UNDERSTANDING MAIL SURVEY RESPONSE BEHAVIOR

UNDERSTANDING MAIL SURVEYRESPONSE BEHAVIORA META-ANALYSIS

FRANCIS J. YAMMARINOSTEVEN J. SKINNERTERRY L CHILDERS

Abstract A meta-analysis of prior studies of techniques de-signed to induce mail survey response rates was conducted. Re-search encompassing 184 effects (study outcomes) in 115 studies(articles) for 17 predictors of response rate was examined. Theaverage effect size across all manipulations was r = .065, indicat-ing an average increase of about 6.5 percent in response ratesfor manipulations. Effect sizes for specific predictors and twopotential moderators of effects were examined. Results indicatedthat repeated contacts in the form of preliminary notification andfollow-ups, appeals, inclusion of a return envelope, postage, andmonetary incentives, were effective in increasing survey re-sponse rates. Significant effect sizes for the predictors rangedfrom an increase in response of 2 percent to 31 percent. Implica-tions of the results for the conduct of mail surveys and futureresearch on mail survey response behavior are discussed.

Researchers have amassed myriad techniques to stimulate mail surveyresponse rates, reduce item omission, speed up response, and reduceresponse bias. Examples include the use of preliminary notification,follow-up, sponsorship, appeals, postage, personalization, incentives,anonymity, prior commitment, and techniques affecting questionnaire

Correspondence regarding this article should be addressed to FRANCIS J. YAMMARINO,School of Management and Center for Leadership Studies, State University of NewYork at Binghamton, P.O. Box 6000, Binghamton, NY 13902-6000. STEVEN J. SKINNERis at the College of Business and Economics, University of Kentucky, Lexington, KY.TERRY L. CHILDERS is at the Carlson School of Management, University of Minnesota,Minneapolis, MN. This research was supported in part by a New Faculty DevelopmentAward to F. J. Yammarino from the NYS/UUP Professional Development and Qualityof Working Life Committee. The views expressed are ours. We gratefully acknowledgethe helpful comments provided by the anonymous reviewers on an earlier version ofthis article.

Public Opinion Quarterly Volume 55:613-639 © 1991 by the American Association for Public Opinion ResearchAll rights nsscrved. 0033-362X/91/5504-0004$02.50

614 Yammarino, Skinner, and Childers

appearance. These methods represent manipulations or predictorswhich, at least in some instances, have served to facilitate surveyparticipation, A comprehensive list and review of studies related tothe effectiveness of these methods can be found in the work of Duncan(1979), Houston and Ford (1976), Kanuk and Berenson (1975), andLinsky (1975).

Linsky (1975), for example, found strong evidence that preliminarynotification, follow-ups, "high powered" postage, sponsorship, smallmonetary incentives, and nonmonetary incentives were very effectivefor, or had positive effects on, increasing response rates. Other manip-ulations, according to Linsky (1975), are equivocal or interact withadditional factors to affect response rates. Likewise, Kanuk and Ber-enson (1975) determined that monetary incentives (especially smallones), follow-up contacts, and perhaps preliminary notification, werethe only techniques that had strong empirical evidence for effective-ness in increasing response rates. Other techniques showed no consis-tent effect.

In a recent qualitative review, Harvey (1987) found that follow-ups,preliminary notification, stamped reply envelopes, and monetary in-centives were important factors for increasing response rates. Harvey(1987) also determined that a number of other techniques such as per-sonalization, length, sponsorship, and appeals had a more ambiguouseffect on response rates.

Although useful, traditional qualitative reviews are limited in thatthey do not explicitly consider differences in studies due to samplesize, sample composition, experimental executions, and effect sizes(Fox, Crask, and Kim 1988). Recently, quantitative reviews such asmeta-analysis, designed to address these limitations, have emerged.Several of these meta-analyses provide important background for thepresent study and are summarized in table 1.

In an early comprehensive review of the mail survey literature,Heberlein and Baumgartner (1978) examined nine factors affecting ini-tial and final survey response rates across 98 studies. They were ableto explain 51 percent of the variance in final response through thenumber of respondent contacts (preliminary and follow-up) and thesaliency of the survey topic. Additionally, Heberlein and Baumgartner(1978) found that sponsorship, length of the questionnaire, type ofpopulation surveyed, incentives, and postage (metered or franked)were also significant determinants of response (see table 1). Goyder(1982) replicated the Heberlein and Baumgartner (1978) study by con-ducting a meta-analysis on 145 citations. His findings were largelyconsistent with the predictive model of Heberlein and Baumgartner,finding that type of population, number of contacts, sponsorship, and

A Meta-analysis of Mail Surveys 615

monetary incentives were most important in predicting final surveyresponse rate.

