Supporting Statement B 1660-0105

B. Collections of Information Employing Statistical Methods.



When Item 17 on the Form OMB 83-I is checked “Yes”, the following documentation should be included in the Supporting Statement to the extent it applies to the methods proposed:

1. Describe (including numerical estimate) the potential respondent universe and any sampling or other respondent selection method to be used. Data on the number of entities (e.g., establishments, State and local government units, households, or persons) in the universe covered by the collection and in the corresponding sample are to be provided in tabular form for the universe as a whole and for each of the strata in the proposed sample. Indicate expected response rates for the collection as a whole. If the collection has been conducted previously, include the actual response rate achieved during the last collection.

Federal Emergency Management Agency (FEMA) Individual and Community Preparedness Division (ICPD) will collect preparedness information from the public via a telephone survey. This collection of information, which began in 2007, is necessary to increase the effectiveness of awareness and recruitment campaigns, messaging and public information, community outreach efforts, and strategic planning initiatives. The household telephone survey will measure public’s knowledge, attitudes, and behaviors relative to preparing for of the following hazards: Tornado, Hurricane, Flood, Earthquakes, Wildfire, Terrorism, Extreme Winter weather, Hazardous materials and Pandemic Flu.

The potential respondent pool includes the entire civilian non-institutionalized U.S. adult population residing in telephone-equipped dwellings or owning a cell phone. This population does not include adults in penal, mental, or other institutions; adults living in dormitories, barracks, or boarding houses; adults living in a dwelling without a telephone; and/or adults who do not speak English or Spanish well enough to be interviewed.

The survey will be conducted twice every year, and this approval period will cover 3 years. The total number of respondents will be 3,000 adults per administration and so it will be 6,000 per year. The total number of respondents (3,000) for any particular administration will include a national level sample of about 1,000 respondents and four separate oversamples (of size 500 each) for four hazard specific areas. The hazard specific areas will be defined in terms of complete counties (or fipscodes). The selection of the hazard profiles to be surveyed in any specific administration will vary across different administrations of the survey. Hazards selected for the 2014 surveys are Flood, Hurricane, Tornado, Wildfire, Earthquake and Winter Storm and Extreme Cold. In each administration, four hazard areas will be surveyed while each of these hazards will be covered in one or both administrations.

The telephone samples will include both landline and cell phones to minimize bias in survey based estimates. In each administration, seven independent telephone samples will be chosen to generate the targeted number of surveys for the national and for each of the six hazard areas. For each sample, the selection of landline numbers will be based on list-assisted RDD (Random Digit Dialing) sampling of telephone numbers for the corresponding geographic area. The cell phone sample will be a simple random sample drawn from all dedicated exchanges for cell phones for the targeted areas. For respondents reached on a landline phone, one respondent will be chosen at random from all eligible adults within a sampled household. For respondents reached on a cell phone, the person answering the call will be selected as the respondent if he or she is otherwise found eligible.

The goal will be to maximize the response rate by taking necessary steps as outlined later in this document on “Methods to maximize response rates.” The calculation of response rates will be based on AAPOR RR3 definition.

2. Describe the procedures for the collection of information including:

-Statistical methodology for stratification and sample selection:

In each administration of the telephone survey, about 1,000 interviews nationwide and 500 interviews for each of the four selected hazard areas will be completed. Samples will be independently drawn for the national and for each of the hazard profile areas defined based on complete counties. In order to minimize bias, both landline and cell phones will be included in the all telephone samples.

For the National sample, the target population will be geographically stratified into four census regions (Northeast, Midwest, South, and West) and sampling will be done independently within each stratum (region). The definition of the four census regions in terms of states is given below.

Northeast: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont, New Jersey, New York, and Pennsylvania.

Midwest: Illinois, Indiana, Michigan, Ohio, Wisconsin, Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota.

South: Delaware, District of Columbia, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia, Alabama, Kentucky, Mississippi, Tennessee, Arkansas, Louisiana, Oklahoma, and Texas.

West: Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming, Alaska, California, Hawaii, Oregon, and Washington.

The sample allocation across the four census regions (Northeast, Midwest, South and West) will be based on proportional allocation i.e. the sample size allocated to any particular region will be roughly in proportion to the size of that region in terms of the estimated number of adults. Using proportional sample allocation, the targeted number of surveys to be completed in each region is expected to be close to those proportions. Within each region, roughly 50 percent of the interviews will be done from the cell phone sample while the rest (50%) will be done from the landline sample. It may be noted that the actual number of completed surveys for each census region (and by landline and cell phone strata within each region) will depend on observed response rates and so they may not exactly match the corresponding targets. However, the goal will be to meet those targets to the extent possible by constant monitoring of the response rates and by optimally releasing the sample in a sequential manner throughout the data collection period.

Within each region, the sampling of landline and cell phones will be carried out separately from the respective sampling frames. The landline RDD (Random Digit Dialing) sample of telephone numbers will be selected (without replacement) following the list-assisted telephone sampling method. For within-household sampling, the contractor will use the “most recent birthday” method to randomly select one eligible person from all eligible adults in each sampled household. Following the “most recent birthday” method, the interviewer asks to speak with the eligible person in the household who most recently had a birthday. This is much less intrusive than the purely random selection method or grid selection that requires enumeration of all household members to make a respondent selection.

The cell phone sample of telephone numbers will be drawn (without replacement) separately from the corresponding dedicated (to cell phones) telephone exchanges. For respondents reached on cell phones, there will not be any additional stage of sampling (as there is with the within-household sampling for landline sample). The person answering the call will be selected for the survey if he/she is found otherwise eligible. For both landline and cell phones, the geographic location of the respondent will be determined based on respondent’s self-reported response to a question on location (like “what is your zip-code?” and what is your county?). Data will be collected from all respondents regardless of whether they have access to a landline, a cell phone or both. Respondents will be asked a series of questions to gather information on his/her use of telephone (cell only, landline only, or dual-user cell mostly and other dual users).

