Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics. 

Comparison of Item and Unit Nonresponse in Household Surveys August 2005

John Dixon, 

Bureau of Labor Statistics, Room 1950, 2 Massachusetts Ave., NE, 

Washington, DC 20212-0001 

Dixon_J@bls.gov

Keywords: Survey nonresponse, Item nonresponse 

1. Introduction 

 Survey quality is thought to be impacted by 

nonresponse, both item nonresponse and survey 

or unit nonresponse. The relationship between 

item nonresponse and unit nonresponse has been 

studied by Dixon (2002). Dahlhamer et al. 

(2003) also studied item nonresponse across 

several federal surveys. 

 The current study examines the nature of item 

nonresponse and the relationship with 

subsequent unit nonresponse for two surveys: the 

Current Population Survey (CPS) and the 

Consumer Expenditure Quarterly Survey (CEQ). 

2. Item nonresponse 

 Item nonresponse is often considered a source 

of nonsampling error. Mason, Lesser and 

Traugott (2002) point to several surveys which 

have found bias due to item nonresponse. Item 

nonresponse may increase with reductions in unit 

nonresponse if respondents who are reluctant to 

participate decline to answer sensitive questions. 

 Item nonresponse can be a predictor of unit 

nonresponse. Loosveldt, G., Rickery, J., and 

Billet, J. (1999) found that increases in item 

nonresponse were related to higher refusal 

probability (unit nonresponse) in subsequent 

panels of a survey. Item characteristics were 

hypothesized to relate to bias. If the refusal is 

related to the survey topic, then bias is highly 

likely. Threatening or sensitive items also are 

likely to produce bias. Income items are a 

common example where response may be related 

to the survey sponsor and the survey topic. 

Other item characteristics which may produce 

refusal are: difficulty, memory demand, access to 

information (such as financial records), response 

complexity, and question format. 

 Item and respondent characteristics can 

interact to produce item nonresponse. Murata 

and Gwartney (1999) used a theoretical grouping 

of items and an expert rating of items to explore 

their characteristics. For respondent 

characteristics they found more education to be 

related to higher item nonresponse, especially for 

women and Hispanics, although the effect was 

confounded with age. For question 

characteristics more “important” questions had 

less nonresponse, and more salient questions 

usually produced lower item nonresponse. 

Question vagueness, difficulty, multiple tasks, 

multiple concepts, and question length were all 

related to higher item nonresponse. Question 

characteristics seemed to have a stronger effect 

than person characteristics, but their survey had 

an unusual variety of questions. In contrast, 

Borgers and Hox (2001) found person 

characteristics more predictive of item 

nonresponse than question characteristics. 

 The impact of item nonresponse on error may 

vary considerably by survey. Mason, Lesser and 

Traugott (2002) found more callbacks and 

refusal conversions didn’t improve estimates in 

the surveys they reviewed. Benchmarking to 

external sources revealed some potential bias in 

some surveys. They suggested that item 

nonresponse may be more of a problem than unit 

nonresponse. Where unit nonresponse is a 

problem, item nonresponse is often also a 

problem. Efforts to convert refusals often 

produced higher item nonresponse in the surveys 

they studied, making the efforts less effective in 

terms of the estimates. 

 Dixon (2002) found two patterns of item 

nonresponse (personal and work related items), 

which indicated that respondents' sensitivity to 

items are different. For some respondents, 

economic items may seem like an intrusion, 

while for other respondents personal items may 

seem irrelevant in an economic survey. This is 

different from the theory that the propensity to 

respond is on a single continuum. The reasons 

for not responding are probably different for 

different items, and may be different for each 

survey. Serfling (2003) found a "reverse 

cooperation continuum" in a German socioeconomic survey, in contrast to the Loosveldt et 

al. (1999) and Dixon (2002) studies. The current 

study attempts to examine these relationships. 

3. Design 

The CPS is a monthly household labor force 

survey conducted by the U.S. Census Bureau for 

the Bureau of Labor Statistics. Approximately 

48,000 eligible households are sampled each 

month in a two-stage clustered design. Sampled 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics. 

households are interviewed a total of eight times 

over a 16 month period. National level data on 

the labor force, employed, and unemployed are 

produced monthly, quarterly, and annually. As a 

household survey the CPS provides rich 

demographic detail, including age, sex, race, 

Hispanic origin, educational attainment, marital 

status and family attributes, foreign-born status, 

veteran status, and other demographic 

characteristics. For the current analysis 

households were matched for the years 2002 

through 2003. Persons in the household who 

were not eligible for the labor force (e.g., under 

16 years old) were excluded. Waves 4 and 8 

were used because more questions were asked 

during those interviews, and 197,077 persons 

were included in the analysis. 

