What characteristic of quantitative research which refers to its necessity to come up to a more reliable data analysis?

Definition

Quantitative research characteristics

Benefits and limitations

Best practices for quantitative research

Definition

Researchers use different research methods as research is carried out for various purposes. Two main forms of research, qualitative and quantitative, are widely used in different fields. While qualitative research involves using non-numeric data, quantitative research is the opposite and utilizes non-numeric data. Although quantitative research data may not offer deeper insights into the issue, it is the best practice in some instances, especially if you need to collect data from a large sample group. Quantitative research is used in various fields, including sociology, politics, psychology, healthcare, education, economics, and marketing.

Earl R. Babbie notes: "Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon."

Quantitative research characteristics

Below are some of the characteristics of quantitative research.

Large sample size

The ability to use larger sample sizes is undoubtedly one of the biggest perks of quantitative research.

Measurability

Due to its quantitative nature, the data gathered through quantitative data collection methods is easily measurable.

Close-ended questions

Quantitative research utilizes close-ended questions, which can be both beneficial and disadvantageous.

Reusability

Since it doesn't involve open-ended questions, quantitative research results can be used in other similar research projects.

Reliability

Quantitative data is considered more reliable since it is usually free of researcher bias.

Generalization

Quantitative research uses larger sample sizes, so it is assumed that it can be generalized easily.

Benefits and limitations

Since quantitative research relies on data that can be measured, there are a lot of benefits offered by quantitative methods.

Quantitative research benefits

Analyzing numeric data is easier; in that context, quantitative research can bring large amounts of data in a short period. There is numerous quantitative data analysis software that lets the researcher analyze the data fast.

• Allows using large sample sizes

Quantitative research involves using close-ended questions or simple "yes and no" questions. Therefore, it is easier to analyze quantitative data. In that sense, it can be distributed to practically as many people as you can. A large sample size usually means more accurate research results.

As quantitative research questions don't feature open-ended questions, participants are more eager to respond to questions. With open-ended qualitative questions, participants sometimes need to write a wall of text, and that is undesirable for many of them. It is easier to choose "yes or no" as it doesn't require much effort. A more engaging research survey means more feedback.  

• Less biased and more accurate

Qualitative research uses open-ended questions, and since the feedback is often open to interpretation, researchers might be biased when analyzing the data. That is not the case with quantitative research, as it involves answers to preset questions. Less biased data means more accurate data.

• Needs less time and effort

In all stages of research, quantitative research requires much less time and effort when compared with qualitative research. With different software, it is possible to create, send and analyze a huge volume of quantitative data in just a few clicks. Unlike qualitative in-depth interviews that usually require participants to be in a specific office, quantitative research isn't geographically bound to any location and can be carried out online.

Quantitative research limitations

• Limited information on the subject - 

Using close-ended questions means there isn't much to interpret. It doesn't allow the researcher to get answers to "why" questions. If you want to get in-depth information on the subject, you need to carry out qualitative research.

Although it allows the researcher to reach a higher sample size, finding a large number of participants is expensive, considering you have to pay each participant.

• Difficulty in confirming the feedback

Quantitative research doesn't usually involve observing participants or talking with them about their answers; therefore, it is difficult to guess if the data gathered from them is accurate all the time. With qualitative methods, you get a chance to observe participants and ask follow-up questions to confirm their answers.

Best practices for quantitative research

What kind of research do you need?

It may sound too obvious, but you may want to think about the type of research you need to carry out before you start with one. Sometimes quantitative research is not the best practice for a given subject, and you may need to go with qualitative research.  

Clear research goals

Setting a research goal is the first thing every researcher does before setting out to carry out actual research. The success of the research hugely depends on the clearly defined research goals. In other words, it's a make or break point for most research projects. Having confusing research goals is what usually fails the entire project and results in a loss of time and money.

Use user-friendly structure

When creating your surveys and questionnaires, use a user-friendly layout and keep it simple, so it's more engaging for the users. A lot of software offers simple survey templates that you can use effectively.

Choose the right sample

Although quantitative research allows the research to use large sample sizes, it is essential to choose the right sample group. The sample group you're trying to get feedback from may not represent your target audience. Therefore, think twice before allocating resources to gathering data from them.

Pay attention to questions

Quantitative research uses closed-ended questions, which means you need to be very careful with the questions you choose. One of the benefits of quantitative research is that it gives you the ability to predetermine the questions, so you need to use this chance and think about the best possible questions you may use for a better result. With quantitative research questions, you usually don't get a chance to ask follow-up questions.

