Empirical investigation of mediators and moderators requires an integrated provide an up-to-date review of the concepts, uses, and The cause-and-effect relationship has been the pursuit of many scholars in the fields of. properties of moderator and mediator variables in such a way as to clarify the different ways economic level as mediators of the relation between locus of control and academic 5 years after the date of publication. For further information. In this regard, both mediation and moderation have to do with checking on how a third variable fits into that relationship. For the purposes of.
We then conduct a literature review of how mediation analysis and moderation analysis have been applied in three top school psychology journals over the past 23 years to gauge current practice. We follow with a presentation of the basic mediation and moderation models, illustrating the most recent recommendations for their statistical estimation, and briefly discuss advanced models that involve the effects.
Why study mediation and moderation? Although evaluating bivariate relations between variables can lend insight into whether a hypothesized relation holds or whether a program appears to work, it cannot address questions of why, how, and for whom the relation holds, or the program works.
Investigating third variables such as moderators and mediators permits the investigation of such questions, informing both theory and evidence-based practice in school psychology. Moderators demonstrate the generalizability of the relation between X and Y, illustrating the context s under which the relation holds. A mediator variable M is a third variable that explains how or why two other variables i.
In a mediation model, the independent variable X predicts the mediator variable M which in turn predicts the outcome Y. Thus, a mediator is intermediate in the relation between X and Y. By modeling an intermediate variable in the X—Y relation, the overall effect between X and Y can be decomposed into component parts called the direct effect of X on Y and the indirect effect of X on Y through M i.
Investigating both direct and indirect effects often provides more insight than simply evaluating the bivariate X—Y relation alone, and researchers have proposed several different ways to statistically test mediation using the component parts. Studying mechanisms of change by investigating mediator variables has the potential to direct and refine the development of evidence-based interventions because it can shed light on how an intervention achieves its effects or alternatively why it fails to achieve effects.
Additionally, studying contextual effects by investigating moderator variables has the potential to extend the generalizability and external validity of evidence-based treatment programs to different cultural groups or in different settings.
By analyzing mediation and moderation effects in this way, researchers can promote theory refinement and positively affect practice. Investigating mediation and moderation effects also works toward fulfilling several recent federal mandates and legislation in education. Department of Education, and the reauthorization of the Individuals With Disabilities Education Improvement Act IDEA, dictate the use of evidence-based practice in the assessment and intervention of students in both general and special education.
Mediators and moderators in early intervention research
This is particularly important given the recent movements towards the Response-to-Intervention RTI model of identification and intervention, where education and placement decisions are based on the degree to which specific students respond to evidence-based treatment. Additionally, analyzing mediation in the development and evaluation of evidence-based programs can identify the effectiveness of individual components of treatment packages.
Programs aim to manipulate these various components in an effort to change the outcome variable. Hypothesis 4 was thus supported: Daily rumination was found to significantly mediate the relationship between daily unpleasant life events and daily unhappy mood.
Specifically, we expected to find that rumination exacerbated the unpleasant events to unhappy mood relationship. We centered all the predictor variables before creating the interaction terms, and we entered trait rumination and trait depression as Level 2 covariates of the Level 1 moderation following the approach of Genet and Siemer, [ 23 ].
We constructed the following Level 1 HLM equation to address the present question: The interaction was graphed and is presented in Figure 1. All simple slopes were significantly different from zero: These slopes show that the relationship between unpleasant events and unhappy mood was positive and significant under all levels of rumination.
Of the three groups described in the figure, high ruminators tended to be consistently unhappy across all degrees of unpleasant events, whereas low ruminators were differentially unhappy depending on the degree of unpleasant events.
Specifically, the exacerbating effect was most evident when the degree of unpleasant events was low: All three moderation groups reported about the same amount of unhappiness under the circumstance of high unpleasant events.
