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Applied Research – Types, Methods and Examples

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Applied Research

Definition:

Applied research is a type of scientific inquiry that focuses on developing practical solutions to real-world problems. It involves the use of existing knowledge, theories, and techniques to address specific problems or challenges in a particular field or industry.

Applied research is often conducted in collaboration with industry or government partners, who provide funding and expertise to support the research. The results of applied research are typically intended to be directly applicable to the real world, and may involve the development of new products, technologies, or processes.

Types of Applied Research

Types of Applied Research are as follows:

Action Research

This type of research is designed to solve specific problems within an organization or community. The research involves collaboration between researchers and stakeholders to develop solutions to issues that affect the organization or community.

Evaluation Research

This type of research is used to assess the effectiveness of a particular program, policy, or intervention. Evaluation research is often used in government, healthcare, and social service settings to determine whether programs are meeting their intended goals.

Developmental Research

This type of research is used to develop new products, technologies, or processes. The research may involve the testing of prototypes or the development of new methods for production or delivery.

Diagnostic Research

This type of research is used to identify the causes of problems or issues. Diagnostic research is often used in healthcare, where researchers may investigate the causes of a particular disease or condition.

Policy Research

This type of research is used to inform policy decisions. Policy research may involve analyzing the impact of existing policies or evaluating the potential outcomes of proposed policies.

Predictive Research

This type of research is used to forecast future trends or events. Predictive research is often used in marketing, where researchers may use data analysis to predict consumer behavior or market trends.

Data Collection Methods

In applied research, data collection methods can be broadly classified into two categories: Quantitative and Qualitative methods:

Quantitative Data Collection

Quantitative research methods involve collecting numerical data that can be analyzed statistically. The most commonly used quantitative data collection methods in applied research include:

• Surveys : Surveys are questionnaires designed to collect data from a large sample of people. Surveys can be conducted face-to-face, over the phone, or online.
• Experiments : Experiments involve manipulating variables to test cause-and-effect relationships. Experiments can be conducted in the lab or in the field.
• Observations : Observations involve watching and recording behaviors or events in a systematic way. Observations can be conducted in the lab or in natural settings.
• Secondary data analysis: Secondary data analysis involves analyzing data that has already been collected by someone else. This can include data from government agencies, research institutes, or other sources.

Qualitative Data Collection

Qualitative research methods involve collecting non-numerical data that can be analyzed for themes and patterns. The most commonly used qualitative data collection methods in applied research include:

• Interviews : Interviews involve asking open-ended questions to individuals or groups. Interviews can be conducted in-person, over the phone, or online.
• Focus groups : Focus groups involve a group of people discussing a topic with a moderator. Focus groups can be conducted in-person or online.
• Case studies : Case studies involve in-depth analysis of a single individual, group, or organization.
• Document analysis : Document analysis involves analyzing written or recorded documents to extract data. This can include analyzing written records, audio recordings, or video recordings.

Data Analysis Methods

In applied research, data analysis methods can be broadly classified into two categories: Quantitative and Qualitative methods:

Quantitative Data Analysis

Quantitative data analysis methods involve analyzing numerical data to identify patterns and trends. The most commonly used quantitative data analysis methods in applied research include:

• Descriptive statistics: Descriptive statistics involve summarizing and presenting data using measures such as mean, median, mode, and standard deviation.
• Inferential statistics : Inferential statistics involve testing hypotheses and making predictions about a population based on a sample of data. This includes methods such as t-tests, ANOVA, regression analysis, and correlation analysis.
• Data mining: Data mining involves analyzing large datasets to identify patterns and relationships using machine learning algorithms.

Qualitative Data Analysis

Qualitative data analysis methods involve analyzing non-numerical data to identify themes and patterns. The most commonly used qualitative data analysis methods in applied research include:

• Content analysis: Content analysis involves analyzing written or recorded data to identify themes and patterns. This includes methods such as thematic analysis, discourse analysis, and narrative analysis.
• Grounded theory: Grounded theory involves developing theories and hypotheses based on the analysis of data.
• Interpretative phenomenological analysis: Interpretative phenomenological analysis involves analyzing data to identify the subjective experiences of individuals.
• Case study analysis: Case study analysis involves analyzing a single individual, group, or organization in-depth to identify patterns and themes.

Applied Research Methodology

Applied research methodology refers to the set of procedures, tools, and techniques used to design, conduct, and analyze research studies aimed at solving practical problems in real-world settings. The general steps involved in applied research methodology include:

• Identifying the research problem: The first step in applied research is to identify the problem to be studied. This involves conducting a literature review to identify existing knowledge and gaps in the literature, and to determine the research question.
• Developing a research design : Once the research question has been identified, the next step is to develop a research design. This involves determining the appropriate research method (quantitative, qualitative, or mixed methods), selecting the data collection methods, and designing the sampling strategy.
• Collecting data: The third step in applied research is to collect data using the selected data collection methods. This can include surveys, interviews, experiments, observations, or a combination of methods.
• Analyzing data : Once the data has been collected, it needs to be analyzed using appropriate data analysis methods. This can include descriptive statistics, inferential statistics, content analysis, or other methods, depending on the type of data collected.
• Interpreting and reporting findings : The final step in applied research is to interpret the findings and report the results. This involves drawing conclusions from the data analysis and presenting the findings in a clear and concise manner.

Applications of Applied Research

Some applications of applied research are as follows:

• Product development: Applied research can help companies develop new products or improve existing ones. For example, a company might conduct research to develop a new type of battery that lasts longer or a new type of software that is more efficient.
• Medical research : Applied research can be used to develop new treatments or drugs for diseases. For example, a pharmaceutical company might conduct research to develop a new cancer treatment.
• Environmental research : Applied research can be used to study and address environmental problems such as pollution and climate change. For example, research might be conducted to develop new technologies for reducing greenhouse gas emissions.
• Agriculture : Applied research can be used to improve crop yields, develop new varieties of plants, and study the impact of pests and diseases on crops.
• Education : Applied research can be used to study the effectiveness of teaching methods or to develop new teaching strategies.
• Transportation : Applied research can be used to develop new technologies for transportation, such as electric cars or high-speed trains.
• Communication : Applied research can be used to improve communication technologies, such as developing new methods for wireless communication or improving the quality of video calls.

Examples of Applied Research

Here are some real-time examples of applied research:

• COVID-19 Vaccine Development: The development of COVID-19 vaccines is a prime example of applied research. Researchers applied their knowledge of virology and immunology to develop vaccines that could prevent or reduce the severity of COVID-19.
• Autonomous Vehicles : The development of autonomous vehicles involves applied research in areas such as artificial intelligence, computer vision, and robotics. Companies like Tesla, Waymo, and Uber are conducting extensive research to improve their autonomous vehicle technology.
• Renewable Energy : Research is being conducted on renewable energy sources like solar, wind, and hydro power to improve efficiency and reduce costs. This is an example of applied research that aims to solve environmental problems.
• Precision Agriculture : Applied research is being conducted in the field of precision agriculture, which involves using technology to optimize crop yields and reduce waste. This includes research on crop sensors, drones, and data analysis.
• Telemedicine : Telemedicine involves using technology to deliver healthcare remotely. Applied research is being conducted to improve the quality of telemedicine services, such as developing new technologies for remote diagnosis and treatment.
• Cybersecurity : Applied research is being conducted to improve cybersecurity measures and protect against cyber threats. This includes research on encryption, network security, and data protection.

Purpose of Applied Research

The purpose of applied research is to solve practical problems or improve existing products, technologies, or processes. Applied research is focused on specific goals and objectives and is designed to have direct practical applications in the real world. It seeks to address problems and challenges faced by individuals, organizations, or communities and aims to provide solutions that can be implemented in a practical manner.

The primary purpose of applied research is to generate new knowledge that can be used to solve real-world problems or improve the efficiency and effectiveness of existing products, technologies, or processes. Applied research is often conducted in collaboration with industry, government, or non-profit organizations to address practical problems and create innovative solutions.

Applied research is also used to inform policy decisions by providing evidence-based insights into the effectiveness of specific interventions or programs. By conducting research on the impact of policies and programs, decision-makers can make informed decisions about how to allocate resources and prioritize interventions.

Overall, the purpose of applied research is to improve people’s lives by developing practical solutions to real-world problems. It aims to bridge the gap between theory and practice, and to ensure that research findings are put into action to achieve tangible benefits.

When to use Applied Research

Here are some specific situations when applied research may be appropriate:

• When there is a need to develop a new product : Applied research can be used to develop new products that meet the needs of consumers. For example, a company may conduct research to develop a new type of smartphone with improved features.
• When there is a need to improve an existing product : Applied research can also be used to improve existing products. For example, a company may conduct research to improve the battery life of an existing product.
• When there is a need to solve a practical problem: Applied research can be used to solve practical problems faced by individuals, organizations, or communities. For example, research may be conducted to find solutions to problems related to healthcare, transportation, or environmental issues.
• When there is a need to inform policy decisions: Applied research can be used to inform policy decisions by providing evidence-based insights into the effectiveness of specific interventions or programs.
• When there is a need to improve efficiency and effectiveness: Applied research can be used to improve the efficiency and effectiveness of processes or systems. For example, research may be conducted to identify ways to streamline manufacturing processes or to improve the delivery of healthcare services.

Characteristics of Applied Research

The following are some of the characteristics of applied research:

• Focus on solving real-world problems : Applied research focuses on addressing specific problems or needs in a practical setting, with the aim of developing solutions that can be implemented in the real world.
• Goal-oriented: A pplied research is goal-oriented, with a specific aim of solving a particular problem or meeting a specific need. The research is usually designed to achieve a specific outcome, such as developing a new product, improving an existing process, or solving a particular issue.
• Practical and relevant: Applied research is practical and relevant to the needs of the industry or field in which it is conducted. It aims to provide practical solutions that can be implemented to improve processes or solve problems.
• Collaborative : Applied research often involves collaboration between researchers and practitioners, such as engineers, scientists, and business professionals. Collaboration allows for the exchange of knowledge and expertise, which can lead to more effective solutions.
• Data-driven: Applied research is data-driven, relying on empirical evidence to support its findings and recommendations. Data collection and analysis are important components of applied research, as they help to identify patterns and trends that can inform decision-making.
• Results-oriented: Applied research is results-oriented, with an emphasis on achieving measurable outcomes. Research findings are often used to inform decisions about product development, process improvement, or policy changes.
• Time-bound : Applied research is often conducted within a specific timeframe, with deadlines for achieving specific outcomes. This helps to ensure that the research stays focused on its goals and that the results are timely and relevant to the needs of the industry or field.

