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A simple statistical mediation model.

In statistics, a mediation model is one that seeks to identify and explicate the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third explanatory variable, known as a mediator variable. Rather than hypothesizing a direct causal relationship between the independent variable and the dependent variable, a mediational model hypothesizes that the independent variable causes the mediator variable, which in turn causes the dependent variable. Thus, the mediator variable, serves to clarify the nature of the relationship between the independent and dependent variables.[1] In other words, mediating relationships occur when a third variable plays an important role in governing the relationship between the other two variables. Researchers are now focusing their studies on better understanding known findings. Mediation analyses are employed to understand a known relationship by exploring the underlying mechanism or process by which one variable (X) influences another variable (Y). For example, a cause X of some variable (Y) presumably precedes Y in time and has a generative mechanism that accounts for its impact on Y. Thus, if gender is thought to be the cause of some characteristic, one assumes that other social or biological mechanisms are present in the concept of gender that can explain how gender-associated differences arise. The explicit inclusion of such a mechanism is called a mediator.

Baron and Kenny's (1968) Steps for Mediation[edit]

Baron and Kenny (1986) laid out several requirements that must be met before one can speak of a mediation relationship. They are outlined below using a real world example. See the diagram above for a visual representation of the overal mediating relationship to be explained.

Step 1:

Regress the dependent variable on the independent variable. In other words, confirm that the independent variable is a significant predictor of the dependent variable.

Independent Variable Dependent Variable


Regress the mediator on the independent variable. In other words, confirm that the independent variable is a significant predictor of the mediator. If the mediator is not associated with the independent variable, then it couldn’t possibly mediate anything.

Independent Variable Mediator

Step 3:

Regress the dependent variable on both the mediator and independent variable. In other words, confirm that the mediator is a significant predictor of the dependent variable, while controlling for the independent variable.

This step involves demonstrating that when the mediator and the independent variable are used simultaneously to predict the dependent variable, the previously significant path between the independent and dependent variable (Step #1) is now greatly reduced, if not nonsignificant. In other words, if the mediator were to be removed from the relationship, the relationship between the independent and dependent variables would be noticeably reduced.


The following example, drawn from Howell (2009), explains each step of Baron and Kenny’s requirements to further understand how a mediation effect is characterized. Step 1 and step 2 use a regression analysis, whereas step 3 uses a multiple regression analysis.

Step 1:

How you were parented (i.e., independent variable) predicts how confident you feel about parenting your own children (i.e., dependent variable).

How you were parented Confidence in own parenting abilities.

Step 2:

How you were parented (i.e., independent variable) predicts your feelings of competence and self esteem (i.e., mediator).

How you were parented Feelings of competence and self esteem.

Step 3:

Your feelings of competence and self esteem (i.e., mediator) predict how confident you feel about parenting your own children (i.e., dependent variable), while controlling for how you were parented (i.e., independent variable).

Such findings would lead to the conclusion implying that your feelings of competence and self esteem mediate the relationship between how you were parented and how confident you feel about parenting your own children.

Note: If step 1 does not yield a significant result, one may still have grounds to move onto step 2. Sometimes there is actually a significant relationship between the independent and dependent variables but because of small sample sizes, or other extraneous factors, there could not be enough power to predict the effect that actually exists.

Direct Versus Indirect Mediation Effects[edit]

In the diagram shown above, the indirect effect is the product of path coefficients "A" and "B". The direct effect is the coefficient "C". The total effect measures the extent to which the dependent variable changes when the independent variable increases by one unit. In contrast, the indirect effect measures the extent to which the dependent variable changes when the independent variable is held fixed and the mediator variable changes to the level it would have attained had the independent variable increased by one unit.[2][3] In linear systems, the total effect is equal to the sum of the direct and indirect effects ("C + AB" in the model above). In nonlinear models, the total effect is not generally equal to the sum of the direct and indirect effects, but to a modified combination of the two.[3]

Full Versus Partial Mediation[edit]

A mediator variable can either account for all or some of the observed relationship between two variables.

Full Mediation

Maximum evidence for mediation, also called full mediation, would occur if inclusion of the mediation variable drops the relationship between the independent variable and dependent variable (see pathway c in diagram above) to zero. This rarely, if ever, occurs. The most likely event is that c becomes a weaker, yet still significant path with the inclusion of the mediation effect.

Partial Mediation

Partial mediation maintains that the mediating variable accounts for some, but not all, of the relationship between the independent variable and dependent variable. Partial mediation implies that there is not only a significant relationship between the mediator and the dependent variable, but also some direct relationship between the independent and dependent variable.

