHA 610 Week 6 Discussion Health and
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Health and Nutritional Status
Since 1971, the National Center for Health
Statistics had been assessing the health and
nutritional status of both children and adults in
the United States, through periodic National
Health and Nutritional Examination Survey
(NHANES) surveys. These surveys are an

2. invaluable resource to epidemiological and public
health research; the surveys can be used to
determine the prevalence of major diseases and
risk factors, to assess nutrition and health
promotion, and to guide public health policy.
All initial and peer postings should be at least 250-
500 words in APA format supported by scholarly
sources.
In 2012, the NHANES National Youth Fitness
Survey (NNYFS) was conducted in conjunction with
NHANES to obtain physical activity and fitness
levels of U.S. youths aged 3 through 15. Initial data
from the NNYFS were released in 2013 and serve
as the basis for this discussion problem.
Begin by downloading the Excel file
MHA610_Week
6_Discussion_NNYFS_workingdata.xls. This
workbook was created by merging two datasets
from the NNYFS: the demographic variables
dataset, and the body measures dataset. For the
purposes of this discussion, many variables were
eliminated from the original datasets, as well as
observations with missing data on height and
weight. The Excel workbook thus consists of one
worksheet, with 1576 rows (the first row contains

3. headers, and the next 1575 rows are observed
values for the participants), and 11 columns of
variables. The columns in the Excel file are the
following:
SEQN the respondent sequence number (index for
all the files)
RIAGENDR gender of the participant, 1 = male, 2 =
female
RIDRETH1 race/Hispanic origin:
1 = Mexican American
2 = other Hispanic
3 = non-Hispanic white
4 = non-Hispanic black
5 = other
RIDEXAGY age in years at time of physical exam
INDHHIN2 annual household income, categorized
INDFMIN2 annual family income, categorized
INDFMPIR ratio of family income to poverty, 0 to 5
BMXWT weight, in kg

4. BMXHT height, in cm
BMXBMI body mass index (kg/m^2)
BMDBMIC BMI category:
1 = underweight
2 = normal weight
3 = overweight
4 = obese
. = missing
More detailed descriptions of these variables are
given at the data documentation web pages for the
NNYFS, at
http://www.cdc.gov/nchs/nnyfs/Y_DEMO.htm and
at http://www.cdc.gov/nchs/nnyfs/Y_BMX.htm.
For purposes of this discussion, you are asked to
answer the three following questions:
• Does BMI vary significantly between boys and
girls?
• Does BMI vary significantly among the
racial/ethnic groups?
• Is there any trend to BMI with age?

5. Comments:
There are several ways to address these questions. For example, you might take BMXBMI as your outcome
variable of interest: it is continuous, so you could then perform a two-sample t test for (1), a one way
analysis of variance for (2), and a simple regression analysis (with age as the predictor variable) for (3).
Alternatively, you might reduce the problem to consideration of binomial probabilities: for example, you
could classify everyone as obese or not obese (or maybe, overweight/obese vs underweight/normal), then
compare binomial outcomes for (1) and (2) (z tests with the normal approximation or contingency tables),
and conduct a t test on ages for (3).
Neither approach is wrong—the key is interpreting your findings!
If you prefer to do the analyses in Stat disk, there is a file, MHA610_Week
6_Discussion_NNYFS_workingdata.csv, ready to be read into Stat disk. (It’s the original Excel workbook,
saved as csv.) No need to go through any additional steps, unless you wish to restructure the data in Excel.
Incidentally, the income variables are not needed for these questions, but as a bonus, you might want to
investigate whether obesity is related to socioeconomic status (as reflected by family income).
Guided Response: Respond to at least two of your peers who chose a different of analysis that you by Day 7,
11:59PM. Did you arrive at the same conclusions as your colleague even though you chose different
methods? If so, which method do you think is preferable and why? If not, which method do you believe
produces more credible results and why? (You might consult the text to support your argument.). All initial
and peer postings should be at least 250-500 words in APA format supported by scholarly sources.

6. Comments:
There are several ways to address these questions. For example, you might take BMXBMI as your outcome
variable of interest: it is continuous, so you could then perform a two-sample t test for (1), a one way
analysis of variance for (2), and a simple regression analysis (with age as the predictor variable) for (3).
Alternatively, you might reduce the problem to consideration of binomial probabilities: for example, you
could classify everyone as obese or not obese (or maybe, overweight/obese vs underweight/normal), then
compare binomial outcomes for (1) and (2) (z tests with the normal approximation or contingency tables),
and conduct a t test on ages for (3).
Neither approach is wrong—the key is interpreting your findings!
If you prefer to do the analyses in Stat disk, there is a file, MHA610_Week
6_Discussion_NNYFS_workingdata.csv, ready to be read into Stat disk. (It’s the original Excel workbook,
saved as csv.) No need to go through any additional steps, unless you wish to restructure the data in Excel.
Incidentally, the income variables are not needed for these questions, but as a bonus, you might want to
investigate whether obesity is related to socioeconomic status (as reflected by family income).
Guided Response: Respond to at least two of your peers who chose a different of analysis that you by Day 7,
11:59PM. Did you arrive at the same conclusions as your colleague even though you chose different
methods? If so, which method do you think is preferable and why? If not, which method do you believe
produces more credible results and why? (You might consult the text to support your argument.). All initial
and peer postings should be at least 250-500 words in APA format supported by scholarly sources.