Research in Nursing & Health, 2000, 23, 415±420

Focus on Research Methods

Mediator and ModeratorVariables in Nursing Research:

Conceptual and StatisticalDifferences

Jill A. Bennett�

School of Nursing, San Diego State University, 5500 Campanile Drive, San Diego, CA

Received 6 July 1999; accepted 17 April 2000

Abstract: Mediators and moderators are variables that affect the associationbetween an independent variable and an outcome variable. Mediatorsprovide additional information about how or why two variables are stronglyassociated. In contrast, moderators explain the circumstances that cause aweak or ambiguous association between two variables that were expected tohave a strong relationship. Mediators and moderators are often overlooked inresearch designs, or the terms are used incorrectly. This article summarizesthe conceptual differences between mediators and moderators. The statisticalanalysis of moderators and mediators in multiple regression is brie¯ydescribed and two examples are presented. ß 2000 John Wiley & Sons, Inc.Res Nurs Health 23:415±420, 2000

Keywords: mediator; moderator; data analysis

Mediator and moderator variables provide usefulinformation about how, why, or when a phenom-enon occurs. Unfortunately, many nurse research-ers fail to test for mediators or moderators in theirdata or use the terms incorrectly. For example,`̀ mediator'' is often used as if it meant `̀ pre-dictor.'' A mediator or moderator is a thirdvariable that changes the association between anindependent variable and an outcome variable(Baron & Kenny, 1986). Thus, consideration of amediator or moderator allows a more precisedescription of the relationship between indepen-dent and outcome variables. If a researcher failsto consider the possibility of a mediator or mode-rator effect in the data, a more exact explanationfor an outcome may be missed. The purpose ofthis paper is to discuss the importance of media-tors and moderators in nursing research and toencourage more widespread analysis of theseeffects in answering nursing questions. To achieve

that purpose, mediators and moderators arede®ned and differentiated. In addition, statisticalmethods for analysis of mediator and moderatoreffects are described and examples presented.

In intervention studies, a mediator or mod-erator can explain why a nursing interventionworks. For example, Kwekkeboom, Huseby-Moore, and Ward (1998) hypothesized that amoderator, imaging ability, could explain whyguided imagery (an intervention) had producedmixed results in reducing cancer pain in earlierstudies. The researchers did an exploratory studyin which guided imagery was used to alleviateanxiety and, in addition, they measured aproposed moderator, individual ability to gener-ate realistic images. The results showed thatparticipants who had high levels of imagingability (the moderator variable) had a reduction inanxiety when the guided imagery interventionwas used, but those with low levels of imaging

*Assistant Professor.

ß2000 John Wiley & Sons, Inc. 415

ability did not reduce their anxiety. Thus, theeffect of the intervention on the outcomedepended on the level of the moderator. Theresearchers hope to continue their research on thismoderator that may explain the previouslyreported weak association between guided ima-gery and cancer pain.

Likewise, the analysis of mediators andmoderators in descriptive studies can elicitinformation about why or how a direct associa-tion occurs between an independent variable andan outcome variable. For example, Schumacher,Dodd, and Paul (1993) studied caregivers ofpersons receiving chemotherapy for cancer andfound that caregiver strain had a signi®cant singleorder correlation with depression. However, theresearchers also investigated possible mediatorsand moderators. They found that coping was amediator in the association and thus provided afuller picture of how caregiver strain can develop(or not) into depression.

THE DEFINITIONS OF MEDIATORAND MODERATOR EFFECTS

Mediators are different from moderators andmany people are confused by the distinctionsbetween the two. It is important to understandwhether a mediator or moderator effect ishypothesized, because they require differentstatistical analyses.

A moderator is an independent variable thataffects the strength and/or direction of theassociation between another independent variableand an outcome variable. Figure 1 illustrates amoderator effect. A moderator variable mayinitially be analyzed in a multiple regressionmodel as one of the independent variables.However, subsequent steps in the analysis mayuncover two distinct regression slopes in the datathat depend on the value of the moderator. Themoderator interacts with the independent variableof interest so that the independent variable'sassociation with the outcome variable is strongeror weaker at different levels of the moderatorvariable. In other words, the association of the

independent variable with the outcome variable`̀ depends on'' the value (or level) of themoderator variable (Cohen & Cohen, 1983).

