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Online Guide to Writing and Research

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• Online Guide to Writing

Planning and Writing a Research Paper

Draw Conclusions

As a writer, you are presenting your viewpoint, opinions, evidence, etc. for others to review, so you must take on this task with maturity, courage and thoughtfulness.  Remember, you are adding to the discourse community with every research paper that you write.  This is a privilege and an opportunity to share your point of view with the world at large in an academic setting.

Because research generates further research, the conclusions you draw from your research are important. As a researcher, you depend on the integrity of the research that precedes your own efforts, and researchers depend on each other to draw valid conclusions. 

To test the validity of your conclusions, you will have to review both the content of your paper and the way in which you arrived at the content. You may ask yourself questions, such as the ones presented below, to detect any weak areas in your paper, so you can then make those areas stronger.  Notice that some of the questions relate to your process, others to your sources, and others to how you arrived at your conclusions.

Checklist for Evaluating Your Conclusions

Key takeaways.

• Because research generates further research, the conclusions you draw from your research are important.
• To test the validity of your conclusions, you will have to review both the content of your paper and the way in which you arrived at the content.

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Table of Contents: Online Guide to Writing

Chapter 1: College Writing

How Does College Writing Differ from Workplace Writing?

What Is College Writing?

Why So Much Emphasis on Writing?

Chapter 2: The Writing Process

Doing Exploratory Research

Getting from Notes to Your Draft

Introduction

Prewriting - Techniques to Get Started - Mining Your Intuition

Prewriting: Targeting Your Audience

Prewriting: Techniques to Get Started

Prewriting: Understanding Your Assignment

Rewriting: Being Your Own Critic

Rewriting: Creating a Revision Strategy

Rewriting: Getting Feedback

Rewriting: The Final Draft

Techniques to Get Started - Outlining

Techniques to Get Started - Using Systematic Techniques

Thesis Statement and Controlling Idea

Writing: Getting from Notes to Your Draft - Freewriting

Writing: Getting from Notes to Your Draft - Summarizing Your Ideas

Writing: Outlining What You Will Write

Chapter 3: Thinking Strategies

A Word About Style, Voice, and Tone

A Word About Style, Voice, and Tone: Style Through Vocabulary and Diction

Critical Strategies and Writing

Critical Strategies and Writing: Analysis

Critical Strategies and Writing: Evaluation

Critical Strategies and Writing: Persuasion

Critical Strategies and Writing: Synthesis

Developing a Paper Using Strategies

Kinds of Assignments You Will Write

Patterns for Presenting Information

Patterns for Presenting Information: Critiques

Patterns for Presenting Information: Discussing Raw Data

Patterns for Presenting Information: General-to-Specific Pattern

Patterns for Presenting Information: Problem-Cause-Solution Pattern

Patterns for Presenting Information: Specific-to-General Pattern

Patterns for Presenting Information: Summaries and Abstracts

Supporting with Research and Examples

Writing Essay Examinations

Writing Essay Examinations: Make Your Answer Relevant and Complete

Writing Essay Examinations: Organize Thinking Before Writing

Writing Essay Examinations: Read and Understand the Question

Chapter 4: The Research Process

Planning and Writing a Research Paper: Ask a Research Question

Planning and Writing a Research Paper: Cite Sources

Planning and Writing a Research Paper: Collect Evidence

Planning and Writing a Research Paper: Decide Your Point of View, or Role, for Your Research

Planning and Writing a Research Paper: Draw Conclusions

Planning and Writing a Research Paper: Find a Topic and Get an Overview

Planning and Writing a Research Paper: Manage Your Resources

Planning and Writing a Research Paper: Outline

Planning and Writing a Research Paper: Survey the Literature

Planning and Writing a Research Paper: Work Your Sources into Your Research Writing

Research Resources: Where Are Research Resources Found? - Human Resources

Research Resources: What Are Research Resources?

Research Resources: Where Are Research Resources Found?

Research Resources: Where Are Research Resources Found? - Electronic Resources

Research Resources: Where Are Research Resources Found? - Print Resources

Structuring the Research Paper: Formal Research Structure

Structuring the Research Paper: Informal Research Structure

The Nature of Research

The Research Assignment: How Should Research Sources Be Evaluated?

The Research Assignment: When Is Research Needed?

The Research Assignment: Why Perform Research?

Chapter 5: Academic Integrity

Academic Integrity

Giving Credit to Sources

Giving Credit to Sources: Copyright Laws

Giving Credit to Sources: Documentation

Giving Credit to Sources: Style Guides

Integrating Sources

Practicing Academic Integrity

Practicing Academic Integrity: Keeping Accurate Records

Practicing Academic Integrity: Managing Source Material

Practicing Academic Integrity: Managing Source Material - Paraphrasing Your Source

Practicing Academic Integrity: Managing Source Material - Quoting Your Source

Practicing Academic Integrity: Managing Source Material - Summarizing Your Sources

Types of Documentation

Types of Documentation: Bibliographies and Source Lists

Types of Documentation: Citing World Wide Web Sources

Types of Documentation: In-Text or Parenthetical Citations

Types of Documentation: In-Text or Parenthetical Citations - APA Style

Types of Documentation: In-Text or Parenthetical Citations - CSE/CBE Style

Types of Documentation: In-Text or Parenthetical Citations - Chicago Style

Types of Documentation: In-Text or Parenthetical Citations - MLA Style

Types of Documentation: Note Citations

Chapter 6: Using Library Resources

Finding Library Resources

Chapter 7: Assessing Your Writing

How Is Writing Graded?

How Is Writing Graded?: A General Assessment Tool

The Draft Stage

The Draft Stage: The First Draft

The Draft Stage: The Revision Process and the Final Draft

The Draft Stage: Using Feedback

The Research Stage

Using Assessment to Improve Your Writing

Chapter 8: Other Frequently Assigned Papers

Reviews and Reaction Papers: Article and Book Reviews

Reviews and Reaction Papers: Reaction Papers

Writing Arguments

Writing Arguments: Adapting the Argument Structure

Writing Arguments: Purposes of Argument

Writing Arguments: References to Consult for Writing Arguments

Writing Arguments: Steps to Writing an Argument - Anticipate Active Opposition

Writing Arguments: Steps to Writing an Argument - Determine Your Organization

Writing Arguments: Steps to Writing an Argument - Develop Your Argument

Writing Arguments: Steps to Writing an Argument - Introduce Your Argument

Writing Arguments: Steps to Writing an Argument - State Your Thesis or Proposition

Writing Arguments: Steps to Writing an Argument - Write Your Conclusion

Writing Arguments: Types of Argument

Appendix A: Books to Help Improve Your Writing

Dictionaries

General Style Manuals

Researching on the Internet

Special Style Manuals

Writing Handbooks

Appendix B: Collaborative Writing and Peer Reviewing

Collaborative Writing: Assignments to Accompany the Group Project

Collaborative Writing: Informal Progress Report

Collaborative Writing: Issues to Resolve

Collaborative Writing: Methodology

Collaborative Writing: Peer Evaluation

Collaborative Writing: Tasks of Collaborative Writing Group Members

Collaborative Writing: Writing Plan

General Introduction

Peer Reviewing

Appendix C: Developing an Improvement Plan

Working with Your Instructor’s Comments and Grades

Appendix D: Writing Plan and Project Schedule

Devising a Writing Project Plan and Schedule

Reviewing Your Plan with Others

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Drawing Conclusions

For any research project and any scientific discipline, drawing conclusions is the final, and most important, part of the process.

