what are the four types of studies in business research

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Business Research: Methods, Types & Examples

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Business research: Definition

Quantitative research methods, qualitative research methods, advantages of business research, disadvantages of business research, importance of business research.

Business research is a process of acquiring detailed information on all the areas of business and using such information to maximize the sales and profit of the business. Such a study helps companies determine which product/service is most profitable or in demand. In simple words, it can be stated as the acquisition of information or knowledge for professional or commercial purposes to determine opportunities and goals for a business.

Business research can be done for anything and everything. In general, when people speak about business research design , it means asking research questions to know where the money can be spent to increase sales, profits, or market share. Such research is critical to make wise and informed decisions.

LEARN ABOUT: Research Process Steps

For example: A mobile company wants to launch a new model in the market. But they are not aware of what are the dimensions of a mobile that are in most demand. Hence, the company conducts business research using various methods to gather information, and the same is then evaluated, and conclusions are drawn as to what dimensions are most in demand.

This will enable the researcher to make wise decisions to position his phone at the right price in the market and hence acquire a larger market share.

LEARN ABOUT: Test Market Demand

Business research: Types and methodologies

Business research is a part of the business intelligence process. It is usually conducted to determine whether a company can succeed in a new region, to understand its competitors, or simply select a marketing approach for a product. This research can be carried out using steps in qualitative research methods or quantitative research methods.

Quantitative research methods are research methods that deal with numbers. It is a systematic empirical investigation using statistical, mathematical, or computational techniques . Such methods usually start with data collection and then proceed to statistical analysis using various methods. The following are some of the research methods used to carry out business research.

LEARN ABOUT: Data Management Framework

Survey research

Survey research is one of the most widely used methods to gather data, especially for conducting business research. Surveys involve asking various survey questions to a set of audiences through various types like online polls, online surveys, questionnaires, etc. Nowadays, most of the major corporations use this method to gather data and use it to understand the market and make appropriate business decisions.

Various types of surveys, like cross-sectional studies , which need to collect data from a set of audiences at a given point of time, or longitudinal surveys which are needed to collect data from a set of audiences across various time durations in order to understand changes in the respondents’ behavior are used to conduct survey research. With the advancement in technology, surveys can now be sent online through email or social media .

For example: A company wants to know the NPS score for their website i.e. how satisfied are people who are visiting their website. An increase in traffic to their website or the audience spending more time on a website can result in higher rankings on search engines which will enable the company to get more leads as well as increase its visibility.

Hence, the company can ask people who visit their website a few questions through an online survey to understand their opinions or gain feedback and hence make appropriate changes to the website to increase satisfaction.

Learn More: Business Survey Template

Correlational research

Correlational research is conducted to understand the relationship between two entities and what impact each one of them has on the other. Using mathematical analysis methods, correlational research enables the researcher to correlate two or more variables .

Such research can help understand patterns, relationships, trends, etc. Manipulation of one variable is possible to get the desired results as well. Generally, a conclusion cannot be drawn only on the basis of correlational research.

For example: Research can be conducted to understand the relationship between colors and gender-based audiences. Using such research and identifying the target audience, a company can choose the production of particular color products to be released in the market. This can enable the company to understand the supply and demand requirements of its products.

Causal-Comparative research

Causal-comparative research is a method based on the comparison. It is used to deduce the cause-effect relationship between variables. Sometimes also known as quasi-experimental research, it involves establishing an independent variable and analyzing the effects on the dependent variable.

In such research, data manipulation is not done; however, changes are observed in the variables or groups under the influence of the same changes. Drawing conclusions through such research is a little tricky as independent and dependent variables will always exist in a group. Hence all other parameters have to be taken into consideration before drawing any inferences from the research.

LEARN ABOUT: Causal Research

For example: Research can be conducted to analyze the effect of good educational facilities in rural areas. Such a study can be done to analyze the changes in the group of people from rural areas when they are provided with good educational facilities and before that.

Another example can be to analyze the effect of having dams and how it will affect the farmers or the production of crops in that area.

LEARN ABOUT: Market research trends

Experimental research

Experimental research is based on trying to prove a theory. Such research may be useful in business research as it can let the product company know some behavioral traits of its consumers, which can lead to more revenue. In this method, an experiment is carried out on a set of audiences to observe and later analyze their behavior when impacted by certain parameters.

LEARN ABOUT: Behavioral Targeting

For example: Experimental research was conducted recently to understand if particular colors have an effect on consumers’ hunger. A set of the audience was then exposed to those particular colors while they were eating, and the subjects were observed. It was seen that certain colors like red or yellow increase hunger.

Hence, such research was a boon to the hospitality industry. You can see many food chains like Mcdonalds, KFC, etc., using such colors in their interiors, brands, as well as packaging.

Another example of inferences drawn from experimental research, which is used widely by most bars/pubs across the world, is that loud music in the workplace or anywhere makes a person drink more in less time. This was proven through experimental research and was a key finding for many business owners across the globe.

Online research / Literature research

Literature research is one of the oldest methods available. It is very economical, and a lot of information can be gathered using such research. Online research or literature research involves gathering information from existing documents and studies, which can be available at Libraries, annual reports, etc.

Nowadays, with the advancement in technology, such research has become even more simple and accessible to everyone. An individual can directly research online for any information that is needed, which will give him in-depth information about the topic or the organization.

Such research is used mostly by marketing and salespeople in the business sector to understand the market or their customers. Such research is carried out using existing information that is available from various sources. However, care has to be taken to validate the sources from where the information is going to be collected.

For example , a salesperson has heard a particular firm is looking for some solution that their company provides. Hence, the salesperson will first search for a decision maker from the company, investigate what department he is from, and understand what the target company is looking for and what they are into.

Using this research, he can cater his solution to be spot on when he pitches it to this client. He can also reach out to the customer directly by finding a means to communicate with him by researching online.’

LEARN ABOUT: 12 Best Tools for Researchers

Qualitative research is a method that has a high importance in business research. Qualitative research involves obtaining data through open-ended conversational means of communication. Such research enables the researcher to not only understand what the audience thinks but also why he thinks it.

In such research, in-depth information can be gathered from the subjects depending on their responses. There are various types of qualitative research methods, such as interviews, focus groups, ethnographic research, content analysis, and case study research, that are widely used.

Such methods are of very high importance in business research as they enable the researcher to understand the consumer. What motivates the consumer to buy and what does not is what will lead to higher sales, and that is the prime objective for any business.

Following are a few methods that are widely used in today’s world by most businesses.

Interviews are somewhat similar to surveys, like sometimes they may have the same types of questions used. The difference is that the respondent can answer these open-ended questions at length, and the direction of the conversation or the questions being asked can be changed depending on the response of the subject.

Such a method usually gives the researcher detailed information about the perspective or opinions of its subject. Carrying out interviews with subject matter experts can also give important information critical to some businesses.

For example: An interview was conducted by a telecom manufacturer with a group of women to understand why they have less number of female customers. After interviewing them, the researcher understood that there were fewer feminine colors in some of the models, and females preferred not to purchase them.

Such information can be critical to a business such as a telecom manufacturer and hence it can be used to increase its market share by targeting women customers by launching some feminine colors in the market.

Another example would be to interview a subject matter expert in social media marketing. Such an interview can enable a researcher to understand why certain types of social media advertising strategies work for a company and why some of them don’t.

LEARN ABOUT: Qualitative Interview

Focus groups

Focus groups are a set of individuals selected specifically to understand their opinions and behaviors. It is usually a small set of a group that is selected keeping in mind the parameters for their target market audience to discuss a particular product or service. Such a method enables a researcher with a larger sample than the interview or a case study while taking advantage of conversational communication.

Focus group is also one of the best examples of qualitative data in education . Nowadays, focus groups can be sent online surveys as well to collect data and answer why, what, and how questions. Such a method is very crucial to test new concepts or products before they are launched in the market.

For example: Research is conducted with a focus group to understand what dimension of screen size is preferred most by the current target market. Such a method can enable a researcher to dig deeper if the target market focuses more on the screen size, features, or colors of the phone. Using this data, a company can make wise decisions about its product line and secure a higher market share.

Ethnographic research

Ethnographic research is one of the most challenging research but can give extremely precise results. Such research is used quite rarely, as it is time-consuming and can be expensive as well. It involves the researcher adapting to the natural environment and observing its target audience to collect data. Such a method is generally used to understand cultures, challenges, or other things that can occur in that particular setting.

For example: The world-renowned show “Undercover Boss” would be an apt example of how ethnographic research can be used in businesses. In this show, the senior management of a large organization works in his own company as a regular employee to understand what improvements can be made, what is the culture in the organization, and to identify hard-working employees and reward them.

It can be seen that the researcher had to spend a good amount of time in the natural setting of the employees and adapt to their ways and processes. While observing in this setting, the researcher could find out the information he needed firsthand without losing any information or any bias and improve certain things that would impact his business.

LEARN ABOUT: Workforce Planning Model

Case study research

Case study research is one of the most important in business research. It is also used as marketing collateral by most businesses to land up more clients. Case study research is conducted to assess customer satisfaction and document the challenges that were faced and the solutions that the firm gave them.

These inferences are made to point out the benefits that the customer enjoyed for choosing their specific firm. Such research is widely used in other fields like education, social sciences, and similar. Case studies are provided by businesses to new clients to showcase their capabilities, and hence such research plays a crucial role in the business sector.

For example: A services company has provided a testing solution to one of its clients. A case study research is conducted to find out what were the challenges faced during the project, what was the scope of their work, what objective was to be achieved, and what solutions were given to tackle the challenges.

The study can end with the benefits that the company provided through its solutions, like reduced time to test batches, easy implementation or integration of the system, or even cost reduction. Such a study showcases the capability of the company, and hence it can be stated as empirical evidence of the new prospect.

Website visitor profiling/research

Website intercept surveys or website visitor profiling/research is something new that has come up and is quite helpful in the business sector. It is an innovative approach to collect direct feedback from your website visitors using surveys. In recent times a lot of business generation happens online, and hence it is important to understand the visitors of your website as they are your potential customers.

Collecting feedback is critical to any business, as without understanding a customer, no business can be successful. A company has to keep its customers satisfied and try to make them loyal customers in order to stay on top.

A website intercept survey is an online survey that allows you to target visitors to understand their intent and collect feedback to evaluate the customers’ online experience. Information like visitor intention, behavior path, and satisfaction with the overall website can be collected using this.

Depending on what information a company is looking for, multiple forms of website intercept surveys can be used to gather responses. Some of the popular ones are Pop-ups, also called Modal boxes, and on-page surveys.

For example: A prospective customer is looking for a particular product that a company is selling. Once he is directed to the website, an intercept survey will start noting his intent and path. Once the transaction has been made, a pop-up or an on-page survey is provided to the customer to rate the website.

Such research enables the researcher to put this data to good use and hence understand the customers’ intent and path and improve any parts of the website depending on the responses, which in turn would lead to satisfied customers and hence, higher revenues and market share.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Business research helps to identify opportunities and threats.
It helps identify research problems , and using this information, wise decisions can be made to tackle the issue appropriately.
It helps to understand customers better and hence can be useful to communicate better with the customers or stakeholders.
Risks and uncertainties can be minimized by conducting business research in advance.
Financial outcomes and investments that will be needed can be planned effectively using business research.
Such research can help track competition in the business sector.
Business research can enable a company to make wise decisions as to where to spend and how much.
Business research can enable a company to stay up-to-date with the market and its trends, and appropriate innovations can be made to stay ahead in the game.
Business research helps to measure reputation management
Business research can be a high-cost affair
Most of the time, business research is based on assumptions
Business research can be time-consuming
Business research can sometimes give you inaccurate information because of a biased population or a small focus group.
Business research results can quickly become obsolete because of the fast-changing markets

Business research is one of the most effective ways to understand customers, the market, and competitors. Such research helps companies to understand the demand and supply of the market. Using such research will help businesses reduce costs and create solutions or products that are targeted to the demand in the market and the correct audience.

In-house business research can enable senior management to build an effective team or train or mentor when needed. Business research enables the company to track its competitors and hence can give you the upper hand to stay ahead of them.

Failures can be avoided by conducting such research as it can give the researcher an idea if the time is right to launch its product/solution and also if the audience is right. It will help understand the brand value and measure customer satisfaction which is essential to continuously innovate and meet customer demands.

This will help the company grow its revenue and market share. Business research also helps recruit ideal candidates for various roles in the company. By conducting such research, a company can carry out a SWOT analysis , i.e. understand the strengths, weaknesses, opportunities, and threats. With the help of this information, wise decisions can be made to ensure business success.

LEARN ABOUT: Market research industry

Business research is the first step that any business owner needs to set up his business to survive or to excel in the market. The main reason why such research is of utmost importance is that it helps businesses to grow in terms of revenue, market share, and brand value.

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Business Research: Types, Methods, Examples

Updated on
Jan 29, 2024

Ever wondered what it takes to build a flourishing business ? Aiming to provide maximum sales and profit, business research helps you to gather comprehensive information about your business and accordingly make relevant changes if required. So, in this process of being successful, we gather all types of data to better define our strategies and understand what products or services customers want. And in case, you’re planning to expand your business, research can help you determine your odds of positive results. In this blog, we’ll help you understand the basics of research and analysis .

“Whoever gets closer to the customer, wins.” – Bernadette Jiwa

This Blog Includes:

What is business research, business research example, importance of business research, types & methods, focus groups , case study research , ethnographic research, survey , correlation research , experimental research , advantages and disadvantages of business research, scope of business research, role of business research, business research books, business research report, top 10 tools for business research, business research partners, top 10 business research topics, career prospects , [bonus] best mba colleges in the world.

Business Research can be simply defined as a process of gathering comprehensive data and information on all the areas of business and incorporating this information for sales and profit maximization. If you are wondering what is Business Research, it is a systematic management activity helping companies to determine which product will be most profitable for companies to produce. Also, there are multiple steps in conducting research, with each thoroughly reviewed to ensure that the best decision is made for the company as a whole.

Also Read: Scope of MBA in International Business

Let’s say there’s an automobile company that is planning to launch a car that runs on CNG. To promote cleaner fuel, the company will be involved in developing different plans and strategies to identify the demand for the car they intend to launch. Other than this, the company will also look for competitors, and the target audience, keeping in mind the distribution of CNG in India. Hence the research is conducted on various ideas to formulate a sustainable and more efficient design.

When it comes to the question of why Business Research is important, it has an essential role to play in varied areas of business. Here are some of the reasons describing the importance of Business Research:

It helps businesses gain better insights into their target customer’s preferences, buying patterns, pain points, as well as demographics.
Business Research also provides businesses with a detailed overview of their target markets, what’s in trend, as well as market demand.
By studying consumers’ buying patterns and preferences as well as market trends and demands with the help of business research, businesses can effectively and efficiently curate the best possible plans and strategies accordingly.
The importance of business research also lies in highlighting the areas where unnecessary costs can be minimized and those areas in a business which need more attention and can bring in more customers and hence boost profits.
Businesses can constantly innovate as per their customers’ preferences and interests and keep their attention on the brand.
Business Research also plays the role of a catalyst as it helps businesses thrive in their markets by capturing all the available opportunities and also meeting the needs and preferences of their customers.

Also Read: Business Analyst vs Data Analyst

Business research plays an important role in the business intelligence process. This is usually conducted to determine if a company can succeed in a new region through competitive analyses and a better marketing approach. Due to this, this broad field has been distinguished into two types namely, Qualitative Research and Quantitative Research Method.

Here are the most important types of Business Research :

Qualitative Research Methods

It involves putting open-ended questions to the audience through different channels of communication to understand why researchers think in a particular manner. Stress is laid on understanding the intent, attitude, and beliefs to figure out the behaviour and response of the customers. Moreover, the goal of Qualitative Business Research is to get in-depth knowledge about the subjects of the research. Moreover, qualitative research enables us to put the perspective of the consumer in front of the researcher so that we can understand and see the alignment of the ideas between the market and the business.

The data collected in this type of business research is by the following methods:

Interviews
Case Study
Ethnographic Research
Website Visitor Profiling
Content Analysis

Also Read: Study MBA in Music Business at Berklee College of Music!

Let us take a detailed look at some of the ways-

Interviews and surveys are similar. The only difference lies in the fact that the responder can put a question in an interview whilst it is not possible during a survey. Through interviews, it is easier to understand the detailed perspective of the person concerning the subject of research. A mobile brand researched to understand why certain colours are preferred by male and female customers. The research revealed that since red is assumed to be a feminine colour, it is more preferred by females than males.

Focus groups are a type of business research that involves only a set of individuals. Each selected individual represents a particular category of the target market. The major difference between interviews and focus groups is the number of people that it involves. To launch a new product for a particular group of society, focus groups prove to be the best way to understand the needs of the local audience.

For example, Tesla decides to launch their latest car model in India. The company, therefore, will require feedback from the Indian audience only.

Did you know? Amazon, the internet giant changed its payment strategy to enter the Indian market. Since the Indian economy was not entirely ready for online modes of payment, amazon introduced a new payment method and came up with ‘ cash on delivery ’ to gain consumers’ trust.

One of the most effective ways for business research is conducting case studies. With the motive to understand customer satisfaction, challenges that usually the customers face while using the product and hence, providing them with the right solution can be achieved by analysing data secured through data secured by case studies. Case study researchers are conducted in many fields of business that ultimately aid organisations in improving their products or services.

Ethnographic Research refers to understanding people as a whole. One must be able to grok their consumers or target audience which will help identify patterns, flaws, etc. Ethnography is a branch of anthropology that is the study of what elements or features make us humans. How did people live? What aspect made us so dependent on smartphones and technology? Why would people buy one product over the other? It refers to asking questions about lifestyle, communities, etc., and trying to gain insight into consumer behaviour and buying patterns.

For example, consider a random product. Are people looking for that product? Do they need it? Is it a necessity or a luxury? Which class of people are most likely to buy it? People often cannot comprehend what they are looking for. Gaining different perceptions can help us tailor our products accordingly to the consumers. Who would have thought that the majority of humans will need face masks for survival?

Also Read: How to Become a Research Analyst?

Quantitative Research Methods

With the employment of mathematical, statistical and computational techniques, quantitative research is carried out to deal with numbers. This systematical empirical investigation starts with the acquisition of the data and then moves on to analyzing it with the help of different tools. The goal is to identify clientele and then meet the targets of the audience. As the method of business research employs a questionnaire to determine the audience’s response, the questions are built around the idea that the audience knows about the product or the services that the firm offers. Some of the key questions answered in quantitative research methods include, who is connected with your network, how they qualify for the ‘product’ or how regularly they visit your website.

