Basic Research Designs Basic Research Designs This module will introduce the basics of choosing an appropriate research design and the key factors that must be considered. Learning Objectives Distinguish between quantitative and qualitative research methods. Identify whether or research project is qualitative or quantitative in nature. List the key factors that must be considered when choosing a research design.
Study Design and Sampling Study Design Cross-sectional studies are simple in design and are aimed at finding out the prevalence of a phenomenon, problem, attitude or issue by taking a snap-shot or cross-section of the population.
This obtains an overall picture as it stands at the time of the study. For example, a cross-sectional design would be used to assess demographic characteristics or community attitudes.
These studies usually involve one contact with the study population and are relatively cheap to undertake. Such studies are often used to measure the efficacy of a program.
These studies can be seen as a variation of the cross-sectional design as they involve two sets of cross-sectional data collection on the same population to determine if a change has occurred. Retrospective studies investigate a phenomenon or issue that has occurred in the past.
Such studies most often involve secondary data collection, based upon data available from previous studies or databases. For example, a retrospective study would be needed to examine the relationship between levels of unemployment and street crime in NYC over the past years.
Prospective studies seek to estimate the likelihood of an event or problem in the future. Thus, these studies attempt to predict what the outcome of an event is to be. General science experiments are often classified as prospective studies because the experimenter must wait until the experiment runs its course in order to examine the effects.
Longitudinal studies follow study subjects over a long period of time with repeated data collection throughout. Some longitudinal studies last several months, while others can last decades.
Most are observational studies that seek to identify a correlation among various factors.
Thus, longitudinal studies do not manipulate variables and are not often able to detect causal relationships. Sample Once the researcher has chosen a hypothesis to test in a study, the next step is to select a pool of participants to be in that study.
However, any research project must be able to extend the implications of the findings beyond the participants who actually participated in the study.
For obvious reasons, it is nearly impossible for a researcher to study every person in the population of interest. The researcher must put some careful forethought into exactly how and why a certain group of individuals will be studied.
This is also known as random sampling. A researcher can simply use a random number generator to choose participants known as simple random samplingor every nth individual known as systematic sampling can be included. Researchers also may break their target population into strata, and then apply these techniques within each strata to ensure that they are getting enough participants from each strata to be able to draw conclusions.
For example, if there are several ethnic communities in one geographical area that a researcher wishes to study, that researcher might aim to have 30 participants from each group, selected randomly from within the groups, in order to have a good representation of all the relevant groups.
Non-Probability Sampling, or convenience sampling, refers to when researchers take whatever individuals happen to be easiest to access as participants in a study.
This is only done when the processes the researchers are testing are assumed to be so basic and universal that they can be generalized beyond such a narrow sample.
Snowball sampling is not a stand-alone tool; the tool is a way of selecting participants and then using other tools, such as interviews or surveys. Sampling Challenges Because researchers can seldom study the entire population, they must choose a subset of the population, which can result in several types of error.
Sometimes, there are discrepancies between the sample and the population on a certain parameter that are due to random differences.
This is known as sampling error and can occur through no fault of the researcher. Far more problematic is systematic error, which refers to a difference between the sample and the population that is due to a systematic difference between the two rather than random chance alone.
The response rate problem refers to the fact that the sample can become self-selecting, and that there may be something about people who choose to participate in the study that affects one of the variables of interest.
For example, in our eye care case, we may experience this kind of error if we simply sample those who choose to come to an eye clinic for a free eye exam as our experimental group and those who have poor eyesight but do not seek eye care as our control group.
It is very possible in this situation that the people who actively seek help happen to be more proactive than those who do not. Because these two groups vary systematically on an attribute that is not the dependent variable economic productivityit is very possible that it is this difference in personality trait and not the independent variable if they received corrective lenses or not that produces any effects that the researcher observes on the dependent variable.
This would be considered a failure in internal validity. Another type of systematic sampling error is coverage error, which refers to the fact that sometimes researchers mistakenly restrict their sampling frame to a subset of the population of interest. This means that the sample they are studying varies systematically from the population for which they wish to generalize their results.
This leaves out all of the more rural populations in developing countries, which have very different characteristics than the urban populations on several parameters.
Thus, the researcher could not appropriately generalize the results to the broader population and would therefore have to restrict the conclusions to populations in urban areas of developing countries.
Errors in sampling can often be avoided by good planning and careful consideration. However, in order to improve a sampling frame, a researcher can always seek more participants.A double blind study is the most rigorous clinical research design because, in addition to the randomization of subjects, which reduces the risk of bias, it can eliminate or minimize the placebo effect which is a further challenge to the validity of a study.
These methods are a common choice in social science research problems and are often used to study ideas, beliefs, human behaviors and other research questions that do not involve studying the relationship between variables.
Studies on design research methodology are infrequent, although there is a consensus that more effort is needed for improving design research quality. Previous calls for exercising better research. The research design for this study is a descriptive and interpretive case study that is analysed through qualitative methods.
Questionnaires were used to evaluate. Empirical Research - The study, based on direct observation, use of statistical records, interviews, or experimental methods, of actual practices or the actual impact of practices or policies. Evaluation Studies - Works consisting of studies determining the effectiveness or utility of processes, personnel, and equipment.