This article is a part of the guide:

• Null Hypothesis
• Research Hypothesis
• Defining a Research Problem
• Selecting Method

Browse Full Outline

• 1 Scientific Method
• 2.1.1 Null Hypothesis
• 2.1.2 Research Hypothesis
• 2.2 Prediction
• 2.3 Conceptual Variable
• 3.1 Operationalization
• 3.2 Selecting Method
• 3.3 Measurements
• 3.4 Scientific Observation
• 4.1 Empirical Evidence
• 5.1 Generalization
• 5.2 Errors in Conclusion

Whichever reasoning processes and research methods were used, the final conclusion is critical, determining success or failure. If an otherwise excellent experiment is summarized by a weak conclusion, the results will not be taken seriously.

Success or failure is not a measure of whether a hypothesis is accepted or refuted, because both results still advance scientific knowledge.

Failure lies in poor experimental design, or flaws in the reasoning processes, which invalidate the results. As long as the research process is robust and well designed, then the findings are sound, and the process of drawing conclusions begins.

The key is to establish what the results mean. How are they applied to the world?

What Has Been Learned?

Generally, a researcher will summarize what they believe has been learned from the research, and will try to assess the strength of the hypothesis.

Even if the null hypothesis is accepted, a strong conclusion will analyze why the results were not as predicted. 

Theoretical physicist Wolfgang Pauli was known to have criticized another physicist’s work by saying, “it’s not only not right; it is not even wrong.”

While this is certainly a humorous put-down, it also points to the value of the null hypothesis in science, i.e. the value of being “wrong.” Both accepting or rejecting the null hypothesis provides useful information – it is only when the research provides no illumination on the phenomenon at all that it is truly a failure.

In observational research , with no hypothesis, the researcher will analyze the findings, and establish if any valuable new information has been uncovered. The conclusions from this type of research may well inspire the development of a new hypothesis for further experiments. 

Generating Leads for Future Research

However, very few experiments give clear-cut results, and most research uncovers more questions than answers.

The researcher can use these to suggest interesting directions for further study. If, for example, the null hypothesis was accepted, there may still have been trends apparent within the results. These could form the basis of further study, or experimental refinement and redesign.

Question: Let’s say a researcher is interested in whether people who are ambidextrous (can write with either hand) are more likely to have ADHD. She may have three groups – left-handed, right-handed and ambidextrous, and ask each of them to complete an ADHD screening.

She hypothesizes that the ambidextrous people will in fact be more prone to symptoms of ADHD. While she doesn’t find a significant difference when she compares the mean scores of the groups, she does notice another trend: the ambidextrous people seem to score lower overall on tests of verbal acuity. She accepts the null hypothesis, but wishes to continue with her research. Can you think of a direction her research could take, given what she has already learnt?

Answer: She may decide to look more closely at that trend. She may design another experiment to isolate the variable of verbal acuity, by controlling for everything else. This may eventually help her arrive at a new hypothesis: ambidextrous people have lower verbal acuity.

Evaluating Flaws in the Research Process

The researcher will then evaluate any apparent problems with the experiment. This involves critically evaluating any weaknesses and errors in the design, which may have influenced the results .

Even strict, ' true experimental ,' designs have to make compromises, and the researcher must be thorough in pointing these out, justifying the methodology and reasoning.

For example, when drawing conclusions, the researcher may think that another causal effect influenced the results, and that this variable was not eliminated during the experimental process . A refined version of the experiment may help to achieve better results, if the new effect is included in the design process.

In the global warming example, the researcher might establish that carbon dioxide emission alone cannot be responsible for global warming. They may decide that another effect is contributing, so propose that methane may also be a factor in global warming. A new study would incorporate methane into the model.

What are the Benefits of the Research?

The next stage is to evaluate the advantages and benefits of the research.

In medicine and psychology, for example, the results may throw out a new way of treating a medical problem, so the advantages are obvious.

In some fields, certain kinds of research may not typically be seen as beneficial, regardless of the results obtained. Ideally, researchers will consider the implications of their research beforehand, as well as any ethical considerations. In fields such as psychology, social sciences or sociology, it’s important to think about who the research serves and what will ultimately be done with the results.

For example, the study regarding ambidexterity and verbal acuity may be interesting, but what would be the effect of accepting that hypothesis? Would it really benefit anyone to know that the ambidextrous are less likely to have a high verbal acuity?

However, all well-constructed research is useful, even if it only strengthens or supports a more tentative conclusion made by prior research.

Suggestions Based Upon the Conclusions

The final stage is the researcher's recommendations based on the results, depending on the field of study. This area of the research process is informed by the researcher's judgement, and will integrate previous studies.

For example, a researcher interested in schizophrenia may recommend a more effective treatment based on what has been learnt from a study. A physicist might propose that our picture of the structure of the atom should be changed. A researcher could make suggestions for refinement of the experimental design, or highlight interesting areas for further study. This final piece of the paper is the most critical, and pulls together all of the findings into a coherent agrument.

The area in a research paper that causes intense and heated debate amongst scientists is often when drawing conclusions .

Sharing and presenting findings to the scientific community is a vital part of the scientific process. It is here that the researcher justifies the research, synthesizes the results and offers them up for scrutiny by their peers.

As the store of scientific knowledge increases and deepens, it is incumbent on researchers to work together. Long ago, a single scientist could discover and publish work that alone could have a profound impact on the course of history. Today, however, such impact can only be achieved in concert with fellow scientists.

Summary - The Strength of the Results

The key to drawing a valid conclusion is to ensure that the deductive and inductive processes are correctly used, and that all steps of the scientific method were followed.

Even the best-planned research can go awry, however. Part of interpreting results also includes the researchers putting aside their ego to appraise what, if anything went wrong. Has anything occurred to warrant a more cautious interpretation of results?

If your research had a robust design, questioning and scrutiny will be devoted to the experiment conclusion, rather than the methods.

Question: Researchers are interested in identifying new microbial species that are capable of breaking down cellulose for possible application in biofuel production. They collect soil samples from a particular forest and create laboratory cultures of every microbial species they discover there. They then “feed” each species a cellulose compound and observe that in all the species tested, there was no decrease in cellulose after 24 hours.

Read the following conclusions below and decide which of them is the most sound:

They conclude that there are no microbes that can break down cellulose.

They conclude that the sampled microbes are not capable of breaking down cellulose in a lab environment within 24 hours.

They conclude that all the species are related somehow.

They conclude that these microbes are not useful in the biofuel industry.

They conclude that microbes from forests don’t break down cellulose.

Answer: The most appropriate conclusion is number 2. As you can see, sound conclusions are often a question of not extrapolating too widely, or making assumptions that are not supported by the data obtained. Even conclusion number 2 will likely be presented as tentative, and only provides evidence given the limits of the methods used.