The data is collected based on the following research:

Correlational
Online
Casual Comparative
Experimental

It is the most common method under quantitative research via which a huge amount of data can be collected concerning a product or service. A common set of questions are asked to the people and they are asked to provide their inputs. To understand the nature of the market in-depth, this method is massively used by leading organisations all across the globe. Analysing data recorded through service helps organisations make suitable decisions.

Under this research, usually two entities are put together to examine the impact they create on each other. As suggested by the name it is the best process to understand patterns, relationships and trends. the data grasped through correlation research is generally combined with other tools as one cannot achieve a firm conclusion using this type of business research.

Experimental research is purely based on proving a particular theory that is pre-assumed. True experimental research companies can understand varied behavioural traits of the customers that further assist them in generating more revenue. Exposing a set of audience to common parameters, their behaviour is recorded and hence analysed. This can be understood as the main basis of the experimental research.

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Business Research Methods: Key Elements & Use Cases

In the cutthroat business world, research is the holy grail that unlocks the doors to success. It is the difference between floundering in obscurity and soaring to greatness.

Successful businesses have one thing in common. They all deeply understand their customers, their competition, and their industry.

And how do they gain this knowledge? Through research.

You must be a research ninja – agile, precise, and armed with the right tools to succeed.

But not just any research will do. To be a true research ninja, you must know the best practices for business research methods.

Lucky for you, we’ve got you covered.

We’ll take you through the best practices for business research methods.

We’ll not stop there. We’ll also look at the use cases and show you how to create a business research report. Consequently, equips you with the skills to sift through the data and uncover valuable insights.

What is Business Research?

Why do you need business research methods, elements of business research methods, use cases for business research methods, how do you write a business research template, how to create a business research report.

Business research refers to the systematic investigation and analysis of various aspects of a business. It involves collecting and interpreting data, conducting surveys, interviews, and experiments, and analyzing trends and patterns. This helps you identify opportunities, improve products or services, and enhance overall business performance.

Have you ever tried to navigate a maze blindfolded? It’s not easy, right? Trying to run a business without proper research methods is like navigating a maze without guidance. You may stumble upon success, but chances are you’ll end up lost and confused. So, why do you need business research methods?

Decision-making: Effective business research methods provide reliable data and insights to support informed decision-making . This helps reduce uncertainty and increases the chances of making successful strategic choices.
Market analysis: Using business research methods, you can analyze market trends, understand customer preferences, and evaluate competitor strategies. This enables you to seize opportunities, develop competitive advantages, and tailor your offerings to meet customer needs.
Product development: Business research helps gather customer feedback and understand their needs and preferences. This aids in developing and improving product offerings, ensuring they align with customer expectations and demands.
Risk management: You can evaluate risks and uncertainties associated with new ventures, product launches, or market expansions. Consequently, mitigate the risks, and improve the likelihood of success.
Performance evaluation: Business research methods enable you to measure and evaluate your performance and that of competitors. This helps identify areas of improvement, track progress, and benchmark against industry standards.
Innovation and adaptation: Business research facilitates the identification of emerging trends, technological advancements, and changing consumer behaviors. This information allows you to innovate, adapt, and stay ahead in dynamic market environments.

The elements of business research methods include:

Research question/objective: Clearly define the purpose of the research and the specific questions or objectives to be addressed.
Research design: Determining the overall approach and structure of the research study. Decide whether it will be qualitative or quantitative , experimental or observational, cross-sectional or longitudinal, etc.
Data collection: Select appropriate methods and tools for gathering relevant data. This may include surveys, interviews, focus groups, observations, experiments, secondary data analysis , or a combination.
Sampling: Determine the target population and select a representative sample from which you will collect the data. This involves defining the sampling frame, selecting sampling techniques, and ensuring adequate sample size.
Data analysis: Apply suitable analytical techniques to interpret the collected data. Such techniques include statistical analysis, content analysis, thematic analysis, regression analysis, or qualitative coding.
Results and findings: Summarize and present the analyzed data , highlighting trends, relationships, or patterns observed during the research process.
Conclusion and recommendations: Draw conclusions based on the research findings and provide recommendations for action or further exploration.
Ethical considerations: Address ethical issues related to data collection, privacy, confidentiality, and informed consent.
Market analysis: Research methods help you understand market trends, customer preferences, and competitive landscapes. This information is crucial for market segmentation , target audience identification, and developing effective marketing strategies.
Product development: Research methods assist in gathering customer feedback and analyzing market demand. This helps refine existing product offerings, identify new opportunities, and optimize features to meet customer needs.
Decision-making: Business research methods provide reliable data, insights, and analysis to support informed decision-making.
Risk assessment and management: By employing research methods, you can identify risks, evaluate market uncertainties, and assess the viability of business ventures. This facilitates effective risk management and informed decision-making to minimize potential losses.

When creating a business research template, consider the following components:

Title and introduction: Provide a concise and informative title for your research project. Also, write an introduction that provides background information and explains the significance of the study.
Research objectives: Clearly define the specific objectives or research questions that your study aims to address.
Literature review: Summarize existing research and literature relevant to your topic. Identify gaps, controversies, or areas that require further exploration.
Methodology: Describe the research design , including whether it is qualitative, quantitative, mixed methods, or other approaches. Explain your data collection methods and tools, such as surveys, interviews, and observations. Furthermore, detail the data analysis techniques or statistical methods you will employ.
Data collection: Outline the procedures for collecting data, including any survey instruments, interview protocols, or observation guidelines. Provide clear instructions for data collection, ensuring consistency and reliability.
Data Analysis: Describe your approach to analyzing the collected data. Explain the statistical or qualitative analysis techniques that you will apply.
Results and findings: Present and interpret the results of your data analysis. Use tables, charts, or graphs to visualize the findings.
Conclusion and recommendations: Summarize the key findings of your research. Draw conclusions based on the results and address the research objectives. In addition, provide recommendations for future action or further research based on your findings.
References: List all the sources cited in your research template. Follow a specific referencing style (e.g., APA, MLA) consistently.
Appendices: Include supplementary materials, such as survey questionnaires , interview transcripts, or additional data charts supporting your research.

Creating a comprehensive business research report that effectively communicates findings and insights can be a daunting task. However, with the help of ChartExpo, an Excel-based data visualization tool , the process becomes more streamlined and efficient.

ChartExpo empowers you to transform raw data into insightful and visually engaging visualizations. This makes it easier to present complex information clearly and concisely.

Benefits of Using ChartExpo

With an intuitive user interface, ChartExpo empowers you to create insightful visualizations without requiring any coding experience.
With ChartExpo, customization options abound, allowing you to create unique visualizations. You can personalize colors, fonts, and styles to align with your brand aesthetics.
ChartExpo offers excellent value for money, with a free 7-day trial and an affordable $10 monthly plan.

How to Install ChartExpo in Excel?

Open your Excel application.
Open the worksheet and click the “ Insert ” menu.
You’ll see the “ My Apps ” option.
In the office Add-ins window, click “ Store ” and search for ChartExpo on my Apps Store.
Click the “ Add ” button to install ChartExpo in your Excel.

ChartExpo charts are available both in Google Sheets and Microsoft Excel. Please use the following CTA’s to install the tool of your choice and create beautiful visualizations in a few clicks in your favorite tool.

Assume you have recently launched a product. You want to gather customer feedback to help you improve. Therefore, you conduct an online customer feedback survey consisting of the following questions.

How often do you use our product?
How satisfied are you with the quality of our product?
How likely are you to recommend our product to others?

Your customers provide an answer to each question using the response scale below.

Let’s say you have organized the results in the data table below.


Never	Rarely	Never
Rarely	Often	Often
Sometimes	Never	Rarely
Often	Sometimes	Sometimes
Sometimes	Often	Often
Never	Often	Never
Often	Never	Sometimes
Often	Often	Often
Sometimes	Rarely	Often
Rarely	Sometimes	Rarely
Never	Always	Often
Often	Always	Sometimes
Often	Never	Often
Always	Sometimes	Always
Sometimes	Always	Always
Always	Always	Always
Always	Never	Sometimes
Sometimes	Always	Always
Rarely	Sometimes	Rarely
Always	Rarely	Sometimes

Follow through as I show you how to create a chart with ChartExpo to make data analysis easier.

To get started with ChartExpo, install ChartExpo in Excel .
Navigate to the INSERT menu and select My Apps.

Select ChartExpo and click the “Insert” button.

Once it loads, choose the “ Likert Scale Chart ” from the charts list.

Select the data from the sheet and click the “ Create Chart From Selection ” button.

Sometimes=3
Once all is set, click the “ Create Chart ” button.

ChartExpo will generate the visualization below for you.

Click the “ Edit Chart ” button to modify your chart.
Click the pencil icon next to the Chart Header to add a title.
This will open the properties dialog.
Add a heading in Line 1 under the Text section and enable the Show option. Then click the “ Apply ” button.

Your final chart will appear below.

20% always recommend your product to others, while 30% indicated that they often do so. On the other hand, 15% of customers mentioned that they never recommend it. 10% rarely recommend it, with 25% doing so sometimes.
Regarding the quality of the product, 25% of customers were always satisfied. 20% stated that they often feel satisfied. Conversely, 20% reported never being satisfied, and 15% were satisfied. Additionally, 20% selected the sometimes option.
Regarding product usage, 20% of customers selected the always option, and 25% chose the often option. Conversely, 15% of customers indicated never using it, and 15% rarely do so. Furthermore, 25% of customers selected the sometimes option.
22% of customers selected the always option
while 25% chose the often option. Additionally
15% selected never
15% rare, and 23% chose sometimes.

What are the 4 types of business research?

The four types of business research are exploratory, descriptive, causal, and predictive. Exploratory research aims to explore a topic, and descriptive research provides a snapshot of a situation. Furthermore, causal research investigates cause-and-effect relationships, and predictive research predicts future outcomes.

What are the 5 classifications of business research?

The five classifications of business research are;

Basic research seeking to expand knowledge.
Applied research focusing on solving practical problems.
Quantitative research involving numerical data.
Qualitative research using non-numerical data.
Mixed-methods research combining quantitative and qualitative approaches.

Understanding and applying best practices for business research methods is essential for conducting effective and insightful studies. Following these practices will help you gather relevant data and gain valuable insights. Consequently, make informed decisions that drive growth and success.

Selecting the appropriate research method is crucial. Whether exploratory, descriptive, causal, or predictive, the choice should align with the research objectives.

Additionally, maintaining research integrity is paramount. This involves ensuring ethical considerations, protecting data privacy, and adhering to rigorous research standards.

Furthermore, a well-designed research plan is essential. Have clear research objectives, define the target population, and carefully construct survey questions or interview protocols. These measures contribute to gathering reliable and meaningful data.

Conclusively, effective communication of research findings is crucial. Present results in a clear, concise, and actionable manner. This enables stakeholders to understand and apply the insights effectively.

Use ChartExpo for Excel. ChartExpo takes the complexity out of the equation by allowing you to effortlessly create insightful, engaging visualizations. No coding is required.

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Doing Research in Business and Management: An Essential Guide to Planning Your Project

Management Decision

ISSN : 0025-1747

Article publication date: 21 June 2013

Berbegal‐Mirabent, J. (2013), "Doing Research in Business and Management: An Essential Guide to Planning Your Project", Management Decision , Vol. 51 No. 6, pp. 1311-1316. https://doi.org/10.1108/MD-06-2012-0505

Emerald Group Publishing Limited

“Publish or perish”. We've all heard this expression referring to the need academic, but especially PhD students and young academics have to make their research results public in order to promote and improve their contractual position, consolidating their careers. Nevertheless, it is also well‐known that this is a long‐distance race conditioned to the capacity to publish the research findings.

This means that students whether undergraduate, postgraduate or enrolled in a PhD programme need to develop their research knowledge from the very begging in order to refine their writing skills, paying attention to a large list of details and requirements which may vary from one situation to another. Indeed, academics invest innumerable hours and resources in writing their research results to succeed when submitting them to a peer‐reviewed journal. Therefore, it is not surprising that planning and writing a research project is often considered by inexperienced students as a challenging, time‐consuming and a demanding task. Yet, despite all these constraints, writing up the findings in a form that can be published and read for others, constitutes a very rewarding effort.

Emerging from the necessity to instruct students in acquiring the appropriate skills and abilities to effectively complete a research project, many courses are now incorporating a research module in their syllabuses as a mean to improve students' probabilities to succeed when doing so.

The editorial industry has also echoed these concerns and demands in the recent years, leading to the flowering of a large number of books and manuals addressing this issue (i.e. Bryman and Bell, 2011 ; Cooper and Schindler, 2011 ; Creswell, 2009 ; Flick, 2011 ; Sekaran, 2010 ). One of the most compelling books tackling this problem is Doing Research in Business and Management: An Essential Guide to Planning Your Project , written by Saunders and Lewis and published in 2012 by Pearson Education Limited.

Saunders and Lewis are experienced professors in teaching research methods in the fields of Business and Management. Indeed, they have published several books offering guidelines for students that need to conduct and produce a competent piece of research. According to the authors it is now fourteen years since their first book on research methods was printed. In 2009 the fifth edition was published ( Saunders et al. , 2009 ) and due to its popularity and good acceptance among academics and students they are now presenting this new and updated approach, providing a detailed coverage of all the relevant aspects (both theoretical and empirical) that may face students when carrying out a research project.

Combining their years of academic practice and their vast practical experience, with this book Andersen and Lewis aim at providing, in an accessible and friendly manner, some assistance to students and early career individuals who need to undertake an assessed research project by means of a written project report. This way, Doing Research in Business and Management provides a comprehensive and holistic overview of how to develop quality research in the fields of business and management, offering a detailed coverage of the relevant research approaches and methods used in these disciplines, and bringing together the essential components of the process of writing up a research project.

The authors take the reader through all the major stages involved in this process, introducing the different contexts and purposes that may guide a research project, as well as highlighting the key methods, strategies, tactics, programmes and processes that are recurrent in a management and business research context.

One noteworthy aspect of this book is that it takes extraordinary care to focus the reader on the logic and techniques of research methods using an accurate but simple language, that is, bearing in mind that the targeted audience is inexperienced students on undergraduate and postgraduate degree programmes in business, management or related disciplines, that may not yet be familiar with the language and style required by the scientific community. Therefore, employing a concise, highly accessible style and a logical structure, this book enables students that face for their first time the challenge of writing a research project to understand the practical relevance of research methods on business and management research. It also discusses the interrelationships of theoretical and empirical research, how these apply to practice, and the most recurrent software packages available in the market.

An important feature of this book is its clear structure and ease of reading. Furthermore, within each chapter, the use of illustrations, contemporary examples and key research terms placed in definition boxes to demystify their meaning, helps breaking up the text and brings it to life due to the practical perspective adopted. Certainly, it is presented in a logical and structured manner, organising the content in eight chapters that correspond to eight stages of the process of writing a research project.

Although the chapters follow a sequential order, the authors have written them in such a way that they stand alone, so any chapter in isolation make sense of it. Therefore, it is not necessary to read this book progressively from Chapter 1 through to Chapter 8, but allow readers to select those chapters that really match with their needs.

Each chapter follows a similar structure, starting with a brief summary that gives some ideas of the content and the approach adopted to afford the topic discussed (“Why read this chapter?”). Also each chapter contains a set of different examples to illustrate, from a practical point of view, some of the points addressed in the chapter (“Research in practice”). Every chapter ends with a “Thinking about” section, where the content is summarised in the form of concise and succinct bullets, and where authors make some suggestions on how to reinforce the contents learned.

Revising each chapter individually, Chapter 1 starts with some preliminary considerations on what is meant by a research process. Conducting research is something more than writing the findings using a formal language and a specific structure. It means to embark on a journey of discovery, where researchers have the chance to express their skills and resourcefulness demonstrating their abilities to carry out research projects of high quality.

Given these initial thoughts, Chapter 1 is then entirely devoted to the first matter academics come across: the selection of the research topic. As any process, a research project starts with the choice of a research theme. This is an issue entailing an extraordinary significance as the student/researcher should have to live with and live for, perhaps for a considerable period of time. Thus, it is important that the research topic matches with his/her expectations and motivations, contributing to the development of his/her personal skills and strengths, rather than being imposed by a supervisor.

The chapter follows with the main difficulties students face when choosing the research topic, and suggests novel ways in which to generate ideas, including for instance the use of relevant literature in the field to identify gaps that may constitute new research avenues. Discussions with friends and lecturers or drawing a concept map, are also listed as intermediate steps that may help filtering ideas, moving from the general to the specific question.

In the latter part of the chapter the authors focus on how to turn an idea into a research topic. At this point Sanders and Lewis recommend the use of the Russian doll principle ( Clough and Nutbrown, 2008 ), consisting of breaking down from the original statement to something which strips away all the complicated layers and obscurities until the heart of the question, in a similar fashion as the Russian doll is taken apart to reveal a tiny doll at the centre (p. 21).

Chapter 2 focuses on the subject of the literature review. A literature review is a very hard and time‐consuming process, as it should discuss relevant previous work and provide a comprehensive review of the major findings in the current area of study. This section is mandatory for any research project as it allows the researcher to demonstrate the need for a new study and that he/she has the appropriate background. Moreover, knowing what is known, it comes out easier to state what is still unknown, facilitating the identification of the gaps in the current understanding of the field, and making it simply to justify the research question(s) underlying the research and the context in which the study is going to be performed.

Thus, in this chapter the authors explain what constitutes an effective critical literature review, giving some ideas on how to start writing it and how to organise ideas and findings in a coherent logical argument.

A careful selection of the references to support statements and previous research findings is essential. In this sense, the authors make explicit differentiation of the main types and sources of literature available. A detailed description of the process of searching for obtaining the relevant literature is then provided, emphasising the use of an abstract to assess likely use of an article.

In accordance with the Harvard College Library (2006) , the authors also suggest that while reading, it is extremely important to take notes, summarise findings, compare information and contrast results. Likewise, they emphasise that a good literature review can lead to interesting insights into possible ways of collecting and analysing data.

Chapter 3 is concerned with how to manage the research process, focusing on four main aspects. First, questioning how to manage respondents, that is, how to access organisations for data collection. Here a set of six strategies are listed. Second, the authors focus on how to manage oneself in terms of keeping up the motivation, organising time and resources, and keeping in touch with individuals who may provide insightful comments on the work in progress. The relationship with the supervisor is believed to be indispensable, as he/she is expected to give advice at every stage of the process. But in order to succeed in this relationship, students are asked to report in a regular basis the partial results throughout the entire length of the project. A fourth issue deals with the management of the university context, that is, how to fit with the regulations, specific norms and other requirements imposed by the university. Paying special attention to the assessment criteria is also essential in order to guarantee that the research outcomes fulfil the requirements.