 The CEQ is a household expenditure survey 

conducted by the U.S. Census Bureau for the 

U.S. Bureau of Labor Statistics. Approximately 

8,910 eligible addresses are sampled each 

quarter, with 6,160 completed interviews typical. 

Households (or more correctly: consumer units) 

were matched for the years 2002 through 2003. 

The response rate is usually in the range of 80-83 

percent. In this study 122,214 persons were 

matched across the five quarters, although only 

Interviews 2 and 3 are used here. 

4. Method 

 Since questions often differ between surveys, 

the comparisons were done with item "concepts" 

(Dahlhamer et al., 2003). Item concepts from 

“demographics” (e.g., Race, Hispanic origin, 

Educational attainment), “labor force” (e.g., class 

of worker), and “income” (e.g., interest, rent, 

dividends) were used. These sets of items had 

been found to be related to unit nonresponse in 

other panels and labor force estimates. Logistic 

models were used to relate the item sets to unit 

nonresponse and whether respondents were 

employed or other (not in the labor force or 

unemployed). The survey weights (probability 

of selection weights) were adjusted to make the 

sample proportions the same for the two surveys 

for ease of interpretation. Item nonresponse 

includes both refusal and "don't know" because 

the coding system for the CEQ didn't 

differentiate the two for the months studied. The 

"don't know" rate is small, so for all practical 

purposes the nonresponse is from refusal. All of 

the models where overdispersion was detected 

use adjusted covariances. The power of the tests 

should be considered in examining the tables. 

The large sample size is offset by the small 

proportions and the multiple tests. Dividing the 

estimate by its standard error should give a better 

index of the size of the effect than the simple 

significance test indicated by italics. 

5. Results 

 Table 1 shows the noncontact and refusal 

rates for the two surveys in the first two rows. 

Nonresponse is lower for the CPS. Noncontact 

is higher in the CPS than the CEQ relative to 

refusal (probably due to a shorter interview 

period). The rest of Table 1 shows the item 

nonresponse rates. The highest nonresponse for 

both surveys was from total income questions. 

Except for education and whether they were ever 

in the armed forces, the items on the CEQ had 

higher nonresponse. 

 Table 2 shows the coefficients and standard 

errors for a number of logistic regressions on 

noncontact. The first set of coefficients (labeled 

“Single”) is from nine separate regressions. The 

strongest relationship was with "income" (.81 

with standard error of .01), although nonresponse 

on all the items was associated with noncontact. 

Significance at the .05 level is indicated by the 

italics. Non-italicized coefficients weren't 

significant. The next two columns are for nine 

models with an indicator for which survey was 

taken added to the models. The positive 

numbers under the "Survey" column indicate the 

CEQ had more noncontact than the CPS. The 

"Item" column can be compared to the first 

column under the "Single" column. While all the 

coefficients are similar to the single variable 

models, many become non-significant when the 

variability of noncontact attributable to 

differences in the surveys is accounted for. The 

last three columns show the models with the 

interaction between the survey and item 

nonresponse. The last column is of most 

interest, since it is the one testing for an 

interaction. Most of the significant coefficients 

indicate a negative relationship; nonresponse on 

the item is associated with less likelihood of 

noncontact in the CEQ relative to the CPS. The 

exception is for "income", where nonresponse is 

associated with a higher likelihood of noncontact 

on the CEQ relative to the CPS. 

 Refusal was studied in a similar series of 

regressions as was done with noncontact. Table 

3 shows the coefficients for the three sets of 

models. The first set shows item nonresponse is 

related to a higher probability of survey refusal 

except for "education." The second two columns 

show that adjusting for differences between 

surveys finds the same higher probability of 

refusal, but with "education" and "armed forces 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics. 

participation" becoming nonsignificant. While 

"income" is still significant, the strength of the 

relationship is diminished similarly to the 

noncontact model. The last three columns show 

the interaction between item nonresponse and 

survey. The nonsignificant interaction term and 

high standard error for "sex" is probably due to 

small cell counts, and should be disregarded. 

