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U S ING CROSS SECTIONALSURVEYS TO PLANMESSAGE STRATEGIES

By Robert Hornik and Kimberly Duyck WoolfINTRODUCTION

This paper identifies methodsfor using cross-sectional survey datato guide the design of the messagestrategies used in the comm unicationcomponent of a social marketing cam-paign. It divides the process into tw ostages: deciding on potential messagestrategies to incorporate them into asurvey instrumen t and then analyzingthe survey results to provide guidancefor message strategy choice.

The analysis requires looking at theevidence to answer three questions: Are there many people wi th the

'wrong' views on the message-relevant belief?

Is there substan tial associationbetween the b elief and the outcome?and

Is the belief one which an educa-tional campaign might affect?The paper presents three illustrative

message strategies relevant to cigarettesmoking among adolescents, the n stepsthrough the analysis of some exampledata and describes some possibleconclusions. The paper concludes withan examination of the strengths andweaknesses of this approach.

There are many ways that empiricalresearch can help guide the developmentof social marketing programs and the ircommun ication components. They canbe used to define the target audience, tochoose focus behaviors, to select amongpossible channels, to provide feedback asto day-to-day successes or failures of theprogram as well as to provide fundamen-

tal evaluation of long-term success.However, among the most importantroles is assistance in the developmentof message strateg ies.

There are manyways tha t empiricalresearch can helpguide the developmentof social, marketingprograms and theircommunicationcomponents.Cross-sectional surveys are one

method for obtaining data to guide themessage strategy development process.Other methods include use of focusgroups, in-depth interviews, l i teraturereviews, expert informants on thequalitative side, or formal laboratoryor field experimentation on thequantitative side.

Mass communication projectsoperate at a distance from the ir audi-ences. This contrasts with conventionaleduc ationa l effo rts. Teachers in aclassroom can hear the questions, readconfused expressions, and tailor theirlessons to meet the needs of theirstudents. If they choose, health workersin a clinic can ask their clients to repeatthe recommendations for home treatmentof a diarrheal episode, to see whetheror not the clients have understood.

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SOCIAL MARKETING QUARTERLY 3 5 .APPLICATIONSCommunication projects may reach much largeraudiences and often are able to assure higher fidelity ofcore messages than classroom teachers or clinicstaffHowever, they do no t have such easy channe ls forfeedback from their audiences.

Communication projects may reach muchlarger audiences and often are ableto assure higher fidelity of core messagesthan classroom teachers or clinic staff.However, they do not have such easychannels for feedback from th eir audiences.As a result, they have a special need forresearch and eva luatio n, for use indesigning th eir messages.

We assume that program planners havechosen a behavior and target audience andare ready to d efine message strate gy. Bymessage strategy, we refer to the essentialbelief(s) tha t a message w il l be designed toimp art. I t is no t the same as the messageitself, which will be the product of a creativeprocess that will turn the strategy into aspecific realization - whether that is atelevision ad, a printed booklet, or a radiosoap opera episo de. The developm ent of thestrategy is the intervening step betweenchoosing a focus behavior and messagecreation. I t wi ll often be the meat of thecreative brief which producersw ill dependupon to guide message production.

THE PROCEDUREWe find it useful to divide the process

of using cross-sectional surveys to assiststrategy development into two dist inctstages: the design of the survey, andthe analysis of the survey to make recom-mend ations of preferred strateg ies. Thedesign of the survey begins with thedevelopment o f hypotheses about factorstha t might explain the behavior. Plannerscan rely on available theory, on the bestadvice of informants, on discussions withgroups representing the target audience -

or on their own judgment - and based onthese cons iderations, suggest a range ofpossible causes that they think mightinfluence audience behavior.

For example, in this paper weusedesireto stop smoking among 12 th grade currentsmokersas the ta rge t outcome (we knowthat the desire to stop smoking is highlypredictive of the behavior of frequentlytryin g to quit smoking). Some mightsuggest that qui tt ing cigarette smokingamong adolescents depends on their percep-tion of the negative health consequences ofsmoking. Others may argue th at rigarettesmoking is a reflection of social expectationsfrom peers - individual use will vary withperceived peer smo king. St ill others migh tsuggest that o verall att itude toward smok-ing, knowledge about ways to qu it smok ing,or perception of whether or not smokers areperceived as'cool' is cru cia l. The process ofgenerating this list is really a creativeprocess, as is hypothesis genera tion, usually.There is no definitive way to know when thelist is f inished, when all the im portantpotential explanations have been offered.However, there are some useful guides forgenerating possible candidate explanations.