As mentioned above, the cell phone numbers will be sampled from the telephone exchanges dedicated to cell phones while the landline numbers will be sampled from all area code exchange combinations for the corresponding geographic area. It may be noted that due to continuous porting of numbers from landline to cell and cell to landline, some numbers from landline exchanges may turn out to be cell phones and conversely, some numbers sampled from the cell phone exchanges may actually be landline numbers. However, such numbers will be relatively rare and the vast majority of landline and cell phone numbers will be from the corresponding frames. The survey will also find out from the respondents if the number called is actually a landline or a cell phone number. It is also possible that an individual respondent may have a telephone number in one region while he/she may actually be living in another region. The physical location of respondents will therefore be based on their self-reported location information (for example, based on their self-reported zip-code or county information) and will not be determined based on their telephone exchange.

The hazard area samples, as mentioned before, will be selected independently following procedures similar to those used for the national sample described above. The target population for each hazard area survey will consist of groups of counties identified based on specific requirements for each hazard. The counties (fips) identified for each hazard are presented in Table 1 through Table 6 below.

Table 1: Counties identified for Hazard - Flood

County	State	FIPS
Jefferson	AL	1073
Coffee	AL	1031
Columbia	AR	5027
Napa	CA	6055
Marin	CA	6041
Sonoma	CA	6097
Monterey	CA	6053
San Luis Obisp	CA	6079
Larimer	CO	8069
Jefferson	CO	8059
Miami-Dade	FL	12086
Broward	FL	12011
*Dade	FL	12025
Linn	IA	19113
Wayne	IA	19185
Johnson	IA	19103
Marshall	IA	19127
Cook	IL	17031
Bartholomew	IN	18005
Morgan	IN	18109
Polk	MN	27119
Roseau	MN	27135
Franklin	MO	29071
Grand Forks	ND	38035
Cass	ND	38017
Somerset	NJ	34035
Sussex	NJ	34037
Washoe	NV	32031
Delaware	NY	36025
Broome	NY	36007
Sullivan	NY	36105
Tioga	NY	36107
Lake	OH	39085
Summit	OH	39153
Luzerne	PA	42079
Susquehanna	PA	42115
Jefferson	PA	42065
Davidson	TN	47037
Gibson	TN	47053
El Paso	TX	48141
Burnet	TX	48053
Washington	UT	49053
Green	WI	55045
Jefferson	WI	55055

Table 2: Counties identified for Hazard - Hurricane

County	State	FIPS
Mobile	AL	1097
Baldwin	AL	1003
Choctaw	AL	1023
Washington	AL	1129
Butler	AL	1013
Clarke	AL	1025
Conecuh	AL	1035
Covington	AL	1039
Crenshaw	AL	1041
Escambia	AL	1053
Monroe	AL	1099
Wilcox	AL	1131
Broward	FL	12011
Collier	FL	12021
Miami-Dade	FL	12086
Palm Beach	FL	12099
Escambia	FL	12033
Okaloosa	FL	12091
Santa Rosa	FL	12113
Charlotte	FL	12015
DeSoto	FL	12027
Lee	FL	12071
Manatee	FL	12081
Sarasota	FL	12115
Brevard	FL	12009
Indian River	FL	12061
Marion	FL	12083
St. Lucie	FL	12111
Volusia	FL	12127
Hardee	FL	12049
Highlands	FL	12055
Polk	FL	12105
*Dade	FL	12025
Monroe	FL	12087
Glades	FL	12043
Hendry	FL	12051
Kauai	HI	15007
Orleans	LA	22071
St. Bernard	LA	22087
Jefferson	LA	22051
St. Tammany	LA	22103
East Baton Rou	LA	22033
Plaquemines	LA	22075
Lafourche	LA	22057
Tangipahoa	LA	22105
Washington	LA	22117
St. John the B	LA	22095
Pointe Coupee	LA	22077
West Feliciana	LA	22125
East Feliciana	LA	22037
St. Helena	LA	22091
Iberville	LA	22047
West Baton Rou	LA	22121
Ascension	LA	22005
Livingston	LA	22063
Assumption	LA	22007
St. James	LA	22093
St. Charles	LA	22089
Terrebonne	LA	22109
Cameron	LA	22023
Vernon	LA	22115
Rapides	LA	22079
Avoyelles	LA	22009
Beauregard	LA	22011
Allen	LA	22003
Evangeline	LA	22039
St. Landry	LA	22097
Calcasieu	LA	22019
Jefferson Davi	LA	22053
Acadia	LA	22001
Lafayette	LA	22055
St. Martin	LA	22099
Vermilion	LA	22113
Iberia	LA	22045
St. Mary	LA	22101
Harrison	MS	28047
Hancock	MS	28045
Jackson	MS	28059
Pearl River	MS	28109
Wilkinson	MS	28157
Amite	MS	28005
Pike	MS	28113
Walthall	MS	28147
Jones	MS	28067
Leake	MS	28079
Warren	MS	28149
Hinds	MS	28049
Lauderdale	MS	28075
Simpson	MS	28127
Lamar	MS	28073
Forrest	MS	28035
Bolivar	MS	28011
Sunflower	MS	28133
Leflore	MS	28083
Grenada	MS	28043
Carroll	MS	28015
Montgomery	MS	28097
Webster	MS	28155
Clay	MS	28025
Lowndes	MS	28087
Choctaw	MS	28019
Oktibbeha	MS	28105
Washington	MS	28151
Humphreys	MS	28053
Holmes	MS	28051
Attala	MS	28007
Winston	MS	28159
Noxubee	MS	28103
Issaquena	MS	28055
Sharkey	MS	28125
Yazoo	MS	28163
Madison	MS	28089
Neshoba	MS	28099
Kemper	MS	28069
Rankin	MS	28121
Scott	MS	28123
Newton	MS	28101
Claiborne	MS	28021
Copiah	MS	28029
Smith	MS	28129
Jasper	MS	28061
Clarke	MS	28023
Jefferson	MS	28063
Adams	MS	28001
Franklin	MS	28037
Lincoln	MS	28085
Lawrence	MS	28077
Jefferson Davi	MS	28065
Covington	MS	28031
Marion	MS	28091
Onslow	NC	37133
Pender	NC	37141
New Hanover	NC	37129
Yadkin	NC	37197
Wilson	NC	37195
Charleston	SC	45019
Georgetown	SC	45043
Horry	SC	45051
Dorchester	SC	45035
York	SC	45091
Sumter	SC	45085
Berkeley	SC	45015
Williamsburg	SC	45089
Jasper	TX	48241
Tyler	TX	48457
Newton	TX	48351
Hardin	TX	48199
Jefferson	TX	48245
Orange	TX	48361
Brazoria	TX	48039
Chambers	TX	48071
Fort Bend	TX	48157
Galveston	TX	48167
Grimes	TX	48185
Harris	TX	48201
Houston	TX	48225
Liberty	TX	48291
Montgomery	TX	48339
Polk	TX	48373
San Jacinto	TX	48407
Trinity	TX	48455
Walker	TX	48471
Waller	TX	48473
Washington	TX	48477
Matagorda	TX	48321

