Quantitative Design
When you are planning on a study, you always want to ensure that the evidence obtained enables you to address the research problem effectively, logically, and as unambiguously as possible. If you begin the investigation too early, without thinking critically about what information is required, the overall problem may not be adequately addressed and the validity of the study will be undermined. Research design integrates all elements needed for the study to maximize validity and balance feasibility prior to beginning the data collection. Our quantitative design planning includes descriptive and experimental designs to provide you a blueprint for the collection, measurement, and analysis of data.
Quantitative Research Design
What is it? Research design is the overall strategy to integrate components of the study in a logical and coherent way. In order to achieve a well-developed design, you need to clearly identify the research program and explicitly specify the research question. You also need to properly align your research goals with your hypothesis because your research problem and research questions determine the type of quantitative design you should use.
There are numerous types of quantitative research design. The most common designs are experimental, quasi-experimental, descriptive, correlational, cross-sectional, longitudinal, survey, and single case designs. Regardless of which type of quantitative research you are designing, it is always recommended to ask yourself who, what, when, where, and how. When designing a study, you want to find the optimal balance between time, resources, and efforts. A well-written research design should be detailed enough that someone else could replicate the study. This includes defining the population, planning instrumentation, selecting methodology and alternative approaches, analysis plan, and more. If you are unsure which research design types are appropriate for you and what components should be included in the design writing, let us help you.
How can we help you?
- Align problem statement, purpose, research question, and hypothesis
- Provide strategies to develop research questions from research problem
- Provide varieties of quantitative design, including but not limited to experimental, quasi-Experimental, non-Experimental, and random clustered trial
- Help determine how to collect the data
- Consider reasonable design methods based on the client’s priorities and practicalities
- Write approach/research design in the research proposal
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Read MoreFAQ About Quantitative Research Design
There is a nearly endless supply of data opportunities. Awareness of potentially untapped resources often requires a research background beyond the specific field in question.
There are multiple platforms for collecting, organizing and analyzing data across different types of operations and infrastructures. The best fit for a particular use involves balancing cost, simplicity, data platform collection approaches, and technical capacity to use particular analytic methods.
There are numerous statistical analytic softwares and platforms capable of modeling and simulation based on known and variable inputs. Experience modeling these simulations helps to properly contextualize and dial results in order to apply and garner feedback useful to processes.
Secondary data sources publish research collected from academic, governmental, philanthropic, journalistic, medical and other sources to public, private, and academic channels. These secondary data sources can complement, replace, reshape, or enhance your research goals and approach.
Existing reviews and studies give access to best practices, market research information and infrastructure developments with limited cost in resources and time. It is incredibly valuable to understand which reviews/studies will be profitable and applicable to portions of your operations.
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