scispace - formally typeset
Search or ask a question
Journal ArticleDOI

The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.

01 Dec 1986-Journal of Personality and Social Psychology (American Psychological Association)-Vol. 51, Iss: 6, pp 1173-1182
TL;DR: This article seeks to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating the many ways in which moderators and mediators differ, and delineates the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena.
Abstract: In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.

Summary (5 min read)

The Nature o f Moderators

  • In general terms, a moderator is a qualitative (e.g., sex, race, class) or quantitative (e.g., level of reward) variable that affects the direction and/or strength of the relation between an independent or predictor variable and a dependent or criterion variable.
  • Specifically within a correlational analysis framework, a moderator is a third variable that affects the zero-order correlation between two other variables.
  • Such an effect would have occurred in the Stern et al.
  • In the dissonanceforced compliance area, for example, it became apparent that the ability of investigators to establish the effects of insufficient justification required the specification of such moderators as commitment, personal responsibility, and free choice (cf. Brehm & Cohen, 1962) .

Toward Establishing an Analytic Framework for Testing Moderator Effects

  • A common framework for capturing both the correlational and the experimental views of a moderator variable is possible by using a path diagram as both a descriptive and an analytic procedure.
  • Glass and Singer's (1972) finding of an interaction of the factors stressor intensity (noise level) and controllability (periodic-aperiodic noise), of the form that an adverse impact on task performance occurred only when the onset of the noise was aperiodic or unsignaled, will serve as their substantive example.
  • There may also be significant main effects for the predictor and the moderator (Paths a and b), but these are not directly relevant conceptually to testing the moderator hypothesis.
  • In addition to these basic considerations, it is desirable that the moderator variable be uncorrelated with both the predictor and the criterion (the dependent variable) to provide a clearly interpretable interaction term.
  • Another property of the moderator variable apparent from Figure 1 is that, unlike the mediator-predictor relation (where the predictor is causally antecedent to the mediator), moderators and predictors are at the same level in regard to their role as causal variables antecedent or exogenous to certain criterion effects.

Choosing an Appropriate Analytic Procedure: Testing Moderation

  • In this section the authors consider in detail the specific analysis procedures for appropriately measuring and testing moderational hypotheses.
  • Within this framework, moderation implies that the causal relation between two variables changes as a function of the moderator variable.
  • The statistical analysis must measure and test the differential effect of the independent variable on the dependent variable as a function of the moderator.
  • Tor is a continuous variable and the independent variable is a categorical variable; and in Case 4, both variables are continuous variables.
  • To ease their discussion, the authors will assume that all the categorical variables are dichotomies.

Case 1

  • For this case, a dichotomous independent variable's effect on the dependent variable varies as a function of another dichotomy.
  • The authors may wish to measure the simple effects of the independent variable across the levels of the moderator (Winer, 1971, pp. 435-436) , but these should be measured only if the moderator and the independent variable interact to cause the dependent variable.

Case 2

  • Here the moderator is a dichotomy and the independent variable is a continuous variable.
  • Gender might moderate the effect of intentions on behavior.
  • If variances differ across levels of the moderator, then for levels of the moderator with less variance, the correlation of the independent variable with the dependent variable tends to be less than for levels of the moderator with more variance.
  • Regression coefficients are not affected by differences in the variances of the independent variable or differences in measurement error in the dependent variable.
  • This can be accomplished within the computer program LISREL-VI (J6reskog & S6rbom, 1984) by use of the multiple-group option.

Case 3

  • The indepen- dent variable might be a rational versus fear-arousing attitudechange message and the moderator might be intelligence as measured by an IQ test.
  • The fear-arousing message may be more effective for low-IQ subjects, whereas the rational message may be more effective for high-IQ subjects.
  • First, the effect of the independent variable on the dependent variable changes linearly with respect to the moderator.
  • Unfortunately, theories in social psychology are usually not precise enough to specify the exact point at which the step in the function occurs.
  • Alternatively, quadratic moderation can be tested by hierarchical regression procedures described by Cohen and Cohen (1983) .

Case 4

  • In this case both the moderator variable and the independent variable are continuous.
  • If one believes that the moderator alters the independent-dependent variable relation in a step function , one can dichotomize the moderator at the point where the step takes place.
  • One should consult Cohen and Cohen (1983) and Cleary and Kessler (1982) for assistance in setting up and interpreting these regressions.
  • Methods presented by Kenny and Judd (1984) can be used to make adjustments for measurement error in the variables, resulting in proper estimates of interactive effects.
  • These methods require that the variables from which the product variable is formed have normal distributions.

The Nature of Mediator Variables

  • Psychologists have long recognized the imporlance of mediating variables.
  • Woodworth's (1928) S-O-R model, which recognizes that an active organism intervenes between stimulus and response, is perhaps the most generic formulation of a mediation hypothesis.
  • The central idea in this model is that the effects of stimuli on behavior are mediated by various transformation processes internal to the organism.
  • Theorists as diverse as Hull, Tolman, and Lewin shared a belief in the importance of postulating entities or processes that intervene between input and output.
  • (Skinner's blackbox approach represents the notable exception.).

General A nalytic Considerations

  • In general, a given variable may be said to function as a mediator to the extent that it accounts for the relation between the predictor and the criterion.
  • Choice may moderate the impact of incentive on attitude change induced by discrepant action, and this effect is in turn mediated by a dissonance arousal-reduction sequence (of. Brehm & Cohen, 1962) .
  • This model assumes a three-variable system such that there are two causal paths feeding into the outcome variable: the direct impact of the independent variable (Path c) and the impact of the mediator (Path b).
  • Iftbe residual Path c is not zero, this indicates the operation of multiple mediating factors.

Testing Mediation

  • An ANOVA provides a limited test ofa mediational hypothesis as extensively discussed in Fiske, Kenny, and Taylor (1982) .
  • Separate coefficients for each equation should be estimated and tested.
  • These three regression equations provide the tests of the linkages of the mediational model.
  • Because a successful mediator is caused by the independent variable and causes the dependent variable, successful mediators measured with error are most subject to this overestimation bias.
  • Models of this type are estimated by two-stage least squares or a related technique.

Overview o f Conceptual Distinctions Between Moderators and Mediators

  • As shown in the previous section, to demonstrate mediation one must establish strong relations between (a) the predictor and the mediating variable and (b) the mediating variable and some distal endogenous or criterion variable.
  • This formulation in no way presupposes that mediators in social psychology are limited to individualistic or "in the head" mechanisms.
  • Group-level mediator constructs such as role conflict, norms, groupthink, and cohesiveness have long played a role in social psychology.
  • In addition, whereas mediator-oriented research is more interested in the mechanism than in the exogenous variable itself (e.g., dissonance and personal-control mediators have been implicated as explaining an almost unending variety of predictors), moderator research typically has a greater interest in the predictor variable per se.

