Non-experimental studies use statistical control to demonstrate nonspuriousness.

In the arrow diagram X T Y, the relationship between X and Y is negative.

A stochastic component typically introduced in survey studies is random sampling.

Controlling for one extraneous variable in elaboration analysis creates a first-order relationship.

In elaboration analysis, “explanation” has a different statistical outcome than “interpretation.”

Replication in elaboration analysis strengthens one’s confidence that a relationship is nonspurious.

Like elaboration analysis, multiple regression analysis is limited to two independent variables.

Multicollinearity indicates that two or more independent variables are associated with the dependent variable.

Dummy coding in regression analysis is limited to nominal-scale variables with two categories, such as gender (male/female) and race (white/nonwhite).

One advantage of path analysis is that it forces researchers to specify the causal relationships among all the variables included in the analysis.