It can also be to investigate a drug, device or procedure that has already been approved but is still in need of further investigation, typically with respect to long-term effects or cost-effectiveness. When choosing a study design, many factors must be taken into account. Different types of studies are product design case study pdf to different types of bias. The nature of this type of analysis tends to overestimate the degree of association between variables.
Additionally, seasonal variations and weather patterns can affect a seasonal study. This use of the term “retrospective study” is misleading, however, and should be avoided because other research designs besides case-control studies are also retrospective in orientation. 6th edn, New York: Oxford University Press. This page was last edited on 11 November 2017, at 12:04. Research design is the framework that has been created to find answers to research questions. There are many ways to classify research designs, but sometimes the distinction is artificial and other times different designs are combined.
Nonetheless, the list below offers a number of useful distinctions between possible research designs. A research design is an arrangement of conditions or collections. Sometimes a distinction is made between “fixed” and “flexible” designs. In fixed designs, the design of the study is fixed before the main stage of data collection takes place. Often, these variables are measured quantitatively.
Flexible designs allow for more freedom during the data collection process. One reason for using a flexible research design can be that the variable of interest is not quantitatively measurable, such as culture. In other cases, theory might not be available before one starts the research. The advantage of confirmatory research is that the result is more meaningful, in the sense that it is much harder to claim that a certain result is generalizable beyond the data set. The reason for this is that in confirmatory research, one ideally strives to reduce the probability of falsely reporting a coincidental result as meaningful. It is also possible to have an idea about a relation between variables but to lack knowledge of the direction and strength of the relation. The advantage of exploratory research is that it is easier to make new discoveries due to the less stringent methodological restrictions.
A distinction can be made between state problems and process problems. State problems aim to answer what the state of a phenomenon is at a given time, while process problems deal with the change of phenomena over time. Examples of state problems are the level of mathematical skills of sixteen-year-old children or the level, computer skills of the elderly, the depression level of a person, etc. Examples of process problems are the development of mathematical skills from puberty to adulthood, the change in computer skills when people get older and how depression symptoms change during therapy. State problems are easier to measure than process problems. State problems just require one measurement of the phenomena of interest, while process problems always require multiple measurements.
Research designs such as repeated measurements and longitudinal study are needed to address process problems. First of all, it is necessary to think of the best way to operationalize the variables that will be measured, as well as which statistical methods would be most appropriate to answer the research question. Thus, the researcher should consider what the expectations of the study are as well as how to analyse any potential results. Finally, in an experimental design the researcher must think of the practical limitations including the availability of participants as well as how representative the participants are to the target population.
It is important to consider each of these factors before beginning the experiment. Non-experimental research designs do not involve a manipulation of the situation, circumstances or experience of the participants. Non-experimental research designs can be broadly classified into three categories. First, in relational designs, a range of variables are measured. These designs are also called correlation studies, because correlation data are most often used in analysis.