This chapter ends with a section covering the subject of research ethics, pointing out the key ethical principles and responsibilities that should be taken into account and that apply to all stages of any research process. This topic has perhaps been underexplored in the management and business literature; however it seems that it is now increasingly entering into the agendas of many academics ( Academy of Management, n.d. ; Frechtling and Boo, 2012 ; Payne, 2000 )).

Chapter 4 begins with a definition of what is meant by secondary data. A full definition is given, differentiating between qualitative and quantitative data and the possible transformation processes data may have suffered. Figure 4.1 (page 86) exhaustively summarises potential forms of secondary data according to their nature.

In this chapter the authors discuss the potential of using secondary data as a method for accessing large datasets and saving time and money, as they can often be found in the public domain as a result of the growth of the Internet. Nevertheless, secondary data also presents some pitfalls. For instance the authors emphasise that data are not always value‐neutral or only meet research needs partially. Although data available are rich and allows the creation of reliable variables, it is relevant to question whether the selected variables represent the core influential factors that match with the exact requirements of the study. The chapter ends with a list of some gateways to secondary data sources that are relevant for its broad scope and content.

Chapter 5 is entirely devoted to research strategy and design. Using the research onion metaphor presented in the authors' previous work ( Saunders et al. , 2009 ), Saunders and Lewis illustrate the different stages that conform the research process. Techniques and procedures for data collection and analysis are placed in the centre of the onion. As we move away from the centre we found the intermediate layers, where the strategies and choices have to be selected. Finally, the outer layers consider the research philosophies and approaches that embody the research. Building on this approach, the different layers of the onion are presented and complemented with readable examples. Definitions are also given, clarifying some terms that may be unknown for the freshman student, for instance, differences between inductive and deductive approaches, and alternative types of studies (exploratory, descriptive and explanatory).

This chapter also includes a section devoted to the description of different strategies that students may use in their attempt to answer their research question(s). Some useful insights are given on the use of experiments, surveys, case studies, action research methods, ethnography studies, grounded theory, archival research methods or the combination of any of the above. Although the aim of this book is not to in‐depth in the technical specifications behind these strategies, Saunders and Lewis provide the reader with the main features characterising each method. Further information on these approaches may be accompanied with the reading of the specific literature on each particular topic, such as Yin (2009) for case studies; the book of Berg and Lune (2008) with two chapters entirely focused on action research methods and ethnography studies; Corbin and Strauss (2008) and their approach to the grounded theory; or Ventresca and Mohr (2002) studying archival research methods. Correspondingly, the books of Miles and Huberman (1994) and Marshall and Rossman (2010) offer an exhaustive review of all aspects related to qualitative data analysis.

Chapter 6 pays attention to the methods used to collect data. The uses of different techniques and statistical procedures to obtain a sample that satisfies the research requirements, or how to draft an effective questionnaire, are some of the issues addressed in this chapter.

Part of this chapter is devoted to the importance of using a pilot test with a small group of respondents in order to corroborate the validity of a test/questionnaire before launching it to the whole sample. The chapter ends with some guidelines on how to successfully conduct interviews (either semi‐structured or unstructured), giving some examples on how to ask questions, how to interact with the respondent and how to prepare the material needed. A transcription of a potential interview is also provided.

The subject of Chapter 7 is about getting data ready for analysis, the use of different techniques for the analysis, and how to interpret the results obtained.

The chapter begins with the particularities of different types of data, namely quantitative and qualitative. Then the authors address each sort of data individually, first focusing on how to prepare and analyse quantitative data, and second, qualitative data. Essential statistical terms are defined, providing the reader with the basic instruments and techniques. Examples on how to interpret descriptives, correlation coefficients, associations between variables, predict cause‐effect relationships, or how to transcript an interview are provided, facilitating the understanding of those terms that the reader may not yet be familiar with. Screenshots of the most commonly used software packages for processing data are also introduced (i.e. SPSS, ATLAS.it, NVivo).

Writing an effective research proposal is a vital part of the research process. In the eighth and last chapter, the authors provide some insightful comments on the writing style students should adopt when compiling their research proposal and suggest some of the criteria against which the quality of the research project may be assessed.

Although each research process has its own particularities and layout, to a great extent, the content of a research project tends to follow a standardized structure: the title; an abstract; an introduction section presenting the research question(s); a review of the literature ending with the formulation of the hypotheses to be tested and the statement of the research objectives; a method section detailing how the research will be carried out; a discussion of the results; the conclusions and limitations of the study; and a list of the cited references. By way of conclusion, two research proposals are presented exemplifying all the aspects and issues discussed throughout the book.

Although the content of this book is quite predictable for a book of this type, it enables an effective learning process, representing a very useful guide for students planning or undertaking a research project or a dissertation in the fields of business and management. An eminently practical approach and a language free of an excessive use of technical terms helps students obtaining a thorough understanding of the main methodological issues a research project entails.

Therefore, taken as a whole, the value and rigour of this book is unquestionable, underpinning the strength of this publication.

A About the reviewer

Jasmina Berbegal‐Mirabent (PhD) received the MS degrees in Industrial Engineering and Industrial Scheduling, both from UPC BarcelonaTech (Spain) in 2008 and 2009, respectively. She is now working as an Associate Professor at the Department of Management at the UPC BarcelonaTech. She has been a Visiting Research Associate at the Institute of Education at the University of London. She has published her works in international and peer‐reviewed journals such as The Service Industries Journal , Management Decision , Journal of Technology Management & Innovation , Intangible Capital and Economía Industrial . She has recently been named Book Review Editor of Management Decision , and she is also on the editorial board of the Journal of Industrial Engineering and Management . Her research interests are in the areas of the management of higher education institutions and their role in regional development; academic entrepreneurship; and technology transfer. Jasmina Berbegal‐Mirabent can be contacted at: [email protected]

Academy of Management ( n.d. ), “Code of ethics”, available at: www.aomonline.org/aomPrint.asp?ID=268&page_ID=240 (accessed 19 July 2012).

Berg , B.L. and Lune , H. ( 2008 ), Qualitative Research Methods for the Social Sciences , 8th ed., international edition, , Person Education , Boston, MA .

Bryman , A. and Bell , E. ( 2011 ), Business Research Methods , 3rd ed. , Oxford University Press , New York, NY .

Clough , P. and Nutbrown , C. ( 2008 ), A Student's Guide to Methodology , 2nd ed. , Sage Publications , London .

Cooper , D.R. and Schindler , P.S. ( 2011 ), Business Research Methods , 11th ed. , McGraw‐Hill Companies , New York, NY .

Corbin , J. and Strauss , A. ( 2008 ), Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory , 3rd ed. , Sage Publications , London .

Creswell , J.W. ( 2009 ), Research Design: Qualitative, Quantitative, and Mixed Methods Approaches , 3rd ed. , Sage Publications , Thousand Oaks, CA .

Flick , U. ( 2011 ), Introducing Research Methodology: A Beginner's Guide to Doing a Research , Sage Publications , Los Angeles, CA .

Frechtling , D.C. and Boo , S. ( 2012 ), “ On the ethics of management research: an exploratory investigation ”, Journal of Business Ethics , Vol. 106 No. 2 , pp. 149 ‐ 160 .

Harvard College Library ( 2006 ), “Interrogating text: 6 reading habits to develop in your first year at Harvard”, available at: http://hcl.harvard.edu/research/guides/lamont_handouts/interrogatingtexts.html (accessed 18 July 2012).

Marshall , C. and Rossman , G.B. ( 2010 ), Designing Qualitative Research , 5th ed. , Sage Publications , London .

Miles , M.B. and Huberman , A.M. ( 1994 ), Qualitative Data Analysis: An Expanded Sourcebook , 2nd ed. , Sage Publications , Beverly Hills, CA .

Payne , S.L. ( 2000 ), “ Challenges for research ethics and moral knowledge construction in the applied social sciences ”, Journal of Business Ethics , Vol. 26 No. 4 , pp. 307 ‐ 318 .

Saunders , M. , Lewis , P. and Thornhill , A. ( 2009 ), Research Methods for Business Students , 5th ed. , Pearson Education , Harlow .

Sekaran , U. ( 2010 ), Research Methods for Business: A Skill Building Approach , 5th ed. , John Wiley & Sons , New York, NY .

Ventresca , M.J. and Mohr , J.W. ( 2002 ), “ Archival research methods ”, in Baum , J.A.C. (Ed.), Companion to Organizations , Blackwell , New York, NY , pp. 805 ‐ 828 .

Yin , R.K. ( 2009 ), Case Study Research: Design and Methods , 5th ed. , Sage Publications , London .

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Types of Research – Explained with Examples

By DiscoverPhDs
October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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Dr Pujada obtained her PhD in Molecular Cell Biology at Georgia State University in 2019. She is now a biomedical faculty member, mentor, and science communicator with a particular interest in promoting STEM education.

Ryan is in the final write up stages of his PhD at the University of Southampton. His research is on understanding narrative structure, media specificity and genre in transmedia storytelling.

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Article contents

Qualitative designs and methodologies for business, management, and organizational research.

Robert P. Gephart Robert P. Gephart Alberta School of Business, University of Alberta
and Rohny Saylors Rohny Saylors Carson College of Business, Washington State University
https://doi.org/10.1093/acrefore/9780190224851.013.230
Published online: 28 September 2020

Qualitative research designs provide future-oriented plans for undertaking research. Designs should describe how to effectively address and answer a specific research question using qualitative data and qualitative analysis techniques. Designs connect research objectives to observations, data, methods, interpretations, and research outcomes. Qualitative research designs focus initially on collecting data to provide a naturalistic view of social phenomena and understand the meaning the social world holds from the point of view of social actors in real settings. The outcomes of qualitative research designs are situated narratives of peoples’ activities in real settings, reasoned explanations of behavior, discoveries of new phenomena, and creating and testing of theories.

A three-level framework can be used to describe the layers of qualitative research design and conceptualize its multifaceted nature. Note, however, that qualitative research is a flexible and not fixed process, unlike conventional positivist research designs that are unchanged after data collection commences. Flexibility provides qualitative research with the capacity to alter foci during the research process and make new and emerging discoveries.

The first or methods layer of the research design process uses social science methods to rigorously describe organizational phenomena and provide evidence that is useful for explaining phenomena and developing theory. Description is done using empirical research methods for data collection including case studies, interviews, participant observation, ethnography, and collection of texts, records, and documents.

The second or methodological layer of research design offers three formal logical strategies to analyze data and address research questions: (a) induction to answer descriptive “what” questions; (b) deduction and hypothesis testing to address theory oriented “why” questions; and (c) abduction to understand questions about what, how, and why phenomena occur.

The third or social science paradigm layer of research design is formed by broad social science traditions and approaches that reflect distinct theoretical epistemologies—theories of knowledge—and diverse empirical research practices. These perspectives include positivism, interpretive induction, and interpretive abduction (interpretive science). There are also scholarly research perspectives that reflect on and challenge or seek to change management thinking and practice, rather than producing rigorous empirical research or evidence based findings. These perspectives include critical research, postmodern research, and organization development.

Three additional issues are important to future qualitative research designs. First, there is renewed interest in the value of covert research undertaken without the informed consent of participants. Second, there is an ongoing discussion of the best style to use for reporting qualitative research. Third, there are new ways to integrate qualitative and quantitative data. These are needed to better address the interplay of qualitative and quantitative phenomena that are both found in everyday discourse, a phenomenon that has been overlooked.

qualitative methods
research design
methods and methodologies
interpretive induction
interpretive science
critical theory
postmodernism
organization development

Introduction

Qualitative research uses linguistic symbols and stories to describe and understand actual behavior in real settings (Denzin & Lincoln, 1994 ). Understanding requires describing “specific instances of social phenomena” (Van Maanen, 1998 , p. xi) to determine what this behavior means to lay participants and to scientific researchers. This process produces “narratives-non-fiction division that link events to events in storied or dramatic fashion” to uncover broad social science principles at work in specific cases (p. xii).

A research design and/or proposal is often created at the outset of research to act as a guide. But qualitative research is not a rule-governed process and “no one knows” the rules to write memorable and publishable qualitative research (Van Maanen, 1998 , p. xxv). Thus qualitative research “is anything but standardized, or, more tellingly, impersonal” (p. xi). Design is emergent and is often created as it is being done.

Qualitative research is also complex. This complexity is addressed by providing a framework with three distinct layers of knowledge creation resources that are assembled during qualitative research: the methods layer, the logic layer, and the paradigmatic layer. Research methods are addressed first because “there is no necessary connection between research strategies and methods of data collection and analysis” (Blaikie, 2010 , p. 227). Research methods (e.g., interviews) must be adapted for use with the specific logical strategies and paradigmatic assumptions in mind.

The first, or methods, layer uses qualitative methods to “collect data.” That is, to observe phenomena and record written descriptions of observations, often through field notes. Established methods for description include participant and non-participant observation, ethnography, focus groups, individual interviews, and collection of documentary data. The article explains how established methods have been adapted and used to answer a range of qualitative research questions.

The second, or logic, layer involves selecting a research strategy—a “logic, or set of procedures, for answering research questions” (Blaikie, 2010 , p. 18). Research strategies link research objectives, data collection methods, and logics of analysis. The three logical strategies used in qualitative organizational research are inductive logic, deductive logic and abductive logic (Blaikie, 2010 , p. 79). 1 Each logical strategy makes distinct assumptions about the nature of knowledge (epistemology), the nature of being (ontology), and how logical strategies and assumptions are used in data collection and analysis. The task is to describe important methods suitable for each logical strategy, factors to consider when selecting methods (Blaikie, 2010 ), and illustrates how data collection and analysis methods are adapted to ensure for consistency with specific logics and paradigms.

The third, or paradigms, layer of research design addresses broad frameworks and scholarly traditions for understanding research findings. Commitment to a paradigm or research tradition entails commitments to theories, research strategies, and methods. Three paradigms that do empirical research and seek scientific knowledge are addressed first: positivism, interpretive induction, and interpretive abduction. Then, three scholarly and humanist approaches that critique conventional research and practice to encourage organizational change are discussed: critical theory and research, postmodern perspectives, and organization development (OD). Paradigms or traditions provide broad scholarly contexts that make specific studies comprehensible and meaningful. Lack of grounding in an intellectual tradition limits the ability of research to contribute: contributions always relate to advancing the state of knowledge in specific unfolding research traditions that also set norms for assessing research quality. The six research designs are explained to show how consistency in design levels can be achieved for each of the different paradigms. Further, qualitative research designs must balance the need for a clear plan to achieve goals with the need for adaptability and flexibility to incorporate insights and overcome obstacles that emerge during research.

Our general goal has been to provide a practical guide to inspire and assist readers to better understand, design, implement, and publish qualitative research. We conclude by addressing future challenges and trends in qualitative research.

The Substance of Research Design

A research design is a written text that can be prepared prior to the start of a research project (Blaikie, 2010 , p. 4) and shared or used as “a private working document.” Figure 1 depicts the elements of a qualitative research design and research process. Interest in a topic or problem leads researchers to pose questions and select relevant research methods to fulfill research purposes. Implementation of the methods requires use of logical strategies in conjunction with paradigms of research to specify concepts, theories, and models. The outcomes, depending on decisions made during research, are scientific knowledge, scholarly (non-scientific) knowledge, or applied knowledge useful for practice.

Figure 1. Elements of qualitative research design.

Research designs describe a problem or research question and explain how to use specific qualitative methods to collect and analyze qualitative data that answer a research question. The purposes of design are to describe and justify the decisions made during the research process and to explain how the research outcomes can be produced. Designs are thus future-oriented plans that specify research activities, connect activities to research goals and objectives, and explain how to interpret the research outcomes using paradigms and theories.

In contrast, a research proposal is “a public document that is used to obtain necessary approvals for a research proposal to proceed” (Blaikie, 2010 , p. 4). Research designs are often prepared prior to creating a research proposal, and research proposals often require the inclusion of research designs. Proposals also require greater formality when they are the basis for a legal contract between a researcher and a funding agency. Thus, designs and proposals are mutually relevant and have considerable overlap but are addressed to different audiences. Table 1 provides the specific features of designs and proposals. This discussion focuses on designs.

Table 1. Decisions Necessitated by Research Designs and Proposals

RESEARCH DESIGNS
Title or topic of project
Research problem and rationale for exploring problem
Research questions to address problem: purpose of study
Choice of logic of inquiry to investigate each research question
Statement of ontological and epistemological assumptions made
Statement or description of research paradigms used
Explanation of relevant concepts and role in research process
Statement of hypotheses to be tested (positivist), orienting proposition to be examined (interpretive) or mechanisms investigated (critical realism)
Description of data sources
Discussion of methods used to select data from sources
Description of methods of data collection, summarization, and analysis
Discussion of problems and limitations
RESEARCH PROPOSALS: add the items below to items above
Statement of aims and research significance
Background on need for research
Budget and justification for each item
Timetable or stages of research process
Specification of expected outcomes and benefits
Statement of ethical issues and how they can be managed
Explanation of how new knowledge will be disseminated

Source: Based on Blaikie ( 2010 ), pp. 12–34.

The “real starting point” for a research design (or proposal) is “the formulation of the research question” (Blaikie, 2010 , p. 17). There are three types of research questions: “what” questions seek descriptions; “why” questions seek answers and understanding; and “how” questions address conditions where certain events occur, underlying mechanisms, and conditions necessary for change interventions (p. 17). It is useful to start with research questions rather than goals, and to explain what the research is intended to achieve (p. 17) in a technical way.

The process of finding a topic and formulating a useful research question requires several considerations (Silverman, 2014 , pp. 31–33, 34–40). Researchers must avoid settings where data collection will be difficult (pp. 31–32); specify an appropriate scope for the topic—neither too wide or too narrow—that can be addressed (pp. 35–36); fit research questions into a relevant theory (p. 39); find the appropriate level of theory to address (p. 42); select appropriate designs and research methods (pp. 42–44); ensure the volume of data can be handled (p. 48); and do an effective literature review (p. 48).

A literature review is an important way to link the proposed research to current knowledge in the field, and to explain what was previously known or what theory suggests to be the case (Blaikie, 2010 , p. 17). Research questions can used to bound and frame the literature review while the literature review often inspires research questions. The review may also provide bases for creating new hypotheses and for answering some of the initial research questions (Blaikie, 2010 , p. 18).

Layers of Research Design

There are three layers of research design. The first layer focuses on research methods for collecting data. The second layer focuses on the logical frameworks used for analyzing data. The third layer focuses on the paradigm used to create a coherent worldview from research methods and logical frameworks.

