DOI: 10.3886/e211641
title: Missouri Firearm Survey (MFS)

description: In July of 2020, Missouri Foundation for Health and its partners conducted the Missouri Firearms Survey of over 1,000 Missouri adults to understand firearm-related beliefs, attitudes, perceptions, and behaviors within the state with the intent of informing stakeholders interested in firearm injury and death prevention.<br><br>
description: The geodemographic benchmarks used to weight the active
panel members for computation of size measures include:

 


 Gender (Male/Female)
 Age (18–29, 30–44, 45–59,
 and 60+)



 Race/Hispanic
 ethnicity (White/Non-Hispanic, Black/Non-Hispanic, Other/Non-Hispanic, 2+
 Races/Non-Hispanic, HispanicEducation
 (Less than High School, High School, Some College, Bachelor and beyondCensus
 Region (Northeast, Midwest, South, West)Household
 income (under $10k, $10K to &lt;$25k, $25K to &lt;$50k, $50K to &lt;$75k,
 $75K to &lt;$100k, $100K to &lt;$150k, and $150K+)Home
 ownership status (Own, Rent/Other)Metropolitan
 Area (Yes, No)Hispanic
 Origin (Mexican, Puerto Rican, Cuban, Other, Non-Hispanic)
Once all survey data have been collected and processed, design
weights are adjusted to account for any differential nonresponse that may have
occurred. Depending on the specific target population for a given study,
geodemographic distributions for the corresponding population are obtained from
the CPS, the U.S. Census Bureau’s American Community Survey (ACS), or in
certain instances from the weighted KnowledgePanel profile data. For this
purpose an iterative proportional fitting (raking) procedure is used to produce
the final weights. In the final step, calculated weights are examined to
identify and, if necessary, trim outliers at the extreme upper and lower tails
of the weight distribution. The resulting weights are then scaled to aggregate
to the total sample size of all eligible respondents. 

For this study, KnowledgePanel profile data was used as the
benchmark for the raking adjustment of weights. The benchmarks were derived from profile data
of those who live in Missouri, first weighted to represent the age 18 and over
Missouri population using ACS as benchmarks.

 

Step1: Design weights for all KnowledgePanel (KP)
assignees were computed to reflect their selection probabilities.

 

Step 2: KP qualified respondents (start with base weights
of assignees) are weighted to represent the age 18+ Missouri population on the following variables:

· 
Age (18-29, 30-44, 45-64, 65+) by Gender (Male,
Female)

<br>· 
Race/Hispanic ethnicity (White/Non-Hispanic,
Black/Non-Hispanic, Other/Non-Hispanic or 2+ Races/Non-Hispanic, Hispanic) <br>

· 
Education (Less than High School or High School,
Some College, Bachelor and higher) 

· 
Metropolitan Status (Metro, Non-Metro)

<br>· 
Household Income (Under $25K, $25K-$49,999,
$50K-$74,999, $75K-$99,999, $100K-$149,999, $150K and over) <br>

· 
Gun Owners (Yes, No) by Rural (Yes, No) <br><br>The resulting weights are trimmed and scaled to sum to the
un-weighted sample size of KP respondents (labeled as kp_wt, N=604).<br><br>Step 3: Opt-In respondents (start with weight=1.0) are
weighted to represent the age 18+ Missouri population on the following
variables:<br>· 
Age (18-29, 30-44, 45-64, 65+) by Gender (Male,
Female)

<br>· 
Race/Hispanic ethnicity (White/Non-Hispanic,
Black/Non-Hispanic, Other/Non-Hispanic or 2+ Races/Non-Hispanic, Hispanic) <br>

· 
Education (Less than High School or High School,
Some College, Bachelor and higher) 

· 
Metropolitan Status (Metro, Non-Metro)

<br>· 
Household Income (Under $25K, $25K-$49,999,
$50K-$74,999, $75K-$99,999, $100K-$149,999, $150K and over) <br>

· 
Gun Owners (Yes, No) by Rural (Yes, No)

