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• Moving While Black: Intergroup Attitudes Influence Judgments of Speed (PDF, 71KB) Journal of Experimental Psychology: General February 2016 by Andreana C. Kenrick, Stacey Sinclair, Jennifer Richeson, Sara C. Verosky, and Janetta Lun
• Recognition Without Awareness: Encoding and Retrieval Factors (PDF, 116KB) Journal of Experimental Psychology: Learning, Memory, and Cognition September 2015 by Fergus I. M. Craik, Nathan S. Rose, and Nigel Gopie
• The Tip-of-the-Tongue Heuristic: How Tip-of-the-Tongue States Confer Perceptibility on Inaccessible Words (PDF, 91KB) Journal of Experimental Psychology: Learning, Memory, and Cognition September 2015 by Anne M. Cleary and Alexander B. Claxton
• Cognitive Processes in the Breakfast Task: Planning and Monitoring (PDF, 146KB) Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale September 2015 by Nathan S. Rose, Lin Luo, Ellen Bialystok, Alexandra Hering, Karen Lau, and Fergus I. M. Craik
• Searching for Explanations: How the Internet Inflates Estimates of Internal Knowledge (PDF, 138KB) Journal of Experimental Psychology: General June 2015 by Matthew Fisher, Mariel K. Goddu, and Frank C. Keil
• Client Perceptions of Corrective Experiences in Cognitive Behavioral Therapy and Motivational Interviewing for Generalized Anxiety Disorder: An Exploratory Pilot Study (PDF, 62KB) Journal of Psychotherapy Integration March 2017 by Jasmine Khattra, Lynne Angus, Henny Westra, Christianne Macaulay, Kathrin Moertl, and Michael Constantino
• Attention-Deficit/Hyperactivity Disorder Developmental Trajectories Related to Parental Expressed Emotion (PDF, 160KB) Journal of Abnormal Psychology February 2016 by Erica D. Musser, Sarah L. Karalunas, Nathan Dieckmann, Tara S. Peris, and Joel T. Nigg
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• "So What Are You?": Inappropriate Interview Questions for Psychology Doctoral and Internship Applicants (PDF, 79KB) Training and Education in Professional Psychology May 2015 by Mike C. Parent, Dana A. Weiser, and Andrea McCourt
• Cultural Competence as a Core Emphasis of Psychoanalytic Psychotherapy (PDF, 81KB) Psychoanalytic Psychology April 2015 by Pratyusha Tummala-Narra
• The Role of Gratitude in Spiritual Well-Being in Asymptomatic Heart Failure Patients (PDF, 123KB) Spirituality in Clinical Practice March 2015 by Paul J. Mills, Laura Redwine, Kathleen Wilson, Meredith A. Pung, Kelly Chinh, Barry H. Greenberg, Ottar Lunde, Alan Maisel, Ajit Raisinghani, Alex Wood, and Deepak Chopra
• Nepali Bhutanese Refugees Reap Support Through Community Gardening (PDF, 104KB) International Perspectives in Psychology: Research, Practice, Consultation January 2017 by Monica M. Gerber, Jennifer L. Callahan, Danielle N. Moyer, Melissa L. Connally, Pamela M. Holtz, and Beth M. Janis
• Does Monitoring Goal Progress Promote Goal Attainment? A Meta-Analysis of the Experimental Evidence (PDF, 384KB) Psychological Bulletin February 2016 by Benjamin Harkin, Thomas L. Webb, Betty P. I. Chang, Andrew Prestwich, Mark Conner, Ian Kellar, Yael Benn, and Paschal Sheeran
• Youth Violence: What We Know and What We Need to Know (PDF, 388KB) American Psychologist January 2016 by Brad J. Bushman, Katherine Newman, Sandra L. Calvert, Geraldine Downey, Mark Dredze, Michael Gottfredson, Nina G. Jablonski, Ann S. Masten, Calvin Morrill, Daniel B. Neill, Daniel Romer, and Daniel W. Webster
• Supervenience and Psychiatry: Are Mental Disorders Brain Disorders? (PDF, 113KB) Journal of Theoretical and Philosophical Psychology November 2015 by Charles M. Olbert and Gary J. Gala
• Constructing Psychological Objects: The Rhetoric of Constructs (PDF, 108KB) Journal of Theoretical and Philosophical Psychology November 2015 by Kathleen L. Slaney and Donald A. Garcia
• Expanding Opportunities for Diversity in Positive Psychology: An Examination of Gender, Race, and Ethnicity (PDF, 119KB) Canadian Psychology / Psychologie canadienne August 2015 by Meghana A. Rao and Stewart I. Donaldson
• Racial Microaggression Experiences and Coping Strategies of Black Women in Corporate Leadership (PDF, 132KB) Qualitative Psychology August 2015 by Aisha M. B. Holder, Margo A. Jackson, and Joseph G. Ponterotto
• An Appraisal Theory of Empathy and Other Vicarious Emotional Experiences (PDF, 151KB) Psychological Review July 2015 by Joshua D. Wondra and Phoebe C. Ellsworth
• An Attachment Theoretical Framework for Personality Disorders (PDF, 100KB) Canadian Psychology / Psychologie canadienne May 2015 by Kenneth N. Levy, Benjamin N. Johnson, Tracy L. Clouthier, J. Wesley Scala, and Christina M. Temes
• Emerging Approaches to the Conceptualization and Treatment of Personality Disorder (PDF, 111KB) Canadian Psychology / Psychologie canadienne May 2015 by John F. Clarkin, Kevin B. Meehan, and Mark F. Lenzenweger
• A Complementary Processes Account of the Development of Childhood Amnesia and a Personal Past (PDF, 585KB) Psychological Review April 2015 by Patricia J. Bauer
• Terminal Decline in Well-Being: The Role of Social Orientation (PDF, 238KB) Psychology and Aging March 2016 by Denis Gerstorf, Christiane A. Hoppmann, Corinna E. Löckenhoff, Frank J. Infurna, Jürgen Schupp, Gert G. Wagner, and Nilam Ram
• Student Threat Assessment as a Standard School Safety Practice: Results From a Statewide Implementation Study (PDF, 97KB) School Psychology Quarterly June 2018 by Dewey Cornell, Jennifer L. Maeng, Anna Grace Burnette, Yuane Jia, Francis Huang, Timothy Konold, Pooja Datta, Marisa Malone, and Patrick Meyer
• Can a Learner-Centered Syllabus Change Students’ Perceptions of Student–Professor Rapport and Master Teacher Behaviors? (PDF, 90KB) Scholarship of Teaching and Learning in Psychology September 2016 by Aaron S. Richmond, Jeanne M. Slattery, Nathanael Mitchell, Robin K. Morgan, and Jared Becknell
• Adolescents' Homework Performance in Mathematics and Science: Personal Factors and Teaching Practices (PDF, 170KB) Journal of Educational Psychology November 2015 by Rubén Fernández-Alonso, Javier Suárez-Álvarez, and José Muñiz
• Teacher-Ready Research Review: Clickers (PDF, 55KB) Scholarship of Teaching and Learning in Psychology September 2015 by R. Eric Landrum
• Enhancing Attention and Memory During Video-Recorded Lectures (PDF, 83KB) Scholarship of Teaching and Learning in Psychology March 2015 by Daniel L. Schacter and Karl K. Szpunar
• The Alleged "Ferguson Effect" and Police Willingness to Engage in Community Partnership (PDF, 70KB) Law and Human Behavior February 2016 by Scott E. Wolfe and Justin Nix
• Randomized Controlled Trial of an Internet Cognitive Behavioral Skills-Based Program for Auditory Hallucinations in Persons With Psychosis (PDF, 92KB) Psychiatric Rehabilitation Journal September 2017 by Jennifer D. Gottlieb, Vasudha Gidugu, Mihoko Maru, Miriam C. Tepper, Matthew J. Davis, Jennifer Greenwold, Ruth A. Barron, Brian P. Chiko, and Kim T. Mueser
• Preventing Unemployment and Disability Benefit Receipt Among People With Mental Illness: Evidence Review and Policy Significance (PDF, 134KB) Psychiatric Rehabilitation Journal June 2017 by Bonnie O'Day, Rebecca Kleinman, Benjamin Fischer, Eric Morris, and Crystal Blyler
• Sending Your Grandparents to University Increases Cognitive Reserve: The Tasmanian Healthy Brain Project (PDF, 88KB) Neuropsychology July 2016 by Megan E. Lenehan, Mathew J. Summers, Nichole L. Saunders, Jeffery J. Summers, David D. Ward, Karen Ritchie, and James C. Vickers
• The Foundational Principles as Psychological Lodestars: Theoretical Inspiration and Empirical Direction in Rehabilitation Psychology (PDF, 68KB) Rehabilitation Psychology February 2016 by Dana S. Dunn, Dawn M. Ehde, and Stephen T. Wegener
• Feeling Older and Risk of Hospitalization: Evidence From Three Longitudinal Cohorts (PDF, 55KB) Health Psychology Online First Publication — February 11, 2016 by Yannick Stephan, Angelina R. Sutin, and Antonio Terracciano
• Anger Intensification With Combat-Related PTSD and Depression Comorbidity (PDF, 81KB) Psychological Trauma: Theory, Research, Practice, and Policy January 2016 by Oscar I. Gonzalez, Raymond W. Novaco, Mark A. Reger, and Gregory A. Gahm
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• Racial and Sexual Minority Women's Receipt of Medical Assistance to Become Pregnant (PDF, 111KB) Health Psychology June 2015 by Bernadette V. Blanchfield and Charlotte J. Patterson
• An Examination of Generational Stereotypes as a Path Towards Reverse Ageism (PDF, 205KB) The Psychologist-Manager Journal August 2017 By Michelle Raymer, Marissa Reed, Melissa Spiegel, and Radostina K. Purvanova
• Sexual Harassment: Have We Made Any Progress? (PDF, 121KB) Journal of Occupational Health Psychology July 2017 By James Campbell Quick and M. Ann McFadyen
• Multidimensional Suicide Inventory-28 (MSI-28) Within a Sample of Military Basic Trainees: An Examination of Psychometric Properties (PDF, 79KB) Military Psychology November 2015 By Serena Bezdjian, Danielle Burchett, Kristin G. Schneider, Monty T. Baker, and Howard N. Garb
• Cross-Cultural Competence: The Role of Emotion Regulation Ability and Optimism (PDF, 100KB) Military Psychology September 2015 By Bianca C. Trejo, Erin M. Richard, Marinus van Driel, and Daniel P. McDonald
• The Effects of Stress on Prospective Memory: A Systematic Review (PDF, 149KB) Psychology & Neuroscience September 2017 by Martina Piefke and Katharina Glienke
• Don't Aim Too High for Your Kids: Parental Overaspiration Undermines Students' Learning in Mathematics (PDF, 164KB) Journal of Personality and Social Psychology November 2016 by Kou Murayama, Reinhard Pekrun, Masayuki Suzuki, Herbert W. Marsh, and Stephanie Lichtenfeld
• Sex Differences in Sports Interest and Motivation: An Evolutionary Perspective (PDF, 155KB) Evolutionary Behavioral Sciences April 2016 by Robert O. Deaner, Shea M. Balish, and Michael P. Lombardo
• Asian Indian International Students' Trajectories of Depression, Acculturation, and Enculturation (PDF, 210KB) Asian American Journal of Psychology March 2016 By Dhara T. Meghani and Elizabeth A. Harvey
• Cynical Beliefs About Human Nature and Income: Longitudinal and Cross-Cultural Analyses (PDF, 163KB) January 2016 Journal of Personality and Social Psychology by Olga Stavrova and Daniel Ehlebracht
• Annual Review of Asian American Psychology, 2014 (PDF, 384KB) Asian American Journal of Psychology December 2015 By Su Yeong Kim, Yishan Shen, Yang Hou, Kelsey E. Tilton, Linda Juang, and Yijie Wang
• Resilience in the Study of Minority Stress and Health of Sexual and Gender Minorities (PDF, 40KB) Psychology of Sexual Orientation and Gender Diversity September 2015 by Ilan H. Meyer
• Self-Reported Psychopathy and Its Association With Criminal Cognition and Antisocial Behavior in a Sample of University Undergraduates (PDF, 91KB) Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement July 2015 by Samantha J. Riopka, Richard B. A. Coupland, and Mark E. Olver

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Chapter 2: Psychological Research

The scientific method.

Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives. In this section, you’ll see how psychologists use the scientific method to study and understand behavior.

Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession (Figure 1). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.

Figure 1 . Some of our ancestors, believed that trephination—the practice of making a hole in the skull—allowed evil spirits to leave the body, thus curing mental illness.

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This module explores how scientific knowledge is generated, and how important that knowledge is in informing decisions in our personal lives and in the public domain.

The Process of Scientific Research

Figure 2 . The scientific method is a process for gathering data and processing information. It provides well-defined steps to standardize how scientific knowledge is gathered through a logical, rational problem-solving method.

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

• Observe a natural phenomenon and define a question about it
• Make a hypothesis, or potential solution to the question
• Test the hypothesis
• If the hypothesis is true, find more evidence or find counter-evidence
• If the hypothesis is false, create a new hypothesis or try again
• Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what he or she thinks the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Video 1.  The Scientific Method explains the basic steps taken for most scientific inquiry.

The Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests (Figure 3).

Figure 3 . The scientific method of research includes proposing hypotheses, conducting research, and creating or modifying theories based on results.

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later module, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race, and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors (Figure 4). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

Figure 4 . Many of the specifics of (a) Freud’s theories, such as (b) his division of the mind into id, ego, and superego, have fallen out of favor in recent decades because they are not falsifiable. In broader strokes, his views set the stage for much of psychological thinking today, such as the unconscious nature of the majority of psychological processes.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Link to Learning

Want to participate in a study? Visit this Psychological Research on the Net website and click on a link that sounds interesting to you in order to participate in online research.

Why the Scientific Method Is Important for Psychology

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a Results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a Discussion and Conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, as you will read in the Tuskegee Syphilis Study, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound. This section presents how ethical considerations affect the design and implementation of research conducted today.

Research Involving Human Participants

Any experiment involving the participation of human subjects is governed by extensive, strict guidelines designed to ensure that the experiment does not result in harm. Any research institution that receives federal support for research involving human participants must have access to an institutional review board (IRB) . The IRB is a committee of individuals often made up of members of the institution’s administration, scientists, and community members (Figure 1). The purpose of the IRB is to review proposals for research that involves human participants. The IRB reviews these proposals with the principles mentioned above in mind, and generally, approval from the IRB is required in order for the experiment to proceed.

Figure 5 . An institution’s IRB meets regularly to review experimental proposals that involve human participants. (credit: modification of work by Lowndes Area Knowledge Exchange (LAKE)/Flickr)

An institution’s IRB requires several components in any experiment it approves. For one, each participant must sign an informed consent form before they can participate in the experiment. An informed consent form provides a written description of what participants can expect during the experiment, including potential risks and implications of the research. It also lets participants know that their involvement is completely voluntary and can be discontinued without penalty at any time. Furthermore, informed consent guarantees that any data collected in the experiment will remain completely confidential. In cases where research participants are under the age of 18, the parents or legal guardians are required to sign the informed consent form.

While the informed consent form should be as honest as possible in describing exactly what participants will be doing, sometimes deception is necessary to prevent participants’ knowledge of the exact research question from affecting the results of the study. Deception involves purposely misleading experiment participants in order to maintain the integrity of the experiment, but not to the point where the deception could be considered harmful. For example, if we are interested in how our opinion of someone is affected by their attire, we might use deception in describing the experiment to prevent that knowledge from affecting participants’ responses. In cases where deception is involved, participants must receive a full debriefing upon conclusion of the study—complete, honest information about the purpose of the experiment, how the data collected will be used, the reasons why deception was necessary, and information about how to obtain additional information about the study.