In another quantitative review of 93 studies for all types of surveys(personal, telephone, and mail), Yu and Cooper (1983) examined 15factors reported potentially to affect survey participation rates. Theyfound, across method of contact, that response rates were improvedby incentives (prepaid and promised), nonmonetary premiums and re-wards, and by increasing the amount of monetary rewards. Other tech-niques that increased response were preliminary notification, foot-in-the-door approaches, personalization, and follow-up contacts (table 1).

In a recent meta-analysis. Fox, Crask, and Kim (1988) examined 82studies with respect to eight factors that have been associated acrossstudies with higher response rates to mail surveys. In their analysis,prenotification, follow-ups, and stamped return postage were found tobe significant determinants of mail survey response rates (table 1).Additionally, sponsorship and small monetary incentives enhanced re-sponse.

Two other more focused quantitative reviews examined the effectsof personalization (Worthen and Valcarce 1985) and several types ofreturn postage (Armstrong and Lusk 1987). Worthen and Valcarce(1985) reviewed 26 studies and concluded that the effect size for per-sonalization from previous research was small, and in a follow-up ex-periment found no effects on response rate due to cover letter person-alization. Armstrong and Lusk (1987) conducted their meta-analysison 34 studies and concluded that the only effect of consequence onresponse rate was first-class postage as compared to business reply,and even that effect was weak (table 1).

In synthesizing across the prior meta-analyses, a subset of the mailsurvey response determinants appears to be consistently predictive ofresponse rates. Drawing from the above studies, repeated contacts,whether they be in the form of preliminary notification of a survey ora survey follow-up, appear to have a considerable positive impact onresponse rates (Fox, Crask, and Kim 1988; Goyder 1982; Heberleinand Baumgartner 1978; Yu and Cooper 1983). Another factor that alsohas been reported to have a consistent positive effect is the use ofmonetary incentives (Fox, Crask, and Kim 1988; Goyder 1982; He-berlein and Baumgartner 1978; Yu and Cooper 1983). In addition, thesponsor of a survey, although less under the control of the researcher,has been a key determinant of survey response (Fox, Crask, and Kim1988; Goyder 1982; Heberlein and Baumgartner 1978). Finally, theinclusion of and type of outgoing and return postage has been observedconsistently to affect response rates (Armstrong and Lusk 1987; Fox,Crask, and Kim 1988).

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The present study complements and extends previous work, includ-ing the meta-analyses of Armstrong and Lusk (1987), Fox, Crask, andKim (1988), Goyder (1982), Heberlein and Baumgartner (1978), Wor-then and Valcarce (1985), and Yu and Cooper (1983), in several ways.First, a meta-analysis is used to assess more factors (17; see table 2)which influence response rate than were examined by previous re-searchers. As summarized in table 1, the present research found sev-eral of these factors to have a significant effect on survey response (seedetails below). Second, a larger number of study outcomes (184 effectsin 115 articles) than in most prior research are included in the currentmeta-analysis. Third, different from previous work, two moderatorvariables (year of publication and type of sample) which may haveaffected the results of previous studies are examined. Given previouswork (Harvey 1987; Heberlein and Baumgartner 1978; Yu and Cooper1983), the publication year of the study was included as a type ofcohort effect because of the changes in respondents' habits and journaleditors' preferences over time, and to reflect any potential accumula-tion of survey administration knowledge. The type of subject sampled(e.g., consumer, educational, industrial) was included as a potentialinfluence on response rates given work and comments on subjects andtarget populations (Goyder 1982; Harvey 1987; Heberlein and Baum-gartner 1978; Kanuk and Berenson 1975; Linsky 1975). In these ways,the purpose of this study was to enhance understanding of mail surveyresponse behavior by presenting a more comprehensive meta-analysisinvestigation of factors that affect mail survey response rates.