Strategic Considerations

  • Moderator variables are typically introduced when there is an unexpectedly weak or inconsistent relation between a predictor and a criterion variable (e.g., a relation holds in one setring but not in another, or for one subpopulation but not for another).
  • In addition, there may be a wide variation in the strategic functions served by moderators and mediators.
  • Therefore, evaluative-anxiety level may be postulated to mediate the differential effectiveness of a given instructional technique.
  • Thus, here the authors have a situation where a moderator variable has been useful in suggesting a possible mediator variable.
  • Race would be preferred over social class as a moderator if race was more able to tell us something about the processes underlying test performance.

Operational Implications

  • First, the moderator interpretation of the relation between the stressor and control typically entails an experimental manipulation of control as a means of establishing independence between the stressor and control as a feature of the environment separate from the stressor.
  • When control is experimentally manipulated in service of a moderator function, one need not measure perceived control, which is the cognitive intraorganismic concept.
  • The most essential feature of the hypothesis is that perceived control is the mechanism through which the stressor affects the outcome variable.
  • Because of the conceptual status of this assessment in the mediator case, one's main concern is the demonstration of construct validity, a situation that ideally requires multiple independent and converging measurements (Campbell & Fiske, 1959) .
  • Thus, when mediation is at issue the authors need to increase both the quality and the quantity of the data.

A Framework for Combining Mediation and Moderation

  • Figure 4 presents a combined model with both mediation and moderation.
  • The variable control has both mediator and moderator status in the model.
  • It can be explained by P because the control manipulation is differentially affecting perceived control for the levels of the stressor.
  • If one wished, further models could be estimated.
  • The second-order interaction effect, CPS, could also be estimated and tested.

I m p l i c a t i o n s and A p p l i c a t i o n s o f the M o d e r a t o r -M e d i a t o r D i s t i n c t i o n

  • The authors take the themes developed in the three previous sections and apply them to three areas of social psychological research.
  • These areas are personal control, the behaviorintention relation, and linking traits and attitudes to behavior.

Clarifying the Meaning of Control

  • Many investigations of the impact of personal control in social and environmental psychology have been methodologically (but not theoretically) ambivalent with respect to the control variable's causal status.
  • This practice leads to serious difficulties of interpretation when a researcher intends to investigate one function of control but studies only the other function.
  • Only when this is done can the authors establish the crucial link between perceived control and the criterion.
  • Such an interpretation would leave open the possibility that other factors, such as an arousal-labeling or an arousal-amplification mechanism, mediate the effects of density (i.e., Freedman, 1975 , Worchel & Teddlie, 1976) .

Behavior Intention-Behavior Relation

  • Because Fishbein and Ajzen's (1975; Ajzen & Fishbein, 1980) attitude theory of reasoned action is in general highly sophisticated at both the conceptual and quantitative levels, it provides a good example of the extent of confusion regarding mediators and moderators.
  • Fishbein and Ajzen assumed that the impact of both attitudes and normative factors on behavior (B) is mediated through behavioral intentions.
  • Surprisingly, however, given the elegance of their general model, similar care was not taken regarding the nature of the BI-B link.
  • From the present perspective, such an approach ignores the possibility that some of these factors are best conceptualized and treated statistically as moderators whereas others are best viewed as mediators.
  • Specifically, Fishbein and Ajzen tested the importance of given factors by looking at the impact on the multiple correla-ti6n of dropping or adding a variable.

Linking Global Dispositions to Behavior: Attitudes and Traits

  • Of all the current areas in social psychology, the one where the use of what the authors have referred to as the combined model is perhaps the strongest is the prediction of social behavior from global dispositional variables.
  • What such suggestions lack is precisely the kind of unified conceptual and analytic framework presented in their combined moderator-mediator example .
  • Specifically, introducing a moderator variable merely involves a relatively static classification procedure.
  • On the other hand, linking the Self-Monitoring Trait relation to a specific mediating mechanism implies that variations in self-monitoring elicit or instigate different patterns of coping or information processing that cause people to become more or less consistent with their attitudes in their behavior.

S u m m a r y

  • First, by carefully elaborating the many ways in which moderators and mediators differ, the authors have tried to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably.
  • Received August 7, 1985 Revision received July 14, 1986 9 Instructions to Authors Authors should prepare manuscripts according to the Publication Manual of the American Psychological Association (3rd ed.).
  • Each copy of a manuscript to be anonymously reviewed should include a separate title page with authors' names and affiliations, and these should not appear anywhere else on the manuscript.
  • Dittoed and mimeographed copies will not be considered.
  • Rejection by one section editor is considered rejection by all, therefore a manuscript rejected by one section editor should not be submitted to another.