Dig Deeper: Ethics and the Tuskegee Syphilis Study

Unfortunately, the ethical guidelines that exist for research today were not always applied in the past. In 1932, poor, rural, black, male sharecroppers from Tuskegee, Alabama, were recruited to participate in an experiment conducted by the U.S. Public Health Service, with the aim of studying syphilis in black men (Figure 6). In exchange for free medical care, meals, and burial insurance, 600 men agreed to participate in the study. A little more than half of the men tested positive for syphilis, and they served as the experimental group (given that the researchers could not randomly assign participants to groups, this represents a quasi-experiment). The remaining syphilis-free individuals served as the control group. However, those individuals that tested positive for syphilis were never informed that they had the disease.

While there was no treatment for syphilis when the study began, by 1947 penicillin was recognized as an effective treatment for the disease. Despite this, no penicillin was administered to the participants in this study, and the participants were not allowed to seek treatment at any other facilities if they continued in the study. Over the course of 40 years, many of the participants unknowingly spread syphilis to their wives (and subsequently their children born from their wives) and eventually died because they never received treatment for the disease. This study was discontinued in 1972 when the experiment was discovered by the national press (Tuskegee University, n.d.). The resulting outrage over the experiment led directly to the National Research Act of 1974 and the strict ethical guidelines for research on humans described in this chapter. Why is this study unethical? How were the men who participated and their families harmed as a function of this research?

Figure 6 . A participant in the Tuskegee Syphilis Study receives an injection.

Visit this CDC website to learn more about the Tuskegee Syphilis Study.

Research Involving Animal Subjects

Figure 7 . Rats, like the one shown here, often serve as the subjects of animal research.

This does not mean that animal researchers are immune to ethical concerns. Indeed, the humane and ethical treatment of animal research subjects is a critical aspect of this type of research. Researchers must design their experiments to minimize any pain or distress experienced by animals serving as research subjects.

Whereas IRBs review research proposals that involve human participants, animal experimental proposals are reviewed by an Institutional Animal Care and Use Committee (IACUC) . An IACUC consists of institutional administrators, scientists, veterinarians, and community members. This committee is charged with ensuring that all experimental proposals require the humane treatment of animal research subjects. It also conducts semi-annual inspections of all animal facilities to ensure that the research protocols are being followed. No animal research project can proceed without the committee’s approval.

• Modification and adaptation. Provided by : Lumen Learning. License : CC BY-SA: Attribution-ShareAlike
• Psychology and the Scientific Method: From Theory to Conclusion, content on the scientific method principles. Provided by : Boundless. Located at : https://courses.lumenlearning.com/boundless-psychology/ . License : CC BY-SA: Attribution-ShareAlike
• Introduction to Psychological Research, Why is Research Important?, Ethics. Authored by : OpenStax College. Located at : http://cnx.org/contents/[email protected]:Hp5zMFYB@9/Why-Is-Research-Important . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/contents/[email protected]
• Research picture. Authored by : Mediterranean Center of Medical Sciences. Provided by : Flickr. Located at : https://www.flickr.com/photos/mcmscience/17664002728 . License : CC BY: Attribution

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Writing Research Papers

• Research Paper Structure

Whether you are writing a B.S. Degree Research Paper or completing a research report for a Psychology course, it is highly likely that you will need to organize your research paper in accordance with American Psychological Association (APA) guidelines.  Here we discuss the structure of research papers according to APA style.

Major Sections of a Research Paper in APA Style

A complete research paper in APA style that is reporting on experimental research will typically contain a Title page, Abstract, Introduction, Methods, Results, Discussion, and References sections. 1  Many will also contain Figures and Tables and some will have an Appendix or Appendices.  These sections are detailed as follows (for a more in-depth guide, please refer to " How to Write a Research Paper in APA Style ”, a comprehensive guide developed by Prof. Emma Geller). 2

What is this paper called and who wrote it? – the first page of the paper; this includes the name of the paper, a “running head”, authors, and institutional affiliation of the authors.  The institutional affiliation is usually listed in an Author Note that is placed towards the bottom of the title page.  In some cases, the Author Note also contains an acknowledgment of any funding support and of any individuals that assisted with the research project.

One-paragraph summary of the entire study – typically no more than 250 words in length (and in many cases it is well shorter than that), the Abstract provides an overview of the study.

Introduction

What is the topic and why is it worth studying? – the first major section of text in the paper, the Introduction commonly describes the topic under investigation, summarizes or discusses relevant prior research (for related details, please see the Writing Literature Reviews section of this website), identifies unresolved issues that the current research will address, and provides an overview of the research that is to be described in greater detail in the sections to follow.

What did you do? – a section which details how the research was performed.  It typically features a description of the participants/subjects that were involved, the study design, the materials that were used, and the study procedure.  If there were multiple experiments, then each experiment may require a separate Methods section.  A rule of thumb is that the Methods section should be sufficiently detailed for another researcher to duplicate your research.

What did you find? – a section which describes the data that was collected and the results of any statistical tests that were performed.  It may also be prefaced by a description of the analysis procedure that was used. If there were multiple experiments, then each experiment may require a separate Results section.

What is the significance of your results? – the final major section of text in the paper.  The Discussion commonly features a summary of the results that were obtained in the study, describes how those results address the topic under investigation and/or the issues that the research was designed to address, and may expand upon the implications of those findings.  Limitations and directions for future research are also commonly addressed.

List of articles and any books cited – an alphabetized list of the sources that are cited in the paper (by last name of the first author of each source).  Each reference should follow specific APA guidelines regarding author names, dates, article titles, journal titles, journal volume numbers, page numbers, book publishers, publisher locations, websites, and so on (for more information, please see the Citing References in APA Style page of this website).

Tables and Figures

Graphs and data (optional in some cases) – depending on the type of research being performed, there may be Tables and/or Figures (however, in some cases, there may be neither).  In APA style, each Table and each Figure is placed on a separate page and all Tables and Figures are included after the References.   Tables are included first, followed by Figures.   However, for some journals and undergraduate research papers (such as the B.S. Research Paper or Honors Thesis), Tables and Figures may be embedded in the text (depending on the instructor’s or editor’s policies; for more details, see "Deviations from APA Style" below).

Supplementary information (optional) – in some cases, additional information that is not critical to understanding the research paper, such as a list of experiment stimuli, details of a secondary analysis, or programming code, is provided.  This is often placed in an Appendix.

Variations of Research Papers in APA Style

Although the major sections described above are common to most research papers written in APA style, there are variations on that pattern.  These variations include: 

• Literature reviews – when a paper is reviewing prior published research and not presenting new empirical research itself (such as in a review article, and particularly a qualitative review), then the authors may forgo any Methods and Results sections. Instead, there is a different structure such as an Introduction section followed by sections for each of the different aspects of the body of research being reviewed, and then perhaps a Discussion section. 
• Multi-experiment papers – when there are multiple experiments, it is common to follow the Introduction with an Experiment 1 section, itself containing Methods, Results, and Discussion subsections. Then there is an Experiment 2 section with a similar structure, an Experiment 3 section with a similar structure, and so on until all experiments are covered.  Towards the end of the paper there is a General Discussion section followed by References.  Additionally, in multi-experiment papers, it is common for the Results and Discussion subsections for individual experiments to be combined into single “Results and Discussion” sections.

Departures from APA Style

In some cases, official APA style might not be followed (however, be sure to check with your editor, instructor, or other sources before deviating from standards of the Publication Manual of the American Psychological Association).  Such deviations may include:

• Placement of Tables and Figures  – in some cases, to make reading through the paper easier, Tables and/or Figures are embedded in the text (for example, having a bar graph placed in the relevant Results section). The embedding of Tables and/or Figures in the text is one of the most common deviations from APA style (and is commonly allowed in B.S. Degree Research Papers and Honors Theses; however you should check with your instructor, supervisor, or editor first). 
• Incomplete research – sometimes a B.S. Degree Research Paper in this department is written about research that is currently being planned or is in progress. In those circumstances, sometimes only an Introduction and Methods section, followed by References, is included (that is, in cases where the research itself has not formally begun).  In other cases, preliminary results are presented and noted as such in the Results section (such as in cases where the study is underway but not complete), and the Discussion section includes caveats about the in-progress nature of the research.  Again, you should check with your instructor, supervisor, or editor first.
• Class assignments – in some classes in this department, an assignment must be written in APA style but is not exactly a traditional research paper (for instance, a student asked to write about an article that they read, and to write that report in APA style). In that case, the structure of the paper might approximate the typical sections of a research paper in APA style, but not entirely.  You should check with your instructor for further guidelines.

Workshops and Downloadable Resources

• For in-person discussion of the process of writing research papers, please consider attending this department’s “Writing Research Papers” workshop (for dates and times, please check the undergraduate workshops calendar).

Downloadable Resources

• How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
• Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – empirical research) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – literature review) [ PDF ]

Further Resources

How-To Videos     

• Writing Research Paper Videos

APA Journal Article Reporting Guidelines

• Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 3.
• Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suárez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 26.  

External Resources

• Formatting APA Style Papers in Microsoft Word
• How to Write an APA Style Research Paper from Hamilton University
• WikiHow Guide to Writing APA Research Papers
• Sample APA Formatted Paper with Comments
• Sample APA Formatted Paper
• Tips for Writing a Paper in APA Style

1 VandenBos, G. R. (Ed). (2010). Publication manual of the American Psychological Association (6th ed.) (pp. 41-60).  Washington, DC: American Psychological Association.

2 geller, e. (2018).  how to write an apa-style research report . [instructional materials]. , prepared by s. c. pan for ucsd psychology.

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• Formatting Research Papers
• Using Databases and Finding References
• What Types of References Are Appropriate?
• Evaluating References and Taking Notes
• Citing References
• Writing a Literature Review
• Writing Process and Revising
• Improving Scientific Writing
• Academic Integrity and Avoiding Plagiarism
• Writing Research Papers Videos

Methods in Psychology

About the journal, aims & scope.

Methods in Psychology considers articles on new, updated, adapted or innovative research methodologies and methods, analytical methods, and research practices across the breadth of psychological research. Articles can be specific to a single sub-discipline of psychology or have relevance to the entire field. We encourage the integration and adaption of methods, methodologies and analytic approaches from one area of psychology or field of research to another, and the adaptation of methods from other disciplines which have relevance for psychological research practice. The journal considers research methods across the whole spectrum, and welcomes articles which take a quantitative, qualitative or mixed-methods approach to research. The journal solicits original theoretical, empirical, and methodological articles that have psychological relevance discussing new innovative applications or adaptations of methodologies and methods, or articles offering reviews of important methodological issues and innovations.

The journal aims to be the leading forum for psychology researchers from all areas of psychology, and seeks to offer original and ground-breaking articles on research methods that advance the field. The intended audience for journal content is academics, researchers, students and practitioners across diverse fields of psychology who seek relevant and informative content on the latest in research methods and research practices. The journal does not seek to publish technical research articles for specialist methodologists, or to publish research where research methods and practices are not the primary focus. Submissions should make explicit how the theories, methods and procedures they discuss enhance the quality and value of psychological research, and should be written to be accessible to a wide non-technical audience across psychology.

Methods in Psychology considers articles on new, updated, adapted or innovative research methodologies and methods, analytical methods, and research practices across the breadth of psychological research. Articles can be specific to a single sub-discipline of psychology or have relevance to the …

Article publishing charge for open access

Editors-in-Chief

Professor kerry chamberlain, ma.

Massey University, Palmerston North, New Zealand

Em. Professor Elizabeth Creamer, EdD

Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America

Professor W. Holmes Finch, PhD

Ball State University, Muncie, Indiana, United States of America

Latest published

Most downloaded, most popular, using design science research to develop and validate the application of client-oriented psychological approaches illustrated by the design of a solution-focused approach, self- and observer ratings of team reflection: a mixed methods approach, love as a concept in academic research: a bibliometric review, a method to simulate multivariate outliers with known mahalanobis distances for normal and non-normal data, a practical applications guide to machine learning regression models in psychology with python, how do you solve a problem like coreq a critique of tong et al.’s (2007) consolidated criteria for reporting qualitative research, using hybrid qualitative analysis to explore lived experience of motherhood and postnatal depression: a thematic-dialogical approach, a systematic – review of academic stress intended to improve the educational journey of learners, more from methods in psychology, call for papers, spotlight on methods in psychology author: dr octavia calder-dawe, spotlight on methods in psychology author: professor iain r. williamson, metip gender pledge, calls for papers, mixed methods research on creativity and innovation (1), mixed methods and mathematics education research, mixed methods research: applying transformative frameworks, special issues and article collections, arts-based methods in psychology, innovations in inclusive methodology, advancing methodological boundaries by innovation in the pairing of mixed methods with qualitative methods, innovations in qualitative research.

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Instant insights, infinite possibilities

61 intriguing psychology research topics to explore

Last updated

11 January 2024

Reviewed by

Brittany Ferri, PhD, OTR/L

Short on time? Get an AI generated summary of this article instead

Psychology is an incredibly diverse, critical, and ever-changing area of study in the medical and health industries. Because of this, it’s a common area of study for students and healthcare professionals.

We’re walking you through picking the perfect topic for your upcoming paper or study. Keep reading for plenty of example topics to pique your interest and curiosity.

• How to choose a psychology research topic

Exploring a psychology-based topic for your research project? You need to pick a specific area of interest to collect compelling data. 

Use these tips to help you narrow down which psychology topics to research:

Focus on a particular area of psychology

The most effective psychological research focuses on a smaller, niche concept or disorder within the scope of a study. 

Psychology is a broad and fascinating area of science, including everything from diagnosed mental health disorders to sports performance mindset assessments. 

This gives you plenty of different avenues to explore. Having a hard time choosing? Check out our list of 61 ideas further down in this article to get started.

Read the latest clinical studies

Once you’ve picked a more niche topic to explore, you need to do your due diligence and explore other research projects on the same topic. 

This practice will help you learn more about your chosen topic, ask more specific questions, and avoid covering existing projects. 

For the best results, we recommend creating a research folder of associated published papers to reference throughout your project. This makes it much easier to cite direct references and find inspiration down the line.

Find a topic you enjoy and ask questions

Once you’ve spent time researching and collecting references for your study, you finally get to explore. 

Whether this research project is for work, school, or just for fun, having a passion for your research will make the project much more enjoyable. (Trust us, there will be times when that is the only thing that keeps you going.) 

Now you’ve decided on the topic, ask more nuanced questions you might want to explore. 

If you can, pick the direction that interests you the most to make the research process much more enjoyable.

• 61 psychology topics to research in 2024

Need some extra help starting your psychology research project on the right foot? Explore our list of 61 cutting-edge, in-demand psychology research topics to use as a starting point for your research journey.

• Psychology research topics for university students

As a university student, it can be hard to pick a research topic that fits the scope of your classes and is still compelling and unique. 

Here are a few exciting topics we recommend exploring for your next assigned research project:

Mental health in post-secondary students

Seeking post-secondary education is a stressful and overwhelming experience for most students, making this topic a great choice to explore for your in-class research paper. 

Examples of post-secondary mental health research topics include:

Student mental health status during exam season

Mental health disorder prevalence based on study major

The impact of chronic school stress on overall quality of life

The impacts of cyberbullying

Cyberbullying can occur at all ages, starting as early as elementary school and carrying through into professional workplaces. 