Let your bias out of the research

We already mentioned that quantitative research is less biased than qualitative research, but it doesn't mean that it's completely free of bias. In this form of research, bias comes with specifically designed questions. The researcher may frame the questions in a way that the feedback may reflect what the researcher wants. In that sense, it is important to leave all the biased questions out you feel can alter the end result of the research.

Quantitative research is a fantastic research methodology as it helps you understand a problem or an already formed hypothesis by generating numerical data. The results don’t end here, as you can process these numbers to get actionable insights that aid in decision-making. 

You can use quantitative research to quantify opinions, behaviors, attitudes, and other definitive variables related to the market, customers, competitors, etc. The research is conducted on a larger sample population to gather a large number of opinions and draw predictive, average-based, pattern-based insights from them. 

Here, we discuss this research methodology at length, and explore the type of quantitative research methods, benefits and examples.

What Is Quantitative Research?

Quantitative research is a research methodology used by researchers to test theories and hypotheses about the attitudes and behaviors of their customers on the basis of numerical and statistical evidence. Researchers use surveys to question a large number of people or customers to obtain measurable and bias-free data in an indirect manner. 

In technical terms, quantitative research is mainly concerned with discovering facts about social phenomena while assuming a fixed and measurable reality. 

Offering numbers and stats-based insights, this research methodology is a crucial part of primary research and helps understand how well an organizational decision is going to work out. 

For example, suppose your qualitative analysis shows that your customers are looking for social media-based customer support. In that case, quantitative analysis will help you see how many of your customers are looking for this support. If 10% of your customers are looking for such a service, you might or might not consider offering this feature. But, if 40% of your regular customers are seeking support via social media, then it is something you just cannot overlook.

Next, we discuss the characteristics of a quantitative research methodology to help you understand it in a better manner.

Characteristics of Quantitative Research

Quantitative research clarifies the fuzziness of research data from qualitative research analysis. With numerical insights, you can formulate a better and more profitable business decision. 

Hence, quantitative research is more readily contestable, sharpens intelligent discussion, helps you see the rival hypotheses, and dynamically contributes to the research process.

Let us have a quick look at some of its characteristics.

The data collection methods in quantitative research are structured and contain items requiring measurable variables, such as age, number of family members, salary range, highest education, etc. 

These structured data collection methods comprise polls, surveys, questionnaires, etc. All of them have measurable approaches. 

For example, they would have survey questions like the ones shown in the following image:

As you can see, all the variables are measurable. 

Ensuring that all the variables are measurable calls for in-depth and less erroneous data that can offer reliable, actionable insights. 

No matter what data analysis methods for quantitative research are being used, the sample size is kept such that it represents the target market. As the main aim of the research methodology is to get numerical insights, the sample size is fairly large. Depending on the survey objective and scope, it might span hundreds of thousands of people. 

There are different types of sampling methods in quantitative research that we will discuss in the next sections. 

However, it is important to note that the sampling methods are chosen on the basis of research objectives.

1. Normal Population Distribution

To maintain the data reliability of a quantitative research methodology, we assume that the population distribution curve is normal. This type of population distribution curve is preferred over a non-normal distribution as the sample size is large and the characteristics of the sample vary with its size. 

This requires adhering to the random sampling principle to avoid the researcher’s bias in result interpretation. Any bias can ruin the fairness of the entire process and defeats the purpose of research.

1. Well-Structured Data Representation

The data analysis methods in quantitative research produce highly structured data results and are represented in well-formed data assessment tools. Some common examples include tables, figures, graphs, etc., that integrate and combine large blocks of data.

So, you can discover hidden data trends, relationships, and differences among various measurable variables. This helps the researchers understand the survey data and formulate actionable insights for business decision-making.

1. Can Produce Predictive Outcomes

Quantitative analysis of data can also be used for estimations and prediction outcomes. You can construct if-then scenarios and analyze the data for identification of any upcoming trends or events. 

However, this requires advanced analytics and involves complex mathematical computations. So, it is mostly done via quantitative research tools that come with advanced analytics capabilities. 

Now that we have had a detailed overview of the quantitative methodology, let us explore its various types. 

Types of Quantitative Research Methods

There can be many ways to collect data via quantitative research methods that are chosen as per the research objective and scope. These methods allow you to build your own observations regarding any hypotheses – unknown, entirely new, or unexplained. 

You can hypothesize a proof and build a prediction of outcomes supporting the same. You can also create a detailed stepwise plan for data collection, analysis, and testing. 

Below, we explore quantitative research methods and discuss some examples to enhance your understanding of them.

