J. R. Statist. Soc. A (1999) 162, Part 3, pp.437-446

A multilevel exploration of the role of interviewers in survey non-response

Colm O'Muircheartaigh

University of Chicago, USA

and Pamela Campanelli

London, UK

[Received November 1998. Revised April 19991

Summary. This paper illustrates the use of multilevel statistical modelling of cross-classified data to explore interviewers' influence on survey non-response. The results suggest that the variability in whole household refusal and non-contact rates is due more to the influence of interviewers than to the influence of areas. The results from separate logistic regression models are compared with the results from multinomial models using a polytomous dependent variable (refusals, non-contacts and responses). Using the cross-classified multilevel approach allows us to estimate correlations between refusals and non-contacts, suggesting that interviewers who are good at reducing whole household refusals are also good at reducing whole household non-contacts.

Keywords: lnterviewer effects; lnterviewer variance; Multilevel models; Multinomial regression; Survey non-response

1. Introduction

Interviewer-based data collectioll is the norm for social and market research surveys in the UK and is likely to remain so for the foreseeable future. But what effect do illterviewers have on survey results? On the one hand, illterviewers are often seen as valuable allies in the data collection process for their role in inillimizillg many potential sources of survey error (e.g. through motivating the respolldellt and controllillg the response process). Yet, at the same time, the interviewer call be one of the principal causes of non-response and response variance in quantitative surveys.

Drawing on data from an interpenetrated sample experiinellt designed by the authors for use in wave 2 of the British Household Panel Study (BHPS), this paper focuses on separating the variability in response rates due to interviewers from that due to areas. The particular focus is on the two main components of non-response, refusals and non-contacts, and the extent to which these are related within interviewers and within areas. These substantive analyses illustrate the use of multilevel analysis for cross-classified data using the software MLwiN (Goldstein et al., 1998). More specifically, this paper focuses on a comparison of cross-classified multilevel logistic regression models with cross-classified multilevel inulti- nomial regression models.

Addrerr for coriespor7derice: Colm O'Muircheartaigh. Irving B. Harris Graduate School of Public Policy Studies, University of Chicago. 1155 East 60th Street. Chicago, IL 60637. USA. E-mail: colm@uchicago.edu

0 1999 Royal Statistical Society 0964-1 99819911 62437

438 C. O'Muircheartaigh and P. Campanelli

1.1. Background on interviewer variability with respect to non-response The effects introduced by interviewers into surveys are typically measured in terins of interviewer variance. Net biases for the individual interviewers call be seen to increase the overall variance of a statistic due to the variability among these biases (see, for example, Kish (1962)). Studies of interviewer variance have typically focused on the effect of interviewers on substantive results rather than on non-response (see, for example, Hansen et 01. (1961), O'Muircheartaigh and Wiggins (1981), Collins and Butcher (1982), Groves and Magilavy (1986) and O'Muircheartaigh and Campanelli (1998)). Viewed from the non-response per- spective, interviewers can be seen to vary systematically in terins of their achieved respoilse rates. The focus is then on this variability among illterviewers rather than on the overall level of non-response.

Of particular interest is the extent to which the variability in response rates is due to inter- viewers rather than to the influence of the areas in which they are working. The former would indicate a need for improved traillillg of interviewers, or the purposeful selection of inter- viewers or both, to improve response rates. The latter, however, offers a much more laborious scenario for survey organizations where special strategies would need to be developed to reach and motivate the types of people who live in 'difficult' areas. There is often, however, an inherent difficulty in separating the variability in response rates due to illterviewers from that due to areas. The norm for face-to-face interview surveys in the UK, for example, is to have the workload from a given primary sampling unit (PSU) assigned to a single interviewer and, moreover, to have each interviewer work in only one PSU. Such designs confound the effects of the interviewer and the effects of the area. What is needed to separate the interviewer variability from the area variability is an interpenetrated design, as pioneered by Mahalanobis (1946), in which households or individuals are assigned at random to interviewers.