Did you find this useful? Give us your feedback

Content maybe subject to copyright    Report

Journal of Pe~nality and Social Psychology Copyright 1986 by the American Psychological Association, Inc.
1986, Vol. 51, No. 6, 1173-1182 0022-3514/86/$00.75
The Moderator-Mediator Variable Distinction in Social Psychological
Research: Conceptual, Strategic, and Statistical Considerations
Reuben M. Baron and David A. Kenny
University of Connecticut
In this article, we attempt to distinguish between the properties of moderator and mediator variables
at a number of levels. First, we seek to make theorists and researchers aware of the importance of
not using the terms
moderator and mediator
interchangeably by carefully elaborating, both concep-
tually and strategically, the many ways in which moderators and mediators differ. We then go beyond
this largely pedagogical function and delineate the conceptual and strategic implications of making
use of such distinctions with regard to a wide range of phenomena, including control and stress,
attitudes, and personality traits. We also provide a specific compendium of analytic procedures ap-
propriate for making the most effective use of the moderator and mediator distinction, both sepa-
rately and in terms of a broader causal system that includes both moderators and mediators.
The purpose of this analysis is to distinguish between the
properties of moderator and mediator variables in such a way
as to clarify the different ways in which conceptual variables
may account for differences in peoples' behavior. Specifically,
we differentiate between two often-confused functions of third
variables: (a) the moderator function of third variables, which
partitions a focal independent variable into subgroups that es-
tablish its domains of maximal effectiveness in regard to a given
dependent variable, and (b) the mediator function of a third
variable, which represents the generative mechanism through
which the focal independent variable is able to influence the
dependent variable of interest.
Although these two functions of third variables have a rela-
tively long tradition in the social sciences, it is not at all uncom-
mon for social psychological researchers to u, the terms
mod-
erator and mediator
interchangeably. For example, Harkins,
Latan6, and Williams 0980) first summarized the impact of
identifiability on social loafing by observing that it "moderates
social loafing" (p. 303) and then within the same paragraph
proposed "that identifiability is an important mediator of social
loafing:' Similarly, Findley and Cooper (1983), intending a
moderator interpretation, labeled gender, age, race, and socio-
economic level as mediators of the relation between locus of
control and academic achievement. Thus, one largely pedagogi-
This research was supported in part by National Science Foundation
Grant BNS-8210137 and National Institute of Mental Health Grant
R01 MH-40295-01 to the second author. Support was also given to him
during his sabbatical year (1982-83) by the MacArthur Foundation at
the Center for Advanced Studies in the Behavioral Sciences, Stanford,
California.
Thanks are due to Judith Harackiewicz, Charles Judd, Stephen West,
and Harris Cooper, who provided comments on an earlier version of
this article. Stephen P. Needel was instrumental in the beginning stages
of this work.
Correspondence concerning this article should be addressed to Reu-
ben M. Baron, Department of Psychology U-20, University of Connect-
icut, Storrs, Connecticut 06268.
cal function of this article is to clarify for experimental re-
searchers the importance of respecting these distinctions.
This is not, however, the central thrust of our analysis. Rather,
our major emphasis is on contrasting the moderator-mediator
functions in ways that delineate the implications of this distinc-
tion for theory and research. We focus particularly on the
differential implications for choice of experimental design, re-
search operations, and plan of statistical analysis.
We also claim that there are conceptual implications of the
failure to appreciate the moderator-mediator distinction.
Among the issues we will discuss in this regard are missed op-
portunities to probe more deeply into the nature of causal
mechanisms and integrate seemingly irreconcilable theoretical
positions. For example, it is possible that in some problem areas
disagreements about mediators can be resolved by treating cer-
tain variables as moderators.
The moderator and mediator functions will be discussed at
three levels: conceptual, strategic, and statistical. To avoid any
misunderstanding of the moderator-mediator distinction by er-
roneously equating it with the difference between experimental
manipulations and measured variables, between situational and
person variables, or between manipulations and verbal self-re-
ports, we will describe both actual and hypothetical examples
involving a wide range of variables and operations. That is,
moderators may involve either manipulations or assessments
and either situational or person variables. Moreover, mediators
are in no way restricted to verbal reports or, for that matter, to
individual-level variables.
Finally, for expository reasons, our analysis will initially
stress the need to make clear whether one is testing a moderator
or a mediator type of model. In the second half of the article,
we provide a design that allows one to test within the structure
of the same study whether a mediator or moderator interpreta-
tion is more appropriate.
Although these issues are obviously important for a large
number of areas within psychology, we have targeted this article
for a social psychological audience because the relevance of this
distinction is highest in social psychology, which uses experi-
1173

1174 REUBEN M. BARON AND DAVID A. KENNY
mental operations and at the same time retains an interest in
organismic variables ranging from individual difference mea-
sures to cognitive constructs such as perceived control.
The Nature of Moderators
In general terms, a moderator is a qualitative (e.g., sex, race,
class) or quantitative (e.g., level of reward) variable that affects
the direction and/or strength of the relation between an inde-
pendent or predictor variable and a dependent or criterion vari-
able.
Specifically within a correlational analysis framework, a
moderator is a third variable that affects the zero-order correla-
tion between two other variables. For example, Stem, McCants,
and Pettine (1982) found that the positivity of the relation be-
tween changing life events and severity of illness was considera-
bly stronger for uncontrollable events (e.g., death of a spouse)
than for controllable events (e.g., divorce). A moderator effect
within a correlational framework may also be said to occur
where the direction of the correlation changes. Such an effect
would have occurred in the Stern et al. study if controllable life
changes had reduced the likelihood of illness, thereby changing
the direction of the relation between life-event change and ill-
ness from positive to negative.
In the more familiar analysis of variance (ANOVA) terms, a
basic moderator effect can be represented as an interaction be-
tween a focal independent variable and a factor that specifies
the appropriate conditions for its operation. In the dissonance-
forced compliance area, for example, it became apparent that
the ability of investigators to establish the effects of insufficient
justification required the specification of such moderators as
commitment, personal responsibility, and free choice (cf.
Brehm & Cohen, 1962).
An example of a moderator-type effect in this context is the
demonstration of a crossover interaction of the form that the
insufficient justification effect holds under public commitment
(e.g., attitude change is inversely related to incentive), whereas
attitude change is directly related to level of incentive when the
counterattitudinal action occurs in private (cf. Collins & Hoyt,
1972). A moderator-interaction effect also would be said to oc-
cur if a relation is substantially reduced instead of being re-
versed, for example, if we find no difference under the private
condition.
Toward Establishing an Analytic Framework
for Testing Moderator Effects
A common framework for capturing both the correlational
and the experimental views of a moderator variable is possible
by using a path diagram as both a descriptive and an analytic
procedure. Glass and Singer's (1972) finding of an interaction
of the factors stressor intensity (noise level) and controllability
(periodic-aperiodic noise), of the form that an adverse impact
on task performance occurred only when the onset of the noise
was aperiodic or unsignaled, will serve as our substantive exam-
ple. Using such an approach, the essential properties of a mod-
erator variable are summarized in Figure 1.
The model diagrammed in Figure 1 has three causal paths
that feed into the outcome variable of task performance: the
Figure 1.
Moderator model.
impact of the noise intensity as a predictor (Path a), the impact
of controllability as a moderator (Path b), and the interaction
or product of these two (Path c). The moderator hypothesis is
supported if the interaction (Path c) is significant. There may
also be significant main effects for the predictor and the moder-
ator (Paths a and b), but these are not directly relevant concep-
tually to testing the moderator hypothesis.
In addition to these basic considerations, it is desirable that
the moderator variable be uncorrelated with both the predictor
and the criterion (the dependent variable) to provide a clearly
interpretable interaction term. Another property of the moder-
ator variable apparent from Figure 1 is that, unlike the media-
tor-predictor relation (where the predictor is causally anteced-
ent to the mediator), moderators and predictors are at the same
level in regard to their role as causal variables antecedent or
exogenous to certain criterion effects. That is, moderator vari-
ables always function as independent variables, whereas medi-
ating events shift roles from effects to causes, depending on the
focus oftbe analysis.
Choosing an Appropriate Analytic Procedure:
Testing Moderation
In this section we consider in detail the specific analysis pro-
cedures for appropriately measuring and testing moderational
hypotheses. Within this framework, moderation implies that
the causal relation between two variables changes as a function
of the moderator variable. The statistical analysis must measure
and test the differential effect of the independent variable on the
dependent variable as a function of the moderator. The way to
measure and test the differential effects depends in part on the
level of measurement of the independent variable and the mod-
erator variable. We will consider four eases: In Case 1, both
moderator and independent variables are categorical variables;
in Case 2, the moderator is a categorical variable and the inde-
pendent variable a continuous variable; in Case 3, the modera-
1 At a conceptual level, a moderator may be more impressive if we go
from a strong to a weak relation or to no relation at all as opposed to
finding a crossover interaction. That is, although crossover interactions
are stronger statistically, as they are not accompanied by residual main
effects, conceptually no effect shifts may be more impressive.