Meta-analysis Method

To accomplish this purpose, a quantitative review of research designeffects on mail survey response rates was conducted using meta-analysis procedures described by Glass (1977), Hunter and Schmidt(1990), and Hunter, Schmidt, and Jackson (1982). The procedures wereused to determine mean effect sizes for factors which affect responserate and whether these effects are the same for different target audi-ences and whether the effects have changed over time. Also, based onthese procedures, a determination can be made whether variation inresults across samples (studies) was due to methodological problemsand statistical artifacts (e.g., sampling error) as compared to actualsubstantive differences in subpopulation correlations of response ratesand design effects identified by underlying moderator variables (e.g.,target groups or time periods) (see details below). Response rate, thedependent variable, was defined as response as a percent of size of


contacted sample (Yu and Cooper 1983). The independent variableswere the manipulated factors designed to affect response rate.

STUDY INCLUSION AND CODING

Individual studies (articles) that were included in the meta-analysiswere identified in several ways. First, reference lists of several pre-viously published review articles on mail surveys were examined (e.g..Fox, Crask, and Kim 1988; Harvey 1987; Heberlein and Baumgartner1978; Kanuk and Berenson 1975; Linsky 1975; Yu and Cooper 1983).Second, a multi-abstracting services computer research was conductedfor all available years in the data bases. These included ABI/Inform(1971+), ERIC (1966+ ), Social SciSearch (1972+ ), Sociological Ab-stracts (1963+ ), and Psylnfo (1967+ ). Key words used in the searchincluded "mail surveys," "response rates," and each of these termscombined with the names and synonyms for the various factors listedin table 1. Third, for recent work (1978+ ), a manual search of 10journals that have a history of publishing mail survey studies was per-formed (i.e., American Sociological Review, Journal of the Academyof Marketing Science, Journal of Advertising Research, Journal ofthe American Statistical Association, Journal of Applied Psychology,Journal of Marketing, Journal of Marketing Research, Journal of theMarket Research Society, Psychological Reports, and Public OpinionQuarterly). The manual search uncovered three articles not identifiedfrom previously published reference lists or from the computer search.

To be included in the meta-analysis, studies must have manipulateda factor that infiuences response rates and reported response rates forthe different experimental conditions. If more than one factor wasmanipulated in a study, each individual effect (study outcome) wasincluded as a separate "data point" for the meta-analysis. Over 25journals from disciplines such as business, education, marketing, polit-ical science, psychology, sociology, and statistics were represented.The 10 journals that yielded the most articles for the meta-analysiswere: Public Opinion Quarterly, 27 articles; Journal of Marketing Re-search, IS SirticlQs; Journal of Applied Psychology, 11 Sirticles; Journalof Marketing and Journal of the Market Research Society, 8 articleseach; Journal of Advertising Research, 1 articles; American Sociologi-cal Review and Journal of the Academy of Marketing Science, 4 arti-cles each; Journal of the American Statistical Association and Psycho-logical Reports, 3 articles each. The remaining journals produced 1 or2 articles for the meta-analysis. The time period was from 1940 toJanuary 1988, and 115 articles yielding 184 data points (effects) for 17different predictors of response rate were included. Although many

620 Yamnnarino, Skinner, and Childers

articles yielded multiple data points, the effects were always for differ-ent predictors of response rate. Thus, for any given predictor, onlyone data point per study was included in the meta-analysis. The articlesincluded in the meta-analysis are listed in the Appendix, and the 17manipulated factors of interest are identified in table 2.

All information relevant to the meta-analysis was coded from thearticles by four trained coders. Data from each article were codedby two individuals independently. The coders then compared theirinformation. In cases of disagreement, a third coder was asked to codethe article in question independently and then all three coders metto determine a consensus coding for the article. Although inter-raterreliability statistics were not calculated, there were very few instancesof nonagreement between raters.

Response rate, the factor that was manipulated (in general, a "yes/no" code was used), sample size, statistics, effect sizes, and directionof relationships were coded. The two moderator variables also werecoded. To ensure an adequate number of data points for meta-analysisfor the predictors of response rates under several conditions of themoderators, publication years were grouped as 1940-70, 1971-75, and1976-87, while sample type was classified to contrast consumers ver-sus various institutional groups (see details below).

