Exploratory research is research conducted for a problem that has not been studied more clearly, intended to establish priorities, develop operational definitions and improve the final research design. Exploratory research helps determine the best research design, data-collection method and selection of subjects. It should draw definitive conclusions only with extreme caution. Given its fundamental nature, exploratory research often concludes that a perceived problem does not actually exist.
Exploratory research often relies on techniques such as:-
- Secondary research – such as reviewing available literature and/or data
- Informal qualitative approaches, such as discussions with consumers, employees, management or competitors
- Formal qualitative research through in-depth interviews, focus groups, projective methods, case studies or pilot studies
The Internet allows for research methods that are more interactive in nature.
Descriptive research is used to describe characteristics of a population or phenomenon being studied. It does not answer questions about how/when/why the characteristics occurred. Rather it addresses the “what” question (what are the characteristics of Minnesota state population or situation being studied?). The characteristics used to describe the situation or population are usually some kind of categorical scheme also known as descriptive categories. For example, the periodic table categorizes the elements. Scientists use knowledge about the nature of electrons, protons and neutrons to devise this categorical scheme. We now take for granted the periodic table, yet it took descriptive research to devise it. Descriptive research generally precedes explanatory research. For example, over time the periodic table’s description of the elements allowed scientists to explain chemical reaction and make sound prediction when elements were combined. Hence, descriptive research cannot describe what caused a situation. Thus, descriptive research cannot be used as the basis of a causal relationship, where one variable affects another. In other words, descriptive research can be said to have a low requirement for internal validity.
EXPERIMENTAL RESEARCH DESIGN:
Experimental Research – An attempt by the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur.
Experimental Design – A blueprint of the procedure that enables the researcher to test his hypothesis by reaching valid conclusions about relationships between independent and dependent variables. It refers to the conceptual framework within which the experiment is conducted.
Validity of Experimental Design:-
- Internal Validity asks did the experimental treatment make the difference in this specific instance rather than other extraneous variables?
- External Validity asks to what populations, settings, treatment variables, and measurement variables can this observed effect be generalized?
- History – The events occurring between the first and second measurements in addition to the experimental variable which might affect the measurement.
Example: Researcher collects gross sales data before and after a 5 day 50% off sale. During the sale a hurricane occurs and results of the study may be affected because of the hurricane, not the sale.
- Maturation – The process of maturing which takes place in the individual during the duration of the experiment which is not a result of specific events but of simply growing older, growing more tired, or similar changes.
Example: Subjects become tired after completing a training session, and their responses on the Post-test are affected.
- Pre-testing – The effect created on the second measurement by having a measurement before the experiment.
Example: Subjects take a Pretest and think about some of the items. On the Posttest they change to answers they feel are more acceptable. Experimental group learns from the pretest
- Measuring Instruments – Changes in instruments, calibration of instruments, observers, or scorers may cause changes in the measurements.
Example: Interviewers are very careful with their first two or three interviews but on the 4th, 5th, 6th become fatigued and are less careful and make errors.
- Statistical Regression – Groups are chosen because of extreme scores of measurements; those scores or measurements tend to move toward the mean with repeated measurements even without an experimental variable.
Example: Managers who are performing poorly are selected for training. Their average Posttest scores will be higher than their Pretest scores because of statistical regression, even if no training were given.
- Differential Selection – Different individuals or groups would have different previous knowledge or ability which would affect the final measurement if not taken into account.
Example: A group of subjects who have viewed a TV program is compared with a group which has not. There is no way of knowing that the groups would have been equivalent since they were not randomly assigned to view the TV program.
- Experimental Mortality – The loss of subjects from comparison groups could greatly affect the comparisons because of unique characteristics of those subjects. Groups to be compared need to be the same after as before the experiment.
Example: Over a 6 month experiment aimed to change accounting practices, 12 accountants drop out of the experimental group and none drop out of the control group. Not only is there differential loss in the two groups, but the 12 dropouts may be very different from those who remained in the experimental group.
Interaction of Factors, such as Selection Maturation, etc. – Combinations of these factors may interact especially in multiple group comparisons to produce erroneous measurements.
Pre-experimental designs are so named because they follow basic experimental steps but fail to include a control group. In other words, a single group is often studied but no comparison between an equivalent non-treatment group is made. Examples include the following:-
- The One-Shot Case Study.
In this arrangement, subjects are presented with some type of treatment, such as a semester of college work experience, and then the outcome measure is applied, such as college grades. Like all experimental designs, the goal is to determine if the treatment had any effect on the outcome. Without a comparison group, it is impossible to determine if the outcome scores are any higher than they would have been without the treatment. And, without any pre-test scores, it is impossible to determine if any change within the group itself has taken place.
- One Group Pretest Posttest Study.
A benefit of this design over the previously discussed design is the inclusion of a pre-test to determine baseline scores. To use this design in our study of college performance, we could compare college grades prior to gaining the work experience to the grades after completing a semester of work experience. We can now at least state whether a change in the outcome or dependent variable has taken place. What we cannot say is if this change would have occurred even without the application of the treatment or independent variable. It is possible that mere maturation caused the change in grades and not the work experience itself.
- The Static Group Comparison Study.
This design attempts to make up for the lack of a control group but falls short in relation to showing if a change has occurred. In the static group comparison study, two groups are chosen, one of which receives the treatment and the other does not. A posttest score is then determined to measure the difference, after treatment, between the two groups. As you can see, this study does not include any pre-testing and therefore any difference between the two groups prior to the study are unknown.
Diagrams of Pre-Experimental Designs