research is objective unbiased and logical

Characteristics of research

• Empirical - based on observations and experimentation
• Systematic - follows orderly and sequential procedure.
• Controlled - all variables except those that are tested/experimented upon are kept constant.
• Employs hypothesis - guides the investigation process
• Analytical - There is critical analysis of all data used so that there is no error in their interpretation
• Objective, Unbiased, & Logical - all findings are logically based on empirical.
• Employs quantitative or statistical methods - data are transformed into numerical measures and are treated statistically.
• Thinking Scientifically
• Writing discipline specific research papers
• Wikipedia: Research
• Wikibooks: Research Methods

Bibliography

• Feigenbaum, Edward A.; McCorduck, Pamela (1983). The fifth generation: Artificial intelligence and Japan's computer challenge to the world . ISBN  978-0-201-11519-2 .  
• Kendal, Simon; Creen, Malcolm (2006-10-04). An Introduction to Knowledge Engineering . ISBN  978-1-84628-475-5 .  
• Russell, Stuart Jonathan; Norvig, Peter (1995). Artificial Intelligence: A Modern Approach . ISBN  0-13-103805-2 .  

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“Objective” and “Unbiased” Qualitative Research

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Scientists like numbers. Words and narrative can help illustrate or describe research results, but scientists typically expect trustworthy results to take a numeric form. Well-trained researchers recognize that quantification does not assure unbiased objectivity. Nevertheless, in scientific communication, numbers enjoy the presumption of innocence while narrative triggers suspicions of subjectivity and bias. How can qualitative data from a small number of people be scientific? How can you interview someone without bias? What about the Hawthorne effect? Without statistics, how can you be sure your findings are real? How can quotes constitute trustworthy results?

Sensitive to these questions, some qualitative researchers respond by striving to adopt quantitative techniques in their qualitative studies. They draw large interview samples and highlight their representativeness. They develop guides to structure interviews or record observations. They train data coders to use a standard approach as they apply a code to each segment of data. They present data in tables; each quote represents a theme; each quote represents a particular type of respondent. Reviewers grade how well studies’ methodology aligns with a standard se t of protocols  that have been adapted from quantitative science.

Other researchers point out, however, that the emperor of objectivity has no clothes. They note, accurately, that science is done by humans, so subjectivity and bias are inescapable. Quantifying techniques, e.g., sampling, surveys, and statistics, may address these limitations to some extent but are no panacea. They do not make science objective and bias-free.

In addition, there are trade-offs when a qualitative study embraces a quantitative logic. On a positive note, when qualitative work embraces standardization it can bolster its legitimacy among quantitatively-oriented disciplines; the trade-off may be necessary to reach certain scholarly communities. But drawing random samples, standardizing data collection, tabulating rather than interpreting results, and summarizing findings in tables and figures — these run counter to the qualitative scholarship tenet  of conveying the world through the eyes and experiences of participants. Conversely, scholars who ground their work in championing subjectivity and rejecting the possibility of objectivity encounter their own trade-offs.  They find themselves in a bind, having lost the ability to dialog with a broad swath of the scientific community.

There is a third way to engage the challenges of objectivity and bias in qualitative research. It neither embraces the quantitative terms of debate nor rejects the value of striving for objectivity. It starts by recognizing that all researchers must address four fundamental questions:

(1) How do I select research subjects? (data collection);

(2) How do I work with subjects to get data? (data collection);

(3) How do I generate defensible findings? (data analysis);

(4) How do I convince readers? (data analysis).

As the list highlights, these questions fall into two familiar buckets. Selecting and engaging subjects occurs during data collection. Identifying and communicating findings are products of data analysis.

Quantitative scientists answer these four questions with R words : drawing Representative samples, minimizing Reactivity during data gathering, generating Reliable statistical results, and emphasizing their potential Replicability. These terms dominate scientific discourse. Many assume that representative samples responding to standardized instruments is THE scientific method for data collection. That statistical analyses and the tables and confidence intervals they generate are THE scientific way to present data.

This is not true. Returning to the four tasks of scientific research helps us recognize the invaluable contributions of qualitative research to scientific discourse. It helps us understand how a scholarly community can strive for s cientific rigor and reproducibility  without using the four R’s.

Selecting participants

Qualitative researchers typically invite people to join their study in a purposeful fashion. Purposeful sampling requires thoughtful design . Designs lead investigators to the proper places and people to recruit. Investigators refer to the design when they describe their value in the study to potential participants. Thoughtful designs also respond and evolve over the course of the work. New data generates new insights and new questions which sends investigators to new places or people or inspires new questions to ask of current participants.

Compared to a purposeful sample, a sample that is merely representative is, at best, inefficient. Representative samples are mechanical. They aim to quantify the presence (or absence) of something measurable within a pre-defined group. In contrast, a purposeful sample enables the researcher to classify, describe, and understand variety. It seeks to document the range of behaviors, opinions, or situations that are relevant to the research question.

Gathering data

Qualitative researchers participate with subjects to gather data. They conduct semi-structured interviews, convene focus groups, and hang out in the field. They select a technique to fit the situation and data needs of the project. Talented fieldworkers shape questions and interactions to respondents’ sensibilities. A flexible participatory approach helps researchers appreciate and understand subjects’ experiences of the world. In contrast, non-reactive approaches record participants’ responses to pre-set research procedures.

The participatory approach means that appearance, identity, and lived experience inevitably shape how fieldworkers and subjects engage each other. Humility and self-reflection are an essential condition of fieldworkers’ craft. But self-reflection can only go so far. In an ideal world, projects enter the field with a diverse team of fieldworkers who respectfully and productively engage participants. Qualitative studies use participation – specifically, the diverse ways researchers engage subjects – to gain insights into their worlds.

Generating defensible findings

Quantitative scholars defend their findings with statistical reliability. In contrast, qualitative scholars produce defensible findings by iteratively and patiently processing their data. They review and critically reflect while collecting it. They identify when and how data seem consistent (or not), and they use those insights to guide ongoing data collection. As the process continues, some findings firm up; they are readily defensible. Counterfactual findings crystalize. To understand and potentially turn those counterfactual findings into defensible ones may require new sites, informants, or techniques. 

These iterative processes align with the well-known grounded theory (GT) approach . But qualitative findings are defensible even if they do not use GT-style inductive reasoning, coding, and theory building. The well-choreographed GT approach – and especially the GT notion of saturation – can sometimes be mistaken for a type of reliability test. But the magic components of GT and other qualitative analyses are the processes of inquiry, reflection, and refinement, not a predefined technical procedure or outcome.

Convincing readers

Quantitative scholars establish credibility by presenting findings in ways that suggest they are readily replicable. Replicability has long been touted as a foundational principle of science. But even in the quantitative social sciences, it can prove easier said than done ; few scholars attempt to replicate others’ results and it can be elusive when attempted. Qualitative data collection is not designed to support replication; the study of a purposeful sample examined via participatory data collection can be done only once.

