Descriptive statistics provide summaries of quantitative data, which can include quantified qualitative data. They are intended to describe the data as they are rather than draw conclusions beyond your sample of participants. Thus, they are used for descriptive research questions. Inferential statistics are needed to address relational or causal research questions.

In the scientific method, we collect data to support or refute hypotheses, not to prove or disprove them. We frame scientific research in this way because there might be factors that we are unaware of that affect the results. In human-subjects research, one reason we cannot prove or disprove hypotheses is that we use samples to represent populations rather than whole populations.

When handling quantitative data, there are a number of steps that need to be completed before you can run your first test. This post describes a basic protocol for data cleaning and tools that you can use for analysis.

Creating a Data File

When you create your data file, analytic software expects that individual participants are represented on the rows and variables are represented on the columns like in the example table below.

Surveys are prominent in quantitative educational research for measuring students’ opinions or self-reported data, such as study habits. Data collected from survey research can be any level of measurement. For example, if you ask the question “How much time did you study per week?” you could collect ordinal data (e.g., “none” “some,” or “a lot”) or ratio data (e.g., number of hours). This example also illustrates the most common criticism of survey data, which is that it can be unreliable. Even if students don’t skew their answers to be socially desirable based on what they think the researcher wants to hear, they might not be able to give an accurate response from memory. For validity, it is better to have direct measurements, but in education, this is often impractical or invasive. Evaluate the trade-offs in your work.

Operationalizing Quantitative Measurements

Your measurements are your dependent variables. In educational research, one of the dependent variables is frequently learning. To measure learning, we have to define exactly what we mean. Learning could be measured by grades on assignments, such as exams or projects, performance on standardized tests, such as concept inventories, or self-report, such as feelings of learning. All of these options are possible, though some are more defensible in scientific research (more on validity later). As a researcher, you need to operationalize, or clearly articulate in a way that can be applied to your research, what you mean when you say learning, or any of your other dependent variables. You might need to operationalize your independent variables, too. For example, instead of saying you’ll measure peer-to-peer interaction, you could operationalize these interactions as number of posts on a peer-to-peer forum and number of contributions during peer-to-peer discussions in class.

Your measurements, or dependent variables, provide the data you will analyze. There are two main types of data.

Quantitative data represent the world with numbers that can be statistically analyzed. They are necessary for relational or causal research questions because these types of questions require inferential statistics that generalize the results from your study to a larger population (more on this later). For example, quantitative data could tell us the average number of forum posts per student or the number of times students watched a video. Quantitative measures are appropriate when you want to confirm a hypothesis (e.g., that students in one group outperform others), but they are close-ended, which does not allow for exploration.

Variables in education research are anything that can have different values or vary across learners. Dependent variables are the outcome variables that you collect data about in research, like learning outcomes. They apply to all research designs: non-experimental and experimental. All measurements used to evaluate or understand learning or a learning environment, such as test scores or attitudes, are dependent variables. Pre-tests and post-tests are dependent variables.