Framing the Research Problem: Basic

Framing the Research Problem: Basic Steps

The specific steps undertaken when framing a research problem for a study will vary with the type of discipline, subject area of research, and the level of accuracy demanded of the research. For example, a small exploratory study designed to see if there is a market for a new fitness studio in a suburban area will demand a different level of scrupulosity than a statistical study designed to see if a new drug has dangerous side effects within certain demographic populations. However, broadly speaking, the steps of the research process are as follows (Marketing research, 2009, Quick MBA):

Define the problem

The problem must be framed in a clear question format, and the data the research is attempting to accumulate must provide a reasonable answer to that question. For example 'is there a statistically significant correlation between hours of television watched and a child's BMI (Body Mass Index)' or 'what characteristics do mothers say influence their breakfast cereal choice when shopping for the family' are both examples of research-based questions.

Most research is framed as a null hypothesis: in other words, the research statement is the opposite of what the researcher actually wants to prove. In the case of a study regarding television watching and childhood obesity, the null hypothesis might state that there is no correlation between hours of television the child watches and the likelihood that the child's BMI will be in the overweight or obese range. The null hypothesis often states conventional wisdom or the status of the control group.

Step 2: Determine research design

Is the research merely designed to describe a specific phenomenon, such as the average age or weight of a consumer of fast food, in the form of descriptive research? Or is it designed to explore possible reasons for the statistical tendency and take the form of exploratory research? Exploratory research might follow a particular population for a period of time to suggest a correlation, such as between obesity and number of fast food restaurants located near a child's school. A causal research design that aims to show a clear cause-and-effect relationship demands a more narrow study design, and usually a control group. It strives to eliminate other possible variables that could influence the outcome: for example, children who live in areas with many fast food establishments near their school might have less access to other leisure-time pursuits because of poverty and a poor diet at home -- factors beyond the location of fast food restaurants might be more of a cause, rather than the availability of fast food alone. More fast food restaurants tend to be in poorer neighborhoods, near schools but also near other establishments, and schools located near fast food restaurants might tend to have poorer and less privileged children -- thus eliminating fast food restaurants from the area will not solve the problem (Marketing research, 2009, Quick MBA).

Step 3: Identify data types and sources: Sample size and collection methods

Data must be drawn from a representative population with a wide variety of norms that could influence the results. For example, in studying the effects of fast food restaurants near schools, the study population of children with many fast food restaurants near their school should be balanced in terms of BMI, gender, socio-economic status and other factors that could influence the results. So must the control group. Of course, some research studies might wish to study intervention techniques in the context of specific population groups, like the effects of a diabetes management program upon a low-income population with a quite limited population base, demographically.

If an ideally balanced sampling is not possible to achieve, nonparametric statistics must be used to allow for an uneven breakdown of demographic data (Rumsey 2007, p.262). Chi-square tests create a balanced group from a large population sample, while the Fisher exact test allows the researcher to balance data of from small samples (Nonparametric statistics, 2010, Stat Soft). Nonparametric methods are useful when the researcher knows little of the parameters of the variable of interest in the population -- for example, if the socioeconomic status of the students might not be able to be known conclusively, given the wide variation that might exist within school districts (Nonparametric statistics, 2010, Stat Soft).