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:
Descriptive:
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.
Comparative:
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.
Relationship:
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.
Predictive:
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.