Layer One: Design as Research Methods

Qualitative research addresses the meanings people have for phenomena. It collects narratives of organizational activity, uses analytical induction to create coherent representations of the truths and meanings in organizational contexts, and then creates explanations of this conduct and its prevalence (Van Maanan, 1998 , pp. xi–xii). Thus qualitative research involves “doing research with words” (Gephart, 2013 , title) in order to describe the linguistic symbols and stories that members use in specific settings.

There are four general methods for collecting qualitative data and creating qualitative descriptions (see Table 2 ). The in-depth case study approach provides a history of an event or phenomenon over time using multiple data sources. Observational strategies use the researcher to observe and describe behavior in actual settings. Interview strategies use a format where a researcher asks questions of an informant. And documentary research collects texts, documents, official records, photographs, and videos as data—formally written or visually recorded evidence that can be replayed and reviewed (Creswell, 2014 , p. 190). These methods are adapted to fit the needs of specific projects.

Table 2. Qualitative Data Collection Methods

Type	Brief Description	Key Example(s) and Reference Source(s)
	Provides thick description of a single event or phenomenon unfolding over time	Perlow ( ); Mills, Duerpos, and Wiebe ( ); Stake ( ); Piekkari and Welch ( )

Participant Observation	Observe, participate in, and describe actual settings and behaviors	McCall and Simmons ( ) Barker ( ) Graham ( )
Ethnography	Insider description of micro-culture developed through active participation in the culture	Van Maanen ( ); Ybema, Yanow, Wels, and Kamsteeg ( ); Cunliffe ( ); Van Maanen ( )
Systematic Self-Observation	Strategy for training lay informants to observe and immediately record selected experiences	Rodrguez, Ryave, and Tracewell ( ); Rodriguez and Ryave ( )

Single-Informant Interviews
Traditional structured interview	Pose preset and fixed questions and record answers to produce (factual) information on phenomena, explore concepts and test theory	Easterby-Smith, Thorpe, and Jackson et al. ( )
Unstructured interview	Use interview guide with themes to develop and pose in situ questions that fit unfolding interview	Easterby-Smith et al. ( )
Active interview	Unstructured interview with questions and answers co-constructed with informant that reveals the co-construction of meaning	Holstein and Gubrium ( )
Ethnographic interview	Meeting where researcher meets informant to pose systematic questions that teach the researcher about the informant’s questions	Spradley ( ) McCurdy, Spradley, and Shandy ( )
Long interview	Extended use of structured interview method that includes demographic and open-ended questions. Designed to efficiently uncover the worldview of informants without prolonged field involvement	McCracken ( ) Gephart and Richardson ( )

Focus Group	A group interview used to collect data on a predetermined topic (focus) and mediated by the researcher	Morgan ( )
Records and Texts
Photographic and visual methods	Produce accurate visual images of physical phenomena in field settings that can be analyzed or used to elicit informant reports	Ray and Smith ( ) Greenwood, Jack, and Haylock ( )
Video methods	Produce “different views’ of activity and permanent record that can be repeatedly examined and used to verify accuracy and validity of research claims	LeBaron, Jarzabkowski, Pratt, and Fetzer ( )
Textual data and documentary data collection		Hodder ( )

The In-Depth Case Study Method

The in-depth case study is a key strategy for qualitative research (Piekkari & Welch, 2012 ). It was the most common qualitative method used during the formative years of the field, from 1956 to 1965 , when 48% of qualitative papers published in the Administrative Science Quarterly used the case study method (Van Maanen, 1998 , p. xix). The case design uses one or more data collection strategies to describe in detail how a single event or phenomenon, selected by a researcher, has changed over time. This provides an understanding of the processes that underlie changes to the phenomenon. In-depth case study methods use observations, documents, records, and interviews that describe the events in the case unfolded and their implications. Case studies contextualize phenomena by studying them in actual situations. They provide rich insights into multiple dimensions of a single phenomenon (Campbell, 1975 ); offer empirical insights into what, how, and why questions related to phenomena; and assist in the creation of robust theory by providing diverse data collected over time (Gephart & Richardson, 2008 , p. 36).

Maniha and Perrow ( 1965 ) provide an example of a case study concerned with organizational goal displacement, an important issue in early organizational theorizing that proposed organizations emerge from rational goals. Organizational rationality was becoming questioned at the time that the authors studied a Youth Commission with nine members in a city of 70,000 persons (Maniha & Perrow, 1965 ). The organization’s activities were reconstructed from interviews with principals and stakeholders of the organization, minutes from Youth Commission meetings, documents, letters, and newspaper accounts (Maniha & Perrow, 1965 ).

The account that emerged from the data analysis is a history of how a “reluctant organization” with “no goals to guide it” was used by other aggressive organizations for their own ends. It ultimately created its own mission (Maniha & Perrow, 1965 ). Thus, an organization that initially lacked rational goals developed a mission through the irrational process of goal slippage or displacement. This finding challenged prevailing thinking at the time.

Observational Strategies

Observational strategies involve a researcher present in a situation who observes and records, the activities and conversations that occur in the setting, usually in written field notes. The three observational strategies in Table 2 —participant observation, ethnography, and systematic self-observation—differ in terms of the role of the researcher and in the data collection approach.

Participant observation . This is one of the earliest qualitative methods (McCall & Simmons, 1969 ). One gains access to a setting and an informant holding an appropriate social role, for example, client, customer, volunteer, or researcher. One then observes and records what occurs in the setting using field notes. Many features or topics in a setting can become a focus for participant observers. And observations can be conducted using continuum of different roles from the complete participant, observer as participant, and participant observer, to the complete observer who observes without participation (Creswell, 2014 , Table 9.2, p. 191).

Ethnography . An ethnography is “a written representation of culture” (Van Maanen, 1988 ) produced after extended participation in a culture. Ethnography is a form of participant observation that focuses on the cultural aspects of the group or organization under study (Van Maanen, 1988 , 2010 ). It involves prolonged and close contact with group members in a role where the observer becomes an apprentice to an informant to learn about a culture (Agar, 1980 ; McCurdy, Spradley, & Shandy, 2005 ; Spradley, 1979 ).

Ethnography produces fine-grained descriptions of a micro-culture, based on in-depth cultural participation (McCurdy et al., 2005 ; Spradley, 1979 , 2016 ). Ethnographic observations seek to capture cultural members’ worldviews (see Perlow, 1997 ; Van Maanen, 1988 ; Watson, 1994 ). Ethnographic techniques for interviewing informants have been refined into an integrated developmental research strategy—“the ethno-semantic method”—for undertaking qualitative research (Spradley, 1979 , 2016 ; Van Maanen, 1981 ). The ethnosemantic method uses a structured approach to uncover and confirm key cultural features, themes, and cultural reasoning processes (McCurdy et al., 2005 , Table 3 ; Spradley, 1979 ).

Systematic Self-Observation . Systematic self-observation (SSO) involves “training informants to observe and record a selected feature of their own everyday experience” (Rodrigues & Ryave, 2002 , p. 2; Rodriguez, Ryave, & Tracewell, 1998 ). Once aware that they are experiencing the target phenomenon, informants “immediately write a field report on their observation” (Rodrigues & Ryave, 2002 , p. 2) describing what was said and done, and providing background information on the context, thoughts, emotions, and relationships of people involved. SSO generates high-quality field notes that provide accurate descriptions of informants’ experiences (pp. 4–5). SSO allows informants to directly provide descriptions of their personal experiences including difficult to capture emotions.

Interview Strategies

Interviews are conversations between researchers and research participants—termed “subjects” in positivist research and informants in “interpretive research.” Interviews can be conducted as individual face-to-face interactions (Creswell, 2014 , p. 190) or by telephone, email, or through computer-based media. Two broad types of interview strategies are (a) the individual interview and (b) the group interview or focus group (Morgan, 1997 ). Interviews elicit informants’ insights into their culture and background information, and obtain answers and opinions. Interviews typically address topics and issues that occur outside the interview setting and at previous times. Interview data are thus reconstructions or undocumented descriptions of action in past settings (Creswell, 2014 , p. 191) that provide descriptions that are less accurate and valid descriptions than direct, real-time observations of settings.

Structured and unstructured interviews. Structured interviews pose a standardized set of fixed, closed-ended questions (Easterby-Smith, Thorpe, & Jackson, 2012 ) to respondents whose responses are recorded as factual information. Responses may be forced choice or open ended. However, most qualitative research uses unstructured or partially structured interviews that pose open-ended questions in a flexible order that can be adapted. Unstructured interviews allow for detailed responses and clarification of statements (Easterby-Smith et al., 2012 ; McLeod, 2014 )and the content and format can be tailored to the needs and assumptions of specific research projects (Gephart & Richardson, 2008 , p. 40).

The informant interview (Spradley, 1979 ) poses questions to informants to elicit and clarify background information about their culture, and to validate ethnographic observations. In interviews, informants teach the researcher their culture (Spradley, 1979 , pp. 24–39). The informant interview is part of a developmental research sequence (McCurdy et al., 2005 ; Spradley, 1979 ) that begins with broad “grand tour” questions that ask an informant to describe an important domain in their culture. The questions later narrow to focus on details of cultural domains and members’ folk concepts. This process uncovers semantic relationships among concepts of members and deeper cultural themes (McCurdy et al., 2005 ; Spradley, 1979 ).

The long interview (McCracken, 1988 ) involves a lengthy, quasi-structured interview sessions with informants to acquire rapid and efficient access to cultural themes and issues in a group. Long interviews differ ethnographic interviews by using a “more efficient and less obtrusive format” (p. 7). This creates a “sharply focused, rapid and highly intense interview process” that avoids indeterminate and redundant questions and pre-empts the need for observation or involvement in a culture. There are four stages in the long interview: (a) review literature to uncover analytical categories and design the interview; (b) review cultural categories to prepare the interview guide; (c) construct the questionnaire; and (d) analyze data to discover analytical categories (p. 30, fig. 1 ).

The active interview is a dynamic process where the researcher and informant co-construct and negotiate interview responses (Holstein & Gubrium, 1995 ). The goal is to uncover the subjective meanings that informants hold for phenomenon, and to understand how meaning is produced through communication. The active approach is common in interpretive, critical, and postmodern research that assumes a negotiated order. For example, Richardson and McKenna ( 2000 ) explored how ex-patriate British faculty members themselves interpreted and explained their expatriate experience. The researchers viewed the interview setting as one where the researchers and informants negotiated meanings between themselves, rather than a setting where prepared questions and answers were shared.

Documentary, Photographic, and Video Records as Data

Documents, records, artifacts, photographs, and video recordings are physically enduring forms of data that are separable from their producers and provide mute evidence with no inherent meaning until they are read, written about, and discussed (Hodder, 1994 , p. 393). Records (e.g., marriage certificate) attest to a formal transaction, are associated with formal governmental institutions, and may have legally restricted access. In contrast, documents are texts prepared for personal reasons with fewer legal restrictions but greater need for contextual interpretation. Several approaches to documentary and textual data analysis have been developed (see Table 3 ). Documents that researchers have found useful to collect include public documents and minutes of meetings; detailed transcripts of public hearings; corporate and government press releases; annual reports and financial documents; private documents such as diaries of informants; and news media reports.

Photographs and videos are useful for capturing “accurate” visual images of physical phenomena (Ray & Smith, 2012 ) that can be repeatedly reexamined and used as evidence to substantiate research claims (LeBaron, Jarzabkowski, Pratt, & Fetzer, 2018 ). Photos taken from different positions in space may also reveal different features of phenomena. Videos show movement and reveal activities as processes unfolding over time and space. Both photos and videos integrate and display the spatiotemporal contexts of action.

Layer Two: Design as Logical Frameworks

The second research design layer links data collection and analysis methods (Tables 2 and 3 ) to three logics of enquiry that answer specific questions: inductive, deductive, and abductive logical strategies (see Table 4 ). Each logical strategy focuses on producing different types of knowledge using distinctive research principles, processes, and types of research questions they can address.

Table 3. Data Analysis and Integrated Data Collection and Analysis Strategies

Strategy	Brief Explanation	Key References
Compassionate Research Methods	Immersive and experimental approach to using ethnographic understanding to enhancing care for others	Dutton, Workman, and Hardin ( ) Hansen and Trank ( )
Computer-Aided Interpretive Textual Analysis	Strategy for computer supported interpretive textual analysis of documents and discourse that capture members’ first-order meanings	Kelle ( ) Gephart ( , )
Content Analysis	Establishing categories for a document or text then counting the occurrences of categories and showing concern with issues of reliability and validity	Sonpar and Golden-Biddle ( ) Duriau, Reger, and Pfarrer ( ) Greckhamer, Misngyi, Elms, and Lacey ( ) Silverman ( )
Document, Record and Artifact Analysis	Uses many procedures for contemporary, non-document data analysis	Hodder ( )
Dream Analysis	Technique for detecting countertransference of emotions from researcher to informant to uncover how researchers are tacitly and unconsciously embedded in their own observations and interpretations	de Rond and Tuncalp ( )
Ethnomethodology	A sociological approach to analysis of sensemaking practices used in face to face communication	Coulon ( ) Garfinkel ( , ) Gephart ( , ) Whittle ( )
Ethnosemantic Analysis	Systematic approach to uncover first-order concepts and terms of members, verify their meaning, and construct folk taxonomies for meaningful cultural domains	Spradley ( ) McCurdy, Spradley, and Shandy ( ) Akeson ( ) Van Maanen ( )
Expansion Analysis	Form of discourse analysis that produces a detailed, line by line, data-driven interpretation of a text or transcript	Cicourel ( ) Gephart, Topal, and Zhang ( )
Grounded Theorizing	Inductive development of theory from systematically obtained and analyzed observations	Glaser and Strauss ( ) Gephart ( ) Locke ( , ) Smith ( ) Walsh et al. ( )
Interpretive Science	A methodology for doing scientific research using abduction that provides discovery oriented replicable scientific knowledge that is interpretive and not positivist	Schutz ( , ) Garfinkel ( ) Gephart ( )
Pattern matching	Unspecified process of matching/finding patterns in qualitative data, often confirmed by subjects’ verbal reports and quantitative analysis	Lee and Mitchell ( ) Lee, Mitchell, Wise, and Fireman ( ) Yan and Gray ( )
Phenomenological Analysis	Methodology/ies for examining individuals’ experiences	Gill ( )
Storytelling Inquiry	Six distinct approaches to storytelling useful for eliciting fine-grained and detailed stories from informants	Boje ( ) Rosile, Boje, Carlon, Downs, and Saylors ( ) Boje and Saylors ( )
Narrative and Textual Analysis	Analysis of written and spoken verbal behavior and documents using techniques from literary criticism, rhetoric, and sociolinguistic analysis to understand discourse	McCloskey ( ) Boje ( ) Gephart ( , , ) Ganzin, Gephart, and Suddaby ( ) Martin ( ) Calas and Smircich ( ) Pollach ( )
Organization Development/Action Research	Approaches to improving organizational structure and functioning through practice-based interventions	Cummings and Worley ( ) Buono and Savall ( ) Worley, Zardet, Bonnet, and Savall ( )

Table 4. Logical Strategies for Answering Qualitative Research Questions with Evidence

Feature	Inductive	Deductive	Abductive
Ontology	Realist	Realist/Objectivist	Interpretive/Constructionist
Assumptions	Objective world that is perceived subjectively; hence perceptions of reality can differ	Single objective reality independent of people’s perceptions
Questions	What—describe and explain phenomena	Why—explain associations between/among phenomena	What, why, and how—describe and explain conditions for occurrence of phenomena from lay and scientific perspectives
Aim
Logic	Linear: Begin with singular statements and conclude via induction with generalizations	Linear: Establish associations via induction or abduction then test them using deductive reasoning	Spiral processes: Analytical process moves from lay actors’ accounts to technical descriptions using scientific accounts Scientist makes an hypothesis that appears to explain observations then proposes what gave rise to it (Blaikie, , p. 164)
Primary Focus	Objective features of settings described through subjective, personal perspectives	Objective features of broad realities described from objective, unbiased perspectives	Intersubjective meanings and interpretations used in everyday life to construct objective features and reveal subjective meanings
Principles	Facts gained by unbiased observations Elimination method Hypotheses are not used to compare facts	Borrow or invent a theory, express it as a deductive argument, deduce a conclusion, test the conclusion. If it passes, treat the conclusion as the explanation.	Construct second-order scientific theories by generalization/induction and inference from observations of actors’ activities, terms, meanings, and theories. Incorporate members’ meanings—phenomena left out of inductive and deductive research.
Outcomes	Describes features of domain of social action and infers from one set of facts to another: hence can confirm existence of phenomena in initial domain but cannot discover phenomena outside of previously known domain	Scientist has great freedom to propose theory but nature decides on the validity of conclusions: knowledge limited to prior hypotheses, no discovery possible (Blaikie, , p. 144)	, p. 165)

Based in part on Blaikie ( 1993 ), ch. 5 & 6; Blaikie ( 2010 ), p. 84, table 4.1

The Inductive Strategy

Induction is the scientific method for many scholars (Blaikie, 1993 , p. 134), and an essential logic for qualitative management research (Pratt, 2009 , p. 856). Inductive strategies ask “what” questions to explore a domain to discover unknown features of a phenomenon (Blaikie, 2010 , p. 83). There are four stages to the inductive strategy: (a) observe and record all facts without selection or anticipating their importance; (b) analyze, compare, and classify facts without employing hypotheses; (c) develop generalizations inductively based on the analyses; and (d) subject generalizations to further testing (Blaikie, 1993 , p. 137).

Inductive research assumes a real world outside human thought that can be directly sensed and described (Blaikie, 2010 ). Principles of inductive research reflect a realist and objectivist ontology. The selection, definition, and measurement of characteristics to be studied are developed from an objective, scientific point of view. Facts about organizational features need to be obtained using unbiased measurement. Further, the elimination method is used to find “the characteristics present in all the positive cases, which are absent in all the negative cases, and which vary in appropriate degrees” (Blaikie, 1993 , p. 135). This requires data collection methods that provide unbiased evidence of the objective facts without pre-supposing their importance.

Induction can establish limited generalizations about phenomena based solely on the observations collected. Generalizations need to be based on the entire sample of data, not on selected observations from large data sets, to establish their validity. The scope of generalization is limited to the sample of data itself. Induction creates evidence to increase our confidence in a conclusion, but the conclusions do not logically follow from premises (Blaikie, 1993 , p. 164). Indeed, inferences from induction cannot be extended beyond the original set of observations and no logical or formal process exists to establish the universality of inferences.

Key data collection methods for inductive designs include observational strategies that allow the researcher to view behavior without making a priori hypotheses, to describe behavior that occurs “naturally” in settings, and to record non-impressionistic descriptions of behavior. Interviews can also elicit descriptions of settings and behavior for inductive qualitative research. Data analysis methods need to describe actual interactions in real settings including discourse among members. These methods include ethnosemantic analysis to uncover key terms and validate actual meanings used by members; analyses of conversational practices that show how meaning is negotiated through sequential turn taking in discourse; and grounded theory-based concept coding and theory development that use the constant comparative method.