1. Survey Research

Surveys are the easiest, most common, and one of the most sought-after quantitative research techniques. The main aim of a survey is to widely gather and describe the characteristics of a target population or customers. Surveys are the foremost quantitative method preferred by both small and large organizations.

They help them understand their customers, products, and other brand offerings in a proper manner. 

Surveys are of two types:

• Cross-sectional Survey: This type of survey is conducted specifically on a target population for a given amount of time, such as retail store surveys, health care trade, etc.
• Longitudinal Surveys: This type of survey is conducted at different time durations, such as applied sciences and medicine surveys.

Some common survey-based quantitative research examples include:

• How frequently do you answer your calls while driving?
• How many texts do you send per day on average?
• How often do you respond to your texts while you are at work?

2. Descriptive Research

This research methodology aims to explain the current status of an identified and measurable variable. 

So, via descriptive research, you can explain and interpret many things, such as:

• Current status of customer satisfaction
• Settings
• Conditions
• Events

In this methodology, it is not mandatory for the researcher to have a hypothesis right from the start. However, once the data is collected, they might form a hypothesis as well to give a proper direction to the research.

Here, the data is assorted systematically, and researchers have to choose the units and measurement of every variable in a careful manner.

Some common examples of descriptive research include:

• Elementary teachers using math manipulatives
• Global warming description for Scientists
• Description of physical activities organized in schools, etc.

3. Correlational Research

This research method is used when you have to establish a relation between two close business entities and figure out how one of them impacts the other. You need at least two separate samples or population groups for correlational research. 

This type of research helps you discover trends and patterns in data, but it does not observe the different patterns. As no variables are manipulated during the process, this methodology is often considered a type of descriptive research.

Some common examples of Correlational research are:

• Relationships between the types of activities done in a particular class and the achievement of students resulting from them
• Relationship between diet and anxiety disorders

4. Experimental Research

This is one of the unique types of quantitative research methods as it is based on one or more theories. It is also termed true experimentation and can use the scientific method for establishing the cause-effect relation over a group of research variables.

You can use multiple theories for conducting this research, and its different components are:

• A comparison group of randomly selected customers or respondents as experimental and control groups
• An independent variable called experimental variable, which can be applied over the experimental groups
• A dependent variable, also called the effect or post-test variable that you can measure identically for all the groups

Some common examples of experimental research include:

• Testing the long-term and short-term effects of a new drug
• Checking who uses social media more for maintaining relationships – men or women

5. Questionnaires

You can use quantitative research questionnaires to collect data for market research as well as for anthropological and social research.

A questionnaire has a list of questions or items that you can use to gather research data from respondents. This data can be about their experiences, attitudes, or opinions. 

The questionnaires are of two types:

• Self-administered: They are shared online, easy to administer for large and small groups, and cost-efficient. However, they can also lead to irrelevant data collection as you might make a mistake while setting questions or while aiming at a particular customer segment. 
• Researcher-administered: They are planned and created by proper researchers and ensure better results. They help you ensure that the respondents are representative of your target audience and there are no unclear or ambiguous questions in the list. 

Common examples of quantitative questionnaires are:

• Open-ended questions
• MCQs
• Ranking questions
• Yes/No questions

6. Interviews

The interviews are one-on-one interactions where the researchers ask questions to the respondents directly. Apart from in-person interviews, the researchers can also have telephonic or online interviews where the respondents don’t have to be physically present. 

The interviews can be a better approach towards gathering data as the researchers have a direct line of contact with the respondents and can take note of the expressions, voice modulations, and behavior as well. 

The researcher can decide how genuine and motivated a particular respondent is based on all these things. Further, many times the respondents are not able to mark a proper response as they are not able to understand a question completely. 

In such cases, researchers can help them in arriving at a conclusion. 

Some common examples of the interviews for qualitative research include:

• How would you choose a suitable subject for your next semester?
• What do you mean when you say that Math is hard?

Next, we discuss two excellent quantitative research methods examples that you can observe to understand how the entire process is done. 

Quantitative Research Methods Examples

Below, we discuss two excellent examples of quantitative research methods that were used by highly distinguished business and consulting organizations. Both the examples show how different types of analysis can be performed with qualitative approaches and how the analysis is done once the data is collected.

1. STEP Project Global Consortium / KPMG 2019 Global Family Business survey

This research leveraged quantitative methods to identify ways to keep the family businesses sustainably profitable with time. 

The study identified the ways in which the family business behavior changed with demographic changes, and had “why” and “how” questions. Their qualitative research methods allowed the KPMG team to dig deeper into the mindsets and perspectives of the business owners and uncover the unexpected research avenues as well. 