META-ANALYSIS CALCULATIONS

To distinguish between artifactual (e.g., sampling error) and substan-tive sources of variation, the procedures developed by Glass (1977),Hunter and Schmidt (1990), and Hunter, Schmidt, and Jackson (1982)were combined in a computer program specifically designed for themeta-analysis in this study. The work of Glass (1977, p. 374) was usedto convert study statistics (e.g., t, F, X^, contingency table, Mann-Whitney U) to uncorrected product-moment correlations. Measure-ment error (reliability) and range restriction affecting these correlationswere generally not issues of concern in the present study because thevariables involved were actual counts of mailings and responses. Ex-cept for potential coding and typographical errors, therefore, reliabilityshould be 1.00 and the range should be completely unrestricted. Thefrequency-weighted mean correlation (r) across studies, the best esti-mate of the population correlation (p), and the frequency-weightedaverage squared error (5^ )̂, the corresponding uncorrected or ob-served variance of correlations across studies, were then computedusing the work of Hunter and Schmidt (1990) and Hunter, Schmidt,and Jackson (1982). Because the observed or uncorrected total vari-ance (5̂ )̂ can be confounded by variation in sample correlations pro-duced by sampling error, it was corrected by removing sampling error


variance as described by Hunter and Schmidt (1990) and Hunter,Schmidt, and Jackson (1982) to derive the best estimate of the popula-tion variance (cr̂ p).

To test the significance of the variance corrected for sampling erroror unexplained variance, a x̂ statistic was used (Hunter and Schmidt1990; Hunter, Schmidt, and Jackson 1982). Because of very high statis-tical power, however, even very small amounts of variation acrossstudies will be statistically significant. That is, variation may be ofminimal magnitude and significant, but when the x̂ is nonsignificant,strong support is provided for the lack of actual variation across stud-ies. As suggested by Hunter and Schmidt (1990) and Hunter, Schmidt,and Jackson (1982), when the unexplained (corrected) variance issmall, inconsistent results across studies are due mainly to statisticalartifacts, and subpopulation correlations do not differ. Thus, it is notnecessary to research for moderator variables. In contrast, when thecorrected (unexplained) variance is large, inconsistent results acrossstudies are not artifactual, and they recommend examination of moder-ator variables (year of publication and type of sample in the presentstudy) to identify subpopulations with different correlational values.

As a general rule, a moderator search should be initiated if the ratioof unexplained (corrected) to total variance is greater than 25 percent,or in contrast, if the ratio of explained (sampling error) to total varianceis less than 75 percent. Regardless of other indicators (e.g., statisticalsignificance of mean correlations), this 75 percent rule is used to deter-mine whether a moderator search is warranted (Hunter and Schmidt1990; Hunter, Schmidt, and Jackson 1982). The above calculationsand procedures are then repeated within each condition (level) of thepotential moderator variables. If (1) the frequency-weighted mean cor-relation varies from subgroup to subgroup and (2) the average cor-rected variance for the subgroups is lower than the corrected variancefor the data set as a whole, then there is evidence for a moderatedeffect. Cle£irly, after examining one moderator, the unexplained (cor-rected) variance may still be greater than zero for each level of amoderator. This suggests that the remaining variance is due to addi-tional moderators or statistical artifacts.

Results

OVERALL RESULTS

The findings of meta-analysis for each of the research design effects(predictors) on mail survey response rates are presented in table 2.

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For each predictor shown in the first column of the table, the followinginformation is presented in columns 2-9: frequency-weighted meancorrelation; 95 percent confidence interval for the correlation; cor-rected (unexplained) variance (i.e., total minus sampling error vari-ance); x̂ test of corrected (unexplained) variance; number of correla-tions (effects); range of the correlations (effects); total sample size;uncorrected total or observed variance in the sample correlations; andvariance explained by sampling error (i.e., ratio of sampling error tototal variance).

Results collapsed across all design effects are shown in the first rowof table 2; that is, regardless of the manipulation in a study, the effect(correlation) was .065, which was not statistically significant (95 percentconfidence interval includes zero). The range of the correlations in thiscase was -.217 to .850. The 184 correlations were distributed asshown in table 3. With the exception of one outlier, the correlationswere generally well dispersed throughout the range and somewhat nor-mally distributed. As indicated by the remaining data in the first rowof table 2, subpopulation correlations should be examined, and thelogical starting point is with the 17 different manipulations also identi-fied in table 2.

