Introduction
The experimental design is among the most systematic and comprehensive survey blueprints. This approach encompasses manipulating a single or more independent variable as treatments, the random allocation of participants to various treatment levels, and the observation of treatments’ consequences on outcomes. There are three primary experimental designs, namely quasi-, true, and pre-experimental designs (Salkind, 2018). The paper provides an in-depth analysis of the aforementioned designs’ specific roles and the importance of external and internal validity.
Pre-Experimental Designs
This approach is not typified by participants’ random selection from a wider populace, and it does not include a controlled grouping. The lack of these two components significantly impedes or reduces a researcher’s capacity to identify the causal nature of the interconnection between dependent and independent variables. Pre-experimental designs also encourage minimal to no control over extraneous variables that may trigger unexpected outcomes (Salkind, 2018). There are two primary types of pre-experimental studies: one-case short survey and one-group pretest-posttest design.
The one-case short study design involves surveying a single grouping at a particular period following a treatment believed to have triggered the change. The outcomes obtained from this instance are then contrasted against the general anticipations of the case results if the intervention had not been employed and other casually observed events (Salkind, 2018). On the other hand, a one-group pretest-posttest design incorporates the observation of a single occurrence at two-period points, before and after treatment. The alterations distinguished in the expected outcome are surmised as the intervention’s results. This study design represents a cost-effective strategy for ascertaining whether the possible explanation deserves additional investigation.
True Experimental Designs
True experimental study approaches allow researchers to incorporate all the phases included in the random selection and allocation of participants, in addition to a controlled grouping. This, in turn, fosters the development of a strong rationalization for the relationship between cause and effect. It encourages the casual selection of subjects, indiscriminate treatment assignments, and unpremeditated allotting to groups (Salkind, 2018). Its sub-types include pretest-posttest control, posttest-only control, and Solomon four grouping design (Salkind, 2018). These sub-categories enhance the applicability of this design in studies.
The pretest-posttest control grouping facilitates random subject allocation to the control and experimental set, pretesting the dependent variable on each lot, treatment applied to the experimental category, and the post-testing approach for both batches. On the other hand, the posttest-only control grouping approach does not encompass the pretest procedure for both lots: experimental and control (Salkind, 2018). This methodology is based on the logic that the process already integrates the random selection of candidates. However, various drawbacks are linked to this study design. First, researchers cannot utilize the pretest procedure to compare participants to other experimental batches. Second, the randomization strategy’s ineffective nature may impact the groups’ equivalence or balance from the beginning.
The Solomon-four grouping blueprint is typified by four major categories: an experimental set who are the intervention’s recipients and three control classes. One of the three comparison groups also receives treatment. It also incorporates the randomization process in participants’ selection and treatment procedures. According to Salkind (2018), this approach fosters a researcher’s capacity to contrast the outcomes between several sets to ascertain the factors accountable for specific results. However, this approach has one primary drawback; it is time-consuming.
Quasi-Experimental Design
This approach typically mimics the experimental research; it incorporates the manipulation of the independent variable. However, it does not allow the random allocation of participants to treatments or conditions. Since it involves the manipulation of independent variables before the computation of the dependent variable, this approach facilitates a researcher’s capacity to eradicate directionality issues (Salkind, 2018). Its sub-types include the static group comparison and nonequivalent control group designs. These two subcategories are similar in many ways except that the former does not have a pretest.
Furthermore, several shortcomings have been identified for each sub-type. Salkind (2018) associates the nonequivalent control grouping design with internal validity threats, particularly in the group selection process. Nonetheless, a pretest may help the researcher to contrast between pretest scores and ascertain the equivalence between the categories. On the other hand, the static group comparison allows minimal control over internal validity threats, for instance, mortality and selection. All external validity risks are also sustained when using this methodology.
Significance of External and Internal Validity
Internal validity relates to the quality or standard of an experimental design in that the outcomes attained by the researcher are ascribed to the independent variable’s manipulation. This concept means that there is proof that the study design selected by the researcher reflects the observed results. Salkind (2018) highlights this conceptualization’s relevance on surveys that attempt to establish a causal interconnection. However, this approach has been linked to significant threats, for instance, maturation, history, selection, testing, instrumentation, mortality, and regression (Salkind, 2018). On the other hand, external validity refers to the generalizability of the outcomes obtained from the original study sample to another one, and later to the populace from which the representative population was sourced. A study with the recommended external validity scores enhances a researcher’s capacity to extrapolate its findings to other settings, measures, and people.
Conclusion
From the above analysis, it can be concluded that experimental designs are more convenient approaches for explanatory studies, whose objective is to investigate cause-effect interrelationships. It is also appropriate for surveys involving a well-defined and constrained group of independent variables that may be controlled or manipulated. External validity facilitates the generalizability of the original study sample’s outcomes to other populations or circumstances. Internal validity is crucial in ascertaining whether the study design selected by the researcher reflects the achieved results.
Reference
Salkind, N. J. (2018). Exploring research (9th ed.). Edinburgh Gate Harlow U.K: Pearson Education Limited.