Table 3: Counties identified for Hazard - Tornado

County	State	FIPS
Tuscaloosa	AL	1125
Limestone	AL	1083
Jefferson	AL	1073
Coffee	AL	1031
St. Clair	AL	1115
Marion	AL	1093
Calhoun	AL	1015
Walker	AL	1127
Tallapoosa	AL	1123
Van Buren	AR	5141
Sebastian	AR	5131
Crawford	AR	5033
Polk	AR	5113
Pulaski	AR	5119
*Dade	FL	12025
Grant	KS	20067
Kiowa	KS	20097
Sedgwick	KS	20173
Bullitt	KY	21029
Hampden	MA	25013
Charles	MD	24017
Prince George'	MD	24033
Genesee	MI	26049
Branch	MI	26023
Hillsdale	MI	26059
Lenawee	MI	26091
Monroe	MI	26115
Hennepin	MN	27053
Nicollet	MN	27103
Jasper	MO	29097
Lawrence	MS	28077
Yazoo	MS	28163
Wake	NC	37183
Cumberland	NC	37051
Lancaster	NE	31109
Wood	OH	39173
Oklahoma	OK	40109
Cleveland	OK	40027
Washita	OK	40149
Rutherford	TN	47149
Davidson	TN	47037
McLennan	TX	48309
Wichita	TX	48485
Bowie	TX	48037
Salt Lake	UT	49035

Table 4: Counties identified for Hazard - Earthquake

FIPS	County	State
15001	Hawaii County	Hawaii
02013	Aleutians East Borough	Alaska
02016	Aleutians West Census Area	Alaska
02020	Anchorage Municipality	Alaska
02060	Bristol Bay Borough	Alaska
02068	Denali Borough	Alaska
02090	Fairbanks North Star Borough	Alaska
02100	Haines Borough	Alaska
02105	Hoonah-Angoon Census Area	Alaska
02122	Kenai Peninsula Borough	Alaska
02150	Kodiak Island Borough	Alaska
02164	Lake and Peninsula Borough	Alaska
02170	Matanuska-Susitna Borough	Alaska
02220	Sitka City and Borough	Alaska
02261	Valdez-Cordova Census Area	Alaska
02282	Yakutat City and Borough	Alaska
05021	Clay County	Arkansas
05031	Craighead County	Arkansas
05035	Crittenden County	Arkansas
05055	Greene County	Arkansas
05093	Mississippi County	Arkansas
05111	Poinsett County	Arkansas
06001	Alameda County	California
06003	Alpine County	California
06011	Colusa County	California
06013	Contra Costa County	California
06015	Del Norte County	California
06019	Fresno County	California
06021	Glenn County	California
06023	Humboldt County	California
06025	Imperial County	California
06027	Inyo County	California
06029	Kern County	California
06031	Kings County	California
06033	Lake County	California
06037	Los Angeles County	California
06041	Marin County	California
06045	Mendocino County	California
06047	Merced County	California
06051	Mono County	California
06053	Monterey County	California
06055	Napa County	California
06059	Orange County	California
06063	Plumas County	California
06065	Riverside County	California
06069	San Benito County	California
06071	San Bernardino County	California
06073	San Diego County	California
06075	San Francisco County	California
06079	San Luis Obispo County	California
06081	San Mateo County	California
06083	Santa Barbara County	California
06085	Santa Clara County	California
06087	Santa Cruz County	California
06091	Sierra County	California
06095	Solano County	California
06097	Sonoma County	California
06099	Stanislaus County	California
06105	Trinity County	California
06111	Ventura County	California
06113	Yolo County	California
15005	Kalawao County	Hawaii
15009	Maui County	Hawaii
17003	Alexander County	Illinois
17087	Johnson County	Illinois
17127	Massac County	Illinois
17153	Pulaski County	Illinois
17181	Union County	Illinois
21007	Ballard County	Kentucky
21039	Carlisle County	Kentucky
21075	Fulton County	Kentucky
21083	Graves County	Kentucky
21105	Hickman County	Kentucky
21145	McCracken County	Kentucky
29023	Butler County	Missouri
29031	Cape Girardeau County	Missouri
29069	Dunklin County	Missouri
29133	Mississippi County	Missouri
29143	New Madrid County	Missouri
29155	Pemiscot County	Missouri
29201	Scott County	Missouri
29207	Stoddard County	Missouri
32005	Douglas County	Nevada
32009	Esmeralda County	Nevada
32019	Lyon County	Nevada
32021	Mineral County	Nevada
32029	Storey County	Nevada
32510	Carson City	Nevada
41011	Coos County	Oregon
41015	Curry County	Oregon
41041	Lincoln County	Oregon
45015	Berkeley County	South Carolina
45035	Dorchester County	South Carolina
47033	Crockett County	Tennessee
47045	Dyer County	Tennessee
47053	Gibson County	Tennessee
47075	Haywood County	Tennessee
47095	Lake County	Tennessee
47097	Lauderdale County	Tennessee
47131	Obion County	Tennessee
47167	Tipton County	Tennessee
47183	Weakley County	Tennessee
53009	Clallam County	Washington
53027	Grays Harbor County	Washington
53029	Island County	Washington
53031	Jefferson County	Washington
53033	King County	Washington
53035	Kitsap County	Washington
53041	Lewis County	Washington
53045	Mason County	Washington
53049	Pacific County	Washington
53053	Pierce County	Washington
53055	San Juan County	Washington
53061	Snohomish County	Washington
53067	Thurston County	Washington
56039	Teton County	Wyoming

