Nonresponse Bias for Survey 

Estimates of Social Activities & 

Roles

Ashley Amaya

December 3, 2015
The Problem

 Nonresponse introduces the potential for nonresponse 

bias.

 If we cannot prevent nonresponse, then we need to 

understand it in order to measure and/or correct for the 

resulting bias.

 One proposed explanation for nonresponse is the 

social integration hypothesis.

 Participation in a broad range of social relationships

 Individuals that are more integrated will be more likely to 

respond to a survey request.

2
The Theory

 Integrated individuals act in accordance with norms 

perpetuated by their social relationships because:

 They want to fit in.

 They want to avoid negative consequences.

 They perceive their participation will benefit 

individuals/groups that they know.

3
Hypotheses

H1: Univariate estimates of social activities and social 

roles should be upwardly biased.

H2: Variables measuring political and civic 

activities/roles should suffer from higher levels of 

nonresponse bias than other social activity and role 

variables.

H3: Coefficients of the independent variables in 

multivariate models used to predict social activities 

and roles should be unbiased.

4
Data

 American Time Use Survey (ATUS)

 General population telephone survey

 Frame is Current Population Survey (CPS) households

 Social indicators available for 5,150 sampled members

 2,779 respondents / 2,371 nonrespondents

 Survey of Health, Ageing and Retirement in Europe 

(SHARE), Wave II

 50+ population in nine European countries

 Frame is Wave I respondents

 Social indicators available for 19,299 sampled members

 12,904 respondents / 6,395 nonrespondents

5
Social Activities & Roles

ATUS SHARE















Dinner w/  Talk politics

family

 Vote

Friend / family

 Internet post

Parent

 Contact 

Spouse official

Sports Group  Boycott

Neighbor  Other org.

Employee  Religious org.













Spouse / 

partner

Contact 

parent

Contact child

Babysit

Help HHM

Help family

 Volunteer

 Sick adult

 Community

group

 Help others

 Training

 Religious org.

 Neighbor

favors

 Civic org.

 Community

officer

 Community

group 6
Testing H1 & H2:

Univariate estimates should be biased.

Civic/Political Variables will be more biased.

7
8

Relative 

Bias of 

Univariate 

Estimates 

(ATUS)
9

Relative 

Bias of 

Univariate 

Estimates 

(ATUS)
10

Relative 

Bias of 

Univariate 

Estimates 

(SHARE)
11

Relative 

Bias of 

Univariate 

Estimates 

(SHARE)
Testing H3:

Multivariate models will be unbiased.

12
Logit Predicting Contacting an Official (ATUS)

Full Respond. Diff.

*** *** ‡

Intercept -2.58 -2.409 0.17

*** *** Home Owner 0.278 0.307 0.029

‡ NH Black -0.063 -0.175 -0.113

Race/Eth. ** Hispanic -0.387 -0.512 -0.125 (ref=NH White)

*** NH Other 0.053 0.307 0.254

*** *** * High School -0.516 -0.628 -0.112

Educ. Some College 0.166 0.223 0.057 (ref=LT HS)

*** *** ** College Degree or More 0.525 0.62 0.095

Married *** 0.003 -0.076 -0.079

** Female 0.034 -0.018 -0.052

Age 0.005 0.004 -0.002

Employed -0.014 -0.029 -0.015

Children in Household -0.062 -0.086 -0.024

Income 

(ref=LT $20k)

* $20,000-$39,999 -0.213 -0.238 -0.025

$40,000-$59,999 0.079 0.094 0.015

$60,000-$99,999 0.128 0.117 -0.01

* * $100,000 or More 0.263 0.27 0.007 13
Logit Predicting Contacting an Official (ATUS)

Full Respond. Diff.