Qualitative authors do not rely on replicability to convince their readers. Rather, they present data with particularity. Data presented with context and forthright detail establishes legitimacy. In a way, qualitative scholars convince readers using the oldest trick in humanity’s book: they tell a believable story.

As noted above, scientists view stories with suspicion. Tidy emplotment may satisfy lay readers, but it can provoke skepticism among scholars. Qualitative results are more convincing when quotes and observations in the results section encourage active and critical thinking. Details should illustrate how context matters; this helps avoid the trap of appearing to over-generalize qualitative findings. Sharing counterfactuals – and their resolution – can help readers recognize the analytic process that generated findings.

The table below summarizes the ways quantitative and qualitative traditions may approach the four basic questions of scientific research. This table illustrates that both traditions have developed a strategy to guide the logistical tasks of collecting and managing data. Both traditions have found ways to analyze and present data so their work conveys its own expression of rigor. Both traditions have done so in ways that are consistent and coherent. Embracing R’s and P’s thus provides researchers with a coherent approach for navigating the four challenges of research. On the other hand, combining R's and P’s in single project may be self-defeating or nonsensical. It is not impossible, but must be done with careful consideration.

Striving to conduct qualitative research in an objective and unbiased fashion is a worthwhile ambition even if, ultimately, an impossible goal. Yet, it is important to do so thoughtfully. Embracing quantitative science’s strategy at best distracts from — and at worse prevents researchers from realizing — the unique strengths of qualitative research. Understanding the ways high-quality research looks similar and how it differs across scholarly traditions will benefit us all.

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• Research Objectives | Definition & Examples

Research Objectives | Definition & Examples

Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.

Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:

• Establish the scope and depth of your project
• Contribute to your research design
• Indicate how your project will contribute to existing knowledge

Table of contents

What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.

Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .

Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.

Research aims

A distinction is often made between research objectives and research aims.

A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.

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Research objectives are important because they:

• Establish the scope and depth of your project: This helps you avoid unnecessary research. It also means that your research methods and conclusions can easily be evaluated .
• Contribute to your research design: When you know what your objectives are, you have a clearer idea of what methods are most appropriate for your research.
• Indicate how your project will contribute to extant research: They allow you to display your knowledge of up-to-date research, employ or build on current research methods, and attempt to contribute to recent debates.

Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.

Step 1: Decide on a general aim

Your research aim should reflect your research problem and should be relatively broad.

Step 2: Decide on specific objectives

Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?

Step 3: Formulate your aims and objectives

Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.

You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.

The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:

• Specific: Make sure your objectives aren’t overly vague. Your research needs to be clearly defined in order to get useful results.
• Measurable: Know how you’ll measure whether your objectives have been achieved.
• Achievable: Your objectives may be challenging, but they should be feasible. Make sure that relevant groundwork has been done on your topic or that relevant primary or secondary sources exist. Also ensure that you have access to relevant research facilities (labs, library resources , research databases , etc.).
• Relevant: Make sure that they directly address the research problem you want to work on and that they contribute to the current state of research in your field.
• Time-based: Set clear deadlines for objectives to ensure that the project stays on track.

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

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

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

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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Reasoning in Research

• First Online: 25 February 2021

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After doing several experiments and carefully analysing the observations recorded, scientists come to certain conclusions. For arriving at correct conclusions, a systematic approach to argument or reasoning is essential. The first systematic approach to reasoning, attributed to the Greeks, was the method of deduction. They used appropriate logic to prove their point.

The moment we want to believe something, we suddenly see all the arguments for it, and become blind to the arguments against it . George Bernard Shaw (1856–1950), Playwright and Nobel Laureate

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What Are Research Objectives and How to Write Them (with Examples)

Table of Contents

Introduction

Research is at the center of everything researchers do, and setting clear, well-defined research objectives plays a pivotal role in guiding scholars toward their desired outcomes. Research papers are essential instruments for researchers to effectively communicate their work. Among the many sections that constitute a research paper, the introduction plays a key role in providing a background and setting the context. 1 Research objectives, which define the aims of the study, are usually stated in the introduction. Every study has a research question that the authors are trying to answer, and the objective is an active statement about how the study will answer this research question. These objectives help guide the development and design of the study and steer the research in the appropriate direction; if this is not clearly defined, a project can fail!

Research studies have a research question, research hypothesis, and one or more research objectives. A research question is what a study aims to answer, and a research hypothesis is a predictive statement about the relationship between two or more variables, which the study sets out to prove or disprove. Objectives are specific, measurable goals that the study aims to achieve. The difference between these three is illustrated by the following example:

• Research question : How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?
• Research hypothesis : Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).
• Research objective : To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

This article discusses the importance of clear, well-thought out objectives and suggests methods to write them clearly.

What is the introduction in research papers?

Research objectives are usually included in the introduction section. This section is the first that the readers will read so it is essential that it conveys the subject matter appropriately and is well written to create a good first impression. A good introduction sets the tone of the paper and clearly outlines the contents so that the readers get a quick snapshot of what to expect.

A good introduction should aim to: 2,3

• Indicate the main subject area, its importance, and cite previous literature on the subject
• Define the gap(s) in existing research, ask a research question, and state the objectives
• Announce the present research and outline its novelty and significance
• Avoid repeating the Abstract, providing unnecessary information, and claiming novelty without accurate supporting information.

Why are research objectives important?

Objectives can help you stay focused and steer your research in the required direction. They help define and limit the scope of your research, which is important to efficiently manage your resources and time. The objectives help to create and maintain the overall structure, and specify two main things—the variables and the methods of quantifying the variables.

A good research objective:

• defines the scope of the study
• gives direction to the research
• helps maintain focus and avoid diversions from the topic
• minimizes wastage of resources like time, money, and energy

Types of research objectives

Research objectives can be broadly classified into general and specific objectives . 4 General objectives state what the research expects to achieve overall while specific objectives break this down into smaller, logically connected parts, each of which addresses various parts of the research problem. General objectives are the main goals of the study and are usually fewer in number while specific objectives are more in number because they address several aspects of the research problem.

Example (general objective): To investigate the factors influencing the financial performance of firms listed in the New York Stock Exchange market.

Example (specific objective): To assess the influence of firm size on the financial performance of firms listed in the New York Stock Exchange market.

In addition to this broad classification, research objectives can be grouped into several categories depending on the research problem, as given in Table 1.

Table 1: Types of research objectives

Characteristics of research objectives

Research objectives must start with the word “To” because this helps readers identify the objective in the absence of headings and appropriate sectioning in research papers. 5,6

• A good objective is SMART (mostly applicable to specific objectives):
• Specific—clear about the what, why, when, and how
• Measurable—identifies the main variables of the study and quantifies the targets
• Achievable—attainable using the available time and resources
• Realistic—accurately addresses the scope of the problem
• Time-bound—identifies the time in which each step will be completed
• Research objectives clarify the purpose of research.
• They help understand the relationship and dissimilarities between variables.
• They provide a direction that helps the research to reach a definite conclusion.

How to write research objectives?