<br>· 
Var1 (Watch TV &lt; 3 hrs/day, Watch TV 3+
hrs/day)

<br>· 
Var2 (Internet for Personal Use &lt; 10
hrs/week, Internet for Personal Use 10 hrs+/week)

<br>· 
Var3 (Express Pol Opinions Online &lt; once a
month/more often, Express Pol Opinions Online - Not at all)

<br>· 
Var4 (Try new products - Not at all/Somewhat,
Try new products - A lot/Completely)

<br><br>The resulting weights are trimmed and scaled to sum to the
un-weighted sample size of Opt-In respondents (labeled as offpanel_wt; n=441)<br><br>Step 4: KP and Opt-In respondents (KP start with kp_wt
and Opt-In start with offpanel_wt) are first combined based on their respective
effective sample sizes. Then, the combined respondents are weighted to
represent the age 18+ Missouri population on the same variables as in Step 2
above. The resulting weights are trimmed
and scaled to sum to the un-weighted sample size of total respondents (labeled
as weight; N=1045).<br>
description: Response Rates: As a member of the American Association of Public Opinion Research
(AAPOR), Ipsos follows the AAPOR standards for response rate reporting. While
the AAPOR standards were established for single survey administrations and not
for multi-stage panel surveys, we use the Callegaro-DiSogra (2008)[1]
algorithms for calculating KnowledgePanel survey response rates. Generally, the KnowledgePanel survey
completion rate is about 60%, with minor variations due to survey length,
topic, sample specifications, and other fielding characteristics. In contrast,
virtually all surveys that employ nonprobability online panels typically
achieve survey completion rates in the low single digits. This means that – aside
from the fact that nonprobability panels are inherently not representative of
any known populations – the effective size of KnowledgePanel (55,000 panel
members × 0.60 completion rate = 33,000 respondents) would be equivalent to a nonprobability
panel with 1,650,000 members that on average secures completion rates close to
2% (1,650,000 panel members x 0.02 = 33,000 respondents).

<br>







[1]
Callegaro, M. and C. DiSogra (2008). “Computing Response Metrics for Online
Panels.” Public Opinion Quarterly,
Vol. 72, No. 5.



<br>
description: KnowledgePanel is the
largest online panel that relies on probability-based sampling techniques for
recruitment; hence, it is the largest national sampling frame from which fully
representative samples can be generated to produce statistically valid
inferences for study populations. <br><br>KnowledgePanel’s recruitment process
was originally based exclusively on a national RDD sampling methodology. In 2009,
in light of the growing proportion of cellphone-only households, Ipsos migrated
to an ABS recruitment methodology via the U.S. Postal Service’s Delivery
Sequence File (DSF). ABS not only improves population coverage, but also provides
a more effective means for recruiting hard-to-reach individuals, such as young
adults and minorities. Households without Internet connection are provided with
a web-enabled device and free internet service.<br><br>For this purpose, we rely on the
latest version of the Delivery Sequence File (DSF) from the USPS to select
address-based samples that are nationally representative of all households. By
taking advantage of a host of ancillary data that are appended to each address,
 we use stratified random sampling to
ensure the geodemographic composition of our panel members mimic those of the
adult population in the U.S.[1]

 

Adults from sampled households are
invited to join KnowledgePanel through a series of mailings, including an
initial invitation letter, a reminder postcard, and a subsequent follow-up
letter. Moreover, telephone refusal-conversion calls are made to nonresponding
households for which a telephone number could be matched to a physical address.
Invited households can join the panel by:

 <br>

· 
Completing
and mailing back a paper form in a postage-paid envelope

<br>· 
Calling
a toll-free hotline phone number maintained by Ipsos

<br>· 
Going
to a designated Ipsos website and completing the recruitment form online

<br>





<br>

<br>
description: Adults (age 18+) in Missouri (firearm and non-firearm owners). Smallest Geographic Unit: State level
description: mail questionnaire; telephone interview; web-based survey;