0.17‡

0.029

-0.113‡

-0.125

0.254***

-0.112*

0.057

0.095**

-0.079***

*** *** Intercept -2.58 -2.409

*** *** Home Owner 0.278 0.307

NH Black -0.063 -0.175

Race/Eth. ** Hispanic -0.387 -0.512 (ref=NH White)

NH Other 0.053 0.307

*** *** High School -0.516 -0.628

Educ. Some College 0.166 0.223 (ref=LT HS)

*** *** College Degree or More 0.525 0.62

Married 0.003 -0.076

** Female 0.034 -0.018 -0.052

Age 0.005 0.004 -0.002

Employed -0.014 -0.029 -0.015

Children in Household -0.062 -0.086 -0.024

* $20,000-$39,999 -0.213 -0.238 -0.025

Income $40,000-$59,999 0.079 0.094 0.015

(ref=LT $20k) $60,000-$99,999 0.128 0.117 -0.01

* * $100,000 or More 0.263 0.27 0.007 14
Number of Significant Differences

by Dependent Variable (ATUS)

<0.001 <0.01 <0.05 n.s.

0

1

1

1

2

1

0

2

Civic Org.† 2 0 15

Dinner w/ Family 1 0 15

Committee Officer 1 0

† 15

Vote† 2 0 14

Religious Org. 1 0

† 14

Friend/Family 3 1 12

Talk Politics† 2 3 12

Contact Official 2 1

† 12

Neighbor Favors 2 3 0 12

Other Org.† 3 2 0 12

Community Group 2 4 1

† 10

Boycott† 0 6 1 10

Internet Post† 2 7 0 8

Neighbor 5 3 1 8

15
Volunteer† 17 1 0 1

<0.001 <0.01 <0.05 n.s.

Sick Adult† 11 1 1 6

Religious Org.† 11 0 3 5

Babysit 11 2 1 5

Help HHM 12 2 0 5

Help Family 11 0 3 5

Contact Children 14 1 0 4

Contact Parent 16 0 1 2

Community Group† 16 1 0 2

Sports Group 17 0 0 2

Help Others† 16 1 1 1

Training† 16 2 0 1

Number of Significant Differences

by Dependent Variable (SHARE)

16
Logit Predicting Contacting an Official (ATUS)

17

Full Respond. Diff.

Intercept -2.58*** -2.409*** 0.17‡

Home Owner 0.278*** 0.307*** 0.029

Race/Eth. 

(ref=NH White)

NH Black -0.063 -0.175 -0.113‡

Hispanic -0.387 -0.512** -0.125

NH Other 0.053 0.307 0.254***

Educ. 

(ref=LT HS)

High School -0.516*** -0.628*** -0.112*

Some College 0.166 0.223 0.057

College Degree or More 0.525*** 0.62*** 0.095**

Married 0.003 -0.076 -0.079***

Female 0.034 -0.018 -0.052**

Age 0.005 0.004 -0.002

Employed -0.014 -0.029 -0.015

Children in Household -0.062 -0.086 -0.024

$20,000-$39,999 -0.213*

-0.238 -0.025

$40,000-$59,999 0.079 0.094 0.015

$60,000-$99,999 0.128 0.117 -0.01

$100,000 or More 0.263* 0.27* 0.007

Income 

(ref=LT $20k)
Number of Differences in the Significance Level

(ATUS)

Full Sample

n.s. p<0.05 p<0.01 p<0.001

Respondents

n.s. 131 12 8 6

p<0.05 5 12 11 5

p<0.01 2 2 4 10

p<0.001 0 0 0 47

18
19

Number of Differences in the Significance Level

(SHARE)

Full Sample

n.s. p<0.05 p<0.01 p<0.001

Respondents

n.s. 81 9

p<0.05 9 3

p<0.01 0 2

p<0.001 0 0

2

6

5

5

5

8

12

81
Summary

 Of the 507 significance tests performed in this section, 

61% yielded significant differences!

 27 of 30 univariate estimates were upwardly biased

 Civic variables trended toward higher levels of bias, but 

not significantly so.

 Multivariate models were biased, but...

 The magnitude of the bias was frequently small.

 The model interpretation was typically unaffected.

20
Next Steps

 Create application procedures

 Taylored contact strategies

 Inclusion of an integration measure into weight 

construction

 Assess the relationship between integration and other 

types of variables

 E.g., health

21
Thank you!

Ashley Amaya

aamaya@umd.edu