There is a great dea l of codified experi-ence concerning likely determinants ofhealth behavior (for useful reviews seeFishbein, Middlestadt, & Hitchcock, 1991;Hornik, 1 991 ; or Maibach & Parrott, 1995).The categories these authors suggestwi ll st imulate useful speculation aboutpossible explanations for many types ofbehaviors. One helpfu l emp irical approachis what Ajzen and Fishbein (1980) call'elicitation research.' This process involvesquestioning sma ll samples of respondents.

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3 6 SOCIAL MAR KETING QUARTERLYAPPLICATIONSThey are asked to describe what good andbad consequences are likely to accrue fromengaging in a particular behavior and tolist which of various influencers aroundthem would expect them to perform or notperform the behavior. The most co nsistentlymentioned consequences and influencersrepresent implicit hypotheses about whatmight influence the behavior, and would beadded to the questionnaire.

However, the development of the listof hypotheses for a communication strategyis subject to a constraint that is not presentin the usual listing of possible explanationsfor a behavior. These explanations musthave clear implications for the developmentof a message strategy. Almost always, tha tconstraint w ill mean that each explanationwill touch on a respondent's beliefs orperceptions, since beliefs are the essenceof what messages address (whether thosebeliefs are about the benefits or costs of abehavior, the expectations of others for therespondent's behavior, or the respondent'sbeliefs about him or herself vis-a-vis thebehavior). As a contrasting exam ple,even if parental education is a predictorof willingness to quit smoking, it has noobvious implication for a message strategy.However, many hypotheses do carrysuch implic ations about message strategies.If the perceived risk of health consequencesis key, then messages about the extent ofrisk might be expected to influence behav-ior . If perceptions of peer behavior arecentral and if others are, in fact, not usingcigarettes, messages emphasizing what peersare doing would be appropriate. Thus, thefirst task is to lay out the range of possibleinfluences on a behavior and their impliedmessage strategies.

This list should drive the design of thesurvey, since the largest number of ques-t ions will be meant to indicate the extentto which the respondent believes or doesn'tbelieve in the truth of a statem ent. For thesake of this article, we will assume that thetarget audience is adolescents, and thetarget behavior is quitt in g smoking. Toillustrate the process of choosing amongmessage strategies, we wil l assume t ha t

there are only three candidate strategies,one of which emphasizes the risk of a healthconsequence from sm oking, the second ofwhich emphasizes a perception that smokingis the norm among friends and the thi rd ,which emphasizes general disapprova l ofsmoking as a behavio r. We use the datafrom the 1996 Monitoring the Future surveyof 12 thgrade students (Bachman, Johnston,& O'Malley, 1998), selecting those studentswho describe themselves as having smokedin th e last 30 days. Our analysis focuses onthe four questions which follow, with theresponse categories in parentheses after thequestions. The firs t three questions embodyone of the three hypotheses, while thefourth question, asking about 'wanting tostop,'will be used as the outcome variable.1) How much do you think people risk

harming themselves (physically or inother ways) if they smoke one or morepacks of cigarettes per day? [no risk,slight risk, moderate risk, great risk].

2) How many of your friends would youestimate smoke cigarettes? [none ofthem,a few of them, half of them, mostof them, all of them].

3) Do you disapprove of people (who are18 or older) smoking one or more packsof cigarettes a day? [don't disapprove,disapprove, strongly disapprove].

4) Do you want to stop smok ing now?[Yes, No]The survey was done with 2,466 12 th

grade students in the Spring of 1996 by theUniversity of Michigan and the data weredownloaded from the website of the Ins t i -tute for Survey Research (Bachman et al.199 8). Our choice of question s to analyzewas constrained by what was available on acommon form in th at data set. To sim plifypresentation, all data were recoded intodichotomous variables and presented ascross-tabulations. Only the 602 currentlysmoking respondents, who answered th equestion about wanting to stop smoking,were included in the tables. How wouldthe results of these analyses be turned intospecific message strategy advice?

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SOC IAL MARK ETING QUARTERLY 3 7APPLICATIONSTABLE 1 - Wanting to Stop Smoking and Belief tha t Cigarettes are Harm ful

Do you w antto stopsmoking?