The interaction terms which are negative indicate 

item nonresponse is associated with lower 

probability of refusal for the CEQ relative to the 

CPS. All of the probabilities were related to 

higher probability of refusal (the column labeled 

"Item"). 

 The relationship of unit noncontact, unit 

refusal, and item nonresponse with labor force 

status was examined with a series of multinomial 

logistic regression models (using "employed" as 

the reference category). 

 Table 4 shows the coefficients and standard 

errors. The first two columns of coefficients 

show the intercepts for "Unemployed" (U) and 

"Not in labor force" (N) relative to "Employed." 

The unemployed are a lower percentage than 

not-in-labor-force. The column labeled "Item" 

shows the coefficient relating the pooled 

Unemployed and Not-in-the-labor-force 

categories to Employed. Noncontact (-0.18) and 

refusal (-0.12) are related to a lower probability 

of being employed, as is nonresponse to "sex" 

(-1.48). The remaining coefficients related item 

nonresponse to a higher probability of being 

employed, although only four were significant. 

 The next four columns show the effect of 

adjusting for survey differences, with only 

nonresponse to "origin" and "income" remaining 

significant. 

 The interaction between nonresponse and 

labor force status shows the CEQ had 

noncontact, refusal and most item nonresponse 

associated with higher probability of 

employment relative to the CPS. The only 

significant item nonresponse with a negative 

relationship was "income". 

 Additional models examining the relationship 

between item nonresponse and labor force status 

adjusting for unit noncontact and unit refusal 

didn't find any substantive differences from the 

models described here and so will not be 

presented. Additional models separating out the 

labor force status into pairs (to avoid 

nonproportional odds) are shown in Tables 5 

through 7. The only strong departures from 

proportional odds were with lower nonresponse 

items. 

 

6. Discussion 

 Many studies of nonresponse are motivated 

by the idea that nonresponse is a propensity 

which, when a threshold is crossed, produces 

refusal. Many factors may affect the threshold: 

survey topic, interviewer characteristics and 

behavior, and previous contact among others. 

 Noncontact is thought to be more complex; 

with the effort of the interviewer, timing of 

attempted contacts, and availability of the 

respondent all interacting to produce the 

outcome. This study found similar relationships 

for most items with both noncontact and refusal. 

Noncontact may be a form of refusal in some 

cases. 

 Item nonresponse is thought to behave in a 

similar manner to unit nonresponse. For those 

who participate in the survey, some items would 

trigger nonresponse (usually refusal) due to 

concerns about privacy and/or confidentiality. 

The current study supported other studies that 

found item nonresponse was related to refusal (as 

well as noncontact). The only items which didn't 

relate positively had very low nonresponse rates, 

suggesting that they were at the end of the 

propensity curve. 

 The propensity theory would suggest that 

surveys with higher unit nonresponse should 

have lower item nonresponse since those who are 

most likely to not respond to some items would 

be eliminated by unit nonresponse. This wasn't 

the case with the two surveys studied here, where 

there was a mix of effects. The most sensitive 

items (income related) had higher nonresponse 

on the CEQ even though the unit nonresponse 

was higher. 

 Some of the items on the CPS which had 

lower nonresponse were items which are used in 

reports (sex, race), so they would be more easily 

justified by the interviewer. Since some of the 

other items not used in reports show the same 

pattern, this explanation isn't sufficient to explain 

the lower item nonresponse. The Mason, Lesser 

and Traugott (2002) study showed more effort 

by interviewers didn't improve estimates in their 

surveys. This may be a confounding factor in 

the surveys studied here, where more effort may 

be required for the CEQ producing some higher 

item nonresponse as the cost of lowering unit 

nonresponse. A future study including call 

history data may clarify the effect. 

 The relationship between unit nonresponse 

(both refusal and noncontact) and labor force 

status was consistent between surveys for the 

employed and not-in-labor-force categories. The 

unemployment category was small and the 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics. 

effects very small. The surveys were consistent 

in the overall relationship between income 

nonresponse (and most of the other nonresponse 

items), and labor force status, but not for 

nonresponse on "armed forces participation," 

birth date, or race. The relation of the item 

nonresponse to labor force status was usually 

opposite the unit nonresponse. This was also 

seen in Dixon (2003). 