THE MODERATOR-MEDIATOR DISTINCTION 1175
tor is a continuous variable and the independent variable is a
categorical variable; and in Case 4, both variables are continu-
ous variables. To ease our discussion, we will assume that all the
categorical variables are dichotomies.
Case 1
This is the simplest case. For this case, a dichotomous inde-
pendent variable's effect on the dependent variable varies as a
function of another dichotomy. The analysis is a 2 2 ANOVA,
and moderation is indicated by an interaction. We may wish to
measure the simple effects of the independent variable across
the levels of the moderator (Winer, 1971, pp. 435-436), but
these should be measured only if the moderator and the inde-
pendent variable interact to cause the dependent variable.
Case 2
Here the moderator is a dichotomy and the independent vari-
able is a continuous variable. For instance, gender might moder-
ate the effect of intentions on behavior. The typical way to mea-
sure this type of moderator effect is to correlate intentions with
behavior separately for each gender and then test the difference.
For instance, virtually all studies of moderators of the attitude-
behavior relation use a correlational test.
The correlational method has two serious deficiencies. First,
it presumes that the independent variable has equal variance at
each level of the moderator. For instance, the variance of inten-
tion must be the same for the genders. If variances differ across
levels of the moderator, then for levels of the moderator with
less variance, the correlation of the independent variable with
the dependent variable tends to be less than for levels of the
moderator with more variance. The source of this difference is
referred to as a restriction in range (McNemar, 1969). Second,
if the amount of measurement error in the dependent variable
varies as a function of the moderator, then the correlations be-
tween the independent and dependent variables will differ spuri-
ously.
These problems illustrate that correlations are influenced by
changes in variances. However, regression coefficients are not
affected by differences in the variances of the independent vari-
able or differences in measurement error in the dependent vari-
able. It is almost always preferable to measure the effect of the
independent variable on the dependent variable not by correla-
tion coefficients but by unstandardized (not betas) regression
coefficients (Duncan, 1975). Tests of the difference between re-
gression coefficients are given in Cohen and Cohen (1983, p.
56). This test should be performed first, before the two slopes
are individually tested.
If there is differential measurement error in the independent
variable across levels of the moderator, bias results. Reliabilities
would then need to be estimated for the different levels of the
moderator, and slopes would have to be disattenuated. This can
be accomplished within the computer program LISREL-VI
(J6reskog & S6rbom, 1984) by use of the multiple-group op-
tion. The levels of the moderator are treated as different groups.
Case 3
In this case, the moderator is a continuous variable and the
independent variable is a dichotomy. For instance, the indepen-
Figure 2.
Three different ways in which the moderator changes the effect
of the independent variable on the dependent variable: linear (top), qua-
dratic (middle), and step (bottom).
dent variable might be a rational versus fear-arousing attitude-
change message and the moderator might be intelligence as
measured by an IQ test. The fear-arousing message may be
more effective for low-IQ subjects, whereas the rational message
may be more effective for high-IQ subjects. To measure modera-
tor effects in this case, we must know a priori how the effect of
the independent variable varies as a function of the moderator.
It is impossible to evaluate the general hypothesis that the effect
of the independent variable changes as a function of the moder-
ator because the moderator has many levels.
Figure 2 presents three idealized ways in which the modera-
tor alters the effect of the independent variable on the dependent
variable. First, the effect of the independent variable on the de-
pendent variable changes linearly with respect to the moderator.
The linear hypothesis represents a gradual, steady change in the
effect of the independent variable on the dependent variable as
the moderator changes. It is this form of moderation that is gen-
erally assumed. The second function in the figure is a quadratic
function. For instance, the fear-arousing message may be more
generally effective than the rational message for all low-IQ sub-
jects, but as IQ increases, the fear-arousing message loses its ad-
vantage and the rational message is more effective.
The third function in Figure 2 is a step function. At some
critical IQ level, the rational message becomes more effective
than the fear-arousing message. This pattern is tested by dichot-
omizing the moderator at the point where the step is supposed
to occur and proceeding as in Case 1. Unfortunately, theories
in social psychology are usually not precise enough to specify
the exact point at which the step in the function occurs.
The linear hypothesis is tested by adding the product of the
moderator and the dichotomous independent variable to the re-