Examples of cyberbullying-based research topics you can study include:

The impact of cyberbullying on self-esteem

Common reasons people engage in cyberbullying 

Cyberbullying themes and commonly used terms

Cyberbullying habits in children vs. adults

The long-term effects of cyberbullying

• Clinical psychology research topics

If you’re looking to take a more clinical approach to your next project, here are a few topics that involve direct patient assessment for you to consider:

Chronic pain and mental health

Living with chronic pain dramatically impacts every aspect of a person’s life, including their mental and emotional health. 

Here are a few examples of in-demand pain-related psychology research topics:

The connection between diabetic neuropathy and depression

Neurological pain and its connection to mental health disorders

Efficacy of meditation and mindfulness for pain management

The long-term effects of insomnia

Insomnia is where you have difficulty falling or staying asleep. It’s a common health concern that impacts millions of people worldwide. 

This is an excellent topic because insomnia can have a variety of causes, offering many research possibilities. 

Here are a few compelling psychology research topics about insomnia you could investigate:

The prevalence of insomnia based on age, gender, and ethnicity

Insomnia and its impact on workplace productivity

The connection between insomnia and mental health disorders

Efficacy and use of melatonin supplements for insomnia

The risks and benefits of prescription insomnia medications

Lifestyle options for managing insomnia symptoms

The efficacy of mental health treatment options

Management and treatment of mental health conditions is an ever-changing area of study. If you can witness or participate in mental health therapies, this can make a great research project. 

Examples of mental health treatment-related psychology research topics include:

The efficacy of cognitive behavioral therapy (CBT) for patients with severe anxiety

The benefits and drawbacks of group vs. individual therapy sessions

Music therapy for mental health disorders

Electroconvulsive therapy (ECT) for patients with depression 

• Controversial psychology research paper topics

If you are looking to explore a more cutting-edge or modern psychology topic, you can delve into a variety of controversial and topical options:

The impact of social media and digital platforms

Ever since access to internet forums and video games became more commonplace, there’s been growing concern about the impact these digital platforms have on mental health. 

Examples of social media and video game-related psychology research topics include:

The effect of edited images on self-confidence

How social media platforms impact social behavior

Video games and their impact on teenage anger and violence

Digital communication and the rapid spread of misinformation

The development of digital friendships

Psychotropic medications for mental health

In recent years, the interest in using psychoactive medications to treat and manage health conditions has increased despite their inherently controversial nature. 

Examples of psychotropic medication-related research topics include:

The risks and benefits of using psilocybin mushrooms for managing anxiety

The impact of marijuana on early-onset psychosis

Childhood marijuana use and related prevalence of mental health conditions

Ketamine and its use for complex PTSD (C-PTSD) symptom management

The effect of long-term psychedelic use and mental health conditions

• Mental health disorder research topics

As one of the most popular subsections of psychology, studying mental health disorders and how they impact quality of life is an essential and impactful area of research. 

While studies in these areas are common, there’s always room for additional exploration, including the following hot-button topics:

Anxiety and depression disorders

Anxiety and depression are well-known and heavily researched mental health disorders. 

Despite this, we still don’t know many things about these conditions, making them great candidates for psychology research projects:

Social anxiety and its connection to chronic loneliness

C-PTSD symptoms and causes

The development of phobias

Obsessive-compulsive disorder (OCD) behaviors and symptoms

Depression triggers and causes

Self-care tools and resources for depression

The prevalence of anxiety and depression in particular age groups or geographic areas

Bipolar disorder

Bipolar disorder is a complex and multi-faceted area of psychology research. 

Use your research skills to learn more about this condition and its impact by choosing any of the following topics:

Early signs of bipolar disorder

The incidence of bipolar disorder in young adults

The efficacy of existing bipolar treatment options

Bipolar medication side effects

Cognitive behavioral therapy for people with bipolar 

Schizoaffective disorder

Schizoaffective disorder is often stigmatized, and less common mental health disorders are a hotbed for new and exciting research. 

Here are a few examples of interesting research topics related to this mental health disorder:

The prevalence of schizoaffective disorder by certain age groups or geographic locations

Risk factors for developing schizoaffective disorder

The prevalence and content of auditory and visual hallucinations

Alternative therapies for schizoaffective disorder

• Societal and systematic psychology research topics

Modern society’s impact is deeply enmeshed in our mental and emotional health on a personal and community level. 

Here are a few examples of societal and systemic psychology research topics to explore in more detail:

Access to mental health services

While mental health awareness has risen over the past few decades, access to quality mental health treatment and resources is still not equitable. 

This can significantly impact the severity of a person’s mental health symptoms, which can result in worse health outcomes if left untreated. 

Explore this crucial issue and provide information about the need for improved mental health resource access by studying any of the following topics:

Rural vs. urban access to mental health resources

Access to crisis lines by location

Wait times for emergency mental health services

Inequities in mental health access based on income and location

Insurance coverage for mental health services

Systemic racism and mental health

Societal systems and the prevalence of systemic racism heavily impact every aspect of a person’s overall health.

Researching these topics draws attention to existing problems and contributes valuable insights into ways to improve access to care moving forward.

Examples of systemic racism-related psychology research topics include: 

Access to mental health resources based on race

The prevalence of BIPOC mental health therapists in a chosen area

The impact of systemic racism on mental health and self-worth

Racism training for mental health workers

The prevalence of mental health disorders in discriminated groups

LGBTQIA+ mental health concerns

Research about LGBTQIA+ people and their mental health needs is a unique area of study to explore for your next research project. It’s a commonly overlooked and underserved community.

Examples of LGBTQIA+ psychology research topics to consider include:

Mental health supports for queer teens and children

The impact of queer safe spaces on mental health

The prevalence of mental health disorders in the LGBTQIA+ community

The benefits of queer mentorship and found family

Substance misuse in LQBTQIA+ youth and adults

• Collect data and identify trends with Dovetail

Psychology research is an exciting and competitive study area, making it the perfect choice for projects or papers.

Take the headache out of analyzing your data and instantly access the insights you need to complete your next psychology research project by teaming up with Dovetail today.

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2.1 Psychologists Use the Scientific Method to Guide Their Research

Learning objectives.

• Describe the principles of the scientific method and explain its importance in conducting and interpreting research.
• Differentiate laws from theories and explain how research hypotheses are developed and tested.
• Discuss the procedures that researchers use to ensure that their research with humans and with animals is ethical.

Psychologists aren’t the only people who seek to understand human behavior and solve social problems. Philosophers, religious leaders, and politicians, among others, also strive to provide explanations for human behavior. But psychologists believe that research is the best tool for understanding human beings and their relationships with others. Rather than accepting the claim of a philosopher that people do (or do not) have free will, a psychologist would collect data to empirically test whether or not people are able to actively control their own behavior. Rather than accepting a politician’s contention that creating (or abandoning) a new center for mental health will improve the lives of individuals in the inner city, a psychologist would empirically assess the effects of receiving mental health treatment on the quality of life of the recipients. The statements made by psychologists are empirical , which means they are based on systematic collection and analysis of data .

The Scientific Method

All scientists (whether they are physicists, chemists, biologists, sociologists, or psychologists) are engaged in the basic processes of collecting data and drawing conclusions about those data. The methods used by scientists have developed over many years and provide a common framework for developing, organizing, and sharing information. The scientific method is the set of assumptions, rules, and procedures scientists use to conduct research .

In addition to requiring that science be empirical, the scientific method demands that the procedures used be objective , or free from the personal bias or emotions of the scientist . The scientific method proscribes how scientists collect and analyze data, how they draw conclusions from data, and how they share data with others. These rules increase objectivity by placing data under the scrutiny of other scientists and even the public at large. Because data are reported objectively, other scientists know exactly how the scientist collected and analyzed the data. This means that they do not have to rely only on the scientist’s own interpretation of the data; they may draw their own, potentially different, conclusions.

Most new research is designed to replicate —that is, to repeat, add to, or modify—previous research findings. The scientific method therefore results in an accumulation of scientific knowledge through the reporting of research and the addition to and modifications of these reported findings by other scientists.

Laws and Theories as Organizing Principles

One goal of research is to organize information into meaningful statements that can be applied in many situations. Principles that are so general as to apply to all situations in a given domain of inquiry are known as laws . There are well-known laws in the physical sciences, such as the law of gravity and the laws of thermodynamics, and there are some universally accepted laws in psychology, such as the law of effect and Weber’s law. But because laws are very general principles and their validity has already been well established, they are themselves rarely directly subjected to scientific test.

The next step down from laws in the hierarchy of organizing principles is theory. A theory is an integrated set of principles that explains and predicts many, but not all, observed relationships within a given domain of inquiry . One example of an important theory in psychology is the stage theory of cognitive development proposed by the Swiss psychologist Jean Piaget. The theory states that children pass through a series of cognitive stages as they grow, each of which must be mastered in succession before movement to the next cognitive stage can occur. This is an extremely useful theory in human development because it can be applied to many different content areas and can be tested in many different ways.

Good theories have four important characteristics. First, good theories are general , meaning they summarize many different outcomes. Second, they are parsimonious , meaning they provide the simplest possible account of those outcomes. The stage theory of cognitive development meets both of these requirements. It can account for developmental changes in behavior across a wide variety of domains, and yet it does so parsimoniously—by hypothesizing a simple set of cognitive stages. Third, good theories provide ideas for future research . The stage theory of cognitive development has been applied not only to learning about cognitive skills, but also to the study of children’s moral (Kohlberg, 1966) and gender (Ruble & Martin, 1998) development.

Finally, good theories are falsifiable (Popper, 1959), which means the variables of interest can be adequately measured and the relationships between the variables that are predicted by the theory can be shown through research to be incorrect . The stage theory of cognitive development is falsifiable because the stages of cognitive reasoning can be measured and because if research discovers, for instance, that children learn new tasks before they have reached the cognitive stage hypothesized to be required for that task, then the theory will be shown to be incorrect.

No single theory is able to account for all behavior in all cases. Rather, theories are each limited in that they make accurate predictions in some situations or for some people but not in other situations or for other people. As a result, there is a constant exchange between theory and data: Existing theories are modified on the basis of collected data, and the new modified theories then make new predictions that are tested by new data, and so forth. When a better theory is found, it will replace the old one. This is part of the accumulation of scientific knowledge.

The Research Hypothesis

Theories are usually framed too broadly to be tested in a single experiment. Therefore, scientists use a more precise statement of the presumed relationship among specific parts of a theory—a research hypothesis—as the basis for their research. A research hypothesis is a specific and falsifiable prediction about the relationship between or among two or more variables , where a variable is any attribute that can assume different values among different people or across different times or places . The research hypothesis states the existence of a relationship between the variables of interest and the specific direction of that relationship. For instance, the research hypothesis “Using marijuana will reduce learning” predicts that there is a relationship between a variable “using marijuana” and another variable called “learning.” Similarly, in the research hypothesis “Participating in psychotherapy will reduce anxiety,” the variables that are expected to be related are “participating in psychotherapy” and “level of anxiety.”

When stated in an abstract manner, the ideas that form the basis of a research hypothesis are known as conceptual variables. Conceptual variables are abstract ideas that form the basis of research hypotheses . Sometimes the conceptual variables are rather simple—for instance, “age,” “gender,” or “weight.” In other cases the conceptual variables represent more complex ideas, such as “anxiety,” “cognitive development,” “learning,” self-esteem,” or “sexism.”

The first step in testing a research hypothesis involves turning the conceptual variables into measured variables , which are variables consisting of numbers that represent the conceptual variables . For instance, the conceptual variable “participating in psychotherapy” could be represented as the measured variable “number of psychotherapy hours the patient has accrued” and the conceptual variable “using marijuana” could be assessed by having the research participants rate, on a scale from 1 to 10, how often they use marijuana or by administering a blood test that measures the presence of the chemicals in marijuana.

Psychologists use the term operational definition to refer to a precise statement of how a conceptual variable is turned into a measured variable . The relationship between conceptual and measured variables in a research hypothesis is diagrammed in Figure 2.1 “Diagram of a Research Hypothesis” . The conceptual variables are represented within circles at the top of the figure, and the measured variables are represented within squares at the bottom. The two vertical arrows, which lead from the conceptual variables to the measured variables, represent the operational definitions of the two variables. The arrows indicate the expectation that changes in the conceptual variables (psychotherapy and anxiety in this example) will cause changes in the corresponding measured variables. The measured variables are then used to draw inferences about the conceptual variables.

Figure 2.1 Diagram of a Research Hypothesis

In this research hypothesis, the conceptual variable of attending psychotherapy is operationalized using the number of hours of psychotherapy the client has completed, and the conceptual variable of anxiety is operationalized using self-reported levels of anxiety. The research hypothesis is that more psychotherapy will be related to less reported anxiety.

Table 2.1 “Examples of the Operational Definitions of Conceptual Variables That Have Been Used in Psychological Research” lists some potential operational definitions of conceptual variables that have been used in psychological research. As you read through this list, note that in contrast to the abstract conceptual variables, the measured variables are very specific. This specificity is important for two reasons. First, more specific definitions mean that there is less danger that the collected data will be misunderstood by others. Second, specific definitions will enable future researchers to replicate the research.

Table 2.1 Examples of the Operational Definitions of Conceptual Variables That Have Been Used in Psychological Research

Conducting Ethical Research

One of the questions that all scientists must address concerns the ethics of their research. Physicists are concerned about the potentially harmful outcomes of their experiments with nuclear materials. Biologists worry about the potential outcomes of creating genetically engineered human babies. Medical researchers agonize over the ethics of withholding potentially beneficial drugs from control groups in clinical trials. Likewise, psychologists are continually considering the ethics of their research.

Research in psychology may cause some stress, harm, or inconvenience for the people who participate in that research. For instance, researchers may require introductory psychology students to participate in research projects and then deceive these students, at least temporarily, about the nature of the research. Psychologists may induce stress, anxiety, or negative moods in their participants, expose them to weak electrical shocks, or convince them to behave in ways that violate their moral standards. And researchers may sometimes use animals in their research, potentially harming them in the process.

Decisions about whether research is ethical are made using established ethical codes developed by scientific organizations, such as the American Psychological Association, and federal governments. In the United States, the Department of Health and Human Services provides the guidelines for ethical standards in research. Some research, such as the research conducted by the Nazis on prisoners during World War II, is perceived as immoral by almost everyone. Other procedures, such as the use of animals in research testing the effectiveness of drugs, are more controversial.

Scientific research has provided information that has improved the lives of many people. Therefore, it is unreasonable to argue that because scientific research has costs, no research should be conducted. This argument fails to consider the fact that there are significant costs to not doing research and that these costs may be greater than the potential costs of conducting the research (Rosenthal, 1994). In each case, before beginning to conduct the research, scientists have attempted to determine the potential risks and benefits of the research and have come to the conclusion that the potential benefits of conducting the research outweigh the potential costs to the research participants.

Characteristics of an Ethical Research Project Using Human Participants

• Trust and positive rapport are created between the researcher and the participant.
• The rights of both the experimenter and participant are considered, and the relationship between them is mutually beneficial.
• The experimenter treats the participant with concern and respect and attempts to make the research experience a pleasant and informative one.
• Before the research begins, the participant is given all information relevant to his or her decision to participate, including any possibilities of physical danger or psychological stress.
• The participant is given a chance to have questions about the procedure answered, thus guaranteeing his or her free choice about participating.
• After the experiment is over, any deception that has been used is made public, and the necessity for it is explained.
• The experimenter carefully debriefs the participant, explaining the underlying research hypothesis and the purpose of the experimental procedure in detail and answering any questions.
• The experimenter provides information about how he or she can be contacted and offers to provide information about the results of the research if the participant is interested in receiving it. (Stangor, 2011)

This list presents some of the most important factors that psychologists take into consideration when designing their research. The most direct ethical concern of the scientist is to prevent harm to the research participants. One example is the well-known research of Stanley Milgram (1974) investigating obedience to authority. In these studies, participants were induced by an experimenter to administer electric shocks to another person so that Milgram could study the extent to which they would obey the demands of an authority figure. Most participants evidenced high levels of stress resulting from the psychological conflict they experienced between engaging in aggressive and dangerous behavior and following the instructions of the experimenter. Studies such as those by Milgram are no longer conducted because the scientific community is now much more sensitized to the potential of such procedures to create emotional discomfort or harm.