 

 

7. Limitations and Future Research

 This study used 85 logistic models to examine 

the relationships between item nonresponse, unit 

nonresponse, and labor force status. While most 

of the models were variations of nested models, 

there is a problem with choosing an appropriate 

confidence region. 

 The CPS is a complex sample (stratified and 

clustered) and the CEQ is a stratified sample. 

While the design effect is small for both surveys, 

correct standard errors should be considered in 

future research (at least for comparison). The 

standard errors would increase by less than 10 

percent in the variables studied here. 

The difference between item refusal and "don't 

know" should be explored. 

References: 

• Borgers, N. and Hox, J., “Item Nonresponse 

in Questionnaire Research With Children”, 

Journal of Official Statistics, Volume 17.2, 

2001, pp. 321-335. 

• Dahlhamer, J., Dixon, J., Doyle, P., Eargle, 

J., Griffin, D., and McGovern, P., “Quality 

at the Item Level: Terms, Methods, and 

Guidelines for Cross-Survey Comparisons”, 

Paper presented at the Federal Conference 

on Statistical Methodology, December 16, 

2003, Washington, D.C. 

• Dixon, J., “Relationship Between Household 

Nonresponse, Demographics, and 

Unemployment Rate in the Current 

Population Survey”, Paper presented at the 

Joint Statistical Meetings, Atlanta, Georgia, 

2001. 

• Dixon, J., “The Effects of Item and Unit 

Nonresponse on Estimates of Labor Force 

Participation”, Paper presented at the Joint 

Statistical Meetings, N.Y., N.Y., 2002. 

• Dolton, P., Lindeboom, M., and Van den 

Berg, G.J., “Survey attrition: A taxonomy 

and the search for valid instruments to 

correct for biases”, in Statistical Policy 

Working Paper 30, 1999 Federal Committee 

on Statistical Methodology Research 

Conference. 

• Fletcher, J., and Schmidt, D., “Measuring 

Response Bias in Survey Research: An 

Analysis of Age Characteristics of Early 

Respondents and Resistors”, Paper 

presented at AAPOR, 2001. 

• Groves, R., and Couper, M., Nonresponse in 

Household Interview Surveys, Wiley, New 

York, 1998. 

• Loosveldt, G., Pickery, J., and Billet, J., 

“Item non-response as a predictor of unit 

non-response in a panel survey”, Paper 

presented at the International Conference on 

Survey Non-response, Portland, Oregon, 

October 28-31, 1999. 

• Mason, R., Lesser, V., and Traugott, M., 

“Effect of Item Nonresponse on 

Nonresponse Error and Inference, In 

Groves, R., Dillman, D., Eltinge, J., and 

Little, R., Survey Nonresponse, Wiley, New 

York, pages 149-162, 2002. 

• Murata, T., and Gwartney, P., “Question 

Salience, Question Difficulty and Item 

Nonresponse in Survey Research”, Paper 

presented at the International Conference on 

Survey Non-response, Portland, Oregon, 

October 28-31, 1999. 

• Serfling, O., "The Interaction Between Unit 

and Item Nonresponse in View of the 

Reverse Cooperation Continuum", Paper 

presented at the Workshop on Item 

Nonresponse and Data Quality in Large 

Social Surveys, Basel, Switzerland, October 

2003. 

• Tucker, C., and Dixon, J., “Predicting 

Interviewer Nonresponse Rates from 

Household and Regional Characteristics”, 

Paper presented at AAPOR, 2000. 

• Tucker, C., and Kojetin, B., “The Impact of 

Nonresponse on the Unemployment Rate in 

the Current Population Survey”, Paper 

presented at the International Workshop on 

Household Survey Nonresponse, 1997. 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics. 