Facts or descriptions of events can be compared to one another and generalizations can be made about the world using induction (Blaikie, 2010 ). Outcomes from inductive analysis include descriptions of features in a limited domain of social action that are inferred to exist in other similar settings. Propositions and broader insights can be developed inductively from these descriptions.

The Deductive Strategy

Deductive logic (Blaikie, 1993 , 2010 ) addresses “why” questions to explain associations between concepts that represent phenomena of interest. Researchers can use induction, abduction, or any means, to develop then test the hypotheses to see if they are valid. Hypotheses that are not rejected are temporarily corroborated. The outcomes from deduction are tested hypotheses. Researchers can thus be very creative in hypothesis construction but they cannot discover new phenomena with deduction that is based only on phenomena known in advance (Blaikie, 2010 ). And there is also no purely logical or mechanical process to establish “the validity of [inductively constructed] universal statements from a set of singular statements” from which deductive hypotheses were formed (Hempel, 1966 , p. 15 cited in Blaikie, 1993 , p. 140).

The deductive strategy uses a realist and objectivist ontology and imitates natural science methods. Useful data collection methods include observation, interviewing, and collection of documents that contain facts. Deduction addresses the assumedly objective features of settings and interactions. Appropriate data analysis methods include content coding to identify different types, features, and frequencies of observed phenomena; grounded theory coding and analytical induction to create categories in data, determine how categories are interrelated, and induce theory from observations; and pattern recognition to compare current data to prior models and samples. Content analysis and non-parametric statistics can be used to quantify qualitative data and make it more amenable to analysis, although quantitative analysis of qualitative data is not, strictly speaking, qualitative research (Gephart, 2004 ).

The Abductive Strategy

Abduction is “the process used to produce social scientific accounts of social life by drawing on the concepts and meanings used by social actors, and the activities in which they engage” (Blaikie, 1993 , p. 176). Abductive reasoning assumes that the socially meaningful world is the world experienced by members. The first abductive task is to discover the insider view that is basic to the actions of social actors (p. 176) by uncovering the subjective meanings held by social actors. Subjective meaning (Schutz, 1973a , 1973b ) refers to the meaning that actions hold for the actors themselves and that they can express verbally. Subjective meaning is not inexpressible ideas locked in one’s mind. Abduction starts with lay descriptions of social life, then moves to technical, scientific descriptions of social life (Blaikie, 1993 , p. 177) (see Table 4 ). Abduction answers “what” questions with induction, why questions with deduction, and “how” questions with hypothesized processes that explain how, and under what conditions, phenomena occur. Abduction involves making a logical leap that infers an explanatory process to explain an outcome in an oscillating logic. Deductive, inductive, and inferential processes move recursively from actors’ accounts to social science accounts and back again in abduction (Gephart, 2018 ). This process enables all theory and second-order scientific concepts to be grounded in actors’ first-order meanings.

The abductive strategy contains four layers: (a) everyday concepts and meanings of actors, used for (b) social interaction, from which (c) actors provide accounts, from which (d) social scientific descriptions are made, or theories are generated and applied, to interpret phenomena (Blaikie, 1993 , p. 177). The multifaceted research process, described in Table 4 , requires locating and comprehending members’ important everyday concepts and theories before observing or creating disruptions that force members to explain the unstated knowledge behind their action. The researcher then integrates members’ first-order concepts into a general, second-order scientific theory that makes first-order understandings recoverable.

Abduction emerged from Weber’s interpretive sociology ( 1978 ) and Peirce’s ( 1936 ) philosophy. But Alfred Schutz ( 1973a , 1973b ) is the contemporary scholar who did the most to extend our understanding of abduction, although he never used the term “abduction” (Blaikie, 1993 , 2010 ; Gephart, 2018 ). Schutz conceived abduction as an approach to verifiable interpretive knowledge that is scientific and rigorous (Blaikie, 1993 ; Gephart, 2018 ). Abduction is appropriate for research that seeks to go beyond description to explanation and prediction (Blaikie, 1993 , p. 163) and discovery (Gephart, 2018 ). It employs an interpretive ontology (Schutz, 1973a , 1973b ) and social constructionist epistemology (Berger & Luckmann, 1966 ), using qualitative methods to discover “why people do what they do” (Blaikie, 1993 ).

Dynamic data collection methods are needed for abductive research to capture descriptions of interactions in actual settings and their meanings to members. Observational and interview approaches that elicit members’ concepts and theories are particularly relevant to abductive understanding (see Table 2 ). Data analysis methods must analyze situated, first-order (common sense) discourse as it unfolds in real settings and then systematically develop second-order concepts or theories from data. Relevant approaches to produce and validate findings include ethnography, ethnomethodology, and grounded theorizing (see Table 3 ). The combination of what, why, and how questions used in abduction produces a broader understanding of phenomena than do what and why deductive and inductive questions.

Layer Three: Paradigms of Research

Scholarly paradigms integrate methods, logics, and intellectual worldviews into coherent theoretical perspectives and form the most abstract level of research design. Six paradigms are widely used in management research (Burrell & Morgan, 1979 ; Cunliffe, 2011 ; Gephart, 2004 , 2013 ; Gephart & Richardson, 2008 ; Hassard, 1993 ). The first three perspectives—positivism, interpretive induction, and interpretive abduction—build on logics of design and seek to produce rigorous empirical research that constitutes evidence (see Table 5 ). Three additional perspectives pursue philosophical, critical, and practical knowledge: critical theory, postmodernism, and organization development (see Table 6 ). Tables 5 and 6 describe important features of each research design to show similarities and differences in the processes through which theoretical meaning is bestowed on research results in management and organization studies.

Table 5. Paradigms, Logical Strategies, and Methodologies for Empirical Research

DIMENSION	Positivism	Interpretive Induction	Interpretive Science
Nature of Reality	Realism: Single objective, durable, knowable reality independent of people	Socially constructed reality with subjective and objective features	Material reality socially constructed through inter-subjective practices that link objective to subjective meanings
Goal	Discover facts and causal interrelationships among facts (variables)	Provide descriptive accounts, theories and data-based understandings of members’ practices	Develop second-order scientific theories from lay members’ first-order concepts and everyday understandings
Research Questions	Why questions	What questions	What, why, and how questions
Methods Foci	Facts Variables, hypotheses, associations, and correlations	Meanings: Describe language use in real life contexts, communication, meaning during organizational action	Meaning: Describe how members construct and maintain a sense of shared meaning and social structure (intersubjectivity)
Methods Orientation
Logical strategies		Induction	Abduction Induction Deduction
Data Collection Methods	Observation Interviews Audio and video records Field notes Document collection	Ethnography Participant observation Interviewing Audio or video tape recording Field notes Document collection	Ethnography Participant observation Informant interviewing Audio or video with detailed transcriptions of conversation and recording Field notes Document collection
Data Analysis Methods	Pattern matching Content analysis Grounded Theory	Analytical induction Grounded theory coding Gioia method	Schutz’s abductive method Expansion analysis Conversation analysis Ethnomethodogy Interpretive textual analysis
Research Process			, p. 90)
Research Design Stages
Research Outcomes
Assessing knowledge
Types of Knowledge Sought	Scientific knowledge	Scholarly knowledge that is interpretive and has scientific features	Scientific knowledge that is replicable, reliable and valid Practice-oriented knowledge of members’ gained based on first-order understandings

Sources: Based on and adapted and extended from Blaikie ( 1993 , pp. 137, 145, & 152); Blaikie ( 2010 , Table 4.1, p. 84); Gephart ( 2013 , Table 9.1, p. 291) and Gephart ( 2018 , Table 3.1, pp. 38–39).

Table 6. Alternative Paradigms, Logical Strategies, and Methodologies

Critical Research	Postmodern Perspectives	Organization Development Research
Dialectical reality with objective contradictions and reified structures that produce power-based inequities
Uncover, dereify, and challenge taken-for-granted meanings and practices to reduce power inequities, enable emancipation, and motivate social change		Reduce hidden costs Enhance value added for humans

Actions and ideologies that create reified, objective social structures that are oppressive—OR—disrupt reified structures	Analysis of texts and discourse that shape and bestow power to show their value-laden nature
Describe and uncover sources of oppression and discord Produce accounts that enable or encourage social action and change Emphasis on description, unveiling of reified structure, change	Describe and uncover sources of oppression and discord Produce accounts that enable or encourage social action and change Emphasis on description, unveiling of reified structure, change
Reflection, Critical reflexivity Dialectical methods	Reflection Deconstruction Linguistic play	Deduction Induction Abduction
All methods possibly useful Case descriptions Document collection	Collect documents and texts	Observations, interviews
All qualitative methods are possibly useful Dialogical Inquiry Critical ethnography	Storytelling inquiry Critical discourse analysis Narrative and rhetorical analysis Deconstruction	Pattern matching Storytelling Qualimetrics Hidden cost analysis
Unmasking of oppression Development of political strategies for action Trigger actions that produce change Trace the conflictual role of power in organizational life	Create texts that disrupt the readers’ conceptions and viewpoints Challenge status quo knowledge Expose hidden knowledge and hidden interests Motivate action to resist categorizations	Qualitative and quantitative improvements in organizational functioning and performance Reduction of hidden costs
Quality of theory developed Positive impacts on management policies and practices to reduce oppression, inequities	Novel research to produce novelinsights	Examineperformance outcomes
Political knowledge, historical knowledge, change orientation	Disruptive knowledge, change orientation, philosophical, literary, and rhetorical texts	Practical knowledge Actionable knowledge

Based in part on Gephart ( 2004 , 2013 , 2018 ).

The Positivist Approach

The qualitative positivist approach makes assumptions equivalent to those of quantitative research (Gephart, 2004 , 2018 ). It assumes the world is objectively describable and comprehensible using inductive and deductive logics. And rigor is important and achieved by reliability, validity, and generalizability of findings (Kirk & Miller, 1986 ; Malterud, 2001 ). Qualitative positivism mimics natural science logics and methods using data recorded as words and talk rather than numerals.

Positivist research (Bitektine, 2008 ; Su, 2018 ) starts with a hypothesis. This can, but need not, be based in data or inductive theory. The research process, aimed at publication in peer-reviewed journals, requires researchers to (a) identify variables to measure, (b) develop operational definitions of the variables, (c) measure (describe) the variables and their inter-relationships, (d) pose hypotheses to test relationships among variables, then (e) compare observations to hypotheses for testing (Blaikie, 2010 ). When data are consistent with theory, theory passes the test. Otherwise the theory fails. This theory is also assessed for its logical correctness and value for knowledge. The positivist approach can assess deductive and inductive generalizations and provide evidence concerning why something occurs—if proposed hypotheses are not rejected.

Positivists view qualitative research as highly subject to biases that must be prevented to ensure rigor, and 23 methodological steps are recommended to enhance rigor and prevent bias (Gibbert & Ruigrok, 2010 , p. 720). Replicability is another concern because methodology descriptions in qualitative publications “insufficiently describe” how methods are used (Lee, Mitchell, & Sablynski, 1999 , p. 182) and thereby prevent replication. To ensure replicability, a qualitative “article’s description of the method must be sufficiently detailed to allow a reader . . . to replicate that reported study either in a hypothetical or actual manner.”

Qualitative research allows positivists to observe naturally unfolding behavior in real settings and allow “the real world” of work to inform research and theory (Locke & Golden-Biddle, 2004 ). Encounters with the actual world provide insights into meaning construction by members that cannot be captured with outsider (etic) approaches. For example, past quantitative research provided inconsistent findings on the importance of pre- and post-recruitment screening interviews for job choices of recruits. A deeper investigation was thus designed to examine how recruitment impacts job selection (Rynes, Bretz, & Gerhart, 1991 ). To do so, students undergoing recruitment were asked to “tell us in their own words” how their recruiting and decision processes unfolded (Rynes et al., 1991 , p. 399). Using qualitative evidence, the researchers found that, in contrast to quantitative findings, “people do make choices based on how they are treated” (p. 509), and the choices impact recruitment outcomes. Rich descriptions of actual behavior can disconfirm quantitative findings and produce new findings that move the field forward.

An important limitation of positivism is its common emphasis on outsiders’ or scientific observers’ objective conceptions of the world. This limits the attention positivist research gives to members’ knowledge and allows positivist research to impose outsiders’ meanings on members’ everyday behavior, leading to a lack of understanding of what the behavior means to members. Another limitation is that no formal, logical, or proven techniques exist to assess the strength of “relationships” among qualitative variables, although such assessments can be formally done using well-formed quantitative data and techniques. Thus, qualitative positivists often provide ambiguous or inexplicit quantitative depictions of variable relations (e.g., “strong relationship”). Alternatively, the analysts quantify qualitative data by assigning numeric codes to categories (Greckhamer, Misngyi, Elms, & Lacey, 2008 ), using non-parametric statistics, or quantitative content analysis (Sonpar & Golden-Biddle, 2008 ) to create numerals that depict associations among variables.

An illustrative example of positivist research . Cole ( 1985 ) studied why and how organizations change their working structures from bureaucratic forms to small, self-supervised work teams that allow for worker participation in shop floor activities. Cole found that existing research on workplace change focused on the micropolitical level of organizations. He hypothesized that knowledge could be advanced differently, by examining the macropolitical change in industries or nations. Next, a testable conclusion was deduced: a macro analysis of the politics of change can better predict the success of work team implementation, measured as the spread of small group work structures, than an examination of the micropolitics of small groups ( 1985 ). Three settings were selected for the research: Japan, Sweden, and the United States. Japanese data were collected from company visits and interviews with employment officials and union leaders. Swedish documentary data on semiautonomous work groups were used and supplemented by interviews at Volvo and Saab, and prior field research in Sweden. U.S. data were collected through direct observations and a survey of early quality circle adopters.

Extensive change was observed in Sweden and Japan but changes to small work groups were limited in the United States (Cole, 1985 ). This conclusion was verified using records of the experiences of the three nations in work reform, compared across four dimensions: timing and scope of changes, managerial incentives to innovate, characteristics of mobilization, and political dimensions of change. Data revealed the United States had piecemeal experimentation and resistance to reform through the 1970s; diffusion emerged in Japan in the early 1960s and became extensive; and Swedish workplace reform started in the 1960s and was widely and rapidly diffused.

Cole then answered the questions of “why” and “how” the change occurred in some countries but not others. Regarding why Japanese and Swedish managers were motivated to introduce workplace change due to perceived managerial problems and the changing national labor market. Differences in the political processes also influenced change. Management, labor, and government interest in workplace change was evident in Japan and Sweden but not in the United States where widespread resistance occurred. As to how, the change occurred through macropolitical processes (Cole, 1985 , p. 120), specifically, the commitment of the national business leadership to the change and whether or not the change was contested or uncontested by labor impacted the adoption of change. Organizational change usually occurs through broad macropolitical processes, hence “the importance of macro-political variables in explaining these outcomes” (p. 122).

Interpretive Induction

Two streams of qualitative research claim the label of “interpretive research” in management and organization studies. The first stream, interpretive induction, emphasizes induction as its primary logical strategy (e.g., Locke, 2001 , 2002 ; Pratt, 2009 ). It assumes a “real world” that is inherently objective but interpreted through subjective lenses, hence different people can perceive or report different things. This research is interpretive because it addresses the meanings and interpretations people give to organizational phenomena, and how this meaning is provided and used. Interpretive induction contributes to scientific knowledge by providing empirical descriptions, generalizations, and low-level theories about specific contexts based on thick descriptions of members’ settings and interactions (first-order understandings) as data.

The interpretive induction paradigm addresses “what” questions that describe and explain the existence and features of phenomena. It seeks to uncover the subjective, personal knowledge that subjects have of the objective world and does so by creating descriptive accounts of the activities of organizational members. Interpretive induction creates inductive theories based on limited samples that provide low-scope, abstract theory. Limitations (Table 5 ) include the fact that inductive generalizations are limited to the sample used for induction and need to be subjected to additional tests and comparisons for substantiation. Second, research reports often fail to provide details to allow replication of the research. Third, formal methods for assessing the accuracy and validity of results and findings are limited. Fourth, while many features of scientific research are evident in interpretive induction research, the research moves closer to humanistic knowledge than to science when the basic assumptions of inductive analysis are relaxed—a common occurrence.

An illustrative example of interpretive induction research . Adler and Adler ( 1988 , 1998 ) undertook a five-year participant-observation study of a college basketball program (Adler, 1998 , p. 32). They sought to “examine the development of intense loyalty in one organization.” Intense loyalty evokes “devotional commitment of . . . (organizational) members through a subordination that sometime borders on subservience” (p. 32). The goal was to “describe and analyze the structural factors that emerged as most related” to intense loyalty (p. 32).

The researchers divided their roles. Peter Adler was the active observer and “expert” who undertook direct observations while providing counsel to players (p. 33). Patricia Adler took the peripheral role of “wife” and debriefed the observer. Two research questions were posed: (a) “what” kinds of organizational characteristics foster intense loyalty? (b) “how” do organizations with intense loyalty differ structurally from those that lack intense loyalty?

The first design stage (Table 5 ) recorded unbiased observations in extensive field notes. Detailed “life history” accounts were obtained from 38 team members interviewed (Adler & Adler, 1998 , p. 33). Then analytical induction and the constant comparative method (Glaser & Strauss, 1967 ) were used to classify and compare observations (p. 33). Once patterns emerged, informants were questioned about variations in patterns (p. 34) to develop “total patterns” (p. 34) reflecting the collective belief system of the group. This process required a “careful and rigorous means of data collection and analysis” that was “designed to maximize both the reliability and validity of our findings” (p. 34). The study found five conceptual elements were essential to the development of intense loyalty: domination, identification, commitment, integration, and goal alignment (p. 35).

The “what” question was answered by inducing a generalization (stage 3): paternalistic organizations with charismatic leadership seek people who “fit” the organization’s style and these people require extensive socialization to foster intense loyalty. This description contrasts with rational bureaucratic organizations that seek people who fit specific, generally known job descriptions and require limited socialization (p. 46). The “how” question is answered by inductive creation of another generalization: organizations that control the extra-organizational activities of members are more likely to evoke intense loyalty by forcing members to subordinate all other interests to those of the organization (p. 46).

The Interpretive Abduction Approach

The second stream of interpretive research—interpretive abduction—produces scientific knowledge using qualitative methods (Gephart, 2018 ). The approach assumes that commonsense knowledge is foundational to how actors know the world. Abductive theory is scientifically built from, and refers to, everyday life meanings, in contrast to positivist and interpretive induction research that omits concern with the worldview of members. Further, interpretive abduction produces second-order or scientific theory and concepts from members’ first-order commonsense concepts and meanings (Gephart, 2018 , p. 34; Schutz, 1973a , 1973b ).