Inspection of the distributions of the correlations for each of the 17manipulations shown in table 2 revealed two somewhat interestingresults. First, for incentives greater than $.50 and less than or equalto $1.00, the frequency-weighted mean correlation of .119 was ob-


tained from two significant effects (.434 and .308) and two nonsignifi-cant effects (.025 and .043). This "split" distribution and small numberof correlations may thus indicate an unstable finding. Second, for ano-nymity, the frequency-weighted mean correlation of .021 was obtainedfrom 10 effects within the - .047 to .089 range and one outlier effect(.850). But this outlier effect was based on a small number of subjects(300 out of 74,259), so the overall effect size was not inflated artifi-cially. For the remaining manipulations, the effects were fairly welldistributed across each of the ranges reported in table 2. Of the 17correlations, only the correlations for appeals (.047), reply/return en-velope (.079), incentives of $.50 or less (.184), incentives greater than$1.00 (. 122), and survey length greater than four pages ( - .078) differedsignificantly from zero (95 percent C.I.).

For appeals and survey length greater than four pages, x̂ tests ofcorrected (unexplained) variance were not significant, and more than75 percent of total (uncorrected) variance was explained by samplingerror variance. A moderator search for these two predictors of re-sponse rate was not necessary—the correlations for appeals and sur-vey length greater than four pages were significant and derived fromone population. Although x̂ tests for special delivery/air mail returnpostage, incentives greater than $1.00, and deadlines were not signifi-cant, the ratio of sampling error to total variance for each is less than75 percent. For the remaining manipulations identified in table 2, x̂tests were significant, and less than 75 percent of total variance wasexplained by sampling error variance. Therefore, except for appealsand survey length greater than four pages, the sample correlationswere derived from more than one population, and moderators need tobe investigated.

MODERATOR RESULTS

Subpopulation correlations were investigated for two potential moder-ators—year of publication and type of sample—of the relationshipbetween research design effects and mail survey response rates. Fre-quencies of correlations across all predictors of response rate for dif-ferent levels of the two moderators were examined. The final categori-zation of year of publication was 1940-70 (62 correlations), 1971-75(55 correlations), and 1976-87 (67 correlations). Type of sample wasoriginally coded as consumer (KM correlations), educational (35 corre-lations), industrial (19 correlations), health care (10 correlations), gov-ernmental (4 correlations), and other institutional (8 correlations)groups. The final categorization of type of sample was consumer andinstitutional groups (108 and 76 correlations, respectively). These finalcategorizations were chosen to ensure an adequate number of correla-


tions per cell for meta-analysis of individual predictors as well as toidentify two independent moderators. Nonsignificant x̂ results (2.116)indicated that the frequencies of correlations did not differ by cell, andthe two moderator coding schemes, year of publication and type ofsample, were independent of one another (r = .093, n.s.; phi = .107).Thus, two separate ways of specifying subpopulations were identified.

Moderator analyses were conducted for all predictors of responserates for which variance explained by sampling error was less than 75percent and which had sufficient numbers of correlations (three ormore) in cells based on time periods or types of samples to performmeta-analysis. To conserve space, only results of comparisons usingthe previously described criteria for determining a significant modera-tor are presented in table 4. The same type of information about corre-lations and variances as displayed in table 2 is shown in table 4 forpredictors of mail survey response rates by year of publication and bytype of sample moderator.

For four predictors of response rate (preliminary notification, reply/return envelope, stamped/metered postage, and personalization), allthree levels of the year of publication moderator were represented. Inonly two cases, however, did there seem to be a moderated effect. Forpreliminary notification, the subgroup correlations varied from .010(1971-75) to .285 (1976-87), the latter one was significantly differentfrom zero, and the average corrected variance in the subgroups was.014 as compared to a corrected variance of .023 for the data as awhole. Although variance explained by sampling error for the sub-groups did not reach 75 percent (suggesting the presence of additionalmoderators), year of publication did appear to moderate the prelimi-nary notification-response rate relationship. For stamped/metered re-turn postage, the subgroup correlations ranged from - .008 (1971-75)to .066 (1940-70), all three differed significantly from zero, the averagecorrected variance in the subgroups was zero as compared to a cor-rected variance of .001 for the data as a whole, and variance explainedby samphng error was 100 pecent in all subgroups. Year of publication,therefore, did appear to be a significant moderator of the stamped/metered return postage-response rate relationship.