On the other hand, a mediator is a variable thatspeci®es how the association occurs between anindependent variable and an outcome variable. Amediator effect is only tested when there is asigni®cant direct effect between the independentvariable and the outcome variable, but there is apossibility that a mediator variable conceptuallyoccurs `̀ between'' the two variables. A mediatoreffect, shown in Figure 2, exists if the followingconditions are met: (a) variations in the indepen-dent variable predict variations in the mediatorvariable, (b) variations in the mediator variablepredict variations in the outcome variable, and (c)When the associations in (a) and (b) arecontrolled in the model, the direct relationshipbetween the independent variable and the out-come variable becomes nonsigni®cant (Baron &Kenny, 1986).

THE DIFFERENCESBETWEEN MEDIATOR AND

MODERATOR EFFECTS

Sometimes it is dif®cult to distinguish mediatorsand moderators when forming hypotheses aboutvariables. The de®nitional difference, that amediator is predicted by the independent variableand a moderator is a separate independentvariable, is important but not always obvious. Avariable such as `̀ coping'' is an example. Someresearchers conceptualize `̀ coping'' as a media-tor because it cannot occur unless a stressor hasoccurred; an independent variable `̀ stressor''predicts the mediator `̀ coping,'' and together theypredict an outcome. In contrast, some researchersconceptualize `̀ coping'' as a moderator variable.In this case, `̀ coping'' is an independent variablethat affects the stressor±outcome relationship;when coping ability is high, the stressor±outcomeassociation is weak, whereas when coping abilityis low, the stressor±outcome association is strong(Holmbeck, 1997). The decision about whether avariable is a mediator or moderator should bebased on theory and the conceptual frameworkthat guides the research.

Baron and Kenny (1986) offer some guidelinesfor determining whether a proposed variable

FIGURE 1. Conceptual model of a moderatoreffect.

FIGURE 2. Conceptual model of a mediatoreffect.

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represents a hypothesized mediator or moderatoreffect. Mediator-oriented research is usually con-cerned with the mechanism of the relationshipbetween the independent variable and the out-come variable. In other words, the `̀ how'' and`̀ why'' is more interesting to the researcher thanthe independent variable itself. In contrast, aresearcher who includes a moderator in the modelwants to know `̀ when'' the relationship occursbetween the independent and outcome variables.Therefore, the researcher is usually more inter-ested in the independent variable than themoderator.

It follows then that mediators are usuallyinvestigated when the relationship between theindependent variable and the outcome variable isstatistically signi®cant. If this relationship isstrong, the researcher is likely to be interestedin ®nding a mediator that explains how or why theindependent variable predicts the outcome. Onthe other hand, if the association between theindependent variable and the outcome variable isweak or inconsistent, the researcher mayhypothesize a moderator, the values of whichcould explain the circumstances that strengthenor weaken the association.

A mediator is likely to be an internal propertyof the person or group being studied. If theindividual is the unit of analysis, the mediatorvariable is often an `̀ in the head'' mechanism thatelaborates the meaning of the relationshipbetween the independent variable and the out-come variable. For example, Song and Lee (1996)found that depression mediated the relationshipbetween sensory de®cits in elders and theirfunctional capacity. However, mediators are notalways individual-level characteristics. If a groupis the unit of analysis, group-level characteristics,such as cohesiveness or role con¯icts, could bemediators.

In contrast, the investigation of a moderatoreffect often includes experimental manipulationof the moderator variable and, therefore, a mode-rator may be a nursing intervention. However,moderators may also be naturally occurring vari-ables. For example, social resources were inves-tigated by Hall, Sachs, and Rayens (1998) as amoderator in the relationship between women'schildhood abuse and their potential for abuse oftheir own children.