The research process, detailed in Table 5 (process and stages), focuses on collecting thick descriptive data on organizations, identifying and interpreting first-order lay concepts, and creating abstract second-order technical constructs of science. The second-order concepts describe the first-order principles and terms social actors use to organize their experience. They compose scientific concepts that form a theoretical system to objectively describe, predict, and explain social organization (Gephart, 2018 , p. 35). This requires researchers to understand the subjective view of the social actors they study, and to develop second-order theory based on actors’ subjective meanings. Subjective meaning can be shared with others through language use and communication and is not private knowledge.

A central analytical task for interpretive abduction is creating second-order, ideal-type models of social roles, motives, and interactions that describe the behavioral trajectories of typical actors. Ideal-type models can be objectively compared to one another and are the special devices that social science requires to address differences between social phenomena and natural phenomena (Schutz, 1973a , 1973b ). The models, once built, are refined to preserve actors’ subjective meanings, to be logically consistent, and to present human action from the actor’s point of view. Researchers can then vary and compare the models to observe the different outcomes that emerge. Scientific descriptions can then be produced, and theories can be created. Interpretive abduction (Gephart, 2018 , p. 35) allows one to addresses what, why, and how questions in a holistic manner, to describe relationships among scientific constructs, and to produce “empirically ascertainable” and verifiable relations among concepts (Schutz, 1973b , p. 65) that are logical, hold practical meaning to lay actors, and provide abstract, objective meaning to interpretive scientists (Gephart, 2018 , p. 35). Abduction produces knowledge about socially shared realities by observing interactions, uncovering members’ first-order meanings, and then developing technical second-order or scientific accounts from lay accounts.

Interpretive abduction (Gephart, 2018 ) uses well-developed methods to create, refine, test, and verify second-order models, and it provides well-developed tools to support technical, second-level analyses. Research using the interpretive abduction approach includes a study of how technology change impacts sales automobile practices (Barley, 2015 ) and an investigation study of how abduction was used to develop new prescription drugs (Dunne & Dougherty, 2016 ).

An illustrative example of the interpretive abduction approach . Perlow ( 1997 ) studied time management among software engineers facing a product launch deadline. Past research verified the widespread belief that long working hours for staff are necessary for organizational success. This belief has adversely impacted work life and led to the concept of a “time bind” faced by professionals (Hochschild, 1997 ). One research question that subsequently emerged was, “what underlies ‘the time bind’ experienced by engineers who face constant deadlines and work interruptions?” (Perlow, 1997 , p. xvii). This is an inductive question about the causes and consequences of long working hours not answered in prior research that is hard to address using induction or deduction. Perlow then explored assumption underlying the hypothesis, supported by lay knowledge and management literature, that even if long working hours cause professionals to destroy their life style, long work hours “further the goals of our organizations” and “maximize the corporation’s bottom line” (Perlow, 1997 , p. 2).

The research commenced (Table 5 , step 1) when Perlow gained access to “Ditto,” a leader in implementing flexible work policies (Perlow, 1997 , p. 141) and spent nine months doing participant observation four days a week. Perlow collected descriptive data by walking around to observe and converse with people, attended meetings and social events, interviewed engineers at work and home and spouses at home, asked participants to record activities they undertook on selected working days (Perlow, 1997 , p. 143), and made “thousands of pages of field notes” (p. 146) to uncover trade-offs between work and home life.

Perlow ( 1997 , pp. 146–147) analyzed first-order concepts uncovered through his observations and interviews from 17 stories he wrote for each individual he had studied. The stories described workstyles, family lives, and traits of individuals; provided objective accounts of subjective meanings each held for work and home; offered background information; and highlighted first-order concepts. Similarities and differences in informant accounts were explored with an empirically grounded scheme for coding observations into categories using grounded theory processes (Gioia, Corley, & Hamilton, 2012 ). The process allowed Perlow to find key themes in stories that show work patterns and perceptions of the requirements of work success, and to create ideal-type models of workers (step 3). Five stories were selected for detailed analysis because they reveal important themes Perlow ( 1997 , p. 147). For example, second-order, ideal-type models of different “roles” were constructed in step 3 including the “organizational superstar” (pp. 15–21) and “ideal female employee” (pp. 22–32) based on first-order accounts of members. The second-order ideal-type scientific models were refined to include typical motives. The models were compared to one another (step 4) to describe and understand how the actions of these employee types differed from other employee types and how these variations produced different outcomes for each trajectory of action (steps 4 and 5).

Perlow ( 1997 ) found that constant help-seeking led engineers to interrupt other engineers to get solutions to problems. This observation led to the abductively developed hypothesis that interruptions create a time crisis atmosphere for engineers. Perlow ( 1997 ) then created a testable, second-order ideal-type (scientific) model of “the vicious working cycle” (p. 96), developed from first-order data, that explains the productivity problems that the firm (and other research and development firms)—commonly face. Specifically, time pressure → crisis mentality → individual heroics → constant interruptions of others’ work to get help → negative consequences for individual → negative consequences for the organization.

Perlow ( 1997 ) then tested the abductive hypothesis that the vicious work cycle caused productivity problems (stage 5). To do so, the vicious work cycle was transformed into a virtuous cycle using scheduling quiet times to prevent work interruptions: relaxed work atmosphere → individuals focus on own work completion → few interruptions → positive consequences for individual and organization. To test the hypothesis, an experiment was conducted (research process 2 in Table 5 ) with engineers given scheduled quiet times each morning with no interruptions. The experiment was successful: the project deadline was met. The hypothesis about work interruptions and the false belief that long hours are needed for success were supported (design stage 6). Unfortunately, the change was not sustained and engineers reverted to work interruptions when the experiment ended.

There are three additional qualitative approaches used in management research that pursue objectives other than producing empirical findings and developing or testing theories. These include critical theory and research, postmodernism, and change intervention research (see Table 6 ).

The Critical Theory and Research Approach

The term “critical” has many meanings including (a) critiques oriented to uncovering ideological manifestations in social relations (Gephart, 2013 , p. 284); (b) critiques of underlying assumptions of theories; and (c) critique as self-reflection that reflexively encapsulates the investigator (Morrow, 1994 , p. 9). Critical theory and critical management studies bring these conceptions of critical to bear on organizations and employees.

Critical theory and research extend the theories Karl Marx, and the Frankfurt School in Germany (Gephart & Kulicki, 2008 ; Gephart & Pitter, 1995 ; Habermas, 1973 , 1979 ; Morrow, 1994 ; Offe, 1984 , 1985 ). Critical theory and research assume that social science research differs from natural science research because social facts are human creations and social phenomena cannot be controlled as readily as natural phenomena (Gephart, 2013 , p. 284; Morrow, 1994 , p. 9). As a result, critical theory often uses a historical approach to explore issues that arise from the fundamental contradictions of capitalism. Critical research explores ongoing changes within capitalist societies and organizations, and analyzes the objective structures that constrain human imagination and action (Morrow, 1994 ). It seeks to uncover the contradictions of advanced capitalism that emerge from the fundamental contradiction of capitalism: owners of capital have the right to appropriate the surplus value created by workers. This basic contradiction produces further contradictions that become sources of workplace oppression and resistance that create labor issues. Thus contradictions reveal how power creates consciousness (Poutanen & Kovalainen, 2010 ). Critical reflection is used to de-reify taken-for-granted structures that create power inequities and to motivate resistance and critique and escape from dominant structures (see Table 6 ).

Critical management studies build on critical theory in sociology. It seeks to transform management and provide alternatives to mainstream theory (Adler, Forbes, & Willmott, 2007 ). The focus is “the social injustice and environmental destruction of the broader social and economic systems” served by conventional, capitalist managers (Adler et al., 2007 , p. 118). Critical management research examines “the systemic corrosion of moral responsibility when any concern for people or for the environment . . . requires justification in terms of its contribution to profitable growth” (p. 4). Critical management studies goes beyond scientific skepticism to undertake a radical critique of socially divisive and environmentally destructive patterns and structures (Adler et al., 2007 , p. 119). These studies use critical reflexivity to uncover reified capitalist structures that allow certain groups to dominate others. Critical reflection is used to de-reify and challenge the facts of social life that are seen as immutable and inevitable (Gephart & Richardson, 2008 , p. 34). The combination of dialogical inquiry, critical reflection, and a combination of qualitative and quantitative methods and data are common in this research (Gephart, 2013 , p. 285). Some researchers use deductive logics to build falsifiable theories while other researchers do grounded theory building (Blaikie, 2010 ). Validity of critical research is assessed as the capability the research has to produce critical reflexivity that comprehends dominant ideologies and transforms repressive structures into democratic processes and institutions (Gephart & Richardson, 2008 ).

An illustrative example of critical research . Barker ( 1998 , p. 130) studied “concertive control” in self-managed work teams in a small manufacturing firm. Concertive control refers to how workers collaborate to engage in self-control. Barker sought to understand how control practices in the self-managed team setting, established to allow workers greater control over their work, differed from previous bureaucratic processes. Interviews, observations, and documents were used as data sources. The resultant description of work activities and control shows that rather than allowing workers greater control, the control process enacted by workers themselves became stronger: “The iron cage becomes stronger” and almost invisible “to the workers it incarcerates” (Barker, 1998 , p. 155). This study shows how traditional participant observation methods can be used to uncover and contest reified structures and taken-for-granted truths, and to reveal the hidden managerial interests served.

Postmodern Perspectives

The postmodern perspective (Boje, Gephart, & Thatchenkery, 1996 ) is based in philosophy, the humanities, and literary criticism. Postmodernism, as an era, refers to the historical stage following modernity that evidences a new cultural worldview and style of intellectual production (Boje et al., 1996 ; Jameson, 1991 ; Rosenau, 1992 ). Postmodernism offers a humanistic approach to reconceptualize our experience of the social world in an era where it is impossible to establish any foundational underpinnings for knowledge. The postmodern perspective assumes that realities are contradictory in nature and value-laden (Gephart & Richardson, 2008 ; Rosenau, 1992 , p. 6). It addresses the values and contradictions of contemporary settings, how hidden power operates, and how people are categorized (Gephart, 2013 ). Postmodernism also challenges the idea that scientific research is value free, and asks “whose values are served by research?”

Postmodern essays depart from concerns with systematic, replicable research methods and designs (Calas, 1987 ). They seek instead to explore the values and contradictions of contemporary organizational life (Gephart, 2013 , p. 289). Research reports have the character of essays that seek to reconceptualize how people experience the world (Martin, 1990 ; Rosenau, 1992 ) and to disrupt this experience by producing “reading effects” that unsettle a community (Calas & Smircich, 1991 ).

Postmodernism examines intertextual relations—how texts become embedded in other texts—rather than causal relations. It assumes there are no singular realities or truths, only multiple realities and multiple truths, none of which are superior to other truths (Gephart, 2013 ). Truth is conceived as the outcome of language use in a context where power relations and multiple realities exist.

From a methodological view, postmodern research tends to focus on discourse: texts and talk. Data collection (in so far as it occurs) focuses on records of discourse—texts of spoken and written verbal communication (Fairclough, 1992 ). Use of formal or official records including recordings, texts and transcripts is common. Analytically, scholars tend to use critical discourse analysis (Fairclough, 1992 ), narrative analysis (Czarniawska, 1998 ; Ganzin, Gephart, & Suddaby, 2014 ), rhetorical analysis (Culler, 1982 ; Gephart, 1988 ; McCloskey, 1984 ) and deconstruction (Calais & Smircich, 1991 ; Gephart, 1988 ; Kilduff, 1993 ; Martin, 1990 ) to understand how categories are shaped through language use and come to privilege or subordinate individuals.

Postmodernism challenges models of knowledge production by showing how political discourses produce totalizing categories, showing how categorization is a tool for social control, and attempting to create opportunities for alternative representations of the world. It thus provides a means to uncover and expose discursive features of domination, subordination, and resistance in society (Locke & Golden-Biddle, 2004 ).

An illustrative example of postmodern research . Martin ( 1990 ) deconstructed a conference speech by a company president. The president was so “deeply concerned” about employee well-being and involvement at work that he encouraged a woman manager “to have her Caesarian yesterday” so she could participate in an upcoming product launch. Martin deconstructs the story to reveal the suppression of gender conflict in the dialogue and how this allows gender conflict and subjugation to continue. This research established the existence of important domains of organizational life, such as tacit gender conflict, that have not been adequately addressed and explored the power dynamics therein.

The Organization Development Approach

OD involves a planned and systematic diagnosis and intervention into an organizational system, supported by top management, with the intent of improving the organization’s effectiveness (Beckhard, 1969 ; Palmer, Dunford, & Buchanan, 2017 , p. 282). OD research (termed “clinical research” by Schein, 1987 ) is concerned with changing attitudes and behaviors to instantiate fundamental values in organizations. OD research often follows the general process of action research (Lalonde, 2019 ) that involves working with actors in an organization to help improve the organization. OD research involves a set of stages the OD practitioner (the leader of the intervention) uses: (a) problem identification; (b) consultation between OD practitioner and client; (c) data collection and problem diagnosis; (d) feedback; (e) joint problem diagnosis; (f) joint action planning; (g) change actions; and (h) further data gathering to move recursively to a refined step 1.

An illustrative example of the organization development approach . Numerous OD techniques exist to help organizations change (Palmer et al., 2017 ). The OD approach is illustrated here by the socioeconomic approach to management (SEAM) (Buono & Savall, 2007 ; Savall, 2007 ). SEAM provides a scientific approach to organizational intervention consulting that integrates qualitative information on work practices and employee and customer needs (socio) with quantitative and financial performance measures (economics). The socioeconomic intervention process commences by uncovering dysfunctions that require attention in an organization. SEAM assumes that organizations produce both (a) explicit benefits and costs and (b) hidden benefits and costs. Hidden costs refer to economic implications of organizational dysfunctions (Worley, Zardet, Bonnet, & Savall, 2015 , pp. 28–29). These include problems in working conditions; work organization; communication, co-ordination, and co-operation; time management; integrated training; and strategy implementation (Savall, Zardet, & Bonnet, 2008 , p. 33). Explicit costs are emphasized in management decision-making but hidden costs are ignored. Yet hidden costs from dysfunctions often greatly outstrip explicit costs.

For example, a fishing company sought to protect its market share by reducing the price and quality of products, leading to the purchase of poor-quality fish (Savall et al., 2008 , pp. 31–32). This reduced visible costs by €500,000. However, some customers stopped purchasing because of the lower-quality product, producing a loss of sales of €4,000,000 in revenue or an overall drop in economic performance of €3,500,000. The managers then changed their strategy to focus on health and quality. They implemented the SEAM approach, assessed the negative impact of the hidden costs on value added and revenue received, and purchased higher-quality fish. Visible costs (expenses) increased by €1,000,000 due to the higher cost for a better-quality product, but the improved quality (performance) cut the hidden costs by increasing loyalty and increased sales by €5,000,000 leaving an increased profit of €4,000,000.

SEAM allows organizations to uncover hidden costs in their operations and to convert these costs into value-added human potential through a process termed “qualimetrics.” Qualimetrics assesses the nature of hidden costs and organizational dysfunctions, develops estimates of the frequencies and amounts of hidden costs in specific organizational domains, and develops actions to reduce the hidden costs and thereby release additional value added for the organization (Savall & Zardet, 2011 ). The qualimetric process is participative and involves researchers who use observations, interviews and focus groups of employees to (a) describe, qualitatively, the dysfunctions experienced at work (qualitative data); (b) estimate the frequencies with which dysfunctions occur (quantitative data); and (c) estimate the costs of each dysfunction (financial data). Then, strategic change actions are developed to (a) identify ways to reduce or overcome the dysfunction, (b) estimate how frequently the dysfunction can be remedied, and (c) estimate the overall net costs of removing the hidden costs to enhance value added. The economic balance is then assessed for changes to transform the hidden costs into value added.

OD research creates actionable knowledge from practice (Lalonde, 2019 ). OD intervention consultants use multistep processes to change organizations that are flexible practices not fixed research designs. OD plays an important role in developing evidence-based practices to improve organizational functioning and performance. Worley et al. ( 2015 ) provide a detailed example of the large-scale implementation of the SEAM OD approach in a large, international firm.

Here we discuss implication of qualitative research designs for covert research, reporting qualitative work and novel integrations of qualitative and quantitative work.

Covert Research

University ethics boards require researchers who undertake research with human participants to obtain informed consent from the participants. Consent requires that all participants must be informed of details of the research procedure in which they will be involved and any risks of participation. Researchers must protect subjects’ identities, offer safeguards to limit risks, and insure informant anonymity. This consent must be obtained in the form of a signed agreement from the participant, obtained prior to the commencement of research observations (McCurdy et al., 2005 , pp. 29–32).

Covert research that fails to fully disclose research purposes or practices to participants, or that is otherwise deceptive by design or tacit practice, has long been considered “suspect” in the field (Graham, 1995 ; Roulet, Gill, Stenger, & Gill, 2017 ). This is changing. Research methodologists have shown that the over/covert dimension is a continuum, not a dichotomy, and that unintended covert elements occur in many situations (Roulet et al., 2017 ). Thus all qualitative observation involves some degree of deception due practical constraints on doing observations since it is difficult to do fully overt research, particularly in observational contexts with many people, and to gain advance consent from everyone in the organization one might encounter.

There are compelling benefits to covert research. It can provide insights not possible if subjects are fully informed of the nature or existence of the research. For example, the year-long, covert observational study of an asylum as a “total institution” (Goffman, 1961 ) showed how ineffective the treatment of mental illness was at the time. This opened the field of mental health to social science research (Roulet et al., 2017 , p. 493). Covert research can also provide access to institutions that researchers would otherwise be excluded from, including secretive and secret organizations (p. 492). This could allow researchers to collect data as an insider and to better see and experience the world from members’ perspective. It could also reduce “researcher demand effects” that occur when informants obscure their normal behavior to conform to research expectations. Thus, the inclusion of covert research data collection in research designs and proposals is an emerging trend and realistic possibility. Ethics applications can be developed that allow for aspects of covert research, and observations in many public settings do not require informed consent.

