There are two types of research methods, quantitative and qualitative. While both types of research have the same objective, which is to investigate the unknown, the data collection techniques and the information yielded differ. Your research goal ultimately influences your choice of methodology – qualitative analysis answers the “Why,” whereas quantitative analysis answers the “What, Where, When, and How.”
In this blog, we’ll cover the quantitative research methodology in detail, including the types of quantitative research designs you can choose from.
What is Quantitative Research Design?
Quantitative research helps collect structured and statistical data to draw broad conclusions.
Through surveys, questionnaires, and polling procedures, enormous amounts of data are collected to uncover patterns, make predictions, and generalize outcomes. Quantitative research provides data that is not subjective (unlike qualitative research) and can be used as an objective input to decision-making.
Key Traits of Quantitative Research
Quantitative research is mainly characterized by the following factors:
- Measurability - One of the primary concerns of quantitative studies is to count social phenomena, i.e., gather numerical data.
- Causality - Quantitative research is highly effective when it comes to establishing causal relationships.
- Generalizability - Quantitative research results generally represent large populations rather than just the sample used in the study.
- Replicability - Quantitative research is concerned with making studies repeatable to ensure that the original researchers' personal biases or traits did not impact the findings.
To summarize its advantages, quantitative studies give reliable data (since it’s numerical), ensure fast and easy data collection and interpretation, and offer a broader scope of data analysis.
Relevant Read: Check out this blog for in-depth information on quantitative vs. qualitative research.
Quantitative Data Collection Methods
Before progressing to the types of quantitative research designs, it helps to understand the data collection methods involved.
The quantitative data collection approach will define how information is acquired and used and the insights it can generate. One or more of these methods are used to collect data in quantitative research:
Data collection today mostly happens online and relies heavily on surveys prepared with online market research tools or survey software. Most quantitative surveys frequently consist of checklists and rating scale questions to measure respondents' attitudes and behaviors quickly and more precisely.
Various types of survey questions can be utilized. Typically, quantitative surveys include close-ended questions that solicit numerical responses.
This type of survey collects data from multiple demographic groups in a specific time period. The data across populations can be compared, and several variables can be tracked using cross-sectional surveys.
An example of a cross-sectional study would be a medical investigation into the incidence of cancer in a particular community. The researcher can assess persons from various ages, ethnic backgrounds, places of residence, and social classes.
This type of survey collects data from a single demographic group across an extended time period. Respondents are contacted every few months or every year to assess how habits evolve and how certain behaviors affect a population over a long duration.
An example of a longitudinal dataset would be individual students and their test results over several years.
Interviews are another popular method of data collection, especially for qualitative studies. To gather quantitative data, however, interviews are more structured, with the researchers asking only a prescribed set of questions and nothing more. Interviews can be conducted face-to-face, over the telephone, or live via online market research software.
Observation is a simple method of gathering data. Structured observation is a type of research in which researchers carefully examine one or more specific behaviors in a more comprehensive or structured context, allowing them to quantify the behavior.
Probability sampling allows researchers to obtain information from individuals of a population they want to investigate. A definitive sampling technique, such as simple random sampling, cluster sampling, or systematic sampling, is carried out by employing some kind of random selection. This enables researchers to make a probability statement based on random information from the targeted demographic.
Types of Quantitative Research Designs
You can employ four main types of research designs to gather quantitative data. Each method collects useful data that can help you hypothesize proofs and develop a prediction of outcomes to back them up. Let's examine each quantitative research design in more detail.
Descriptive research helps understand the current condition of a phenomenon or population, including demographics, behaviors, and attitudes. It aims to outline in detail the present characteristics without manipulating the variables involved.
This design is employed to identify characteristics, categories, and trends or confirm existing phenomena. Surveys, case studies, and observation can be leveraged to collect descriptive data.
Surveys, for instance, can be used to analyze the demographics of a specific area. Observations can help comprehend how respondents behave in real-life scenarios. Case studies can compile in-depth data and uncover traits of a specific subject, helping in hypothesis formulation.
An example of a descriptive study would be an athletic apparel brand performing a demographic study to learn more about a specific population’s preference for its products.
While descriptive research uncovers new facts by understanding a situation in detail, correlational research measures two variables and attempts to find a relationship between them. Similar to descriptive research, the variables involved are not manipulated.
Surveys and observation are easy ways to conduct correlational research. Secondary research can also be used, but there’s a possibility that the data may not be entirely relevant.
Correlational data identifies trends and patterns with the help of the correlation coefficient:
- There is a positive correlation when two variables fluctuate in the same direction.
- There is a negative correlation when variables fluctuate in opposite directions.
- There can also be zero correlation, indicating no correlation between the examined variables.
For example, correlational studies can help determine if there’s a relationship between the social media shares for your website and the Google ranking.
It’s good to note that while correlational data denotes the existence of a relationship, it does not establish causality. In simpler terms, it cannot prove that one variable causes the other.
This type of research establishes a cause-and-effect relationship between variables. It determines the effect of one or more independent variables on a dependent variable. It includes categorizing subjects into groups using non-random criteria and shows how the same situation impacts various groups.
For example, a study may find students likelier to achieve good test scores if they study for an extra hour each day. To arrive at this conclusion, the researcher would first calculate the subjects' daily study time (independent variable) and test scores (dependent variable). The dependent variable's value depends entirely on changes in the independent variable and is unaffected by any other variables.
When true experiments cannot be conducted for practical or ethical reasons, causal-comparative/quasi-experimental research is used instead. This design has the distinct advantage of having higher external validity than most genuine experiments since it frequently uses real-world settings rather than ones performed in a controlled lab.
Like causal-comparative research, experimental research aims to establish a cause-and-effect relationship between variables using scientific methods, i.e., establishing a hypothesis and proving or disproving it.
Also called true experimentation, this design includes the following three steps:
- The researcher measures the variables.
- The researcher manipulates the variables (affects or alters them somehow).
- To determine how the intervention altered the variables, the researcher re-measures them.
This design helps guide decision-making and is commonly used in natural or social sciences. An example of true experimentation would be testing a drug’s immediate and long-term effects.
Or, as part of your marketing strategy, you can run two separate advertisement versions and monitor each ad's performance to ascertain which is most successful. You can also show the best-performing ad to two target groups to see which group produced better results. True experimentation can also help with prototype testing, helping you choose the more functional design.
While it’s necessary to be aware of the different types of quantitative research designs and the kind of data they can generate, it’s also important to ensure effective quantitative research techniques.
With beneficial features like a robust survey module, AI-led behavioral insights, online panel, benchmarking, and more, a comprehensive market research tool can help you seamlessly employ any research design you want.