Research objectives can be written using the following steps: 7

• State your main research question clearly and concisely.
• Describe the ultimate goal of your study, which is similar to the research question but states the intended outcomes more definitively.
• Divide this main goal into subcategories to develop your objectives.
• Limit the number of objectives (1-2 general; 3-4 specific)
• Assess each objective using the SMART
• Start each objective with an action verb like assess, compare, determine, evaluate, etc., which makes the research appear more actionable.
• Use specific language without making the sentence data heavy.
• The most common section to add the objectives is the introduction and after the problem statement.
• Add the objectives to the abstract (if there is one).
• State the general objective first, followed by the specific objectives.

Formulating research objectives

Formulating research objectives has the following five steps, which could help researchers develop a clear objective: 8

• Identify the research problem.
• Review past studies on subjects similar to your problem statement, that is, studies that use similar methods, variables, etc.
• Identify the research gaps the current study should cover based on your literature review. These gaps could be theoretical, methodological, or conceptual.
• Define the research question(s) based on the gaps identified.
• Revise/relate the research problem based on the defined research question and the gaps identified. This is to confirm that there is an actual need for a study on the subject based on the gaps in literature.
• Identify and write the general and specific objectives.
• Incorporate the objectives into the study.

Advantages of research objectives

Adding clear research objectives has the following advantages: 4,8

• Maintains the focus and direction of the research
• Optimizes allocation of resources with minimal wastage
• Acts as a foundation for defining appropriate research questions and hypotheses
• Provides measurable outcomes that can help evaluate the success of the research
• Determines the feasibility of the research by helping to assess the availability of required resources
• Ensures relevance of the study to the subject and its contribution to existing literature

Disadvantages of research objectives

Research objectives also have few disadvantages, as listed below: 8

• Absence of clearly defined objectives can lead to ambiguity in the research process
• Unintentional bias could affect the validity and accuracy of the research findings

Key takeaways

• Research objectives are concise statements that describe what the research is aiming to achieve.
• They define the scope and direction of the research and maintain focus.
• The objectives should be SMART—specific, measurable, achievable, realistic, and time-bound.
• Clear research objectives help avoid collection of data or resources not required for the study.
• Well-formulated specific objectives help develop the overall research methodology, including data collection, analysis, interpretation, and utilization.
• Research objectives should cover all aspects of the problem statement in a coherent way.
• They should be clearly stated using action verbs.

Frequently asked questions on research objectives

Q: what’s the difference between research objectives and aims 9.

A: Research aims are statements that reflect the broad goal(s) of the study and outline the general direction of the research. They are not specific but clearly define the focus of the study.

Example: This research aims to explore employee experiences of digital transformation in retail HR.

Research objectives focus on the action to be taken to achieve the aims. They make the aims more practical and should be specific and actionable.

Example: To observe the retail HR employees throughout the digital transformation.

Q: What are the examples of research objectives, both general and specific?

A: Here are a few examples of research objectives:

• To identify the antiviral chemical constituents in Mumbukura gitoniensis (general)
• To carry out solvent extraction of dried flowers of Mumbukura gitoniensis and isolate the constituents. (specific)
• To determine the antiviral activity of each of the isolated compounds. (specific)
• To examine the extent, range, and method of coral reef rehabilitation projects in five shallow reef areas adjacent to popular tourist destinations in the Philippines.
• To investigate species richness of mammal communities in five protected areas over the past 20 years.
• To evaluate the potential application of AI techniques for estimating best-corrected visual acuity from fundus photographs with and without ancillary information.
• To investigate whether sport influences psychological parameters in the personality of asthmatic children.

Q: How do I develop research objectives?

A: Developing research objectives begins with defining the problem statement clearly, as illustrated by Figure 1. Objectives specify how the research question will be answered and they determine what is to be measured to test the hypothesis.

Q: Are research objectives measurable?

A: The word “measurable” implies that something is quantifiable. In terms of research objectives, this means that the source and method of collecting data are identified and that all these aspects are feasible for the research. Some metrics can be created to measure your progress toward achieving your objectives.

Q: Can research objectives change during the study?

A: Revising research objectives during the study is acceptable in situations when the selected methodology is not progressing toward achieving the objective, or if there are challenges pertaining to resources, etc. One thing to keep in mind is the time and resources you would have to complete your research after revising the objectives. Thus, as long as your problem statement and hypotheses are unchanged, minor revisions to the research objectives are acceptable.

Q: What is the difference between research questions and research objectives? 10

Q: Are research objectives the same as hypotheses?

A: No, hypotheses are predictive theories that are expressed in general terms. Research objectives, which are more specific, are developed from hypotheses and aim to test them. A hypothesis can be tested using several methods and each method will have different objectives because the methodology to be used could be different. A hypothesis is developed based on observation and reasoning; it is a calculated prediction about why a particular phenomenon is occurring. To test this prediction, different research objectives are formulated. Here’s a simple example of both a research hypothesis and research objective.

Research hypothesis : Employees who arrive at work earlier are more productive.

Research objective : To assess whether employees who arrive at work earlier are more productive.

To summarize, research objectives are an important part of research studies and should be written clearly to effectively communicate your research. We hope this article has given you a brief insight into the importance of using clearly defined research objectives and how to formulate them.

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• Understanding the difference between research questions and objectives. Accessed June 21, 2023. https://board.researchersjob.com/blog/research-questions-and-objectives

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Objectivity and Subjectivity in Social Sciences Research

Research is an integral part of not only academia but society as well because academic research is what allows individuals to understand the world in a factual sense. Data provides us with facts and these facts structure the world we live in. Society and every aspect of it has been studied for decades now and researchers have come up with an array of new methods to accomplish this. As society progressed civilisation moved from a society centred around religious governance to one rooted around science. Sociologists wanted to study human behaviour the same way in which natural sciences were studied-  thus the emergence of positivism . This is where objectivity and subjectivity become important.

Objectivity and Subjectivity

Objectivity     

Approaches in Sociology

Sociological approaches that are objective in nature adopt a viewpoint that is external or transcendent to individual experience. Examples include Marxism, Functionalism, Critical Theory, and Structuralism. The actor’s motives, choices, and reasoning are not of importance. In a way, it assumes that social reality does not stem from subjective experiences, rather the macro structures that bind society actively constructs reality (Greiffenhagen & Sharrock, 2008). Durkheim through his writings establish two things. In an attribute to scientific discourse, objectivity must be understood in an epistemological sense. He argues that an opposition between methodological analysis (analysis of subjective motives as explanatory factors) and the objective observation, comparison and explanation of social facts as ‘things’ must be established (Paoletti, 2004). The second question he raises, which takes an ontological approach, is whether representations actually represent something and if they do, what is it? Through his works he tackles these questions and comes to the conclusion that social reality is independent of our representations.

Ontology is about things where as epistemology is about knowledge. Ontologically, objectivity is independent of the mind whereas subjectivity is dependent on it. In the realm of Epistemology, however, objectivity is dependent on the view of rational thinkers. (Powell et al., 2014) If the knowledge or thing in question is true for all rational thinkers, then it has attained objectivity. Something becomes subjective when there is disagreement on the conclusion despite being presented with the same evidence. Durkheim and Weber believed in producing knowledge that is epistemologically objective however their opinions on how important ontologically subjective and objective things are to sociological explanation differed (Powell et al., 2014).

