Mediator vs Moderator Comp Prep

mediation analysis uses what statistical analysis

ANOVA or linear regression

Moderators can be

Categorical variables such as ethnicity, race, religion, favorite colors, health status, or stimulus type, Quantitative variables such as age, weight, height, income, or visual stimulus size.

Moderator example

In a study on work experience and salary, you hypothesize that: years of work experience predicts salary, when controlling for relevant variables, gender identity moderates the relationship between work experience and salary.

If something is a mediator

It's caused by the independent variable. It influences the dependent variable When it's taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn't considered.

Mediator vs Moderator

The key difference between the concepts can be compared to a case where a moderator lets you know when an association will occur while a mediator will inform you how or why it occurs. A mediating variable (or mediator) explains the process through which two variables are related, while a moderating variable (or moderator) affects the strength and direction of that relationship.

mediator example

We want to design reusable components, but dependencies between the potentially reusable pieces demonstrates the "spaghetti code" phenomenon (trying to scoop a single serving results in an "all or nothing clump").

Full Mediation Model

a mediator fully explains the relationship between the independent and dependent variable: without the mediator in the model, there is no relationship.

Moderators

extraneous variables that affect the relationship among the independent and dependent variables; affects the strength and direction of the relationship between two variables..

Mediators

extraneous variables that come between the independent and dependent variables; explains the process through which two variables are related..

A moderator

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.

Mediator and Moderator variables can help

researchers avoid or mitigate several research biases, like observer bias, survivorship bias, under-coverage bias, or omitted variable bias

To test a moderator statistically

researchers can use a multiple regression analysis where we can compare the statistical significance of the model with and without the moderator included to determine whether it moderates the relationship between the DV and the IV

Partial Mediation Model

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.

Moderators usually help

you judge the external validity 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.