STATISTICAL TESTS FOR AMODERATOR EFFECT

Though the approach to testing for statisticalsigni®cance of a moderator effect varies if the

independent variables are categorical or contin-uous, the general strategy is to test for aninteraction using hierarchical multiple regressionanalysis. In the ®rst step (or steps) of theregression, the independent variables (includingthe moderator) are entered into the model aspredictors of the outcome variable. The indepen-dent variables do not have to be signi®cantpredictors of the outcome variable in order to testfor an interaction in the next step. In a separatestep, an interaction term (the product of twoindependent variables, which represents themoderator effect) is entered. If the interactionterm explains a statistically signi®cant amount ofvariance in the dependent variable, a moderatoreffect is present. Figure 3 show a diagram of thisprocedure.

The interaction term represents a joint relation-ship between the two independent variables andthis relationship accounts for additional variancein the outcome variable beyond that explained byeither single variable alone. In other words,several different regression slopes represent theassociation, rather than just one, and the associa-tion of the independent variable with the outcomevariable depends on the value of the moderatorvariable. It should be noted that this conditionalrelationship is symmetrical; it can also be saidthat the association of the moderator and theoutcome variable depends on the value of theindependent variable (Cohen & Cohen, 1983).

A hierarchical regression in which the interac-tion term is entered in its own step allows theresearcher to see the main effects of theindependent variables (the independent variableof interest and the moderator) in the earlier stepsseparately from the effect of the moderator in the®nal step. It is also possible to use a regressionmodel in which all the variables, including theinteraction term, are entered in a single step. Inthis case, the signi®cance of the semi-partial

FIGURE 3. Statistical model of a moderatoreffect.

MEDIATOR AND MODERATOR VARIABLES / BENNETT 417

correlation of the interaction term will show if amoderator effect is present. However, the maineffects of the independent variables cannot beseen unless they are entered in a separate step,because the presence of the interaction term in thesame step alters the variance explained by theindependent variables alone. Thus, the usualprocedure is to use a multi-step hierarchicalregression.

An interaction effect may be dif®cult to detectstatistically if the sample does not have a fullrange of values for the independent variable andthe moderator variable. Thus, a homogeneoussample may weaken detection of a moderatoreffect. In addition, measurement error in either ofthe variables is compounded in the interactionvariable. This error can be dif®cult to detect usingmultiple regression analysis and may result in anunderestimation of the moderator effect (Holm-beck, 1997).

STATISTICAL TESTS FOR AMEDIATOR EFFECT

A mediator effect can also be tested with multipleregression analysis. However, mediator effectsshould only be tested if there is a signi®cant directassociation between an independent variable andan outcome variable, otherwise there is norelationship to mediate. An important assumptionof this method is that the outcome variable doesnot predict the mediator variable.

The method, shown in Figure 4, uses threeregression equations to test for the statisticalsigni®cance of a mediator effect. The ®rstequation should show that the independentvariable is a signi®cant predictor of the mediator.The second equation should show that theindependent variable is a signi®cant predictor ofthe outcome variable. The third equation shouldcontain both the independent and mediatorvariables entered simultaneously with the out-come variable. Two conditions must be met in thethird equation if a mediator effect is present: (a)the mediator is a signi®cant predictor of the

outcome variable and (b) the direct relationshipof the independent variable to the outcomevariable is less signi®cant than it was in thesecond equation (Baron & Kenny, 1986).

AN EXAMPLE OF AMEDIATOR EFFECT

Yarcheski, Scoloveno, and Mahon (1994)reported their investigation of a mediator effectin an article titled `̀ Social Support and Well-Being In Adolescents: The Mediating Role ofHopefulness.'' The authors provided a theoreticalbasis for hopefulness as a mediating variable byciting earlier research studies that providedtheoretical and empirical evidence for an associa-tion between social support and well-being,between social support and hope, and betweenhope and well-being. They then hypothesizedthat, in some instances, individuals may believethat hopefulness is important to their well-beingbecause of their relationships with other people.Thus, the purpose of the study was correctly andclearly de®ned as testing the hypothesis thathopefulness mediated, and therefore helpedexplain, the relationship between perceived socialsupport and general well-being in adolescents.

Data from 99 participants between 15 and 17years old were analyzed to test the hypothesis.First, the single order relationships among thevariables were con®rmed by statistically signi®-cant Pearson correlations in the expected direc-tion. As predicted, perceived social support andwell-being were correlated (r� .55), perceivedsocial support and hopefulness were correlated(r� .57), and hopefulness and well-being werecorrelated (r� .60).