Table 5: Counties identified for Hazard - Wildfire

FIPS	County	State
01003	Baldwin County	Alabama
01097	Mobile County	Alabama
04013	Maricopa County	Arizona
04019	Pima County	Arizona
06007	Butte County	California
06019	Fresno County	California
06029	Kern County	California
06037	Los Angeles County	California
06053	Monterey County	California
06059	Orange County	California
06061	Placer County	California
06065	Riverside County	California
06071	San Bernardino County	California
06073	San Diego County	California
06083	Santa Barbara County	California
06085	Santa Clara County	California
06113	Yolo County	California
08013	Boulder County	Colorado
08041	El Paso County	Colorado
08069	Larimer County	Colorado
12001	Alachua County	Florida
12005	Bay County	Florida
12009	Brevard County	Florida
12011	Broward County	Florida
12015	Charlotte County	Florida
12017	Citrus County	Florida
12019	Clay County	Florida
12021	Collier County	Florida
12031	Duval County	Florida
12035	Flagler County	Florida
12053	Hernando County	Florida
12057	Hillsborough County	Florida
12061	Indian River County	Florida
12069	Lake County	Florida
12071	Lee County	Florida
12083	Marion County	Florida
12085	Martin County	Florida
12086	Miami-Dade County	Florida
12091	Okaloosa County	Florida
12095	Orange County	Florida
12097	Osceola County	Florida
12099	Palm Beach County	Florida
12101	Pasco County	Florida
12105	Polk County	Florida
12107	Putnam County	Florida
12109	St. Johns County	Florida
12113	Santa Rosa County	Florida
12115	Sarasota County	Florida
12127	Volusia County	Florida
13187	Lumpkin County	Georgia
15009	Maui County	Hawaii
16001	Ada County	Idaho
20103	Leavenworth County	Kansas
20161	Riley County	Kansas
22103	St. Tammany Parish	Louisiana
28047	Harrison County	Mississippi
28059	Jackson County	Mississippi
32003	Clark County	Nevada
34005	Burlington County	New Jersey
34029	Ocean County	New Jersey
35001	Bernalillo County	New Mexico
45015	Berkeley County	South Carolina
48027	Bell County	Texas
48135	Ector County	Texas
48139	Ellis County	Texas
48181	Grayson County	Texas
48187	Guadalupe County	Texas
48303	Lubbock County	Texas
48329	Midland County	Texas
48355	Nueces County	Texas
48367	Parker County	Texas
48375	Potter County	Texas
48381	Randall County	Texas
48439	Tarrant County	Texas
48441	Taylor County	Texas
48485	Wichita County	Texas
49011	Davis County	Utah
49049	Utah County	Utah
49057	Weber County	Utah
53005	Benton County	Washington
53063	Spokane County	Washington
54039	Kanawha County	West Virginia
54081	Raleigh County	West Virginia

