One of the most common challenges with a quantitative study, from my experience, is that learners often choose too broad of a topic. For example, attempting to derive an algorithm using the attendance, socio-economic, attendance, special need proportions, and other data captured from various educational institutions, and how they affect average yearly progress AYP goals is a worthwhile objective, but often too broad for one dissertation. However, studying how one after-school remedial math course might affect standardized test scores in math is doable because it is much more focused.
There are usually two groups in this category - learners that already have the data and are contemplating how to leverage it, and those that have in mind a way to collect it. My advice for the second group is to be cautiously optimistic about the collection of data through Survey Monkey, and other instruments. Often participants are less willing to participate than originally planned, which means that response bias will also have to be something that you discuss in your dissertation.
If your research involves a field experiment, successfully encouraging subjects to participate will be an essential component of the success of your research.
This is one of the easiest challenges to remedy, as your methodologist, and related studies, should guide your selection of statistical methods. Of course, you will need to explain why you used a particular method in your dissertation. Finally, what the numbers actually "tell us" can be materially different from what we expected.
The results can either be inconclusive, or they may actually reveal that the opposite of our assumptions was actually true. In this case, would it be possible for you to leverage this new, surprising, knowledge in some way?
For example, if extra instruction actually lowers test scores in an educational setting, then are the courses worth the extra resources for the organization? Hopefully, this list of considerations, offered as just a start of some of the challenges of a quantitative dissertation, will generate some thought at just the right time for you in the doctoral process!
An Overview of Quantitative Research This modules provides a basic overview of quantitative research, including its key characteristics and advantages.
Describe the uses of quantitative research design. Provide examples of when quantitative research methodology should be used. Discuss the strengths and weaknesses of quantitative research.
The data collected is numeric, allowing for collection of data from a large sample size. Statistical analysis allows for greater objectivity when reviewing results and therefore, results are independent of the researcher.
Numerical results can be displayed in graphs, charts, tables and other formats that allow for better interpretation. Data analysis is less time-consuming and can often be done using statistical software. Results can be generalized if the data are based on random samples and the sample size was sufficient.
Data collection methods can be relatively quick, depending on the type of data being collected. Numerical quantitative data may be viewed as more credible and reliable, especially to policy makers, decision makers, and administrators. How often do college students between the ages of access Facebook? What is the difference in the number of calories consumed between male and female high school students? What percentage of married couples seek couples counseling?
How many organized sports activities has the average 10 year old child competed in? Planning, conducting, and evaluating quantitative. Data is in the form of words, pictures or objects. Data is in the form of numbers and statistics. Qualitative data is more 'rich', time consuming, and less able to be generalized.
Quantitative data is more efficient, able to test hypotheses, but may miss contextual detail. Researcher tends to become subjectively immersed in the subject matter. Researcher tends to remain objectively separated from the subject matter. Qualitative versus Quantitative Research: Key Points in a Classic Debate. James Neill Last updated:
As is the case for qualitative researchers, Creswell () is one of the best sources for some best practices of quantitative studies, and he outlines some basic principles of a quantitative study on pages
Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an .
Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality. Quantitative research deals in numbers, logic, and an objective stance. What are quantitative methods of research? What is its definition, when are these methods used and what are its characteristics? This article defines quantitative methods and lists seven characteristics that discriminate these research methods from qualitative research approaches.
QUALITATIVE RESEARCH DESIGNS. Comparison of qualitative & quantitative research: Qualitative: Quantitative: Definitions: Purpose - to describe a culture's characteristics: Method: Identify culture, variables for study, & review literature;. Quantitative research involves analysis of numerical data. The summarizing characteristics of qualitative and quantitative research in more detail; Quantitative Research Designs (notes from a post-graduate research methods class) Qualitative Research Exam;.