No

Yes

(N)

Perceived risk tha t people wi ll harm themselvesif they smoke one or more packs per day.

None or s light risk

80

20

15 (85)

Moderate orgreat risk58

42

85 (483)

(N)61(349)39(219)568

A message strategy is promising insofaras it satisfies three criteria:1) There are a su bst an tial number of people

who are not in the desired position onthe message strategy-relevan t variable.

2) There is a subs tantial relation betweenthe message strategy-relev ant variableand the outcome variable . Differentvalues on the message strategy-relevantvariable should predict who will and whowill not engage in the desired behavior.

3) I t wil l be feasible to move the targetaudience on the message strategy-relevant variable. I t is a beliefthat might be learned from aneducational campaign.For each of the three candidate stra te-

gies,we can address each of those ques-t ions . A dichotom ized version of eachvariable is used in Table 1, Table 2 orTable 3, which present results for therisk, normative and general disapprovalstrategies, respectively.

EVALUATION OF STRATEGIESWe begin with the risk strategy.

The first criterion is whether there are asubstantial number of people who hold the'wrong' belief or whether the target popula-

tio n already holds the 'correct' belief. InTable 1 , the answer is fai rly clear. Eighty-five percent of the respondents alreadyrecognize the risk of cigarette use. Thispotential strategy doesn't look promisingon those grounds, since there are only15 of the population who hold the'wrong' posit ion.

We go on to consider the secondcr i ter ion: Does status on the messagestrategy variable actually predict behavior(or in this case intention to undertakea behavio r)? Here, the data are moreoptim ist ic; while 20 of those who don'tperceive much risk want to stop smoking,42 of those who do perceive the risk wantto stop. A typ ica l way of summarizing thisrelationship is the relative odds ratio. Thisis calculated by comparing the ratios in thetwo columns: (.42/.58)/(.20/.80) which inthi s case is equal to 2 .90. Thus the oddsof wanting to quit smoking are 2.9 timesgreater if one perceives the risk of smokingthan if one doesn't.

The final criterion is whether or notthe predictor belief is 'movable.' In thejudgment of the planner, will it be possibleto convince people of the risk of harm fromsmoking? In the end, this is a judgm entcall . On the one hand, the fac t th at manypeople already hold this belief makes itpromising on this criterion . I f 85 of



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3 8 SOCIAL MARKETING QUARTERLYAPPLICATIONSthe population already believe that the riskis substantial, shouldn't it be possible toconvince the others? A contrary view wouldbe that if there was enough public informa-tion in the environment to convince 85 ofthe populatio n, the 15 who were notconvinced must be quite resistant tomessages. On thos e grounds , a new set ofmessages might not be expected to behelpful . We return to deciding whether ornot this strategy is promising after weexamine the remaining strategies.

This analysis of the first messagestrategy contrasts with the sense one canmake of the data in Table 2, which examinesthe association between normative percep-tions and willingness to quit smoking.

Against the f ir st c riterion, whether ornot there is anyone left to convince, thispredictor variable fares much better than thefirs t. Sixty-thre e percent of this sample ofsmokers believe that most of their friendsare smok ing. Agains t the second crit erio n,the presence of a substan tial associationbetween the belief and the outcome (inten-tions), this strategy doesn't look very good:39 of those who have few friends smokingintend to quit themselves; virtually thesame pro por tion, 37 , of those who havemany friends smoking intend to qui t.Again, this can be summarized as relativeodds, the ratio of the numbers in each

column (.3 9/.6 1)/( .37 /.63 ), equal to 1.09,essentially showing no relationship.

This lack of a relationship suggests thata social expectations strategy would not beso prom ising. However, we examine thefinal criterion, whether or not people aremovable on the targeted belief. As before,jud gm ent comes to the fore. Even if the rewas a relationship, is it reasonable to expectto convince people that their friends do notsmoke when their direct perceptions are thatthey do? If the respondents are accuratelyreporting their close friends' behavior, itseems dou btful that any com municationstrategy will convince them otherwise.

There are two substantive commentsabout this result worth noting . On the onehand, despite the lack of relationship withdesire to quit smoking here, in data notpresented here, friends' smoking behavioris highly related to respondent's currentsmoking behavior. This suggests tha t peerinfluences are worth attention if the issue isini t ia t io n of smoking. I t may also be tha tfriends' smoking behavior is not the same aspeer expectations that the respondent stopsmoking. Friends may be saying th at therespondent ought to quit, even if they aresmoking themselves. I f we had a measureof perceived expectation of others that onestop smoking, there might have beena relat ionship.