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Martyn Shuttleworth , Lyndsay T Wilson (Jul 22, 2008). Drawing Conclusions. Retrieved Dec 03, 2023 from Explorable.com: https://explorable.com/drawing-conclusions

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Why do the closing remarks in speeches always begin with the phrase, "In conclusion"? It's remarkably the same thought process that occurs when a group of astronomers look at a blip on a computer screen and soon announce the discovery of some distant celestial object. How is that possible? Well, the person concluding their speech and the enthusiastic astronomer are satisfied that their work is coming to an end. They have done their duty to the best of their ability and are confident that they have covered all the bases, and it is safe to conclude proceedings. In the case of the astronomer, though, the process is a bit more scientifically rigorous. In this article, we will discuss what it means to draw a conclusion and how it can be done, scientifically.

The definition of drawing a conclusion

An experimenter aims to test a hypothesis (which is a statement about what the experimenter expects will happen in the experiment) and possibly answer some larger, important question. At the end of each experiment, an experimenter makes a statement that summarizes what they have learnt from the conducted observation. This is called a conclusion , and we can define the drawing of a conclusion as follows.

We can define the drawing of a conclusion as stating the insight gained from experimenting.

All that is learned during an investigation can be summarised in a concluding statement, called the conclusion . In simple terms, the conclusion of any research should be based purely on the findings of that research. It is supported by facts and proof from the research conducted.

The steps involved in drawing conclusions

In conducting scientific research, an experimenter will follow the scientific method described in the steps below. The experimenter will:

• ask a question and formulate a hypothesis,
• conduct an experiment or investigation,
• collect, represent and analyse information,
• interpret the results,
• and draw a conclusion .

The steps above outline the scientific method very briefly. As scientists, we must first formulate a hypothesis or a research question. This will determine the path that our research journey will take. Next, we will conduct an experiment or investigation to test our hypothesis. The results from our investigation will be collected, analysed and interpreted. We should have gained enough information to answer our research question, and the final step in conducting research is then to draw a conclusion . We'll discuss the scientific method in more detail in the next section. The figure below shows a simple representation of the steps involved in conducting research and arriving at a conclusion.

Using the scientific method to draw a conclusion

The steps above, from creating a hypothesis to drawing a conclusion, form the scientific method, as we've just mentioned. There are other steps in the scientific method that we have omitted for brevity (e.g. communicating findings), but for now, we'll deal with the experiment and its immediate outcomes. The figure below shows how this process can be repeated to continuously refute science with better science.

Ideally, the conclusion of an investigation should prove or disprove the hypothesis and answer the research question. This is not always the case, as the scientific inferences may leave the scientist no nearer to the answer they require.

An example of drawing a conclusion

The example below outlines the steps involved in the scientific method and eventually reaches the final step, which is the focus of this article; drawing a conclusion.

Assume Mark and Joseph create a hypothesis regarding the January temperatures in their neighbourhood. They have followed the steps mentioned above to come to a conclusion.

Step 1: Formulating the hypothesis

Hypothesis 1: January days are hottest before 14:00, according to Mark.

Hypothesis 2: The warmest time of January days is after four o'clock in the afternoon, according to Joseph.

After setting their hypotheses, they want to perform an experiment and gather data to validate them.

Step 2: Performing an experiment

They decide to use a digital thermometer to measure the temperature outside at specific times during each day for January.

Step 3: Collecting and representing the data

The temperature data is collected for January and then averaged, as indicated in the diagram below.

Step 4: Interpretation of results

By simply looking at the data that is visualized by the vertical bar graph above, one can notice that t he temperature increases from 08:00 until 12:00, at which point it reaches a maximum and decreases thereafter.

Step 5: Drawing conclusions

Joseph can tell from the graph that the investigation's findings contradict his ideas. Based on the data recorded and the observations, the hottest temperature occurs before 14:00, and not after 4 o'clock in the afternoon.

The findings corroborate Mark's premise and he can derive the following conclusion that validates his initial hypothesis.

Conclusion: Winter days are the hottest before 14:00.

The example above highlights the importance of representing data. Data that is well-collected and well-represented can make analysis and inference much easier. In turn, this can make it easier to draw conclusions.

Even if you put major efforts into preparing data, analyzing results, and performing observations, the conclusion is crucial in deciding whether the project will succeed or fail.

On one hand, the results will not be taken seriously if an otherwise good experiment is summarized by a poor conclusion. On the other hand, even if the set-up and the data gathered are valid, but the conclusion drawn is not correct, the experiment will not be valid.

Keep in mind that whether a theory is accepted or disproven is not a measure of success or failure, because both outcomes contribute to scientific knowledge.

The differences between inferences and drawing conclusions

It may seem as if the words are interchangeable but there are differences between inferences and conclusions.

An inference is a fact that is assumed based on the information that is provided.

Simply, an inference is an assumed fact based on other facts. Here's an example that will make this idea clearer.

Imagine that you observe someone slamming a door. You might infer that this person is angry. That is, you used the fact that the door was slammed to assume the fact that this person is angry.

Inferences are important because scientists can often pose and answer questions about things that are not immediately apparent. Next, we can define a conclusion.

A conclusion refers to an explanation or interpretation of an observation . It is the next step in the information process and comes after critical thought and logical reasoning.

Let us revisit the previous example to illustrate the difference between inference and conclusion.

Imagine that you observe someone slamming a door. You might infer that this person is angry. This cannot be your conclusion, however, since critically you would know that more information is required. A conclusion could be that this person is strong enough to slam a door.

We can see that there is a clear difference between making an inference and drawing a conclusion. A good scientific example would be the one below.

Dinosaurs have been extinct for millions of years, so simply observing them is not a possible way of determining their diet. What we can do is study fossils of dinosaur droppings and determine the type of food they ate. The following events would occur in the given order.

Observation : Studies of some dinosaur droppings show signs of crushed bones.

Inference : These dinosaurs preyed on herbivores that were smaller than themselves. This is a pretty safe assumption to make but we don't know this for certain.

Conclusion : These dinosaurs ate animals. However, they could have been predators, scavengers, or maybe even cannibals.

Drawing Conclusions - Key takeaways

• Drawing conclusions is the final step in any research or any scientific investigation.

We can define the drawing of a conclusion as the insight gained from experimenting. All that is learned during an investigation can be summarised in a concluding statement.

• Ideally, the conclusion of an investigation should prove or disprove the hypothesis and answer the research question.
• and draw a conclusion.
• A conclusion refers to an explanation or interpretation of an observation . It is the next step in the information process and comes after critical thought and logical reasoning. It is a fact that follows logically from the information that is provided.
• Fig. 1- Four stage scientific method (https://commonswikimedia.org/wiki/File:4_stage_Scientific_Method.jpg) by Brightyellowjeans is licensed by CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/deed.en).
• Fig. 2- The Scientific Method (https://commons.wikimedia.org/wiki/File:The_Scientific_Method.svg) by Efbrazil is licensed by CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/deed.en).

Frequently Asked Questions about Drawing Conclusions

--> what is drawing conclusion .

The drawing conclusion is a statement, at the end of each experiment, that summarizes what the experimenter have learnt from the conducted observation. This is what we call drawing conclusions.