Another goal of ethical research is to guarantee that participants have free choice regarding whether they wish to participate in research. Students in psychology classes may be allowed, or even required, to participate in research, but they are also always given an option to choose a different study to be in, or to perform other activities instead. And once an experiment begins, the research participant is always free to leave the experiment if he or she wishes to. Concerns with free choice also occur in institutional settings, such as in schools, hospitals, corporations, and prisons, when individuals are required by the institutions to take certain tests, or when employees are told or asked to participate in research.

Researchers must also protect the privacy of the research participants. In some cases data can be kept anonymous by not having the respondents put any identifying information on their questionnaires. In other cases the data cannot be anonymous because the researcher needs to keep track of which respondent contributed the data. In this case one technique is to have each participant use a unique code number to identify his or her data, such as the last four digits of the student ID number. In this way the researcher can keep track of which person completed which questionnaire, but no one will be able to connect the data with the individual who contributed them.

Perhaps the most widespread ethical concern to the participants in behavioral research is the extent to which researchers employ deception. Deception occurs whenever research participants are not completely and fully informed about the nature of the research project before participating in it . Deception may occur in an active way, such as when the researcher tells the participants that he or she is studying learning when in fact the experiment really concerns obedience to authority. In other cases the deception is more passive, such as when participants are not told about the hypothesis being studied or the potential use of the data being collected.

Some researchers have argued that no deception should ever be used in any research (Baumrind, 1985). They argue that participants should always be told the complete truth about the nature of the research they are in, and that when participants are deceived there will be negative consequences, such as the possibility that participants may arrive at other studies already expecting to be deceived. Other psychologists defend the use of deception on the grounds that it is needed to get participants to act naturally and to enable the study of psychological phenomena that might not otherwise get investigated. They argue that it would be impossible to study topics such as altruism, aggression, obedience, and stereotyping without using deception because if participants were informed ahead of time what the study involved, this knowledge would certainly change their behavior. The codes of ethics of the American Psychological Association and other organizations allow researchers to use deception, but these codes also require them to explicitly consider how their research might be conducted without the use of deception.

Ensuring That Research Is Ethical

Making decisions about the ethics of research involves weighing the costs and benefits of conducting versus not conducting a given research project. The costs involve potential harm to the research participants and to the field, whereas the benefits include the potential for advancing knowledge about human behavior and offering various advantages, some educational, to the individual participants. Most generally, the ethics of a given research project are determined through a cost-benefit analysis , in which the costs are compared to the benefits. If the potential costs of the research appear to outweigh any potential benefits that might come from it, then the research should not proceed.

Arriving at a cost-benefit ratio is not simple. For one thing, there is no way to know ahead of time what the effects of a given procedure will be on every person or animal who participates or what benefit to society the research is likely to produce. In addition, what is ethical is defined by the current state of thinking within society, and thus perceived costs and benefits change over time. The U.S. Department of Health and Human Services regulations require that all universities receiving funds from the department set up an Institutional Review Board (IRB) to determine whether proposed research meets department regulations. The Institutional Review Board (IRB) is a committee of at least five members whose goal it is to determine the cost-benefit ratio of research conducted within an institution . The IRB approves the procedures of all the research conducted at the institution before the research can begin. The board may suggest modifications to the procedures, or (in rare cases) it may inform the scientist that the research violates Department of Health and Human Services guidelines and thus cannot be conducted at all.

One important tool for ensuring that research is ethical is the use of informed consent . A sample informed consent form is shown in Figure 2.2 “Sample Consent Form” . Informed consent , conducted before a participant begins a research session, is designed to explain the research procedures and inform the participant of his or her rights during the investigation . The informed consent explains as much as possible about the true nature of the study, particularly everything that might be expected to influence willingness to participate, but it may in some cases withhold some information that allows the study to work.

Figure 2.2 Sample Consent Form

The informed consent form explains the research procedures and informs the participant of his or her rights during the investigation.

Adapted from Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Because participating in research has the potential for producing long-term changes in the research participants, all participants should be fully debriefed immediately after their participation. The debriefing is a procedure designed to fully explain the purposes and procedures of the research and remove any harmful aftereffects of participation .

Research With Animals

Because animals make up an important part of the natural world, and because some research cannot be conducted using humans, animals are also participants in psychological research. Most psychological research using animals is now conducted with rats, mice, and birds, and the use of other animals in research is declining (Thomas & Blackman, 1992). As with ethical decisions involving human participants, a set of basic principles has been developed that helps researchers make informed decisions about such research; a summary is shown below.

APA Guidelines on Humane Care and Use of Animals in Research

The following are some of the most important ethical principles from the American Psychological Association’s guidelines on research with animals.

• Psychologists acquire, care for, use, and dispose of animals in compliance with current federal, state, and local laws and regulations, and with professional standards.
• Psychologists trained in research methods and experienced in the care of laboratory animals supervise all procedures involving animals and are responsible for ensuring appropriate consideration of their comfort, health, and humane treatment.
• Psychologists ensure that all individuals under their supervision who are using animals have received instruction in research methods and in the care, maintenance, and handling of the species being used, to the extent appropriate to their role.
• Psychologists make reasonable efforts to minimize the discomfort, infection, illness, and pain of animal subjects.
• Psychologists use a procedure subjecting animals to pain, stress, or privation only when an alternative procedure is unavailable and the goal is justified by its prospective scientific, educational, or applied value.
• Psychologists perform surgical procedures under appropriate anesthesia and follow techniques to avoid infection and minimize pain during and after surgery.
• When it is appropriate that an animal’s life be terminated, psychologists proceed rapidly, with an effort to minimize pain and in accordance with accepted procedures. (American Psychological Association, 2002)

Psychologists may use animals in their research, but they make reasonable efforts to minimize the discomfort the animals experience.

Because the use of animals in research involves a personal value, people naturally disagree about this practice. Although many people accept the value of such research (Plous, 1996), a minority of people, including animal-rights activists, believes that it is ethically wrong to conduct research on animals. This argument is based on the assumption that because animals are living creatures just as humans are, no harm should ever be done to them.

Most scientists, however, reject this view. They argue that such beliefs ignore the potential benefits that have and continue to come from research with animals. For instance, drugs that can reduce the incidence of cancer or AIDS may first be tested on animals, and surgery that can save human lives may first be practiced on animals. Research on animals has also led to a better understanding of the physiological causes of depression, phobias, and stress, among other illnesses. In contrast to animal-rights activists, then, scientists believe that because there are many benefits that accrue from animal research, such research can and should continue as long as the humane treatment of the animals used in the research is guaranteed.

Key Takeaways

• Psychologists use the scientific method to generate, accumulate, and report scientific knowledge.
• Basic research, which answers questions about behavior, and applied research, which finds solutions to everyday problems, inform each other and work together to advance science.
• Research reports describing scientific studies are published in scientific journals so that other scientists and laypersons may review the empirical findings.
• Organizing principles, including laws, theories and research hypotheses, give structure and uniformity to scientific methods.
• Concerns for conducting ethical research are paramount. Researchers assure that participants are given free choice to participate and that their privacy is protected. Informed consent and debriefing help provide humane treatment of participants.
• A cost-benefit analysis is used to determine what research should and should not be allowed to proceed.

Exercises and Critical Thinking

• Give an example from personal experience of how you or someone you know have benefited from the results of scientific research.
• Find and discuss a research project that in your opinion has ethical concerns. Explain why you find these concerns to be troubling.
• Indicate your personal feelings about the use of animals in research. When should and should not animals be used? What principles have you used to come to these conclusions?

American Psychological Association. (2002). Ethical principles of psychologists. American Psychologist, 57 , 1060–1073.

Baumrind, D. (1985). Research using intentional deception: Ethical issues revisited. American Psychologist, 40 , 165–174.

Kohlberg, L. (1966). A cognitive-developmental analysis of children’s sex-role concepts and attitudes. In E. E. Maccoby (Ed.), The development of sex differences . Stanford, CA: Stanford University Press.

Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper and Row.

Plous, S. (1996). Attitudes toward the use of animals in psychological research and education. Psychological Science, 7 , 352–358.

Popper, K. R. (1959). The logic of scientific discovery . New York, NY: Basic Books.

Rosenthal, R. (1994). Science and ethics in conducting, analyzing, and reporting psychological research. Psychological Science, 5 , 127–134.

Ruble, D., & Martin, C. (1998). Gender development. In W. Damon (Ed.), Handbook of child psychology (5th ed., pp. 933–1016). New York, NY: John Wiley & Sons.

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Thomas, G., & Blackman, D. (1992). The future of animal studies in psychology. American Psychologist, 47 , 1678.

Introduction to Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Scientific Psychology: Introduction to Research Methods and Research Designs - Research Methodology

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• Edition: 3rd (2007)
• Publisher: Pearson Custom Publishing
• Editors: Justin M. Joffe and Julianne Krulewitz

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APA Sample Paper: Experimental Psychology

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• Library & Learning Center
• Research Guides

PSYCH 122 - Research Methods

• APA Style, 7th Edition
• Select Your Topic
• Develop Your Topic
• Know Your Sources
• How to Search the Literature
• Finding Peer-Reviewed Articles

APA Tutorial

Formatting your paper, headings organize your paper (2.27), video tutorials, reference list format (9.43).

• Elements of a Reference

Reference Examples (Chapter 10)

Dois and urls (9.34-9.36), in-text citations.

• In-Text Citations Format
• In-Text Citations for Specific Source Types

NoodleTools

What is apa style.

APA style was created by social and behavioral scientists to standardize scientific writing. APA style is most often used in:

• psychology,
• social sciences (sociology, business), and

If you're taking courses in any of these areas, be prepared to use APA style.

For in-depth guidance on using this citation style, refer to Publication Manual of the American Psychological Association , 7th ed. We have several copies available at the MJC Library at the call number  BF 76.7 .P83 2020 .

APA Style, 7th ed.

In October 2019, the American Psychological Association made radical changes its style, especially with regard to the format and citation rules for students writing academic papers. Use this guide to learn how to format and cite your papers using APA Style, 7th edition.

You can start by viewing the  video tutorial .

For help on all aspects of formatting your paper in APA Style, see   The Essentials  page on the APA Style website.

• sans serif fonts such as 11-point Calibri, 11-point Arial, or 10-point Lucida Sans Unicode, or
• serif fonts such as 12-point Times New Roman, 11-point Georgia, or normal (10-point) Computer Modern (the default font for LaTeX)
• There are exceptions for the  title page ,  tables ,  figures ,  footnotes , and  displayed equations .
• Margins :  Use 1-in. margins on every side of the page.
• Align the text of an APA Style  paper to the left margin . Leave the right margin uneven, or “ragged.”
• Do not use full justification for student papers.
• Do not insert hyphens (manual breaks) in words at the end of line. However, it is acceptable if your word-processing program automatically inserts breaks in long hyperlinks (such as in a DOI or URL in a reference list entry).
• Indent the first line of each paragraph of text 0.5 in . from the left margin. Use the tab key or the automatic paragraph-formatting function of your word-processing program to achieve the indentation (the default setting is likely already 0.5 in.). Do not use the space bar to create indentation. 
• There are exceptions for the  title page ,  section labels ,  abstract ,  block quotations ,  headings ,  tables and figures ,  reference list , and  appendices .

Paper Elements

Student papers generally include, at a minimum: 

• Title Page (2.3)
• Text (2.11)
• References  (2.12)

Student papers may include additional elements such as tables and figures depending on the assignment. So, please check with your teacher!

Student papers generally  DO NOT  include the following unless your teacher specifically requests it:

• Running head
• Author note

For complete information on the  order of pages , see the APA Style website.

Number your pages consecutively starting with page 1. Each section begins on a new page. Put the pages in the following order:

• Page 1: Title page
• Page 2: Abstract (if your teacher requires an abstract)
• Page 3: Text 
• References begin on a new page after the last page of text
• Footnotes begin on a new page after the references (if your teacher requires footnotes)
• Tables begin each on a new page after the footnotes (if your teacher requires tables) 
• Figures begin on a new page after the tables (if your teacher requires figures)
• Appendices begin on a new page after the tables and/or figures (if your teacher requires appendices)

Sample Papers With Built-In Instructions

To see what your paper should look like, check out these sample papers with built-in instructions.

APA Style uses five (5) levels of headings to help you organize your paper and allow your audience to identify its key points easily. Levels of headings establish the hierarchy of your sections just like you did in your paper outline.

APA tells us to use "only the number of headings necessary to differentiate distinct section in your paper." Therefore, the number of heading levels you create depends on the length and complexity of your paper.

See the chart below for instructions on formatting your headings:

Use Word to Format Your Paper:

Use Google Docs to Format Your Paper:

Placement:  The reference list  appears at the end of the paper, on its own page(s). If your research paper ends on page 8, your References begin on page 9.

Heading:  Place the section label References  in bold at the top of the page, centered.

Arrangement:  Alphabetize entries by author's last name. If source has no named author, alphabetize by the title, ignoring A, An, or The. (9.44-9.48)

Spacing:  Like the rest of the APA paper, the reference list is double-spaced throughout. Be sure NOT to add extra spaces between citations.

Indentation:  To make citations easier to scan, add a  hanging indent  of 0.5 in. to any citation that runs more than one line. Use the paragraph-formatting function of your word processing program to create your hanging indent.  

See Sample References Page (from APA Sample Student Paper):

Elements of Reference List Entries: (Chapter 9)

References generally have four elements, each of which has a corresponding question for you to answer:

• Author:   Who is responsible for this work? (9.7-9.12)
• Date:   When was this work published? (9.13-9.17)
• Title:   What is this work called? (9.18-9.22)
• Source:   Where can I retrieve this work? (9.23-9.37)

By using these four elements and answering these four questions, you should be able to create a citation for any type of source.

For complete information on all of these elements, checkout the APA Style website.

This infographic shows the first page of a journal article. The locations of the reference elements are highlighted with different colors and callouts, and the same colors are used in the reference list entry to show how the entry corresponds to the source.

To create your references, you'll simple look for these elements in your source and put them together in your reference list entry.

American Psychological Association.  Example of where to find reference information for a journal article  [Infographic]. APA Style Center. https://apastyle.apa.org/style-grammar-guidelines/references/basic-principles

Below you'll find two printable handouts showing APA citation examples. The first is an abbreviated list created by MJC Librarians. The second, which is more comprehensive, is from the APA Style website. Feel free to print these for your convenience or use the links to reference examples below:

• APA Citation Examples Created by MJC Librarians for you.
• Common References Examples (APA Handout) Printable handout from the American Psychological Association.
• Journal Article
• Magazine Article
• Newspaper Article
• Edited Book Chapter
• Webpage on a Website

Classroom or Intranet Sources

• Classroom Course Pack Materials
• How to Cite ChatGPT
• Dictionary Entry
• Government Report
• Legal References (Laws & Cases)
• TED Talk References
• Religious Works
• Open Educational Resources (OER)
• Archival Documents and Collections

You can view the entire Reference Examples website below and view a helpful guide to finding useful APA style topics easily:

• APA Style: Reference Examples
• Navigating the not-so-hidden treasures of the APA Style website
• Missing Reference Information

Sometimes you won't be able to find all the elements required for your reference. In that case, see the  instructions in Table 9.1 of the APA style manual in section 9.4 or the APA Style website below:

• Direct Quotation of Material Without Page Numbers

The DOI or URL is the final component of a reference list entry. Because so much scholarship is available and/or retrieved online, most reference list entries end with either a DOI or a URL.