Advantages of Applied Research

Some of the advantages of applied research are as follows:

• Practical solutions: Applied research is focused on developing practical solutions to real-world problems, making it highly relevant to the needs of the industry or field in which it is conducted. The solutions developed through applied research are often highly effective and can be implemented quickly to address specific issues.
• Improved processes: Applied research can help organizations to improve their processes, leading to increased efficiency and productivity. The research can identify areas for improvement, such as bottlenecks or inefficiencies, and provide recommendations for optimizing processes.
• Innovation: Applied research can lead to the development of new products, services, and technologies that can transform industries and create new opportunities for growth and innovation. The research can help organizations to identify unmet needs and develop new solutions to meet them.
• Collaboration : Applied research often involves collaboration between researchers and practitioners, leading to the exchange of knowledge and expertise. Collaboration can result in more effective solutions and can help to build partnerships between academia and industry.
• Increased competitiveness : Applied research can help organizations to stay competitive by enabling them to adapt to changing market conditions and customer needs. The research can provide insights into emerging trends and technologies, helping organizations to stay ahead of the curve.
• Economic growth: Applied research can contribute to economic growth by creating new industries and jobs. The research can lead to the development of new technologies and products that can drive economic growth and create new opportunities for entrepreneurship and innovation.

Limitations of Applied Research

Some of the limitations of applied research are as follows:

• Limited generalizability: Applied research often focuses on specific contexts and may not be generalizable to other settings. This means that the findings of applied research may not be applicable to other industries, regions, or populations.
• Time and resource constraints: Applied research is often conducted within a specific timeframe and with limited resources. This can limit the scope and depth of the research and may prevent researchers from exploring all possible avenues.
• Potential for bias: Applied research may be influenced by the interests and perspectives of the organization or industry funding the research. This can lead to a bias in the research and potentially compromise the objectivity and validity of the findings.
• Ethical considerations: Applied research may raise ethical concerns, particularly if it involves human subjects or sensitive issues. Researchers must adhere to ethical standards and ensure that the research is conducted in a responsible and respectful manner.
• Limited theoretical development: Applied research tends to focus on practical solutions and may not contribute significantly to theoretical development in a particular field. This can limit the broader impact of the research and may hinder the development of new theories and frameworks.
• Limited focus on long-term impact: Applied research often focuses on short-term outcomes, such as developing a new product or improving a process. This may limit the focus on long-term impacts, such as the sustainability of the solution or its broader implications for the industry or society.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• What is Applied Research? + [Types, Examples & Method]

Every research begins with a clear delineation of the purpose of the investigation as this goes a long way to determine the research process or methodology adopted. In this sense, a researcher may choose to carry out basic research or applied research. 

Applied research is set on providing answers to specific questions in a bid to provide a solution to a defined problem. In this article, we will outline the features of this method of systematic investigation as well as how it differs from other approaches to research. 

What is Applied Research?

Applied research is a type of research design that seeks to solve a specific problem or provide innovative solutions to issues affecting an individual, group or society. It is often referred to as a scientific method of inquiry or contractual research because it involves the practical application of scientific methods to everyday problems. 

When conducting applied research, the researcher takes extra care to identify a problem, develop a research hypothesis and goes ahead to test these hypotheses via an experiment. In many cases, this research approach employs empirical methods in order to solve practical problems. 

Applied research is sometimes considered to be a non-systematic inquiry because  of its direct approach in  seeking a solution to a problem. It is typically a follow-up research design that further investigates the findings of pure or basic research in order to validate these findings and apply them to create innovative solutions.     

Types of Applied Research

There are 3 types of applied research. These are evaluation research, research and development, and action research.

• Evaluation Research

Evaluation research is a type of applied research that analyses existing information about a research subject to arrive at objective research outcomes or reach informed decisions. This type of applied research is mostly applied in business contexts, for example, an organisation may adopt evaluation research to determine how to cut down  overhead costs.

• Research and Development

Research and development is a type of applied research that is focused on developing new products and services based on the needs of target markets. It focuses on gathering information about marketing needs and finding ways to improve on an existing product or create new products that satisfy the identified needs.

• Action Research

Action research is a type of applied research that is set on providing practical solutions to specific business problems by pointing the business in the right directions. Typically, action research is a process of reflective inquiry that is limited to specific contexts and situational in nature.

Examples of Applied Research 

Applied research is relevant in different fields of study; especially science and social science-related fields. Examples of applied research can be seen in medicine, education, business, engineering, psychology and health, and these would be further explicated below. 

Applied Research Example in Business

Applied research is used in business to build knowledge and develop product solutions. It enables organisations to identify the peculiar needs of target markets and this would help them to create different business strategies that would allow them to satisfy these needs. 

In addition, conducting contractual research would help business owners to get insightful feedback on product gaps that may have, otherwise, been ignored. This is a great way to get first-hand information on target market reactions which can inform brand decisions. 

Applied research also helps employers of labour to identify and address the productivity needs of their workforce. For instance, an organization may carry out applied research in order to measure the effectiveness of its recruitment practices or of its organisational structure. 

• Applied research to improve an organization’s hiring process.
• Applied research to improve workplace efficiency and organizational policies.
• Applied research to bridge skill gaps in the workplace.

Applied Research Examples in Education  

In education, applied research is used to test pedagogic processes in order to discover the best teaching and learning methods. It is also used to test educational policies before implementation and to address different issues associated with teaching paradigms and classroom dynamics for a better learning experience. 

Educational applied research attempts solving a problem by gathering data from primary sources using a combination of qualitative and quantitative data collection methods. This data serves as empirical evidence which is then subjected to rigorous analysis and description in order to arrive at valid conclusions.

The goal of this research methodology is to determine the applicability of educational theory and principles by way of subjecting hypotheses to experimentation within specific settings. Applied research in education is also more utilitarian as it gathers practical evidence that can inform pragmatic solutions to problems. 

Characteristics of Applied Research in Education 

• It clearly highlights generalizations and hypotheses that inform the research findings.
• It relies on empirical evidence.
• It is set at providing solutions to a defined problem.
• It requires accurate observation and description.
• A study into the way to improve teacher-learner classroom engagements.
• A study into the way to improve a school’s readiness for its students.
• A study to build students’ interests in Mathematics.

Applied Research Example in Science

In itself, applied research is a scientific method of investigation because it applies existing scientific knowledge to practical situations. It is useful in different fields including thermodynamics, physics, material sciences and microbiology. 

Examples of applied research in science include the following: 

• Applied research to improve agricultural crop production
• Applied research to treat or cure a specific disease.

Applied Research Examples in Psychology  

There are different reasons psychologists would make use of applied research in the course of their work. In many cases, industrial-psychologists concerned with workplace behavior, human resources and organisational development combine psychological principles with applied research to proffer solutions. 

Examples of applied research in psychology include:

• Applied research to improve workplace commitment by arriving at practical worker-motivation strategies.
• Investigating treatment and management options for anxiety and panic attacks.
• Investigating factors that improve worker’s productivity.

Applied Research Example in Health   

In health and medical sciences, applied research serves as the background to evidence-based and solution-oriented medicine. It effectively merges scientific knowledge and methods with health experiences in order to arrive at accurate and verifiable results; using empirical research data or evidence. 

The adaptation of applied research to medicine is referred to as applied clinical research . Many health and medical practitioners use applied research to measure the extent to which the findings of basic or pure research can be adopted or modified into a solution-oriented approach.

Examples of applied research in health include:

• An investigation to identify the healing properties of a specific herb.
• An investigation to identify the side effects of using a particular drug.

APPLIED RESEARCH METHODS

Qualitative and quantitative data collection methods are used in applied research to gather empirical evidence that is further subjected to experimentation in order to arrive at valid research outcomes. The following are data collection methods in applied research:

An interview is a qualitative method of data collection that involves having a one-on-one interaction or discussion with the research subjects in order to gather relevant information that can serve as empirical data. It can be conducted with the use of an audio recorder, digital camera or camcorder.

Even though it is time-consuming and expensive, interviews allow the researcher to gather the most relevant data which gives him or her in-depth knowledge about the research subjects. An interview may be structured, semi-structured or unstructured; depending on the research purpose. 

• Surveys/Questionnaires

A questionnaire is an instrument that is typically used for quantitative data gathering . It outlines a series of questions relating to the research context and requires the research subjects to choose or provide responses that reflect their knowledge and experiences.

There are different types of questions that can be contained in a questionnaire including rating scale question s, close and open-ended questions and fixed alternatives. You can create and administer your applied research survey using data-collection platforms like Formplus . 

You can also start choosing from our over 200 online survey/questionnaire templates.

Here is a step-by-step guide on  how to create and administer questionnaires for applied research using Formplus

Sign in to Formplus

In the Formplus builder, you can easily create different questionnaires for applied research by dragging and dropping preferred fields into your form. To access the Formplus builder, you will need to create an account on Formplus. 

Once you do this, sign in to your account and click on “Create Form ” to begin.

Edit Form Title

Click on the field provided to input your form title, for example, “Applied Research Questionnaire”.

Click on the edit button to edit the form.

i. Add Fields: Drag and drop preferred form fields into your form in the Formplus builder inputs column. There are several field input options for questionnaires in the Formplus builder. 

ii. Edit fields

iii. Click on “Save”

iv. Preview form. 

Form Customization

With the form customization options in the form builder, you can easily improve on the appearance of your questionnaire and make it more unique and personalized. Formplus allows you to change your form theme, add background images and even change the font according to your needs. 

Multiple Sharing Options

Formplus also provides multiple form sharing options which enables you to easily share your questionnaire with respondents. With the direct social media sharing buttons, you can swiftly share your applied research questionnaire link to your organization’s social media pages. 

You can send out your questionnaire as email invitations to your research subjects too. Formplus also allows you to share your form’s QR code or embed it in your organization’s website for easy access. 

• Data Reporting

The process of gathering useful information about a research subject which can be used for further research. This can be done through not-for-profit reports, newspapers, website articles and hospital records.

It helps you  gather relevant data that results in more insightful decisions.  However, it is susceptible to bias because the information can easily be exaggerated by the individual or group collecting the data. 

• Observation

A type of data gathering method in applied research that requires the researcher to pay close attention to a subject (s) in order to gather useful information about it. Although bias may arise with this method, observation is widely considered as a universally accepted research practice.

Observation helps the researcher to gather empirical data and thus, it is the starting point for the formulation of a hypothesis. There are different techniques for observation including complete observer, complete participant, participant as observer and observer as participant. 

• Focus Groups

A focus group is a type of qualitative data collection process that allows the researcher to gather information about the disposition, feelings and opinions of the research subjects about a specific issue.

Here, the researcher engages a group comprising 6-10 individuals with a range of open-ended questions with the aim of gathering feedback about their emotional disposition to the issue at hand. This method is cost-effective compared to one-on-one interviews, and the information obtained is insightful and detailed. 

How is Applied Research Different from Basic Research?

Applied research and basic research are common methods of inquiry, based on purpose or utility. However, there are key differences between these 2 research approaches and these would be clearly outlined below: 

Applied research is a type of research that is aimed at the practical application of science in order to solve practical problems. On the other hand, basic research is a type of research that is aimed at expanding knowledge rather than solving problems. 

Basic research is theoretical in nature while applied research is practical and descriptive in nature. Basic research explores and generates theories that may be abstract while applied research tests these theories in order to solve a problem. 

Basic research is universal while applied research is limited. Basic research can focus on diverse or multiple contexts while applied research focuses on specific contexts with the aim of providing a solution to an identified problem. 