1176 REUBEN M. BARON AND DAVID A. KENNY
gression equasion, as described by Cohen and Cohen (1983) and
Cleary and Kessler (1982). So if the independent variable is de-
noted as X, the moderator as Z, and the dependent variable as
Y, Y is regressed on X, Z, and
XZ.
Moderator effects are indi-
cated by the significant effect of
XZ
while X and Z are con-
trolled. The simple effects of the independent variable for
different levels of the moderator can be measured and tested by
procedures described by Aiken and West (1986). (Measurement
error in the moderator requires the same remedies as measure-
ment error in the independent variable under Case 2.)
The quadratic moderation effect can be tested by dichotomiz-
ing the moderator at the point at which the function is pre-
sumed to accelerate. If the function is quadratic, as in Figure 2,
the effect of the independent variable should be greatest for
those who are high on the moderator. Alternatively, quadratic
moderation can be tested by hierarchical regression procedures
described by Cohen and Cohen (1983). Using the same notation
as in the previous paragraph, Y is regressed on
X, Z, XZ, Z 2,
and
XZ 2.
The test of quadratic moderation is given by the test
of XZ 2. The interpretation of this complicated regression equa-
tion can be aided by graphing or tabling the predicted values
for various values ofXand Z.
Case 4
In this case both the moderator variable and the independent
variable are continuous. If one believes that the moderator al-
ters the independent-dependent variable relation in a step func-
tion (the bottom diagram in Figure 2), one can dichotomize the
moderator at the point where the step takes place. After dichot-
omizing the moderator, the pattern becomes Case 2. The mea-
sure of the effect of the independent variable is a regression co-
efficient.
If one presumes that the effect of the independent variable
(X) on the dependent variable (Y) varies linearly or quadrati-
cally with respect to the moderator (Z), the product variable
approach described in Case 3 should be used. For quadratic
moderation, the moderator squared must be introduced. One
should consult Cohen and Cohen (1983) and Cleary and Kessler
(1982) for assistance in setting up and interpreting these regres-
sions.
The presence of measurement error in either the moderator
or the independent variable under Case 4 greatly complicates
the analysis. Busemeyer and Jones (1983) assumed that the
moderation is linear and so can be captured by an
XZ
product
term. They showed that measuring multiplicative interactions
when one of the variables has measurement error results in low
power in the test of interactive effects. Methods presented by
Kenny and Judd (1984) can be used to make adjustments for
measurement error in the variables, resulting in proper esti-
mates of interactive effects. However, these methods require
that the variables from which the product variable is formed
have normal distributions.
The Nature of Mediator Variables
Although the systematic search for moderator variables is rel-
atively recent, psychologists have long recognized the impor-
lance of mediating variables. Woodworth's (1928) S-O-R
model, which recognizes that an active organism intervenes be-
tween stimulus and response, is perhaps the most generic for-
mulation of a mediation hypothesis. The central idea in this
model is that the effects of stimuli on behavior are mediated
by various transformation processes internal to the organism.
Theorists as diverse as Hull, Tolman, and Lewin shared a belief
in the importance of postulating entities or processes that inter-
vene between input and output. (Skinner's blackbox approach
represents the notable exception.)
General A nalytic Considerations
In general, a given variable may be said to function as a medi-
ator to the extent that it accounts for the relation between the
predictor and the criterion. Mediators explain how external
physical events take on internal psychological significance.
Whereas moderator variables specify when certain effects will
hold, mediators speak to how or why such effects occur. For
example, choice may moderate the impact of incentive on atti-
tude change induced by discrepant action, and this effect is in
turn mediated by a dissonance arousal-reduction sequence (of.
Brehm & Cohen, 1962).
To clarify the meaning of mediation, we now introduce a path
diagram as a model for depicting a causal chain. The basic
causal chain involved in mediation is diagrammed in Figure 3.
This model assumes a three-variable system such that there are
two causal paths feeding into the outcome variable: the direct
impact of the independent variable (Path c) and the impact of
the mediator (Path b). There is also a path from the independent
variable to the mediator (Path a).
A variable functions as a mediator when it meets the follow-
ing conditions: (a) variations in levels of the independent vari-
able significantly account for variations in the presumed media-
tor (i.e., Path a), (b) variations in the mediator significantly ac-
count for variations in the dependent variable (i.e., Path b), and
(c) when Paths a and b are controlled, a previously significant
relation between the independent and dependent variables is no
longer significant, with the strongest demonstration of media-
tion occurring when Path c is zero. In regard to the last condi-
tion we may envisage a continuum. When Path c is reduced to
zero, we have strong evidence for a single, dominant mediator.
Iftbe residual Path c is not zero, this indicates the operation of
multiple mediating factors. Because most areas of psychology,
including social, treat phenomena that have multiple causes, a
more realistic goal may be to seek mediators that significantly
decrease Path c rather than eliminating the relation between the
independent and dependent variables altogether. From a theo-
retical perspective, a significant reduction demonstrates that a
given mediator is indeed potent, albeit not both a necessary and
a sufficient condition for an effect to occur.

THE MODERATOR-MEDIATOR DISTINCTION 1177
Testing Mediation
An ANOVA provides a limited test ofa mediational hypothesis
as extensively discussed in Fiske, Kenny, and Taylor (1982).
Rather, as recommended by Judd and Kenny (1981 b), a series
of regression models should be estimated. To test for mediation,
one should estimate the three following regression equations:
first, regressing the mediator on the independent variable; sec-
ond, regressing the dependent variable on the independent vari-
able; and third, regressing the dependent variable on both the
independent variable and on the mediator. Separate coefficients
for each equation should be estimated and tested. There is no
need for hierarchical or stepwise regression or the computation
of any partial or semipartial correlations.
These three regression equations provide the tests of the link-
ages of the mediational model. To establish mediation, the fol-
lowing conditions must hold: First, the independent variable
must affect the mediator in the first equation; second, the inde-
pendent variable must be shown to affect the dependent variable
in the second equation; and third, the mediator must affect the
dependent variable in the third equation. If these conditions all
hold in the predicted direction, then the effect of the indepen-
dent variable on the dependent variable must be less in the third
equation than in the second. Perfect mediation holds if the inde-
pendent variable has no effect when the mediator is controlled.
Because the independent variable is assumed to cause the me-
diator, these two variables should be correlated. The presence
of such a correlation results in multicollinearity when the
effects of independent variable and mediator on the dependent
variable are estimated. This results in reduced power in the test
of the coefficients in the third equation. It is then critical that
the investigator examine not only the significance of the co-
efficients but also their absolute size. For instance, it is possible
for the independent variable to have a smaller coefficient when
it alone predicts the dependent variable than when it and the
mediator are in the equation but the larger coefficient is not
significant and the smaller one is.
Sobel (1982) provided an approximate significance test for
the indirect effect of the independent variable on the dependent
variable via the mediator. As in Figure 3, the path from the
independent variable to the mediator is denoted as a and its
standard error is sa; the path from the mediator to the depen-
dent variable is denoted as b and its standard error is sb. The
exact formula, given multivariate normality for the standard er-
ror of the indirect effect or
ab,
is this:
Vb2sa 2 q- a2Sb 2 d- Sa2Sb 2
Sobel's method omits the term
Sa2Sb 2,
but that term ordinarily
is small. His approximate method can be used for more compli-
cated models.
The use of multiple regression to estimate a mediational
model requires the two following assumptions: that there be no
measurement error in the mediator and that the dependent vari-
able not cause the mediator.
The mediator, because it is often an internal, psychological
variable, is likely to be measured with error. The presence of
measurement error in the mediator tends to produce an under-
estimate of the effect of the mediator and an overestimate of
the effect of the independent variable on the dependent variable
when all coefficients are positive (Judd & Kenny, 198 la). Obvi-
ously this is not a desirable outcome, because successful media-
tors may be overlooked.
Generally the effect of measurement error is to attenuate the
size of measures of association, the resulting estimate being
closer to zero than it would be if there were no measurement
error (Judd & Kenny, 198 la). Additionally, measurement error
in the mediator is likely to result in an overestimate in the effect
of the independent variable on the dependent variable. Because
of measurement error in the mediator, effects of the mediator
on the dependent variable cannot totally be controlled for when
measuring the effects of the independent variable on the depen-
dent variable.
The overestimation of the effects of the independent variable
on the dependent variable is enhanced to the extent that the
independent variable causes the mediator and the mediator
causes the dependent variable. Because a successful mediator is
caused by the independent variable and causes the dependent
variable, successful mediators measured with error are most
subject to this overestimation bias.
The common approach to unreliability is to have multiple
operations or indicators of the construct. Such an approach re-
quires two or more operationalizations or indicators of each
construct. One can use the multiple indicator approach and es-
timate mediation paths by latent-variable structural modeling
methods. The major advantages of structural modeling tech-
niques are the following: First, although these techniques were
developed for the analysis of nonexperimental data (e.g., field-
correlational studies), the experimental context actually
strengthens the use of the techniques. Second, all the relevant
paths are directly tested and none are omitted as in ANOVA.
Third, complications of measurement error, correlated mea-
surement error, and even feedback are incorporated directly
into the model. The most common computer program used to
estimate structural equation models is LISREL-VI (JSreskog
& S6rbom, 1984). Also available is the program EQS (Bentler,
1982).
We now turn our attention to the second source of bias in
the mediational chain: feedback. The use of multiple regression
analysis presumes that the mediator is not caused by the depen-
dent variable. It may be possible that we are mistaken about
which variable is the mediator and which is the dependent vari-
able.
Smith (1982) has proposed an ingenious solution to the prob-
lem of feedback in mediational chains. His method involves the
manipulation of two variables, one presumed to cause only the
mediator and not the dependent variable and the other pre-
sumed to cause the dependent variable and not the mediator.
Models of this type are estimated by two-stage least squares or
a related technique. Introductions to two-stage least squares are
in James and Singh (1978), Duncan (1975), and Judd and
Kenny (1981a). The earlier-mentioned structural modeling
procedures can also be used to estimate feedback models.
Overview of Conceptual Distinctions
Between Moderators and Mediators
As shown in the previous section, to demonstrate mediation
one must establish strong relations between (a) the predictor