Appendix: 

Table 1: Noncontact and Refusal, and Item Nonresponse 

 survey 

 CEQ CPS 

noncontact .07572 .05894 

refuse .16901 .02975 

armforce .00059 .00441 

birthdate .02332 .02091 

education .00281 .01638 

sex .00640 .00007 

marital .02010 .00561 

origin .01507 .01639 

race .00933 .00299 

income .19850 .14241 

phone .02368 .00500 

Table 2: Logistic regressions for Noncontact (standard errors are show in parentheses) 

Item 

nonresponse 

Single With Survey Interaction 

 Survey Item Survey Item Survey*Item 

Armed force 0.65(.10) .136(.04) .758(.63) .138(.01) -4.1(16.3) -5.1(16.3) 

Birth date 0.44(.04) .134(.06) .436(.33) .144(.01) .448(.04) -0.34(.04) 

Education 0.26(.06) .137(.07) .360(.64) .142(.01) -1.4(.39) -1.93(.39) 

Sex 0.77(.08) .132(.002) .653(.03) .132(.01) .471(.52) 0.18(.52) 

Marital Status 0.38(.05) .132(.06) .305(.42) .138(.01) .542(.05) -0.48(.05) 

Origin 0.41(.04) .135(.003) .414(.02) .134(.01) .413(.04) 0.19(.04) 

Race 0.78(.06) .131(.005) .715(.04) .132(.01) .744(.07) -0.05(.07) 

Income 0.81(.01) .108(.20) .792(.43) -0.01(.01) .666(.02) 0.46(.02) 

Telephone 0.60(.04) .129(.03) .515(.19) .132(.01) .672(.05) -0.24(.05) 

Table 3: Logistic regressions for Refusal (standard errors are show in parentheses) 

Item 

nonresponse 

Single With Survey Interaction 

 Survey Item Survey Item Survey*Item 

Armed force 0.29(.10) .950(.06) 1.20(.80) .954(.01) .56(.15) -1.0(.15) 

Birth date 0.84(.03) .950(.06) .857(.25) .961(.01) 1.01(.03) -0.29(.03) 

Education -0.20(.06) .951(.10) .583(.83) .963(.01) 0.14(.08) -0.98(.08) 

Sex 1.95(.06) .940(.012) 1.36(.10) .940(.01) -2.30(15.0) 3.62(15.0) 

Marital Status 1.08(.03) .940(.04) .725(.18) .944(.01) .970(.05) -0.30(.05) 

Origin 1.05(.03) .950(.03) 1.19(.11) .946(.01) 1.12(.04) 0.132(.04) 

Race 2.00(.04) .940(.03) 1.77(.15) .937(.01) 1.59(.07) 0.240(.07) 

Income 1.55(.01) .928(.22) 1.50(.35) .773(.01) 1.18(.02) 0.48(.02) 

Telephone 1.20(.03) .937(.04) .798(.19) .942(.01) 1.09(.05) -0.34(.05) 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics. 

Table 4: Labor force status (Using Employed as the reference) 

Item 

nonresponse 

Single With Survey 

 U N Item U N Survey Item 

Noncontact -3.65(.05) -0.64(.02) -0.18(.07) -3.66(.06) -0.64(.02) -0.04(.02) -0.17(.08) 

Refusal -3.65(.03) -0.64(.01) -0.12(.03) -3.66(.06) -0.65(.02) -0.10(.07) -0.03(.02) 

Armed force -3.66(.03) -0.64(.01) 0.24(.21) -3.67(.03) -0.66(.01) -0.04(.01) 0.21(.16) 

Birth date -3.66(.06) -0.65(.02) 0.23(.17) -3.67(.03) -0.66(.01) -0.04(.01) 0.20(.09) 

Education -3.67(.12) -0.65(.04) 0.59(.38) -3.67(.06) -0.66(.02) -0.03(.02) 0.56(.19) 

Sex -3.66(.01) -0.65(.002) -1.48(.36) -3.67(.02) -0.65(.01) -0.04(.01) -1.46(1.06) 

Marital Status -3.66(.01) -0.65(.003) 0.22(.05) -3.67(.03) -0.65(.01) -0.04(.01) 0.19(.15) 

Origin -3.66(.03) -0.65(.01) 0.24(.11) -3.67(.03) -0.66(.01) -0.04(.01) 0.21(.09) 

Race -3.66(.01) -0.65(.002) 0.09(.04) -3.67(.03) -0.65(.01) -0.04(.01) 0.06(.22) 

Income -3.72(.18) -0.70(.06) 0.31(.15) -3.73(.14) -0.71(.05) -0.05(.04) 0.32(.11) 

Telephone -3.66(.02) -0.65(.01) 0.07(.06) -3.67(.02) -0.66(.01) -0.04(.01) 0.10(.05) 

Item 

nonresponse 

Interaction 

 U N Survey Item S*I 

Noncontact -3.65(.05) -0.65(.004) -0.04(.004) -0.18(.02) 0.13(.02) 