According to Durkheim social facts exist objectively, they are in a sphere that is independent of the actors who carry them out. He believes that it is not important to study the meanings behind those actions, rather social facts must be entirely based upon objective properties. This is reflected in his work revolving around suicide- though the act initself might seem to be highly subjective he believed that was not the case. As he pointed out in his book, there are four different types of suicide and he mapped out how this highly individualistic feature is actually a structural phenomenon. Weber on the other hand, disagreed with both his claims. For him, social reality is actively constructed because of the individual and their actions (Powell et al., 2014). The only thing that exists in society are the individuals and their subsequent actions, hence, in order to study social facts and reality the meanings behind these actions must be studied.

During the early years of psychology, psychologists used a positivist approach to mirror the objective nature of hard sciences, especially during the 19th century, positivism was welcomed with open arms. Laboratory experiments were particularly famous around this time and were the favoured method owing to their success and reliability. This further helped dawn in the positivist framework. However, as time progressed, researchers raised doubts regarding the limitations of objectivity in scientific inquiry and split into four camps: tempered positivist view, relative positivist view, constructivist view, and the subjectivist view.

Those who followed the tempered positivist view regarded objectivity as an ideal that is preferred but never attainable. It can never truly be attained because of the unintentional or intentional bias of the researcher. They try to bridge the gap by ensuring that these biases are eliminated. The relative positivists on the other hand believe that the matter studied in social sciences is inherently value-laden, hence this subjectivity cannot be escaped. The solution to minimising subjectivity is to put the work under rigorous review and constantly re-evaluate the work being produced. The latter two camps will be introduced when discussing subjectivity.

Research Methods: Objectivity

Researchers who strive for objectivity in their work and field incorporate methods that are objective, unbiased and the closest to scientific truth. As mentioned earlier, the method in which the subject matter is being studied can entirely change the trajectory of the end result. Hence, it is crucial to choose the most appropriate research method and design. Objective research claims to illustrate reality that is ‘true’ and ‘correct’, it exists independently of those that are being studied, similar to it theoretical counterparts. Therefore, the methods and apparatus used in objective research is modelled after those in the hard sciences. Researchers use experiments, surveys with closed-ended questions and observations in strict controlled environments. These methods allow researchers to collect numerical data which can then be interpreted and analysed in an objective, unbiased manner as it does not require the researchers’ input. Experiments work under a controlled environment which is free of the researchers subjective feelings and interpretations, the situation can be replicated and hence provide reliability and validity to the work.

Subjectivity

According to Schutz, social sciences are constructs of the second degree, meaning, they are constructs of constructs made by individuals (actors). Therefore, in order to understand this, the social scientist must observe and explain this in accordance with the rules of his science (Greiffenhagen & Sharrock, 2008). In this case that translates to observing and understanding the root of social interaction and action that constructs meaning in everyday life.  Bittner was hesitant in terming these approaches as subjectivist as it would get lost within the discourse around objectivity and subjectivity. He argued that experience cannot entirely be construed as ‘subjective’ as it includes references to an objective social world. To characterise phenomenology (inclusive of the other approaches mentioned above) as subjective opens a dangerous territory. While it is inherently subjective, it is not void of objectivity. Bittner urges to draw attention to the fact that the factual reality of the world actually has an effect on the subject (Greiffenhagen & Sharrock, 2008).

Research Methods: Subjectivity

Experiments have always been a preferred method to study human behavior in both disciplines. However, as approaches and theories developed researchers now use a range of methods to obtain qualitative data that is rich. Interpretivism integrates human interest into the study and aim to uncover the reasoning behind human behavior and actions. While experiments allow for a description of unbiased data, it may not always reflect what is happening in the real world. A controlled environment is not the same as everyday life and what researchers are interested in studying is human behaviour in real life. Field experiments, observations and qualitative interviews are examples of research methods that allow for a subjective interpretation of behaviour. Qualitative interviews, for example, provides subjects the platform to express their thought process and with precautions bias that could confound data can be avoided. These tools are greatly useful when wanting to describe lived experiences.

While both views raise valid points it must be noted that they have both been crticised to be reductionist in their thinking. It would prove to be useful if researchers found a way to incorporate the important aspects of both subjectivity and objectivity to their approach as they are equally important.

Powell, C., Says:, S., says:, C. P., says:, B. A. B. A. lukman, says:, J., says:, C., says:, A. E., Says:, H., says:, A., & says:, H. H. (2014, March 10). Objectivity and Subjectivity in Classical Sociology . The Practical Theorist. https://practicaltheorist.wordpress.com/2014/03/10/objectivity-and-subjectivity-in-classical-sociology/.

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Moving towards less biased research

Mark yarborough.

Bioethics Program, University of California Davis, Sacramento, California, USA

Introduction

Bias, perhaps best described as ‘any process at any stage of inference which tends to produce results or conclusions that differ systematically from the truth,’ can pollute the entire spectrum of research, including its design, analysis, interpretation and reporting. 1 It can taint entire bodies of research as much as it can individual studies. 2 3 Given this extensive detrimental impact, effective efforts to combat bias are critically important to biomedical research’s goal of improving healthcare. Champions for such efforts can currently be found among individual investigators, journals, research sponsors and research regulators. The central focus of this essay is assessing the effectiveness of some of the efforts currently being championed and proposing new ones.

Current efforts fall mainly into two domains, one meant to prevent bias and one meant to detect it. Much like a proverbial chain, efforts in either domain are hampered by their weakest components. Hence, it behoves us to constantly probe antibias tools so that we can identify weak components and seek ways to compensate for them. Further, given the high stakes—conclusions that align with rather than diverge from truth—it further behoves the biomedical research community to prioritise to the extent possible bias prevention over bias detection. The less likely any given study is to be tainted by bias, the fewer research publications reporting biased results there will be. The value of detected bias pales in comparison, for it extends only as far as those who are aware of that detection after the fact, meaning that biased conclusions at variance with the truth can mislead those unaware of the bias that taints them for as long as the affected publications endure.

With these preliminary considerations about bias in mind, let us first examine some current antibias efforts and probe their weaknesses. Doing so will show why we need to develop additional strategies for preventing bias in the first place, and space is set aside at the end to examine two related candidate strategies for how we could attempt to do that.

Current bias countermeasures

Table 1 reflects some current countermeasures being employed to combat various kinds of biases. Though the table is far from comprehensive, (dozens of biases have been catalogued) 1 it does include major biases of concern, representative countermeasures to combat them, whether those countermeasures prevent or detect bias, and their likely relative strength.