Next, three regression analyses were per-formed, following the method speci®ed by Baronand Kenny (1986) and depicted in Figure 5. The®rst equation regressed hopefulness on perceivedsocial support (F(1,97)� 45.90, p< .001). Socialsupport explained 32% of the variance in hope-fulness. The second equation regressed well-being on social support (F(1,97)� 41.84,p< .001). Social support explained 30% of thevariance in well-being. The third equationregressed well-being on both hopefulness andperceived social support. This ®nal equation metthe two requirements for a mediator effect: (a)The hypothesized mediator, hopefulness, was asigni®cant predictor (t� 4.69, p< .001) andexplained 19% of the variance in well-being,and (b) the variance in well-being explained byperceived social support was reduced from 30%FIGURE 4. Statistical model of a mediator effect.

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in the second equation to 9% in the thirdequation. Thus, the reduced direct associationbetween perceived social support and well-beingwhen hopefulness was in the model supported thehypothesis that hopefulness was at least one ofthe mediators in the relationship between per-ceived social support and general well-being.

AN EXAMPLE OF AMODERATOR EFFECT

Spitzer, Bar-Tal, and Golander (1995) investi-gated the hypothesized moderator effect ofdemographic variables on the relationshipbetween stress and coping effectiveness. In theirpaper, `̀ The Moderating Effect of DemographicVariables on Coping Effectiveness,'' the research-ers stated that the reason for conducting theanalysis was the lack of empirical evidence in theliterature for the theoretical proposition thatcoping is a moderator in the relationship betweenstress and adaptation. Therefore, they proposedthat personal and situational variables maymoderate the effectiveness of coping strategiesin a stressful situation. In other words, a particularcoping strategy would be more or less effectivedepending on the level of a given demographicvariable. Data from 78 adults who were treated atan outpatient clinic (mean age 54.2 years) wereanalyzed with eight hierarchical regressions foreach of three different coping strategies.

For each coping strategy that might predictcoping effectiveness, the following hierarchicalsteps were followed. Stress was entered alone inthe ®rst step. One demographic variable andone coping strategy were entered as a blockin the second step. Three interaction terms(stress� coping strategy, stress� demographiccharacteristic, demographic characteristic�coping strategy) were entered as a block in thethird step. A second level interaction term(stress� coping strategy� demographic charac-

teristic) was entered as a block in the fourth step.The dependent variable was coping effectiveness.Thus, the analysis followed the correct procedurefor testing moderator effects by entering thepredictors, including the proposed moderatorvariables, into the model before the interactionterms were entered in a separate step.

The results showed that some interaction termswere signi®cant for some coping strategy models,indicating that particular demographic variablesmoderated the effectiveness of the strategy. Forexample, the strategy of active cognitive copingwas more effective for younger subjects than forolder (i.e., age was a moderator in the relationshipbetween coping strategy and coping effective-ness).

INCORPORATING MEDIATORSAND MODERATORS INNURSING RESEARCH

Analysis of mediator or moderator effects maysupply more in-depth information about aresearch phenomenon than can be explained bydirect effects alone. Four elements that should beincluded in reports of mediator or moderatorresearch are (a) correct de®nition and use of theterms mediator or moderator, (b) a rationale forthe hypothesized mediator or moderator effectand evidence for the hypothesis based onliterature and/or conceptual framework, (c)statistical analysis that is matched to thehypothesized mediator or moderator effect, and(d) interpretation of the mediator or moderatoreffect in the ®ndings. Nurse scientists who areinterested in exploring more than just the directeffects of predictor variables on outcome vari-ables may want to consider hypotheses aboutmediators and moderators that could provideadditional information about why an observedphenomenon occurs or under what circumstancesa nursing intervention has the greatest effect.

FIGURE 5. Example of statistical analysis of a mediator effect (datafrom Yarcheski, Scoloveno, and Mahon, 1990).

MEDIATOR AND MODERATOR VARIABLES / BENNETT 419

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