Applied research is focused on providing answers or solutions to a specific research question while basic research focuses on multiple concepts at the same time in its quest to expand knowledge. 

• Applied research pays attention to external validity while basic research is more focused on internal validity .

Characteristics of Applied Research 

• Applied research is solution-specific and it addresses practical problems. Unlike basic research that is aimed at theorizing and expanding knowledge, applied research focuses on addressing a particular problem using a range of science-based approaches.
• Applied research is descriptive in nature as it arrives at solutions by experimenting on empirical evidence and describing research outcomes.
• Usually, applied research tests theories arrived at by pure research in order to determine the usefulness of these theories in solving practical problems.
• It describes the relationship between research variables by measuring the characteristics of dependent and independent variables.
• Applied research relies on empirical evidence in order to arrive at valid research outcomes.
• It is not theoretical and it is not directly concerned with the expansion of knowledge.
• Applied research is synthetic in nature.
• It is aimed at the cost-effective reduction of social problems.
• Applied research is action-oriented.

Advantages of Applied Research

• Validity: Applied research is unbiased in nature because it tests empirical evidence in order to arrive at valid research outcomes. It employs carefully mapped-out procedures, and this makes it a more valid research approach.
• It is useful in solving specific problems. It helps individuals and organizations to find solutions to specific problems.

Disadvantages of Applied Research 

• It is not flexible in nature as it is restricted to a stipulated deadline.
• Applied research is limited in nature and it cannot be generalized. In other words, the findings from applied research cannot be generalized.

Conclusion 

Applied research is an important research approach because it helps organisations to arrive at practical solutions to specific problems while improving their productivity and output. Unlike basic research that focuses on generating theories that explain phenomena, applied research pays attention to describing empirical evidence with the aim of providing solutions. 

In carrying out applied research, the researcher combines a number of qualitative and quantitative data-gathering methods including questionnaires, observation methods, and interviews. This helps the researcher to gather empirical evidence that is then subjected to experimentation depending on the type of applied research and the overall focus. 

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What is Applied Research? Definition, Types, Examples

Appinio Research · 10.01.2024 · 35min read

Ever wondered how groundbreaking solutions to real-world challenges are developed, or how innovations come to life? Applied research holds the key. In this guide, we will delve deep into the world of applied research, uncovering its principles, methodologies, and real-world impact.  From harnessing cutting-edge technology to address healthcare crises to revolutionizing industries through data-driven insights, we'll explore the diverse domains where applied research thrives.

What is Applied Research?

Applied research is a systematic and organized inquiry aimed at solving specific real-world problems or improving existing practices, products, or services. Unlike basic research, which focuses on expanding general knowledge, applied research is all about using existing knowledge to address practical issues.

The primary purpose of applied research is to generate actionable insights and solutions that have a direct impact on practical situations. It seeks to bridge the gap between theory and practice by taking existing knowledge and applying it in real-world contexts. Applied research is driven by the need to address specific challenges, make informed decisions, and drive innovation in various domains.

Importance of Applied Research

Applied research holds immense significance across various fields and industries. Here's a list of reasons why applied research is crucial:

• Problem Solving:  Applied research provides effective solutions to real-world problems, improving processes, products, and services.
• Innovation:  It drives innovation by identifying opportunities for enhancement and developing practical solutions.
• Evidence-Based Decision-Making:  Policymakers and decision-makers rely on applied research findings to make informed choices and shape effective policies.
• Competitive Advantage:  In business, applied research can lead to improved products, increased efficiency, and a competitive edge in the market.
• Social Impact:  Applied research contributes to solving societal issues, from healthcare improvements to environmental sustainability.
• Technological Advancement:  In technology and engineering, it fuels advancements by applying scientific knowledge to practical applications.

Applied Research vs. Basic Research

Applied research differs from basic research in several key ways:

• Objectives:  Applied research aims to address specific practical problems or improve existing processes, while basic research seeks to expand general knowledge.
• Focus:  Applied research focuses on solving real-world challenges, whereas basic research explores fundamental principles and concepts.
• Applicability:  Applied research findings are directly applicable to practical situations, while basic research often lacks immediate practical applications.
• Immediate Impact:  Applied research has a more immediate impact on solving problems and improving practices, whereas basic research may have longer-term or indirect effects on knowledge and innovation.
• Research Questions:  Applied research formulates research questions related to practical issues, while basic research poses questions to explore theoretical or fundamental concepts.

Understanding these distinctions is essential for researchers, policymakers, and stakeholders in various fields, as it guides the choice of research approach and the expected outcomes of a research endeavor.

Types of Applied Research

Applied research encompasses various types, each tailored to specific objectives and domains. Understanding these types is essential for choosing the right approach to address real-world problems effectively. Here are some common types of applied research, each with its distinct focus and methodologies.

Evaluation Research

Purpose:  Evaluation research assesses the effectiveness, efficiency, and impact of programs, interventions, or policies. It aims to determine whether these initiatives meet their intended goals and objectives.

Methodology:  Researchers employ a range of quantitative and qualitative methods , including surveys, interviews, observations, and data analysis, to evaluate the outcomes and outcomes of programs or interventions.

Example:  Evaluating the impact of a public health campaign aimed at reducing smoking rates by analyzing pre- and post-campaign survey data on smoking habits and attitudes.

Action Research

Purpose:  Action research focuses on solving practical problems within a specific organizational or community context. It involves collaboration between researchers and practitioners to implement and assess solutions.

Methodology:  Action research is iterative and participatory, with researchers and stakeholders working together to identify problems, develop interventions, and assess their effectiveness. It often involves cycles of planning, action, reflection, and adjustment.

Example:  Teachers collaborating with researchers to improve classroom teaching methods and student outcomes by implementing and refining innovative teaching strategies.

Case Study Research

Purpose:   Case study research investigates a particular individual, organization, or situation in-depth to gain a comprehensive understanding of a specific phenomenon or issue.

Methodology:  Researchers collect and analyze a wealth of data, which may include interviews, documents, observations, and archival records. The goal is to provide a detailed and context-rich description of the case.

Example:  A detailed examination of a successful startup company's growth strategies and challenges, offering insights into factors contributing to its success.

Applied Experimental Research

Purpose:  Applied experimental research seeks to establish causal relationships between variables by manipulating one or more factors and observing their impact on outcomes. It helps determine cause-and-effect relationships in real-world settings.

Methodology:  Researchers conduct controlled experiments, similar to those in basic research, but within practical contexts. They manipulate variables and use statistical analysis to assess their effects on specific outcomes.

Example:  Testing the impact of different website designs on user engagement and conversion rates by randomly assigning visitors to various design versions and measuring their interactions.

Survey Research

Purpose:   Survey research involves collecting data from a sample of individuals or organizations to understand their opinions, attitudes, behaviors, or characteristics. It is commonly used to gather quantitative data on specific topics.

Methodology:  Researchers design surveys with carefully crafted questions and administer them to a representative sample of the target population . Statistical analysis is used to draw conclusions based on survey responses.

Example:  Conducting a national survey to assess public sentiment and preferences on environmental conservation initiatives and policies.

These types of applied research provide a framework for approaching real-world challenges systematically. Researchers can choose the most appropriate type based on their research goals, objectives, and the nature of the problem or phenomenon they seek to address. By selecting the right approach, applied researchers can generate valuable insights and practical solutions in various fields and disciplines.

How to Prepare for Applied Research?

In the preparatory phase of your applied research journey, you'll lay the groundwork for a successful study. This phase involves a series of crucial steps that will shape the direction and ethics of your research project.

Identifying Research Questions

One of the key starting points for any applied research endeavor is identifying the right research questions. Your research questions should be clear, specific, and directly related to the problem or issue you aim to address.

• Engage with Stakeholders:  Reach out to individuals or groups who are affected by or have an interest in the issue you're researching. Their perspectives can help you formulate relevant questions.
• Consider Feasibility:  Ensure that your research questions are feasible within your available resources, including time, budget, and access to data or participants.
• Prioritize Impact:  Focus on questions that have the potential to create meaningful change or provide valuable insights in your chosen field.

Formulating Hypotheses

Hypotheses serve as the guiding stars of your research, providing a clear direction for your investigation. Formulating hypotheses is a critical step that sets the stage for testing and validating your ideas.

• Testable Predictions:  Your hypotheses should be testable and capable of being proven or disproven through empirical research.
• Informed by Literature:  Base your hypotheses on existing knowledge and insights gained from the literature review. They should build upon what is already known and aim to expand that knowledge.
• Clarity and Precision:  Write your hypotheses in a clear and precise manner, specifying the expected relationship or outcome you intend to explore.

Literature Review

Conducting a thorough literature review is like embarking on a treasure hunt through existing knowledge in your field. It's a comprehensive exploration of what other researchers have already discovered and what gaps in knowledge still exist.

• Search Strategies:  Utilize academic databases, journals, books, and credible online sources to search for relevant literature.
• Analyze Existing Research:  Examine the findings, methodologies, and conclusions of previous studies related to your research topic.
• Identify Research Gaps:  Look for areas where current knowledge is insufficient or contradictory. These gaps will be the foundation for your own research.

Data Collection Methods

Selecting the proper data collection methods is crucial to gather the information needed to address your research questions. The choice of methods will depend on the nature of your research and the type of data you require.

• Quantitative vs. Qualitative:  Decide whether you will collect numerical data (quantitative) or focus on descriptive insights and narratives (qualitative).
• Survey Design :  If surveys are part of your data collection plan, carefully design questions that are clear, unbiased, and aligned with your research goals.
• Sampling Strategies:  Determine how you will select participants or data points to ensure representativeness and reliability.

Ethical Considerations

Ethical considerations are at the heart of responsible research. Ensuring that your study is conducted ethically and with integrity is paramount.

• Informed Consent:  Obtain informed consent from participants, ensuring they understand the purpose of the research, potential risks, and their right to withdraw at any time.
• Confidentiality:  Safeguard participants' personal information and ensure their anonymity when reporting findings.
• Minimizing Harm:  Take measures to mitigate any physical or emotional harm that participants may experience during the research process.
• Ethical Reporting:  Accurately represent your research findings, avoiding manipulation or selective reporting that may mislead readers or stakeholders.

By diligently addressing these aspects of research preparation, you are building a solid foundation for your applied research project, setting the stage for effective data collection and meaningful analysis in the subsequent phases of your study.

How to Design Your Research Study?

When it comes to applied research, the design of your study is paramount. It shapes the entire research process, from data collection to analysis and interpretation. In this section, we will explore the various elements that make up the foundation of your research design.

Research Design Types

Your choice of research design is like selecting the blueprint for your research project. Different research design types offer unique advantages and are suited for different research questions. Here are some common research design types:

• Experimental Design :  In this design, researchers manipulate one or more variables to observe their impact on outcomes. It allows for causal inference but may not always be feasible in applied research due to ethical or practical constraints.
• Descriptive Design:  This design aims to describe a phenomenon or population without manipulating variables. It is often used when researchers want to provide a snapshot of a situation or gain insights into a specific context.
• Correlational Design :  In this design, researchers examine relationships between variables without manipulating them. It helps identify associations but does not establish causation.
• Longitudinal Design :   Longitudinal studies involve collecting data from the same subjects over an extended period. They are valuable for tracking changes or developments over time.
• Cross-Sectional Design :  This design involves data collection from a diverse group of subjects at a single point in time. It's helpful in studying differences or variations among groups.