Sponsorship bias

The bias that probably draws the most attention is what is known as sponsorship bias, 4 5 wherein pecuniary interests undermine the disinterestedness meant to prevail in scientific investigations. 6 The most prominent countermeasure against it consists in multiple disclosure practices that flag financial relationships between scientists and private companies. For example, academic institutions may require faculty to disclose annually their financial relationships with private companies; research sponsors may require applicants to make such disclosures when submitting applications; and journals typically require authors to make such disclosures when submitting manuscripts. The right-hand column of table 1 prompts the question, ‘to what extent do such disclosures actually prevent sponsorship bias?’ There is now ample conceptual analysis 7–10 and empirical evidence produced over many years such that we can safely state that there is an over-reliance on disclosure.

This extensive prior work shows, for example, that journal disclosure policies targeting authors fail to capture many financial ties between researchers and industry. Recent studies show that consulting agreements between researchers and companies, as well as financial ties between biomedical companies and organisations that produce clinical practice guidelines, often go undisclosed. 11 12 Looking at journal disclosure policies, we see further evidence of disclosure’s limited ameliorative effect. A recent study that randomised article reviewers into one group that received financial interests disclosures along with the manuscripts to be reviewed and another group that did not found that the disclosures had no effect on reviewer assessments of the manuscripts. 13 Another recent study looked at editorial practices regarding the financial interests of authors at 30 leading medical journals and found that none had actual tools for determining whether and how disclosed financial relationships might have impacted any given research report. 14

Additional considerations help to further explain the weaknesses of journal disclosure policies. First, disclosures are usually mistimed. When financial relationships bias studies, that bias occurs long before anyone discloses the relationships in reports about the studies. 15 Second, it is those, and only those, designated as authors who are subject to them. Often those who lead the design, conduct, analysis and reporting of a study are not in fact considered authors of it. 16 Private companies that sponsor the majority of drug studies and/or contract research organisations they hire control the design, manage the conduct, and analyse the data, as well as write the articles about that analysis for studies. 17 Journal disclosure mandates leave untouched the bias that these conflicted sponsors can introduce into clinical trials because of sizeable holes in the International Committee of Medical Journal Editors (ICMJE) authorship policy. Followed by an outsized portion of biomedical research journals, it ‘support[s] practices of commercial data control, content development and attribution that run counter to science’s values of openness, objectivity and truthfulness’ because ‘the ICMJE accepts the use of commercial editorial teams to produce manuscripts, which is a potential source of bias, and accepts private company ownership and analysis of clinical trial data.’ 16 In other words, even though readers of journals assume that journals accurately attribute those, and only those, who are responsible for the design, conduct, analysis and reporting of a study, authorship practices do not in fact require such accurate attribution. Thus, we are relying on disclosure, often after the fact of conducting a study, to combat the bias that financial entanglements can cause prior to a study’s launch and the disclosure practices themselves often mistarget those who should be making the disclosures. The end result is that current disclosure practices can conceal rather than reveal the prospect of sponsorship bias.

Furthermore, even if disclosures were better targeted, this would not negate the potential that disclosures themselves have to cause unintended detrimental consequences. Commentators long ago noted that disclosing financial relationships may contribute to people having a sense of ‘moral license to (act in biased ways more) than they would without disclosure. With disclosure, (acting in a biased way) might seem like fair play. While most professionals might care about their (audience), disclosure (practices) can encourage these professionals to exhibit this concern in a merely perfunctory way.’ 18

There are two final considerations about disclosure that need to be noted. First, disclosure is not meant to actually detect bias. Rather, it is meant to alert people to its possibility. Thus, even though disclosure is our major tool for combating one of the most detrimental forms of bias, it is not clear what good it actually does, which leads us to the second consideration. Since disclosure does nothing to prevent sponsorship bias, more substantial countermeasures aimed at prevention are needed. It is beyond the scope of this essay to examine the suitability of possible countermeasures for preventing sponsorship bias, such as sequestering investigators from private companies whenever possible. 15 Referencing this one example, though, highlights the substantial difference there can be between detecting bias on the one hand and actually preventing it on the other, a topic we will return to later.

Returning for the moment, though, to detection of sponsorship bias, these collective concerns about the most prevalent safeguard against it suggest that it can facilitate rather than detect, let alone prevent, bias. By stopping at disclosure, it suggests that financial entanglements are often permissible; we just need to make sure they are relatively transparent to others. The end result is that there is a pall of uncertainty cast over a large body of published research, including a major portion of the clinical trials that society relies on to improve healthcare. 17

Additional major sources of bias

Evidence about the effectiveness of safeguards against other prominent sources of bias besides sponsorship bias is equally disconcerting. Consider, for example, biases that impact the design, conduct and reporting of preclinical animal studies. This class of studies is of particular concern for multiple reasons, not the least of which is the fact that early phase clinical trials, and the risks intrinsic to them, can launch on a single, highly prized ‘proof-of-concept finding in an animal model without wider preclinical validation.’ 19 This risk is particularly grave when we consider the interests and welfare of the patients who volunteer for the early phase clinical trials. 20

Given such high stakes, it is critical that there be effective safeguards that, once again, counter biases that undermine the rigour that studies capable of producing reliable findings require. Here too table 1 prompts investigation of how well current safeguards actually work. Evidence about excess significance bias, a publishing bias due in large part to selective publishing of results by both authors and journals, shows major limitations in their effectiveness. Looking, for example, at the neurosciences preclinical studies generally 2 and stroke studies specifically, 21 we see that excess significance bias is a major contributor to well documented failure 22 23 to successfully ‘translate preclinical animal research (results) to clinical trials.’ 24

When we look at biases resulting from poor study design, across all fields of preclinical inquiry, we find that studies that lack construct, internal and/or external validity that produce biased research reports are ubiquitous. 25 Not only have such findings contributed to ‘spectacular failures of irreproducibility’ 25 that cast concern over entire fields of research, 3 they also forecast failure for the clinical trials that seek to translate preclinical findings into clinical therapies. 26 Illustrating this is a recent study estimating that a majority of the reports of positive findings from animal studies meant to inform clinical studies of acute stroke actually report what are likely to be false positive results. 27

With this evidence in mind, we must consider anew the harm caused by, for example, toxicities, personal expenses and opportunity costs 28 that phase 1 trial participants endure in trials that launch on the basis of preclinical studies whose biased design produces unreliable research reports used to justify the clinical trials. 29 Those participants have no choice but to rely on a properly functioning research oversight system to protect their interests and welfare. Alas, that oversight system is much weaker than the research and research oversight communities likely would care to admit. 30 All the more reason, then, that our efforts to guard against bias should be as varied and robust as its many sources.