Citations
More filters
Journal ArticleDOI
TL;DR: An overview of simple and multiple mediation is provided and three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model are explored.
Abstract: Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.

25,799 citations


Cites background or methods from "The moderator–mediator variable dis..."

  • ...For example, the ability to include covariates permits the testing of mediated moderation effects (Baron & Kenny, 1986), in which interaction effects are hypothesized to be mediated; future research might also address how mediated moderation effects may be contrasted in a pairwise manner....

    [...]

  • ...By far the most commonly used is the causal steps strategy, popularized by Baron and Kenny (1986), in which the investigator estimates the paths of the model in Figure 1, using OLS regression or SEM, and assesses the extent to which several criteria are met....

    [...]

  • ...By far the most commonly used is the causal steps strategy, popularized by Baron and Kenny (1986), in which the investigator estimates the paths of the model in Figure 1, using OLS regression or SEM, and assesses the extent to which several criteria are met. Variable M is a mediator if X significantly accounts for variability in M, X significantly accounts for variability in Y, M significantly accounts for variability in Y when controlling for X, and the effect of X on Y decreases substantially when M is entered simultaneously with X as a predictor of Y. As Kenny, Kashy, and Bolger (1998) note, however, the latter criterion will be satisfied when the first and third criteria are satisfied and when the signs of the effects are consistent with the proposed mediation process....

    [...]

Journal ArticleDOI
TL;DR: It is argued the importance of directly testing the significance of indirect effects and provided SPSS and SAS macros that facilitate estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtaining confidence intervals to enhance the frequency of formal mediation tests in the psychology literature.
Abstract: Researchers often conduct mediation analysis in order to indirectly assess the effect of a proposed cause on some outcome through a proposed mediator. The utility of mediation analysis stems from its ability to go beyond the merely descriptive to a more functional understanding of the relationships among variables. A necessary component of mediation is a statistically and practically significant indirect effect. Although mediation hypotheses are frequently explored in psychological research, formal significance tests of indirect effects are rarely conducted. After a brief overview of mediation, we argue the importance of directly testing the significance of indirect effects and provide SPSS and SAS macros that facilitate estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtaining confidence intervals, as well as the traditional approach advocated by Baron and Kenny (1986). We hope that this discussion and the macros will enhance the frequency of formal mediation tests in the psychology literature. Electronic copies of these macros may be downloaded from the Psychonomic Society's Web archive at www.psychonomic.org/archive/.

15,041 citations


Cites background or methods from "The moderator–mediator variable dis..."

  • ...After a brief overview of mediation, we argue the importance of directly testing the significance of indirect effects and provide SPSS and SAS macros that facilitate estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtaining confidence intervals, as well as the traditional approach advocated by Baron and Kenny (1986). We hope that this discussion and the macros will enhance the frequency of formal mediation tests in the psychology literature....

    [...]

  • ...Rozeboom (1956) coined the term mediation to describe a particular pattern of linear prediction among measured variables, but Judd and Kenny (1981) and Baron and Kenny (1986) are mainly responsible for popularizing mediation models in psychology....

    [...]

  • ...Curiously, the Sobel test is discussed, with requisite formulas, by Baron and Kenny (1986), but it is rarely used in practice (cf. MacKinnon et al., 2002)....

    [...]

  • ...The Need for a Formal Test Given that Baron and Kenny (1986) provide a conceptually appealing recipe to follow in order to determine the presence or absence of a mediation effect, one may well ask why it is necessary to perform a formal significance test of the indirect effect if the Baron and Kenny criteria have been met. Two broad benefits of formal testing may be suggested. First, there are shortcomings inherent in the Baron and Kenny method. For example, Holmbeck (2002) points out that it is possible to observe a change from a significant XÆY path to a nonsignificant XÆY path upon the addition of a mediator to the model with a very small change in the absolute size of the coefficient. This pattern of results may lead a researcher to erroneously conclude that a mediation effect is present (Type I error). Conversely, it is possible to observe a large change in the XÆY path upon the addition of a mediator to the model without observing an appreciable drop in statistical significance (Type II error). The latter situation is especially likely to occur when large samples are employed because those are the conditions under which even small regression weights may remain statistically significant. Finally, it is possible for a Type I error about mediation to occur if either a or b appears to be statistically different from zero when one of them is in fact zero in the population. A Type I error in the test of either a or b (or both) could lead to an incorrect conclusion about mediation. Second, testing the hypothesis of no difference between the total effect (c) and the direct effect (c¢) more directly addresses the mediation hypothesis than does the series of regression analyses recommended by Baron and Kenny (1986). In the case of simple mediation, the indirect effect of X on Y through M is measured as the product of the XÆM and MÆY paths (ab), which is equivalent to (c c¢) in most situations. Therefore, a significance test associated with ab should address mediation more directly than a series of separate significance tests not directly involving ab. In addition, it has been found that the method described by Baron and Kenny (1986) suffers from low statistical power in most situations (MacKinnon et al....