Table 2 - Wanting to Stop Smoking and Friends' Smoking

Do you wantto stopsmoking?

No

Yes

(N)

Belief that fr iends smokeMost of them ,al l of them .

63

37

63 (334)

None of them,a few of them,some of them613937 (194)

(N )62(327)38(201)528

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SOCIAL MARKETING QUARTERLY 3 9APPLICATIONSTable 3 - Want ing to Stop Smoking and Disapproval of Smoking

Do youwant tostop smoking?

No

Yes

(N )

Disapproval of people smoking oneor more packs of cigarettes a day

Don't Disapprove

67

33

73 (284)

Disapprove, StronglyDisapprove54

46

27 (103)

(N )64(246)36(141)387*

*Note:Disapproval questions asked only of a subset of the sample.

A final example considers the issue ofgeneralized disapproval of sm oking. Table 3presents the data. Against the first cr ite -rion; havin g some members of th e audiencewho hold the 'wrong' view, this predictordoeswel l , wit h 73 who 'don 't disapprove,'perhaps not surprising for a sample ofsmokers. Against the second criterio n,association with desire to stop smoking,the variable does only moderately well,wit h 33 of those who do n't disapprove and46 of those who do disapprove wanting toquit. The relative odds would be (.4 6/ .54 )/(.33/. 67)= 1.73, n ot as strong as in the firsttable, but st il l of some size. For the lastcriterion, 'movableness,' many messageplanners would be hop efu l. While mo stsmokers don't disapprove, the fact thatone quarter of them do disapprovesuggests that it is something that evensmokers might accept.

So against the three criteria, the threemessage strategies fare differe ntly . The fir ststrategy is probably not promising: While ithas the strongest relationship w ith behavior,there are few people left unconvinced of thisbelief to promise much reward. This issupported by an estimate of how muchchange in the population would occur if acampaign focusing on this message strategywas completely successful and everyone

believed the statement. In Table 1, 39 ofall of the respondents w ant to stop smoking.Imagine that every one of the people whodid not believe in the risk of smoking becameconvince d, and looked ju st like the peoplewho are believers in Table 1. Then, we wouldexpect th at 42 of them would intend toqu it. The maximum change we could expectfrom a comp letely successful implem entationof this strategy would be a change from 39to 42 wanting to qui t , a minima l change.

The second strategy is quite promisingon the first cr iterion, lots of people tochange, but i t fai ls on the second, sincethere is no relationsh ip. Also, if, in reality,fr iends are smoking, it would seem difficultto imagine messages which would producechange in that belief.

The third strategy is perhaps the mostprom ising, given the d ata. There are alarge number of people to move, there isa moderate relationship between the beliefand the intended behavior, and it maybe possible to develop a set of messagesth at w il l increase disapproval of smoking.Complete success would mean the re wouldbe 46 rather tha n 36 of smokers wan tingto quit now, a worthwhile change.

Clearly, these analyses are only illustra-tive, and they are constrained by thesecondary data available to us, thus the



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4 0 SOCIAL MARKETING QUARTERLY PPLIC TIONSchoices of hypotheses we could test.Certainly, other potential explanations forbehavior, and thus other message strategies,might have been tested had we started theprocess from sc ratch . Nonetheless, the y givesome notion of what the process of analysisand interpretation might look l ike whileusing widely available data.

If 8 5 of th e popu lationalready believethat therisk is substan tial,shouldn't it be possibleto convince the others? contraryview would bethat ifthere wasenoughpublic information in theenvironment: to convince85 of the population, the15 who were notconvinced must be quiteresistant to messages.IMPLICATIONS FORSOCIAL MARKETING

This analysis illustrate s t he process amessage planner might go through if he orshe wanted to explo it survey data to informmessage strategy ch oice. The streng ths ofthis approach are substa ntial. By laying outhypotheses explicitly and subjecting them tosystematic em pirical tests on a representa-t ive sample of the po pulat ion, one w i l lproduce evidence tha t can help elimina tesome potential approaches and providesuppo rt for oth ers. Surveys provide evidencethat is somewhat independent of theobserver, reducing dependence on a priorisuppositions about the audience that mayhave been based on anecdotal evidence orinsights derived from conversations w ithunrepresentative informants.