In order for either full or partial mediation to be established, the reduction in variance explained by the independent variable must be significant as determined by one of several tests, such as the Sobel test (1982). The effect of an independent variable on the dependent variable can become nonsignificant when the mediator is introduced simply because a trivial amount of variance is explained (i.e., not true mediation). Thus, it is imperative to show a significant reduction in variance explained by the independent variable before asserting either full or partial mediation. Hayes (2009) shows that it is possible to have statistically significant indirect effects in the absence of a total effect. This can be explained by the presence of several mediating paths that cancel each other out, and become noticeable when one of the cancelling mediators is controlled for. This implies that the terms 'partial' and 'full' mediation should always be interpreted relative to the set of variables that are present in the model. In all cases, the operation of "fixing a variable" must be distinguished from that of "controlling for a variable," which has been inappropriately used in the literature.[4][2][5] The former stands for physically fixing, while the latter stands for conditioning on, adjusting for, or adding to the regression model. The two notions coincide only when all error terms (not shown in the diagram) are statistically uncorrelated. When errors are correlated, adjustments must be made to neutralize those correlations before embarking on mediation analysis (see Bayesian Networks).

Sobel's Test[edit]

As mentioned above, Sobel’s test is calculated to determine if the relationship between the independent variable and dependent variable has been significantly reduced after inclusion of the mediator variale. In other words, this test assesses whether a mediation effect is significant.


Examines the relationship between the independent variable and the dependent variable compared to the relationship between the independent variable and dependent variable including the mediation factor.

The Sobel test is more accurate than the Baron and Kenny steps explained above, however it does have low statistical power. As such, large sample sizes are required in order to have sufficient power to detect significant effects. This is because the key assumption of Sobel’s test is the assumption of normality. Because Sobel’s test evaluates a given sample on the normal distribution, small sample sizes and skewness of the sampling distribution can be problematic (See Normal Distribution for more details). Thus, the general rule of thumb as suggested by MacKinnon et al., (2003) is that a sample size of 1000 is required to detect a small effect, a sample size of 100 is sufficient in detecting a medium effect, and a sample size of 50 is required to detect a large effect.

Preacher & Hayes (2004) Bootstrap Method[edit]

The bootstrapping method provides some advantages to the Sobel’s test, primarily an increase in power. The Precher and Hayes Bootstrapping method is a non-parametric test (See non Parametric Statistics for a discussion on why non parametric tests have more power). As such, the bootstrap method does not violate assumptions of normality and is therefore recommended for small sample sizes. Bootstrapping involves repeatedly randomly sampling observations with replacement from the data set to compute the desired statistic in each resample. Over hundreds, or thousands, of bootstrap resamples provide an approximation of the sampling distribution of the statistic of interest. Hayes offers a macro ( that calculates bootstrapping directly within SPSS, a computer program used for statistical analyses. This method provides point estimates and confidence intervals by which one can assess the significance or nonsignificance of a mediation effect. Point estimates reveal the mean over the number of bootstrapped samples and if zero does not fall between the resulting confidence intervals of the bootstrapping method, one can confidently conclude that there is a significant mediation effect to report.

Significance of Mediation[edit]

As outlined above, there are a few different options one can choose from to evaluate a mediation model.

Bootstrapping [1] [2][3] is becoming the most popular method of testing mediation because it does not require the normality assumption to be met, and because it can be effectively utilized with smaller sample sizes (N<25). However, mediation continues to be most frequently determined using the the logic of Baron and Kenny [6] or the Sobel test. Unfortunately, it is becoming increasingly more difficult to publish tests of mediation based purely on the Baron and Kenny method or tests that make distributional assumptions such as the Sobel test. Thus, it is important to consider your options when choosing which test to conduct. See Hayes (2009) for a discussion.

Approaches to Mediation[edit]

While the concept of mediation as defined within psychology is theoretically appealing, the methods used to study mediation empirically have been challenged by statisticians and epidemiologists[7][4][2] and interpreted formally.[3]

(1) Experimental-Causal-Chain Design

An experimental-causal-chain design is used when the proposed mediator is experimental manipulated. Such a design implies that one manipulates some controlled third variable that they have reason to believe could be the underlying mechanism of a given relationship.

(2) Measurement-of-Mediation Design

A measurement-of-mediation design can be conceptualized as a statistical approach. Such a design implies that one measures the proposed intervening variable and then uses statistical analyses to establish mediation. This approach does not involve manipulation of the hypothesized mediating variable, but only involves measurement.

See Spencer et al., 2005 for a discussion on the approaches mentioned above.

Criticisms of Mediation Measurement[edit]

Experimental approaches to mediation must be carried out with caution. First, it is important to have strong theoretical support for the exploratory investigation of a potential mediating variable. A criticism of a mediation approach rests on the ability to manipulate and measure a mediating variable. Thus, one must be able to manipulate the proposed mediator in an acceptable and ethical fashion. As such, one must be able to measure the intervening process without interfering with the outcome. The mediator must also be able to establish construct validity of manipulation. One of the most common criticisms of the measurement-of-mediation approach is that it is ultimately a correlational design. Consequently, it is possible that some other third variable, independent from the proposed mediator, could be responsible for the proposed effect. However, researchers have worked hard to provide counter evidence to this disparagement. Specifically, Cohen et al., 2003 put forward these counter arguments:

(1) Temporal precedence. For example, if the independent variable precedes the dependent variable in time, this would provide evidence suggesting a directional, and potentially causal, link from the independent variable to the dependent variable.

(2) Nonspuriousness and/or no confounds. For example, should one identify other third variables and prove that they do not alter the relationship between the independent variable and the dependent variable he/she would have a stronger argument for their mediation effect. See other 3rd variables below.

