Using the Multi-Level Integrated Database Approach 

Tom W. Smith 

NORC/University of Chicago 

Federal Committee on Statistical Methodology Research Conference 

Arlington, VA 

August, 2007 

Non-response is a key component of total survey error (Couper and Groves, 1996; 

Groves, 2005; Groves and Couper, 1998; Martin, 2004; Smith, 2005; Stoop, 2005) and 

non-response has been increasing in magnitude over time (de Leeuw and de Heer, 2002; 

Smith, 1995; 2002a; Zukin, 2006). The two basic approaches to dealing with nonresponse 

are: 1) developing approaches to increase the response rate and 2) measuring nonresponse bias and using weighting and/or imputation to compensate for the detected bias 

(Dillman et al., 2002; Groves, 2005; Groves and Couper, 1998; Lessler and Kalsbeek, 

1992; Zanutto and Zaslavsky, 2002). Both of these approaches have yielded only limited 

success. Despite extensive tests of new interviewing techniques involving a range of 

treatments (e.g. the use of incentives, tailoring by interviewers, improved interviewer 

training) and increases in the level of effort (e.g. longer field periods, more call backs, 

more use of converters), response rates have been falling. Procedures to measure and 

adjust for non-response bias have been hampered by the fact that often little is known 

about non-respondents and without information on non-respondents the possibility of 

both measuring non-response bias and compensating for it is severely limited. 

We live in an information age in which considerable data exist in many forms and 

at many levels (e.g. individuals, households, neighborhoods, communities, etc.). These 

data sources can be tapped to aid in the collection, adjustment, and analysis of surveys. 

As Stoop (2004) has observed, by linking sampled cases “to a large number of other 

registers and administrative records, a large amount of data is available on 

nonrespondents and respondents with which nonresponse can be analyzed” and as 

Zanutto and Zaslavsky (2002) have noted, “Administrative records are a relatively 

inexpensive source of detailed information, especially as technology increases our ability 

to manipulate large datasets.” 

The multi-level, integrated database approach (MIDA) described in this proposal 

can help to deal with the problems of both low response rates and the detection and 

adjustment for non-response bias. In addition, MIDA will also further substantive 

analysis by providing aggregate-level information for contextual analysis. Thus, MIDA 

will both advance survey-research methodology as well has enhance substantive research. 

Description of MIDA 

The essence of MIDA is to use databases to collect as much information as 

practical about the target sample at both the case-level and at various aggregate levels 

1 
 

 

 

 

during the initial sampling stage. The following description of MIDA will use the 

example of national US samples of households based on addresses and as such is directly 

appropriate for postal and in-person samples. However, similar approaches can be 

applied to other modes and populations (e.g. national, RDD, telephone samples; panel 

studies; list-based samples; and local surveys). 

The first step in MIDA is to extract all relevant, public information at both the 

case-level and aggregate levels from the sampling frame from which the sample 

addresses are drawn. In European samples based on population registers, there is often 

very useful information on such matters as gender, age, and household composition 

(Bethlehem, 2002; Stoop, 2004; van Goor, Jansma, and Veenstra, 2005; Voogt and Van 

Kempen, 2002) and list samples (e.g. of employees and HMO enrollees) often have a 

wealth of sampling frame information (Fowler et al., 2002; Groves, 2006; Kennickell, 

2005; Lessler and Kalsbeek, 1992; Moore and Tarnai, 2002; Smith, 1999). But in the US, 

general population samples of addresses are typically nearly void of household-level 

information. However, US address samples are rich in aggregate-level information. 

Address/location of course is the one known attribute of all cases, whether respondents or 

non-respondents. Moreover, address-based sampling frames are typically based on the 

US Census and as such the appropriate Census data from blocks, tracts, place, etc. are 

part of the sampling frame and linked to each address. (That is, the local sample points 

are selected based on the Census and then addresses within those sample points are 

obtained from the United States Postal Service Delivery Sequence File and/or special 

field listings – the later especially typical for rural areas(O’Muircheartaigh, 2003).) 