We predicted that high ruminators would manifest stronger relationships between negative life events and unhappy mood, and this result was not obtained. However, we considered Hypothesis 5 to be partially supported because rumination exacerbated unhappy mood under the condition of low unpleasant events.
Daily unhappy mood as a function of daily unpleasant events and daily rumination. Trait Rumination at Level 2 as a Moderator on the Mediated Relationship between Unpleasant Events, Rumination, and Unhappy Mood at Level 1 Given evidence of significant mediation by rumination between unpleasant events and daily unhappy mood, the last question to be addressed was whether trait rumination at Level 2 might have explained some of the variability in this mediation relationship.
Our prediction was that the Level 2 moderator of trait rumination W would significantly strengthen the mediation relationships between daily unpleasant events X and daily rumination Mi. We adopted the suggestions put forward by Bauer et al.
As shown in the findings under Hypothesis 4, we found evidence of significant random effects for the mediation at Level 1, and we aimed to add a Level 2 factor to the model to explain further variance. According to Bauer et al. Depiction of Level 2 trait moderation of the Level 1 mediation. Discussion The present study aimed to replicate and extend past empirical findings concerning how rumination is related to stressful events and negative mood. As expected, we found significant and positive associations among momentary reports of unpleasant events, rumination, and unhappy mood.
An investigation of day-to-day stability of the three constructs showed that momentary reports of rumination and unhappy mood evidenced reasonable stability from one day to the next, but, as expected, unpleasant events did not demonstrate the same level of stability.
Additionally, we found a significant bi-directional cross-lagged relationship between rumination and negative mood, but we did not find the anticipated link between unpleasant events to rumination over contiguous days.
Two central predictions of the current study were that momentary rumination would mediate and moderate the relationship between unpleasant life events and unhappy mood, and both of these predictions were supported. And finally, we obtained empirical support for our moderated mediation prediction such that trait rumination was found to significantly moderate the momentary mediation relationship demonstrated among daily unpleasant events, rumination, and unhappiness.
We will now discuss each finding in turn within the context of existing literature. Rumination is a well-established risk factor for emotional disorders; the precipitating factor leading to rumination, however, is less well understood.
Jose and Brown [ 11 ] and Michl et al. In particular, Michl et al. Consistent with these studies, we found that momentary reports of unpleasant events, rumination, and unhappy mood were positively related to each other within a given day.
The direction and strength of these momentary relationships from one day to the next has not been examined previously, so to elucidate these important phenomena we chose to conduct stability and cross-lagged analyses on our day-lagged momentary unpleasant events, rumination, and unhappy mood data. Consistent with our second hypothesis, both rumination and unhappy mood were relatively stable across successive days, but unpleasant events did not manifest day-to-day stability given its uncontrollable and exogenous nature.
We also found that rumination and unhappy mood, but not unpleasant events, evidenced significant stability over a two-day period, so the findings suggest relative stability of these two constructs over time. Examination of cross-lagged relationships over time allowed us to obtain a picture of how these variables affected each other from day to day. We found two marginally significant cross-lagged findings: Although we predicted that the occurrence of stressful events on a given day would trigger more rumination on the following day, we obtained no empirical support for this contention.
Taken together these results suggest that intrapsychic dynamics i. This view would be consistent with the cognitive approach to emotional disturbance e.
Mediators and moderators in early intervention research
Another approach commonly used to examine the relationships among these three variables is statistical mediation. A number of studies have showed that daily or state rumination mediates the effects of unpleasant daily events on negative mood e. Although the results suggest the presence of a causal path beginning with stressful events, proceeding through ruminative thought, and ending with negative mood, in truth, this method does not well capture the day to day temporal influences among these three variables compared to the day-lagged analyses.
Moderation, on the other hand, is not based on the same assumption of causal relationships among variables. In other words, the exacerbating effect of daily rumination was most evident under conditions of low levels of unpleasant events. In our case we found that the slope of the high rumination group was flatter than the other two groups. How can we explain this discrepancy?