• A  DOI  is a unique alphanumeric string that identifies content and provides a persistent link to its location on the internet. DOIs can be found in database records and the reference lists of published works.
• A  URL  specifies the location of digital information on the internet and can be found in the address bar of your internet browser. URLs in references should link directly to the cited work when possible.

When to Include DOIs and URLs:

• Include a DOI for all works that have a DOI, regardless of whether you used the online version or the print version.
• If an online work has both a DOI and a URL, include only the DOI.
• For works without DOIs from websites (not including academic research databases), provide a URL in the reference (as long as the URL will work for readers).
• For works without DOIs from most academic research databases, do not include a URL or database information in the reference because these works are widely available. The reference should be the same as the reference for a print version of the work.
• For works from databases that publish original, proprietary material available only in that database (such as the UpToDate database) or for works of limited circulation in databases (such as monographs in the ERIC database), include the name of the database or archive and the URL of the work. If the URL requires a login or is session-specific (meaning it will not resolve for readers), provide the URL of the database or archive home page or login page instead of the URL for the work. (See APA Section 9.30 for more information). 
• If the URL is no longer working or no longer provides readers access to the content you intend to cite, try to find an archived version using the Internet Archive , then use the archived URL. If there is no archived URL, do not use that resource.

Format of DOIs and URLs:

Your DOI should look like this: 

https://doi.org/10.1037/a0040251

Follow these guidelines from the APA Style website.

APA Style uses the  author–date citation system , in which a brief in-text citation points your reader to the full reference list entry at the end of your paper. The in-text citation appears within the body of the paper and briefly identifies the cited work by its author and date of publication. This method enables your reader to locate the corresponding entry in the alphabetical reference list at the end of your paper.

Each work you cite  must  appear in the reference list, and each work in the reference list must be cited in the text (or in a table, figure, footnote, or appendix) except for the following (See APA, 8.4):

• Personal communications (8.9)
• General mentions of entire websites, whole periodicals (8.22), and common software and apps (10.10) in the text do not require a citation or reference list entry.
• The source of an epigraph does not usually appear in the reference list (8.35)
• Quotations from your research participants do not need citations or reference list entries (8.36)
• References included in a statistical meta-analysis, which are marked with an asterisk in the reference list, may be cited in the text (or not) at the author’s discretion. This exception is relevant only to authors who are conducting a meta-analysis (9.52).

Formatting Your In-Text Citations

Parenthetical and Narrative Citations: ( See APA Section  8.11)

In APA style you use the author-date citation system for citing references within your paper. You incorporate these references using either a  parenthetical   or a  narrative  style.

Parenthetical Citations

• In parenthetical citations, the author name and publication date appear in parentheses, separated by a comma. (Jones, 2018)
• A parenthetical citation can appear within or at the end of a sentence.
• When the parenthetical citation is at the end of the sentence, put the period or other end punctuation after the closing parenthesis.
• If there is no author, use the first few words of the reference list entry, usually the "Title" of the source: ("Autism," 2008) See APA 8.14
• When quoting, always provide the author, year, and specific page citation or paragraph number for nonpaginated materials in the text (Santa Barbara, 2010, p. 243).  See APA 8.13
• For most citations, the parenthetical reference is placed BEFORE the punctuation: Magnesium can be effective in treating PMS (Haggerty, 2012).

Narrative Citations 

In narrative citations, the author name or title of your source appears within your text and the publication date appears in parentheses immediately after the author name. 

• Santa Barbara (2010) noted a decline in the approval of disciplinary spanking of 26 percentage points from 1968 to 1994.

In-Text Citation Checklist

• In-Text Citation Checklist Use this useful checklist from the American Psychological Association to ensure that you've created your in-text citations correctly.

In-Text Citations for Specific Types of Sources

Quotations from Research Participants

Personal Communications

Secondary Sources  

Use NoodleTools to Cite Your Sources  

NoodleTools can help you create your references and your in-text citations.

• NoodleTools Express No sign in required . When you need one or two quick citations in MLA, APA, or Chicago style, simply generate them in NoodleTools Express then copy and paste what you need into your document. Note: Citations are not saved and cannot be exported to a word processor using NoodleTools Express.
• NoodleTools (Login Full Database) This link opens in a new window Create and organize your research notes, share and collaborate on research projects, compose and error check citations, and complete your list of works cited in MLA, APA, or Chicago style using the full version of NoodleTools. You'll need to Create a Personal ID and password the first time you use NoodleTools.

See How to Use NoodleTools Express to Create a Citation in APA Format

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Chapter 3. Psychological Science

3.1 Psychologists Use the Scientific Method to Guide Their Research

Learning objectives.

• Describe the principles of the scientific method and explain its importance in conducting and interpreting research.
• Differentiate laws from theories and explain how research hypotheses are developed and tested.
• Discuss the procedures that researchers use to ensure that their research with humans and with animals is ethical.

Psychologists aren’t the only people who seek to understand human behaviour and solve social problems. Philosophers, religious leaders, and politicians, among others, also strive to provide explanations for human behaviour. But psychologists believe that research is the best tool for understanding human beings and their relationships with others. Rather than accepting the claim of a philosopher that people do (or do not) have free will, a psychologist would collect data to empirically test whether or not people are able to actively control their own behaviour. Rather than accepting a politician’s contention that creating (or abandoning) a new centre for mental health will improve the lives of individuals in the inner city, a psychologist would empirically assess the effects of receiving mental health treatment on the quality of life of the recipients. The statements made by psychologists are empirical, which means they are based on systematic collection and analysis of data .

The Scientific Method

All scientists (whether they are physicists, chemists, biologists, sociologists, or psychologists) are engaged in the basic processes of collecting data and drawing conclusions about those data. The methods used by scientists have developed over many years and provide a common framework for developing, organizing, and sharing information. The scientific method  is the set of assumptions, rules, and procedures scientists use to conduct research .

In addition to requiring that science be empirical, the scientific method demands that the procedures used be objective , or free from the personal bias or emotions of the scientist . The scientific method proscribes how scientists collect and analyze data, how they draw conclusions from data, and how they share data with others. These rules increase objectivity by placing data under the scrutiny of other scientists and even the public at large. Because data are reported objectively, other scientists know exactly how the scientist collected and analyzed the data. This means that they do not have to rely only on the scientist’s own interpretation of the data; they may draw their own, potentially different, conclusions.

Most new research is designed to replicate — that is, to repeat, add to, or modify — previous research findings. The scientific method therefore results in an accumulation of scientific knowledge through the reporting of research and the addition to and modification of these reported findings by other scientists.

Laws and Theories as Organizing Principles

One goal of research is to organize information into meaningful statements that can be applied in many situations. Principles that are so general as to apply to all situations in a given domain of inquiry are known as laws . There are well-known laws in the physical sciences, such as the law of gravity and the laws of thermodynamics, and there are some universally accepted laws in psychology, such as the law of effect and Weber’s law. But because laws are very general principles and their validity has already been well established, they are themselves rarely directly subjected to scientific test.

The next step down from laws in the hierarchy of organizing principles is theory. A theory  is an integrated set of principles that explains and predicts many, but not all, observed relationships within a given domain of inquiry . One example of an important theory in psychology is the stage theory of cognitive development proposed by the Swiss psychologist Jean Piaget. The theory states that children pass through a series of cognitive stages as they grow, each of which must be mastered in succession before movement to the next cognitive stage can occur . This is an extremely useful theory in human development because it can be applied to many different content areas and can be tested in many different ways.

Good theories have four important characteristics. First, good theories are general , meaning they summarize many different outcomes . Second, they are parsimonious , meaning they provide the simplest possible account of those outcomes . The stage theory of cognitive development meets both of these requirements. It can account for developmental changes in behaviour across a wide variety of domains, and yet it does so parsimoniously — by hypothesizing a simple set of cognitive stages. Third, good theories provide ideas for future research. The stage theory of cognitive development has been applied not only to learning about cognitive skills, but also to the study of children’s moral (Kohlberg, 1966) and gender (Ruble & Martin, 1998) development.

Finally, good theories are falsifiable  (Popper, 1959), which means the variables of interest can be adequately measured and the relationships between the variables that are predicted by the theory can be shown through research to be incorrect . The stage theory of cognitive development is falsifiable because the stages of cognitive reasoning can be measured and because if research discovers, for instance, that children learn new tasks before they have reached the cognitive stage hypothesized to be required for that task, then the theory will be shown to be incorrect.

No single theory is able to account for all behaviour in all cases. Rather, theories are each limited in that they make accurate predictions in some situations or for some people but not in other situations or for other people. As a result, there is a constant exchange between theory and data: existing theories are modified on the basis of collected data, and the new modified theories then make new predictions that are tested by new data, and so forth. When a better theory is found, it will replace the old one. This is part of the accumulation of scientific knowledge.

The Research Hypothesis

Theories are usually framed too broadly to be tested in a single experiment. Therefore, scientists use a more precise statement of the presumed relationship between specific parts of a theory — a research hypothesis — as the basis for their research. A research hypothesis  is a specific and falsifiable prediction about the relationship between or among two or more variables , where a variable  is any attribute that can assume different values among different people or across different times or places . The research hypothesis states the existence of a relationship between the variables of interest and the specific direction of that relationship. For instance, the research hypothesis “Using marijuana will reduce learning” predicts that there is a relationship between one variable, “using marijuana,” and another variable called “learning.” Similarly, in the research hypothesis “Participating in psychotherapy will reduce anxiety,” the variables that are expected to be related are “participating in psychotherapy” and “level of anxiety.”

When stated in an abstract manner, the ideas that form the basis of a research hypothesis are known as conceptual variables. Conceptual variables  are abstract ideas that form the basis of research hypotheses . Sometimes the conceptual variables are rather simple — for instance, age, gender, or weight. In other cases the conceptual variables represent more complex ideas, such as anxiety, cognitive development, learning, self-esteem, or sexism.

The first step in testing a research hypothesis involves turning the conceptual variables into measured variables , which are variables consisting of numbers that represent the conceptual variables . For instance, the conceptual variable “participating in psychotherapy” could be represented as the measured variable “number of psychotherapy hours the patient has accrued,” and the conceptual variable “using marijuana” could be assessed by having the research participants rate, on a scale from 1 to 10, how often they use marijuana or by administering a blood test that measures the presence of the chemicals in marijuana.

Psychologists use the term operational definition  to refer to a precise statement of how a conceptual variable is turned into a measured variable . The relationship between conceptual and measured variables in a research hypothesis is diagrammed in Figure 3.1. The conceptual variables are represented in circles at the top of the figure (Psychotherapy and anxiety), and the measured variables are represented in squares at the bottom (number of hours the patient has spent in psychotherapy and anxiety concerns as reported by the patient). The two vertical arrows, which lead from the conceptual variables to the measured variables, represent the operational definitions of the two variables. The arrows indicate the expectation that changes in the conceptual variables (psychotherapy and anxiety) will cause changes in the corresponding measured variables (number of hours in psychotherapy and reported anxiety concernts). The measured variables are then used to draw inferences about the conceptual variables.

Table 3.1 lists some potential operational definitions of conceptual variables that have been used in psychological research. As you read through this list, note that in contrast to the abstract conceptual variables, the measured variables are very specific. This specificity is important for two reasons. First, more specific definitions mean that there is less danger that the collected data will be misunderstood by others. Second, specific definitions will enable future researchers to replicate the research.

Conducting Ethical Research

One of the questions that all scientists must address concerns the ethics of their research. Physicists are concerned about the potentially harmful outcomes of their experiments with nuclear materials. Biologists worry about the potential outcomes of creating genetically engineered human babies. Medical researchers agonize over the ethics of withholding potentially beneficial drugs from control groups in clinical trials. Likewise, psychologists are continually considering the ethics of their research.

Research in psychology may cause some stress, harm, or inconvenience for the people who participate in that research. For instance, researchers may require introductory psychology students to participate in research projects and then deceive these students, at least temporarily, about the nature of the research. Psychologists may induce stress, anxiety, or negative moods in their participants, expose them to weak electrical shocks, or convince them to behave in ways that violate their moral standards. And researchers may sometimes use animals in their research, potentially harming them in the process.

Decisions about whether research is ethical are made using established ethical codes developed by scientific organizations, such as the Canadian Psychological Association, and federal governments. In Canada, the federal agencies, Health Canada, and the Canadian Institute for Health Research provide the guidelines for ethical standards in research. Some research, such as the research conducted by the Nazis on prisoners during World War II, is perceived as immoral by almost everyone. Other procedures, such as the use of animals in research testing the effectiveness of drugs, are more controversial.

Scientific research has provided information that has improved the lives of many people. Therefore, it is unreasonable to argue that because scientific research has costs, no research should be conducted. This argument fails to consider the fact that there are significant costs to not doing research and that these costs may be greater than the potential costs of conducting the research (Rosenthal, 1994). In each case, before beginning to conduct the research, scientists have attempted to determine the potential risks and benefits of the research and have come to the conclusion that the potential benefits of conducting the research outweigh the potential costs to the research participants.

Characteristics of an Ethical Research Project Using Human Participants

• Trust and positive rapport are created between the researcher and the participant.
• The rights of both the experimenter and participant are considered, and the relationship between them is mutually beneficial.
• The experimenter treats the participant with concern and respect and attempts to make the research experience a pleasant and informative one.
• Before the research begins, the participant is given all information relevant to his or her decision to participate, including any possibilities of physical danger or psychological stress.
• The participant is given a chance to have questions about the procedure answered, thus guaranteeing his or her free choice about participating.
• After the experiment is over, any deception that has been used is made public, and the necessity for it is explained.
• The experimenter carefully debriefs the participant, explaining the underlying research hypothesis and the purpose of the experimental procedure in detail and answering any questions.
• The experimenter provides information about how he or she can be contacted and offers to provide information about the results of the research if the participant is interested in receiving it. (Stangor, 2011)

This list presents some of the most important factors that psychologists take into consideration when designing their research. The most direct ethical concern of the scientist is to prevent harm to the research participants. One example is the well-known research of Stanley Milgram (1974) investigating obedience to authority. In these studies, participants were induced by an experimenter to administer electric shocks to another person so that Milgram could study the extent to which they would obey the demands of an authority figure. Most participants evidenced high levels of stress resulting from the psychological conflict they experienced between engaging in aggressive and dangerous behaviour and following the instructions of the experimenter. Studies such as those by Milgram are no longer conducted because the scientific community is now much more sensitized to the potential of such procedures to create emotional discomfort or harm.

Another goal of ethical research is to guarantee that participants have free choice regarding whether they wish to participate in research. Students in psychology classes may be allowed, or even required, to participate in research, but they are also always given an option to choose a different study to be in, or to perform other activities instead. And once an experiment begins, the research participant is always free to leave the experiment if he or she wishes to. Concerns with free choice also occur in institutional settings, such as in schools, hospitals, corporations, and prisons, when individuals are required by the institutions to take certain tests, or when employees are told or asked to participate in research.

Researchers must also protect the privacy of the research participants. In some cases data can be kept anonymous by not having the respondents put any identifying information on their questionnaires. In other cases the data cannot be anonymous because the researcher needs to keep track of which respondent contributed the data. In this case, one technique is to have each participant use a unique code number to identify his or her data, such as the last four digits of the student ID number. In this way the researcher can keep track of which person completed which questionnaire, but no one will be able to connect the data with the individual who contributed them.