The second step is to augment the sampling frame by linking all cases in the 

sample to other databases. As Groves (2005) has noted, “Collecting auxiliary variables 

on respondents and nonrespondents to guide attempts to balance response rates across 

key subgroups is wise.” 

At the case-level that means linking the addresses to such sources as telephone 

directories, credit records, property records, voter-registration lists, and other public 

sources (Berge et al., 2005; Brick et al., 2000; Cantor and Cunningham, 2002; Cox, 2006; 

Davern, 2006; Johnston et al., 2000; Marcus at al., 2006; Williams et al., 2006).1 

Examples of specific useful databases/providers are Accurint, Century List Services, 

Donnelley/infoUSA, Emerges.Com, Equifax, Experian, Fundrace, Info Quest, 

Peoplefinders, TARGUSinfo, Telematch, Transunion, the Ultimates, and the US Data 

Corporation. A number of special procedures have also been developed to use databases 

in ways not commonly expected and thereby extract much more information than 

available from more limited and superficial applications (Cantor and Cunningham, 2002; 

Smith, 2006a; Traub, Pilhuj, and Mallet, 2005; Williams et al., 2006).2

 The information obtained would include first of all whether a match was or was 

not found (e.g. listed in telephone directory or not) and, if matched, whatever particular 

information is available (e.g. names, telephone numbers, credit reports, voter registration 

status). 

1

 For a general discussion of record linkage involving surveys see Fair, 1996 and Jenkins et al., 2005. On 

linking surveys to administrative records see Obenski, 2006 and Davern, 2006. 

2

 Survey researchers already have considerable experience in using databases, but further consultation will 

be carried out with other experts such as data librarians, geographical information systems specialists, 

cyber-information technicians and data miners, and records searchers such as paralegals and investigators. 

2 
 

 

 

 

 

 

 

 

 

 

 

At the aggregate level, this means merging information from sources other than 

those in the sampling frame.3 Examples of aggregate-level data beyond that from the 

Census that could be appended are consumer information from such sources as Claritas 

PRIZM NE and Donnelley Marketing’s FIND index, the National Address Server, voting 

information from national elections, and data on such other matters as vital statistics 

(Salvo and Lobo, 2003); crime rates (FBI, 2004), religion (Jones, 2002), public housing 

(HUD, 1998), HIV/STD rates (CDC), and public welfare utilization (Salvo and Lobo, 

2003). An example of the extensive information that can be linked from databases to 

schools and school districts is illustrated by a recent NORC project (Hoffer, Ghadialy, 

and Halverson, 2006).4 

The linked data would include information from multiple-levels of aggregation. 

The multi-level analysis will start with household-based data and include neighborhoodlevel data from Census track and zip code-based data sources, community-level data from 

the Census, election counts, crime rates, and other sources, and higher level aggregations 

(e.g. metropolitan areas and Census divisions).5 

The third step in MIDA is to take information gained from the initial case-level 

linkages to secure additional information. For example, securing a name and telephone 

number from a telephone-directory search can lead to households being found in 

databases when a mere address was insufficient to allow a match. Also, once a 

respondent was identified, links to that person in addition to household-level matching 

could be carried out. Thus, the process of augmenting the sampling frame is iterative and 

continues during the data-collection phase. 

The final step is to record, process, clean, and maintain a large amount of paradata 

for each case (Couper and Lyberg, 2005; Scheuren, 2000). This would include having 

interviewers systematically record information about the sample residence (e.g. dwelling 

type, condition of dwelling), contacts or call attempts, interactions with household 

members, and observations on the composition and demographics of the household) 

(Bethlehem, 2002; Cantor and Cunningham, 2002; Gfroerer, Lessler, and Parsley, 1997; 

Groves, 2006; Kennickell, 2005; Lynn et al., 2002; Safir et al., 2002; Smith, 1983; Stoop, 

2004).6

 As Cantor and Cunningham (2002) note surveys “should maintain the date and 

result of each contact or attempt to contact each subject (and each lead)... The reports 

3 When starting with addresses without prior Census information as part of the sampling frame, Census and 

other geographic-based information can be obtained by linking addresses to the geo units (e.g. Census tract, 

zip code, place/community, etc.) that they fall in. That is, the Census data are added as part of step two if 

they are not already available as part of the sampling frame. Address linkages to Census tract and higher 

geo units are possible for from 95-100% of cases (Geronimus, Bound, and Neidert, 1996; Groves and 

Couper, 1998; Kim, Smith, and Sokolowski, 2006).