Sampling Methods

Sampling methods determine who or what will be included in your study. The choice of sampling method has a significant impact on the generalizability of your findings. Here are some standard sampling methods:

• Random Sampling:  This method involves selecting participants or data points entirely at random from the population. It ensures every element has an equal chance of being included, which enhances representativeness .
• Stratified Sampling:  In stratified sampling, the population is divided into subgroups or strata, and then random samples are drawn from each stratum. This method ensures that each subgroup is adequately represented.
• Convenience Sampling:  Researchers choose subjects or data points that are readily available and accessible. While convenient, this method may lead to sampling bias as it may not accurately represent the entire population.
• Purposive Sampling:  In purposive sampling, researchers deliberately select specific individuals or groups based on their expertise, experience, or relevance to the research topic. It is often used when seeking specialized knowledge.

Data Collection Tools

Selecting the right data collection tools is essential to gather accurate and relevant information. Your choice of tools will depend on the research design and objectives. Standard data collection tools include:

• Questionnaires and Surveys:  These structured instruments use standardized questions to gather data from participants. They are suitable for collecting large amounts of quantitative data.
• Interviews:   Interviews can be structured, semi-structured, or unstructured. They provide an opportunity to gather in-depth, qualitative insights from participants.
• Observation:  Direct observation involves systematically watching and recording behaviors or events. It's valuable for studying behaviors or phenomena in their natural context.
• Secondary Data :  Researchers can also utilize existing data sources, such as government reports, databases, or historical records, for their research.

Variables and Measurement

Defining variables and choosing appropriate measurement methods is crucial for ensuring the reliability and validity of your research. Variables are characteristics, phenomena, or factors that can change or vary in your study. They can be categorized into:

• Independent Variables:  These are the variables you manipulate or control in your study to observe their effects on other variables.
• Dependent Variables:  These are the variables you measure to assess the impact of the independent variables.

Choosing the right measurement techniques, scales, or instruments is essential to accurately quantify variables and collect valid data. It's crucial to establish clear operational definitions for each variable to ensure consistency in measurement.

Data Analysis Techniques

Once you have collected your data, the next step is to analyze it effectively. Data analysis involves:

• Data Cleaning:  Removing any errors, inconsistencies, or outliers from your dataset to ensure data quality.
• Statistical Analysis :  Depending on your research design and data type, you may use various statistical techniques such as regression analysis , t-tests, ANOVA, or chi-square tests.
• Qualitative Analysis:  For qualitative data, techniques like thematic analysis, content analysis, or discourse analysis help uncover patterns and themes.
• Data Visualization:  Using graphs, charts, and visual representations to present your data effectively.

Selecting the appropriate analysis techniques depends on your research questions, data type, and objectives. Proper data analysis is crucial for drawing meaningful conclusions and insights from your research.

With a solid understanding of research design, sampling methods, data collection tools, variables, and measurement, you are well-equipped to embark on your applied research journey. These elements lay the groundwork for collecting valuable data and conducting meaningful analyses in the subsequent phases of your study.

How to Conduct Applied Research?

Now that you've prepared and designed your research study, it's time to delve into the practical aspects of conducting applied research. This phase involves the execution of your research plan, from collecting data to drawing meaningful conclusions. Let's explore the critical components in this stage.

Data Collection Phase

The data collection phase is where your research plan comes to life. It's a crucial step that requires precision and attention to detail to ensure the quality and reliability of your data.

• Implement Data Collection Methods:   Execute the data collection methods you've chosen, whether they involve surveys, interviews, observations, or the analysis of existing datasets.
• Maintain Consistency:  Ensure that data collection is carried out consistently according to your research design and protocols. Minimize any variations or deviations that may introduce bias .
• Document the Process:  Keep thorough records of the data collection process. Note any challenges, unexpected occurrences, or deviations from your original plan. Documentation is essential for transparency and replication.
• Quality Assurance:  Continuously monitor the quality of the data you collect. Check for errors, missing information, or outliers. Implement data validation and cleaning procedures to address any issues promptly.
• Participant Engagement:  If your research involves human participants, maintain open and respectful communication with them. Address any questions or concerns and ensure participants' comfort and willingness to participate.

Data Analysis Phase

Once you've collected your data, it's time to make sense of the information you've gathered. The data analysis phase involves transforming raw data into meaningful insights and patterns.

• Data Preparation:  Start by organizing and cleaning your data. This includes dealing with missing values, outliers, and ensuring data consistency.
• Selecting Analysis Methods:  Depending on your research design and data type, choose the appropriate statistical or qualitative analysis methods. Common techniques include regression analysis , content analysis, or thematic coding .
• Conducting Analysis:  Perform the chosen analysis systematically and according to established protocols. Ensure that your analysis is reproducible by documenting every step.
• Interpreting Results:  Interpretation involves making sense of your findings in the context of your research questions and hypotheses. Consider the statistical significance of the results and any practical implications they may have.
• Visualization:  Create visual representations of your data, such as graphs, charts, or tables, to convey your findings effectively. Visualizations make complex data more accessible to a broader audience.

Interpretation of Results

Interpreting research results is a critical step that bridges the gap between data analysis and drawing conclusions. This process involves making sense of the patterns and insights that emerge from your analysis.

• Relate to Hypotheses:  Determine whether your results support or refute your hypotheses. Be prepared to explain any unexpected findings.
• Contextualize Findings:  Consider the broader context in which your research takes place. How do your results fit into the larger body of knowledge in your field?
• Identify Patterns :  Highlight significant trends, correlations, or relationships you've uncovered. Discuss their practical implications and relevance.
• Acknowledge Limitations:  Be transparent about any limitations in your study that may affect the interpretation of results. This includes sample size, data quality, and potential biases.

Drawing Conclusions

Drawing conclusions is the ultimate goal of your research. It involves synthesizing your findings and answering the research questions you initially posed.

• Answer Research Questions:  Explicitly address the research questions you formulated at the beginning of your study. State whether your findings confirm or challenge your initial hypotheses.
• Highlight Insights:  Emphasize the key insights and contributions of your research. Discuss the practical implications of your findings and their relevance to the field.
• Recommend Actions:  Based on your conclusions, suggest practical steps, recommendations, or future research directions. How can your research contribute to addressing the problem or challenge you investigated?
• Consider Implications:  Reflect on the broader implications of your research for stakeholders, policymakers, or practitioners in your field.

Common Pitfalls to Avoid

During the data collection, analysis, interpretation, and conclusion-drawing phases, it's essential to be aware of common pitfalls that can affect the quality and integrity of your research.

• Sampling Bias :  Ensure that your sample is representative of the population you intend to study. Address any bias that may have been introduced during data collection.
• Data Manipulation:  Avoid manipulating or selectively reporting data to fit preconceived notions. Maintain transparency in your analysis and reporting.
• Overinterpretation:  Be cautious of drawing overly broad conclusions based on limited data. Acknowledge the limitations of your study.
• Ignoring Ethical Considerations:  Continuously uphold ethical standards in your research, from data collection to reporting. Protect participants' rights and privacy.
• Lack of Validation:  Ensure that the methods and tools you use for data collection and analysis are valid and reliable. Validation helps establish the credibility of your findings.

By navigating the data collection, analysis, interpretation, and conclusion-drawing phases with care and attention to detail, you'll be well-prepared to confidently share your research findings and contribute to advancing knowledge in your field.

How to Report Applied Research Results?

Now that you've conducted your applied research and drawn meaningful conclusions, it's time to share your insights with the world. Effective reporting and communication are crucial to ensure that your research has a real impact and contributes to the broader knowledge base.

Writing Research Reports

Writing a comprehensive research report is the cornerstone of communicating your findings. It provides a detailed account of your research process, results, and conclusions. Here's what you need to consider:

Structure of a Research Report

• Title:  Create a concise, informative title that reflects the essence of your research.
• Abstract:  Summarize your research in a clear and concise manner, highlighting key objectives, methods, results, and conclusions.
• Introduction:  Provide an overview of your research topic, objectives, significance, and research questions.
• Literature Review:  Summarize relevant literature and identify gaps in existing knowledge that your research addresses.
• Methodology:  Describe your research design, sampling methods, data collection tools, and data analysis techniques.
• Results:  Present your findings using tables, charts, and narratives. Be transparent and objective in reporting your results.
• Discussion:  Interpret your results, discuss their implications, and relate them to your research questions and hypotheses.
• Conclusion:  Summarize your main findings, their significance, and the implications for future research or practical applications.
• References:  Cite all sources and studies you referenced in your report using a consistent citation style (e.g., APA, MLA).

Writing Tips

• Use clear and concise language, avoiding jargon or overly technical terms.
• Organize your report logically, with headings and subheadings for easy navigation.
• Provide evidence and data to support your claims and conclusions.
• Consider your target audience and tailor the report to their level of expertise and interest.

Creating Visualizations

Visualizations are powerful tools for conveying complex data and making your research findings more accessible. Here are some types of visualizations commonly used in research reports:

Charts and Graphs

• Bar Charts:  Ideal for comparing categories or groups.
• Line Charts:  Effective for showing trends or changes over time.
• Pie Charts:  Useful for displaying proportions or percentages.
• Data Tables:  Present numerical data in an organized format.
• Cross-tabulations:  Show relationships between variables.

Diagrams and Maps

• Flowcharts:  Visualize processes or workflows.
• Concept Maps:  Illustrate connections between concepts.
• Geographic Maps:  Display spatial data and patterns.

When creating visualizations:

• Choose the correct type of visualization for your data and research questions.
• Ensure that visualizations are labeled, clear, and easy to understand.
• Provide context and explanations to help readers interpret the visuals.

Presenting Your Research

Presenting your research to an audience is an opportunity to engage, educate, and inspire. Whether it's through a conference presentation, seminar, or webinar, effective communication is vital.

• Know Your Audience:  Tailor your presentation to the interests and expertise of your audience.
• Practice:  Rehearse your presentation to ensure a smooth delivery and confident demeanor.
• Use Visual Aids:  Enhance your presentation with visual aids such as slides, images, or videos.
• Engage with Questions:  Encourage questions and discussions to foster interaction and clarify points.
• Stay within Time Limits:  Respect time constraints and stay on schedule.

Peer Review Process

Before your research is published, it typically undergoes a peer review process. This involves experts in your field evaluating the quality, validity, and significance of your work. The peer review process aims to ensure the integrity and credibility of your research.