The two main components of non-response are refusals and non-contacts. Past research suggests that the types of people who tend to refuse to participate in a survey differ from those who cannot be contacted (see, for example, Wilcox (1977), Kalton et al. (1990) and Cainpanelli et al. (1997)). In addition, it can be argued that combatting refusals and non- contacts requires different skills of the interviewer. Minimizing whole household refusals requires interviewers to draw on their persuasive skills (see, for example, Groves et nl. (1992), Morton Williams (1993) and Campanelli et al. (1997)), whereas minimizing whole household non-contacts requires their persistence In making follow-up calls. Although survey organiza- tions offer guidelines to interviewers about how many calls to make and the timing of these calls, good interviewers often go well beyond the required lluinber of calls and differ in the way that they structure their calls (see, for example, Lievesley (1986) and Campanelli et al. (1997)). Given these different challenges, we would hypothesize a negative relationship between interviewers' whole household refusal rates and their whole household non-contact rates, with some interviewers being better at reducing the refusal side of non-response and others being better at reducing the non-contact side of non-response. To our knowledge this hypothesis has never been tested experimentally.

Certain characteristics of areas have been shown to increase non-response. Increased urbanicity usually trallslates into both increased refusal rates and increased non-contact rates. The increased refusal rates may arise from reduced norms of helping behaviour and greater fear of crlme (see, for example, House and Wolf (1978)). Non-contact rates may increase as the life style of urban residents may mean that they are more difficult to find at home or that they are simply more difficult to contact because they live In inultistorey blocks of flats (see, for example, Lylln (1996)). Campanelli et al. (1997) explored several other aspects of areas in relation to non-response by using 1991 census small area data. They

Role of Interviewers in Survey Non-response 439

found that refusal and non-contact rates were higher in areas in which the residents were economically less well off. They found, in addition to the relationship with direct urbanicity measures, higher non-contacts in areas with high proportions of one-person households, of unrelated sharers or of people between 15 and 34 years of age. They found that refusals were more likely in areas with a higher percentage of renters, lone parents with dependent children, non-white residents, the elderly or migrants, and in areas with a higher percentage of indus- tries focusing on distribution, hotel and catering and other services. Along the urban-rural dimension of areas, we would hypothesize that the refusal and non-contact components would be positively correlated. With respect to other characteristics of areas, however, it is unclear how the two components would relate.

2. The data

The data come from wave 2 of the BHPS, a inultitopic panel survey of all household members aged 16 years and over with a multistage stratified cluster sample design covering all of Great Britain.

An interpenetrated sampling scheme was designed by the authors for use in wave 2 of the BHPS. Field requirements and travel costs led to the adoption of a constrailled form of randomization in which addresses were allocated to illterviewers at ralldom within geo- graphic pools, consisting of two or three nearby PSUs. A systematic random sainple of 30 pools, from a frame of 70 pools, was selected to form the interpenetrating sample design. This resulted in 1493 households, roughly a quarter of the full sample, and 65 interviewers. (For a full description of the data and the interpenetrated design see O'Muircheartaigh and Campanelli (1998).)

For wave 2, the interpenetrated design creates a hierarchical cross-classification in which households are nested within the cross-classification of illterviewer by PSU, which is in turn nested within the larger geographic pools, thus allowing for the separation of interviewer and PSU effects within geographic pools.

At wave 1, all individuals in 69% of households in the full sample were either interviewed directly or by proxy. Collditiolling on the wave 1 participating original sainple members, the response rates for wave 2 of the interpenetrated sample are 87% co-operating and partially co-operating households, 7% refusal households and 5% non-contacts households. The 0.3% other non-interview households were excluded from the analyses in this paper.

Data at the level of illdividuals within households are available, but we restrict ourselves here to the analysis of the household level data.

3. Methods

3.1. Multilevel models Separating interviewer effects from area effects on refusals and non-contacts can be accom- plished through separate cross-classified multilevel logistic regression models. Explorillg the covariance between these two components for both interviewers and PSUs, however, requires a multillomial approach with the polytoinous dependent variable: refusals, non-contacts and interviews.

The general usefullless of the multilevel modelling approach to the study of interviewer effects has been demonstrated by several researchers (see, for example, Hox et nl. (1991) and Wiggins et

nl. (1992)). But it is only recently that a cross-classified multilevel analysis for binary dependent variables has become feasible (see Goldstein et nl. (1998) for a basic description of multilevel

440 C. O'Muircheartaigh and P. Campanelli

models and the MLwiN software, and O'Muircheartaigh and Campallelli (1998) for the use of this type of model with substantive interviewer effects). In addition, this paper represents one of the first uses of cross-classified multilevel multinomial models.