4

 The most extensive example of linking all sample cases (i.e. both respondents and non-respondents) to 

databases is the matching of cases from six major government surveys to the 1990 Census (Gfroerer, 

Lessler, and Parsley, 1997; Groves and Couper, 1998). Unfortunately the Census can not be generally used 

for this purpose because of the Bureau of the Census’ no access policy to household-level information. 

Although household-level linkage with the Census is not a viable option, the study demonstrates that 1) a 

very high level of matching can be achieved between surveys and other records using addresses (96% of 

non-respondents on the surveys and 97% of respondents were linked to the Census) and 2) the information 

on the characteristics of non-respondents was very useful in modeling and adjusting for non-response bias. 

5

 For multi-level analysis see Bryk and Raudenbush, 1988; DiPrete and Forristal, 1994; and Raudenbush 

and Bryk, 2002. 

6

 This is obviously not possible for postal surveys. 

3 
 

should provide cost and hit data for each method to help manage the data collection 

effort. In the end it helps to determine those methods that were the most and least cost 

effective for searching for the population of interest, and this knowledge can be used for 

planning future surveys.” 

The Utility of MIDA 

Consider how the multi-level information in this greatly enriched, sampling frame 

can be used to advantage for data collection, non-response measurement and adjustment, 

interview validation, and substantive analysis. 

Data Collection 

First, more information on the target sample will make data collection both more 

efficient and more effective. For example, securing names and phone numbers can be 

very helpful in making contact with households and are particularly useful in the case of 

locked building, gated communities, and other hard to access residences. More 

information about households before the start of the data-collection phase can greatly 

ease making contact with households and thus allow efforts to be concentrated on gaining 

respondent cooperation. It is also very useful if a multiple-mode approach is used (e.g. 

data collection combining in-person + telephone). 

Once contact is made, tailoring is very important in gaining cooperation (Couper 

and Groves, 1996; Groves and Couper, 1998; Smith, forthcoming). The more information 

that one has about the household (e.g. whether they have a listed phone number, home 

owner or renter, etc.), the better able one is to shape interviewers’ approaches and to 

provide and highlight information most salient to the sampled household (Groves, 2006; 

Groves, Singer, and Corning, 2000). It is not that well-run surveys do not already make 

some use of databases to assist interviewers, but what is not done is the careful evaluation 

of various databases and the retention of the information for other than data-collection 

efforts. 

Non-Response Measurement and Adjustment 

Second, while this added information will assist interviewers and decrease the 

overall non-response error, there will still remain a notable amount of non-response on 

even the better surveys. The information in the MIDA-augmented sampling frame will 

then be used to measure and adjust for non-response error.7

 Having a wide range of caselevel and aggregate-level information is important both to test the representativeness of 

the achieved sample across as many variables as possible and because surveys covering 

different topics are likely to have different non-response profiles (e.g. non-voters underrepresented in political surveys and the wealthy in the Survey of Consumer Finance – 

Kennickell, 1997; 2005). Having more relevant information on non-respondents allows 

7

 It is likely that some information will be most valuable at the data-collection stage and other at the nonresponse adjustment stage. For example, name and telephone number would be most useful to aid the field 

work and having a listed/unlisted telephone number, mobility history, and housing tenure would likely be 

more valuable for non-response adjustments. 

4 
for better modeling of non-response bias and the creation of weights that more fully 

account for the biases and has the particular advantage of having augmented data for all 

sample cases (Groves, 2005). It also makes fresh, cross-sectional studies more like 

reinterview, panel studies where the bias from attrition can be well-modeled based on 

time 1 data (Lepkowski and Couper, 2002). 