• Submission:  Submit your research manuscript to a journal or conference for review.
• Editorial Review:  The editorial team assesses your submission's fit with the journal's scope and may conduct an initial review for quality and compliance.
• Peer Review:  Your manuscript is sent to peer reviewers who evaluate it for methodology, validity, significance, and adherence to ethical standards.
• Feedback and Revision:  Based on reviewers' feedback, you may be asked to revise and improve your research.
• Acceptance or Rejection:  After revisions, the manuscript is reevaluated, and a decision is made regarding publication.

Publishing Your Research

Publishing your research is the final step in sharing your findings with the broader scientific community. It allows others to access and build upon your work. Consider the following when choosing where to publish:

• Journal Selection:  Choose a reputable journal that aligns with your research field and target audience.
• Review Process:  Understand the journal's peer review process and requirements for submission.
• Open Access:  Consider whether you want your research to be open access, freely accessible to all.

Once published, actively promote your research through academic networks, conferences, and social media to maximize its reach and impact.

By effectively reporting and communicating your research findings, you contribute to the advancement of knowledge, inspire others, and ensure that your hard work has a meaningful impact on your field and beyond.

Applied Research Examples

To provide a deeper understanding of applied research's impact and relevance, let's delve into specific real-world examples that demonstrate how this type of research has addressed pressing challenges and improved our lives in tangible ways.

Applied Medical Research: mRNA Vaccines

Example:  mRNA (messenger RNA) vaccine technology, exemplified by the COVID-19 vaccines developed by Pfizer-BioNTech and Moderna, is a remarkable achievement in the field of applied medical research.

Applied researchers in this domain utilized mRNA technology to create vaccines that provide immunity against the SARS-CoV-2 virus. Unlike traditional vaccines, which use weakened or inactivated viruses, mRNA vaccines instruct cells to produce a harmless spike protein found on the virus's surface. The immune system then recognizes this protein and mounts a defense, preparing the body to combat the actual virus.

Impact:  The rapid development and deployment of mRNA vaccines during the COVID-19 pandemic have been groundbreaking. They've played a crucial role in controlling the spread of the virus and saving countless lives worldwide. This example underscores how applied research can revolutionize healthcare and respond swiftly to global health crises.

Environmental Science and Applied Research: Ocean Cleanup

Example:  The Ocean Cleanup Project, founded by Boyan Slat, is an ambitious endeavor rooted in applied research to combat plastic pollution in the world's oceans.

This project employs innovative technology, such as large-scale floating barriers and autonomous systems, to collect and remove plastic debris from the ocean. Applied researchers have played a pivotal role in designing, testing, and optimizing these systems to make them efficient and environmentally friendly.

Impact:  The Ocean Cleanup Project is a testament to the power of applied research in addressing pressing environmental challenges. By removing plastic waste from the oceans, it mitigates harm to marine ecosystems and raises awareness about the urgent need for sustainable waste management.

Business and Applied Research: E-commerce Personalization

Example:   E-commerce giants like Amazon and Netflix use applied research to develop sophisticated personalization algorithms that tailor product recommendations and content to individual users.

Applied researchers in data science and machine learning analyze user behavior, preferences, and historical data to create recommendation systems. These algorithms utilize predictive analytics to suggest products, movies, or shows that align with a user's interests.

Impact:  The application of research-driven personalization has transformed the e-commerce and streaming industries. It enhances user experiences, increases customer engagement, and drives sales by presenting customers with products or content they are more likely to enjoy.

Education and Applied Research: Flipped Classroom Model

Example:  The Flipped Classroom Model is an applied research-based teaching approach that has gained popularity in education.

In this model, instructors leverage technology to deliver instructional content (such as video lectures) outside of class, allowing in-class time for active learning, discussions, and problem-solving. Applied research has informed the design and implementation of this pedagogical approach.

Impact:  The Flipped Classroom Model has shown promise in enhancing student engagement and learning outcomes. It capitalizes on research findings about how students learn best, emphasizing active participation and collaborative learning.

Agriculture and Applied Research: Precision Agriculture

Example:  Precision agriculture employs data-driven technology and applied research to optimize farming practices.

Farmers utilize satellite imagery, sensors, and data analytics to monitor crop conditions, soil health, and weather patterns. Applied research guides the development of precision farming techniques, enabling more efficient resource allocation and reducing environmental impact.

Impact:  Precision agriculture increases crop yields, conserves resources (such as water and fertilizer), and minimizes the ecological footprint of farming. This approach contributes to sustainable and economically viable agriculture.

These real-world examples underscore the versatility and impact of applied research across diverse domains. From healthcare and environmental conservation to business, education, and agriculture, applied research continually drives innovation, addresses critical challenges, and enhances the quality of life for individuals and communities worldwide.

Conclusion for Applied Research

Applied research is a powerful force for solving real-world problems and driving progress. By applying existing knowledge and innovative thinking, we can address healthcare challenges, protect our environment, improve businesses, enhance education, and revolutionize agriculture. Through this guide, you've gained valuable insights into the what, why, and how of applied research, unlocking the potential to make a positive impact in your field. So, go forth, conduct meaningful research, and be part of the solution to the world's most pressing issues. Remember, applied research is not just a concept; it's a practical approach that empowers individuals and teams to create solutions that matter. As you embark on your own applied research endeavors, keep the spirit of inquiry alive, remain open to new ideas, and never underestimate the transformative power of knowledge put into action.

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Applied Research

Applied research “aims at finding a solution for an immediate problem facing a society, or an industrial/business organisation, whereas fundamental research is mainly concerned with generalisations and with the formulation of a theory” [1] . Applied research is considered to be non-systematic inquiry and it is usually launched by a company, agency or an individual in order to address a specific problem. [2]

   Applied research can be divided into the following three categories:

1. Evaluation research . This type of research focuses on analysing existing information about the phenomenon in order to generate objective research outcomes. A study into the ways of reducing supply-chain costs can be mentioned as an example for an evaluation research.

2. Research and Development . It is a type of applied research that focuses on the development of new products and services to satisfy needs and wants of target customer segment. This type of applied research is the least relevant to a business dissertation.

3. Action research . This type of study aims to tackle specific business problems. For example, a research into the ways of restoring Starbucks brand image in UK after the tax scandal can be classified as an action research.

Differences between Applied Research and Fundamental (Basic) Research

The difference between applied and  fundamental or basic research  is straightforward – findings of applied research can be applied to resolve problems, whereas fundamental studies are used simply to explore certain issues and elements. Applied research can be a follow-up to the findings of a fundamental research.

Moreover, differences between applied and basic research can be summarized into three points:

1. Differences in purpose . Purpose of applied studies is closely associated with the solution of specific problems, while the purpose of fundamental studies relate to creation of new knowledge or expansion of the current knowledge without any concerns to applicability.

2. Differences in context . In applied studies, research objectives are set by clients or sponsors as a solution to specific problems they are facing. Fundamental studies, on the other hand, are usually self-initiated in order to expand the levels of knowledge in certain areas.

3. Differences in methods .  Research validity is an important point to be addressed in all types of studies. Nevertheless, applied studies are usually more concerned with external validity, whereas internal validity can be specified as the main point of concern for fundamental researchers.

Examples of Applied Research

The following are examples for applied research. You can notice that each of these studies aim to resolve a specific and an immediate problem.

• A study into marketing strategies to appeal to the aspirations of millenials in China
• An investigation into the ways of improving employee motivation in Marriot Hotel, Hyde Park
• Development of strategies to introduce change in Starbucks global supply-chain management with the view on cost reduction
• A study into the ways of fostering creative deviance amongst employees without compromising respect for authority.

Advantages and Disadvantages of Applied Research

The advantages and disadvantages of applied and fundamental research mirror and contrast each other. On the positive side, applied research can be helpful in solving specific problems in business and other settings.

On the negative side, findings of applied research cannot be usually generalized. In other words, applicability of the new knowledge generated as a result of this type of research is limited to the research problem. Moreover, applied studies usually have tight deadlines which are not flexible.

You need to specify the type of your research in the earlier part of the research methodology chapter in about one short paragraph. Also, in this paragraph you will have to justify your choice of research type.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance   contains discussions of research types and application of research methods in practice. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  research design ,  methods of data collection  and  data analysis , sampling and others are explained in this e-book in simple words.

John Dudovskiy

[1] Kothari, C.R. (2008) “Research Methodology: Methods and Techniques” New Age International

[2] Bajpai, N. (2011) “Business Research Methods” Pearson Education India

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• 09 January, 2024

Commencing a research journey involves a distinct definition of the investigation’s purpose, guiding the selection of the research procedure or approach. In this context, researchers can opt for either basic or applied research , each with its unique characteristics and objectives.

Embarking on the journey of conducting applied research requires a blend of theoretical knowledge and practical application. Understanding the nuances of applied research and its distinctions from basic research is crucial before delving into the intricacies of crafting an impactful paper.

What is Applied Research?

Applied research addresses specific problems with the goal of finding practical solutions. Distinguishing it from basic research, which primarily seeks to expand theoretical knowledge, applied research focuses on resolving real-world issues. It serves as a follow-up to basic or pure research, aiming to identify solutions to specific issues at individual, group, or societal levels.

Applied research encompasses various types, each tailored to address specific challenges and practical issues in different domains. The following are three common types of applied research with examples:

1. Evaluation Research:

• Purpose: Evaluation research aims to assess the effectiveness, efficiency, and relevance of programs, policies, or interventions. It seeks to determine the impact and outcomes of specific actions and initiatives.

• Application: Often employed in business, government, and non-profit sectors, evaluation research helps organisations make informed decisions by providing data-driven insights into the success or shortcomings of their endeavors.

• Example: Evaluating the impact of a workplace training program on employee productivity and job satisfaction.

2. Research and Development (R&D):

• Purpose: Research and Development applied research focuses on creating or enhancing products, goods, or services to meet the needs of specific markets or industries. It involves innovation and design to improve existing offerings or introduce new solutions.

• Application: Commonly found in industries such as technology, pharmaceuticals, and manufacturing, R&D applied research supports the creation of cutting-edge products and processes, contributing to market competitiveness.

• Example: Conducting R&D to develop a new pharmaceutical drug with improved efficacy and fewer side effects.

3. Action Research:

• Purpose: Action research aims to address real-world problems by actively engaging with and observing everyday life and organisational dynamics. It involves a cyclical process of planning, acting, observing, and reflecting to bring about positive change.

• Application: Widely used in fields like education, healthcare, and organisational development, action research empowers practitioners to collaboratively solve problems, improve processes, and enhance outcomes in their specific contexts.

• Example: Implementing action research in a primary school to integrate AI-driven personalised learning platforms. By actively observing the impact of AI on student engagement, understanding, and academic performance, the research aims to refine teaching strategies and optimise the integration of AI in the classroom.

Step-by-Step Guide to Writing an Applied Research Paper

Writing an applied research paper involves a systematic and purposeful approach to address practical issues in a specific field. The following steps provide a comprehensive guide for crafting an effective applied research paper:

1. Selecting a Relevant Topic:

• Identify a specific problem or question within your field of study that requires practical solutions.

• Ensure your topic aligns with the goals of applied research, focusing on real-world issues and challenges.

2. Conducting a Thorough Literature Review:

• Explore existing literature related to your chosen topic to understand the current state of knowledge.

