See instructions below.
This program predicts the good-bad impression of an actor, from pre-existing feelings about the actor, behavior, and object of an event. You define the pre-existing feelings by rating the actor, behavior, and object on EPA scales. The program computes how good or bad the actor seems as a result of the event and reports the outcome impression as a numerical rating.
Click positions on the rating scales in the yellow box in order to specify pre-event feelings toward actor, behavior, and object. The numerical value of each rating shows up in a box on the right.
The outcome impression resulting from the event with the pre-existing feelings turns up in the box at the top of the form. Interpret outcome impressions using the rating scale for good-bad feelings.
The outcome impression also is represented on a bar graph that is green for good impressions, black for bad impressions, and red for an exactly neutral impression.
Sentiments measured in a group of people rarely have a value greater than 3.0, and outcome impressions rarely get beyond 4.0. The program allows you to exceed these numerical values so that you can see how impression-formation processes work.
An impression-formation equation obtained by empirical research is used to predict the numerical good-bad impression that forms from pre-existing feelings as a result of the event. Each term in the equation is given in the blue box at the bottom of the form. The terms represent mental processes, and names for some of them are indicated in parentheses - e.g., stability effect, morality effect.
A prediction is obtained by substituting current EPA values for actor, behavior, and object into variables like "Actor E", computing products, and summing everything. Click the checkboxes in the blue section to see the effect of removing a particular term from the equation. (Some terms have not been included in this model because their coefficients are small: Actor P, Actor A, Object P, Object A, Actor E*Object E, Actor E*Object P, Actor P*Object E, Actor P*Behavior P, Actor P*Object P, Actor P*Object A, Actor A*Behavior A, Behavior P*Object A.)