For eight design effects on response rates (preliminary notification,follow-ups/repeated contacts, stamped/metered postage, personaliza-tion, incentives less than or equal to $.50, nonmonetary incentives,survey appearance, and anonymity), both levels of the type of samplemoderator were represented. In only two cases, however, did thereseem to be a moderated effect. For follow-ups/repeated contacts, thesubgroup correlations were .073 (consumer groups) and .306 (institu-tional groups), the latter one was significantly different from zero, andthe average corrected variance in the subgroups was .002 as compared

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to a corrected variance of .015 for the data as a whole. Althoughvariance explained by sampling error for the subgroups did not reach75 percent (suggesting the presence of additional moderators), type ofsample did appear to moderate the follow-ups/repeated contacts-response rate relationship. For stampedlmetered return postage, thesubgroup correlations were .009 (consumer groups) and .061 (institu-tional groups), the latter one was significantly different from zero, andthe average corrected variance in the subgroups was .000 as comparedto a corrected variance of .001 for the data as a whole. In the institu-tional (but not in the consumer) subgroup, the variance explained bysampling error exceeded 75 percent. Although weak, type of sampledid seem to moderate the stamped/metered return postage-responserate relationship.

Discussion

KEY RESULTS AND IMPLICATIONS

Four key findings can be identified from the meta-analysis results re-ported in this study. First, the relationships between appeals and re-sponse rate, and survey length greater than four pages and responserate, were statistically significant and derived from one population;that is, the relationships are generalizable across the studies investi-gated. Thus, in designing mail surveys, regardless of the target popula-tion, researchers that use a cover letter that includes appeals and asurvey of less than four pages should be able to increase their responserates.

Second, the associations between reply/return envelope and re-sponse rate, incentives less than or equal to $.50 and response rate,and incentives greater than $1.00 and response rate, were statisticallysignificant but derived from more than one population; that is, therelationships are situation specific and there is a need to examine po-tential moderators. Because the moderators in this study were notsignificant in the cases of these three predictors, to increase responserates by using one of these factors, researchers must consider othervariables that may influence individuals' willingness to respond (e.g.,situational factors, personality characteristics).

Third, for the remaining predictor-response rate relationships inves-tigated, the correlations were not statistically significant, and subgroupcorrelations identified by moderators need to be examined. Thus, with-out consideration of other variables that may influence individuals'response tendencies, these factors do not appear to enhance response.In this study, two potential moderators were examined.


The fourth finding pertains to the two moderators, year of publica-tion and type of sample. In terms of the meta-analysis results for theyear of publication moderator, several key findings can be specified.First, the relationship between preliminary notification and responserate varied across time periods, and for more recent times (1976-87),the correlation was statistically significant. Preliminary notification hasbeen most effective in recent time periods, suggesting that respon-dents' changing attitudes are involved. Current researchers shouldtherefore continue to use preliminary notification to enhance responserates. Although time or cohort influences appear to be significant, theresults also suggest other moderators, and thus subgroups, could beaffecting this relationship. Second, the association between stamped/metered return postage and response rate also varied with time, and allthree correlations were statistically significant. Moreover, the resultssuggest that searching for additional moderators of this relationshipmay not be necessary. Third, for the remaining predictor-response raterelationships, year of publication was not concluded to be a significantmoderator.

Several key findings also can be specified for the meta-analysis re-sults for the type of sample moderator. First, the association betweenfollow-ups/repeated contacts and response rate differed for samples ofconsumer groups and institutional groups, and the correlation for thelatter was statistically significant. Thus follow-ups/repeated contactsseemed to have a greater effect on institutional than consumer groups'response rates, and the designers of mail surveys for these differenttarget groups should be aware of such differential responses. For bothsubgroups, however, additional moderators may be influencing thisassociation. Second, the relationship between stamped/metered returnpostage and response rate differed (albeit weakly) for samples of con-sumer and institutional groups, and again, the latter correlation wasstatistically significant. There seemed to be a greater effect on theresponse rates of institutional as compared to consumer groups. How-ever, for the consumer groups, at least, additional moderators may beoperating. Third, for the remaining predictor-response rate relation-ships, type of sample was not concluded to be a significant moderator.

