Missouri Firearm Survey (MFS)

Name: Missouri Firearm Survey (MFS)
Published: 2024-11-21
License: https://creativecommons.org/licenses/by/4.0

Principal Investigator(s): View help for Principal Investigator(s) Daniel Lee, University of Michigan Institute for Firearm Injury Prevention; Patrick Carter, University of Michigan Institute for Firearm Injury Prevention; Megan Simmons, Missouri Foundation for Health

Version: View help for Version V1

Institute for Firearm Injury Prevention Data Repository

Name	File Type	Size	Last Modified
KnowledgePanel Calibration Weighting_Q3_13.docx	application/vnd.openxmlformats-officedocument.wordprocessingml.document	45 KB	11/21/2024 09:24:AM
KnowledgePanel Methodology (1).pdf	application/pdf	486.9 KB	11/21/2024 09:24:AM
MFFH Firearms Questionnaire Main_Final (2).docx	application/vnd.openxmlformats-officedocument.wordprocessingml.document	215.6 KB	11/21/2024 09:28:AM
MFFH_Missouri_Firearms_Client_08_26_2020_no_qzip.sav	application/x-spss-sav	932.9 KB	11/21/2024 09:49:AM

Project Citation:

Lee, Daniel, Carter, Patrick, and Simmons, Megan. Missouri Firearm Survey (MFS). Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-11-21. https://doi.org/10.3886/E211641V1

Project Description

Summary: 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.

Funding Sources: Missouri Foundation for Health

Scope of Project

Subject Terms: Firearm ; Firearm Attitudes; Firearm Behaviors

Geographic Coverage: Missouri, USA

Time Period(s): 7/1/2020 – 7/31/2020

Collection Date(s): 7/1/2020 – 7/31/2020

Universe: Adults (age 18+) in Missouri (firearm and non-firearm owners)

Data Type(s): survey data

Methodology

Response Rate: 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).

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

Sampling: 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.

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.

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:
·         Completing and mailing back a paper form in a postage-paid envelope
·         Calling a toll-free hotline phone number maintained by Ipsos
·         Going to a designated Ipsos website and completing the recruitment form online

Data Source: The Missouri Firearm Survey

Collection Mode(s): mail questionnaire; telephone interview; web-based survey

Weights: 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, Hispanic
Education (Less than High School, High School, Some College, Bachelor and beyond
Census Region (Northeast, Midwest, South, West)
Household income (under $10k, $10K to <$25k, $25K to <$50k, $50K to <$75k, $75K to <$100k, $100K to <$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)
·         Race/Hispanic ethnicity (White/Non-Hispanic, Black/Non-Hispanic, Other/Non-Hispanic or 2+ Races/Non-Hispanic, Hispanic)
·         Education (Less than High School or High School, Some College, Bachelor and higher) ·         Metropolitan Status (Metro, Non-Metro)
·         Household Income (Under $25K, $25K-$49,999, $50K-$74,999, $75K-$99,999, $100K-$149,999, $150K and over)
·         Gun Owners (Yes, No) by Rural (Yes, No)

The resulting weights are trimmed and scaled to sum to the un-weighted sample size of KP respondents (labeled as kp_wt, N=604).

Step 3: Opt-In respondents (start with weight=1.0) 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)
·         Race/Hispanic ethnicity (White/Non-Hispanic, Black/Non-Hispanic, Other/Non-Hispanic or 2+ Races/Non-Hispanic, Hispanic)
·         Education (Less than High School or High School, Some College, Bachelor and higher) ·         Metropolitan Status (Metro, Non-Metro)
·         Household Income (Under $25K, $25K-$49,999, $50K-$74,999, $75K-$99,999, $100K-$149,999, $150K and over)
·         Gun Owners (Yes, No) by Rural (Yes, No)
·         Var1 (Watch TV < 3 hrs/day, Watch TV 3+ hrs/day)
·         Var2 (Internet for Personal Use < 10 hrs/week, Internet for Personal Use 10 hrs+/week)
·         Var3 (Express Pol Opinions Online < once a month/more often, Express Pol Opinions Online - Not at all)
·         Var4 (Try new products - Not at all/Somewhat, Try new products - A lot/Completely)

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)

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).

Unit(s) of Observation: Individual Respondents

Geographic Unit: State level

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Published Versions

V1 [2024-11-21]

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