The study was a joint effort, in which STEP Project Global Consortium collected 26 cases and KPMG collected 11 cases. 

The research reached the stage of data analysis in 2020, and the analysis process spanned over 4 stages. 

The results, which were also the reasons why family businesses tend to lose their strength with time, were found to be:

• Succession
• Women
• Family governance
• Family business legacy

1. EY Seren Teams Research 2020

This is yet another commendable example of qualitative research where the EY Seren Team digs into the unexplored depths of human behavior and how it affected their brand or service expectations. 

The research was done across 200+ sources, and involved in-depth virtual interviews with people, in their homes exploring their current needs and wishes. It also involved diary studies across the entire UK customer base to analyze human behavior changes and patterns. 

The study also included interviews with professionals and design leaders from a wide range of industries to explore how COVID-19 transformed their industries. Finally, quantitative surveys were conducted to gain insights into the EY community after every 15 days. 

The insights and results were:

• A culture of fear, daily resilience, and hopes for a better world and a better life – these were the macrotrends
• People felt massive digitization to be a resourceful yet demanding aspect as they have to adapt everyday
• Some people wished to have a new world with lots of possibilities and some were looking for a new purpose

Both the examples belong to entirely different categories, and show how beneficial and insightful quantitative survey methods can be.

Let us move ahead by exploring the different types of sampling methods in the quantitative methodology of research.

Sampling Methods in Quantitative Research

As mentioned earlier, sample refers to the number of people or target group that you wish to conduct research on. While qualitative studies have small groups, 6 to 10 people only, quantitative studies gain their reliability from large data sets. 

Hence, the sample size is large, which also results in normal curves for data analysis purposes. 

This sampling method uses the theory of probability to create samples, which means choosing the respondents or samples from the target population. Random selection methods are utilized to choose the participants of a sample or group. This ensures that every member of the target population has an equal opportunity to be chosen for the sample group.

Probability sampling is of four types:

• Cluster Sampling: The main segment gets divided into clusters by using geographic and demographic segmentation parameters.
• Simple Random Sampling: Here, the elements or participants of a sample are selected randomly, and this technique is used in studies that are conducted over considerably large audiences.
• Systematic Sampling: In this method, only the starting point of the sample is randomly chosen. All the other participants are chosen using a fixed interval. Researchers calculate this interval by dividing the size of the study population by the target sample size.
• Stratified Random Sampling: In this method, the entire population is divided into strata or groups, and the sample members get chosen randomly from these strata only. It is always ensured that different segregated strata do not overlap with each other.

In this sampling method, the researchers create the samples as per their knowledge and experience. As the samples are not randomly chosen, not everyone in the target population has an equal probability of getting to be a part of a sample.

Non-probability sampling is of five different types:

• Consecutive Sampling: The researchers can opt for a single participant or a group of samples to conduct quantitative research in a consecutive manner for a significant period of time. Once this is over, they can conduct the research from the start over some other samples.
• Convenience Sampling: This sampling method resembles consecutive sampling as the elements or participants are chosen on the basis of their nearness to the researcher. The people in close proximity can be studied and analyzed easily and quickly, as there is no other selection criterion involved.
• Judgmental Sampling: In this method, samples are formulated purely on the basis of the skill and experience of the researcher.
• Quota Sampling: Here, researchers leverage their knowledge of target traits and personalities to select elements and form strata. Next, the researchers again use their knowledge to compile samples by choosing members from various strata.
• Snowball Sampling: This sampling method is used where the target audiences are difficult to contact and interviewed for data collection. It also works well where it is difficult to get them all together at one place for research.

Once the samples are formed, and data is collected via different types of data collection methods in quantitative research, the researchers move on to the next phase of analyzing this data. 

Let us find out how this is exactly done and what processes are involved in this step.

Quantitative Data Analysis

Before the researchers begin the process of data analysis, they perform data cleaning to remove the errors, duplicate records, whitespaces, etc. Once this is done, the data is ready for analysis whereby the researchers use quantitative research tools to interpret and make sense of the collected data. 

This process finally brings the results that you wanted – the data-driven actionable insights that help you in decision-making and strategizing.

There are various data analysis methods for quantitative research that we will discuss next. 

This analysis looks for answers to the “Whys,” like “Why did this not happen?” or “Why are people doing this?” 

This method is generally used after the Statistical analysis to dig into similar patterns that were present in previous researches. Afterwards, the researchers can use the previous solutions to overcome the present or current challenges.

This analysis focuses on the “What” aspects of the research, such as “What are your customers hating or loving about your product?“

Here also, the researchers use patterns from older research as well as current findings, to predict future events. 