The regression equation for the cross-classified multilevel logistic model is similar to that of a standard logistic regressioll model with ralldoin effects terms added to capture the ralldoin components due to the geographic pool and the cross-classification of PSU by iilterviewer (see Goldstein (1995)). The cross-classification is implemented in MLwiN by viewing one member of the cross-classification as an additional level above the other. In the case of multi- nomial models, the dependent variable is now a vector of t proportions. As one is chose11 as the base category, t - 1 equations are necessary. 111 this paper the three-category dependent variable, interviews, refusals and non-contacts (coded 0, 1 aild 2 respectively) is considered with 'interviews' chosen as the base category. Thus, the two equations are

where y,(,,), is the response for household i within the cross-classification of PSU j aild interviewer k , within geographic pool I. The u-terms represent random departures due to PSU j, interviewer k and geographic pool I respectively and are assumed to be norinally distrib- uted. In MLwiN this multivariate response vector is modelled by 'using dummy variables with no variation at level 1 and the true level 1 covariallce matrix specified at level 2' (see Goldstein (1995), p. 105). Levels 3 and 4 are used to capture the cross-classification of PSUs and interviewers. Geographic pool variation call then be assessed at level 5 or, with the block structure of our data, it call also be assessed at level 4.

3.2. Analysis plan Binary variance components models are considered first, separately for refusals and 11011- contacts. These are then compared with a multinomial model in which the covariance between the random term for refusals and the random term for non-contacts can be estimated for interviewers, for PSUs and for geographic pools. (To ensure comparability between these two approaches, the dependent variables for the two logistic models are set up to model the odds of being a refusal as opposed to being an interview and the odds of being a non-contact as opposed to being an interview.) Known correlates of refusals and non-contacts were then introduced to see whether ally of the random variation could be explained. The covariates of most interest would be expected to be the characteristics of illterviewers and the character- istics of areas. As reported in Cainpanelli and O'Muircheartaigh (1999), however, the easily measurable characteristics of interviewers (such as their age, gender, years of experience and grade level) offer virtually no explanation for the variability of interviewer response rates in BHPS data. With respect to areas, earlier work by Campallelli et id. (1997) suggested that, although area characteristics were significant predictors of refusals and non-contacts in bivariate analyses of BHPS data, they were simply proxies for the characteristics of the actual sample members. Once characteristics of individuals, households and measures of respondent colltactability and co-operation were controlled for, area effects virtually disappeared. We therefore decided to include as covariates just those variables which had been useful in our earlier work. Given the complex estimation requirements of the cross-classified inultilevel multinoinial models, the covariate list was further restricted to those that were found to make a statistically significant contribution at the 0.01 level in the previous studies.

Role of Interviewers in Survey Non-response 441

Table 1. Wave 2 binary and multinomial cross-classified multilevel regression: variance com- ponents models using restricted iterative generalized least squares and penalized quasi-likelihood estimation and assuming binomial or multinomial variation at level I t

Morlel HozcselloM level non-resl~onse it7 = 1365)

lWoilel I , binnr~) Model 2 , binnyl: Model 3, nn~ltinoininl iefirml,? non-coniocts

Esrilnnte Co~~nrir~ncebetu:een r(~ndon~tern?,?

Fiserl effect? Refusals -

No11-contacts

Rnnilon~ effecfs Geographic pools

Refusals Non-contacts

PSUs Refusals Non-contacts

Illterviewers Refusals Non-contacts

Households

?Second-order estimation did not converge for the lnultinon~ial model, Restricted iterative generalized least squares and penalized quasi-likelihood were used for the binary models to facilitate a con~parisoll with the n~ultinomial model. $NA, not applicable. $ p = 0.06.

The multilevel covariate models were then compared with the multilevel variance com- ponents models under both the binary and the multillomial conditions. Any cases in which missing data were illcurred by adding the covariates were also excluded from the variance components models. Furthermore, to increase comparability, the same covariates have been used in all the models. Thus, though some covariates are predictors only of refusals whereas others are predictors only of non-contacts, both sets must be illcluded in the multinomial model; both sets were also included in the binary response models.

4. Results

4.1. Technical aspects The six household level multilevel models are presented in Tables 1 and 2. Before discussing these models directly, a few technical points should be considered.

As described in the literature (see, for example, Goldstein (1995)), the best estimation method for our non-response data with their rather small probabilities of refusals and non- contacts is restricted iterative generalized least squares with penalized (or predictive) quasi- likelihood and the second-order approximation. Con~parability across models, however, was complicated by the fact that the second-order approximation did not converge for the multinomial variance components model. The first-order versiolls of the binary variance components models were therefore used.