Refusal -3.65(.05) -0.65(.012) -0.04(.012) -0.20(.06) 0.17(.06) 

Armed force -3.67(.01) -0.65(.004) -0.04(.004) -3.70(12.48) -3.93(12.48) 

Birth date -3.67(.01) -0.66(.004) -0.04(.004) 0.08(.09) -0.13(.09) 

Education -3.67(.01) -0.66(.004) -0.03(.004) 0.40(.22) -0.16(.22) 

Sex -3.67(.01) -0.56(.004) -0.04(.004) -4.56(16.27) -4.06(16.27) 

Marital Status -3.67(.01) -0.66(.004) -0.04(.004) 0.70(.18) 0.55(.18) 

Origin -3.67(.01) -0.66(.004) -0.04(.004) 0.61(.17) 0.41(.17) 

Race -3.67(.01) -0.66(.004) -0.04(.004) 0.76(.22) 0.78(.22) 

Income -3.73(.01) -0.71(.004) -0.03(.004) 0.31(.01) -0.14(.01) 

Telephone -3.67(.01) -0.66(.004) -0.04(.004) 0.14(.04) -0.07(.04) 

Table 5: Unemployed Labor force status (Using Employed as the reference) 

Item 

nonresponse 

Single With Survey 

 U Item U Survey Item 

Noncontact -3.26(.01) -0.04(.05) -3.27(.01) -0.03(.01) -0.03(.04) 

Refusal -3.26(.01) -0.07(.04) -3.26(.01) -0.03(.01) -0.05(.01) 

Armed force -3.26(.01) -0.34(.29) -3.27(.03) -0.03(.01) - 0.36(.17) 

Birth date -3.26(.01) -0.20(.12) -3.27(.03) -0.03(.01) -0.23(.09) 

Education -3.26(.01) -0.40(.17) -3.27(.01) -0.03(.01) -0.43(.13) 

Sex -3.26(.01) -8.06(70.) -3.27(.01) -0.03(.01) -8.04(1.41) 

Marital Status -3.26(.01) 0.17(.20) -3.27(.01) -0.03(.01) 0.15(.18) 

Origin -3.26(.01) 0.02(.12) -3.27(.01) -0.03(.01) -0.01(.12)

Race -3.26(.01) 0.02(.28) -3.27(.01) -0.03(.01) 0.01(.17) 

Income -3.26(.01) 0.003(.03) -3.27(.04) -0.03(.04) 0.01(.11) 

Telephone -3.27(.01) 0.24(.10) -3.27(.01) -0.03(.01) 0.27(.01) 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics. 

(table 5 continued) 

Item 

nonresponse 

Interaction 

 U Survey Item S*I 

Noncontact -3.27(.01) -0.03(.012) -0.03(.05) -0.04(.05) 

Refusal -3.26(.01) -0.03(.013) -0.04(.05) -0.01(.05) 

Armed force -3.27(.01) -0.03(.012) -3.70(33.87) -3.34(33.87) 

Birth date -3.27(.01) -0.03(.012) -0.47(.39) -0.26(.39) 

Education -3.27(.01) -0.03(.012) -4.22(40.83) -3.80(40.83) 

Sex -3.27(.01) -0.03(.012) -8.05(73.45) 0.03(73.45) 

Marital Status -3.27(.01) -0.03(.012) -3.43(28.09) -3.59(28.09) 

Origin -3.27(.01) -0.03(.012) -4.01(40.14) -4.01(40.14) 

Race -3.27(.01) -0.03(.012) -3.51(40.19) -3.52(40.19) 

Income -3.28(.01) -0.05(.013) 0.01(.03) 0.11(.03) 

Telephone -3.27(.01) -0.03(.012) 0.24(.11) -0.01(.11) 

Table 6: Not in Labor force status (Using Employed as the reference) 

Item 

nonresponse 

Single With Survey 

 N Item N Survey Item 

Noncontact -0.71(.01) -0.20(.02) -0.72(.04) -0.04(.04) -0.19(.15) 

Refusal -0.71(.01) -0.12(.01) -0.72(.01) -0.00(.04) -0.10(.15) 

Armed force -0.72(.01) 0.31(.08) -0.73(.01) -0.04(.01) - 0.28(.19) 

Birth date -0.73(.01) 0.27(.03) -0.73(.01) -0.04(.01) -0.25(.05) 

