# Quantitative Research Approach

Quantitative research most often uses deductive logic, in which researchers start with hypotheses and then collect data which can be used to determine whether empirical evidence to support that hypothesis exists.

Quantitative analysis requires numeric information in the form of variables.  A variable is a way of measuring any characteristic that varies or has two or more possible values.  Many characteristics are naturally numeric in nature (such as years of education, age, income); for these numeric variables, the numbers used to measure the characteristic are meaningful in that they measure the amount of that characteristic that is present.  Often researchers are interested in characteristics which are not numeric in nature (such as gender, race, religiosity), but even these variables are assigned numeric values for use in quantitative analysis although these numbers do not measure the amount of the characteristic present.  For example, although the categories of the variable “gender” may be coded as female=1, male=2 this does not imply that males have twice the amount of the characteristic “gender” compared to females.  Variables can thus be divided into numeric variables (in which the numbers have meaning) and categorical variables (which are commonly words or ranges).

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Quantitative data can be collected in a variety of ways.  In experimental settings, researchers can directly collect quantitative data (such as reaction times, blood pressure) or such data can be self-reported by research participants on a pretest or posttest.  Questionnaires – either interviewer- or self-administered – are commonly used to collect quantitative data by asking respondents to report attitudes, experiences, demographics, etc.  Direct observation of quantitative data which has been gathered for another purpose is also common, such as quantitative data that is recorded in patients’ medical charts or the results of students’ standardized tests.