Table 6: Counties identified for Hazard – Winter Storm and Extreme Cold

FIPS	County	State
02180	Nome Census Area	Alaska
02185	North Slope Borough	Alaska
02188	Northwest Arctic Borough	Alaska
02261	Valdez-Cordova Census Area	Alaska
02290	Yukon-Koyukuk Census Area	Alaska
04005	Coconino County	Arizona
04017	Navajo County	Arizona
04021	Pinal County	Arizona
06071	San Bernardino County	California
06093	Siskiyou County	California
06103	Tehama County	California
06107	Tulare County	California
06109	Tuolumne County	California
08035	Douglas County	Colorado
08059	Jefferson County	Colorado
08077	Mesa County	Colorado
08085	Montrose County	Colorado
08097	Pitkin County	Colorado
08103	Rio Blanco County	Colorado
08107	Routt County	Colorado
08113	San Miguel County	Colorado
08123	Weld County	Colorado
09001	Fairfield County	Connecticut
09005	Litchfield County	Connecticut
09009	New Haven County	Connecticut
10001	Kent County	Delaware
10003	New Castle County	Delaware
10005	Sussex County	Delaware
13241	Rabun County	Georgia
16013	Blaine County	Idaho
16029	Caribou County	Idaho
16037	Custer County	Idaho
16055	Kootenai County	Idaho
16059	Lemhi County	Idaho
16077	Power County	Idaho
16081	Teton County	Idaho
16087	Washington County	Idaho
17011	Bureau County	Illinois
17067	Hancock County	Illinois
17071	Henderson County	Illinois
17109	McDonough County	Illinois
17131	Mercer County	Illinois
17155	Putnam County	Illinois
17187	Warren County	Illinois
19011	Benton County	Iowa
19019	Buchanan County	Iowa
19031	Cedar County	Iowa
19045	Clinton County	Iowa
19055	Delaware County	Iowa
19057	Des Moines County	Iowa
19061	Dubuque County	Iowa
19087	Henry County	Iowa
19095	Iowa County	Iowa
19097	Jackson County	Iowa
19101	Jefferson County	Iowa
19103	Johnson County	Iowa
19105	Jones County	Iowa
19107	Keokuk County	Iowa
19111	Lee County	Iowa
19113	Linn County	Iowa
19115	Louisa County	Iowa
19119	Lyon County	Iowa
19139	Muscatine County	Iowa
19143	Osceola County	Iowa
19163	Scott County	Iowa
19177	Van Buren County	Iowa
19183	Washington County	Iowa
23001	Androscoggin County	Maine
23003	Aroostook County	Maine
23005	Cumberland County	Maine
23007	Franklin County	Maine
23009	Hancock County	Maine
23011	Kennebec County	Maine
23013	Knox County	Maine
23015	Lincoln County	Maine
23017	Oxford County	Maine
23019	Penobscot County	Maine
23021	Piscataquis County	Maine
23023	Sagadahoc County	Maine
23025	Somerset County	Maine
23027	Waldo County	Maine
23029	Washington County	Maine
23031	York County	Maine
24001	Allegany County	Maryland
24003	Anne Arundel County	Maryland
24005	Baltimore County	Maryland
24013	Carroll County	Maryland
24021	Frederick County	Maryland
24023	Garrett County	Maryland
24025	Harford County	Maryland
24027	Howard County	Maryland
24031	Montgomery County	Maryland
24033	Prince George's County	Maryland
24043	Washington County	Maryland
24510	Baltimore city	Maryland
25003	Berkshire County	Massachusetts
26009	Antrim County	Michigan
26013	Baraga County	Michigan
26029	Charlevoix County	Michigan
26033	Chippewa County	Michigan
26041	Delta County	Michigan
26047	Emmet County	Michigan
26053	Gogebic County	Michigan
26055	Grand Traverse County	Michigan
26061	Houghton County	Michigan
26079	Kalkaska County	Michigan
26083	Keweenaw County	Michigan
26089	Leelanau County	Michigan
26103	Marquette County	Michigan
26131	Ontonagon County	Michigan
26137	Otsego County	Michigan
26153	Schoolcraft County	Michigan
27001	Aitkin County	Minnesota
27005	Becker County	Minnesota
27007	Beltrami County	Minnesota
27021	Cass County	Minnesota
27027	Clay County	Minnesota
27029	Clearwater County	Minnesota
27033	Cottonwood County	Minnesota
27061	Itasca County	Minnesota
27063	Jackson County	Minnesota
27081	Lincoln County	Minnesota
27083	Lyon County	Minnesota
27089	Marshall County	Minnesota
27101	Murray County	Minnesota
27105	Nobles County	Minnesota
27107	Norman County	Minnesota
27111	Otter Tail County	Minnesota
27117	Pipestone County	Minnesota
27119	Polk County	Minnesota
27133	Rock County	Minnesota
27137	St. Louis County	Minnesota
27167	Wilkin County	Minnesota
29045	Clark County	Missouri
29199	Scotland County	Missouri
30003	Big Horn County	Montana
30047	Lake County	Montana
30071	Phillips County	Montana
30073	Pondera County	Montana
30077	Powell County	Montana
30081	Ravalli County	Montana
30085	Roosevelt County	Montana
30089	Sanders County	Montana
30093	Silver Bow County	Montana
30099	Teton County	Montana
30105	Valley County	Montana
31031	Cherry County	Nebraska
32031	Washoe County	Nevada
33001	Belknap County	New Hampshire
33003	Carroll County	New Hampshire
33007	Coos County	New Hampshire
33009	Grafton County	New Hampshire
33013	Merrimack County	New Hampshire
33015	Rockingham County	New Hampshire
33017	Strafford County	New Hampshire
33019	Sullivan County	New Hampshire
34001	Atlantic County	New Jersey
34005	Burlington County	New Jersey
34007	Camden County	New Jersey
34009	Cape May County	New Jersey
34011	Cumberland County	New Jersey
34015	Gloucester County	New Jersey
34019	Hunterdon County	New Jersey
34021	Mercer County	New Jersey
34023	Middlesex County	New Jersey
34025	Monmouth County	New Jersey
34027	Morris County	New Jersey
34029	Ocean County	New Jersey
34031	Passaic County	New Jersey
34033	Salem County	New Jersey
34035	Somerset County	New Jersey
34037	Sussex County	New Jersey
34039	Union County	New Jersey
34041	Warren County	New Jersey
35055	Taos County	New Mexico
36001	Albany County	New York
36011	Cayuga County	New York
36019	Clinton County	New York
36025	Delaware County	New York
36031	Essex County	New York
36033	Franklin County	New York
36035	Fulton County	New York
36039	Greene County	New York
36041	Hamilton County	New York
36043	Herkimer County	New York
36045	Jefferson County	New York
36047	Kings County	New York
36049	Lewis County	New York
36053	Madison County	New York
36057	Montgomery County	New York
36065	Oneida County	New York
36067	Onondaga County	New York
36075	Oswego County	New York
36083	Rensselaer County	New York
36089	St. Lawrence County	New York
36091	Saratoga County	New York
36093	Schenectady County	New York
36095	Schoharie County	New York
36103	Suffolk County	New York
36111	Ulster County	New York
36113	Warren County	New York
36115	Washington County	New York
36119	Westchester County	New York
37011	Avery County	North Carolina
37021	Buncombe County	North Carolina
37075	Graham County	North Carolina
37087	Haywood County	North Carolina
37089	Henderson County	North Carolina
37099	Jackson County	North Carolina
37113	Macon County	North Carolina
37115	Madison County	North Carolina
37121	Mitchell County	North Carolina
37173	Swain County	North Carolina
37175	Transylvania County	North Carolina
37199	Yancey County	North Carolina
38003	Barnes County	North Dakota
38005	Benson County	North Dakota
38017	Cass County	North Dakota
38019	Cavalier County	North Dakota
38027	Eddy County	North Dakota
38035	Grand Forks County	North Dakota
38039	Griggs County	North Dakota
38063	Nelson County	North Dakota
38067	Pembina County	North Dakota
38071	Ramsey County	North Dakota
38073	Ransom County	North Dakota
38077	Richland County	North Dakota
38081	Sargent County	North Dakota
38091	Steele County	North Dakota
38097	Traill County	North Dakota
38099	Walsh County	North Dakota
39007	Ashtabula County	Ohio
39035	Cuyahoga County	Ohio
39055	Geauga County	Ohio
39085	Lake County	Ohio
41029	Jackson County	Oregon
41035	Klamath County	Oregon
41037	Lake County	Oregon
41059	Umatilla County	Oregon
41065	Wasco County	Oregon
42011	Berks County	Pennsylvania
42025	Carbon County	Pennsylvania
42027	Centre County	Pennsylvania
42035	Clinton County	Pennsylvania
42039	Crawford County	Pennsylvania
42045	Delaware County	Pennsylvania
42049	Erie County	Pennsylvania
42071	Lancaster County	Pennsylvania
42077	Lehigh County	Pennsylvania
42081	Lycoming County	Pennsylvania
42089	Monroe County	Pennsylvania
42095	Northampton County	Pennsylvania
42101	Philadelphia County	Pennsylvania
42111	Somerset County	Pennsylvania
42125	Washington County	Pennsylvania
42127	Wayne County	Pennsylvania
45045	Greenville County	South Carolina
45073	Oconee County	South Carolina
45077	Pickens County	South Carolina
46011	Brookings County	South Dakota
46051	Grant County	South Dakota
46067	Hutchinson County	South Dakota
46077	Kingsbury County	South Dakota
46083	Lincoln County	South Dakota
46087	McCook County	South Dakota
46099	Minnehaha County	South Dakota
46101	Moody County	South Dakota
46109	Roberts County	South Dakota
46125	Turner County	South Dakota
49047	Uintah County	Utah
50001	Addison County	Vermont
50003	Bennington County	Vermont
50005	Caledonia County	Vermont
50007	Chittenden County	Vermont
50011	Franklin County	Vermont
50013	Grand Isle County	Vermont
50015	Lamoille County	Vermont
50017	Orange County	Vermont
50019	Orleans County	Vermont
50021	Rutland County	Vermont
50023	Washington County	Vermont
50025	Windham County	Vermont
50027	Windsor County	Vermont
51017	Bath County	Virginia
51043	Clarke County	Virginia
51059	Fairfax County	Virginia
51069	Frederick County	Virginia
51079	Greene County	Virginia
51107	Loudoun County	Virginia
51113	Madison County	Virginia
51139	Page County	Virginia
51153	Prince William County	Virginia
51157	Rappahannock County	Virginia
51165	Rockingham County	Virginia
51171	Shenandoah County	Virginia
51187	Warren County	Virginia
51540	Charlottesville city	Virginia
51820	Waynesboro city	Virginia
53025	Grant County	Washington
53047	Okanogan County	Washington
53071	Walla Walla County	Washington
53077	Yakima County	Washington
54003	Berkeley County	West Virginia
54023	Grant County	West Virginia
54027	Hampshire County	West Virginia
54031	Hardy County	West Virginia
54037	Jefferson County	West Virginia
54057	Mineral County	West Virginia
54065	Morgan County	West Virginia
54067	Nicholas County	West Virginia
54075	Pocahontas County	West Virginia
54083	Randolph County	West Virginia
54093	Tucker County	West Virginia
54101	Webster County	West Virginia
55003	Ashland County	Wisconsin
55007	Bayfield County	Wisconsin
55021	Columbia County	Wisconsin
55025	Dane County	Wisconsin
55027	Dodge County	Wisconsin
55031	Douglas County	Wisconsin
55039	Fond du Lac County	Wisconsin
55045	Green County	Wisconsin
55047	Green Lake County	Wisconsin
55049	Iowa County	Wisconsin
55051	Iron County	Wisconsin
55055	Jefferson County	Wisconsin
55059	Kenosha County	Wisconsin
55065	Lafayette County	Wisconsin
55075	Marinette County	Wisconsin
55077	Marquette County	Wisconsin
55079	Milwaukee County	Wisconsin
55083	Oconto County	Wisconsin
55089	Ozaukee County	Wisconsin
55101	Racine County	Wisconsin
55105	Rock County	Wisconsin
55111	Sauk County	Wisconsin
55117	Sheboygan County	Wisconsin
55125	Vilas County	Wisconsin
55127	Walworth County	Wisconsin
55131	Washington County	Wisconsin
55133	Waukesha County	Wisconsin
56007	Carbon County	Wyoming
56021	Laramie County	Wyoming
56031	Platte County	Wyoming
56045	Weston County	Wyoming