    [...]

  • ...The macros provide unstandardized coefficients for regression Equations 1–3 given above and discussed by Baron and Kenny (1986) as required to test mediation....

    [...]

Journal ArticleDOI
TL;DR: Efron and Tibshirani as discussed by the authors used bootstrap tests to assess mediation, finding that the sampling distribution of the mediated effect is skewed away from 0, and they argued that R. M. Kenny's (1986) recommendation of first testing the X --> Y association for statistical significance should not be a requirement when there is a priori belief that the effect size is small or suppression is a possibility.
Abstract: Mediation is said to occur when a causal effect of some variable X on an outcome Y is explained by some intervening variable M. The authors recommend that with small to moderate samples, bootstrap methods (B. Efron & R. Tibshirani, 1993) be used to assess mediation. Bootstrap tests are powerful because they detect that the sampling distribution of the mediated effect is skewed away from 0. They argue that R. M. Baron and D. A. Kenny's (1986) recommendation of first testing the X --> Y association for statistical significance should not be a requirement when there is a priori belief that the effect size is small or suppression is a possibility. Empirical examples and computer setups for bootstrap analyses are provided.

8,940 citations


Cites background or methods from "The moderator–mediator variable dis..."

  • ...In this article, we focus on the approach to mediation analysis that was articulated by Kenny and his colleagues ( Baron & Kenny, 1986; Judd & Kenny, 1981; Kenny et al., 1998)....

    [...]

  • ...14 Collins et al. (1998) and MacKinnon and colleagues (MacKinnon, 2000; MacKinnon et al., 2000) recommended dropping the first step of Baron and Kenny (1986) for a different reason than we do. They considered inconsistent mediating variables that may have effects that go in opposite directions, so the total effect may seem to disappear....

    [...]

  • ...The statistical model in this case is technically misspecified because it does not take into account the interaction between group and mediation process ( Baron & Kenny, 1986; described as moderated mediation effects by James & Brett, 1984)....

    [...]

  • ... Baron and Kenny (1986) made Equation 2b wellknown, but for large samples Equation 2a is nearly the same magnitude and has been recommended (Mac-Kinnon et al., 1995)....

    [...]

  • ... Baron and Kenny (1986) reported that the standard error of the indirect effect estimate can be calculated using a large-sample test provided by Sobel (1982, 1986)....

    [...]

Journal ArticleDOI
TL;DR: A quantitative integration and review of research on the Theory of Planned Behaviour and the subjective norm, which found that intentions and self-predictions were better predictors of behaviour than attitude, subjective norm and PBC.
Abstract: The Theory of Planned Behaviour (TPB) has received considerable attention in the literature. The present study is a quantitative integration and review of that research. From a database of 185 independent studies published up to the end of 1997, the TPB accounted for 27% and 39% of the variance in behaviour and intention, respectively. The perceived behavioural control (PBC) construct accounted for significant amounts of variance in intention and behaviour, independent of theory of reasoned action variables. When behaviour measures were self-reports, the TPB accounted for 11% more of the variance in behaviour than when behaviour measures were objective or observed (R2s = .31 and .21, respectively). Attitude, subjective norm and PBC account for significantly more of the variance in individuals' desires than intentions or self-predictions, but intentions and self-predictions were better predictors of behaviour. The subjective norm construct is generally found to be a weak predictor of intentions. This is partly attributable to a combination of poor measurement and the need for expansion of the normative component. The discussion focuses on ways in which current TPB research can be taken forward in the light of the present review.

8,889 citations

Journal ArticleDOI
TL;DR: A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect and found two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power.
Abstract: A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect. An intervening variable (mediator) transmits the effect of an independent variable to a dependent variable. The commonly used R. M. Baron and D. A. Kenny (1986) approach has low statistical power. Two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power except in 1 important case in which Type I error rates are too high. The best balance of Type I error and statistical power across all cases is the test of the joint significance of the two effects comprising the intervening variable effect.

8,629 citations


Cites background or methods from "The moderator–mediator variable dis..."

  • ...This approach can be traced to the seminal work of Judd and Kenny (1981a, 1981b) and Baron and Kenny (1986) and is the most commonly used approach in the psychological literature....

    [...]

  • ...The Baron and Kenny (1986) method had greater power ast8 increased and the Judd and Kenny (1981b) method had less power ast8 increased....

    [...]

  • ...…reasons including omitting a variable from the path model, an interaction betweenI and X, incorrect specification of the functional form of the relations, error of measurement in the intervening variable, and a bidirectional causal relation between I and Y (Baron & Kenny, 1986; MacKinnon, 1994)....

    [...]

  • ...The most widely used methods proposed by Judd and Kenny (1981b) and Baron and Kenny (1986) have Type I error rates that are too low in all the simulation conditions and have very low power, unless the effect or sample size is large....

    [...]

  • ...The majority of the statistical tests of intervening effects reported in psychology journals test the significance of the indirect effectab or each of the causal steps proposed by Judd and Kenny (1981b) or Baron and Kenny (1986)....

    [...]

References
More filters
Book
01 Jan 1975
TL;DR: In this article, the Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements is presented. But it does not address the problem of missing data.
Abstract: Contents: Preface. Introduction. Bivariate Correlation and Regression. Multiple Regression/Correlation With Two or More Independent Variables. Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems I. Data-Analytic Strategies Using Multiple Regression/Correlation. Quantitative Scales, Curvilinear Relationships, and Transformations. Interactions Among Continuous Variables. Categorical or Nominal Independent Variables. Interactions With Categorical Variables. Outliers and Multicollinearity: Diagnosing and Solving Regression Problems II. Missing Data. Multiple Regression/Correlation and Causal Models. Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Model. Random Coefficient Regression and Multilevel Models. Longitudinal Regression Methods. Multiple Dependent Variables: Set Correlation. Appendices: The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements. Determination of the Inverse Matrix and Applications Thereof.

29,764 citations

Book
17 Mar 1980
TL;DR: In this paper, the author explains "theory and reasoned action" model and then applies the model to various cases in attitude courses, such as self-defense and self-care.
Abstract: Core text in attitude courses. Explains "theory and reasoned action" model and then applies the model to various cases.