The fact of the matter, though, is that the most prominent safeguard against them is peer review. Since it occurs at the reporting stage of the research continuum, it is preceded by other safeguards, such as reporting guidelines, which are reviewed below. None of these other safeguards are as ubiquitous as peer review, however, and it is the gate that publications must ultimately navigate through. Given this level of significance, its effectiveness warrants careful scrutiny. Scrutiny begins by noting that peer review is meant to detect rather than prevent bias. One perhaps could counter that peer review actually is a hybrid countermeasure since it is capable of actually preventing bias at times, or at the least the dissemination of reports tainted by it since, when peer review works, it can prevent publication of suspect findings. However, though it is no doubt true that peer reviewers can reject manuscripts out of concern for bias, concerns about false positive findings, and the like, there is no assurance that manuscripts rejected at one journal will be rejected by all journals. Hence, even if one were to confer it a hybrid status wherein it can both prevent and detect bias, the extent of bias that has long been documented in peer-reviewed journals reveals major weaknesses in peer review. Recent high-profile COVID-19 -related retractions 31 and commentary 32 further confirms these weaknesses. Consequently, we need to be guarded in our expectations about the central antibias safeguard and its ability to assure the reliability of published research findings.

The upshot of all this is that current bias safeguards do little to alert clinical investigators, research ethics review committees, and others to the prospects of biased findings in either pivotal preclinical studies that are the precursors to clinical trials or the full spectrum of clinical trials themselves. This raises genuine concerns that far too many ill-advised clinical trials get conducted rather than avoided. It also underscores the need for conducting the individual studies that constitute any given body of preclinical or clinical research in a manner that is free of bias in the first place. Additional safeguards that prevent rather than detect bias will be needed if we are to succeed at this. No doubt multiple ones are needed. In the balance of this piece, I will focus on ones that could be used for preclinical studies, leaving clinical studies safeguards for other occasions.

Preventing bias

Examples of current bias prevention tools.

We are fortunate that there are some safeguards for combatting bias in preclinical studies already in place. Perhaps the most notable are reporting guidelines such as the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines. 33 Recently revised, 34 the guidelines are designed to assure transparency of critical methodological aspects of animal studies. If widely enough adopted, they should promote greater rigour in animal research and thus prevent much of the bias that currently plagues it. Unfortunately, though, uptake of the guidelines has been lacklustre to date, mainly because too many animal researchers are either unaware of them or do not follow them. 35 Not all the evidence about reporting guidelines is so discouraging though. A recent study of reporting guidelines tailored for the journal Stroke found that they substantially improved the quality of published preclinical studies when compared with reports in other journals that did not require use of the same guidelines. 36 37

Despite the mixed evidence about the effectiveness of reporting guidelines, both general and journal-tailored reporting guidelines do have value that is worth noting. Even though they target the reporting stage of research, their use can influence how researchers design and conduct their studies. This highlights the true promise of reporting guidelines: they can incline researchers toward well-designed research and robust reports about it. To the extent that this occurs, they function as true bias prevention safeguards.

Nevertheless, enthusiasm for reporting guidelines must be tempered by the mixed evidence about them to date. It suggests that reporting guidelines will have an incremental effect at best on preventing bias. This is borne out by evidence, for example, pertaining to the TREAT-NMD Advisory Committee for Therapeutics. Although this committee does not promulgate specific reporting guidelines, it does promote the kinds of research practices that reporting guidelines are meant to foster. It does this by ‘provid(ing) detailed constructive feedback on clinical proposals for neuromuscular diseases submitted by researchers in both academia and industry.’ This group provided feedback on just under 60 preclinical research programmes between 2010 and 2019. It reports having raised concerns in just under a third of their reviews about the use of control groups, blinding and randomisation with researchers whose preclinical research they reviewed. They also report raising concerns about a misalignment between preclinical data and claimed preclinical efficacy almost a third of the time as well. 19 While some may take comfort in the fact that the group’s reviews found deficiencies in basic elements of sound research in far less than half of the studies they reviewed, all likely agree that the frequency of deficiencies still remains troubling.

Two new strategies for preventing bias in preclinical studies

Experience with the ARRIVE guidelines to date suggest that systematic adoption of new research practices will be sporadic, though, rather than widespread until we find ways to systematically move towards widespread adoption of reforms aimed at preventing bias. Perhaps the first step in moving in that direction is collectively grappling with an obvious inference to be drawn from all the evidence noted above: current success metrics in research can too often reward rather than prevent biased research. People may enjoy rewards from design-deficient studies, in the form of publications and funding, as well as the prestige that follows both. This suggests that efforts to combat bias are not just hampered by ineffective and often ill-timed bias countermeasures. They are also hampered by current flawed and entrenched incentive structures and researcher performance metrics that Hardwicke and Ioannidis contend ‘preferentially valu[e] aesthetics over authenticity.’ 38 While many readers may not agree that the current incentive structures are this far askew, we nevertheless must worry, based on the assembled evidence, that research institutions and sponsors may often incentivise biases in very much the same way that private sponsors can cause sponsorship bias.

If this analysis is sound, then widespread adoption of research practices capable of preventing bias will hinge on resisting current incentive structures. The most logical opportunity for generating such resistance resides jointly, I think, with institutional leaders and individual investigators. Though systems-level incentive structures contribute to biased research, the fact of the matter is that investigative teams conduct research and their members are trained at and often employed by research institutions. Thus, the path forward seems to depend on finding ways to get both investigators and research institutions to prize ‘authenticity’ more. This, no doubt, will prove challenging given the extent to which both groups can flourish under current rewards structures.

There are at least two complimentary strategies to look at that might prove beneficial. One encourages both investigators and research institutions to recognise the extent to which they are entangled in a major conflict of interest. Their primary interest in conducting authentic science is too often at odds with the secondary interest in being successful and enjoying the individual and institutional rewards of that success. Though we typically do not label this situation as a conflict of interest, often preferring instead the nomenclature of conflicts of commitment, the situation most assuredly is just as deeply conflicted as are the financial relationships that create sponsorship bias. If it was so designated, continued indifference about it would be difficult to maintain. That prospect alone warrants us labelling the situation the conflict of interest that it is.

The other strategy might provide additional motivation. It requires research teams and research institutions, either separately or jointly, to carefully examine the extent to which they may be contributing to the production of biased research. Here is one way they could do that: identify a systematic review of a given body of research in a given field that those participating in the exercise agree employed a reliable meta-analysis plan that identified bias and/or research deficiencies, determine whether any of the published studies included in the review originated from one’s lab or institution, and determine whether that study may have been at risk for contributing to the bias/deficiencies reported in the systematic review. If no studies from a lab group or the institution were included in the systematic review, they could still determine whether there are any published studies from the lab or institution that could have been included in the systematic review and, if so, whether their studies would have contributed to the worrisome findings reported in the systematic review. With these results in hand, the next step would be to develop a prevention plan that is designed to prevent future studies from exhibiting those problems. With the prevention plan in place, one could then determine what institutional and/or lab-level changes would be required in order to implement the prevention plan.