Education -0.73(.01) 0.70(.04) -0.74(.01) -0.04(.01) 0.68(.03) 

Sex -0.72(.01) -1.39(.75) -0.73(.01) -0.04(.01) -1.38(1.4) 

Marital Status -0.72(.01) 0.23(.07) -0.73(.01) -0.04(.01) 0.20(.25) 

Origin -0.73(.01) 0.27(.03) -0.73(.01) -0.04(.01) 0.24(.12) 

Race -0.72(.01) 0.11(.09) -0.73(.02) -0.04(.02) 0.07(.40) 

Income -0.79(.01) 0.35(.01) -0.80(.07) -0.05(.07) 0.37(.17) 

Telephone -0.72(.01) 0.05(.04) -0.73(.01) -0.04(.01) 0.08(.07) 

Item 

nonresponse 

Interaction 

 N Survey Item S*I 

Noncontact -0.72(.01) -0.05(.004) -0.20(.02) 0.15(.02) 

Refusal -0.72(.01) -0.04(.004) -0.22(.02) 0.19(.02) 

Armed force -0.73(.01) -0.04(.004) -3.66(12.78) -3.95(12.78) 

Birth date -0.73(.01) -0.04(.004) 0.13(.09) -0.12(.09) 

Education -0.74(.01) -0.04(.004) 0.52(.23) -0.16(.23) 

Sex -0.73(.01) -0.04(.004) -4.51(16.67) -4.10(16.67) 

Marital Status -0.73(.01) -0.04(.004) -0.86(.20) -0.70(.20) 

Origin -0.73(.01) -0.04(.004) 0.75(.18) .53(.18) 

Race -0.73(.01) -0.04(.004) 1.01(.27) 1.02(.27) 

Income -0.79(.01) -0.02(.004) 0.36(.01) -0.17(.01) 

Telephone -0.73(.01) -0.04(.004) 0.13(.04) -0.08(.04) 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics. 

Table 7: Unemployed Labor force status (Using NILF as the reference) 

Item 

nonresponse 

Single With Survey 

 U Item U Survey Item 

Noncontact -2.55(.01) 0.17(.05) -2.55(.05) 0.01(.05) 0.17(.19) 

Refusal -2.54(.01) 0.06(.04) -2.54(.01) 0.01(.05) 0.05(.16) 

Armed force -2.54(.01) -0.65(.29) -2.54(.01) 0.01(.01) -0.64(.30) 

Birth date -2.54(.01) -0.48(.12) -2.53(.01) 0.01(.01) -0.47(.04) 

Education -2.53(.01) -1.11(.17) -2.53(.01) 0.01(.01) -1.10(.11) 

Sex -2.54(.01) -7.12(88.) -2.54(.01) 0.01(.01) -7.11(88.) 

Marital Status -2.54(.01) -0.05(.20) -2.54(.02) 0.01(.02) -0.05(.36) 

Origin -2.54(.01) -0.25(.12) -2.54(.02) 0.01(.02) -0.25(.20)

Race -2.54(.01) -0.08(.29) -2.54(.02) 0.01(.02) -0.07(.45) 

Income -2.48(.01) -0.35(.03) -2.47(.11) 0.02(.10) -0.35(.28) 

Telephone -2.54(.01) 0.19(.10) -2.54(.01) 0.01(.01) 018(.06) 

Item 

nonresponse 

Interaction 

 U Survey Item S*I 

Noncontact -2.55(.01) 0.02(.013) 0.17(.05) -0.19(.05) 

Refusal -2.54(.01) 0.02(.013) 0.18(.06) -0.21(.06) 

Armed force 

Birth date -2.53(.01) 0.01(.012) -0.60(.39) -0.13(.39) 

Education -2.53(.01) 0.01(.012) -4.62(35.94) -3.52(35.94) 

Sex 

Marital Status -2.54(.01) 0.01(.012) -4.56(36.95) -4.57(36.95) 

Origin -2.54(.01) 0.01(.012) -4.68(36.80) -4.45(36.80) 

Race -2.54(.01) 0.01(.012) -4.56(41.84) -4.58(41.84) 

Income -2.49(.01) -0.03(.013) -0.35(.03) 0.27(.03) 

Telephone -2.54(.01) 0.01(.012) 0.14(.12) 0.07(.12)