--> What is an example of drawing conclusion ?

An example of drawing conclusion can be the following situation:

After repeating the experiment 10 times, we were able to validate the initial hypothesis, and confirm that the distilled water boils at 100 degrees Celsius. This is an example of a conclusion. The process of reaching this conclusion is called drawing a conclusion.

--> What are the 3 steps for drawing conclusions? 

The 3 steps for drawing conclusions are:

• Refer to your experiment's hypothesis.
• Examine the results of your experiment. Analyze the data, doing any computations or graphs necessary to spot trends or patterns in your findings.
• Check to see if your evidence backs up your theory or proves it to be wrong. Make a statement that summarizes your findings.

--> How to draw a conclusion in the scientific method ?

To draw a conclusion in the scientific method, we can follow the next steps:

• State if you agree   or   disagree   with your hypothesis. 
•  Support your statement with particular facts (proof) from your experiment.
• Talk about if   the   problem/question   has   been   resolved.
•  Describe   further   difficulties   or   experiments that should be carried out.

--> What is the differences between drawing conclusion and inferences?

The differences between drawing conclusion and interferences are that an inference is a fact that is assumed based on the information provided.  A conclusion is logically and factually based on data that is observed, recorded and well represented.

Final Drawing Conclusions Quiz

Drawing conclusions quiz - teste dein wissen.

How can we define the phrase "drawing a conclusion"? 

Show answer

Show question

Is the conclusion linked to the hypothesis? 

Yes. Ideally, the conclusion of an investigation should prove or disprove the hypothesis.

Drawing conclusions is the last step of the scientific method.

A conclusion for a scientific experiment can be drawn without collecting data or conducting research.

On what part of the experiment should the conclusion be based? 

How do you support your conclusion of an experiment? 

It should be supported with particular facts and proof from the experiment.

What is an inference? 

An inference is a fact that is assumed based on the information that is provided. Simply, an inference is an assumed fact based on other facts.

Why is inference important in science? 

Inferences are important because scientists can often pose and answer questions about things that are not immediately apparent.

Scientists discover crushed bones in the fossils of dinosaur droppings. They decide that the dinosaur had eaten other dinosaurs. In this case, did the scientists provide a conclusion or inference?   

You notice a classmate crying on the day that the examination results were released. You decide that they must be sad because they obtained poor results. Is this an example of a conclusion or inference?

Which of these is not a step of the scientific method?

Ask a question and formulate a hypothesis.

If the conclusion was not aligned with your initial hypothesis, then the experiment was not successful. Is this statement true or false? 

False. Whether a theory is accepted or disproved is not a measure of success or failure, because both outcomes contribute to scientific knowledge. 

Can data representation aid the process of drawing conclusions? 

Yes, since if data is well-represented, valid conclusions can be drawn more easily by analysing the data representation. 

After repeating the experiment 10 times, we were able to validate the initial hypothesis, and confirming that the distilled water boils at 100 degree Celsius. Is this statement a conclusion or an interference? 

Conclusion.

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2.7 Drawing Conclusions and Reporting the Results

Learning objectives.

• Identify the conclusions researchers can make based on the outcome of their studies.
• Describe why scientists avoid the term “scientific proof.”
• Explain the different ways that scientists share their findings.

Drawing Conclusions

Since statistics are probabilistic in nature and findings can reflect type I or type II errors, we cannot use the results of a single study to conclude with certainty that a theory is true. Rather theories are supported, refuted, or modified based on the results of research.

If the results are statistically significant and consistent with the hypothesis and the theory that was used to generate the hypothesis, then researchers can conclude that the theory is supported. Not only did the theory make an accurate prediction, but there is now a new phenomenon that the theory accounts for. If a hypothesis is disconfirmed in a systematic empirical study, then the theory has been weakened. It made an inaccurate prediction, and there is now a new phenomenon that it does not account for.

Although this seems straightforward, there are some complications. First, confirming a hypothesis can strengthen a theory but it can never prove a theory. In fact, scientists tend to avoid the word “prove” when talking and writing about theories. One reason for this avoidance is that the result may reflect a type I error.  Another reason for this  avoidance  is that there may be other plausible theories that imply the same hypothesis, which means that confirming the hypothesis strengthens all those theories equally. A third reason is that it is always possible that another test of the hypothesis or a test of a new hypothesis derived from the theory will be disconfirmed. This  difficulty  is a version of the famous philosophical “problem of induction.” One cannot definitively prove a general principle (e.g., “All swans are white.”) just by observing confirming cases (e.g., white swans)—no matter how many. It is always possible that a disconfirming case (e.g., a black swan) will eventually come along. For these reasons, scientists tend to think of theories—even highly successful ones—as subject to revision based on new and unexpected observations.

A second complication has to do with what it means when a hypothesis is disconfirmed. According to the strictest version of the hypothetico-deductive method, disconfirming a hypothesis disproves the theory it was derived from. In formal logic, the premises “if  A  then  B ” and “not  B ” necessarily lead to the conclusion “not  A .” If  A  is the theory and  B  is the hypothesis (“if  A  then  B ”), then disconfirming the hypothesis (“not  B ”) must mean that the theory is incorrect (“not  A ”). In practice, however, scientists do not give up on their theories so easily. One reason is that one disconfirmed hypothesis could be a missed opportunity (the result of a type II error) or it could be the result of a faulty research design. Perhaps the researcher did not successfully manipulate the independent variable or measure the dependent variable.

A disconfirmed hypothesis could also mean that some unstated but relatively minor assumption of the theory was not met. For example, if Zajonc had failed to find social facilitation in cockroaches, he could have concluded that drive theory is still correct but it applies only to animals with sufficiently complex nervous systems. That is, the evidence from a study can be used to modify a theory.  This practice does not mean that researchers are free to ignore disconfirmations of their theories. If they cannot improve their research designs or modify their theories to account for repeated disconfirmations, then they eventually must abandon their theories and replace them with ones that are more successful.

The bottom line here is that because statistics are probabilistic in nature and because all research studies have flaws there is no such thing as scientific proof, there is only scientific evidence.

Reporting the Results

The final step in the research process involves reporting the results. As described in the section on Reviewing the Research Literature in this chapter, results are typically reported in peer-reviewed journal articles and at conferences.

The most prestigious way to report one’s findings is by writing a manuscript and having it published in a peer-reviewed scientific journal. Manuscripts published in psychology journals typically must adhere to the writing style of the American Psychological Association (APA style). You will likely be learning the major elements of this writing style in this course.

Another way to report findings is by writing a book chapter that is published in an edited book. Preferably the editor of the book puts the chapter through peer review but this is not always the case and some scientists are invited by editors to write book chapters.

A fun way to disseminate findings is to give a presentation at a conference. This can either be done as an oral presentation or a poster presentation. Oral presentations involve getting up in front of an audience of fellow scientists and giving a talk that might last anywhere from 10 minutes to 1 hour (depending on the conference) and then fielding questions from the audience. Alternatively, poster presentations involve summarizing the study on a large poster that provides a brief overview of the purpose, methods, results, and discussion. The presenter stands by his or her poster for an hour or two and discusses it with people who pass by. Presenting one’s work at a conference is a great way to get feedback from one’s peers before attempting to undergo the more rigorous peer-review process involved in publishing a journal article.