The Appropriate Style for Reporting Qualitative Work

The appropriate style for reporting qualitative research has become an issue of concern. For example, editors of the influential Academy of Management Journal have noted the emergence of an “AMJ style” for qualitative work (Bansal & Corley, 2011 , p. 234). They suggest that all qualitative work should use this style so that qualitative research can “benefit” from: “decades of refinement in the style of quantitative work.” The argument is that most scholars can assess the empirical and theoretical contributions of quantitative work but find it difficult to do so for qualitative research. It is easier for quantitatively trained editors and scholars “to spot the contribution of qualitative work that mimics the style of quantitative research.” Further, “the majority of papers submitted to . . . AMJ tend to subscribe to the paradigm of normal science that aims to find relationships among valid constructs that can be replicated by anyone” (Bansal, Smith, & Vaara, 2018 , p. 1193). These recommendations appear to explicitly encourage the reporting of qualitative results as if they were quantitatively produced and interpreted and highlights the advantage of conformity to the prevailing positivist perspective to gain publication in AMJ.

Yet AMJ editors have also called for researchers to “ensure that the research questions, data, and analysis are internally consistent ” (Bansal et al., 2018 , p. 1193) and to “Be authentic , detailed and clear in argumentation” (emphasis added) (Bansal et al., 2018 , p. 1193). These calls for consistency appear to be inconsistent with suggestions to present all qualitative research using a style that mimics quantitative, positivist research. Adopting the quantitative or positivist style for all qualitative reports may also confuse scholars, limit research quality, and hamper efforts to produce innovative, non-positivist research. This article provides six qualitative research designs to ensure a range of qualitative research publications are internally consistent in methods, logics, paradigmatic commitments, and writing styles. These designs provide alternatives to positivist mimicry in non-positivist scholarly texts.

Integrating Qualitative and Quantitative Research in New Ways

Qualitative research often omits consideration of the naturally occurring uses of numbers and statistics in everyday discourse. And quantitative researchers tend to ignore qualitative evidence such as stories and discourse. Yet knowledge production processes in society “rely on experts and laypeople and, in so doing, make use of both statistics and stories in their attempt to represent and understand social reality” (Ainsworth & Hardy, 2012 , p. 1649). Numbers and statistics are often used in stories to create legitimacy, and stories provide meaning to numbers (Gephart, 1988 ). Hence stories and statistics cannot be separated in processes of knowledge production (Ainsworth & Hardy, 2012 , p. 1697). The lack of attention to the role of quantification in everyday life means a huge domain of organizational discourse—all talk that uses numbers, quantities, and statistics—is largely unexplored in organizational research.

Qualitative research has, however, begun to study how words and numbers are mutually used for organizational storytelling (Ainsworth & Hardy, 2012 ; Gephart, 2016 ). This focus offers the opportunity to develop research designs to explore qualitative features and processes involved in quantitative phenomena such as financial crises (Gephart, 2016 ), to address how stories and numbers need to work together to create legitimate knowledge (Ainsworth & Hardy, 2012 ), and to show how statistics are used rhetorically to convince others of truths in organizational research (Gephart, 1988 ).

Ethnostatistics (Gephart, 1988 ; Gephart & Saylors, 2019 ) provides one example of how to integrate qualitative and quantitative research. Ethnostatistics examines how statistics are constructed and used by professionals. It explores how statistics are constructed in real settings, how violations of technical assumptions impact statistical outcomes, and how statistics are used rhetorically to convince others of the truth of research outcomes. Ethnostatistics has been used to reinterpret data from four celebrated network studies that themselves were reanalyzed (Kilduff & Oh, 2006 ). The ethnostatistical reanalyses revealed how ad hoc practices, including judgment calls and the imputation of new data into old data set for reanalysis, transformed the focus of network research from diffusion models to structural equivalence models.

Another innovative study uses a Bayesian ethnostatistical approach to understand how the pressure to produce sophisticated and increasingly complex theoretical narratives for causal models has impacted the quantitative knowledge generated in top journals (Saylors & Trafimow, 2020 ). The use of complex causal models has increased substantially over time due to a qualitative and untested belief that complex models are true. Yet statistically speaking, as the number of variables in a model increase, the likelihood the model is true rapidly decreases (Saylors & Trafimow, 2020 , p. 3).

The authors test the previously untested (qualitative) belief that complex causal models can be true. They found that “the joint probability of a six variable model is about 3.5%” (Saylors & Trafimow, 2020 , p. 1). They conclude that “much of the knowledge generated in top journals is likely false” hence “not reporting a (prior) belief in a complex model” should be relegated to the set of questionable research practices. This study shows how qualitative research that explores the lay theories and beliefs of statisticians and quantitative researchers can challenge and disrupt conventions in quantitative research, improve quantitative practices, and contribute qualitative foundations to quantitative research. Ethnostatistics thus opens the qualitative foundations of quantitative research to critical qualitative analyses.

The six qualitative research design processes discussed in this article are evident in scholarly research on organizations and management and provide distinct qualitative research designs and approaches to use. Qualitative research can provide research insights from several theoretical perspectives, using well-developed methods to produce scientific and scholarly insights into management and organizations. These approaches and designs can also inform management practice by creating actionable knowledge. The intended contribution of this article is to describe these well-developed methods, articulate key practices, and display core research designs. The hope is both to better equip researchers to do qualitative research, and to inspire them to do so.

Acknowledgments

The authors wish to acknowledge the assistance of Karen Lund at The University of Alberta for carefully preparing Figure 1 . Thanks also to Beverly Zubot for close reading of the manuscript and helpful suggestions.

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1. The fourth logic is retroduction. This refers to the process of building hypothetical models of structures and mechanisms that are assumed to produce empirical phenomena. It is the primary logic used in the critical realist approach to scientific research (Avenier & Thomas, 2015 ; Bhaskar, 1978 ). Retroduction requires the use of inductive or abductive strategies to discover the mechanisms that explain regularities (Blaikie, 2010 , p. 87). There is no evident logic for discovering mechanisms and this requires disciplined scientific thinking aided by creative imagination, intuition, and guesswork (Blaikie, 2010 ). Retroduction is likr deduction in asking “what” questions and differs from abduction because it produces explanations rather than understanding, causes rather than reasons, and hypothetical conceptual mechanisms rather than descriptions of behavioral processes as outcomes. Retroduction is becoming important in the field but has not as yet been extensively used in management and organization studies (for examples of uses, see Avenier & Thomas, 2015 ); hence, we do not address it at length in this article.

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What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:

Your overall research objectives and approach
Whether you’ll rely on primary research or secondary research
Your sampling methods or criteria for selecting subjects
Your data collection methods
The procedures you’ll follow to collect data
Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative approach	Quantitative approach
	and describe frequencies, averages, and correlations about relationships between variables

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

How much time do you have to collect data and write up the research?
Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

Experimental and quasi-experimental designs allow you to test cause-and-effect relationships
Descriptive and correlational designs allow you to measure variables and describe relationships between them.

Type of design	Purpose and characteristics
Experimental	relationships effect on a
Quasi-experimental	)
Correlational
Descriptive

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Type of design	Purpose and characteristics


Grounded theory
Phenomenology

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling	Non-probability sampling

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Questionnaires	Interviews
	)

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Quantitative observation

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

Field	Examples of data collection methods
Media & communication	Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives
Psychology	Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time
Education	Using tests or assignments to collect data on knowledge and skills
Physical sciences	Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

Reliability	Validity
	) )

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

How many participants do you need for an adequate sample size?
What inclusion and exclusion criteria will you use to identify eligible participants?
How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

The distribution of the data (e.g., the frequency of each score on a test)
The central tendency of the data (e.g., the mean to describe the average score)
The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

Make estimates about the population based on your sample data.
Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

Approach	Characteristics
Thematic analysis
Discourse analysis

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

Your research questions and/or hypotheses
Your overall approach (e.g., qualitative or quantitative )
The type of design you’re using (e.g., a survey , experiment , or case study )
Your data collection methods (e.g., questionnaires , observations)
Your data collection procedures (e.g., operationalization , timing and data management)
Your data analysis methods (e.g., statistical tests or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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The Four Types of Research Design — Everything You Need to Know

Updated: December 11, 2023

Published: January 18, 2023

When you conduct research, you need to have a clear idea of what you want to achieve and how to accomplish it. A good research design enables you to collect accurate and reliable data to draw valid conclusions.

research design used to test different beauty products

In this blog post, we'll outline the key features of the four common types of research design with real-life examples from UnderArmor, Carmex, and more. Then, you can easily choose the right approach for your project.

Table of Contents

What is research design?

The four types of research design, research design examples.

Research design is the process of planning and executing a study to answer specific questions. This process allows you to test hypotheses in the business or scientific fields.

Research design involves choosing the right methodology, selecting the most appropriate data collection methods, and devising a plan (or framework) for analyzing the data. In short, a good research design helps us to structure our research.

Marketers use different types of research design when conducting research .

There are four common types of research design — descriptive, correlational, experimental, and diagnostic designs. Let’s take a look at each in more detail.

Researchers use different designs to accomplish different research objectives. Here, we'll discuss how to choose the right type, the benefits of each, and use cases.

Research can also be classified as quantitative or qualitative at a higher level. Some experiments exhibit both qualitative and quantitative characteristics.

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Experimental

An experimental design is used when the researcher wants to examine how variables interact with each other. The researcher manipulates one variable (the independent variable) and observes the effect on another variable (the dependent variable).

In other words, the researcher wants to test a causal relationship between two or more variables.

In marketing, an example of experimental research would be comparing the effects of a television commercial versus an online advertisement conducted in a controlled environment (e.g. a lab). The objective of the research is to test which advertisement gets more attention among people of different age groups, gender, etc.

Another example is a study of the effect of music on productivity. A researcher assigns participants to one of two groups — those who listen to music while working and those who don't — and measure their productivity.

The main benefit of an experimental design is that it allows the researcher to draw causal relationships between variables.

One limitation: This research requires a great deal of control over the environment and participants, making it difficult to replicate in the real world. In addition, it’s quite costly.

Best for: Testing a cause-and-effect relationship (i.e., the effect of an independent variable on a dependent variable).

Correlational

A correlational design examines the relationship between two or more variables without intervening in the process.

Correlational design allows the analyst to observe natural relationships between variables. This results in data being more reflective of real-world situations.

For example, marketers can use correlational design to examine the relationship between brand loyalty and customer satisfaction. In particular, the researcher would look for patterns or trends in the data to see if there is a relationship between these two entities.

Similarly, you can study the relationship between physical activity and mental health. The analyst here would ask participants to complete surveys about their physical activity levels and mental health status. Data would show how the two variables are related.

Best for: Understanding the extent to which two or more variables are associated with each other in the real world.

Descriptive

Descriptive research refers to a systematic process of observing and describing what a subject does without influencing them.

Methods include surveys, interviews, case studies, and observations. Descriptive research aims to gather an in-depth understanding of a phenomenon and answers when/what/where.

SaaS companies use descriptive design to understand how customers interact with specific features. Findings can be used to spot patterns and roadblocks.

For instance, product managers can use screen recordings by Hotjar to observe in-app user behavior. This way, the team can precisely understand what is happening at a certain stage of the user journey and act accordingly.

Brand24, a social listening tool, tripled its sign-up conversion rate from 2.56% to 7.42%, thanks to locating friction points in the sign-up form through screen recordings.

different types of research design: descriptive research example.

Carma Laboratories worked with research company MMR to measure customers’ reactions to the lip-care company’s packaging and product . The goal was to find the cause of low sales for a recently launched line extension in Europe.

The team moderated a live, online focus group. Participants were shown w product samples, while AI and NLP natural language processing identified key themes in customer feedback.

This helped uncover key reasons for poor performance and guided changes in packaging.

S. Sreejesh 4 ,
Sanjay Mohapatra 5 &
M. R. Anusree 6

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The objective of this chapter is to define and explain research design in detail. In this chapter, we discussed three major types of research designs, such as exploratory, descriptive and causal research designs. We also explained the mode of data used in each of these designs and the techniques to collect these data, which would ultimately helps the researcher to decide appropriate analysis technique. This chapter concludes with budgeting and scheduling of a business research project and elaborated the guidelines for writing a business research proposal. This chapter designed in such a way that the reader can appreciate these concepts by considering the examples and cartoon illustrations, which would better elicit and convince the concept understanding.

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The Use of Experiments in Business Research

Basic Research Methods

Writing about Research Design

By David Voreacos, Alex Nussbaum and Greg Farrel, Johnson and Johnson reaches a band-aid, Bloomberg Business week.

Due of lack of representativeness it is not possible to compare the results from different groups in a strict quantitative sense.

See Ref. Sudman ( 1980 ).

See Ref. Cook and Campbell ( 1979 ).

ASQ Statistics Division Newsletter (2000), vol 19(issue 2)

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Beri GC (1989) Marketing research, 1st edn. Tata McGraw-Hill, New Delhi

Cook T, Campbell D (1979) Quasi experimentation: design and analysis issues for field settings. Houghton Mifflin Company, Boston

Sudman S (1980) Improving the quality of shopping centre sampling. J Mark Res 17:423–431

Zikmund WG (2003) Business research methods, 7th edn. Thomson South-Western, Singapore, p 281

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IBS Hyderabad, IFHE University, Hyderabad, 501504, India

S. Sreejesh

Xavier Institute of Management, Xavier Road, Bhubaneswar, Orissa, 751013, India

Sanjay Mohapatra

Department of Statistics, University of Kerala, Trivandrum, Kerala, 695581, India

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In an experimental design, the primary goal is to isolate and identify the effects produced by the…

Dependent variable

Extraneous variable

Independent variable

An experiment has high… if one has confidence that the experiential treatment has been the source of change in the dependent variable.

Internal validity

External validity

Internal and external validity

Internal and external reliability

Which of the following is a threat to internal validity of an experimental design

Interaction of setting and treatment

Interaction effects of pre-testing

Reactive effects of experimental design

Which of the following effect in internal validity occurs when test units with extreme scores move closer to the average score during the course of the experiment

Statistical Regression

Selection bias

Instrumentation

Which of the following statement is incorrect with respect to ‘An experimental design is a set of procedures specifying

How to test units (subjects) are divided into homogeneous sub samples

What independent variables or treatments are to be measured

What dependent variables are to be measured

How the extraneous variables are to be controlled

Randomization of test units is a part of…

A characteristic that distinguishes true experiments from weaker experimental designs is that true experiments include

Random assignment

Repeated measurements of the dependent variable

Random sampling

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Sreejesh, S., Mohapatra, S., Anusree, M.R. (2014). Business Research Design: Exploratory, Descriptive and Causal Designs. In: Business Research Methods. Springer, Cham. https://doi.org/10.1007/978-3-319-00539-3_3

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Research Data – Types Methods and Examples

Table of Contents

Research Data

Research data refers to any information or evidence gathered through systematic investigation or experimentation to support or refute a hypothesis or answer a research question.

It includes both primary and secondary data, and can be in various formats such as numerical, textual, audiovisual, or visual. Research data plays a critical role in scientific inquiry and is often subject to rigorous analysis, interpretation, and dissemination to advance knowledge and inform decision-making.

Types of Research Data

There are generally four types of research data:

Quantitative Data

This type of data involves the collection and analysis of numerical data. It is often gathered through surveys, experiments, or other types of structured data collection methods. Quantitative data can be analyzed using statistical techniques to identify patterns or relationships in the data.

Qualitative Data

This type of data is non-numerical and often involves the collection and analysis of words, images, or sounds. It is often gathered through methods such as interviews, focus groups, or observation. Qualitative data can be analyzed using techniques such as content analysis, thematic analysis, or discourse analysis.

Primary Data

This type of data is collected by the researcher directly from the source. It can include data gathered through surveys, experiments, interviews, or observation. Primary data is often used to answer specific research questions or to test hypotheses.

Secondary Data

This type of data is collected by someone other than the researcher. It can include data from sources such as government reports, academic journals, or industry publications. Secondary data is often used to supplement or support primary data or to provide context for a research project.

Research Data Formates

There are several formats in which research data can be collected and stored. Some common formats include:

Text : This format includes any type of written data, such as interview transcripts, survey responses, or open-ended questionnaire answers.
Numeric : This format includes any data that can be expressed as numerical values, such as measurements or counts.
Audio : This format includes any recorded data in an audio form, such as interviews or focus group discussions.
Video : This format includes any recorded data in a video form, such as observations of behavior or experimental procedures.
Images : This format includes any visual data, such as photographs, drawings, or scans of documents.
Mixed media: This format includes any combination of the above formats, such as a survey response that includes both text and numeric data, or an observation study that includes both video and audio recordings.
Sensor Data: This format includes data collected from various sensors or devices, such as GPS, accelerometers, or heart rate monitors.
Social Media Data: This format includes data collected from social media platforms, such as tweets, posts, or comments.
Geographic Information System (GIS) Data: This format includes data with a spatial component, such as maps or satellite imagery.
Machine-Readable Data : This format includes data that can be read and processed by machines, such as data in XML or JSON format.
Metadata: This format includes data that describes other data, such as information about the source, format, or content of a dataset.

Data Collection Methods

Some common research data collection methods include:

Surveys : Surveys involve asking participants to answer a series of questions about a particular topic. Surveys can be conducted online, over the phone, or in person.
Interviews : Interviews involve asking participants a series of open-ended questions in order to gather detailed information about their experiences or perspectives. Interviews can be conducted in person, over the phone, or via video conferencing.
Focus groups: Focus groups involve bringing together a small group of participants to discuss a particular topic or issue in depth. The group is typically led by a moderator who asks questions and encourages discussion among the participants.
Observations : Observations involve watching and recording behaviors or events as they naturally occur. Observations can be conducted in person or through the use of video or audio recordings.
Experiments : Experiments involve manipulating one or more variables in order to measure the effect on an outcome of interest. Experiments can be conducted in a laboratory or in the field.
Case studies: Case studies involve conducting an in-depth analysis of a particular individual, group, or organization. Case studies typically involve gathering data from multiple sources, including interviews, observations, and document analysis.
Secondary data analysis: Secondary data analysis involves analyzing existing data that was collected for another purpose. Examples of secondary data sources include government records, academic research studies, and market research reports.

Analysis Methods

Some common research data analysis methods include:

Descriptive statistics: Descriptive statistics involve summarizing and describing the main features of a dataset, such as the mean, median, and standard deviation. Descriptive statistics are often used to provide an initial overview of the data.
Inferential statistics: Inferential statistics involve using statistical techniques to draw conclusions about a population based on a sample of data. Inferential statistics are often used to test hypotheses and determine the statistical significance of relationships between variables.
Content analysis : Content analysis involves analyzing the content of text, audio, or video data to identify patterns, themes, or other meaningful features. Content analysis is often used in qualitative research to analyze open-ended survey responses, interviews, or other types of text data.
Discourse analysis: Discourse analysis involves analyzing the language used in text, audio, or video data to understand how meaning is constructed and communicated. Discourse analysis is often used in qualitative research to analyze interviews, focus group discussions, or other types of text data.
Grounded theory : Grounded theory involves developing a theory or model based on an analysis of qualitative data. Grounded theory is often used in exploratory research to generate new insights and hypotheses.
Network analysis: Network analysis involves analyzing the relationships between entities, such as individuals or organizations, in a network. Network analysis is often used in social network analysis to understand the structure and dynamics of social networks.
Structural equation modeling: Structural equation modeling involves using statistical techniques to test complex models that include multiple variables and relationships. Structural equation modeling is often used in social science research to test theories about the relationships between variables.