Research has shown that neighborhood, community, and higher level attributes of 

areas are correlates of non-response. For example, non-response is consistently and 

notably higher in large cities than in small towns (Groves and Couper, 1998; Smith, 

1983; Smith, 1984; Steeh et al., 2001), in some regions and metropolitan areas vs. others 

(Groves and Couper, 1998; Johnson and Cho, 2004; Lepkowski and Couper, 2002; 

Montaquila and Brick, 1997; Murray et al., 2003; Smith, 1983); and related to other 

aggregate-level attributes such as density, crime rate/fear of crime, social disorganization, 

geographic mobility, and family structure (Couper and Groves, 1996; Groves, 2006; 

Groves and Couper, 1998; Goyder, Lock, and McNair, 1992; Gfroerer, Lessler, and 

Parsley, 1997; Johnson and Cho, 2004; Johnson et al., 2006; Kim, Smith, and 

Sokolowski, 2006; Kojetin, 1994; O’Hare, Ziniel, and Groves, 2005; van Goor, Jansma, 

and Veenstra, 2005; Voogt and van Kempen, 2002). Thus, aggregate-level variables are 

very useful for assessing, understanding, and adjusting for non-response bias (Brick and 

Broene, 1997; Johnson and Cho, 2004; Kalsbeek, Yang, and Agans, 2002; Kennedy and 

Bannister, 2005; Kennickell, 2005; Montaquila and Brick, 1997; Nolin et al., 2000; 

Turrell et al., 2003). 

While MIDA is designed to address the matter of nonresponse bias in general, 

special attention will be focused on examining several prominent theories about the 

nature and source of nonresponse bias: social disorganization, social isolation, 

overextension, and structural barriers. MIDA will provide an opportunity to compare and 

evaluate these theories of non-response. 

First, social disorganization theory holds that social structural conditions 

influence the social relations of people. Wirth (1938) notes that population size, density, 

and heterogeneity accompanying urbanization weaken individual, family, neighborhood, 

and social ties. Shaw and McKay (1969) show an association between certain structural 

conditions and the concentration of social ills such as delinquency. They attribute the 

higher prevalence of social ills in socially and economically disadvantaged areas to the 

differences in social organization in the community. Treating refusal rates in Primary 

Sampling Units (PSUs) “as a behavioral measure of interpersonal trust or helpfulness,” 

House and Wolf (1978:1030) show a positive relationship between crime rate and refusal 

rate, and find that the total crime rate provides the strongest positive explanatory power 

on variation of refusal rates among different places. Groves and Couper (1998) show that, 

controlling for household characteristics, population density and the percentage of 

individuals under 20 years of age are positively related to survey cooperation. The 

individual and especially the aggregate level data collected here will provide multiple 

measures of social disorganization (e.g. crime level, concentration of poverty, residential 

instability). 

Related to social disorganization theory is the concept of collective efficacy 

which holds areas vary in the willingness of people to “intervene on behalf of the 

common good” (Johnson et al., 2006; Sampson, Morenoff, and Earls, 1997). Collective 

efficacy is related to such neighborhood traits as low population turnover, higher 

5 
 

education, higher income, low density, fewer immigrants, and more intact families. 

Research has found that this propensity is related cooperation in surveys (Couper, Singer, 

and Kulka, 1998). 

Second, social isolation theory argues that nonrespondents are likely to be poorly 

integrated members of society (Groves and Couper, 1998; Looseveldt and Carton, 2001; 

Stoop, 2005). According to this theory social isolates are likely to be non-respondents 

both because of personal misanthropy and because of social and civic disengagement. 

Personally, social isolates try to minimize inter-personal contacts with others and as such 

are disinclined to want to cooperate with and engage in an interview (i.e. a conversational 

interaction) with an interviewer (Converse and Schuman, 1974). Socially and civically, 

social isolates have little interest in general societal and community affairs and neither 

follow such matters nor are interested in discussing such topics in an interview. Thus, for 

these distinct, but associated, reasons social isolates are expected to be overrepresented 

among non-respondents. It will be possible to examine these expectations by both 

comparing households that are socially isolated (e.g. with no listed number, no members 

registered to vote nor belonging to large voluntary associations, etc.) to less isolated 

households and by comparing more engaged areas (e.g. higher voter turnout, more 

magazine/newspaper subscriptions) vs. less involved neighborhoods and communities. 