The statistically significant effects in this study were moderate inmagnitude. The practical implications of these findings can be illus-trated in terms of a binomial effect size display (BESD; Rosenthal1984; Wolf 1986). The predictor with the largest frequency-weightedmean correlation with response rate was incentives less than or equalto $.50 (r = .184). Using this incentive would increase mail surveyresponse rates approximately 18,4 percent, or from a response rate ofabout 40.8 percent to 59.2 percent (see Rosenthal 1984, p. 131). Incen-tives of $1.00 or more would improve response rates 12.2 percent.


This is consistent with the findings of Goyder (1982), Heberlein andBaumgartner (1978), and Yu and Cooper (1983).

Preliminary notification for studies conducted between 1976 and1987 and follow-ups for institutional respondent groups were also sig-nificantly related to response rates. All previous meta-analyses, withthe exception of Armstrong and Lusk (1987), who did not examine anytype of contact, report similar findings. The present results suggestthat preliminary notification (1976-87) increased response rate by 28.5percent, while follow-ups for institutional groups increased responserate by 30.6 percent.

Like Fox, Crask, and Kim (1988) and Armstrong and Lusk (1987),in the current study, postage was an important predictor of responserate. For all time periods, stamped/mete red return postage was signifi-cantly related to response rate. Most recently (1976-87), stamped-metered postage increased response rate 2.4 percent, while in the past(1940-70), the enhanced effect on response rates was even greater (6.6percent). The negative effect obtained for the period 1971-75, how-ever, may be a statistical anomaly due to the small number of observedcorrelations (5). On the other hand, the trend over the 47-year timeperiod indicates that the affect of postage is declining and may signalan expectation on the part of respondents that postage is a surveyrequirement and its absence may only tend to deter rather than moti-vate response. Moreover, for institutional samples, stamped/meteredreturn postage increased response rates about 6.1 percent. Again, how-ever, this result is based on a small number of correlations (5).

Compatible with the work of Heberlein and Baumgartner (1978),results from the current study indicated that response rates would bereduced by 7.8 percent for survey lengths of greater than four pages.In contrast to Yu and Cooper (1983), appeals (4.7 pecent) and reply/return envelope (7.9 percent) had a positive effect on response ratesin the present investigation. Overall, the average effect size across allpredictors of response rate (r = .065) in the current study would indi-cate an increase in response rates of about 6.5 percent, or from 46.75percent to 53.25 percent (see Rosenthal 1984, p. 131).

Two current findings were not consistent with previous research.WhUe Fox, Crask, and Kim (1988), Goyder (1982), and Heberlein andBaumgartner (1978) reported a significant effect for sponsorship, thepresent results did not support this finding. One reason for this differ-ence may stem from the level of aggregation of studies used in thepresent study. By aggregating groups within the institutional category,subpopulation effects may have been obscured. For instance, He-berlein and Baumgartner (1978) found that government sponsorshipincreased response, while this study was unable to investigate thisissue because only six data points were available for analysis. Also,


Yu and Cooper (1983) reported a significant relationship between non-monetary incentives and response rate. Nonmonetary incentives werenot a significant predictor of response rates in the present study, al-though the overall correlation did indicate the presence of a positiveeffect on response. This result as well as those discussed earlier maydiverge from the present results, in part due to the inclusion of bothmore recent studies and a greater number of effect sizes than in pastwork.

LIMITATIONS AND FUTURE WORK

Nevertheless, the results of this investigation are generally compatiblewith and extend those of prior review (e.g., Harvey 1987; Kanuk andBerenson 1975; Linsky 1975) and meta-analysis (e.g.. Fox, Crask,and Kim 1988; Goyder 1982; Heberlein and Baumgartner 1978; Yu andCooper 1983) articles about mail surveys. The extensions involve theclarification of prior work by examining a greater number of factorsthat influence response rate through a large number of study outcomesand the investigation of moderators. The current study, however, isnot without limitations, and thus, several directions for future researchcan be suggested.

