Experimental Design : Basic concept

Experimental Design:

Planning an experiment properly is very important in order to ensure that the right type of data and a sufficient sample size and power are available to answer the research questions of interest as clearly and efficiently as possible. Experimental designs are often touted as the most "rigorous" of all research designs or, as the "gold standard" against which all other designs are judged. The design of any experiment is of utmost importance because it has the power to be the most rigid type of research. The design, however, is always dependent on feasibility. 

The overall data collection and analysis plan considers how the experimental factors, both controlled and uncontrolled, fit together into a model that will meet the specific objectives of the experiment and satisfy the practical constraints of time and money. The data collection and analysis plan provides the maximum amount of information that is relevant to a problem by using the available resources most efficiently. Understanding how the relevant variables fit into the design structure indicates whether the appropriate data will be collected in a way that permits an objective analysis that leads to valid inferences with respect to the stated problem. The desired result is to produce a layout of the design along with an explanation of its structure and the necessary statistical analyses.

Thus Experimental design can be defined as the process of planning a study to meet specified objectives.

Defining the experimental design consists of the following steps:

1. Identify the experimental unit.

2. Identify the types of variables.

3. Define the treatment structure.

4. Define the design structure.

1.      Identify the experimental  unit:

An experimental or sampling unit is the person or object that will be studied by the researcher. This is the smallest unit of analysis in the experiment from which data will be collected. For example, depending on the objectives, experimental or sampling units can be individual persons, students in a classroom, the classroom itself, an animal or a litter of animals, a plot of land, patients from a doctor's office, and so on.

2.      Identify types of variables:

A data collection plan considers how four important variables: background (covariates), constant, uncontrollable, and primary, fit into the study. It is important to consider all the relevant variables (even those variables that might, at first, appear to be unnecessary) before the final data collection plan is approved in order to maximize confidence in the final results.

3.       Treatment Structure

The treatment structure consists of factors that the researcher wants to study and about which the researcher will make inferences. The primary factors are controlled by the researcher and are expected to show the effects of greatest interest on the response variable(s).

4.      Design Structure

Most experimental designs require experimental units to be allocated to treatments either randomly or randomly with constraints. The design structure consists of those factors that define the blocking of the experimental units into clusters

Designing design for an Experiment:

Much contemporary social research is devoted to examining whether a program, treatment, or manipulation causes some outcome or result. Cook and Campbell (1979) argue that three conditions must be met before we can infer that such a cause-effect relation exists:

1. Co variation

Changes in the presumed cause must be related to changes in the presumed effect. Thus, if we introduce, remove, or change the level of a treatment or program, we should observe some change in the outcome measures.

2. Temporal Precedence

The presumed cause must occur prior to the presumed effect.

3. No Plausible Alternative Explanations.

The presumed cause must be the only reasonable explanation for changes in the outcome measures. If there are other factors which could be responsible for changes in the outcome measures we cannot be confident that the presumed cause-effect relationship is correct.