Third, overextension theory argues that it is people leading busy lives that tend to 

be non-respondents (Campanelli, Sturgis, and Purdon, 1997; Groves and Couper, 1998; 

Lynn, 2002; Smith, 1984). This would include people working full time in general and 

especially those putting in over time, those with open-ended management responsibilities, 

and those whose work involves travel. It would naturally include people with multiple, 

major roles such as full-time employees and parents of small children or those providing 

in-home eldercare. Databases can often provide useful information on employment status 

and household composition that can be used to test this hypothesis. 

Additionally, many structural factors such as gated communities, locked 

buildings, policies of gatekeepers, etc. influence contact rates and ultimately response 

rates and these can be observed and recorded by interviewers and examined by 

researchers. Including these structural impediments will better specify the overall nonresponse model. 

Interview Validation 

 Interviews are checked or validated through a combination of close supervision of 

field interviewers and the recontacting of respondents to verify that an interview had been 

conducted with the eligible respondent. Invalid interviews are a relatively small 

component of total survey error. MIDA will reduce it even further by allowing the 

information from the databases to be used along with recontacts to help corroborate that 

interviews were truly and correctly done. 

Substantive Analysis 

Finally, for respondents the case-level and aggregate-level data in the augmented 

sampling frame can be utilized for crucial substantive analysis. While most case-level 

information would come from the interviews with the respondents, the added case-level 

6 
 

data would include both information uncovered in any particular survey and data that can 

be used to corroborate information reported by respondents.8 Procedures for crosschecking information from different databases and between databases and surveys are 

discussed below. 

Aggregate-level information is of great utility for research. Research has 

demonstrated that contextual, aggregate-level geographic effects in general and 

neighborhood characteristics in particular influence a wide range of attitudes and 

behaviors independent of the attributes of individuals. For example, research has shown 

that impacts exist on 1) political involvement (Bobo and Gilliam 1990; Cohen and 

Dawson 1993; Gilbert 1991), 2) residential and social mobility (Lee, Oropesa, and Kanan 

1994; Massey and Eggers 1990; Massey et al. 1994; South, Baumer, and Lutz 2003), 3) 

the sexual and reproductive activities of youths and adults (Billy and Moore 1992; 

Brewster 1994a; Brooks-Gunn et al. 1993; Browning and Olinger-Wilbon 2003; 

Browning, Leventhal, and Brooks-Gunn, 2004; Cohen et al 2000; Crane 1991; South and 

Baumer 2001), 4) responses to poverty (Jencks and Mayer 1990; McLeod and Edwards 

1995; Oreopoulos 2003), 5) racism and tolerance (Gibson 1995), 6) fear of and 

involvement in crime (Covington and Taylor 1991; Peeples and Loeber 1994; Sampson, 

Raudenbush, and Earls, 1997), 7) minorities politically (Cohen and Dawson 1993), 

economically (Lee et al. 1994; Massey and Eggers 1990), and in other ways (Brewster 

1994b; Smith 1994a), 8) social capital and better health (Mellor and Milyo 2004), 9) 

group membership and economic improvement (Tolbert, Lyson, and Irwin 1998); 10) 

inequality and political trust (Rahn and Rudolph 2005); 11) religion and deviant behavior 

(Regnerus 2003), 12) drug use (Boardman et al. 2001; Ford and Beveridge, 2006;Galea, 

Ahern, and Vlahov, 2003; Snedker, Herting, and Walton, 2006), and 13) depression 

(Latkin and Curry, 2003). 

Among the contextual effects that have been examined from the General Social 

Survey (GSS)(Davis, Smith, and Marsden, 2007) specifically are the following: 1) racial 

composition of the local population predicts levels of racial prejudice (Alesina and 