Perhaps the most widespread ethical concern to the participants in behavioural research is the extent to which researchers employ deception. Deception   occurs whenever research participants are not completely and fully informed about the nature of the research project before participating in it . Deception may occur in an active way, such as when the researcher tells the participants that he or she is studying learning when in fact the experiment really concerns obedience to authority. In other cases the deception is more passive, such as when participants are not told about the hypothesis being studied or the potential use of the data being collected.

Some researchers have argued that no deception should ever be used in any research (Baumrind, 1985). They argue that participants should always be told the complete truth about the nature of the research they are in, and that when participants are deceived there will be negative consequences, such as the possibility that participants may arrive at other studies already expecting to be deceived. Other psychologists defend the use of deception on the grounds that it is needed to get participants to act naturally and to enable the study of psychological phenomena that might not otherwise get investigated. They argue that it would be impossible to study topics such as altruism, aggression, obedience, and stereotyping without using deception because if participants were informed ahead of time what the study involved, this knowledge would certainly change their behaviour. The codes of ethics of the Canadian Psychological Association and the Tri-Council Policy Statement of Canada’s three federal research agencies (the Canadian Institute of Health Research [CIHR], the Natural Sciences and Engineering Research Council of Canada [NSERC], and the Social Sciences and Humanities Research Council of Canada [SSHRC] or “the Agencies”) allow researchers to use deception, but these codes also require them to explicitly consider how their research might be conducted without the use of deception.

Ensuring that Research Is Ethical

Making decisions about the ethics of research involves weighing the costs and benefits of conducting versus not conducting a given research project. The costs involve potential harm to the research participants and to the field, whereas the benefits include the potential for advancing knowledge about human behaviour and offering various advantages, some educational, to the individual participants. Most generally, the ethics of a given research project are determined through a cost-benefit analysis , in which the costs are compared with the benefits . If the potential costs of the research appear to outweigh any potential benefits that might come from it, then the research should not proceed.

Arriving at a cost-benefit ratio is not simple. For one thing, there is no way to know ahead of time what the effects of a given procedure will be on every person or animal who participates or what benefit to society the research is likely to produce. In addition, what is ethical is defined by the current state of thinking within society, and thus perceived costs and benefits change over time. In Canada, the Tri-Council regulations require that all universities receiving funds from the Agencies set up an Ethical Review Board (ERB) to determine whether proposed research meets department regulations. The ERB  is a committee of at least five members whose goal it is to determine the cost-benefit ratio of research conducted within an institution . The ERB must approve the procedures of all the research conducted at the institution before the research can begin. The board may suggest modifications to the procedures, or (in rare cases) it may inform the scientist that the research violates Tri-Council Research Policy Statement and thus cannot be conducted at all.

One important tool for ensuring that research is ethical is the use of informed consent . A sample informed consent form is shown in Figure 3.2, Informed consent , conducted before a participant begins a research session, is designed to explain the research procedures and inform the participant of his or her rights during the investigation . The informed consent explains as much as possible about the true nature of the study, particularly everything that might be expected to influence willingness to participate, but it may in some cases withhold some information that allows the study to work.

The informed consent form explains the research procedures and informs the participant of his or her rights during the investigation. Informed consent should address the following issues:

• A very general statement about the purpose of the study
• A brief description of what the participants will be asked to do
• A brief description of the risks, if any, and what the researcher will do to restore the participant
• A statement informing participants that they may refuse to participate or withdraw at any time without being penalized
• A statement regarding how the participant’s confidentiality will be protected
• Encouragement to ask questions about participation
• Instructions regarding whom to contact if there are concerns
• Information regarding where the subjects may be informed about the study’s findings

Because participating in research has the potential for producing long-term changes in the research participants, all participants should be fully debriefed immediately after their participation. The debriefing  is a procedure designed to fully explain the purposes and procedures of the research and remove any harmful after-effects of participation .

Research with Animals

Because animals make up an important part of the natural world, and because some research cannot be conducted using humans, animals are also participants in psychological research (Figure 3.3). Most psychological research using animals is now conducted with rats, mice, and birds, and the use of other animals in research is declining (Thomas & Blackman, 1992). As with ethical decisions involving human participants, a set of basic principles has been developed that helps researchers make informed decisions about such research; a summary is shown below.

Canadian Psychological Association Guidelines on Humane Care and Use of Animals in Research

The following are some of the most important ethical principles from the Canadian Psychological Association’s (CPA) guidelines on research with animals.

• II.45 Not use animals in their research unless there is a reasonable expectation that the research will increase understanding of the structures and processes underlying behaviour, or increase understanding of the particular animal species used in the study, or result eventually in benefits to the health and welfare of humans or other animals.
• II.46 Use a procedure subjecting animals to pain, stress, or privation only if an alternative procedure is unavailable and the goal is justified by its prospective scientific, educational, or applied value.
• II.47 Make every effort to minimize the discomfort, illness, and pain of animals. This would include performing surgical procedures only under appropriate anaesthesia, using techniques to avoid infection and minimize pain during and after surgery and, if disposing of experimental animals is carried out at the termination of the study, doing so in a humane way. (Canadian Code of Ethics for Psychologists)
• II.48 Use animals in classroom demonstrations only if the instructional objectives cannot be achieved through the use of video-tapes, films, or other methods, and if the type of demonstration is warranted by the anticipated instructional gain  (Canadian Psychological Association, 2000).

Because the use of animals in research involves a personal value, people naturally disagree about this practice. Although many people accept the value of such research (Plous, 1996), a minority of people, including animal-rights activists, believe that it is ethically wrong to conduct research on animals. This argument is based on the assumption that because animals are living creatures just as humans are, no harm should ever be done to them.

Most scientists, however, reject this view. They argue that such beliefs ignore the potential benefits that have come, and continue to come, from research with animals. For instance, drugs that can reduce the incidence of cancer or AIDS may first be tested on animals, and surgery that can save human lives may first be practised on animals. Research on animals has also led to a better understanding of the physiological causes of depression, phobias, and stress, among other illnesses. In contrast to animal-rights activists, then, scientists believe that because there are many benefits that accrue from animal research, such research can and should continue as long as the humane treatment of the animals used in the research is guaranteed.

Key Takeaways

• Psychologists use the scientific method to generate, accumulate, and report scientific knowledge.
• Basic research, which answers questions about behaviour, and applied research, which finds solutions to everyday problems, inform each other and work together to advance science.
• Research reports describing scientific studies are published in scientific journals so that other scientists and laypersons may review the empirical findings.
• Organizing principles, including laws, theories, and research hypotheses, give structure and uniformity to scientific methods.
• Concerns for conducting ethical research are paramount. Researchers ensure that participants are given free choice to participate and that their privacy is protected. Informed consent and debriefing help provide humane treatment of participants.
• A cost-benefit analysis is used to determine what research should and should not be allowed to proceed.

Exercises and Critical Thinking

• Give an example from personal experience of how you or someone you know has benefited from the results of scientific research.
• Find and discuss a research project that in your opinion has ethical concerns. Explain why you find these concerns to be troubling.
• Indicate your personal feelings about the use of animals in research. When should and should not animals be used? What principles have you used to come to these conclusions?

Image Attributions

Figure 3.3: “ Wistar rat ” by Janet Stephens (http://en.wikipedia.org/wiki/File:Wistar_rat.jpg) is in the public domain .

Baumrind, D. (1985). Research using intentional deception: Ethical issues revisited.  American Psychologist, 40 , 165–174.

Canadian Psychological Association. (2000). Canadian code of ethics for psychologists (third edition) [PDF] . Retrieved July 2014 from http://www.cpa.ca/cpasite/userfiles/Documents/Practice_Page/Ethics_Code_Psych.pdf

Kohlberg, L. (1966). A cognitive-developmental analysis of children’s sex-role concepts and attitudes. In E. E. Maccoby (Ed.),  The development of sex differences . Stanford, CA: Stanford University Press.

Milgram, S. (1974).  Obedience to authority: An experimental view . New York, NY: Harper and Row.

Plous, S. (1996). Attitudes toward the use of animals in psychological research and education.  Psychological Science, 7 , 352–358.

Popper, K. R. (1959).  The logic of scientific discovery . New York, NY: Basic Books.

Rosenthal, R. (1994). Science and ethics in conducting, analyzing, and reporting psychological research.  Psychological Science, 5 , 127–134.

Ruble, D., & Martin, C. (1998). Gender development. In W. Damon (Ed.),  Handbook of child psychology  (5th ed., pp. 933–1016). New York, NY: John Wiley & Sons.

Stangor, C. (2011).  Research methods for the behavioral sciences  (4th ed.). Mountain View, CA: Cengage.

Thomas, G., & Blackman, D. (1992). The future of animal studies in psychology.  American Psychologist, 47 , 1678.

Long Descriptions

Figure 3.2 long description: Sample research consent form.

My name is [insert your name], and this research project is part of the requirement for a [insert your degree program] at [blank] University. My credentials with [blank] university can be established by telephoning [insert name and number of supervisor].

This document constitutes an agreement to participate in my research project, the objective of which is to [insert research objectives and the sponsoring organization here].

The research will consist of [insert your methodology] and its foreseen to last [insert amount of time]. The foreseen questions will refer to [insert summary of foreseen questions]. In addition to submitting my final report to [blank] University in partial fulfillment for a [insert your degree program], I will also be sharing my search findings with [insert your sponsoring organization]. [Disclose all the purposes to which the research data is going to be put, e.g. journal articles, books, etc.].

Information will be recorded in hand-written format (or taped/videotaped, etc) and where appropriate, summarized, in anonymous format, in the body of the final report. At no time will any specific comments be attributed to any individual unless specific agreement has been obtained beforehand. All documentation will be kept strictly confidential.

A copy of the final report will be published. A copy will be housed at [blank] university, available online through [blank] and will be publicly accessible. Access and distribution will be unrestricted.

[Disclose any and all conflicts of interest and how those will be managed.]

You are not compelled to participate in this research project. If you do choose to participate, you are free to withdraw at any time without prejudice. Similarly, if you choose not to participate in this research project, this information will also be maintained in confidence.

By signing this letter, you give free and informed consent to participate in this project.

Name (Please print), Signed: Date: [Return to Figure 3.2]

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How to Write an Introduction for a Psychology Paper

• Writing Tips

If you are writing a psychology paper, it is essential to kick things off with a strong introduction. The introduction to a psychology research paper helps your readers understand why the topic is important and what they need to know before they delve deeper.

Your goal in this section is to introduce the topic to the reader, provide an overview of previous research on the topic, and identify your own hypothesis .

At a Glance

Writing a great introduction can be a great foundation for the rest of your psychology paper. To create a strong intro:

• Research your topic
• Outline your paper
• Introduce your topic
• Summarize the previous research
• Present your hypothesis or main argument

Before You Write an Introduction

There are some important steps you need to take before you even begin writing your introduction. To know what to write, you need to collect important background information and create a detailed plan.

Research Your Topic

Search a journal database, PsychInfo or ERIC, to find articles on your subject. Once you have located an article, look at the reference section to locate other studies cited in the article. As you take notes from these articles, be sure to write down where you found the information.

A simple note detailing the author's name, journal, and date of publication can help you keep track of sources and avoid plagiarism.

Create a Detailed Outline

This is often one of the most boring and onerous steps, so students tend to skip outlining and go straight to writing. Creating an outline might seem tedious, but it can be an enormous time-saver down the road and will make the writing process much easier.

Start by looking over the notes you made during the research process and consider how you want to present all of your ideas and research.

Introduce the Topic

Once you are ready to write your introduction, your first task is to provide a brief description of the research question. What is the experiment or study attempting to demonstrate? What phenomena are you studying? Provide a brief history of your topic and explain how it relates to your current research.

As you are introducing your topic, consider what makes it important. Why should it matter to your reader? The goal of your introduction is not only to let your reader know what your paper is about, but also to justify why it is important for them to learn more.

If your paper tackles a controversial subject and is focused on resolving the issue, it is important to summarize both sides of the controversy in a fair and impartial way. Consider how your paper fits in with the relevant research on the topic.

The introduction of a research paper is designed to grab interest. It should present a compelling look at the research that already exists and explain to readers what questions your own paper will address.

Summarize Previous Research

The second task of your introduction is to provide a well-rounded summary of previous research that is relevant to your topic. So, before you begin to write this summary, it is important to research your topic thoroughly.

Finding appropriate sources amid thousands of journal articles can be a daunting task, but there are several steps you can take to simplify your research. If you have completed the initial steps of researching and keeping detailed notes, writing your introduction will be much easier.

It is essential to give the reader a good overview of the historical context of the issue you are writing about, but do not feel like you must provide an exhaustive review of the subject. Focus on hitting the main points, and try to include the most relevant studies.

You might describe previous research findings and then explain how the current study differs or expands upon earlier research.

Provide Your Hypothesis

Once you have summarized the previous research, explain areas where the research is lacking or potentially flawed. What is missing from previous studies on your topic? What research questions have yet to be answered? Your hypothesis should lead to these questions.

At the end of your introduction, offer your hypothesis and describe what you expected to find in your experiment or study.

The introduction should be relatively brief. You want to give your readers an overview of a topic, explain why you are addressing it, and provide your arguments.

Tips for Writing Your Psychology Paper Intro

• Use 3x5 inch note cards to write down notes and sources.
• Look in professional psychology journals for examples of introductions.
• Remember to cite your sources.
• Maintain a working bibliography with all of the sources you might use in your final paper. This will make it much easier to prepare your reference section later on.
• Use a copy of the APA style manual to ensure that your introduction and references are in proper APA format .

What This Means For You

Before you delve into the main body of your paper, you need to give your readers some background and present your main argument in the introduction of you paper. You can do this by first explaining what your topic is about, summarizing past research, and then providing your thesis.

Armağan A. How to write an introduction section of a scientific article ?  Turk J Urol . 2013;39(Suppl 1):8-9. doi:10.5152/tud.2013.046

Fried T, Foltz C, Lendner M, Vaccaro AR. How to write an effective introduction .  Clin Spine Surg . 2019;32(3):111-112. doi:10.1097/BSD.0000000000000714

Jawaid SA, Jawaid M. How to write introduction and discussion .  Saudi J Anaesth . 2019;13(Suppl 1):S18-S19. doi:10.4103/sja.SJA_584_18

American Psychological Association. Information Recommended for Inclusion in Manuscripts That Report New Data Collections Regardless of Research Design . Published 2020.

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

Research Methods In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

The researcher decides where the experiment will take place, at what time, with which participants, in what circumstances,  using a standardized procedure.

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

Meta-analysis is a statistical procedure used to combine and synthesize findings from multiple independent studies to estimate the average effect size for a particular research question.

Meta-analysis goes beyond traditional narrative reviews by using statistical methods to integrate the results of several studies, leading to a more objective appraisal of the evidence.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

• Strengths : Increases the conclusions’ validity as they’re based on a wider range.
• Weaknesses : Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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Overview of the Scientific Method

Here is the abstract of a 2014 article in the journal Psychological Science.

