{"id":14844,"date":"2021-03-01T10:48:56","date_gmt":"2021-03-01T09:48:56","guid":{"rendered":"https:\/\/www.scribbr.com\/?p=14844"},"modified":"2023-06-22T10:29:11","modified_gmt":"2023-06-22T08:29:11","slug":"mediator-vs-moderator","status":"publish","type":"post","link":"https:\/\/www.scribbr.com\/methodology\/mediator-vs-moderator\/","title":{"rendered":"Mediator vs. Moderator Variables | Differences & Examples"},"content":{"rendered":"

A mediating variable<\/strong><\/span> (or mediator<\/strong>) explains the process through which two variables<\/a> are related, while a moderating variable<\/strong><\/span> (or moderator<\/strong>) affects<\/a> the strength and direction of that relationship.<\/p>\n

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. These variables are important to consider when studying complex correlational<\/a> or causal relationships between variables.<\/p>\n

Including these variables can also help you avoid or mitigate several research biases<\/a>, like observer bias<\/a>, survivorship bias<\/a>, undercoverage bias<\/a>, or omitted variable bias<\/a><\/p>\n

<\/p>\n

What\u2019s the difference?<\/h2>\n

You can think of a mediator<\/strong> as a go-between for two variables. For example, sleep quality (an independent variable<\/a>) can affect academic achievement (a dependent variable) through the mediator of alertness. In a mediation relationship, you can draw an arrow from an independent variable to a mediator and then from the mediator to the dependent variable.<\/p>\n

In contrast, a moderator<\/strong> is something that acts upon the relationship between two variables and changes its direction or strength. For example, mental health status may moderate the relationship between sleep quality and academic achievement: the relationship might be stronger for people without diagnosed mental health conditions than for people with them.<\/p>\n

In a moderation relationship, you can draw an arrow from the moderator to the relationship between an independent and dependent variable.<\/p>\n

\"Mediator<\/p>\n

Mediating variables<\/span><\/h2>\n

A mediator<\/strong> is a way in which an independent variable impacts a dependent variable. It\u2019s part of the causal pathway of an effect, and it tells you how or why an effect takes place.<\/p>\n

If something is a mediator:<\/p>\n

    \n
  1. It\u2019s caused by the independent variable.<\/li>\n
  2. It influences the dependent variable<\/li>\n
  3. When it\u2019s taken into account, the statistical<\/a> correlation between the independent and dependent variables is higher than when it isn\u2019t considered.<\/li>\n<\/ol>\n

    Mediation analysis is a way of statistically testing whether a variable is a mediator using linear regression analyses<\/a> or ANOVAs<\/a>.<\/p>\n

    In full mediation<\/strong>, a mediator fully explains the relationship between the independent and dependent variable: without the mediator in the model, there is no relationship.<\/p>\n

    In partial mediation<\/strong>, there is still a statistical relationship between the independent and dependent variable even when the mediator is taken out of a model: the mediator only partially explains the relationship.<\/p>\n

    Example: Mediator variables<\/figcaption>In a study on socioeconomic status and reading ability in children, you hypothesize that parental education level is a mediator. This means that socioeconomic status affects reading ability mainly through its influence on parental education levels.<\/p>\n

    \"Example<\/p>\n

    You use a descriptive research<\/a> design for this study. After collecting data on each of these variables, you perform statistical analysis to check whether:<\/p>\n

      \n
    1. Socioeconomic status predicts parental education levels,<\/li>\n
    2. Parental education levels predicts child reading ability,<\/li>\n
    3. The correlation between socioeconomic status and child reading ability is greater when parental education levels are taken into account in your model.<\/li>\n<\/ol>\n<\/figure>\n

      Moderating variables<\/span><\/h2>\n

      A moderator<\/strong> influences the level, direction, or presence of a relationship between variables. It shows you for whom, when, or under what circumstances a relationship will hold.<\/p>\n

      Moderators usually help you judge the external validity<\/a> of your study by identifying the limitations of when the relationship between variables holds. For example, while social media use can predict levels of loneliness, this relationship may be stronger for adolescents than for older adults. Age is a moderator here.<\/p>\n

      Moderators can be:<\/p>\n