LaFerrara 2000; Charles 2003; Dixon and Rosenbaum 2004; Taylor 1998 and 2002) and 

class voting (Weakliem 1997), 2) higher collective levels of trust and civic engagement 

are associated with lower homicide rates (Rosenfeld et al. 1999 and 2001) and lower 

mortality in general (Kawachi et al. 1997b), 3) areas with greater aggregate happiness 

have lower mortality (Jencks 1999), 4) higher levels of anomia are related to higher local 

crime rates (Rosenfeld and Messner 1998), 5) community-level differences in attitudes on 

gender roles do not affect the demand for female labor (Cotter et al. 1998), 6) the 

prevalence of Fundamentalists reduces support for feminism (Moore 1999), 7) a higher 

level of people on welfare reduces support for welfare spending (Luttmer 1998), 8) living 

around gun owners increases one’s likelihood of acquiring a gun (Glaeser and Glendon 

1998), 9) lower income equality is associated with lower social trust and group 

membership (Kawachi et al. 1997a), 10) community heterogeneity influences civic 

engagement (Costa and Kahn 2002), 11) community norms shape attitudes toward capital 

punishment (Baumer, Messner, and Rosenfeld 2003), 12) state and regional differences 

may be declining over time (Weakliem and Biggert 1999), 13) voting and civic 

involvement vary by community as well as individual demographics (D’Urso 2003), 14) 

8Examples of collaboration are the voter validations studies – Anderson and Silver, 1986; Burden, 2000; 

Silver, Anderson, and Abrahamson, 1986. 

7 
greater community acceptance of immigrants relates to more occupational achievement 

by immigrants (De Jong and Steinmetz 2004), 15) community religious beliefs and 

behaviors influence gender roles (Moore and Vanneman 2003), and 16) aggregate public 

opinion affects public policies on such as abortion laws, welfare payments, and AIDSrelated funding (Brace et al. 2002). 

The coding of a rich array of aggregate-level data from the sampling frame and a 

wide range of databases will facilitate such contextual analysis and make it a regular part 

of survey analysis rather than an occasional approach carried out only when special 

multi-level data are added, often after the fact, to standard surveys. In brief, the 

information in the augmented sampling frame that can be used to assist data collection 

and adjust for non-response bias can in turn be used for multi-level, contextual analysis. 

MIDA Expansion over Existing Practices 

While all of the elements of MIDA have been used in some way or another in 

some existing surveys, the use of case-level and aggregate-level linkage to databases has 

not be used in an integrated, systematic manner. The use of databases has been quite 

limited in terms of both what sources are used and how the linked information is utilized, 

and the databases and the information from them have not be assessed and evaluated. 

One of the limitations of existing approaches is that databases are not used in a 

systematic manner. For example, telephone directories are often used to try and find the 

name and number associated with a sampled address or to track a respondent in a panel 

who has moved. The telephone-directory searches are often quite helpful for these 

purposes, but their use is purely operational. The information gathered is used by 

interviewers to help locate respondents, but seldom, if ever, systematically analyzed, used 

for non-response adjustment, or retained as part of the final analysis file. Conversely, 

linkage data are sometimes collected for substantive purposes (e.g. to see if graduates of a 

job-training program end up on welfare), but this information is not used for field 

operations or non-response adjustment purposes. 

A second limitation is that the use of different databases has apparently never 

been systematically assessed. Different practitioners use different data sources (e.g. 

telephone directories, credit records, various public, governmental files) based on their 

familiarity with data sets and/or the data providers and other general preferences. 

Apparently no rigorous comparisons of the ease-of-use, cost, and yield of various 

databases have been conducted and none have closely examined the cumulative gain 

from the use of multiple data sets (Smith, 2006a). 

A third limitation has been that few databases have been typically utilized. 

Telephone directories are the only commonly used database. Other databases such as 

credit records, property records, and voter registration have been used only occasionally 

(and only for limited purposes when used at all). Many other potentially valuable 

databases have apparently never been used (e.g. political contribution lists, membership 

lists, subscription lists). 

A final limitation is that the uses of databases have generally focused on only 

information obtained about respondents who are found in particular sources. Typically, 

searches in telephone directories are deemed useful when the target individual or 

household is located and as not useful when no match occurs (as is the case with the large 

8 
 

 

proportion of households with unlisted numbers plus those with no telephone). But being 

found or not found in a database is in itself a useful piece of information and should be 

recorded for comparing respondents and non-respondents. For example, those listed in 

the telephone directory are much more likely to be respondents than those not included 

(Brick et al., 2003; Brick, Montaquila, and Scheuren, 2002; Harvey et al., 2003; Minato 

and Luo, 2004; O’Hare, Ziniel, and Groves, 2005). 