It is likely that few, if any, prevention plans would need to start from scratch. As most readers of this journal are no doubt aware, there is already a wealth of published scholarship about how to improve the quality of biomedical research. Some of the most relevant examples from it include routine use of study preregistration 39 40 and research reports, 38 41 42 supplementing the 3Rs 43 of animal studies with the 3 Vs of scientific validity, 25 and clearly reporting whether a study is a hypothesis generating or a hypothesis confirming study. 26

We must acknowledge at the outset, though, that developing a prevention plan will likely prove much easier than fully adopting one because adoption will reveal how deeply entrenched the conflict of interest between professional success and rewards and good science often is. For example, clearly labelling research studies as exploratory ones in publications will temper claims about innovation that researchers may be accustomed to making about their work. Similarly, employing research reports will restrict study analyses and descriptions, which will often result in more constrained publications. 41 Different researchers no doubt will respond differently to these changes, but one can hope that enough of them will feel empowered by the changes to become champions of science reforms within their institutions and professional societies meant to align success metrics with good research. Supporting this expectation are recent studies reporting that researchers are eager for improved research climates at their organisations. 44 45

While research teams develop and implement prevention plans, institutional leaders will need to take responsibility for eliminating the conflicts of interest that promote bias in research. They would not need to start from scratch either, since important preliminary work that could help with this is already underway. This work includes efforts that show how to align institutional metrics of professional success with good science. 46–48 An additional resource they could fruitfully draw from is the recently published ‘Hong Kong Principles for assessing researchers.’ 49 Here too it will no doubt be easier to develop than implement plans meant to avoid the entrenched conflict of interest. But benefits may quickly materialise as soon as the work to develop prevention plans materialise. Once institutions name, and thus acknowledge, the conflict of interest that they are helping to perpetuate, maintaining the status quo should prove that much more difficult. This should help to create at least some inertia tilted toward reform and thus away from stasis.

Many readers will no doubt be less sanguine about the success prospects for either strategy. The teams and institutions that choose to adopt them would no doubt have concerns that they would be unilaterally placing themselves at a disadvantage to those that choose not to burden themselves with the demands of either of the proposed strategies. With such concerns in mind, it is helpful to ponder how we might address them. Probably the best option for doing so is to implement some pilot projects to test the use of systematic reviews to develop bias prevention plans. There are at least two options for implementing such pilot projects.

One is for either an institution or a professional society to host a competition where the team that develops the best prevention plan for their work receives some kind of institutional/professional society recognition or reward. Institutional rewards might be monetary in the form of travel stipends for graduate students or postdoctoral fellows to attend conferences. Professional society rewards might be a plenary session at a society’s annual meeting where the winning team could present its bias prevention plan.

The other option is for research institutions to work through their main research officers to sponsor audits of the work of research teams. The audits would be informed by relevant systematic reviews. The audits could either be random or limited to teams that volunteer. To ensure that the audits are not seen or experienced as punitive, the launch of the audits would need to be preceded by a communication campaign that explained the purpose and value of the audits. Others may identify additional options for implementing pilot projects. Whatever options research teams, institutions, and/or professional societies might use, such pilot projects should prove valuable. They are likely the quickest way to learn whether systematic reviews could be used to interrogate research quality at the local level and to develop prevention plans for reducing bias in research.

There is no one panacea capable of turning away all the contributors to decades of disappointing clinical translation efforts. And even if we could snap our fingers and banish overnight the biases that are among the contributors to the disappointing results, science still may not take us to the goal of improved clinical treatments that we seek. After all, we are dealing with science, not magic. But if we could muster the desire and discipline to better combat bias in research, at least we could take comfort in the fact that what we are calling science is in fact actual science, as free of bias as we can possibly make it. The two complimentary strategies described above are offered in hopes that they could help to muster that desire and discipline. If either or both were to prove beneficial, we would find ourselves in a place far preferable to the one we are in now.

Acknowledgments

The author would also like to acknowledge the support of Fondation Brocher, the thoughtful suggestions of several reviewers, and useful input from colleagues Robert Nadon and Fernando Fierro.

Correction notice: This article has been corrected since it was published Online First. In the Acknowledgments, name "Fernando Feraro" has been corrected to "Fernando Fierro".

Contributors: The author conceived the ideas for the manuscript and exclusively wrote all versions of the manuscript, including the final one.

Funding: A portion of the author’s time was supported by the National Centre for Advancing Translational Sciences, National Institutes of Health, through grant number UL1 TR001860.

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement: Data sharing not applicable as no datasets generated and/or analysed for this study. This manuscript does not report about any original empirical research and thus there are no research data to share.

Open peer review: Prepublication and Review History is available online at http://dx.doi.org/10.1136/bmjos-2020-100116 .

Quantitative Research: Definition, Types and Examples

02 February, 2021

14 minutes read

Author:  Mathieu Johnson

Quantitative research is one popular research method that has been in existence for several decades. Unlike qualitative research which involves collating and analyzing non-numerical data, this type of research involves using statistical methods to analyze data. It is majorly used in the natural and social sciences as well as professional fields like marketing.

This leads us to the big question: what exactly is quantitative research? Why is this research method so popular and when should you use it?

Well, this article will provide a detailed insight into the world of quantitative research and when this research method should be used. 

What Is Quantitative Research? 

Wondering what quantitative research is all about? Here is a quick definition of this statistics-based research method:

Quantitative Research Definition 

Quantitative research is generally defined as a methodical investigation of certain phenomena by collating quantifiable data and analyzing the data through mathematical or statistical techniques. In simpler terms, this research method just involves gathering countable data and using mathematical methods to analyze the data. 

Quantitative research method is typically objective and aims to achieve logical, unbiased results. Under this research method, data is collected from a large sample that represents the entire population. The data is then investigated in order for the researcher to gain further insight and arrive at a logical conclusion. 

Quantitative Research Methods (Types) 

There are different types of quantitative research methods. They include:

Survey research

Survey research is one of the most fundamental types of quantitative research designs and is quite popular among students and professionals. Typically, this kind of research aims at explaining the peculiar characteristics of a particular population. As such, most organizations prefer carrying out surveys in order to get a clearer insight into customer behavior.

Survey research may be carried out using questionnaires, online polls, online surveys and so on. Once the researcher has conducted the survey on a pool of respondents, they can then go on to analyze the collected data and produce numerical results. 

Originally, this type of quantitative research was typically carried out through phone calls or face-to-face meetings. However, the advent of social media has made it possible for researchers to carry out surveys via online mediums. 

When carrying out this kind of research, it is important to ensure that your respondents are randomly selected. This way, you can end up with more accurate results and theories. 

Correlational research 

Correlational research is majorly conducted to draw a relationship between two entities or phenomena and examine the impact of one on the other. Usually, a minimum of two different groups are required to carry out this type of research successfully. 

When it comes to correlation research, it can be tempting to jump into hasty conclusions or make assumptions. However, the researcher is expected to conduct the study without unnecessary manipulations of any variable. 

Descriptive research

Descriptive research is quite simplistic in nature. Rather than drawing a comparison between two or more variables, it merely tries to explain and interpret the current state of a particular variable. It focuses on answering all the questions of a research problem in order to have a proper understanding of what it is about. 

For any researcher, it is important to get a clear picture of what a research problem is all about before going ahead to investigate the cause of the problem. This is where descriptive research comes in. 

For instance, an activist seeking to launch a campaign on climate change would need to understand the current state of the climate and how it has progressed or regressed over the years before investigating the causes of the change. 