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• Nov 26, 2018
• 11 min read

12 Ways To Draw Conclusions From Information

Updated: Sep 25

There are a LOT of ways to make inferences – that is, for drawing conclusions based on information, evidence or data. In fact, there are many more than most people realize. All of them have strengths and weaknesses that render them more useful in some situations than in others.

Here's a brief key describing most popular methods of inference, to help you whenever you're trying to draw a conclusion for yourself. Do you rely more on some of these than you should, given their weaknesses? Are there others in this list that you could benefit from using more in your life, given their strengths? And what does drawing conclusions mean, really? As you'll learn in a moment, it encompasses a wide variety of techniques, so there isn't one single definition.

1. Deduction

Common in: philosophy, mathematics

If X, then Y, due to the definitions of X and Y.

X applies to this case.

Therefore Y applies to this case.

Example: “Plato is a mortal, and all mortals are, by definition, able to die; therefore Plato is able to die.”

Example: “For any number that is an integer, there exists another integer greater than that number. 1,000,000 is an integer. So there exists an integer greater than 1,000,000.”

Advantages: When you use deduction properly in an appropriate context, it is an airtight form of inference (e.g. in a mathematical proof with no mistakes).

Flaws: To apply deduction to the world, you need to rely on strong assumptions about how the world works, or else apply other methods of inference on top. So its range of applicability is limited.

2. Frequencies

Common in: applied statistics, data science

95% of the time that X occurred in the past, Y occurred also.

X occurred.

Therefore Y is likely to occur (with high probability).

Example: “95% of the time when we saw a bank transaction identical to this one, it was fraudulent. So this transaction is fraudulent.”

Advantages: This technique allows you to assign probabilities to events. When you have a lot of past data it can be easy to apply.

Flaws: You need to have a moderately large number of examples like the current one to perform calculations on. Also, the method assumes that those past examples were drawn from a process that is (statistically) just like the one that generated this latest example. Moreover, it is unclear sometimes what it means for “X”, the type of event you’re interested in, to have occurred. What if something that’s very similar to but not quite like X occurred? Should that be counted as X occurring? If we broaden our class of what counts as X or change to another class of event that still encompasses all of our prior examples, we’ll potentially get a different answer. Fortunately, there are plenty of opportunities to make inferences from frequencies where the correct class to use is fairly obvious.

If you've found this article valuable so far, you may also like our free tool

Common in : financial engineering, risk modeling, environmental science

Given our probabilistic model of this thing, when X occurs, the probability of Y occurring is 0.95.

Example: “Given our multivariate Gaussian model of loan prices, when this loan defaults there is a 0.95 probability of this other loan defaulting.”

Example: "When we run the weather simulation model many times with randomization of the initial conditions, rain occurs tomorrow in that region 95% of the time."

Advantages: This technique can be used to make predictions in very complex scenarios (e.g. involving more variables than a human mind can take into account at once) as long as the dynamics of the systems underlying those scenarios are sufficiently well understood.

Flaws: This method hinges on the appropriateness of the model chosen; it may require a large amount of past data to estimate free model parameters, and may go haywire if modeling assumptions are unrealistic or suddenly violated by changes in the world. You may have to already understand the system deeply to be able to build the model in the first place (e.g. with weather modeling).

4. Classification

Common in: machine learning, data science

In prior data, as X1 and X2 increased, the likelihood of Y increased.

X1 and X2 are at high levels.

Therefore Y is likely to occur.

Example: “Height for children can be approximately predicted as an (increasing) linear function of age (X1) and weight (X2). This child is older and heavier than the others, so we predict he is likely to be tall.”

Example: "We've trained a neural network to predict whether a particular batch of concrete will be strong based on its constituents, mixture proportion, compaction, etc."

Advantages: This method can often produce accurate predictions for systems that you don't have much understanding of, as long as enough data is available to train the regression algorithm and that data contains sufficiently relevant variables.

Flaws: This method is often applied with simple assumptions (e.g. linearity) that may not capture the complexity of the inference problem, but very large amounts of data may be needed to apply much more complex models (e.g to use neural networks, which are non-linear). Regression also may produce results that are hard to interpret – you may not really understand why it does a good job of making predictions.

5. Bayesianism

Common in: the rationality community

Given my prior odds that Y is true...

And given evidence X...

And given my Bayes factor, which is my estimate of how much more likely X is to occur if Y is true than if Y is not true...

I calculate that Y is far more likely to be true than to not be true (by multiplying the prior odds by the Bayes factor to get the posterior odds).

Therefore Y is likely to be true (with high probability).

Example: “My prior odds that my boss is angry at me were 1 to 4, because he’s angry at me about 20% of the time. But then he came into my office shouting and flipped over my desk, which I estimate is 200 times more likely to occur if he’s angry at me compared to if he’s not. So now the odds of him being angry at me are 200 * (1/4) = 50 to 1 in favor of him being angry.”

Example: "Historically, companies in this situation have 2 to 1 odds of defaulting on their loans. But then evidence came out about this specific company showing that it is 3 times more likely to end up defaulting on its loans than similar companies. Hence now the odds of it defaulting are 6 to 1 since: (2/1) * (3/1) = 6. That means there is an 85% chance that it defaults since 0.85 = 6/(6+1)."

Advantages: If you can do the calculations in a given instance, and have a sensible way to set your prior probabilities, this is probably the mathematically optimal framework to use for probabilistic prediction. For instance, if you have a belief about the probability of something, then you gain some new evidence, you can prove mathematically that Bayes's rule tells you how to calculate what your new probability should now be that incorporates that evidence. In that sense, we can think of many of the other approaches on this list as (hopefully pragmatic) approximations of Bayesianism (sometimes good approximations, sometimes bad ones).

Flaws: It's sometimes hard to know how to set your prior odds, and it can be very hard in some cases to perform the Bayesian calculation. In practice, carrying out the calculation might end up relying on subjective estimates of the odds, which can be especially tricky to guess when the evidence is not binary (i.e not of the form “happened” vs. “didn’t happen”), or if you have lots of different pieces of evidence that are partially correlated.

If you’d like to learn more about using Bayesian inference in everyday life, try our mini-course on The Question of Evidence . For a more math-oriented explanation, check out our course on Understanding Bayes’s Theorem .

6. Theories

Common in: psychology, economics

Given our theory, when X occurs, Y occurs.

Therefore Y will occur.

Example: “One theory is that depressed people are most at risk for suicide when they are beginning to come out of a really bad depression. So as depression is remitting, patients should be carefully screened for potentially increasing suicide risk factors.”

Example: “A common theory is that when inflation rises, unemployment falls. Inflation is rising, so we should predict that unemployment will fall.”

Advantages: Theories can make systems far more understandable to the human mind, and can be taught to others. Sometimes even very complex systems can be pretty well approximated with a simple theory. Theories allow us to make predictions about what will happen while only having to focus on a small amount of relevant information, without being bogged down by thousands of details.