First, the moderators that were chosen for investigation did not clar-ify the relationships as much as had been expected. Perhaps this wasdue to a poor coding scheme or perhaps the choice of moderatorswhich were too global in nature. For instance, to ensure an adequatenumber of correlations for meta-analyses of individual predictors, itwas necessary to combine educational, industrial, health care, govern-mental, and other institutional samples into one subgroup. This combi-nation may have obscured or perhaps cancelled important differencesin results among the groups. With a few exceptions, predictor-responserate relationships appear to be moderated by other variables. Futurework concerning moderators might focus on characteristics of respon-dents (e.g., personality, prior knowledge, involvement, expertise), ad-ditional details about samples (e.g., academic, government, generalpublic), and the situations (e.g., work vs. home or on-site intercepts) inwhich surveys are administered and/or completed. Also, results fromcensus pretesting (Riche 1987) indicate there are significant geographicdifferences in response rates due to regional as well as metropolitanversus rural respondents. These results as well as those from BLSsurveys (Riche 1987) and the Goyder (1982) study indicate that otherpotential moderators to be examined may relate to cultural and subcul-tural (e.g., blacks, Hispanics) differences in response to surveys.

Second, the predictors of response rate were investigated in a limitedway in this study; in general, a simple "yes/no" coding scheme was


used. Along the lines of the work of Worthen and Valcarce (1985)and Armstrong and Lusk (1987), future studies could investigate eachpredictor in this study in greater detail as well as focus on other andmultiple predictors of response rate simultaneously. Likewise, someof the findings from the current research may be unstable due to thesmall number of effect sizes that were available to be synthesized (e.g.,appeals, deadlines, incentives greater than $.50 and less than or equalto $1.00). As such, additional primary research on these predictorsof response rate is necessary. In these ways, more comprehensivemeta-analyses could be conducted in future work.

Third, because the 184 data points (study outcomes) for the meta-analysis were obtained from 115 studies (articles), an average of 1.6per study, there is nonindependence of study outcomes in severalcases. For any given predictor of response rate, however, only oneeffect size per study was used in the meta-analyses. Although noninde-pendence may not be of concern when an individual factor influencingresponse rate is examined, it affects the overall meta-analysis encom-passing all predictors. To address this issue, the average correlationper study across factors manipulated in that study was used in theoverall meta-analysis. In future work, various statistical adjustmentsto the correlations or limiting the number of correlations included fromany one study could be investigated as alternatives to the approachused here. Likewise, whether more than one manipulation (e.g., per-sonalization and return postage) for the same subjects in one studyreduces or enhances response rates could be investigated in futurework.

Fourth, a "file drawer problem" may exist in that inclusion of miss-ing studies could alter the results of this investigation (Rosenthal 1979).The inclusion of unpublished studies, especially in cases where resultsare based on only a few published studies or the confidence intervalsare close to zero, could affect the findings reported here. In futureinvestigations, researchers might include both published and unpub-lished work in meta-analyses to obtain more comprehensive findings.

Fifth, although guided by prior theoretical work, this investigationdid not present a complete conceptualization or test of how and whypeople respond to mail surveys. Given the results of this investigation,future work could focus on testing, with meta-analysis as well as newdata and experimental studies, a comprehensive model of mail surveyresponse behavior. Candidates for the comprehensive theory includesuch social psychological theories as attribution and self-perceptiontheory (cf. Hansen 1980; Reingen and Kernan 1977, 1979) and disso-nance theory (Furse and Stewart 1982; Hackler and Bourgette 1973)as well as the equity/exchange model (Childers 1986; Childers andSkinner 1985). In the latter, the three constructs of cooperation, com-

632 Yannmarino, Skinner, and Childers

mitment, and trust are integrated to provide an explanation for theimpact of such significant effects as preliminary notification, follow-ups, and appeals (increasing respondent outcomes) and postage/returnenvelope and questionnaire length (reducing respondent inputs) as wellas incentives and sponsorship (increasing respondent trust).

Despite some limitations, the current research incorporated severalaspects of a properly conducted meta-analysis as identified byBangert-Drowns (1986) and Bullock and Svyantek (1985). Moreover,meta-analysis procedures of Glass (1977), Hunter and Schmidt (1990),and Hunter, Schmidt, and Jackson (1982) were combined in this study.The results of this research, in conjunction with those from the sug-gested future research efforts, should clarify understanding of mailsurvey response behavior and, thus, permit the design of surveyswhich increase response rates. The current investigation was intendedas a step in that direction.

AppendixStudies Included in Meta-anaiysis

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636 Yammariffio, Skinner, and Childers

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UNDERSTANDING MAIL SURVEY RESPONSE BEHAVIOR

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