Taking notes on laptops rather than in longhand is increasingly common. Many researchers have suggested that laptop note taking is less effective than longhand note taking for learning. Prior studies have primarily focused on students’ capacity for multitasking and distraction when using laptops. The present research suggests that even when laptops are used solely to take notes, they may still be impairing learning because their use results in shallower processing. In three studies, we found that students who took notes on laptops performed worse on conceptual questions than students who took notes longhand. We show that whereas taking more notes can be beneficial, laptop note takers’ tendency to transcribe lectures verbatim rather than processing information and reframing it in their own words is detrimental to learning. (Mueler & Oppenheimer, 2014, p. 1159) [1]

In this abstract, the researcher has identified a research question—about the effect of taking notes on a laptop on learning—and identified why it is worthy of investigation—because the practice is ubiquitous and may be harmful to learning. In this chapter, we give you a broad overview of the various stages of the research process. These include finding a topic of investigation, reviewing the literature, refining your research question and generating a hypothesis, designing and conducting a study, analyzing the data, coming to conclusions, and reporting the results.

• Mueller, P. A., & Oppenheimer, D. M. (2014). The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychological Science, 25 (6), 1159-1168. ↵

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Perspective: Dimensions of the scientific method

Eberhard o. voit.

Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America

The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining–inspired, and “allochthonous” knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation.

The traditional scientific method: Hypothesis-driven deduction

Research is the undisputed core activity defining science. Without research, the advancement of scientific knowledge would come to a screeching halt. While it is evident that researchers look for new information or insights, the term “research” is somewhat puzzling. Never mind the prefix “re,” which simply means “coming back and doing it again and again,” the word “search” seems to suggest that the research process is somewhat haphazard, that not much of a strategy is involved in the process. One might argue that research a few hundred years ago had the character of hoping for enough luck to find something new. The alchemists come to mind in their quest to turn mercury or lead into gold, or to discover an elixir for eternal youth, through methods we nowadays consider laughable.

Today’s sciences, in stark contrast, are clearly different. Yes, we still try to find something new—and may need a good dose of luck—but the process is anything but unstructured. In fact, it is prescribed in such rigor that it has been given the widely known moniker “scientific method.” This scientific method has deep roots going back to Aristotle and Herophilus (approximately 300 BC), Avicenna and Alhazen (approximately 1,000 AD), Grosseteste and Robert Bacon (approximately 1,250 AD), and many others, but solidified and crystallized into the gold standard of quality research during the 17th and 18th centuries [ 1 – 7 ]. In particular, Sir Francis Bacon (1561–1626) and René Descartes (1596–1650) are often considered the founders of the scientific method, because they insisted on careful, systematic observations of high quality, rather than metaphysical speculations that were en vogue among the scholars of the time [ 1 , 8 ]. In contrast to their peers, they strove for objectivity and insisted that observations, rather than an investigator’s preconceived ideas or superstitions, should be the basis for formulating a research idea [ 7 , 9 ].

Bacon and his 19th century follower John Stuart Mill explicitly proposed gaining knowledge through inductive reasoning: Based on carefully recorded observations, or from data obtained in a well-planned experiment, generalized assertions were to be made about similar yet (so far) unobserved phenomena [ 7 ]. Expressed differently, inductive reasoning attempts to derive general principles or laws directly from empirical evidence [ 10 ]. An example is the 19th century epigram of the physician Rudolf Virchow, Omnis cellula e cellula . There is no proof that indeed “every cell derives from a cell,” but like Virchow, we have made the observation time and again and never encountered anything suggesting otherwise.

In contrast to induction, the widely accepted, traditional scientific method is based on formulating and testing hypotheses. From the results of these tests, a deduction is made whether the hypothesis is presumably true or false. This type of hypotheticodeductive reasoning goes back to William Whewell, William Stanley Jevons, and Charles Peirce in the 19th century [ 1 ]. By the 20th century, the deductive, hypothesis-based scientific method had become deeply ingrained in the scientific psyche, and it is now taught as early as middle school in order to teach students valid means of discovery [ 8 , 11 , 12 ]. The scientific method has not only guided most research studies but also fundamentally influenced how we think about the process of scientific discovery.

Alas, because biology has almost no general laws, deduction in the strictest sense is difficult. It may therefore be preferable to use the term abduction, which refers to the logical inference toward the most plausible explanation, given a set of observations, although this explanation cannot be proven and is not necessarily true.

Over the decades, the hypothesis-based scientific method did experience variations here and there, but its conceptual scaffold remained essentially unchanged ( Fig 1 ). Its key is a process that begins with the formulation of a hypothesis that is to be rigorously tested, either in the wet lab or computationally; nonadherence to this principle is seen as lacking rigor and can lead to irreproducible results [ 1 , 13 – 15 ].

The central concept of the traditional scientific method is a falsifiable hypothesis regarding some phenomenon of interest. This hypothesis is to be tested experimentally or computationally. The test results support or refute the hypothesis, triggering a new round of hypothesis formulation and testing.

Going further, the prominent philosopher of science Sir Karl Popper argued that a scientific hypothesis can never be verified but that it can be disproved by a single counterexample. He therefore demanded that scientific hypotheses had to be falsifiable, because otherwise, testing would be moot [ 16 , 17 ] (see also [ 18 ]). As Gillies put it, “successful theories are those that survive elimination through falsification” [ 19 ]. Kelley and Scott agreed to some degree but warned that complete insistence on falsifiability is too restrictive as it would mark many computational techniques, statistical hypothesis testing, and even Darwin’s theory of evolution as nonscientific [ 20 ].

While the hypothesis-based scientific method has been very successful, its exclusive reliance on deductive reasoning is dangerous because according to the so-called Duhem–Quine thesis, hypothesis testing always involves an unknown number of explicit or implicit assumptions, some of which may steer the researcher away from hypotheses that seem implausible, although they are, in fact, true [ 21 ]. According to Kuhn, this bias can obstruct the recognition of paradigm shifts [ 22 ], which require the rethinking of previously accepted “truths” and the development of radically new ideas [ 23 , 24 ]. The testing of simultaneous alternative hypotheses [ 25 – 27 ] ameliorates this problem to some degree but not entirely.

The traditional scientific method is often presented in discrete steps, but it should really be seen as a form of critical thinking, subject to review and independent validation [ 8 ]. It has proven very influential, not only by prescribing valid experimentation, but also for affecting the way we attempt to understand nature [ 18 ], for teaching [ 8 , 12 ], reporting, publishing, and otherwise sharing information [ 28 ], for peer review and the awarding of funds by research-supporting agencies [ 29 , 30 ], for medical diagnostics [ 7 ], and even in litigation [ 31 ].

A second dimension of the scientific method: Data-mining–inspired induction

A major shift in biological experimentation occurred with the–omics revolution of the early 21st century. All of a sudden, it became feasible to perform high-throughput experiments that generated thousands of measurements, typically characterizing the expression or abundances of very many—if not all—genes, proteins, metabolites, or other biological quantities in a sample.

The strategy of measuring large numbers of items in a nontargeted fashion is fundamentally different from the traditional scientific method and constitutes a new, second dimension of the scientific method. Instead of hypothesizing and testing whether gene X is up-regulated under some altered condition, the leading question becomes which of the thousands of genes in a sample are up- or down-regulated. This shift in focus elevates the data to the supreme role of revealing novel insights by themselves ( Fig 2 ). As an important, generic advantage over the traditional strategy, this second dimension is free of a researcher’s preconceived notions regarding the molecular mechanisms governing the phenomenon of interest, which are otherwise the key to formulating a hypothesis. The prominent biologists Patrick Brown and David Botstein commented that “the patterns of expression will often suffice to begin de novo discovery of potential gene functions” [ 32 ].

Data-driven research begins with an untargeted exploration, in which the data speak for themselves. Machine learning extracts patterns from the data, which suggest hypotheses that are to be tested in the lab or computationally.

This data-driven, discovery-generating approach is at once appealing and challenging. On the one hand, very many data are explored simultaneously and essentially without bias. On the other hand, the large datasets supporting this approach create a genuine challenge to understanding and interpreting the experimental results because the thousands of data points, often superimposed with a fair amount of noise, make it difficult to detect meaningful differences between sample and control. This situation can only be addressed with computational methods that first “clean” the data, for instance, through the statistically valid removal of outliers, and then use machine learning to identify statistically significant, distinguishing molecular profiles or signatures. In favorable cases, such signatures point to specific biological pathways, whereas other signatures defy direct explanation but may become the launch pad for follow-up investigations [ 33 ].

Today’s scientists are very familiar with this discovery-driven exploration of “what’s out there” and might consider it a quaint quirk of history that this strategy was at first widely chastised and ridiculed as a “fishing expedition” [ 30 , 34 ]. Strict traditionalists were outraged that rigor was leaving science with the new approach and that sufficient guidelines were unavailable to assure the validity and reproducibility of results [ 10 , 35 , 36 ].

From the view point of philosophy of science, this second dimension of the scientific method uses inductive reasoning and reflects Bacon’s idea that observations can and should dictate the research question to be investigated [ 1 , 7 ]. Allen [ 36 ] forcefully rejected this type of reasoning, stating “the thinking goes, we can now expect computer programs to derive significance, relevance and meaning from chunks of information, be they nucleotide sequences or gene expression profiles… In contrast with this view, many are convinced that no purely logical process can turn observation into understanding.” His conviction goes back to the 18th century philosopher David Hume and again to Popper, who identified as the overriding problem with inductive reasoning that it can never truly reveal causality, even if a phenomenon is observed time and again [ 16 , 17 , 37 , 38 ]. No number of observations, even if they always have the same result, can guard against an exception that would violate the generality of a law inferred from these observations [ 1 , 35 ]. Worse, Popper argued, through inference by induction, we cannot even know the probability of something being true [ 10 , 17 , 36 ].

Others argued that data-driven and hypothesis-driven research actually do not differ all that much in principle, as long as there is cycling between developing new ideas and testing them with care [ 27 ]. In fact, Kell and Oliver [ 34 ] maintained that the exclusive acceptance of hypothesis-driven programs misrepresents the complexities of biological knowledge generation. Similarly refuting the prominent rule of deduction, Platt [ 26 ] and Beard and Kushmerick [ 27 ] argued that repeated inductive reasoning, called strong inference, corresponds to a logically sound decision tree of disproving or refining hypotheses that can rapidly yield firm conclusions; nonetheless, Platt had to admit that inductive inference is not as certain as deduction, because it projects into the unknown. Lander compared the task of obtaining causality by induction to the problem of inferring the design of a microprocessor from input-output readings, which in a strict sense is impossible, because the microprocessor could be arbitrarily complicated; even so, inference often leads to novel insights and therefore is valuable [ 39 ].

An interesting special case of almost pure inductive reasoning is epidemiology, where hypothesis-driven reasoning is rare and instead, the fundamental question is whether data-based evidence is sufficient to associate health risks with specific causes [ 31 , 34 ].

Recent advances in machine learning and “big-data” mining have driven the use of inductive reasoning to unprecedented heights. As an example, machine learning can greatly assist in the discovery of patterns, for instance, in biological sequences [ 40 ]. Going a step further, a pithy article by Andersen [ 41 ] proffered that we may not need to look for causality or mechanistic explanations anymore if we just have enough correlation: “With enough data, the numbers speak for themselves, correlation replaces causation, and science can advance even without coherent models or unified theories.”

Of course, the proposal to abandon the quest for causality caused pushback on philosophical as well as mathematical grounds. Allen [ 10 , 35 ] considered the idea “absurd” that data analysis could enhance understanding in the absence of a hypothesis. He felt confident “that even the formidable combination of computing power with ease of access to data cannot produce a qualitative shift in the way that we do science: the making of hypotheses remains an indispensable component in the growth of knowledge” [ 36 ]. Succi and Coveney [ 42 ] refuted the “most extravagant claims” of big-data proponents very differently, namely by analyzing the theories on which machine learning is founded. They contrasted the assumptions underlying these theories, such as the law of large numbers, with the mathematical reality of complex biological systems. Specifically, they carefully identified genuine features of these systems, such as nonlinearities, nonlocality of effects, fractal aspects, and high dimensionality, and argued that they fundamentally violate some of the statistical assumptions implicitly underlying big-data analysis, like independence of events. They concluded that these discrepancies “may lead to false expectations and, at their nadir, even to dangerous social, economical and political manipulation.” To ameliorate the situation, the field of big-data analysis would need new strong theorems characterizing the validity of its methods and the numbers of data required for obtaining reliable insights. Succi and Coveney go as far as stating that too many data are just as bad as insufficient data [ 42 ].

While philosophical doubts regarding inductive methods will always persist, one cannot deny that -omics-based, high-throughput studies, combined with machine learning and big-data analysis, have been very successful [ 43 ]. Yes, induction cannot truly reveal general laws, no matter how large the datasets, but they do provide insights that are very different from what science had offered before and may at least suggest novel patterns, trends, or principles. As a case in point, if many transcriptomic studies indicate that a particular gene set is involved in certain classes of phenomena, there is probably some truth to the observation, even though it is not mathematically provable. Kepler’s laws of astronomy were arguably derived solely from inductive reasoning [ 34 ].

Notwithstanding the opposing views on inductive methods, successful strategies shape how we think about science. Thus, to take advantage of all experimental options while ensuring quality of research, we must not allow that “anything goes” but instead identify and characterize standard operating procedures and controls that render this emerging scientific method valid and reproducible. A laudable step in this direction was the wide acceptance of “minimum information about a microarray experiment” (MIAME) standards for microarray experiments [ 44 ].

A third dimension of the scientific method: Allochthonous reasoning

Parallel to the blossoming of molecular biology and the rapid rise in the power and availability of computing in the late 20th century, the use of mathematical and computational models became increasingly recognized as relevant and beneficial for understanding biological phenomena. Indeed, mathematical models eventually achieved cornerstone status in the new field of computational systems biology.

Mathematical modeling has been used as a tool of biological analysis for a long time [ 27 , 45 – 48 ]. Interesting for the discussion here is that the use of mathematical and computational modeling in biology follows a scientific approach that is distinctly different from the traditional and the data-driven methods, because it is distributed over two entirely separate domains of knowledge. One consists of the biological reality of DNA, elephants, and roses, whereas the other is the world of mathematics, which is governed by numbers, symbols, theorems, and abstract work protocols. Because the ways of thinking—and even the languages—are different in these two realms, I suggest calling this type of knowledge acquisition “allochthonous” (literally Greek: in or from a “piece of land different from where one is at home”; one could perhaps translate it into modern lingo as “outside one’s comfort zone”). De facto, most allochthonous reasoning in biology presently refers to mathematics and computing, but one might also consider, for instance, the application of methods from linguistics in the analysis of DNA sequences or proteins [ 49 ].

One could argue that biologists have employed “models” for a long time, for instance, in the form of “model organisms,” cell lines, or in vitro experiments, which more or less faithfully reflect features of the organisms of true interest but are easier to manipulate. However, this type of biological model use is rather different from allochthonous reasoning, as it does not leave the realm of biology and uses the same language and often similar methodologies.

A brief discussion of three experiences from our lab may illustrate the benefits of allochthonous reasoning. (1) In a case study of renal cell carcinoma, a dynamic model was able to explain an observed yet nonintuitive metabolic profile in terms of the enzymatic reaction steps that had been altered during the disease [ 50 ]. (2) A transcriptome analysis had identified several genes as displaying significantly different expression patterns during malaria infection in comparison to the state of health. Considered by themselves and focusing solely on genes coding for specific enzymes of purine metabolism, the findings showed patterns that did not make sense. However, integrating the changes in a dynamic model revealed that purine metabolism globally shifted, in response to malaria, from guanine compounds to adenine, inosine, and hypoxanthine [ 51 ]. (3) Data capturing the dynamics of malaria parasites suggested growth rates that were biologically impossible. Speculation regarding possible explanations led to the hypothesis that many parasite-harboring red blood cells might “hide” from circulation and therewith from detection in the blood stream. While experimental testing of the feasibility of the hypothesis would have been expensive, a dynamic model confirmed that such a concealment mechanism could indeed quantitatively explain the apparently very high growth rates [ 52 ]. In all three cases, the insights gained inductively from computational modeling would have been difficult to obtain purely with experimental laboratory methods. Purely deductive allochthonous reasoning is the ultimate goal of the search for design and operating principles [ 53 – 55 ], which strives to explain why certain structures or functions are employed by nature time and again. An example is a linear metabolic pathway, in which feedback inhibition is essentially always exerted on the first step [ 56 , 57 ]. This generality allows the deduction that a so far unstudied linear pathway is most likely (or even certain to be) inhibited at the first step. Not strictly deductive—but rather abductive—was a study in our lab in which we analyzed time series data with a mathematical model that allowed us to infer the most likely regulatory structure of a metabolic pathway [ 58 , 59 ].