• Identify gaps or areas where applied research can contribute valuable insights.

3. Defining Clear Objectives and Hypotheses:

• Clearly outline the goals and hypotheses of your research to guide the direction of your investigation.

• Ensure that your objectives align with the practical implications you aim to address.

4. Choosing an Appropriate Research Methodology:

• Select a methodology that aligns with your research objectives. This could involve qualitative, quantitative, or mixed methods.

• Justify your choice of methodology and discuss how it will address the practical aspects of your research.

5. Collecting and Analysing Data:

• Implement your chosen methodology to collect relevant data. Ensure that your data collection methods are appropriate for the practical nature of your research.

• Thoroughly analyse the data using appropriate statistical or qualitative analysis techniques.

6. Presenting Results and Drawing Conclusions:

• Clearly present your findings, using tables, charts, or graphs if necessary.

• Connect your results back to your research objectives and draw meaningful conclusions that address the practical implications of your study.

7. Crafting a Well-structured Paper:

• Follow the specific format and guidelines provided by your university or institution.

• Typically, an applied research paper includes sections such as an abstract, introduction, literature review, methodology, results, discussion, and conclusion.

8. Providing Recommendations for Practice:

• Offer practical recommendations based on your research findings. Discuss how these recommendations can be implemented in real-world scenarios.

• Emphasise the actionable nature of your suggestions.

9. Acknowledging Limitations:

• Address any limitations or constraints in your research methodology or data collection.

• Acknowledge potential challenges and discuss their impact on the reliability and validity of your findings.

10. Citing Relevant Literature:

• Ensure proper citation of all sources used in your research. Follow the citation style recommended by your institution.

11. Reviewing and Revising:

• Proofread your paper for clarity, coherence, and grammatical accuracy.

• Seek feedback from peers or mentors and be open to making revisions based on constructive input.

By following these steps, researchers can produce applied research papers that not only contribute to academic knowledge but also offer practical solutions to real-world challenges in their respective fields.

In conclusion, the significance of applied research cannot be understated. With their practical orientation and real-world solutions, they serve as invaluable assets across industries, academia, and societal sectors. They are instrumental in addressing pressing challenges, guiding informed decision-making, fostering innovation, and contributing to positive changes in various fields. Applied research papers bridge the gap between theory and practice, providing actionable insights that enhance efficiency, optimise processes, and lead to tangible improvements. As agents of continuous learning and development, these papers play a pivotal role in shaping the future landscape of industries, organisations, and communities. In a world that demands pragmatic solutions, the importance of applied research papers lies in their ability to make a lasting and meaningful impact on the way we approach and solve real-world problems.

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How Applied Research Is Used in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

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Basic vs. Applied Research

How it works, potential challenges.

• Real-World Applications

Applied research refers to scientific study and research that seeks to solve practical problems. This type of research plays an important role in solving everyday problems that can have an impact on life, work, health, and overall well-being. For example, it can be used to find solutions to everyday problems, cure illness, and develop innovative technologies.

There are many different types of psychologists who perform applied research. Human factors or industrial/organizational psychologists often do this type of research.

A few examples of applied research in psychology include:

• Analyzing what type of prompts will inspire people to volunteer their time to charities
• Investigating if background music in a work environment can contribute to greater productivity
• Investigating which treatment approach is the most effective for reducing anxiety
• Researching which strategies work best to motivate workers
• Studying different keyboard designs to determine which is the most efficient and ergonomic

As you may notice, all of these examples explore topics that will address real-world issues. This immediate and practical application of the findings is what distinguishes applied research from basic research , which instead focuses on theoretical concerns.  

Basic research tends to focus on "big picture" topics, such as increasing the scientific knowledge base around a particular topic. Applied research tends to work toward solving specific problems that affect people in the here and now.

For example a social psychologist may perform basic research on how different factors may contribute to violence in general. But if a social psychologist were conducting applied research, they may be tackling the question of what specific programs can be implemented to reduce violence in school settings.

However, basic research and applied research are actually closely intertwined. The information learned from basic research often builds the basis on which applied research is formed.

Basic research often informs applied research, and applied research often helps basic researchers refine their theories.

Applied research usually starts by identifying a problem that exists in the real world. Then psychologists begin to conduct research in order to identify a solution.

The type of research used depends on a variety of factors. This includes unique characteristics of the situation and the kind of problem psychologists are looking to solve.

Researchers might opt to use naturalistic observation to see the problem as it occurs in a real-world setting. They may then conduct experiments to determine why the problem occurs and to explore different solutions that may solve it.

As with any type of research, challenges can arise when conducting applied research in psychology. Some potential problems that researchers may face include:

Ethical Challenges

When conducting applied research in a naturalistic setting, researchers have to avoid ethical issues, which can make research more difficult. For example, they may come across concerns about privacy and informed consent.

In some cases, such as in workplace studies conducted by industrial-organizational psychologists, participants may feel pressured or even coerced into participating as a condition of their employment. Such factors sometimes impact the result of research studies.

Problems With Validity

Since applied research often takes place in the field, it can be difficult for researchers to maintain complete control over all of the variables . Extraneous variables can also exert a subtle influence that experimenters may not even consider could have an effect on the results.

In many cases, researchers are forced to strike a balance between a study's ecological validity (which is usually quite high in applied research) and the study's internal validity .  

Since applied research focuses on taking the results of scientific research and applying it to real-world situations, those who work in this line of research tend to be more concerned with the external validity of their work.

External validity refers to the extent that scientific findings can be generalized to other populations.

Researchers don't just want to know if the results of their experiments apply to the participants in their studies, rather they want these results to also apply to larger populations outside of the lab.

External validity is often of particular importance in applied research. Researchers want to know that their findings can be applied to real people in real settings.

How It's Used in the Real-World

Here are some examples of how applied research is used to solve real-world problems:

• A hospital may conduct applied research to figure out how to best prepare patients for certain types of surgical procedures.
• A business may hire an applied psychologist to assess how to design a workplace console to maximize efficiency and productivity while minimizing worker fatigue and error.
• An organization may hire an applied researcher to determine how to select employees that are best suited for certain positions within the company.

Applied research is an important tool in the process of understanding the human mind and behavior. Thanks to much of this research, psychologists are able to investigate problems that affect people's daily lives. This kind of research specifically targets real-world issues, however it also contributes to knowledge about how people think and behave.

National Science Foundation. Definitions of research and development: An annotated compilation of official sources .

CDC. Evaluation briefs .

Helmchen H. Ethical issues in naturalistic versus controlled trials .  Dialogues Clin Neurosci . 2011;13(2):173‐182.

Truijens FL, Cornelis S, Desmet M, De Smet MM, Meganck R. Validity beyond measurement: Why psychometric validity is insufficient for valid psychotherapy research .  Front Psychol . 2019;10:532. doi:10.3389/fpsyg.2019.00532

 McBride D.  The Process Of Research In Psychology . SAGE Publications; 2018.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Basic vs. applied research

• Coding qualitative data for valuable insights

What is the difference between applied research and basic research?

Examples of basic research vs. applied research, basic vs. applied research: a comparative analysis, the interplay between basic and applied research, introduction.

Basic and applied research look at existing knowledge and create new knowledge in different ways. They share the same basic principles of contributing to knowledge through research findings, but their aims and objectives are distinctly different.

In the vast realm of scientific inquiry, research stands as the cornerstone for advancement, driving our understanding of the world and fostering innovation. At its core, research can be bifurcated into two primary types: applied and basic research . While both serve pivotal roles in contributing to our collective knowledge, they operate with distinct objectives and outcomes.

Any approach that is called basic research delves into the foundational principles and theories of science. It is driven by a researcher's curiosity and the aspiration to expand the frontiers of understanding. The primary goal isn't to solve an immediate problem but to garner knowledge for the sake of understanding.

On the other hand, applied research focuses on analysis intended to solve practical problems. Conducting applied research means seeking solutions to specific, tangible challenges that society or industries face. Using the principles derived from basic research, applied research aims to bring about real-world impact and deliver pragmatic solutions.

Basic research

Basic research, often called "pure" or "fundamental" research, is characterized by its intrinsic quest to unravel the mysteries of nature and society. It is an investigation into the very core of phenomena, aiming to discover new principles, theories, or facts without an immediate application in mind. This kind of research is often propelled by the researcher's curiosity, a thirst to understand the "why" and "how" of things, rather than the "what can we do with it."

Basic research has a relatively broad scope and aims to enhance the existing body of knowledge in a particular field. It's not about creating a new product, improving a process, or solving a current societal problem. Instead, it's about laying the groundwork for future investigations, paving the way for applied research to build upon. Basic research poses questions like, "What are the fundamental principles of this phenomenon?" or "How does this process work at different levels?"

Such goals provide the essential framework upon which much of our modern understanding and technological advancement rests. Without the exploratory and explanatory nature of basic research, the foundational knowledge needed to drive innovation would be missing.

Applied research

While basic research focuses on curiosity and the pursuit of knowledge for its own sake, applied research takes a different approach by examining how real-world phenomena or outcomes can be altered. At its core, applied research is oriented towards identifying practical solutions to specific problems. Its primary objective is not just to add to the existing knowledge base but to leverage that knowledge to develop solutions, innovations, or interventions that can be directly applied in the real world.

Applied research is deeply rooted in real-world issues. Whether it's finding a cure for a specific disease, developing a new technological solution for environmental challenges, or creating strategies to improve education in underprivileged communities, the primary goal is to generate practical outcomes that can be directly implemented. Its relevance is often immediately apparent, as it's tailored to answer particular challenges faced by society, industries, or organizations.

The line between basic and applied research can sometimes blur, especially when foundational discoveries from basic research lead directly to tangible applications. However, the main distinction lies in the intent: while basic research seeks to understand the fundamental nature of phenomena, applied research aims to harness that understanding for tangible benefits.

Applied research is invaluable as it accelerates the transition of theoretical knowledge into practical, impactful solutions. Through applied research, the abstract findings of basic research are transformed into actionable insights, tools, and technologies that shape our daily lives and address pressing challenges.

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Research in the social sciences encompasses a broad spectrum of topics, ranging from understanding human behavior and societal structures to exploring the dynamics of interpersonal relationships. Basic and applied research methods in the social sciences offer unique insights into these areas. Let's delve into some examples to understand their distinct approaches.

Basic research examples

The social construction of reality

A classic area of investigation in sociology is understanding how societies construct reality. This kind of research delves deep into the ways cultures, languages, and institutions shape our understanding of the world. It doesn't immediately aim to solve societal problems but provides essential insights into how perceptions and beliefs are formed. Research methods often used for this type of study include in-depth interviews , participant observations , and ethnographic studies .

Attachment theory in psychology

Attachment theory seeks to understand the deep emotional and physical attachment between a child and at least one primary caregiver. It delves into the nature of attachment and its implications for personal development. The research often involves longitudinal studies that observe behaviors over extended periods.

Applied research examples

Interventions for at-risk youth

Applied researchers might design programs or interventions to help at-risk youth, building on the foundational knowledge of psychology, sociology, and education. The research might involve evaluating the effectiveness of a particular program, using methods like surveys , focus groups , and pre-and-post assessments.