When the assumption of binomial or inultinoinial variation at level 1 was relaxed, all six models showed apparent ullderdispersioll with values for level 1 variation ranging from 0.66 to 0.89 (and their associated standard errors rallgillg from 0.02 to 0.04). This is interesting

442 C. O'Muircheartaigh and P. Campanelli

Table 2. Wave 2 binary and multinomial cross-classified multilevel regression: covariates models using restricted iterative generalized least squares, penalized quasi-likelihood and second-order estimation and assuming binomial or multinomial variation at level 1

Model Hozrseliold level non-response in = 1365)

Model 4, lWodel 5, Model 6 , inulrinonzial binary binary

refusai~ non-contacts Esriilzare Coi~ariaizce between ritnclonz

terii~s

Fixed effects Constant Refusals -3.80 (0.33)

Non-contacts -4.91 (0.42)

Indicators of co-ol~erafioiz anrl confnctabilitj) Number of calls made by Refusals -0.02 (0.06)

interviewer at wave 1 Non-contacts 0.16 (0.06)t Total number of calls until Refusals 0.24 (0.08)t

1st contact at wave 2 Non-contacts 0 29 (0 07)t

Wave 1 co-operating households (base category: with no individual refusals) With at least 1 individual Refusals 1.95 (0.38)t

refusal With at least 1 individual Non-contacts 0.30 (0.74)

refusal

Wave 1 respondent co-operation rating (base category: very good) Missing Refusals 0.53 (1.42) Good or fair 1.14 (0.27)t Poor or very poor 1.94 (0.75)t Missing Non-contacts Good or fair Poor or very poor

Wave 1 rating of problems with English (base category: no) Missing Refusals -1.32 (1.39) Yes 0.71 (0.80) Missing Non-contacts Yes

Houselzold leilel clirrracreristics Number of pellsiollers in wave 1 household (base category: none)

At least 1 pensioner in Refusals 1.30 (0.61)t household

At least 1 pensioner in Non-contacts household

Age of head of household at wave 1 (base category: 26-59 years) 15-25 years Refusals -0.22 (0.54) 60-93 years -0.22 (0.60) 15-25 years Non-contacts

3.57 (1.35)t 1.08 (0.35)t 1.61 (1.30)

-3.38 (1.64)t 1.96 (0.68)t

0.82 (1.01)

1.81 (0.33)t 60-93 years -1.91 (1.13)

Rar~dorn effects Geographic pools Refusals 0.0

Non-contacts 0.0 PSUs Refusals 0.0

Non-contacts 0.21 (0.37) Interviewers Refusals 0.38 (0.24)

Non-contacts 0.54 (0.42) Households 1 1

PCoefficient significant at p < 0.05 (11 < 0.01 and p < 0.001 not shown). $NA. not applicable.

Role of Interviewers in Survey Non-response 443

given that in practice the most coininon tendency is for overdispersion (McCullagh and Nelder, 1989). This underdispersion was partly alleviated by the second-order estimation. Given that we have no theoretical explanation for the apparent underdispersion, we have opted for the more parsimonious model that constrains the level 1 variance to 1. As the random and fixed effects coefficients change in size in relation to the size of the level 1 variance, this constraint also facilitates the comparison of coefficients across models.

An examination of the residuals for the levels associated with interviewers and areas for Tables 1 and 2 suggested that normality was a valid assumption, despite the small prob- abilities associated with being a refusal or a non-contact.

4.2, Substantive aspects 4.2.1. Randorw effects in tlze binary vlzodels Models 1 and 2 in Table 1 show interviewer effects for both categories of non-response, although these do not reach significance. After controlling for important covariates of non- response in models 4 and 5 , similar conclusions are reached. In comparison, there appear to be no effects of area (either PSU or geographic pool) on whole household refusals, but small effects on whole household non-contacts.

4.2.2. Random effects in the ~?ultinornial n~odels As expected, allowing covariances between the random terms for refusals and non-contacts, within interviewers and within areas, changes the size of the random coefficients in the multi- nomial models slightly. For example, in the covariates model, we can see that the coefficients for interviewers are larger in the multinomial model than in the separate logistic regression models and that the coefficient for PSUs is smaller. Nevertheless, the cross-classified multi- level multinomial models point to the same conclusions as the binary models.