26,683 citations


"The moderator–mediator variable dis..." refers background in this paper

  • ...Because Fishbein and Ajzen's (1975; Ajzen & Fishbein, 1980 ) attitude theory of reasoned action is in general highly sophisticated at both the conceptual and quantitative levels, it provides a good example of the extent of confusion regarding mediators and moderators....

    [...]

Book
B. J. Winer1
01 Jan 1962
TL;DR: In this article, the authors introduce the principles of estimation and inference: means and variance, means and variations, and means and variance of estimators and inferors, and the analysis of factorial experiments having repeated measures on the same element.
Abstract: CHAPTER 1: Introduction to Design CHAPTER 2: Principles of Estimation and Inference: Means and Variance CHAPTER 3: Design and Analysis of Single-Factor Experiments: Completely Randomized Design CHAPTER 4: Single-Factor Experiments Having Repeated Measures on the Same Element CHAPTER 5: Design and Analysis of Factorial Experiments: Completely-Randomized Design CHAPTER 6: Factorial Experiments: Computational Procedures and Numerical Example CHAPTER 7: Multifactor Experiments Having Repeated Measures on the Same Element CHAPTER 8: Factorial Experiments in which Some of the Interactions are Confounded CHAPTER 9: Latin Squares and Related Designs CHAPTER 10: Analysis of Covariance

25,607 citations

Journal ArticleDOI
TL;DR: This transmutability of the validation matrix argues for the comparisons within the heteromethod block as the most generally relevant validation data, and illustrates the potential interchangeability of trait and method components.
Abstract: Content Memory (Learning Ability) As Comprehension 82 Vocabulary Cs .30 ( ) .23 .31 ( ) .31 .31 .35 ( ) .29 .48 .35 .38 ( ) .30 .40 .47 .58 .48 ( ) As judged against these latter values, comprehension (.48) and vocabulary (.47), but not memory (.31), show some specific validity. This transmutability of the validation matrix argues for the comparisons within the heteromethod block as the most generally relevant validation data, and illustrates the potential interchangeability of trait and method components. Some of the correlations in Chi's (1937) prodigious study of halo effect in ratings are appropriate to a multitrait-multimethod matrix in which each rater might be regarded as representing a different method. While the published report does not make these available in detail because it employs averaged values, it is apparent from a comparison of his Tables IV and VIII that the ratings generally failed to meet the requirement that ratings of the same trait by different raters should correlate higher than ratings of different traits by the same rater. Validity is shown to the extent that of the correlations in the heteromethod block, those in the validity diagonal are higher than the average heteromethod-heterotrait values. A conspicuously unsuccessful multitrait-multimethod matrix is provided by Campbell (1953, 1956) for rating of the leadership behavior of officers by themselves and by their subordinates. Only one of 11 variables (Recognition Behavior) met the requirement of providing a validity diagonal value higher than any of the heterotrait-heteromethod values, that validity being .29. For none of the variables were the validities higher than heterotrait-monomethod values. A study of attitudes toward authority and nonauthority figures by Burwen and Campbell (1957) contains a complex multitrait-multimethod matrix, one symmetrical excerpt from which is shown in Table 6. Method variance was strong for most of the procedures in this study. Where validity was found, it was primarily at the level of validity diagonal values higher than heterotrait-heteromethod values. As illustrated in Table 6, attitude toward father showed this kind of validity, as did attitude toward peers to a lesser degree. Attitude toward boss showed no validity. There was no evidence of a generalized attitude toward authority which would include father and boss, although such values as the VALIDATION BY THE MULTITRAIT-MULTIMETHOD MATRIX

15,795 citations


"The moderator–mediator variable dis..." refers background in this paper

  • ...Because of the conceptual status of this assessment in the mediator case, one's main concern is the demonstration of construct validity, a situation that ideally requires multiple independent and converging measurements (Campbell & Fiske, 1959)....

    [...]

  • ...Because of the conceptual status of this assessment in the mediator case, one's main concern is the demonstration of construct validity, a situation that ideally requires multiple independent and converging measurements ( Campbell & Fiske, 1959 )....

    [...]

Book
01 Jan 1935
TL;DR: In this paper, Neuberg and Heine discuss the notion of belonging, acceptance, belonging, and belonging in the social world, and discuss the relationship between friendship, membership, status, power, and subordination.
Abstract: VOLUME 2. Part III: The Social World. 21. EVOLUTIONARY SOCIAL PSYCHOLOGY (Steven L. Neuberg, Douglas T. Kenrick, and Mark Schaller). 22. MORALITY (Jonathan Haidt and Selin Kesebir). 23. AGGRESSION (Brad J. Bushman and L. Rowell Huesmann). 24. AFFILIATION, ACCEPTANCE, AND BELONGING: THE PURSUIT OF INTERPERSONAL CONNECTION (Mark R. Leary). 25. CLOSE RELATIONSHIPS (Margaret S. Clark and Edward P. Lemay, Jr.). 26. INTERPERSONAL STRATIFICATION: STATUS, POWER, AND SUBORDINATION (Susan T. Fiske). 27. SOCIAL CONFLICT: THE EMERGENCE AND CONSEQUENCES OF STRUGGLE AND NEGOTIATION (Carsten K. W. De Dreu). 28. INTERGROUP RELATIONS 1(Vincent Yzerbyt and Stephanie Demoulin). 29. INTERGROUP BIAS (John F. Dovidio and Samuel L. Gaertner). 30. SOCIAL JUSTICE: HISTORY, THEORY, AND RESEARCH (John T. Jost and Aaron C. Kay). 31. INFLUENCE AND LEADERSHIP (Michael A. Hogg). 32. GROUP BEHAVIOR AND PERFORMANCE (J. Richard Hackman and Nancy Katz). 33. ORGANIZATIONAL PREFERENCES AND THEIR CONSEQUENCES (Deborah H. Gruenfeld and Larissa Z. Tiedens). 34. THE PSYCHOLOGICAL UNDERPINNINGS OF POLITICAL BEHAVIOR (Jon A. Krosnick, Penny S. Visser, and Joshua Harder). 35. SOCIAL PSYCHOLOGY AND LAW (Margaret Bull Kovera and Eugene Borgida). 36. SOCIAL PSYCHOLOGY AND LANGUAGE: WORDS, UTTERANCES, AND CONVERSATIONS (Thomas Holtgraves). 37. CULTURAL PSYCHOLOGY (Steven J. Heine). AUTHOR INDEX. SUBJECT INDEX.

13,453 citations