MIDA is designed to overcome each of these standard limitations by comparing 

and evaluating data sources, flagging both matched and unmatched records, and retaining 

data for use in all phases of research. 

MIDA Test and Application 

To develop and test MIDA, we first propose to draw a sample of 400 addresses 

from the NORC national sampling frame (Davis, Smith, and Marsden, 2007) and link 

these to a large number of public databases at the household and/or aggregate levels. 

These will include, but will not be limited to 1) telephone directories, 2) voterregistration lists, and 3) credit records9

. In addition, a wide range of innovative data 

sources will be explored such as group memberships, magazine subscriptions, political 

and charitable donations, property records, court records, etc. (The distinction is that the 

certain databases have been used enough to be of known value (e.g. telephone directories 

and credit records), while others are potentially very useful, but have not been utilized at 

all or at least not enough to know how valuable they are). 

Attention will focus on general, national data sources rather than local, state, and 

otherwise restricted listings, but some of these will also be explored (e.g. Goyder, Lock, 

McNair, 1992; Harvey, 2003; Moore and Tarnai, 2002; Murray et al., 2003; Salvo and 

Lobo, 2003). The potential for acquiring useful linked information is great and often 

unappreciated. As Cantor and Cunningham (2002) note, “Proprietary databases available 

on the Internet and elsewhere contain detailed information on large numbers of people. 

Access to the databases is often restricted. However, these restrictions are often 

negotiable for limited searches for legitimate research purposes.” 

Careful account will be kept of the ease, cost, and linkage rates for each type of 

records obtained, on the consistency of results across different databases, and the 

cumulative results and total amount of information obtained across the multiple records. 

This will be used to identify the best databases for general and specific uses. 

Second, another important step in the evaluation of MIDA will be checks on the 

quality of the data from the various databases (Brick et al., 2000; Groves, 2005; Zanutto 

and Zaslavsky, 2002). As Prewitt (2006) has advocated, rigorous tests of the quality of 

data from the databases will be conducted. Previous work at NORC gives reason for 

being optimistic. Work that NORC did in 2004 comparing a sample from a Donnelley 

database to that from a national survey of the elderly found that in terms of accuracy of 

information the database was “on par with the field sample” and concluded “our 

9

 Credit records and other commercial databases include a lot more than the bottom line credit scores and 

related financial records, they typically include considerable information of mobility, home and vehicle 

ownership, various legal statuses, ages, household composition, etc. (Brick et al., 2000; Cantor and 

Cunningham, 2002). 

9 
 

preliminary work with the Donnelley list was encouraging and [we] recommend its use in 

future...” 

Completeness, accuracy, and up-to-datedness will be assessed in two principal 

ways. First, for all sample cases (respondents and non-respondents), the data from 

alternative data sources will be compared to one another to get a measure of inter-record 

consistency. For example, there are three major credit records (Experian, Equifax, and 

Transunion) and a sample of cases will be linked to all three databases and the 

information contained within them compared. Other inter-record evaluations will also be 

conducted. Second, a sample of respondents from the GSS scheduled for reinterviews 

could be matched to databases and the information from the GSSs could be compared to 

information from the databases. These comparisons will assess all of the databases 

examined. 

Assessing and Adjusting for Non-response 

The MIDA dataset will contain much more data about non-respondents than are 

usually available. The full dataset will have household and aggregate-level data for both 

respondents and non-respondents. Such a rich dataset is uncommon in nationally 

representative demographic/attitudinal surveys. It provides an opportunity to explore 

different approaches to estimating and adjusting for non-response bias. For the many 

variables for which the dataset contains values for both respondents and non-respondents, 

it will be possible to explore the hypothetical effects of non-response by comparing 

estimates from these variables for the full dataset with estimates on the respondent cases 

only. These analyses will suggest which estimates would be most vulnerable to nonresponse bias. This knowledge will then inform our understanding of the error implicit in 

estimates from the survey variables themselves, for which non-respondent data are not 

available. (Gelman and Carlin, 2002; Geronimus, Bound, and Neidert, 1996; Groves, 

2005; Groves, 2006; Marker, Judkins, and Winglee, 2002; Meng, 2002; Zanutto and 

Zaslavsky, 2002). 