Experimental research 

Also known as true experimentation, experimental research is an age-long research method that relies on unproven theories. Under this type of research, the researcher identifies a theory that has not been proven in the past and then carries out an analysis to either prove or disprove the theory. 

Although experimental research is majorly used in the natural and social sciences, other fields often employ this research method to establish the truth value of a statement. 

Causal-comparative research

Just like the name implies, causal-comparative research is based on comparison. This fundamental quantitative research method is typically used by researchers to establish a cause-effect relationship between two or more variables. 

This research method is often mistaken for correlational research but there’s a whole world of difference between them. Here, one of the variables being studied is dependent on the other independent variable. Causal-comparative research also extends to studying how various groups or variables evolve when subjected to the same influencing factors. 

Quantitative Research Questions 

What are the typical quantitative research questions? When carrying out any type of quantitative research, it is important to map out appropriate questions for your respondents that would guarantee clear, unambiguous answers. 

There are three major types of quantitative research questions that you may employ depending on the type of research you want to conduct. These are:

Descriptive research questions 

This type of research questions is often used when the quantitative research analyst wants to conduct descriptive research. These questions are targeted at getting the respondent’s answer or reaction to a particular variable. Most descriptive research questions may be aimed at quantifying one or more variables and could look like this:

• How much will you be willing to pay for a shot of the COVID-19 vaccine? 
• How often do you purchase skincare products? 
• How regularly do you invest in ponzi schemes? 
• What’s your maximum budget for a week-long vacation on a tropical island? 
• How much will you be willing to spend on a designer bag? 

Comparative research questions

Comparative research questions are often used to compare or draw clear differences between two or more groups. Some examples of this type of research questions include:

• What are the differences between attitudes to social media trends of millenials and middle-aged adults? 
• What is the difference between the social behavior of college students and high school students? 

Relationship-based research questions

This type of research question aims to describe a relationship or correlation between two or more variables within a particular group. Some examples are:

• What is the relationship between drug addiction and neurological disorders in young adults? 
• What is the correlation between latent sexism and the gender pay gap? 

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Quantitative Research Examples 

Looking for an example of quantitative research and the way it is used? Here are a few to get you started:

• If an organization is about to create and launch a new product such as a food delivery app, they may collect quantitative data and metrics on consumer behavior towards online grocery shopping. This data can be collected by running online and physical surveys or polls. Once the organization has collated quantifiable data from the surveys, they can go on to analyze the data and determine the size of their target market and the implications on their estimated ROI . 
• If a marketing brand is trying to gain a clearer insight into social media habits for their next marketing campaign, they could carry out a survey on different demographics or groups of people. They could conduct a survey on teenagers, middle-aged adults and elderly people to examine how they react to social media trends and how this affects brand-consumer relationships. 

Pros and Cons of Quantitative Research 

There are many advantages or benefits of quantitative research. This explains why this method is widely used among researchers and organizations. Some of the merits of using the quantitative research method are:

• It helps the researcher collect accurate and reliable data: The major goal of any research or study is to end up with accurate and reliable results. With quantitative research, this goal is fairly easy to achieve. All data is collated, analyzed and presented in numbers, thereby offering a reliable and genuine picture of the research. This way, researchers can end up with true results rather than mere assumptions or hasty conclusions. 
• Eliminates bias: When it comes to research, it’s quite easy for any researcher to let their personal bias seep into the work. However, quantitative research offers no avenue for personal opinions or manipulation of results. Since data analysis is done through numerical methods, the researcher is sure to arrive at an objective, unbiased conclusion. 
• Quick and seamless data collection and analysis: Collecting data is usually one of the most time consuming phases of conducting a research. However, quantitative research allows for a quick and seamless data collection process. The research is typically carried out on a group of respondents that represent a larger population. The use of pure statistics to analyze the data gotten from these respondents makes the entire process swift and straightforward. 
• Direct comparisons of results: In quantitative research, where one research work stops, another begins. The research can be reproduced by switching up the variables. For instance, another researcher may reproduce the study in a different cultural setting or with a different group of participants. This would make it easier to compare results statistically and point out marginal differences. 

Disadvantages of quantitative research 

Even though quantitative research is highly beneficial to any researcher who decides to employ this research method, it also has a few limitations which include:

• Structural bias: Although quantitative research is often touted as a reliable and objective research method, structural bias may still seep into the picture and affect the entire research work. For instance, missing data or use of the wrong sampling methods can utterly ruin the research and lead to the wrong conclusion. 
• False focus on numbers: Since quantitative research deals solely with numbers and mathematical techniques, researchers often focus so hard on pursuing statistical relationships that they end up missing the bigger picture. Some situations or theories may have broader themes or relationships. However, most quantitative research analysts focus solely on numbers and eventually miss the bigger information. 
• It can be difficult: Most researchers who conduct this type of research are often under undue pressure to set up a concrete research model and end up with organized, reliable results. However, this is quite difficult to achieve as the slightest error or execution slip can invalidate all your results and render the work unreliable. 
• Does not explore the “why” and “how” of a phenomenon: Quantitative research never explains the causes or background context of a phenomenon. Rather, it conducts a number-based analysis of the phenomenon and leaves the rest to qualitative research. 

When to Use Quantitative Research 

Wondering the best time to use quantitative research? Most researchers use quantitative research when they want to confirm a theory or hypothesis. It is also used to get logical, objective answers or conclusions. 

However, some researchers may choose to combine this research method with qualitative research in order to get a more comprehensive understanding of the research problem. In this case, one may decide to use qualitative research to explore a problem and scope out the research question. 

Subsequently, you may then use quantitative research to get answers to the questions raised during the qualitative research phase. 

Tips for Carrying out Quantitative Research 

Here are some suggestions to help you conduct quantitative research:

Make your research goals clear and concise

Before you hop into the data collection process, it’s important to know your research goals. What exactly are you trying to achieve by the end of the study? What tools would help you achieve your aim? By mapping out your goals before data collection, you’d be able to get the right amount of data and secondary tools needed for the study. 

Choose a suitable sample size

The ideal sample size for quantitative research largely depends on the general population and aim of the research. When carving out a sample size, ensure that it ably represents the population and is large enough to have statistical significance. 

Stick to clear, simple questions 

For researchers who may be carrying out surveys, it’s important to stick to only simple questions. Most surveys involve a large demographic of people and the data collection process will be exhausting if respondents have to pore over complex questions. As such, it is highly advisable to set clear questions that respondents can understand easily. 

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Why research is logical and objective?

it is because of the heat of the sun sampack to our brain

Add your answer:

What is the difference between a research question and a research objective?

the difference between a research question and objective is the question is about the research the objective is what you want to prove with the research

What is the difference between a research question and an objective?

I think a research question is in a question form, starts with How, What, ... while a research objective starts with To identify......, To explore.......

What is the definition of objective procedures?

Objective procedures are ones that are not influenced by emotions. In research, objective procedures help answer questions without any interference.

Why is a crescent moon called a paper moon?

Perhaps it is because it looks paper thin. No research done, it just seems logical.

What is the definition of procedure?

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