Moderator Variable

A moderator variable, commonly denoted as just M, is a third variable that affects the strength of the relationship between a dependent and independent variable In correlation, a moderator is a third variable that affects the correlation of two variables.  In a causal relationship, if x is the predictor variable and y is an outcome variable, then z is the moderator variable that affects the casual relationship of x and y.  Most of the moderator variables measure causal relationship using regression coefficient.  The moderator variable, if found to be significant, can cause an amplifying or weakening effect between x and y.  In ANOVA, the moderator variable effect is represented by the interaction effect between the dependent variable and the factor variable.

Questions Answered:

Does gender effectively moderate the relationship between desire to marry and attitudes of marriage?

Does Z treatment effect the impact of X drug onto Y symptoms?

 

Moderated regression analysis: This is a regression based technique that is used to identify the moderator variable.  To explain how MRA technique works, we can use the following example:

Let
(1)
(2)
(3)

In this equation, if  (the interaction between the independent variable and moderator variable) is not statistically significant, then Z is not a moderator variable, it is just an independent variable.  If is statistically significant, then Z will be a moderator variable, and thus moderation is supported.

Linear vs. non-linear measurement: In a regression equation, when the relationship between the dependent variable and the independent variable is linear, then the dependent variable may change when the value of the moderator variable changes.  In a linear relationship, the following equation is used to represent the effect:

In this equation, the relationship is linear and represents the interaction effect of the moderator and the independent variable.  When the relationship is non-linear, the following equation shows the effect of the moderator variable effect:

In this equation, the relationship between the dependent and the independent variable is non-linear, so and shows the interaction effect. In a repeated measure design moderator, the variable can also be used.  In multi-level modeling, if a variable predicts the effect size, that variable is called the moderator variable.

  1. Alternative: In a non-linear relationship, a significant value of a moderator variable does not prove the true moderator effect.  Unless the moderator is a manipulated variable, we cannot say if the moderator variable is a true moderator or if it is just used as a proxy.
  2. Level of measurement: The moderator variable is an independent variable that is used to measure the causal relationship.  Like other independent variables, it may be categorized or continuous.

Assumptions:

  1. Causal assumption: When x variable is not randomized, then causation must be assumed.  The moderator variable can reversely effect the causation, if the causation between x and y is not presumed.
  2. Causal variable relationship: The moderator variable and independent variable, in principal, should not be related.  No special interpretation can be found between a correlated independent and moderator variable.  However, they should not be too highly correlated, otherwise, estimation problems may occur.  The moderator variable must be related to the dependent variable.
  3. Measurement: Usually, the moderation effect is represented by the interaction effect between the the dependent and independent varaible.  In a multiple regression equation, the moderator variable is as follows:

In this equation, the interaction effect between X and Z measures the moderation effect.  Typically, if there is no significant relationship on the dependent variable from the interaction between the moderator and independent variable, moderation is not supported.

 

Related Pages:

Statistics Solutions can assist with your quantitative analysis by assisting you to develop your methodology and results chapters. The services that we offer include:

Data Analysis Plan

Edit your research questions and null/alternative hypotheses

Write your data analysis plan; specify specific statistics to address the research questions, the assumptions of the statistics, and justify why they are the appropriate statistics; provide references

Justify your sample size/power analysis, provide references

Explain your data analysis plan to you so you are comfortable and confident

Two hours of additional support with your statistician

Quantitative Results Section (Descriptive Statistics, Bivariate and Multivariate Analyses, Structural Equation Modeling, Path analysis, HLM, Cluster Analysis)

Clean and code dataset

Conduct descriptive statistics (i.e., mean, standard deviation, frequency and percent, as appropriate)

Conduct analyses to examine each of your research questions

Write-up results

Provide APA 6th edition tables and figures

Explain chapter 4 findings

Ongoing support for entire results chapter statistics

Please call 727-442-4290 to request a quote based on the specifics of your research, schedule using the calendar on t his page, or email [email protected]