Flaws: It can be very challenging to come up with reliable theories, and often you will not know how accurate such a theory is. Even if it has substantial truth to it and is right often, there may be cases where the opposite of what was predicted actually happens, and for reasons the theory can’t explain. Theories usually only capture part of what is going on in a particular situation, ignoring many variables so as to be more understandable. People often get too attached to particular theories, forgetting that theories are only approximations of reality, and so pretty much always have exceptions.

Common in: engineering, biology, physics

We know that X causes Y to occur.

Example: “Rusting of gears causes increased friction, leading to greater wear and tear. In this case, the gears were heavily rusted, so we expect to find a lot of wear.”

Example: “This gene produces this phenotype, and we see that this gene is present, so we expect to see the phenotype in the offspring.”

Advantages: If you understand the causal structure of a system, you may be able to make many powerful predictions about it, including predicting what would happen in many hypothetical situations that have never occurred before, and predicting what would happen if you were to intervene on the system in a particular way. This contrasts with (probabilistic) models that may be able to accurately predict what happens in common situations, but perform badly at predicting what will happen in novel situations and in situations where you intervene on the system (e.g. what would happen to the system if I purposely changed X).

Flaws: It’s often extremely hard to figure out causality in a highly complex system, especially in “softer” or "messier" subjects like nutrition and the social sciences. Purely statistical information (even an infinite amount of it) is not enough on its own to fully describe the causality of a system; additional assumptions need to be added. Often in practice we can only answer questions about causality by running randomized experiments (e.g. randomized controlled trials), which are typically expensive and sometimes infeasible, or by attempting to carefully control for all the potential confounding variables, a challenging and error-prone process.

Common in: politics, economics

This expert (or prediction market, or prediction algorithm) X is 90% accurate at predicting things in this general domain of prediction.

X predicts Y.

Example: “This prediction market has been right 90% of the time when predicting recent baseball outcomes, and in this case predicts the Yankees will win.”

Advantages: If you can find an expert or algorithm that has been proven to make reliable predictions in a particular domain, you can simply use these predictions yourself without even understanding how they are made.

Flaws: We often don’t have access to the predictions of experts (or of prediction markets, or prediction algorithms), and when we do, we usually don’t have reliable measures of their past accuracy. What's more, many experts whose predictions are publicly available have no clear track record of performance, or even purposely avoid accountability for poor performance (e.g. by hiding past prediction failures and touting past successes).

9. Metaphors

Common in: self-help, ancient philosophy, science education

X, which is what we are dealing with now, is metaphorically a Z.

For Z, when W is true, then obviously Y is true.

Now W (or its metaphorical equivalent) is true for X.

Therefore Y is true for X.

Example: “Your life is but a boat, and you are riding on the waves of your experiences. When a raging storm hits, a boat can’t be under full sail. It can’t continue at its maximum speed. You are experiencing a storm now, and so you too must learn to slow down.”

Example: "To better understand the nature of gasses, imagine tons of ping pong balls all shooting around in straight lines in random directions, and bouncing off of each other whenever they collide. These ping pong balls represent molecules of gas. Assuming the system is not inside a container, ping pong balls at the edges of the system have nothing to collide with, so they just fly outward, expanding the whole system. Similarly, the volume of a gas expands when it is placed in a vacuum."

Advantages: Our brains are good at understanding metaphors, so they can save us mental energy when we try to grasp difficult concepts. If the two items being compared in the metaphor are sufficiently alike in relevant ways, then the metaphor may accurately reveal elements of how its subject works.

Flaws: Z working as a metaphor for X doesn’t mean that all (or even most) predictions that are accurate for situations involving Z are appropriate (or even make any sense) for X. Metaphor-based reasoning can seem profound and persuasive even in cases when it makes little sense.

10. Similarities

Common in: the study of history, machine learning

X occurred, and X is very similar to Z in properties A, B and C.

When things similar to Z in properties A, B, and C occur, Y usually occurs.

Example: “This conflict is similar to the Gulf War in various ways, and from what we've learned about wars like the Gulf War, we can expect these sorts of outcomes.”

Example: “This data point (with unknown label) is closest in feature space to this other data point which is labeled ‘cat’, and all the other labeled points around that point are also labeled ‘cat’, so this unlabeled point should also likely get the label ‘cat’.”

Advantages: This approach can be applied at both small scale (with small numbers of examples) and at large scale (with millions of examples, as in machine learning algorithms), though of course large numbers of examples tend to produce more robust results. It can be viewed as a more powerful generalization of "frequencies"-based reasoning.

Flaws: In the history case, it is difficult to know which features are the appropriate ones to use to evaluate the similarity of two cases, and often the conclusions this approach produces are based on a relatively small number of examples. In the machine learning case, a very large amount of data may be needed to train the model (and it still may be unclear how to measure which examples are similar to which other cases, even with a lot of data). The properties you're using to compare cases must be sufficiently relevant to the prediction being made for it to work.

11. Anecdotes

Common in: daily life

In this handful of examples (or perhaps even just one example) where X occurred, Y occurred.

Example: “The last time we took that so-called 'shortcut' home, we got stuck in traffic for an extra 45 minutes. Let's not make that mistake again.”

Example: “My friend Bob tried that supplement and said it gave him more energy. So maybe it will give me more energy too."

Advantages: Anecdotes are simple to use, and a few of them are often all we have to work with for inference.

Flaws: Unless we are in a situation with very little noise/variability, a few examples likely will not be enough to accurately generalize. For instance, a few examples is not enough to make a reliable judgement about how often something occurs.

12. Intuition

My intuition (that I may have trouble explaining) predicts that when X occurs, Y is true.

Therefore Y is true.

Example: “The tone of voice he used when he talked about his family gave me a bad vibe. My feeling is that anyone who talks about their family with that tone of voice probably does not really love them.”

Example: "I can't explain why, but I'm pretty sure he's going to win this election."

Advantages: Our intuitions can be very well honed in situations we’ve encountered many times, and that we've received feedback on (i.e. where there was some sort of answer we got about how well our intuition performed). For instance, a surgeon who has conducted thousands of heart surgeries may have very good intuitions about what to do during surgery, or about how the patient will fare, even potentially very accurate intuitions that she can't easily articulate.

Flaws: In novel situations, or in situations where we receive no feedback on how well our instincts are performing, our intuitions may be highly inaccurate (even though we may not feel any less confident about our correctness).

Do you want to learn more about drawing conclusions from data?

If you'd like to know more about when intuition is reliable, try our 7-question guide to determining when you can trust your intuition.

We also have a full podcast episode about Mental models that apply across disciplines that you may like:

Click here to access other streaming options and show notes.

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Subject index

This guide to the essentials of doing participatory methods in a broad range of health contexts covers all of the stages of the research process, from research design right through to dissemination. With chapters from international contributors, each with many years’ experience using participatory qualitative approaches, it provides guidance on. - Ethical issues in Participatory Research - Designing and conduction Participatory Research projects - Data management and analysis - Researching with different populations - New technologies Packed full of up to date and engaging case studies, Using Participatory Qualitative Research Methodologies in Health offers a wide range of perspectives and voices on the practicalities and theoretical issues involved in conducting participatory research today. It is the ideal resource for students and researchers embarking upon a participatory research project.

