What does SDAR mean in RESEARCH

SDAR is an approach to researching a topic by collecting existing or already collected data, rather than gathering new data through primary research. SDAR can be used for a variety of purposes such as making predictions, testing hypotheses, or analyzing patterns in the data. This method of research has gained popularity in recent years due to its cost-effectiveness and quick turnaround time.

SDAR can use any type of quantitative or qualitative data depending on the research question being addressed. This could include survey responses, census information, demographic data, survey responses, observational studies and many more.

The main benefit of SDAR is that it is cost-effective compared to other forms of primary research. Additionally, this form of research generally yields results quickly and without needing to conduct additional field work; this is particularly useful for researchers with limited resources who need to produce timely results.

Yes; one limitation with this approach to research is that it may not provide up-to-date information since the data being analyzed was likely collected at some point in the past. Additionally, the interpretation of the data relies heavily on existing knowledge on the subject matter which may limit its applicability or accuracy.

Before committing to a particular strategy for your project, you should consider what kind of questions you want answered and how much time and money you have available. If your goal is to test certain hypotheses or make predictions but you don't have a lot resources then SDAR may be an effective option for you. On the other hand if your goal is to learn more about something in greater depth then primary research might better suit your needs.

You can start by searching for publicly available datasets from government organizations or online sources such as Kaggle or Datahub before going directly to the source and requesting special access rights if needed. Once you have identified suitable datasets make sure they are reliable by verifying their provenance before using them in your analysis.

The type of analyses depends entirely on what kind of questions you are trying to answer with your study; however common techniques include correlation analyses (statistical tests that measure relationships between different variables), predictive modeling (using historical/existing data points to generate forecasts) and Bayesian networks (using conditional probability models).

Yes! In fact combining both sources may provide even more insight into understanding a particular issue since they will complement each other’s strengths and weaknesses while allowing more flexibility when drawing conclusions from your findings.

To ensure validity it's important that you first check the quality of the dataset by examining things like completeness, accuracy, range etc., before beginning your analysis; additionally try using different methods/models on the same set so that there's room for comparison/evaluation if necessary and remember to document all steps taken during this process as well including assumptions made along the way!