Mediation can be an extremely useful and powerful statistical test, however it must be used properly. It is important that the measures used to assess the mediator and the dependent variable are theoretically distinct and that the independent variable and mediator cannot interact. Should there be an interaction between the independent variable and the mediator one would have grounds to investigate Moderation.

Other Third Variables[edit]

(1) Confounding:

Another model that is often tested is one in which competing variables in the model are alternative potential mediators or an unmeasured cause of the dependent variable. An additional variable in a causal model may obscure or ""confound"" the relationship between the independent and dependent variables. Potential confounders are variables that may have a causal impact on both the independent variable and dependent variable. They include common sources of measurement error (as discussed above) as well as other influences shared by both the independent and dependent variables.

In experimental studies, there is a special concern about aspects of the experimental manipulation or setting that may account for study effects, rather than the motivating theoretical factor. Any of these problems may produce spurious relationships between the independent and dependent variables as measured. Ignoring a confounding variable may bias empirical estimates of the causal effect of the independent variable.

(2) Suppression:

Suppression variables increase the predictive validity of another variable by its inclusion into a regression equation. For example, higher intelligence scores (X) cause a decrease in errors made at work on an assembly line (Y). However an increase in intelligence (X) could cause an increase in errors made on an assembly line (Y) as it may also relate to an increase in boredom while at work (Z) thereby introducing an element of carelessness resulting in a higher percentage of errors made on the job. Such a suppressor variable will lead to an increase in magnitude of the relationship between two variables.

In general, the omission of suppressors or confounders will lead to either an underestimation or an overestimating of the effect of X on Y, thereby either reducing or artificially inflating the magnitude of a relationship between two variables.

(3) Moderators:

Other important third variables are moderators. Moderators are variables that can make the relationship between two variables either stronger or weaker. Such variables further characterize interactions in regression by affecting the direction and/or strength of the relationship between X and Y. A moderating relationship can be thought of as an interaction. It occurs when the relationship between variables A and B depends on the level of C. See Moderation for further discussion.

Mediator Variable[edit]

A mediator variable (or mediating variable, or intervening variable) in statistics is a variable that describes how rather than when effects will occur by accounting for the relationship between the independent and dependent variables. A mediating relationship is one in which the path relating A to C is mediated by a third variable (B).

For example, a mediating variable explains the actual relationship between the following variables. Most people will agree that older drivers (up to a certain point), are better drivers. Thus:

Aging Better driving

But what is missing from this relationship is a mediating variable that is actually causing the improvement in driving: experience. The mediated relationship would look like the following:

Aging Increased experience driving a car Better driving

Mediating variables are often contrasted with moderating variables, which pinpoint the conditions under which an independent variable exerts its effects on a dependent variable.

Moderated mediation[edit]

Mediation and moderation can co-occur in statistical models. It is possible to mediate moderation and moderate mediation.

Moderated mediation is when the effect of the treatment effect A on the mediator B, and/or when the partial effect of B on C, depends on levels of another variable (D). Essentially, in moderated mediation, mediation is first established, and then one investigates if the mediation effect that describes the relationship between the independent variable and dependent variable is moderated by different levels of another variable (i.e., a moderator). This definition has been outlined by Muller, Judd, and Yzerbyt (2005)[8] and Preacher, Rucker, and Hayes (2007).[9]

Mediated moderation[edit]

Mediated moderation is a variant of both moderation and mediation. This is where there is initially overall moderation and the direct effect of the moderator variable on the outcome, is mediated either at the AB path or at the BC.

The main difference between mediated moderation and moderated mediation is that for the former there is initial moderation and this effect is mediated and for the latter there is no moderation but the effect of either the treatment (A) on the mediator (B) is moderated or the effect of the mediator (B) on the outcome (C) is moderated.[8]


  1. ^ MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis. New York: Erlbaum.
  2. ^ a b c Robins, J. M., Greenland, S. (1992). "Identifiability and exchangeability for direct and indirect effects". Epidemiology, 3(2):143–55.
  3. ^ a b c Pearl, J. (2001) "Direct and indirect effects". Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, 411–420.
  4. ^ a b Kaufman, J. S., MacLehose R. F., Kaufman S (2004). A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation. Epidemiology Innovations and Perspectives, 1:4.
  5. ^ Cite error: The named reference Pearl-00 was invoked but never defined (see the help page).
  6. ^ "Mediation". Retrieved April 25, 2012.
  7. ^ Bullock, J. G., Green, D. P., Ha, S. E. (2010). Yes, but what's the mechanism? (Don't expect an easy answer). Journal of Personality & Social Psychology, 98(4):550-558.
  8. ^ a b Muller, D., Judd, C. M., Yzerbyt, V. Y. (2005). When moderation is mediated and mediation is moderated. Journal of Personality and Social Psychology, 89(6), 852–863.
  9. ^ Preacher, K. J., Rucker, D. D. & Hayes, A. F. (2007). Assessing moderated mediation hypotheses: Strategies, methods, and prescriptions. Multivariate Behavioral Research, 42, 185–227.

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