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.
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A common quantitative approach is known as secondary data analysis, in which a researcher analyzes data that were originally collected by another research team. Often these are large-scale, nationally-representative data sets that require extensive resources to collect; such data sets are made available by many organizations to allow many researchers to conduct independent research using high quality data.
Hypotheses for quantitative analysis tend to be highly specific, describing clear relationships between the independent and dependent variables. For hypotheses involving two numeric variables, the expected direction of the relationship will be described. For example, a hypothesis might read: we expect that age and functional limitations are related; as age increases, the number of functional limitations individuals experience will also increase. Hypotheses for categorical variables specify which category of the independent will be more likely to report a certain category of the dependent variable; for example: gender is associated with having experienced sexual harassment; women are more likely to report ever having experienced sexual harassment than men.
The results of quantitative analysis are most commonly reported in the form of statistical tables or graphs. The presentation of results usually begins with descriptive statistics describing who is in the sample. This can take the form of univariate statistics (such as frequency distributions, means, standard deviations) or simple graphs (such as pie charts, bar graphs, or historgrams). Bivariate results are commonly presented next to show the demographic distributions of key variables of interest. For example, the crosstabulation of gender and attitudes toward abortion may be reported to establish whether a bivariate relationship exists between these variables. Finally, the results of statistical models in which control variables are included are presented and interpreted. Such models allow researchers to rule out alternative explanations and to specify the conditions under which their hypotheses are upheld.
The quantitative approach is especially useful for addressing specific questions about relatively well-defined phenomena. Quantitative analysis requires high-quality data in which variables are measured well (meaning the values of the variables must accurately represent differences in the characteristics of interest); this can be challenging when conducting research on complicated or understudied areas that do not lend themselves well to being measured with specific variables. Because it uses deductive logic and is therefore more easily viewed as “real science,” the quantitative approach is often perceived as providing stronger empirical evidence than other research approaches.