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.
Researchers often study non-numeric characteristics (like gender, race, or religiosity), but they assign numeric values for quantitative analysis, even though these numbers don’t measure the amount of the characteristic. For example, coding “gender” as female=1 and male=2 doesn’t mean males have twice the amount of “gender” compared to females. They divide variables into numeric variables, where numbers have meaning, and categorical variables, which are often words or ranges.
Researchers can collect quantitative data in various ways. In experimental settings, they can directly collect data like reaction times or blood pressure, or participants can self-report data on pretests or posttests. Researchers commonly use questionnaires, either interviewer- or self-administered, to collect quantitative data on attitudes, experiences, demographics, etc. They also frequently observe quantitative data gathered for other purposes, such as data recorded in patients’ medical charts or results from students’ standardized tests.
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Secondary data analysis involves analyzing data originally collected by another research team. Large-scale, nationally-representative data sets, requiring extensive resources, are available for researchers to conduct independent research with high-quality data.
Quantitative hypotheses are specific, outlining clear relationships between independent and dependent variables. For two numeric variables, the expected direction of the relationship is specified.
For example, a hypothesis might state that as age increases, so do functional limitations. Hypotheses for categorical variables identify which independent category is more likely to report a certain dependent category. For example, women are more likely than men to report experiencing sexual harassment.
Quantitative results are shown in tables or graphs, starting with descriptive statistics like means or simple graphs.
Bivariate results follow, showing the demographic distributions of key variables. For example, a crosstabulation of gender and abortion attitudes may reveal a relationship. Finally, researchers present results from statistical models with control variables to rule out alternatives and support their hypotheses.
The quantitative approach is especially useful for addressing specific questions about well-defined phenomena. It requires researchers to collect high-quality data, where variables accurately represent differences in characteristics. This is challenging when researching complex or understudied areas that don’t easily fit specific variables. The quantitative approach uses deductive logic, leading people to perceive it as “real science”. Further giving it more empirical weight than other research methods.