Emerging naturally from the six previous rules, cross-national surveys should ideally be confined to the smallest number of countries consistent with their aims, rather than celebrating as many nations as possible in their purview. Cross-national surveys should pay as much attention to the choice and compilation of aggregate-level contextual variables, as they do to individual-level dependent and independent variables relevant level-2 variables.
Social scientists contemplating or engaged in cross-national studies should be as open about their limitations as they are enthusiastic about their explanatory powers. The fact is that only certain subjects, and only certain aspects of those subjects, can successfully be measured cross-nationally. Stringent and well-policed ground rules for comparable survey methods should become much more common in comparative studies than they are now.
To avoid infringing well-established cultural norms in one country or another, substantial national variations in methods are sometimes tolerated that should render comparisons invalid. To transform cross-national surveys from parallel exercises into joint ones, collective development work, experimentation, scale construction, and piloting should be undertaken in all participating nations.
One should routinely include methodological experiments in cross-national research. Analysts of cross-national data should try to suspend initial belief in any major inter-country differences they discover. It is also used by market researchers to judge the habits of customers, or by companies wishing to judge the morale of staff. The results from a descriptive research can in no way be used as a definitive answer or to disprove a hypothesis but, if the limitations are understood, they can still be a useful tool in many areas of scientific research.
The subject is being observed in a completely natural and unchanged natural environment. A good example of this would be an anthropologist who wanted to study a tribe without affecting their normal behavior in any way. True experiments , whilst giving analyzable data, often adversely influence the normal behavior of the subject. Descriptive research is often used as a pre-cursor to quantitative research designs, the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
Quantitative experiments are often expensive and time-consuming so it is often good sense to get an idea of what hypotheses are worth testing. Because there are no variables manipulated , there is no way to statistically analyze the results.
In addition, the results of observational studies are not repeatable , and so there can be no replication of the experiment and reviewing of the results. Descriptive research design is a valid method for researching specific subjects and as a precursor to more quantitative studies. Whilst there are some valid concerns about the statistical validity , as long as the limitations are understood by the researcher, this type of study is an invaluable scientific tool.
Whilst the results are always open to question and to different interpretations, there is no doubt that they are preferable to performing no research at all. Check out our quiz-page with tests about:. Martyn Shuttleworth Sep 26, 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. If the researcher does not have any specific hypotheses beforehand, the study is exploratory with respect to the variables in question although it might be confirmatory for others.
The advantage of exploratory research is that it is easier to make new discoveries due to the less stringent methodological restrictions. In other words, if the researcher simply wants to see whether some measured variables could be related, he would want to increase the chances of finding a significant result by lowering the threshold of what is deemed to be significant.
Sometimes, a researcher may conduct exploratory research but report it as if it had been confirmatory 'Hypothesizing After the Results are Known', HARKing—see Hypotheses suggested by the data ; this is a questionable research practice bordering on fraud. 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. In an experimental design, the researcher actively tries to change the situation, circumstances, or experience of participants manipulation , which may lead to a change in behaviour or outcomes for the participants of the study.
The researcher randomly assigns participants to different conditions, measures the variables of interest and tries to control for confounding variables. Therefore, experiments are often highly fixed even before the data collection starts. In a good experimental design , a few things are of great importance. 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.
Use comparative research design when the necessary funding and resources are available. When Not to Use It. Do not use comparative research design with little funding, limited access to necessary technology and few team members. Because of the larger scale of these studies, they should be conducted only if adequate population samples are available.
NOTE: To search for scholarly resources on specific research designs and methods, use the SAGE Research Methods Online and Cases database. The database contains links to more than , pages of SAGE publisher's book, journal, and reference content on .
Comparative research is a research methodology in the social sciences that aims to make comparisons across different countries or cultures. He noticed there was a difference in types of social welfare systems, and compared them based on their level of decommodification of social welfare goods. Basic Research Designs. Causal Comparative. Compare two groups with the intent of understanding the reasons or causes for the two groups being different. Research Designs - This web link explores the main types of research design and provides additional links for more information.
On the other hand, the practicalities of different types of comparative research designs will be examined in detail, by following all the hands-on steps: prior arbitrations and . What are the main types of quantitative approaches to research? It is easier to understand the different types of quantitative research designs if you consider how the researcher designs for control of the variables in the investigation.. If the researcher views quantitative design as a continuum, one end of the range represents a design where the variables are not controlled at all and only.