The first step in developing research is identifying the appropriate quantitative design as well as target population and sample.
Please access the NU library database "SAGE Research Methods" for help in identifying the appropriate design for your quantitative doctoral project or dissertation-in-practice.
Quantitative studies are experimental, quasi-experimental, or non-experimental.
Experimental is the traditional study you may be familiar with – random sampling and experimental and control groups investigating the cause-and-effect relationship between dependent variable(s) and independent variable(s). The independent variable is manipulated by the researcher. The researcher also designs the intervention. Some examples of designs are independent measures/between groups, repeated measures/with-in groups, and matched pairs.
Quasi-experimental is when the sample cannot be randomly sampled but still focuses on the cause-and-effect relationship between dependent variable(s) and independent variable(s). The researcher does not have control over the intervention, i.e., the groups already exist, and the independent variable (intervention/treatment) is not manipulated. The intervention/treatment has usually occurred prior to the current study. Control groups can be used but are not required like in an experimental study. Some examples of designs are causal comparative, regression analysis, and pre-test/posttest.
NOTE: Quasi-experimental is often used interchangeably with ex-post facto design, which means “after the fact.”
Non-experimental is when the sample is not randomly sampled and cause-and-effect are neither desired nor possible. These studies often can find a relationship between variables, but not which variable caused the other to change. Therefore, these studies do not have dependent nor independent variables. Some examples of designs are correlational, cross-sectional, and observational.
The primary non-experimental quantitative design is correlational. However, you need to keep in mind that correlational just confirms if a relationship exists between two variables, not the degree or strength of that relationship NOR the cause of the relationship.
NOTE: Variables in correlational studies are NOT dependent and independent, they are just variables.
If you wish to conduct a more rigorous type of quantitative study still looking at relationships, you can choose regression analysis, which will demonstrate how one variable affects the other. In regression analysis, the “independent variable(s)” should be referred to as “predictor variable(s)” and the “dependent variable(s)” as “outcome variable(s).”
Also, a causal-comparative design (which is a quasi-experimental design) can help determine differences between groups due to an independent variable’s effect on them.
The target population is the population that the sample will be drawn from. It is all individuals who possess the desired characteristics (inclusion criteria) to participate in the Doctoral Project or Applied Dissertation.
The sampling design represents the plan for obtaining a sample from the target population. A sampling frame can be employed to identify participants and can provide access to the population for recruitment of sample.
To identify all individuals in the Doctoral Project or dissertation-in-practice population a sampling frame is identified and provides access to the population for recruitment of sample. Review Trochim's Knowledge Base at http://www.socialresearchmethods.net/kb/ for more information.
Use the script below by replacing the italicized text with the appropriate information to state the target population.
"The target population for the proposed study is comprised of all (individuals with relevant characteristics), within (describe the sampling frame)."
The sample is a subset of the target population. Participants comprise the sample and should be labeled with relevant characteristics to the doctoral project or dissertation-in-practice. The sampling method is the technique used to obtain the sample. Review Trochim's Knowledge Base at http://www.socialresearchmethods.net/kb/ for more information.
A G*Power analysis is often conducted to determine the minimum sample size needed for a quantitative study. There are calculators to help with this analysis - https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html.
NOTE: It is important to understand the target population to determine the correct minimum sample size.
Use the script below to state the sample.
"A (sampling method) was used to determine a sample of (sample number) participants to be recruited for this study. The following inclusion criteria (list relevant characteristics needed to participate) must be met."