Drawing Conclusions from Your Research

• By: Gina Higginbottom
• In: Participatory Qualitative Research Methodologies in Health
• Chapter DOI: https:// doi. org/10.4135/9781473919945
• Subject: Health , Nursing , Medicine
• Keywords: knowledge
• Show page numbers Hide page numbers

A conclusion is not simply a summary of the findings and the research conducted. Importantly, the conclusion provides a significant and vital opportunity to explain to the reader exactly what the research means to the various audiences who have an interest in the research. The conclusion provides the potential to explore in depth and detail the broader implications of the findings, while stating the limitations of the research and clearly delineating the parameters. Like all stages of participatory research, drawing conclusions and dissemination of research findings must adhere to the principles of participatory research.

In this chapter, we map out and explore strategies for drawing conclusions from participatory qualitative research studies.

• To explicate in detail how to map out the broader implications of participatory research findings with precision and relevance.
• To elucidate knowledge translation, transfer and exchange strategy conclusions that researchers may need to employ for specific audiences. For example, policy-relevant conclusions may be very different from those presented to practitioners working at the health and social care interface.

After reading this chapter, readers will be able to better understand strategies that can be used to draw a conclusion from participatory qualitative research.

Introduction

Participatory research (PR) is a collaborative process of research, education and action with the final goal of social transformation (Kindon, Pain & Kesby, 2007). This approach is a subsection of the larger framework of action research, in which knowledge is produced through reflecting on actions that aim to bring about change (Denzin & Lincoln, 2005). Vollman, Anderson and McFarlane have defined participatory action research as ‘a philosophical approach to research that recognizes the need for persons being studied to participate in the design and conduct of all phases (e.g., design, execution and dissemination) of any research that affects them’ (cited in MacDonald, 2012). As noted in Chapter 1 , these methodologies view participants as being knowledgeable about their own social realities and therefore best able to rearticulate this knowledge as research evidence.

The aims of participatory research, and participatory action research in particular, are to promote social justice, participation, the development of communities and empowerment (MacDonald, 2012), with the notions of participation and action forming the basis of this methodology (Walter, 2009). Participation refers to the collaborative nature of participatory research and the need for all of the people involved in the study to partake in planning and conducting each stage of the research process (Denzin & Lincoln, 2011). The purpose of conducting research is not only to gather information but also to include an action component to bring about change by moving away from social injustice and towards an improved life (Denzin & Lincoln, 2005; Walter, 2009). The research topic of interest comes from the community itself (Walter, 2009), and the methodology emerges from a co-construction of the research processes and products (Jagosh et al., 2012). Therefore, participatory research approaches are distinctive in their collaborative nature, political involvement and goal of removing social injustice (Denzin & Lincoln, 2005). These three components distinguish participatory research from other more conventional and linear research methods (MacDonald, 2012), and set the stage for drawing conclusions and designing dissemination strategies at the end of research projects. In this chapter, we map out and explore strategies for drawing conclusions from participatory qualitative research studies. We also elucidate knowledge translation, transfer and exchange strategies that may need to be employed for specific audiences.

The importance of good conclusions

Conclusions are not simply a summary of the findings and the research that you have conducted. Importantly, the conclusion provides researchers with a significant and vital opportunity to explain to the reader exactly what the research means to the various audiences who have an interest in that research. The conclusion provides researchers with the potential to explore in depth and detail the broader implications of their findings. A conclusion must state the limitations of the research by clearly delineating the parameters.

A precursor to the development of meaningful and comprehensive conclusions and knowledge translation is the development and maintenance of [Page 82] a clear and transparent audit trail. This is essential in order to demonstrate how the findings and interpretations have evolved from the raw data and how the elicited evidence supports the subsequent conclusions. Audit trails are particularly important in participatory qualitative research, since these studies involve a reflexive process with participants going through a spiral of cycles involving self-critical action and reflection processes (McTaggart, 1997). Detailed documentation of steps taken, reflections, and decisions made enable knowledge users to establish the rigour and robustness of the research and the scientific methodologies employed.

Drawing conclusions from participatory research: a collaborative process

A vital component of participatory qualitative research is consultation with the key stakeholders and collaborators (Jagosh et al., 2012; Weller & Malheiros da Silva, 2011) with regard to the integration of their ideas and interpretations of the data and analytical process. This collaborative process extends to the development and construction of the research conclusions. Conclusions are drawn from the evidence presented in the findings, which are a result of the joint analysis and the interpretation of data by the participants/community and researcher (see Table 1.1 in Chapter 1 ). Given the philosophical underpinnings of this methodology, a key consideration in participatory qualitative research is the extent to which participants’ ‘voices’ and perspectives are represented in the conclusion, rather than the academic or professional interpretation. Case 5.1 illustrates how co-researchers and community members’ perspectives were central to a participatory research project.

[Page 83] Table 5.1 provides a useful guide for establishing collaboration and partnership processes in the various stages of the research. Use the questions to determine the type and degree of collaboration and involvement you engage in.

A second major concern in constructing conclusions is the broader implications for the communities with whom the researcher has collaborated. The researcher must be able to demonstrate the value of the work simultaneously to various key stakeholders, such as regional and national policymakers, community groups and associations, practitioners and the academic research community. It is also extremely important to delineate the parameters of the research – in other words, what is the [Page 84] research team not able to evidence – by clearly mapping out the parameters and extent of the investigations. There may be quite a few unanswered questions; therefore, perhaps it might be pertinent to suggest new areas of research or research questions. For example, in the previously mentioned case study (Kildea, Barclay, Wardaguga & Dawumal, 2009), the authors discuss the challenge of not being able to meet some of the community’s articulated desires through one research study. In this case, there was a desire by community members to return Aboriginal birthing services to the community where older women could be directly involved in supporting younger women and health service providers. However, it was clear that the research process had given community members a platform to express their interests through increased consciousness and empowerment (Kildea et al., 2009). In turn, the academic and co-researchers were able to continue to pursue their interest in service redesign through involvement in another project.

Case 5.1 Improving access to Australian Aboriginal cultural knowledge on pregnancy/childbirth

In an article describing the participatory research process used to develop and evaluate an Internet-based resource for healthcare professionals to improve access to Australian Aboriginal cultural knowledge specific to pregnancy and childbirth, two academic researchers and two Aboriginal healthcare workers (Kildea, Barclay, Wardaguga & Dawumal, 2009) demonstrated how voices of community participants and Aboriginal co-researchers were central to this study. Aboriginal knowledge was incorporated into the participative methodology through the guidance of the two Aboriginal co-researchers and research participants. This resulted in the recording and representation of women’s stories in a resource website called the ‘Birthing Business in the Bush Website’. Direct quotes from all phases of the study were included in the article, including quotes from early group discussions to identify research goals, public comments on the website, and personal reflections on research conclusions and delimitations. As a result, it is evident to the reader that principles of participatory research were adhered to throughout this research process and there was a strong collaboration between the academic researchers and the community members.