Communication strategies for public health

Understanding human behavior is crucial for successful public health campaigns. Researchers might study the best ways to communicate vital health information to various populations, especially in times of crisis like pandemics. Methods often include A/B testing of messages, surveys to assess message efficacy, and observational studies to gauge real-world behavior following communication campaigns.

The distinction between basic and applied research is not just a matter of intent or outcome; it also encompasses differences in methodologies , scopes, and approaches. Let's undertake a comparative analysis to illuminate these distinctions further, particularly in the context of the social sciences.

Purpose and motivation

Basic research is motivated by the quest for knowledge. It seeks to answer fundamental questions about human behavior, societal structures, and the interplay between various social factors. The driving force here is curiosity. In contrast, applied research is driven by the need to address specific societal or practical problems. Its purpose is to take the theoretical knowledge derived from basic research and convert it into actionable solutions.

Methodological approaches

It's important to acknowledge that there is no one universal research method that can address all potential research inquiries. Moreover, the same research methods, such as conducting interviews or engaging in inductive and deductive reasoning , can be utilized in basic and applied research, but they will differ in their scope and objectives. While applied research is more experimental or confirmatory, a basic research approach is often exploratory or explanatory in nature. Basic research methods include ethnography , in-depth interviews , or longitudinal studies to gain a deep understanding of a topic. The focus is on generating theories and understanding patterns.

Applied research, on the other hand, often employs more structured and targeted methodologies. Surveys , experiments, and evaluations are commonly used to verify propositions, assess the efficacy of interventions, or gauge public opinion. The approach is more pragmatic, seeking results that can inform decisions and guide actions.

Outcomes and results

Basic research outcomes are usually theoretical contributions: new concepts, theories, or insights into existing phenomena. The results expand the academic literature and provide a foundation for future studies.

Applied research results in tangible solutions or recommendations. The outcomes might include a new social program, policy recommendations, interventions, or communication strategies. The results are geared towards immediate implementation and often have direct implications for organizations, governments, or communities.

The discourse on basic and applied research often sets them apart, emphasizing their distinct objectives and methodologies. However, it's crucial to recognize that these research types aren't isolated from each other. They coexist in a symbiotic relationship, where the findings from basic research often provide the foundational knowledge for applied research, and the results of applied research can inspire further basic investigations.

The transition of knowledge

One of the most notable instances of the interplay is how basic research's findings become the bedrock for applied research projects. For example, a basic research study on cognitive development in children might reveal specific patterns or stages. An applied researcher, recognizing the implications of these findings, could then design educational interventions tailored to these developmental stages.

How one complements the other

Basic research pushes the boundaries of our understanding, expanding the horizon of what we know. Applied research, on the other hand, can reframe this expansive knowledge and make it relevant and actionable for society's immediate needs.

But the relationship is reciprocal. Applied research can also highlight gaps in our understanding, pointing out areas where basic research is needed. For instance, if an intervention designed based on current knowledge fails to achieve its intended results, it signals to basic researchers that there might be underlying factors or dynamics not yet understood.

The dynamic continuum

Instead of viewing basic and applied research as two separate entities, it's more accurate to see them as points on a continuum. The knowledge generated by basic research flows towards applied projects, which in turn can inspire further basic investigations. This dynamic loop ensures that research in the social sciences remains both grounded in fundamental understanding and relevant to real-world challenges.

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Applied Research: Definition, Types and Examples

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Every research project begins with a clear definition of the research purpose, which helps determine the research procedure or approach used. In this sense, a researcher can Applied research operate.

This research focuses on answering specific questions to solve a particular problem. It aims to find a solution to a cultural or organizational problem and is usually a research plan following basic or pure research.

In this article we explain the characteristics and types of applied research and provide some examples.

• 1 What is applied research?
• 2 Main areas of application of applied research
• 3.1 Applied research solves specific problems
• 3.2 Increased collaboration between academia and the business sector
• 3.3 Contribute to the economy
• 3.4 Improving quality of life through applied research
• 3.5 Advance knowledge
• 4.1 Evaluative research
• 4.2 Research and Development
• 4.3 Action research
• 5 How to conduct applied research?
• 6.1 Applied research in economics
• 6.2 Applied Research in Education
• 6.3 Applied Research in Science
• 6.4 Applied research in psychology
• 6.5 Applied research in healthcare
• 7 Conclusion
• 8 1:1 Live Online Presentation: QUESTIONPRO MARKET RESEARCH SOFTWARE
• 9 Try software for market research and experience management now for 10 days free of charge!

What is applied research?

Applied research is a non-systematic way to find solutions to specific problems or questions. These problems or questions can be at an individual, group or societal level. It is called “unsystematic” because it goes straight to finding solutions.

It is often called the “scientific process” because it takes the available scientific tools and puts them into practice to find answers.

As in normal research, the researcher identifies the problem, formulates a hypothesis, and then conducts experiments to test it.

Applied research aims to use the results of scientific research to improve the quality of life of people, promote economic and social development and solve specific social problems.

Main areas of application of applied research

Applied research is used to solve practical problems and improve the effectiveness of existing processes and technologies in a variety of areas, such as: B. in medicine, engineering, education, psychology, sociology and many other areas. Here are some examples of their most important areas of use:

• Development of new products and technologies : This research is used to develop new products and technologies to meet the needs of the market. For example, it can be used to develop new technologies in electronics, renewable energy, autonomous vehicles, etc.
• Improving the quality and efficiency of processes : Applied research is used to improve the quality and efficiency of existing processes in a variety of areas. For example, to optimize production and reduce costs.
• Development of policies and corporate strategies : It is used to develop policies and corporate strategies. Thus, governments can use applied research to develop policies to solve social and economic problems, while companies can use it to develop marketing strategies and plan business expansion.
• Development of educational programs: It is used to develop educational programs that are more effective for students. For example, it is used to improve teaching techniques and develop more personalized education programs.

Benefits of applied research

Some benefits of applied research are:

Applied research solves specific problems

Applied research focuses on solving specific problems that affect people and society. Therefore, this research can help find concrete and practical solutions to problems in areas such as health, education, technology and others.

Increased collaboration between academia and the business sector

Applied research can be a way to combine the efforts of universities and companies to develop new products, processes and services that benefit society. This can lead to greater collaboration between sectors and more innovation.

Contribute to the economy

Applied research can help create new economic opportunities through the development of innovative products and services that can create jobs and wealth.

Improving quality of life through applied research

It can improve people's quality of life by providing practical and effective solutions to important problems. For example, applied research in health can lead to the development of new medical treatments and therapies that can improve people's health.

Advance knowledge

Applied research can help expand knowledge in a specific area by testing theories and concepts in real-world situations. In this way, she can help make new discoveries that can have a significant impact on society.

Types of applied research

There are three types of applied research:

• evaluative research,
• Research and development and
• Action research.

The individual types are briefly explained below:

Evaluative research

Evaluative research is one of the types of research that is applied. It examines information on a research topic. These types of research lead to objective research or help people make better decisions. Most often, evaluative research is used in companies.

The organization uses this research to figure out how to reduce overhead costs.

Research and Development

Research and development is the second type of applied research. Their main goal is to create or design new products, goods or services that meet the needs of specific markets in society. She finds out what the needs of the market are. She focuses on finding new ways to improve products that already meet an organization's needs.

Action research

Action research is the third type of applied research. Action research is a method of researching things that occur in everyday life and in nature. It aims to find real solutions to business problems by providing the right direction.

How to conduct applied research?

Here are the steps to conduct applied research:

• Identify the problem : The first step is to clearly identify the problem to be solved. The problem must be clearly and specifically defined.
• Review of the topic : The existing literature on the identified problem should be reviewed. This makes it possible to know the progress made in solving the problem, the methods used and the results achieved.
• Formulation of the hypothesis: After reviewing the literature, a hypothesis must be formulated that explains the problem and the proposed solution.
• Research design : To test the hypothesis, a research design needs to be created. This includes defining the sample, selecting the variables to be measured and choosing the analysis methods.
• data collection : The data necessary to carry out the investigation must be collected. This may include interviews, surveys, observations or experiments.
• Intelligent Data Analysis : The data collected needs to be analysed to determine whether the hypothesis is valid or not. Statistical methods should be used to analyse the data.
• Interpretation of the results : Interpret the results of data analysis and determine whether the hypothesis is valid or not.
• Drawing conclusions : Conclusions must be drawn from the results obtained to determine whether the problem posed has been solved.
• Communication of results: The results of the research should be communicated. This may include presenting the results at conferences or publishing the results in scientific journals.

Examples of applied research

Applied research is used in many areas of study and research. Let's look at how it is used and what examples there are:

Applied research in economics

Applied research in business depends entirely on its products and services. It helps companies understand market needs and trends and design their products to meet customer needs.

Companies benefit from this research because it allows them to uncover gaps in their performance and gain primary information about the preferences of the target market.

• It can improve recruitment.
• Improving work and politics.
• Identifying skills gaps in the workplace.

Applied Research in Education

Applied research is used in education to test different teaching methods and find better teaching and learning methods. Before new education policies are introduced, they are tested to see how they work and what impact they have on teaching and performance in the classroom.

Applied educational research uses quantitative and qualitative methods to collect first-hand data. This information is then examined and interpreted in different ways to produce valuable insights or conclusions.

Most research in this area is designed to develop and test different approaches and try them out in different situations. It is based on accurate observations and descriptions of the real world.

• Applied study to understand the scope of online learning initiatives.
• Applied study to promote engagement between teachers and learners in the classroom.
• Applied study of the new mathematics curriculum.

Applied Research in Science

As previously mentioned, applied research uses the scientific tools available to find answers. It can be used in physics, microbiology, thermodynamics and other fields.

• Applied research is put into practice to cure a disease.
• It is used to improve agricultural practices.
• Applied research is used to test new laboratory equipment.

Applied research in psychology

Researchers use applied research in psychology to find out how people behave in the workplace, how human resources work, and how the organization develops and changes to find solutions.

It is often used in areas where researchers are trying to understand how people think and then develop solutions that best fit their behavior.

• Applied study to find new ways to treat depression.
• Study to improve students' grades by emphasizing practical training.
• Applied research to create a plan that keeps workers coming to work regularly.

Applied research in healthcare

This research is used to investigate new drugs in the medical industry. It combines scientific knowledge and procedures with healthcare experience to achieve evidence-based results.

• Applied study in cardiac surgery.
• Applied research to determine the effectiveness of a drug.
• Studying the undesirable effects of a drug.

Applied research is an important form of research because it helps organizations find real-world solutions to specific problems while improving their performance and productivity.

Unlike basic research, which focuses on developing theories to explain things, applied research focuses on describing facts to find solutions.

In applied research, the researcher uses qualitative and quantitative methods to collect data such as: b. Questionnaires, interviews and observation methods.

At QuestionPro we offer researchers access to data collection tools such as: B. our survey software.

