 Schmidt then systematically works through examples of how each function can be achieved by altering and/or holding a different class or classes of variable constant. For example, to fulfil the function of controlling for sampling error  (Function 1),  one should alter only variables regarding participant recruitment (Class 3), attempting to keep variables in all other classes as close to the original study as possible. To control for artefacts  (Function 2),  one should alter variables concerning the context and dependent variable measures (variables in Classes 2 and 4 respectively), but keep variables in 1 and 3 (information conveyed to participants and participant recruitment) as close to the original as possible. Schmidt, like most other authors in this area, acknowledges the practical limits of being able to hold all else constant. Controlling for fraud  (Function 3)  is served by the same arrangements as controlling for artefacts  (Function 2).  In Schmidt’s account, controlling for sampling error, artefacts and fraud (Functions 1 to 3) are connected by a theme of confirming the results of the original study. Functions 4 and 5 go beyond this—generalizing to new populations  (Function 4)  which is served by changes to participant recruitment (Class 3) and confirming the underlying hypothesis  (Function 5),  which served by changes to the information conveyed, the context and dependant variable measures (Classes 1, 2 and 4 respectively) but not changes to participant recruitment (Class 3, although Schmidt acknowledges that holding the latter class of variables constant whilst varying everything else is often practically impossible). Attempts to enable verification of the underlying research hypothesis (i.e., to fulfil Function 5) alone are what Schmidt classifies as conceptual replications, following Rosenthal (1991). Attempts to fulfil the other four functions are considered variants of direct replications.