Writing a quantitative research question

Posted February 19, 2013

Formulating a quantitative research question can often be a difficult task.  When composing a research question, a researcher needs to determine if they want to describe data, compare differences among groups, assess a relationship, or determine if a set of variables predict another variable.  The type of question the researcher asks will help to determine the type of statistical analysis that needs to be conducted.  It is also important to consider what specific variables need to be assessed when writing a research question.  The researcher must be certain all variables are quantifiable, or measurable. Measuring variables can be as simple as having participants report their age or as involved as having participants answer survey questions that make up a reliable instrument.  Some examples of different types of research questions are presented below:
Describe the teachers’ perceptions of the newly implemented reading assessment program.
The goal of a descriptive research question is to describe the data.  The researcher cannot infer any conclusions from this type of analysis; it simply presents data.  Descriptive questions do not have corresponding null and alternative hypotheses because the researcher is not making inferences.  Descriptive studies can be conducted on categorical or continuous data.
Are there differences in students’ grades by gender (male vs. female)?
Are there differences in job level (entry vs. mid vs. executive) by gender (male vs. female)?
Comparative questions can be assessed using a continuous variable and a categorical grouping variable, as well as with two categorical grouping variables.  They type of analysis will vary depending on the types of data.
Is there a relationship between age and fitness level?
Is there a relationship between ice cream sales and temperature at noon?
Questions that assess relationships do not require a definitive independent and dependent variable, but two variables are required; they can be considered variables of interest as opposed to independent and dependent variables.  Data used for this type of analysis can be dichotomous, ordinal, or continuous.  They type of analysis will vary depending on the types of data.
Do age, gender, and education predict income?
Does a pitcher’s ERA predict the number of wins the team has?
Predictive questions have a definitive independent and dependent variable.  Typically, the independent variable should be continuous or dichotomous, but nominal and ordinal variables can be used.  When nominal and ordinal variables are used as predictors, they must be dummy coded.  Like the independent variable, the dependent variable is typically continuous or dichotomous, but can also be ordinal or nominal.  The type of analysis that is appropriate will vary based upon the type of data.

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