Experimental and Quasi-Experimental Designs for Research
Nonfiction, Social & Cultural Studies, Social Science, Statistics
| Author: | Donald T. Campbell, Julian C. Stanley | ISBN: | 1230001942538 |
| Publisher: | Ravenio Books | Publication: | September 3, 2015 |
| Imprint: | Language: | English |
| Author: | Donald T. Campbell, Julian C. Stanley |
| ISBN: | 1230001942538 |
| Publisher: | Ravenio Books |
| Publication: | September 3, 2015 |
| Imprint: | |
| Language: | English |
Experimental and Quasi-Experimental Designs for Research
DONALD T. CAMPBELL
Northwestern University
JULIAN C. STANLEY
Johns Hopkins University
In this chapter we shall examine the validity of 16 experimental designs against 12 common threats to valid inference. By experiment we refer to that portion of research in which variables are manipulated and their effects upon other variables observed. It is well to distinguish the particular role of this chapter. It is not a chapter on experimental design in the Fisher (1925, 1935) tradition, in which an experimenter having complete mastery can schedule treatments and measurements for optimal statistical efficiency, with complexity of design emerging only from that goal of efficiency. Insofar as the designs discussed in the present chapter become complex, it is because of the intransigency of the environment: because, that is, of the experimenter’s lack of complete control.
Experimental and Quasi-Experimental Designs for Research
DONALD T. CAMPBELL
Northwestern University
JULIAN C. STANLEY
Johns Hopkins University
In this chapter we shall examine the validity of 16 experimental designs against 12 common threats to valid inference. By experiment we refer to that portion of research in which variables are manipulated and their effects upon other variables observed. It is well to distinguish the particular role of this chapter. It is not a chapter on experimental design in the Fisher (1925, 1935) tradition, in which an experimenter having complete mastery can schedule treatments and measurements for optimal statistical efficiency, with complexity of design emerging only from that goal of efficiency. Insofar as the designs discussed in the present chapter become complex, it is because of the intransigency of the environment: because, that is, of the experimenter’s lack of complete control.