Writing conclusions

Keeping in mind the aims of participatory research (i.e., transformation of societal structures and relationships), skills in writing are essential to convey the significance, relevance and applicability of your findings. In order to achieve this, a narrative of the conclusion must be constructed in a way that will engage the specific audience. In other words, messages must be compelling and audience-specific. The conclusions are not simply a reiteration of the findings, but a synthesis of all components of the research illustrated usually with verbatim comments that most vividly and accurately convey the meaning of the conclusions. It is generally agreed that conclusions do not include new material; however, the conclusions must intersect well with the original research question, showing clearly how the team’s research has shed light on the phenomena and answered the research questions. The reliability of conclusions will be evaluated in light of the supporting evidence (e.g., the findings). In qualitative research, it is generally tempting to make statements that go beyond the supporting evidence. Therefore, care and caution must be exercised in ensuring the conclusions do not extend beyond the evidence in the data.

Knowledge translation strategies

The Canadian Institutes for Health Research (CIHR) (2014) defines knowledge translation in the following manner:

Knowledge translation (KT) is defined as a dynamic and iterative process that includes synthesis, dissemination, and exchange and ethically-sound application of knowledge to improve the health of Canadians, provide more effective health services and products and strengthen the health care system.

This process takes place within a complex system of interactions between researchers and knowledge users which may vary in intensity, complexity and level of engagement depending on the nature of the research and the findings as well as the needs of the particular knowledge user (CIHR, 2014: para 1).

[Page 85] The primary goal of knowledge translation (KT) is to ensure that key messages are delivered in an audience-specific manner such that they align with the needs of integrated knowledge users. Integrated knowledge users in participatory qualitative research are the key stakeholders and collaborators (Weller & Malheiros da Silva, 2011) with whom the researcher has engaged from the outset. Figure 5.1 below depicts the cyclical nature of the stages of participatory research when KT is centralized and aligned with integrated knowledge users. In other words, the KT should be focused on optimizing the impact of the research findings on policy and practice change throughout the health service, public health and community sectors. In order to achieve this in the 21st century, we must use all available mechanisms, including new technologies such as social networking and webinars, to ensure maximum coverage and dissemination. The creation of audience-specific messages is vital to the diffusion of the research findings.

Figure 5.1 Knowledge transfer

Mixed audiences.

Examples of mixed-audience knowledge transfer strategies include consultation with participants and key stakeholders in respect of their preferred method of knowledge translation. Some ethnocultural groups, for example, may prefer visual methods of knowledge transfer, and these must be tailored to meet specific needs. More generally, knowledge transfer might include the production of (a) research briefings for health practitioners, policymakers and decision-makers or (b) professionally designed accessible plain-language fact sheets (single page, double-sided on high quality paper). Both types of documents might include knowledge transfer activities to date as well as web links for academic team members and integrated knowledge users. The research briefings might be inserted into conference packages, for example, and distributed directly to appropriate decision-makers wherever practical. The fact sheets may facilitate the transfer of key messages directly to the public as well as to healthcare professionals, policymakers and other knowledge users. Both types of documents can be designed using the input of integrated knowledge users and key collaborators and posted on the Internet in a variety of formats.

Other knowledge transfer activities may include targeting radio and television media to sensitize a wider audience to key findings. Live or web-based seminars and conferences might be organized with presentations given by the study-specific integrated knowledge users. Social media platforms such as Blogger, Facebook and Twitter may also be used, as appropriate, for further knowledge transfer. A good example of a mixed audience knowledge transfer strategy is described by Sloane et al. (2003). They used a community-based participatory method to build health promotion capacity (nutrition and healthy living) among African–American community residents in the Los Angeles metropolitan area. Results from this study were shared with participants, community members and policymakers through a programme presented by community-based grant subcontractors at community events called Indabas (a Zulu word meaning ‘deep talk’). Indabas were marketed as opportunities for the wider community to discuss nutrition challenges, or ‘brown bananas and bad meat’, in their markets, which was a pertinent finding in the study.

Community groups and agencies

Knowledge translation begins, as suggested earlier, in the project planning stages and might conclude with public dissemination through community meetings. Knowledge users might be invited to attend community-based initiatives such as seminars , workshops and other creative forms of dissemination, as demonstrated in Case 5.2 . Publicizing findings at local community events will target community, provincial and/or national leaders.

Case 5.2 Hope and post-war experiences of refugee children and youth

After concluding an arts-based project examining hope and post-war experiences of refugee children in Canada (Yohani, 2008), youth participants decided to showcase their stories as depicted in photographs, collages and a quilt at a variety of community settings, including an immigrant-serving agency, a hospital, a university and a number of schools. These creative public dissemination activities allowed youth to speak about their experiences in a comfortable manner using mediums that reduced language barriers and were appropriately child-centred. These youth-led activities allowed for a variety of knowledge users (settlement service providers, education/healthcare professionals, and community members) to learn directly from participants about their challenges and opportunities during their early years of resettlement in Canada.

Policymakers

Knowledge transfer mechanisms to inform policy and practice should include presentations to international, national and regional networks and at conferences attended by policymakers. Establishing links with the regional or national ministers responsible for the researcher’s area of investigation is highly desirable as research findings can be shared directly with influential decision-makers.

Healthcare practitioners

The mixed-audience knowledge transfer strategies described above will already target healthcare practitioners. Nonetheless, additional workshops and seminars might be facilitated at national health and qualitative research conferences, and webinars can be presented for regional healthcare providers. Case 5.3 provides an example of a creative approach that targeted healthcare practitioners. To ensure optimal direct access of healthcare practitioners, consideration might be given to the establishment of an E-Community of Practice with the help of integrated knowledge users in the healthcare provider community.

Case 5.3 People with schizophrenia’s experiences with medical professionals

As part of the dissemination activities of a project that explored the experiences of people with schizophrenia with medical professionals, participants shared results and recommendations through a readers’ theatre presentation. The script was written by the academic researcher based on participant suggestions about content and included quotations from interviews selected by participants. In addition, in order to reach a wider audience, the academic researcher sought the permission of the participants to write an academic paper and include them as co-authors. At the time of the publication of the academic paper (Scheider et al., 2004), the presentation had been performed seven times by participants and had been seen by several hundred healthcare professionals. It is interesting to note that this researcher stated that while the article was written by the lead researcher, ‘true participation belongs to those who take part, not those who write about them’ (Scheider et al., 2004: 567).

Contributions to academic theory and practice occur through the publication of findings in high-impact international journals and international conferences. Examples of journals for participatory researchers include Action Research and International Journal of Action Research .

Participatory research is a research methodology that promotes collaboration among community members and researchers. This collaboration is critical for drawing conclusions that are relevant to the goals of the study and the needs of the integrated knowledge users. Conclusions also provide the research team with an opportunity to explain to the reader exactly what the research means to the various audiences who have an interest in the research. As such, skills in writing are essential to convey the significance, relevance and applicability of findings in a manner that will engage the specific audience. Since the purpose of participatory research is to collect practical knowledge that can be used to generate social change, knowledge translation and knowledge transfer, goals should be focused on optimizing the impact of research findings on policy and practice change throughout the health service, public health and community sectors.

Data Management, Analysis and Interpretation

Engaging Older People in Participatory Research

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