Purpose of Research Data

Research data serves several important purposes, including:

Supporting scientific discoveries : Research data provides the basis for scientific discoveries and innovations. Researchers use data to test hypotheses, develop new theories, and advance scientific knowledge in their field.
Validating research findings: Research data provides the evidence necessary to validate research findings. By analyzing and interpreting data, researchers can determine the statistical significance of relationships between variables and draw conclusions about the research question.
Informing policy decisions: Research data can be used to inform policy decisions by providing evidence about the effectiveness of different policies or interventions. Policymakers can use data to make informed decisions about how to allocate resources and address social or economic challenges.
Promoting transparency and accountability: Research data promotes transparency and accountability by allowing other researchers to verify and replicate research findings. Data sharing also promotes transparency by allowing others to examine the methods used to collect and analyze data.
Supporting education and training: Research data can be used to support education and training by providing examples of research methods, data analysis techniques, and research findings. Students and researchers can use data to learn new research skills and to develop their own research projects.

Applications of Research Data

Research data has numerous applications across various fields, including social sciences, natural sciences, engineering, and health sciences. The applications of research data can be broadly classified into the following categories:

Academic research: Research data is widely used in academic research to test hypotheses, develop new theories, and advance scientific knowledge. Researchers use data to explore complex relationships between variables, identify patterns, and make predictions.
Business and industry: Research data is used in business and industry to make informed decisions about product development, marketing, and customer engagement. Data analysis techniques such as market research, customer analytics, and financial analysis are widely used to gain insights and inform strategic decision-making.
Healthcare: Research data is used in healthcare to improve patient outcomes, develop new treatments, and identify health risks. Researchers use data to analyze health trends, track disease outbreaks, and develop evidence-based treatment protocols.
Education : Research data is used in education to improve teaching and learning outcomes. Data analysis techniques such as assessments, surveys, and evaluations are used to measure student progress, evaluate program effectiveness, and inform policy decisions.
Government and public policy: Research data is used in government and public policy to inform decision-making and policy development. Data analysis techniques such as demographic analysis, cost-benefit analysis, and impact evaluation are widely used to evaluate policy effectiveness, identify social or economic challenges, and develop evidence-based policy solutions.
Environmental management: Research data is used in environmental management to monitor environmental conditions, track changes, and identify emerging threats. Data analysis techniques such as spatial analysis, remote sensing, and modeling are used to map environmental features, monitor ecosystem health, and inform policy decisions.

Advantages of Research Data

Research data has numerous advantages, including:

Empirical evidence: Research data provides empirical evidence that can be used to support or refute theories, test hypotheses, and inform decision-making. This evidence-based approach helps to ensure that decisions are based on objective, measurable data rather than subjective opinions or assumptions.
Accuracy and reliability : Research data is typically collected using rigorous scientific methods and protocols, which helps to ensure its accuracy and reliability. Data can be validated and verified using statistical methods, which further enhances its credibility.
Replicability: Research data can be replicated and validated by other researchers, which helps to promote transparency and accountability in research. By making data available for others to analyze and interpret, researchers can ensure that their findings are robust and reliable.
Insights and discoveries : Research data can provide insights into complex relationships between variables, identify patterns and trends, and reveal new discoveries. These insights can lead to the development of new theories, treatments, and interventions that can improve outcomes in various fields.
Informed decision-making: Research data can inform decision-making in a range of fields, including healthcare, business, education, and public policy. Data analysis techniques can be used to identify trends, evaluate the effectiveness of interventions, and inform policy decisions.
Efficiency and cost-effectiveness: Research data can help to improve efficiency and cost-effectiveness by identifying areas where resources can be directed most effectively. By using data to identify the most promising approaches or interventions, researchers can optimize the use of resources and improve outcomes.

Limitations of Research Data

Research data has several limitations that researchers should be aware of, including:

Bias and subjectivity: Research data can be influenced by biases and subjectivity, which can affect the accuracy and reliability of the data. Researchers must take steps to minimize bias and subjectivity in data collection and analysis.
Incomplete data : Research data can be incomplete or missing, which can affect the validity of the findings. Researchers must ensure that data is complete and representative to ensure that their findings are reliable.
Limited scope: Research data may be limited in scope, which can limit the generalizability of the findings. Researchers must carefully consider the scope of their research and ensure that their findings are applicable to the broader population.
Data quality: Research data can be affected by issues such as measurement error, data entry errors, and missing data, which can affect the quality of the data. Researchers must ensure that data is collected and analyzed using rigorous methods to minimize these issues.
Ethical concerns: Research data can raise ethical concerns, particularly when it involves human subjects. Researchers must ensure that their research complies with ethical standards and protects the rights and privacy of human subjects.
Data security: Research data must be protected to prevent unauthorized access or use. Researchers must ensure that data is stored and transmitted securely to protect the confidentiality and integrity of the data.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

Quantitative Data – Types, Methods and Examples

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Business research: what it is & how to use it for business results.

10 min read What is business research and why does it matter? Here are some of the ways business research can be helpful to your company, whichever method you choose to carry it out.

What is business research?

Business research helps companies make better business decisions by gathering information. The scope of the term business research is quite broad – it acts as an umbrella that covers every aspect of business, from finances to advertising creative. It can include research methods which help a company better understand its target market. It could focus on customer experience and assess customer satisfaction levels. Or it could involve sizing up the competition through competitor research.

Often when carrying out business research, companies are looking at their own data, sourced from their employees, their customers and their business records. However, business researchers can go beyond their own company in order to collect relevant information and understand patterns that may help leaders make informed decisions. For example, a business may carry out ethnographic research where the participants are studied in the context of their everyday lives, rather than just in their role as consumer, or look at secondary data sources such as open access public records and empirical research carried out in academic studies.

There is also a body of knowledge about business in general that can be mined for business research purposes. For example organisational theory and general studies on consumer behaviour.

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Why is business research important?

We live in a time of high speed technological progress and hyper-connectedness. Customers have an entire market at their fingertips and can easily switch brands if a competitor is offering something better than you are. At the same time, the world of business has evolved to the point of near-saturation. It’s hard to think of a need that hasn’t been addressed by someone’s innovative product or service.

The combination of ease of switching, high consumer awareness and a super-evolved marketplace crowded with companies and their offerings means that businesses must do whatever they can to find and maintain an edge. Business research is one of the most useful weapons in the fight against business obscurity, since it allows companies to gain a deep understanding of buyer behaviour and stay up to date at all times with detailed information on their market.

Thanks to the standard of modern business research tools and methods, it’s now possible for business analysts to track the intricate relationships between competitors, financial markets, social trends, geopolitical changes, world events, and more.

Find out how to conduct your own market research and make use of existing market research data with our Ultimate guide to market research

Types of business research

Business research methods vary widely, but they can be grouped into two broad categories – qualitative research and quantitative research .

Qualitative research methods

Qualitative business research deals with non-numerical data such as people’s thoughts, feelings and opinions. It relies heavily on the observations of researchers, who collect data from a relatively small number of participants – often through direct interactions.

Qualitative research interviews take place one-on-one between a researcher and participant. In a business context, the participant might be a customer, a supplier, an employee or other stakeholder. Using open-ended questions , the researcher conducts the interview in either a structured or unstructured format. Structured interviews stick closely to a question list and scripted phrases, while unstructured interviews are more conversational and exploratory. As well as listening to the participant’s responses, the interviewer will observe non-verbal information such as posture, tone of voice and facial expression.

Focus groups

Like the qualitative interview, a focus group is a form of business research that uses direct interaction between the researcher and participants to collect data. In focus groups, a small number of participants (usually around 10) take part in a group discussion led by a researcher who acts as moderator. The researcher asks questions and takes note of the responses, as in a qualitative research interview. Sampling for focus groups is usually purposive rather than random, so that the group members represent varied points of view.

Observational studies

In an observational study, the researcher may not directly interact with participants at all, but will pay attention to practical situations, such as a busy sales floor full of potential customers, or a conference for some relevant business activity. They will hear people speak and watch their interactions , then record relevant data such as behaviour patterns that relate to the subject they are interested in. Observational studies can be classified as a type of ethnographic research. They can be used to gain insight about a company’s target audience in their everyday lives, or study employee behaviours in actual business situations.

Ethnographic Research

Ethnographic research is an immersive design of research where one observes peoples’ behaviour in their natural environment. Ethnography was most commonly found in the anthropology field and is now practices across a wide range of social sciences.

Ethnography is used to support a designer’s deeper understanding of the design problem – including the relevant domain, audience(s), processes, goals and context(s) of use.

The ethnographic research process is a popular methodology used in the software development lifecycle. It helps create better UI/UX flow based on the real needs of the end-users.

If you truly want to understand your customers’ needs, wants, desires, pain-points “walking a mile” in their shoes enables this. Ethnographic research is this deeply rooted part of research where you truly learn your target audiences’ problem to craft the perfect solution.

Case study research

A case study is a detailed piece of research that provides in depth knowledge about a specific person, place or organisation. In the context of business research, case study research might focus on organisational dynamics or company culture in an actual business setting, and case studies have been used to develop new theories about how businesses operate. Proponents of case study research feel that it adds significant value in making theoretical and empirical advances. However its detractors point out that it can be time consuming and expensive, requiring highly skilled researchers to carry it out.

Quantitative research methods

Quantitative research focuses on countable data that is objective in nature. It relies on finding the patterns and relationships that emerge from mass data – for example by analysing the material posted on social media platforms, or via surveys of the target audience. Data collected through quantitative methods is empirical in nature and can be analysed using statistical techniques. Unlike qualitative approaches, a quantitative research method is usually reliant on finding the right sample size, as this will determine whether the results are representative. These are just a few methods – there are many more.

Surveys are one of the most effective ways to conduct business research. They use a highly structured questionnaire which is distributed to participants, typically online (although in the past, face to face and telephone surveys were widely used). The questions are predominantly closed-ended, limiting the range of responses so that they can be grouped and analysed at scale using statistical tools. However surveys can also be used to get a better understanding of the pain points customers face by providing open field responses where they can express themselves in their own words. Both types of data can be captured on the same questionnaire, which offers efficiency of time and cost to the researcher.

Correlational research

Correlational research looks at the relationship between two entities, neither of which are manipulated by the researcher. For example, this might be the in-store sales of a certain product line and the proportion of female customers subscribed to a mailing list. Using statistical analysis methods, researchers can determine the strength of the correlation and even discover intricate relationships between the two variables. Compared with simple observation and intuition, correlation may identify further information about business activity and its impact, pointing the way towards potential improvements and more revenue.

Experimental research

It may sound like something that is strictly for scientists, but experimental research is used by both businesses and scholars alike. When conducted as part of the business intelligence process, experimental research is used to test different tactics to see which ones are most successful – for example one marketing approach versus another. In the simplest form of experimental research, the researcher identifies a dependent variable and an independent variable. The hypothesis is that the independent variable has no effect on the dependent variable, and the researcher will change the independent one to test this assumption. In a business context, the hypothesis might be that price has no relationship to customer satisfaction. The researcher manipulates the price and observes the C-Sat scores to see if there’s an effect.

The best tools for business research

You can make the business research process much quicker and more efficient by selecting the right tools. Business research methods like surveys and interviews demand tools and technologies that can store vast quantities of data while making them easy to access and navigate. If your system can also carry out statistical analysis, and provide predictive recommendations to help you with your business decisions, so much the better.

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Related resources

Qualitative research design 12 min read, qualitative vs quantitative research 13 min read, video in qualitative research 10 min read, correlation research 11 min read, market intelligence tools 10 min read, qualitative research questions 11 min read, primary vs secondary research 14 min read, request demo.

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Business Research: Definition, Types & Methods
Types of business research. Business research methods vary widely, but they can be grouped into two broad categories ... In the context of business research, case study research might focus on organizational dynamics or company culture in an actual business setting, and case studies have been used to develop new theories about how businesses ...
Business Research: Methods, Types & Examples
Business research: Definition. Business research is a process of acquiring detailed information on all the areas of business and using such information to maximize the sales and profit of the business. Such a study helps companies determine which product/service is most profitable or in demand. In simple words, it can be stated as the acquisition of information or knowledge for professional or ...
PDF An Introduction to Business Research
Put another way, in the honeycomb, the six main elements - namely: (1) research philosophy; (2) research approach; (3) research strategy; (4) research design; (5) data collection and (6) data analysis techniques - come together to form research methodology. This structure is characteristic of the main headings you will find in a methodology ...
Types of Business Research
Types of Business Research Designs, Methods, Examples, Explorative, Descriptive, Causal, Applied and Basic; What is business research, its types & more! ... A case study is a type of business research used to analyse complex issues. It involves in-depth analyses and investigation of an issue within a real-life context. Case studies are used in ...
Business Research: Types, Methods, Examples
Business Research also plays the role of a catalyst as it helps businesses thrive in their markets by capturing all the available opportunities and also meeting the needs and preferences of their customers. Also Read: Business Analyst vs Data Analyst. Types & Methods. Business research plays an important role in the business intelligence process.
PDF Research Methods in Business Studies
Challenges and Ambiguities of Business Research 1 1 Introduction 3 1.1 Purpose of the Book 3 1.2 Readership 5 1.3 On the Use of the Book 7 2 Research in Business Studies 8 2.1 The Nature of Knowledge Development 9 2.2 Why We Do Research 10 2.3 Research versus Common Sense 12 2.4 Different Research Orientations 16 2.5 Induction, Deduction, and ...
Types of Research Designs Compared
Other interesting articles. If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples. Statistics. Normal distribution. Skewness. Kurtosis. Degrees of freedom. Variance. Null hypothesis.
Business Research Methods: Key Elements & Use Cases
Elements of Business Research Methods. The elements of business research methods include: Research question/objective: Clearly define the purpose of the research and the specific questions or objectives to be addressed. Research design: Determining the overall approach and structure of the research study. Decide whether it will be qualitative ...
Business Research: Definition, Types, Pros, and Cons
This video breaks down two main types of research: applied and fundamental research. Research helps businesses gather information to make decisions that can improve their profits and market share. There are several types of research businesses can conduct, such as surveys, interviews, focus groups, and experimental research.
Doing Research in Business and Management: An Essential Guide to
This way, Doing Research in Business and Management provides a comprehensive and holistic overview of how to develop quality research in the fields of business and management, offering a detailed coverage of the relevant research approaches and methods used in these disciplines, and bringing together the essential components of the process of ...
Types of Research
Classification of Types of Research. There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It's important to note that a research project will not be limited to one type of research, but will likely use several.
Research Methods
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Qualitative Designs and Methodologies for Business, Management, and
Description is done using empirical research methods for data collection including case studies, interviews, participant observation, ethnography, and collection of texts, records, and documents.The second or methodological layer of research design offers three formal logical strategies to analyze data and address research questions: (a ...
What Is a Research Design
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
Research Methods
Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.
The Four Types of Research Design
In short, a good research design helps us to structure our research. Marketers use different types of research design when conducting research. There are four common types of research design — descriptive, correlational, experimental, and diagnostic designs. Let's take a look at each in more detail.
What is Business Research? Its Types and Methods With Example
Business research methodologies can be broadly categorized into two main types: qualitative and quantitative methods. Qualitative Research Methods. In business, qualitative research aims to understand people's thoughts, emotions, and motivations. These methods help explore new ideas, create new products, and understand customer needs.
19 Types of Research (With Definitions and Examples)
Example: A researcher examines if and how employee satisfaction changes in the same employees after one year, three years and five years with the same company. 16. Mixed research. Mixed research includes both qualitative and quantitative data. The results are often presented as a mix of graphs, words and images.
Case Study Method: A Step-by-Step Guide for Business Researchers
By keeping in mind research students, this article presents a systematic step-by-step guide to conduct a case study in the business discipline. Research students belonging to said discipline face issues in terms of clarity, selection, and operationalization of qualitative case study while doing their final dissertation.
Business Research Design: Exploratory, Descriptive and ...
Abstract. The objective of this chapter is to define and explain research design in detail. In this chapter, we discussed three major types of research designs, such as exploratory, descriptive and causal research designs. We also explained the mode of data used in each of these designs and the techniques to collect these data, which would ...
6 Basic Types of Research Studies (Plus Pros and Cons)
Here are six common types of research studies, along with examples that help explain the advantages and disadvantages of each: 1. Meta-analysis. A meta-analysis study helps researchers compile the quantitative data available from previous studies. It's an observational study in which the researchers don't manipulate variables.
Case Study 1 Hero Builders
Which of the four types of studies are presented in this case? Upon reading the Hero Builders case study, I realize there are multiple types of studies and research methods presented this in case. Business research is defined as a systematic inquiry that provides information to guide a specified managerial, in this case CEO, decision (Schindler ...
Research Data
There are generally four types of research data: ... academic research studies, and market research reports. Analysis Methods. Some common research data analysis methods include: ... Business and industry: Research data is used in business and industry to make informed decisions about product development, marketing, and customer engagement ...
Business Research: How to Use it to See Results
In the context of business research, case study research might focus on organisational dynamics or company culture in an actual business setting, and case studies have been used to develop new theories about how businesses operate. Proponents of case study research feel that it adds significant value in making theoretical and empirical advances.

Business Research Methods by Emma Bell
Business Research Methods by Donald R. Cooper
Essentials of Business Research Methods by Joseph F. Hair Jr
Management and Business Research by SAGE Publications
Data Analysis in Business Research by D Isarel


	USA	20 Months	=1
Stanford Graduate School of Business	USA	2 Years	1
	Multiple Campuses	10 Months	6
	USA	2 Years	3
	USA	2 Years	5
	UK	15-21 Months	7
	France	16 Months	5
	USA	21 Months	11=
IESE Business School	Spain	15/19 Months	11=
Judge Business School	UK	1 Year	15
Saïd Business School	UK	1 Year	16
ESADE Business School	Spain	12/15/18 Months	13
Yale School of Management	USA	2 Years	17=
Imperial College Business School	UK	1 Year	22
	Italy	1 Year	23

Business Research: Methods, Types & Examples

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Article contents

Introduction

The Substance of Research Design

Table 1. Decisions Necessitated by Research Designs and Proposals

Layers of Research Design

Layer One: Design as Research Methods

Table 2. Qualitative Data Collection Methods

The In-Depth Case Study Method

Observational Strategies