Hazard samples will also be stratified by census region and so each of the four strata for any hazard will consist of counties identified for that hazard in that particular census region. The sample allocation across strata will be proportional to the size of the stratum derived as the estimated total adult population of the counties selected for that hazard in that particular stratum. As proposed for national sample, both landline and cell phone numbers will be included in hazard area samples and the total number of completed surveys will be roughly split equally between the landline and cell phone samples. For within household selection of respondent, the most recent birthday method will be used. For cell phone sample, the person answering the phone will be selected for interview as long as he/she is otherwise found eligible for the survey.

-Estimation procedure:

Each of the five samples (the national and the four hazard samples) will be weighted independently. Once those weights are finalized, the sample data consisting of the national sample (1,000 completed surveys) and four hazard area level surveys (500 completes each) will also be combined (composite weighting) and then weighted (post-stratified) to generate estimates for unknown populations parameters at various levels (national, regional or for other subgroups of interest).

For the National sample, weighting will be carried out within each stratum (region) to adjust for (i) unequal probability of selection in the sample and (ii) nonresponse. Once the sampling weights are generated, weighted estimates can be produced for different unknown population parameters (means, proportions etc.) for the target population and also for population subgroups.

The weighting for this study will be done following the basic approach described in Kennedy, Courtney (2007): Evaluating the Effects of Screening for Telephone Service in Dual Frame RDD Surveys, Public Opinion Quarterly, Special Issue 2007, Volume 71 / Number 5: 750-771. In studies dealing with both landline and cell phone samples, one approach is to screen for “cell only” respondents by asking respondents reached on the cell phones whether or not they also have access to a landline and then interviewing all eligible persons from the landline sample whereas interviewing only “cell only” persons from the cell phone sample. The samples from such designs are stratified, with each frame constituting its own stratum. In this study, however, a dual-frame design is proposed where dual users (those with access to both landline and cell phones) can be interviewed in either sample. This will result in two estimates for the dual users based on the two samples (landline and cell). The two estimates for the dual users will then be combined and added to the estimates based on landline-only and cell-only population to generate the estimate for the whole population.

Composite pre-weight— For the purpose of sample weighting of the national sample, the four census regions will be used as weighting adjustment classes. Following Kennedy, Courtney (2007), the composite pre-weight will be generated within each weighting class. The weight assigned to the i^th respondent in the h^th weighting class (h=1, 2, 3, 4) will be calculated as follows:

W_{(landline,hi)} = (N_hl/n_hl)(1/RR_hl)(n_cwa/n_ll)(λ^IDual) for landline sample cases (1)

W_(Cell,hi) = (N_hc/n_hc)(1/RR_hc)(1 – λ)^IDual for cellular sample cases (2)

where

N_hl: size of the landline RDD frame in weighting class h

n_hl: sample size from landline frame in weighting class h

RR_hl: response rate in weighting class h associated with landline frame

n_cwa: number of adults in the sampled household

n_ll: number of residential telephone landlines in sampled household

I^Dual: indicator variable with value 1 if the respondent is a dual user and value 0 otherwise

N_hc: size of the Cell RDD frame in weighting class h

n_hc: sample size from Cell frame in weighting class h

RR_hc: response rate in weighting class h associated with Cell frame

‘λ’ is the “mixing parameter” with a value between 0 and 1. If roughly the same number of dual users is interviewed from both samples (landline and cell) within each census region, then 0.5 will serve as a reasonable approximation to the optimal value for λ. This adjustment of the weights for the dual users based on the value of the mixing parameter ‘λ’ will be carried out within each census region. For this study, the plan is to use a value of ‘λ’ equal to the ratio of the number of dual users interviewed from the landline frame and the total number dual users interviewed from both frames within each region.