To conduct applied research with QuestionPro, it is important that you are clear about the goal you want to achieve. You can design your questionnaire on our platform, making sure it contains clear and concise questions that will give you the data you need to achieve your research goal.

If you already know who you want to interview and how you want to reach them, that's perfect. Remember that QuestionPro can also help you select your research sample through our audience service, and you can send your surveys via email, social media, a link on your website, etc.

Once you have designed your questionnaire and selected your sample, it is time to conduct the survey. In QuestionPro you can see the results of your survey in real time and know how many people responded, how many questions they answered, etc.

The tool also gives you a variety of tools to analyse your survey data, from graphs and tables to text analysis and statistical comparisons. Use the results of your survey to make informed decisions and improve your company, product or service.

Remember that QuestionPro is a very useful tool for conducting applied research, but it is always important to have a clear and precise methodology to achieve the best results. Create a free account or request a demo to learn more.

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FURTHER KEYWORDS

Types of research | Empirical research | Research design

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KEYWORDS OF THIS BLOG POST

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Applied Research Examples: Empowering Real-World Solutions

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Applied research plays a crucial role in various fields, providing practical solutions to real-world problems and driving advancements in technology, healthcare, business, and more. It bridges the gap between theory and practice by translating scientific knowledge into tangible outcomes that positively impact individuals, organizations, communities, or industries. Applied research enables us to develop innovative solutions, refine existing practices, and make informed decisions based on evidence. By focusing on practical applications, applied research contributes to advancements in various fields, ultimately leading to societal progress and improvement. It serves as a driving force for innovation, economic growth, and the overall betterment of individuals and communities. 

What is Applied Research and its purposes?

Applied research is a systematic and practical approach to investigating real-world problems and finding practical solutions. It makes application of scientific methods and techniques to gather and analyze data, conduct experiments, and make evidence-based recommendations. The primary purpose of applied research is to address specific issues or challenges in various fields. It aims to make better-existing practices, processes, or products, validate the effectiveness of interventions or programs, inform policy decisions, and contribute to the development and implementation of evidence-based strategies. 

When using Applied Research

Some common applications of applied research include:

Business and Marketing

Through the utilization of applied research, organizations can gain valuable insights into consumer behavior, evaluate marketing strategies, assess market trends, and identify opportunities for product development and innovation.

Healthcare and Medicine

Applied research is conducted to evaluate the effectiveness of medical treatments, interventions, and healthcare policies. It helps in the advancement of patient outcomes, optimizes healthcare delivery systems, and informs evidence-based medical practices.

In education, applied research informs curriculum development, evaluates program effectiveness, guides evidence-based instruction, informs policy decisions, supports professional development, and optimizes student assessment and evaluation.

Public Policy and Governance

Policymakers can leverage data-driven insights to inform decision-making, evaluate program effectiveness, and enhance governance practices. This approach fosters evidence-based policymaking, promotes transparency, and facilitates effective and equitable governance.

Environmental Studies

Applied research is used to address environmental challenges, such as climate change, pollution, and natural resource management. It helps develop sustainable practices, assess the environmental impact of policies and projects, and guide conservation efforts.

Technology and Engineering

Optimizing efficiency and advancing technology, applied research in technological and engineering fields develops and upgrades products, systems, and processes, addressing practical problems with innovative solutions.

Agriculture and Food Science

Applied research is conducted to raise agricultural practices and crop yields, ensure food safety, and develop sustainable farming methods. It addresses challenges related to food production, distribution, and environmental impact.

Types of Applied Research

Applied research includes various types tailored to address specific practical issues and inform decision-making. Some common types of applied research are:

Evaluation Research

This type of research focuses on assessing the effectiveness, efficiency, and impact of programs, interventions, policies, or initiatives. It measures outcomes, identifies strengths and weaknesses, and provides recommendations for improvement.

Action Research

Action research involves collaborative inquiry and problem-solving in real-world settings. It emphasizes the active participation of stakeholders to identify and address practical challenges, often leading to immediate changes or interventions.

Research and Development

R&D is a type of applied research with the objective to create innovative products and services to meet market needs. It requires gathering market information, improving existing products, and developing new ones to fulfill customer demands and enhance organizational effectiveness.

Policy Research

Generating evidence-based recommendations for policymakers, policy research plays a crucial role in informing policy development, implementation, and evaluation. By analyzing existing policies and identifying areas for improvement, it aims to shape effective and informed decision-making processes.

Data Collection Methods

Data collection methods refer to the techniques and approaches used to gather information or data for research purposes. These methods vary depending on the nature of the research question, the type of data needed, and the resources available. Here are some common data collection methods:

This collects data through structured questionnaires or interviews. They can be administered in person, over the phone, through mail, or online. Surveys are useful for gathering information from a large number of participants and obtaining self-reported data on attitudes, opinions, behaviors, or demographics.

Interviews conduct one-on-one or group conversations with participants to gather detailed information. Interviews can be structured (with predetermined questions), semi-structured (with a set of guiding questions), or unstructured (allowing for open-ended discussion). Interviews are useful for exploring complex topics, capturing in-depth insights, and understanding participants’ perspectives.

Observations

The method of observation watches and records behaviors, actions, or events in their natural settings. Researchers can be participant observers (actively participating in the observed context) or non-participant observers (observing from a distance). Observations are valuable for studying social interactions, behaviors, and patterns in real-life contexts.

Experiments

Experiments manipulate variables under controlled conditions to determine cause-and-effect relationships. Participants are assigned to different experimental conditions, and data is collected to assess the impact of the manipulated variables. Experiments allow researchers to study causal relationships and test hypotheses.

Data Analysis Methods

Data analysis methods are the techniques and procedures used to analyze and interpret data collected during a research study. These methods help researchers make sense of the data, identify patterns, draw conclusions, and answer research questions. Data analysis plays a crucial role in research as it transforms raw data into meaningful insights and supports evidence-based decision-making. Some common data analysis methods are:

Descriptive Statistics

Descriptive statistics summarize and describe the main characteristics of the data. They include measures such as mean, median, mode, standard deviation, and frequency distributions. Descriptive statistics provide a snapshot of the data’s central tendency, dispersion, and distribution.

Inferential Statistics

Inferential statistics make inferences or draw conclusions about a population based on a sample. These methods help researchers test hypotheses, determine statistical significance, and make generalizations. Examples of inferential statistics include t-tests, analysis of variance (ANOVA), regression analysis, and chi-square tests.

Data Mining

Data mining uses computational algorithms to discover patterns, trends, and relationships within large datasets. It helps identify hidden insights and generate predictive models. Data mining techniques include association rule mining, classification, clustering, and anomaly detection.

Applied Research Methodology

Applied research methodology refers to the systematic approach used to conduct applied research studies. It is a series of steps and procedures designed to gather relevant data, analyze it, and draw meaningful conclusions to address real-world problems or provide practical solutions. The methodology for applied research typically includes the following key components:

Problem Identification

Clearly defining and understanding the specific problem or issue to be addressed is the first step in applied research methodology. Conducting a thorough literature review, consulting with experts, and engaging stakeholders are essential steps to gain insights into the problem’s context, scope, and potential impact.

Research Design

Developing a research design involves determining the appropriate research approach, such as quantitative, qualitative, or mixed methods, based on the research objectives and the nature of the problem. It also includes selecting the appropriate data collection methods, sampling techniques, and data analysis procedures.

Data Collection

Data collection methods are chosen based on the research design and the type of data required. Common data collection methods include surveys, interviews, observations, experiments, case studies, and document analysis. Rigorous data collection techniques ensure the collection of accurate and reliable data relevant to the research problem.

Data Analysis

Data analysis is about processing, organizing, and interpreting the collected data to derive meaningful insights. Depending on the nature of the data, quantitative analysis techniques such as statistical analysis, regression analysis, or data mining may be used. Qualitative analysis techniques, such as thematic analysis or content analysis, can be employed for textual or qualitative data.

Results and Conclusion

The analyzed data is used to draw conclusions, identify patterns, and make inferences related to the research problem. The results are presented in a clear and concise manner, often through tables, charts, or visualizations. Conclusions should be supported by evidence from the data analysis and aligned with the research objectives.

Examples of Applied Research

The applied research examples illustrate how this addresses real-world issues and aims to provide practical solutions that can be implemented and make a meaningful impact in various domains. Here are some applied research examples across different fields:

In healthcare, the focus is finding practical solutions to improve patient care and outcomes. For example, a study investigating the effectiveness of a new medical treatment or therapy for a specific condition would be considered applied research.

The primary objective is to increase teaching methods, curriculum development, and student learning outcomes. This involves evaluating the effectiveness of various instructional approaches and designing interventions to foster improved student engagement and achievement.

In the domain of business and marketing, the emphasis is on tackling practical issues encountered by organizations. This can include analyzing consumer behavior to devise impactful marketing strategies or conducting market research to evaluate the viability of introducing a new product. Such endeavors align with the principles of applied research.

Environmental Science

The objective is to devise practical solutions for addressing environmental challenges. This can encompass studying the effects of pollution on ecosystems, formulating sustainable practices, or assessing the efficacy of conservation initiatives. These pursuits align with the principles of applied research.

Engineering and Technology

The emphasis of applied research on engineering and technology is to create inventive solutions for real-world problems. This could entail research endeavors aimed at enhancing energy efficiency, refining manufacturing techniques, or pioneering novel materials.

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Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

Online Certificate in Applied Behavior Analysis

Join a community of scholars whose mission is to improve the lives of individuals with disabilities..

• School Peabody College
• Credits 21 credits
• Format Online, asynchronous & synchronous sessions
• Enrollment Fall

About the Program

Vanderbilt University’s Peabody College’s Online Post-Master’s Certificate in Applied Behavior Analysis is grounded in educational research and delivers high-quality behavioral services, equipping students with the necessary skills to apply the behavioral analysis learned in the program into practice. Non degree-seeking individuals who have already completed a master’s degree in a related field should enroll in this certificate program. The program challenges students to look beyond the standard applied behavior analysis practices and think critically about how to effectively work with individuals with disabilities. 

Following the completion of this certificate program, students will be equipped to pursue careers working with individuals with disabilities and have satisfied all coursework required to sit for the Board Certified Behavior Analyst (BCBA) examination.  

Facts & Stats

• 14% increase in Board Certified Behavior Analyst job postings between 2022 to 2023

Key Takeaways

• Housed within Peabody’s esteemed Department of Special Education, renowned for pioneering research, inventive methodologies, and widespread practices
• Flexible online courses, both synchronous and asynchronous, cater to the schedules of working professionals
• Equips students to take the Board Certified Behavior Analysis (BCBA) examination
• Interact with and gain insights from distinguished Vanderbilt faculty and grow extensive professional connections

Who Should Enroll?

The Vanderbilt Online Post-Master’s Certificate in Applied Behavior Analysis is an ideal choice for individuals who already hold a master’s degree in a related field. The coursework is delivered online, featuring a blend of synchronous and asynchronous learning modules. Upon program completion, students will be prepared for careers in challenging, real-world conditions, assisting and improving the lives of individuals with disabilities.