A typical allochthonous investigation begins in the realm of biology with the formulation of a hypothesis ( Fig 3 ). Instead of testing this hypothesis with laboratory experiments, the system encompassing the hypothesis is moved into the realm of mathematics. This move requires two sets of ingredients. One set consists of the simplification and abstraction of the biological system: Any distracting details that seem unrelated to the hypothesis and its context are omitted or represented collectively with other details. This simplification step carries the greatest risk of the entire modeling approach, as omission of seemingly negligible but, in truth, important details can easily lead to wrong results. The second set of ingredients consists of correspondence rules that translate every biological component or process into the language of mathematics [ 60 , 61 ].

This mathematical and computational approach is distributed over two realms, which are connected by correspondence rules.

Once the system is translated, it has become an entirely mathematical construct that can be analyzed purely with mathematical and computational means. The results of this analysis are also strictly mathematical. They typically consist of values of variables, magnitudes of processes, sensitivity patterns, signs of eigenvalues, or qualitative features like the onset of oscillations or the potential for limit cycles. Correspondence rules are used again to move these results back into the realm of biology. As an example, the mathematical result that “two eigenvalues have positive real parts” does not make much sense to many biologists, whereas the interpretation that “the system is not stable at the steady state in question” is readily explained. New biological insights may lead to new hypotheses, which are tested either by experiments or by returning once more to the realm of mathematics. The model design, diagnosis, refinements, and validation consist of several phases, which have been discussed widely in the biomathematical literature. Importantly, each iteration of a typical modeling analysis consists of a move from the biological to the mathematical realm and back.

The reasoning within the realm of mathematics is often deductive, in the form of an Aristotelian syllogism, such as the well-known “All men are mortal; Socrates is a man; therefore, Socrates is mortal.” However, the reasoning may also be inductive, as it is the case with large-scale Monte-Carlo simulations that generate arbitrarily many “observations,” although they cannot reveal universal principles or theorems. An example is a simulation randomly drawing numbers in an attempt to show that every real number has an inverse. The simulation will always attest to this hypothesis but fail to discover the truth because it will never randomly draw 0. Generically, computational models may be considered sets of hypotheses, formulated as equations or as algorithms that reflect our perception of a complex system [ 27 ].

Impact of the multidimensional scientific method on learning

Almost all we know in biology has come from observation, experimentation, and interpretation. The traditional scientific method not only offered clear guidance for this knowledge gathering, but it also fundamentally shaped the way we think about the exploration of nature. When presented with a new research question, scientists were trained to think immediately in terms of hypotheses and alternatives, pondering the best feasible ways of testing them, and designing in their minds strong controls that would limit the effects of known or unknown confounders. Shaped by the rigidity of this ever-repeating process, our thinking became trained to move forward one well-planned step at a time. This modus operandi was rigid and exact. It also minimized the erroneous pursuit of long speculative lines of thought, because every step required testing before a new hypothesis was formed. While effective, the process was also very slow and driven by ingenuity—as well as bias—on the scientist’s part. This bias was sometimes a hindrance to necessary paradigm shifts [ 22 ].

High-throughput data generation, big-data analysis, and mathematical-computational modeling changed all that within a few decades. In particular, the acceptance of inductive principles and of the allochthonous use of nonbiological strategies to answer biological questions created an unprecedented mix of successes and chaos. To the horror of traditionalists, the importance of hypotheses became minimized, and the suggestion spread that the data would speak for themselves [ 36 ]. Importantly, within this fog of “anything goes,” the fundamental question arose how to determine whether an experiment was valid.

Because agreed-upon operating procedures affect research progress and interpretation, thinking, teaching, and sharing of results, this question requires a deconvolution of scientific strategies. Here I proffer that the single scientific method of the past should be expanded toward a vector space of scientific methods, with spanning vectors that correspond to different dimensions of the scientific method ( Fig 4 ).

The traditional hypothesis-based deductive scientific method is expanded into a 3D space that allows for synergistic blends of methods that include data-mining–inspired, inductive knowledge acquisition, and mathematical model-based, allochthonous reasoning.

Obviously, all three dimensions have their advantages and drawbacks. The traditional, hypothesis-driven deductive method is philosophically “clean,” except that it is confounded by preconceptions and assumptions. The data-mining–inspired inductive method cannot offer universal truths but helps us explore very large spaces of factors that contribute to a phenomenon. Allochthonous, model-based reasoning can be performed mentally, with paper and pencil, through rigorous analysis, or with a host of computational methods that are precise and disprovable [ 27 ]. At the same time, they are incomparable faster, cheaper, and much more comprehensive than experiments in molecular biology. This reduction in cost and time, and the increase in coverage, may eventually have far-reaching consequences, as we can already fathom from much of modern physics.

Due to its long history, the traditional dimension of the scientific method is supported by clear and very strong standard operating procedures. Similarly, strong procedures need to be developed for the other two dimensions. The MIAME rules for microarray analysis provide an excellent example [ 44 ]. On the mathematical modeling front, no such rules are generally accepted yet, but trends toward them seem to emerge at the horizon. For instance, it seems to be becoming common practice to include sensitivity analyses in typical modeling studies and to assess the identifiability or sloppiness of ensembles of parameter combinations that fit a given dataset well [ 62 , 63 ].

From a philosophical point of view, it seems unlikely that objections against inductive reasoning will disappear. However, instead of pitting hypothesis-based deductive reasoning against inductivism, it seems more beneficial to determine how the different methods can be synergistically blended ( cf . [ 18 , 27 , 34 , 42 ]) as linear combinations of the three vectors of knowledge acquisition ( Fig 4 ). It is at this point unclear to what degree the identified three dimensions are truly independent of each other, whether additional dimensions should be added [ 24 ], or whether the different versions could be amalgamated into a single scientific method [ 18 ], especially if it is loosely defined as a form of critical thinking [ 8 ]. Nobel Laureate Percy Bridgman even concluded that “science is what scientists do, and there are as many scientific methods as there are individual scientists” [ 8 , 64 ].

Combinations of the three spanning vectors of the scientific method have been emerging for some time. Many biologists already use inductive high-throughput methods to develop specific hypotheses that are subsequently tested with deductive or further inductive methods [ 34 , 65 ]. In terms of including mathematical modeling, physics and geology have been leading the way for a long time, often by beginning an investigation in theory, before any actual experiment is performed. It will benefit biology to look into this strategy and to develop best practices of allochthonous reasoning.

The blending of methods may take quite different shapes. Early on, Ideker and colleagues [ 65 ] proposed an integrated experimental approach for pathway analysis that offered a glimpse of new experimental strategies within the space of scientific methods. In a similar vein, Covert and colleagues [ 66 ] included computational methods into such an integrated approach. Additional examples of blended analyses in systems biology can be seen in other works, such as [ 43 , 67 – 73 ]. Generically, it is often beneficial to start with big data, determine patterns in associations and correlations, then switch to the mathematical realm in order to filter out spurious correlations in a high-throughput fashion. If this procedure is executed in an iterative manner, the “surviving” associations have an increased level of confidence and are good candidates for further experimental or computational testing (personal communication from S. Chandrasekaran).

If each component of a blended scientific method follows strict, commonly agreed guidelines, “linear combinations” within the 3D space can also be checked objectively, per deconvolution. In addition, guidelines for synergistic blends of component procedures should be developed. If we carefully monitor such blends, time will presumably indicate which method is best for which task and how the different approaches optimally inform each other. For instance, it will be interesting to study whether there is an optimal sequence of experiments along the three axes for a particular class of tasks. Big-data analysis together with inductive reasoning might be optimal for creating initial hypotheses and possibly refuting wrong speculations (“we had thought this gene would be involved, but apparently it isn’t”). If the logic of an emerging hypotheses can be tested with mathematical and computational tools, it will almost certainly be faster and cheaper than an immediate launch into wet-lab experimentation. It is also likely that mathematical reasoning will be able to refute some apparently feasible hypothesis and suggest amendments. Ultimately, the “surviving” hypotheses must still be tested for validity through conventional experiments. Deconvolving current practices and optimizing the combination of methods within the 3D or higher-dimensional space of scientific methods will likely result in better planning of experiments and in synergistic blends of approaches that have the potential capacity of addressing some of the grand challenges in biology.

Acknowledgments

The author is very grateful to Dr. Sriram Chandrasekaran and Ms. Carla Kumbale for superb suggestions and invaluable feedback.

Funding Statement

This work was supported in part by grants from the National Science Foundation ( https://www.nsf.gov/div/index.jsp?div=MCB ) grant NSF-MCB-1517588 (PI: EOV), NSF-MCB-1615373 (PI: Diana Downs) and the National Institute of Environmental Health Sciences ( https://www.niehs.nih.gov/ ) grant NIH-2P30ES019776-05 (PI: Carmen Marsit). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Scientific Research Question Generator

Feeling stuck trying to make a fresh and creative research question? Try our free research question generator! Choose a suitable question from a list of suggestions or build your own.

• Question Maker
• Question Finder

Make a scientific research question with this tool in 3 simple steps:

Please try again with some different keywords.

• 🧪 What Is This Tool?

‼️ Why Are Research Questions Important?

• 📃 How to Create a Science Paper?
• 🔗 References

🧪 Scientific Research Question Generator: What Is It?

Welcome to the page of our scientific research question generator! Right about now, you’re probably wondering – what is this tool, and how does it work? We present you with two options – a generator and a builder. You can read more about them below.

Scientific Question Generator

Deciding to use a question generator is a great alternative to save time and get what you want. You won’t have to suffer for hours looking for a fresh and creative idea! Once you customize the generator to your requirements, you’ll get incredible results.

What is good about this option? Simply put, you’ll only need to follow a few basic steps to create a research question. First, enter the keywords for your future work. You can also select a research area to optimize the generator’s search. Run a search for results and choose a question option from the many suggested ideas! You can refresh your search until you find the research question that fits and inspires you the most.

Research Question Builder

This tool has another feature that may come in handy – a generator of individual research questions from scratch. You don’t need to come up with your own options and guess how to write a well-written idea. It is a valuable function that will save time and produce more creative outcomes. To generate it, you’ll have to specify more qualifying study details.

As the first step, decide your study group and the factor that affects it . Next, try to formulate a measurable outcome of your work. You can add another study group to expand the generator’s capabilities. And finally, specify the time frame of the study. As a result, you have a ready-made individualized research question.

A research question is a helpful tool both for students and researchers. Sound and well-constructed questions are the ones that can shape the structure of your study. They should be grounded in consciously chosen data, instead of random variables. We can use these important questions not only for academic objectives but also in other life situations. For example, by studying the research questions of a potential employer, you can understand the suitability of the company and this kind of job for you.

A well-worded question will be easier for you to answer. You can also use it to outline your research and identify possible problems. That approach will reduce the time it takes to prepare the design of your study. To create a good research question , you need to:

• Choose an area of interest.
• Focus on a specific topic.
• Compose smaller support questions .
• Select the type of data collection and review the applicable literature.
• Identify your target audience.

📃 How to Create a Good Science Paper?

Scientific research papers are similar to the standard essays you are used to writing in school and college. But they have their specificities that you should be aware of. In this section, we have broken down the structure of a typical science paper and explained what goes into each part.

Your title should be specific and concise. It should also describe the subject and be comprehensive. However, it should be clear enough to be understood by a broader target audience, not just narrowly focused specialists.

The abstract is often a necessary component of academic work. The principal aim is to allow the reader a quick look at the scientific material and decide whether they are interested. However, this part shouldn’t be as technical as the main study, so as not to distract them. The abstract consists of general objectives, methods, results, and conclusions, and is approximately 150 to 250 words long. Note that you shouldn’t include citations, notations, and abbreviations.

Introduction

You should write an introduction describing the statement of your problem, and why it’s relevant and worthwhile. A few paragraphs will be enough. You can mention the main sources you have been working with to keep your audience involved. Also, remember to provide the necessary context and background information for your research. You can finish the introduction by explaining the essence of your research question and the value of your answer.

Methods & Materials

In this section of the paper, you should provide the methods and materials you have used for your study. It’s necessary to make your results replicable, and use qualitative or quantitative research methods (or a mix of both). You can use tables, diagrams, and charts to visually represent this information. You shouldn’t disclose your work findings, but you can include preview conclusions for reference.

At this point, we present the final study results, outlining the essential conclusions. Remember, there is no need to discuss the findings or cause-and-effect relationships. Avoid including subtotal results you have received and don’t affect the bottom line. Also, avoid manipulating your audience or exaggerating your achievements, as your results should be testable.

Provide the most meaningful results for discussion . Describe how these results relate to your question and how they are consistent with the results of other researchers. Indicate if the results coincided with your expectations and how you can interpret them. Also, mention if your findings raise issues and how they impact the scope of the study. You may finish up with the relevance of your conclusions.

When you give data in tables or charts, be sure to include a header describing the information in them. Don’t use tables or charts if they are irrelevant. Also, don’t insert them if you need to display data that can fit into a couple of sentences. Make sure to annotate all the visual data you end up using and mention them in the list of figures in the appendix.

Every scientific research paper must have a list of references at the end. This is to avoid plagiarism and to support the validity of your study. Remember to use notations as you go along and indicate them in the text. Then, you must list all the literature used in alphabetical order at the end of the paper. Double-check the citation style of your institution before making this list.

We hope you found our tool helpful in your work! Be sure to check out the FAQ section below if you still have any questions.

❓ Scientific Question Generator – FAQ

❓ how do you develop a scientific question.

Formulate the question in such a way that you can study it. It should be clear, understandable, and brief. After reading your research question, the reader should understand what your paper will be about. Therefore, it should have an objective , relevance, and meaning.

❓ What are good examples of a science research question?

“What are the legal aspects affecting the decrease in people who drive under the influence of alcohol in the USA?” — This question focuses on a defined topic and reviews the effectiveness of existing legislation.  

“How can universities improve the environment for students to become more LGBT-inclusive?” — This question focuses on one specific issue and addresses a narrowly targeted area.

❓ What are the 3 qualities of a good scientific question?

A good question should be feasible in the context of the research accessible to the field of study, ethical, sufficient methods, and materials. It should be interesting, engaging, and intriguing to the target audience. Finally, it should also be relevant and provide new ideas to the chosen field for future research.

Updated: Sep 13th, 2024

📎 References

• Scientific Writing Made Easy: A Step-by-Step Guide to Undergraduate Writing in the Biological Sciences – Sheela P. Turbek, Taylor M. Chock, Kyle Donahue, Caroline A. Havrilla, Angela M. Oliverio, Stephanie K. Polutchko, Lauren G. Shoemaker & Lara Vimercati, Ecological Society of America
• Writing the Scientific Paper – Emily Wortman-Wunder & Kate Kiefer, Colorado State University
• Organizing Your Social Sciences Research Paper, University of Southern California
• Your research question – Imperial College London
• Developing research questions – Monash University