There are some lim itation s t o thisapproach, of course. The imp lication of this

analysis is that if one changes a predictorbelief, one can expect change in thepredicted outcom e. But, these are cross-sectional data, and one ought n ot confuseobserved association with claims of causa-t i on . There remains a risk th at some oth erconfounding factor accounts for an observedrelation or that there is reverse causation -with intentions to stop smoking causingbeliefs, rather tha n vice versa. The presenceof an association cannot guarantee that achange in the predictor belief w il l produce achange in the outcome.

There are weaknesses in this approach ,surely. They constrain one's confidencein the recommendations that derive fromthe a nalysis. However, th e issue here isnot whether this analysis w il l provide anincontrovertible foundation for messageplan ning . The crite rion for assessing th eworth of this approach is whether it im-proves judg me nt, making the planner lesslikely to go off in a problematic direction.It should be valuable, in this way, at bothstages. The process of generating candidatestrategies will force planners to be explicitabout what they m ight consider. Theprocess of analyzing the results againstestablished criteria w il l lead to the rejectionof some approaches and support for thecandidacy of others.

Unlike some qualitative approaches,for example, focus groups or in-de pthinterviews, survey research may not be souseful in generating the first l ist of ideasfor possible message strateg ies. Ind ee d, itwill often make sense to delay survey workuntil exploratory research is complete andcan be used as the basis for the developmentof candidate message strateg ies. However,the survey approach has the advantage ofbeing systematic and offers the abil i ty toexamine candidate strategies on a represen-tative sample of the target population.

In sum, cross-sectional survey researchcan both inform and be informed by thejudgm ent of the planners. We think th at i tpromises to improve the quality of messagestrategy planning.

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SOCIAL MARKETING QUARTERLY 4 1APPLICATIONSREFERENCESAJZEN, I. &FISHBEIN,M. (1980). Understanding attitudes and predicting social behavior.Englewood Cliffs, N J: Prentice Hall, Inc .BACHMAN, J. G., JOHNSTON, L. D. & O'MALLEY, P. M . (1998). Monitoring the Future: Acontinuing study of American youth 12 th grade survey) 1996 [Computer fi le ] . Conducted byUniversity of M ichigan, Survey Research Center. ICPSR ed. Ann Arbor, M I: Int er-U nive rsityConsortium for Political and Social Research [Producer and Distributor].FISHBEIN, M., MIDDLESTADT, S. E. & HITCHCOCK, P. 3. (1991). Using inform ation to changesexually transmitted disease-related behaviors: An analysis based on the theory of reasonedact ion . In J.N . Wasserheit, S.O. Aral & K.K Holmes (Eds.),Research ssues in human behaviorand sexually transmitted diseases in the AIDS era (pp. 243-257). Washington, DC: AmericanSociety for Microbiology.HORNIK, R. (199 1). Altern ative models of behavior change. In J.N. Wasserheit, S.O. Aral &K.K Holmes (Eds.),Research ssues in human behavior and sexually transmitted diseases inthe A IDS era(pp. 201-2 18). Was hington, DC: American Society for Microbiology.MAIBACH, E. & PARROTT, R. L. (Eds.).(1995). Designing health messages: Approaches fromcommunication theory and public health practice. Thousand Oaks, CA: Sage.

ABOUT THE AUTHORSRobert Hornik, Ph.D.,is a Professor of C omm unication, and holds the Wilbur Schramm Chairin Comm unication and Health Policy at th e Annenberg School for C omm unication, U niversityof Pennsylvania. He has led efforts to design and evaluate large-scale public health comm uni-cation a nd education programs. Some major projects for which Hornik has been prin cipalinvestigator include USAID-sponsored evaluations of national AIDS education programs in fourdeveloping countries, of com munication for child survival programs in 10 developing countries,and CDC-sponsored research on determinants o f immu niza tion status in P hiladelphia . He iscurrently co-principal investigator and scientific director for the evaluation of the NationalYouth An ti-drug Media Campaign, as well as principal investigator on two evaluations ofdomestic violence prevention p rojects, one in Philadelphia, and one nation wide.Kimberly Duyck Woolf, M.A., provided research assistance wit h th is paper. She is adoc toral candidate at the Annenberg School for C omm unication, University of Pennsylvania.Her research interests include developm ental differences in comprehension o f media messagesand the effects such messages have on children's attitudes and behavior.Note: T he authors are grateful for the helpful comments from Martin Fishbein.

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