While 100% accurate forecasting is not feasible, if the researchers have plenty of detailed information and skills to complete the data analysis thoroughly, they can improve the accuracy as much as they want.

This analysis combines the elements from all the other analysis methods. This technique is perfect for research cases where one type of analysis method is not producing wholesome results.

This method allows you to factor information about:

• Possible scenarios
• Past performances
• Available resources
• Current performance

Based on this information, the prescriptive technique suggests the right strategy or course of action that you can follow. The technique uses ML for this purpose, and also uses predictive analytics to determine the outcomes in near-future. 

Some ideal use cases for prescriptive analysis are:

• Hospitals – Improving patient outcome by evaluating the official clinical methods
• Airlines – Automated adjustment of ticket prices and availability depending on various factors to ensure high profitability

This approach aims at finding the answers to “What happened?” questions and comprises data collection, analysis, modeling, followed by data interpretation and presentation. 

This method uses dashboards, and is further divided into two sub-categories:

• Descriptive Analysis: This method works perfectly for complete selections of summarized numerical data sets. It shows means and deviations in continuous data sets and frequencies and percentages in categorical data sets.
• Inferential Analysis: This method is used where samples are derived from complete research data. The researchers can choose different samplings and might reach different conclusions even when they analyze the same comprehensive data set.

This is one of the most unique data analysis methods in quantitative research and is becoming increasingly popular with the emergence of ML and AI-based data analytics technologies. It is also called data mining and uses data mining tools as well as databases to uncover hidden patterns in large data sets.

It helps businesses make sense of raw data in the form of chats, messages, feedback, reviews, etc, and gather important business insights from them. This is also considered the most direct method of research data analysis.

Advantages of Quantitative Research Methods

Quantitative research data is often standardized and can be easily used to generalize findings for taking crucial business decisions and uncover insights to supplement the qualitative research findings. 

Here are some core benefits this research methodology offers.

As the studies can be replicated for different cultural settings and different times, even with different groups of participants, they tend to be extremely useful. The researchers can compare the results of different studies in a statistical manner and arrive at comprehensive conclusions for a broader understanding.

Researchers can repeat the study by using standardized data collection protocols over the well-structured data sets. They can also apply tangible definitions of abstract concepts to arrive at different conclusions for similar research objectives with minor variations.

As the research data comes from large samples, the researchers can process and analyze the data via highly reliable and consistent analysis procedures. They can arrive at well-defined conclusions that can be used to make the primary research more thorough and reliable.

This research methodology follows standardized and established hypothesis testing procedures. So, you have to be careful while reporting and analyzing your research data, and the overall quality of results gets improved.

Disadvantages of Quantitative Research Methods

Quantitative methods presume that the research data is controlled and measured no matter your research objective and scale. They also tend to keep the target audience fairly large to facilitate better fact accumulation and cause determination. 

Hence, the results might be statistically significant and reliable, but they don’t fare well when it comes to the “human” sentiment. This is why primary research tends to use both qualitative and quantitative methodologies. 

Below, we share some other disadvantages of the quantitative approach of research.

Having highly restrictive and precise operational definitions might not be able to represent complex concepts in an adequate manner. 

For example, when it comes to human emotions and behavioral characteristics, quantitative research might simply attach a number to it. However, such aspects are better explored with qualitative methodologies as they help in broadening the scope of research.

As the researchers have many preset notions about the data sets, data collections, sample, and even sample formulation, they might never approach the research with an open mind.

They make assumptions to assign a number to everything, while this can affect the overall outcome or research results.

Quantitative methods are also subject to the structural bias of different types, such as missing data, inaccurate sampling techniques, and imprecise measurements. 

When the researcher has to use their skills and experience to form samples, it is highly probable for them to arrive at wrong conclusions because of sampling biases.

This research methodology has professional or unnatural settings, such as labs or computerized offices. Hence, the researchers tend to ignore the historical and cultural aspects that influence the responses of participants. 

This can also lead to errors in data collections, and hence, the results.

Unlock Success in Quantitative Research: Invest in Right Research Software

While no single research methodology can produce 100% reliable results, you can always opt for a hybrid research method by opting for the methods that are most relevant to your objective.

This understanding comes gradually, as you learn how to implement the correct combination of qualitative and quantitative research methods for your research projects. For the best results, we recommend investing in smart, efficient, and scalable research tools that come with delightful reporting and advanced analytics to make every research initiative a success. 

These software tools, such as survey makers come with pre-built survey templates, question libraries and allow you to create a high-converting survey within a few minutes. 

So, choose your right research partner, create the right research plan and gather insights that drive your business towards sustainable growth.