Please enable JavaScript.
Coggle requires JavaScript to display documents.
Chapter 10 - Coggle Diagram
Chapter 10
Experimental research, often considered to be the "gold standard" in research designs, is one of the most rigorous of all research designs.
The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.
Experimental research is best suited for explanatory research (rather than for descriptive or exploratory research), where the goal of the study is to examine cause-effect relationships.
Laboratory experiments, conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalizability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world.
Field experiments, conducted in field
settings such as in a real organization, and high in both internal and external validity.
Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs.
Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not.
In experimental research, some subjects are
administered one or more experimental stimulus called a treatment (the treatment group)
while other subjects are not given such a stimulus (the control group).
Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods.
Any measurements conducted before the treatment is administered are called retest measures, while those
conducted after the treatment are posttest measures.
Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame.
Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats.
History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment.
Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects rather than the experimental treatment.
Testing threat is a threat in pre-post designs where subjects' posttest responses are conditioned by their pretest responses.
Instrumentation threat, which also occurs in pre-post designs, refers to the possibility
that the difference that the difference between pretest and posttest scores is not due to the remedial math
program, but due that the difference between pretest and posttest scores is not due to the remedial math
program, but due to changes in the administered test, such as the posttest having a
higher or lower degree of difficulty than the pretest.
- 1 more item...
The two basic two-group designs are the pretest-posttest control group design and the posttest-only control group design, while variations may include covariance designs.
These designs are often depicted using a standardized design notation, where R represents random assignment of subjects to groups, X represents the treatment administered to the treatment group, and O represents pretest or posttest observations of the dependent variable (with different subscripts to distinguish between pretest and posttest observations of treatment and control groups).
-
Factorial Designs
Two-group designs are inadequate if your research requires manipulation of two or more independent variables (treatments).
Each independent variable in this design is called a factor, and each sub-division of a factor is called a level.
Sometimes, due to resource constraints, some cells in such factorial designs may not receive any
treatment at all, which are called incomplete factorial designs.
In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors.
An interaction effect exists when the
effect of differences in one factor depends upon the level of a second factor.
-
-