In addition, the availability of data on non-respondents can improve weighting 

techniques. In recent rounds, the GSS has incorporated a non-response adjustment at the 

level of the Primary Sampling Unit (PSU) which are metro areas or non-metro counties. 

It assumes that the non-respondents in a given area are more like the respondents near 

them than other respondents. This assumption has been empirically verified and is 

probably the most common type of non-respondent adjustment used in national, in-person 

surveys But the use of PSU to form non-response adjustment cells is limited in the 

improvement it can provide and is based primarily on a heuristic of availability rather 

than relying on specific theoretical connection with the study variables. The MIDAenriched dataset, by providing data on both respondents and non-respondents on many 

variables, will allow for more discretion in creating non-response adjustment cells and for 

more sophisticated weighting adjustments (Bethlehem, 2001; Kalton and Kasprzyk, 

1986). 

Response-propensity weighting is a common method of adjusting for nonresponse. The theory behind this approach is that all cases, both responding and nonresponding, have a non-zero propensity to respond which can be modeled with a logistic 

regression. The dependent variable is a dummy variable indicating response and the 

10 
independent variables are those that predict response: urbanicity, region, household size 

and composition, interest in the survey topic, etc. Responding cases are then weighted by 

the inverse of their response propensity to account for the non-responding cases, with 

low-propensity cases given more weight than high. Like the non-response weighting 

adjustment discussed above, this method often suffers from a lack of frame variables: the 

right hand side variables are usually those that are available for all cases rather than those 

that would be most appropriate. MIDA will permit more thoughtful choices in the 

independent variables and should improve the response-propensity weighting adjustment. 

(Ekholm and Laaksonen, 1991). 

In addition to giving one a wider selection of variables with which to adjust the 

weights, MIDA will also provide data with which to compare and evaluate the adjustment 

methods. These results could greatly improve the weighting methods used for surveys in 

general. 

Similarly, having more variables in the MIDA dataset will improve imputation 

techniques. Hot-deck imputation fills in values that are missing due to item-non-response 

by matching cases with missing data to cases without missing data. MIDA will allow 

better matches and should thus improve the imputation. Also, if the imputation technique 

chosen involves modeling (e.g., mean regression or multiple imputation), the MIDA 

dataset will allow better models to be formed with the additional variables. Either way, 

MIDA will improve the imputation techniques available to surveys in general (Marker et 

al., 2002). 

Conclusion 

MIDA has the potential to advance social-science research in general by notably 

improving survey-research methodology. Moreover, it does so by drawing on one of the 

major societal changes in recent decades, the development of large-scale, computerized 

databases that hold extensive information about individuals, households, neighborhoods, 

and other societal units. As part of the testing of MIDA all databases utilized will be 

evaluated for usefulness and reliability. 

Methodologically, it should help to increase response rates, allow for a much 

more comprehensive assessment of non-response bias, and facilitate the calculation of 

weights and imputations to adjust for the detected non-response bias. Besides providing 

for a general approach to deal with non-response, it will in particular permit the testing of 

several prominent theories and hypotheses explaining non-response: social 

disorganization theory, social isolation theory, overextension theory, structural 

impediments, etc. The auxiliary data from the databases will permit an examination of 

general, non-response models (Groves and Couper, 1998). 

Substantively, MIDA will improve analysis by easily and automatically making 

multi-level, contextual variables as ready for analysis as data directly collected in 

surveys. As the list of examples cited above attest, geographic context has notable 

impacts on many aspects of people’s lives. The contextual data from sampling frames and 

augmented from multiple databases will provide a rich, contextual array of data for 

analysis across scores of central substantive topics. 

The incorporation of auxiliary data will become more common in surveys as 

researchers strive to improve contact strategies, increase response rates, and better adjust 

11 
for non-response. This is the right moment for a full, rigorous evaluation of the 

advantages and challenges in using databases, other metadata, and paradata to advance 

survey research. 

12 
 

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