Question 1
The foundation of your original research question begins here. Select one "HP2020 Objective, Dataset, and Outcome Variable" from the following table:
Copy and paste the row in the answer text.
HP2020 Objective
Dataset
Outcome Variable
SA-14.3: Reduce the proportion of persons engaging in binge drinking during the past 30 days—adults aged 18 years and older
BRFSS 2018
_RFBING5
SA-14.4: Reduce the proportion of persons engaging in binge drinking during the past month—adolescents aged 12 to 17 years
YRBSS 2017
QN44
SH-4: Increase the proportion of adults who get sufficient sleep
BRFSS 2018
SLEPTIM1
SH-3: Increase the proportion of students in grades 9 through 12 who get sufficient sleep
YRBSS 2017
QN88
TU-1.1: Reduce cigarette smoking by adults
BRFSS 2018
_SMOKER3
TU-2.2: Reduce use of cigarettes by adolescents (past month)
YRBSS 2017
QN33
Question 2
1) Attach one peer-reviewed journal article (i.e., full-text) that identifies a modifiable predictor (i.e., risk factor, protective factor) associated with the outcome variable and population selected in question #1.
2) Enter the citation for the article in APA 6th edition (or later) format into the text box.
Question 3
1) Describe the target population from the research article attached in question #2.
Example 1: male high school students
Example 2: students in grades 9–12
2) Provide the dataset and variable information from the codebook that can be used to identify the population:
Example 1: YRBSS Q2 "Sex", 2=Male (PDF Page 69)
NOTE: If the target population will only include males then the variable "Sex" cannot be included as a non-modifiable socio-demographic variable because there is no comparison group.
Example 2: N/A - Entire YRBSS population (i.e., described in the methodology)
Purpose: This information will be used to ensure your SAS results are representative of the described target population. If a subsample is used (e.g., male) the information will be used in the SAS code to restrict your dataset sample to the target population.
Question 4
Provide one modifiable predictor from the codebook for your selected dataset. The variable should be similar* to the modifiable predictor in the peer-reviewed journal article provided question #2.
Include the following information for the modifiable predictor from the dataset codebook:
1) SAS Variable; 2) Variable label; 3) Values; and 4) Value Labels.
Note: Copying and pasting from the codebook is allowed.
*The variable selected from the dataset codebook does not need to be an exact match to the article; however, it should have a similar meaning and/or measurement(s).
Question 5
Provide one non-modifiable socio-demographic variable (e.g., age, grade, sex, marital status, educational attainment, poverty level, employment, race/ethnicity) relevant to your outcome variable.*
Include the following information for the modifiable predictor from the dataset codebook:
1) SAS Variable; 2) Variable label; 3) Values; and 4) Value Labels.
Note: Copying and pasting from the codebook is allowed.
*The variable should either be a similar to the peer-reviewed journal article or HP2020 "Spotlight on Disparities" for your selected objective.
Question 6
Provide one non-modifiable socio-demographic variable (e.g., age, grade, sex, marital status, educational attainment, poverty level, employment, race/ethnicity) relevant to your outcome variable.*
Include the following information for the modifiable predictor from the dataset codebook:
1) SAS Variable; 2) Variable label; 3) Values; and 4) Value Labels.
Note: Copying and pasting from the codebook is allowed.
*The variable should either be a similar to the peer-reviewed journal article or HP2020 "Spotlight on Disparities" for your selected objective.
Question 7
Provide one non-modifiable socio-demographic variable (e.g., age, grade, sex, marital status, educational attainment, poverty level, employment, race/ethnicity) relevant to your outcome variable.*
Include the following information for the modifiable predictor from the dataset codebook:
1) SAS Variable; 2) Variable label; 3) Values; and 4) Value Labels.
Note: Copying and pasting from the codebook is allowed.
*The variable should either be a similar to the peer-reviewed journal article or HP2020 "Spotlight on Disparities" for your selected objective.
Question 8
Use the information in the previous questions to create an original research question using the template below. As needed, refer back to resources at the beginning of the quiz.
Behavioral Risk Factor Surveillance System 2018 Summary Data Quality Report
Page 1 of 26
Behavioral Risk Factor Surveillance System
2018 Summary Data Quality Report
July 17, 2019
Page 2 of 26
Table of Contents
Introduction .......................................................................................................................................... XXXXXXXXXX3
Interpretation of BRFSS Response Rates ............................................................................................ XXXXXXXXXX4
BRFSS 2018 Call Outcome Measures and Response Rate Formulae ................................................. XXXXXXXXXX5
Tables of Outcomes and Rates by State ............................................................................................. XXXXXXXXXX10
References .......................................................................................................................................... XXXXXXXXXX26
Page 3 of 26
Introduction
The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based, CDC-assisted health-data collection
project and partnership of state health departments, CDC’s Division of Population Health, and other CDC
programs and offices. It comprises telephone surveys conducted by the health departments of all 50 states, the
District of Columbia, Puerto Rico, and Guam.
This Summary Data Quality Report presents detailed descriptions of the 2018 BRFSS calling outcomes and call
summary information for each of the states and te
itories that participated. All BRFSS public-use data are
collected by landline telephone and cellular telephone to produce a single data set aggregated from the 2018
BRFSS te
itorial- and state-level data sets. The variables and outcomes provided in this document are
applicable to a combined data set of responses from participants using landline telephones and cellular
telephones within each of the states and te
itories.
The inclusion of data from cellular telephone interviews in the BRFSS public release data set has been standard
protocol since 2011. In many respects, 2011 was a year of change—both in BRFSS’s approach and
methodology. As the results of cellular telephone interviews were added in 2011, so were new weighting
procedures that could accommodate the inclusion of new weighting variables. Data users should note that
weighting procedures are likely to affect trend lines when comparing BRFSS data collected before and after
2011. Because of these changes, users are advised NOT to make direct comparisons with pre-2011 data, and
instead, should begin new trend lines with that year. Details of changes beginning with the 2011 BRFSS are
provided in the Mo
idity and Mortality Weekly Report (MMWR), which highlights weighting and coverage
effects on trend lines.1 Since 2011, each yearly data set has included a larger percentage of calls from the cell
phone sample. In 2018, a majority of the BRFSS interviews were conducted by cell phone. The annual
codebooks provide information on the number and percentage of calls conducted by landline and cell phone by
year.
The measures presented in this document are designed to summarize the quality of the 2018 BRFSS survey
data. Response rates, cooperation rates, and refusal rates for BRFSS are calculated using standards set by the
American Association for Public Opinion Research (AAPOR).2 The BRFSS has calculated 2018 response rates
using AAPOR Response Rate #4, which is in keeping with rates provided by BRFSS before 2011 using rates
from the Council of American Survey Research Organizations (CASRO).3
On the basis of the AAPOR guidelines, response rate calculations include assumptions of eligibility among
potential respondents or households that are not interviewed. Changes in the geographic distribution of cellular
telephone numbers by telephone companies and the portability of landline telephone numbers are likely to make
it more difficult than in the past to determine which telephone numbers are out-of-sample and which telephone
numbers represent likely households. The BRFSS calculates likely households and eligible persons using the