It may be noted that equation (2) above for cellular sample cases doesn’t include weighting adjustments for (i) number of adults and (ii) telephone lines. For cellular sample cases, as mentioned before, there is no within-household random selection. The random selection can be made from all persons sharing a cell phone but the percentage of those sharing a cell phone is rather small and it will also require additional questionnaire time to try to capture such information. The person answering the call will be selected as the respondent if he or she is otherwise found eligible and hence no adjustment based on “number of eligible adults in the household” will be necessary. The information on the number of cell phones owned by a respondent could also be asked to make adjustments based on number of cell phones. However, the percentage of respondents owning more than one cell phone is expected to be too low to have any significant impact on sampling weights. For landline sample cases, the values for (i) number of adults (n_cwa) and (ii) number of residential telephone lines (n_ll) may have to be truncated to avoid extreme weights. The cutoff value for truncation will be determined after examining the distribution of these variables in the sample. It is anticipated that these values may be capped at 2 or 3.

Response rate: The response rates (RR_hl and RR_hc mentioned above in equations (1) and (2)), will be measured using the AAPOR (3) definition of response rate within each weighting class and will be calculated as follows:

RR = (number of completed interviews) / (estimated number of eligibles)

= (number of completed interviews) / (known eligibles + presumed eligibles)

It will be straightforward to find the number of completed interviews and the number of known eligibles. The estimation of the number of “presumed eligibles” will be done in the following way: In terms of eligibility, all sample records (irrespective of whether any contact/interview was obtained) may be divided into three groups: i) known eligibles (i.e., cases where the respondents, based on their responses to screening questions, were found eligible for the survey), ii) known ineligibles (i.e., cases where the respondents, based on their responses to screening questions, were found ineligible for the survey), and iii) eligibility unknown (i.e., cases where all screening questions could not be asked, as there was never any human contact or cases where respondents answered the screening questions with a “Don’t Know” or “Refused” response and hence the eligibility is unknown).

Based on cases where the eligibility status is known (known eligible or known ineligible), the eligibility rate (ER) is computed as:

ER = (known eligibles) / (known eligibles + known ineligibles)

Thus, the ER is the proportion of eligibles found in the group of respondents for whom the eligibility could be established.

At the next step, the number of presumed eligibles is calculated as:

Presumed eligibles = ER × number of respondents in the eligibility unknown group

The basic assumption is that the eligibility rate among cases where eligibility could not be established is the same as the eligibility rate among cases where eligibility status was known. The response rate formula presented above is based on standard guidelines on definitions and calculations of Response Rates provided by AAPOR (American Association for Public Opinion Research).

Post-stratification weight— Once the landline and cell samples are combined using the composite weight (equations (1) and (2) above), a post-stratification weighting step will be carried out, following Kennedy (2007), to simultaneously rake the combined sample to (i) known characteristics of the target population (adults 18 years of age or older) and (ii) an estimated parameter for relative telephone usage (landline-only, cell only, cell mostly, other dual users). The demographic variables to be used for weighting will include Age, gender, Race, Ethnicity (Hispanic/Non-Hispanic), and Education. The target numbers for post-stratification weighting will be obtained from the latest available Current Population Survey (CPS) data. The collapsing of categories for post-stratification weighting may become necessary where the sample sizes are going to be relatively small.

The target numbers for the relative telephone usage parameter will be based on the latest estimates from NHIS (National Health Interview Survey). For the purpose of identifying the “cell mostly” respondents among the group of dual users, a question similar to the following question will be included in the survey.

Question: Of all the telephone calls your household receives (read 1-3)?

1 All or almost all calls are received on cell phones

2 Some are received on cell phones and some on regular phones, OR

3 Very few or none are received on cell phones

4 (DK)

5 (Refused)

Respondents choosing response category 1 (all or almost all calls are received on cell phones) will be identified as “cell mostly” respondents.

After post-stratification weighting, the distribution of the final weights will be examined and trimming of extreme weights, if any, will be carried out if necessary to minimize the effect of large weights on variance of estimates.

Each of the four hazard samples will be weighted separately following procedures similar to those described above for the national sample. For the hazard samples, the weighting classes will be based on county (or groups of counties) depending on the definition of specific counties. Once each of the five samples (the national and the four hazard samples) are weighted separately, they will also be pulled together into one combined sample using composite weighting. The combined sample will then be post-stratified to known characteristics of the target population (i.e. the national population) for this study.

-Degree of accuracy needed for the purpose described in the justification:

We plan to complete about 3,000 completed telephone interviews per administration including about 1,000 interviews using a national level sample and around 500 interviews each for each of the hazard areas. The survey estimates of unknown population parameters (for example, population proportions) based on a sample size of 3,000 will have a precision (margin of error) of about +1.8 percentage points at 95% level of significance. This is under the assumption of no design effect and also under the most conservative assumption that the unknown population proportion is around 50%. The margin of error (MOE) for estimating the unknown population proportion ‘P’ at the 95% confidence level can be derived based on the following formula:

MOE = 1.96 * where “n” is the sample size (i.e. the number of completed surveys).

In this survey, where the total sample size (3,000) will include oversamples from four hazard areas and therefore may be subject to a relatively higher design effect. A design effect of 2, for example, will result in effective sample size of 1,500 and a margin of error around +2.5% at 95% confidence level. The sampling error associated with an estimate based on just the national sample size of 1,000 with a design effect of 1.25 will still be below ±3.5 points. For each of the hazard areas with about 500 completed interviews, an estimate for an unknown population proportion will have margin of error around ±4.4 points ignoring any design effect. With an anticipated design effect of about 1.25, the precision will be around ±4.9 percentage points. Hence, the accuracy and reliability of the information collected in this study will be adequate for its intended uses. The sampling error of estimates for this survey will be computed using special software (like SUDAAN) that calculates standard errors of estimates by taking into account the complexity, if any, in the sample design and the resulting set of unequal sample weights.

