The Most Effective Quantitative Data Collection Methods

In many fields, possessing a bachelor’s degree is sufficient to land an entry-level job. However, if you aspire to achieve greater career success, you may need to improve your academic qualifications. Many professionals decide to return to school to earn a doctoral degree and qualify for high-level, supervisory or executive positions. 

If this sounds like you, you may be researching your options and pondering questions such as how to write a dissertation and whether you should opt for quantitative or qualitative research methods. These are important questions you’ll need to answer with the help of your dissertation advisor and other dissertation committee members. If you do choose quantitative research methods for your dissertation, you can explore the following quantitative data collection methods as you begin your research. 

Writing Your Dissertation

For new and aspiring doctoral students, the idea of writing a dissertation of several hundred pages can be overwhelming. Fortunately, it’s possible to break the process down into concrete steps that are easier to manage. Long before you dive into quantitative data collection methods, you’ll need to complete the following steps: 

1. Select your topic with the guidance of your dissertation advisor.
2. Develop an outline that offers a full background of your topic.
3. Complete a thorough literature review that explores existing research on your subject.

Only after your literature review is complete should you turn your attention to your data collection methods. At this point in the process, you will develop an outline of your data collection methodologies and all the steps involved in your research. Finally, you will analyze your findings in dissertation form.

What Is Quantitative Data?

All quantitative data are expressed in numeric form. Quantitative research strives to answer questions such as “How much?” “How often?” and “How many?”.

There are two primary categories of quantitative data: discrete and continuous. Discrete data are any data that cannot be further refined. For example, a family can have two or three children but not 2.5 children. Continuous data, on the other hand, can be further refined in multiple ways. For example, when taking a person’s height measurement, you can continuously refine it from feet to inches to centimeters to millimeters.

You can collect discrete or continuous data in the following forms:

• Counting: A count is associated with entities. For example, you can count the number of students in a class.
• Measurements: This refers to measurements of physical objects. For instance, you can measure the size of a student’s desk.
• Data projection: Data projection is the anticipation of future data. You may take existing data and extrapolate future data from it. For instance, a marketing professional might use current sales figures for an existing product to project expected sales figures for a new product.
• Quantification of qualitative data: This is among the trickiest of all quantitative data because it is based on qualitative data. The quantification of qualitative data involves assigning a numeric value to a qualitative concept, theory or experience. A common example is the 0–10 scale that healthcare providers use to evaluate the severity of a patient’s pain.

Once you have an idea of the types of data you need to collect for your research, you’ll be able to pinpoint the most effective quantitative data collection methods for your work.

Quantitative Data Collection Methods

Students can choose from several primary methods of collecting quantitative data to shape their dissertation. This includes the following methods:

Surveys and Questionnaires

Of all the quantitative data collection methods, surveys and questionnaires are among the easiest and most effective. Many graduate students conducting doctoral research use this method because surveys and questionnaires are applicable to both quantitative and qualitative research.

You should consider a few questions when planning a survey or questionnaire:

• Who is your target demographic?
• Which questions will you ask? (These questions will show the focus of your research.)
• How will you eliminate biases?
• How will you distribute your surveys?
• Will you need to incentivize participation?

When collecting quantitative data, remember to keep your questions closed-ended rather than open-ended. Scales, multiple choice questions, yes or no questions and other types of closed-ended questions work well.

Additionally, you should be aware of the potential for unintended biases to sneak into the wording of your questions. You’ll want to avoid using any language that isn’t strictly neutral. Consider the following example:

• Biased: Don’t students love it when teachers incentivize learning?
• Neutral: Do your students respond better, worse or the same to incentivized learning vs. non-incentivized learning?

Note that you must ensure that your survey respondents align with your target demographic. The first few questions of your survey should strive to filter out potential participants who do not meet the inclusion criteria. For example, you might ask the respondents to indicate their age range and occupation.

Interviews

Although interviews are used more frequently for qualitative research, they can still work for quantitative data collectors. If you do attempt to utilize interviews in a quantitative design, be careful to structure your interview questions accordingly. You will need to stick to closed-ended questions, such as yes or no questions and scales.

The way in which you conduct the interviews is another consideration. To avoid flaws in your research, you will need to employ the same script for every interview. This means reading the same questions in the same order and refraining from inserting additional words or phrases. Even if an impromptu question occurs to you in the midst of an interview, you cannot ask it because you cannot deviate from the script.

Observations

Sometimes, researchers must visit a particular environment and make specific observations to inform their dissertation analyses. Like interviews, observations can be used to collect either quantitative or qualitative data. For quantitative data, researchers will generally conduct structured observations.

To conduct structured observations, you will first identify the specific behaviors on which you intend to focus. Then, you will record those behaviors as they happen. This process will produce your calculations (e.g., how many times a particular event happens).

Let’s go back to the previous hypothetical example of conducting research in the field of education. Perhaps your goal is to evaluate the effectiveness of incentives in the special education classroom. In collaboration with a teacher, you might visit a special education classroom and sit quietly in a corner to observe the class.

Because you have identified the behaviors of interest and consulted with the teacher, you know that at some point during your observation, the teacher will announce to the class that every student who is able to focus on an assignment for 15 minutes without being distracted will receive a lollipop. As the students start working, you’ll count the number of times they look away from their assignments. You might also decide to count the number of times a student gets up from their desk.

Of course, for an accurate experiment, you’ll need to study multiple groups of participants. This means that you’ll need to visit another special education classroom where the teacher does not offer a lollipop reward but still announces to the class that the students are expected to work for 15 minutes without distraction. This would allow you to compare the number of distracted behaviors exhibited by one group of students to the number of distracted behaviors exhibited by the second group.

You’ll also need a control group. For this, you would visit a third special education classroom where the teacher distributes an assignment but does not announce expected behaviors. You’ll count the number of distracted behaviors in this control group.

Document or Archival Reviews

Conducting a review of documents or archival data is another effective quantitative data collection method. Your document review may concern either of the following types of documents:

• Public records (e.g. census records)
• Personal documents (e.g. student report cards)

You should always identify the type of information you are seeking before you begin your review. For example, you may decide to review census records to evaluate the average grade level of education that adults in California completed from 1850 through 1900. During your review, you’ll ignore irrelevant data, such as the respondents’ city of residence and occupation, and focus solely on respondents’ educational achievements.

Enhance your career qualifications and become recognized as an expert in your field by earning your doctoral degree at Grand Canyon University. The College of Doctoral Studies offers a variety of degree options, including the Doctor of Business Administration: Data Analytics (Quantitative Research) degree and the Doctor of Education in Organizational Leadership: Special Education (Quantitative Research) program. If you’re passionate about reaching the pinnacle of academic achievement, click on the Request Info button at the top of your screen to begin planning your doctoral education.