to as β. In other words, if you want to see whether some of your measured variables could be related, you would want to increase your chances of finding a significant result by lowering the threshold of what you deem to be significant.State problems versus process problems 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. An example of state problems is the types of failure in a communication system. An example of process problems is the build-up of packet queue under a particular communication scenario.
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.
Experimental versus Non-experimental research 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. Therefore, it is important to consider how the variable(s) will be measured, as well as which methods would be most appropriate to answer the research question. In addition, the statistical analysis has to be taken into account. The researcher should consider what the expectations of the study are as well as how to analyze this outcome. Finally, in an experimental design the researcher must think of the practical limitations including the availability of data-set or experimental set-up that are representative of the real situations. 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, relational designs, in which a range of variables is measured. These designs are also called correlational studies. Correlation does not imply causation, and rather identifies dependence of one variable on another. Correlational designs are helpful in identifying the relation of one variable to another, and seeing the frequency of co-occurrence in two natural groups. The second type is comparative research. These designs compare two or more groups on one or more variable, such as the effect of gender on grades. The third type of non-experimental research is a longitudinal design. A longitudinal design examines variables such as performance exhibited by a group or groups over time.