The necessary sample size for a two-sample proportion test (one-tailed test) can be derived as follows:

n = [{z_(1-α) SQRT (2p^*q^*) + z_(1-β) SQRT(p₁q₁ + p₂q₂)} /{p₂ – p₁)}] ² (3)

where

n: sample size (number of completed surveys) required per group to achieve the desired statistical power

z_(1-α), z_(1-β) are the normal abscissas that correspond to the respective probabilities

p₁, p₂ are the two proportions in the two-sample test

and p* is the simple average of p₁ and p₂ and q* = 1 – p*.

For example, the required sample size, ignoring any design effect, will be around 310 per group (top and bottom halves) with β=.2 (i.e., with 80% power), α=.05 (i.e., with 5% level of significance), and p₁=.55 and p₂=0.45. The sample size requirement is highest when p₁ and p₂ are around 50% and so, to be most conservative, those values (.55 and .45) of p₁ and p₂ were chosen. The proposed sample size will therefore meet the sample size requirements for estimation and testing statistical hypotheses not only at the national level but also for a wide variety of subgroups that may be of special interest in this study.

-Unusual problems requiring specialized sampling procedures: (e.g., special hard to reach populations, bias toward landline verses cell phone respondents, populations that need to be reached via other methods such as those who do not use telephones for religious reasons, large non-English speaking populations expected to be surveyed but only English questionnaires available, exclusion of elderly using computer response only, etc.)

Note: For surveys with particularly low response rates and a substantial suspicion of non-response bias, it may be necessary to collect an additional sub-sample of completed surveys from non-respondents in order to confirm if non-response bias is present in the sample and make adjustments if appropriate.

Unusual problems requiring specialized sampling procedures are not anticipated at this time. If response rates fall below the expected levels, additional sample will be released to generate the targeted number of surveys. However, all necessary steps to maximize response rates will be taken throughout the data collection period and hence such situations are not anticipated.

-Any use of periodic (less frequent than annual) data collection cycles to reduce burden:

During each administration of the survey, independent samples will be drawn and so the probability of selecting the same respondent in multiple administrations will be quite low.

1. Describe methods to maximize response rates and to deal with issues of non-response. The accuracy and reliability of information collected must be shown to be adequate for intended uses. For collections based on sampling, a special justification must be provided for any collection that will not yield “reliable” data that can be generalized to the universe studied.

Methods to maximize response rates— In order to maximize response rates, Gallup will use a comprehensive plan that focuses on (1) a call design that will ensure call attempts are made at different times of the day and different days of the week to maximize contact rates, (2) conducting an extensive interviewer briefing prior to the field period that educates interviewers about the content of the survey as well as how to handle reluctance and refusals, (3) having strong supervision that will ensure that high-quality data are collected throughout the field period, (4) using troubleshooting teams to attack specific data collection problems that may occur during the field period, and (5) customizing refusal aversion techniques. A 5 + 5 call design, i.e., a maximum of five calls will be made on the phone number to reach the specific person we are attempting to contact and up to another five calls will be made to complete the interview with that selected person.

Issues of Non-Response— Survey based estimates for this study will be weighted to minimize any potential bias, including any bias that may be associated with unit level nonresponse. All estimates will be weighted to reduce bias and it will be possible to calculate the sampling error associated with any subgroup estimate in order to ensure that the accuracy and reliability is adequate for intended uses of any such estimate. Based on experience from conducting similar surveys previously and given that the mode of data collection for the proposed survey is telephone, the extent of missing data at the item level is expected to be minimal. We, therefore, do not anticipate using any imputation procedure to handle item-level missing data.

Non-response bias Study and analysis— A nonresponse bias analysis will be conducted to examine the non-response pattern and identify potential sources of nonresponse bias. No additional follow-up data collection for the non-respondents is planned for this study. Hence the proposed non-response analysis will be based on survey data collected in the main survey.

Nonresponse bias associated with estimates consists of two factors—the amount of nonresponse and the difference in the estimate between the groups of respondents and non-respondents. Bias may therefore be caused by significant differences in estimates between respondents and non-respondents further magnified by lower response rates. As described earlier in this section, necessary steps will be taken to maximize response rates and thereby minimize the effect, if any, of lower non-response rates on non-response bias. Also, nonresponse weighting adjustments will be carried out to minimize potential nonresponse bias. However, despite all these attempts, nonresponse bias can still persist in estimates.

As part of the non-response analysis, the respondents will be split into two groups: (i) early or ‘easy to reach’ and (ii) late or ‘difficult to reach’ respondents. The call design for this survey, as mentioned before, will be 5 + 5 and so a maximum of up to 10 calls may be made to each sampled phone number. The total number of calls required to complete an interview with a respondent will be used to identify these two groups – “early” and “late” respondents. This comparison will be based on the assumption that the latter group may in some ways resemble the population of non-respondents. The goal of the analysis plan will be to assess the nature of non-response pattern in this survey. Nonresponse bias analysis may also involve comparison of survey-based estimates of important characteristics of the adult population to external estimates. This process will help identify estimates that may be subject to nonresponse bias. If non-response is found to be associated with certain variables, then weighting based on those variables will be attempted to minimize non-response bias.

Note: Describe all possible actions you plan to take to maximize response including incentives, call-backs, follow up, survey length kept to a minimum to increase participation, letters urging the importance of their contribution to this data collection, etc.

4. Describe any tests of procedures or methods to be undertaken. Testing is encouraged as an effective means of refining collections of information to minimize burden and improve utility. Tests must be approved if they call for answers to identical questions from 10 or more respondents. A proposed test or set of tests may be submitted for approval separately or in combination with the main collection of information.

The CATI survey will be tested with fewer than 10 respondents, prior to fielding, to ensure correct skip patterns and procedures.

5. Provide the name and telephone number of individuals consulted on statistical aspects of the design and the name of the agency unit, contractor(s), grantee(s), or other person(s) who will actually collect and/or analyze the information for the agency.

The information collection is conducted for the Individual and Community Preparedness Division by a contractor:

The representatives of the contractor who consulted on statistical aspects of design are:

Camille Lloyd

Research Director

Gallup Inc.

901 F Street NW

Washington, DC 20004

202-715-3188

[email protected]

Manas Chattopadhyay

Chief Methodologist

Gallup Inc.

901 F Street NW

Washington, DC 20004

202-715-3030

[email protected]

37