It is in the covariances between random terms in the multinomial model, however, that the main interest lies. In both cases, large positive covariances were found between refusals and non-contacts for interviewers. (In terms of correlations rather than covariances, the values were 0.96 and 0.72 respectively.) Overall, these findings suggest that good interviewers are good at reducing both aspects of non-response and poor interviewers are generally poor at both tasks. This is contrary to our hypothesis, suggesting that, although persuading a house- hold to co-operate and finding them at home in the first place require different types of skills from the interviewer, interviewers are generally good or poor at mastering both.

In the case of PSU (area) variance, no covariances were computed because the between- area variance was estimated to be 0 for the refusal component of non-response in these models.

4.2.3. Fixed effects in tlze binary and multinonzial lnodels When we compare the binary covariates models (models 4 and 5 ) with the multinomial covariates model (model 6), the two sets of analyses yield very similar conclusions about the size of the fixed effects. The covariances introduce differences in the specification of the random parameters for the two kinds of model, and the fixed parameters are estimated on the basis of the random effects; thus we would expect differences between the models. The only difference, however, is for the category of respondents who were rated with 'poor or very poor' co-operation as opposed to 'very good' co-operation at wave 1. Respondents with poor

444 C. O'Muircheattaigh and P. Campanelli

or very poor co-operation at wave 1 were significantly more likely to be non-contacts in wave 2 in the multinomial model.

5. Summarizing remarks and discussion

5.1. General advantages of multilevel modelling Using data from an interpenetrated sample experiment, this paper has explored the random effects of interviewers in survey non-response. It is only through the use of cross-classified multilevel multinomial regression models that we could examine the correlation between refusals and non-contacts within interviewers and areas. Given models where the fixed effects rather than the random effects are the main focus, multilevel modelling allows for the incorporation of the complex variance-covariance structure that is present in almost all survey data directly into the substantive analysis. For example, a majority of large social surveys in the UK employ face-to-face hierarchical designs which introduce correlatio~ls between units due to sample clustering and to the clustering of units within interviewers' workloads.

5.2. Summary of results Our results suggest that the variance induced by systematic differences between interviewers is greater than the variance between geographic areas (PSUs and geographic pools). The pattern holds after controlling for the characteristics of wave 1 households and various measures of respondent co-operation and contactability. The stronger effects of interviewers rather than areas suggests the need for survey organizations to focus on the training of inter- viewers as a means of minimizing non-response even in what classically have been considered the most difficult areas (such as London and other urban areas).

The cross-classified multilevel multinomial model allows us to ascertain for the first time the covariance between the refusals and non-contact cases for interviewers and areas. Large, though non-significant, positive covariances were found for the interviewers' part in gen- erating refusals and non-contacts. Although it is not conclusive, this finding suggests that interviewers who are generally good at converting refusals are also generally good at chasing up non-contacts. With respect to areas (PSUs), no covariances were found. The presence of the covariance for interviewers and not for areas provides useful inforn~ation for survey managers.

Of particular interest, from a modelling standpoint, is the comparison of the individual cross-classified multilevel logistic regressions with the corresponding multinomial regressions. We would expect differences because the cross-classified multilevel multinomial model allows for the estimation of the covariances between the categories of the dependent variable with respect to the random effects terms. Conclusions with respect to the fixed effects, however, were essentially the same in both cases.

5.3. Limitations of the research Overall, these results should be considered indicative rather than definitive, for two main reasons. First, the number of areas and interviewers were small and so the random effects of interviewers and areas were estimated with substantial error. Moreover, despite the magni- tude of the correlatio~ls between the refusal and non-contact categories for interviewers, these were not statistically significant at conventional levels.

Role of Interviewers in Survey Non-response 445

A more general concern is the extent to which these results, taken from the second wave of a panel survey, can generalize to non-response patterns from the initial wave and from other one-off surveys. Although we can only hypothesize about the pattern of random effects for interviewers and areas for wave 1 (e.g. the possibility of area effects closer in size to inter- viewer effects), it is interesting that past research has shown a strong similarity between the demographic and econon~ic profiles of attrition non-respondents and first-wave non-respondents (see, for example, Farrant and O'Muircheartaigh (1991), Gray, et al. (1996) and Campanelli et al. (1997)).

Acknowledgements

The data used in this paper were made available through the Ecoilon~ic and Social Research Council (ESRC) Data Archive, UK. The data were originally collected by the ESRC Research Centre on Micro-social Change at the University of Essex. Neither the original collectors of the data nor the Archive bear any responsibility for the analyses or inter-pretations presented here.

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