is there research methods in paper 3 psychology

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• December 31, 2022

Your Ultimate Guide for Acing IB Psychology Paper 3 (HL only)

Written By Rashi S.

Before beginning, feel free to check out how to tackle IB Psychology Paper 1 and Paper 2 if you have not yet and/or are interested!

Paper 3 (P3) is only for HL students, focusing on the research methods in psychology. It consists of 3 parts, all of which must be answered (no choice of questions). A research scenario is presented and students answer questions related to the methods and conclusions of it.

Question (Q) 1 consists of 3 parts, all of which must be answered for a total of 3 marks each, 9 marks in total.

  1. (a) Identify the research method used and outline two characteristics of the method.

I do not recommend using bullet points for this question; nevertheless, ensure that your answer is clear for the examiner. Generally, the IB requires that the number of maximum marks you can gain on a question is the number of points that you need to make. In this case, you need to make three points as the question is worth three marks; one mark for identifying the correct research method and two marks for stating its two correct characteristics. Additionally, I would recommend mentioning an additional point to the two points that you are already made, to ensure that you maximize the number of marks you earn (i.e. in case the point you make is not on the mark scheme or a point made is ambiguous).

• The research method used is semi-structured interviews. Some characteristics of it are that the interviewer pre-determines topics or themes to explore. Moreover, it contains both open-ended and close-ended questions; whilst the former allows the respondents to elaborate, the latter invites brief and precise answers. Lastly, semi-structured interviews are informal and conversational, facilitating a rapport between the interviewer and the interviewee.

(b) Describe the sampling method used in the study.

The strategy to employ for tackling this question is the same as the one used in 1(a). This question requires that you correctly identify the sampling method used in the study (1 mark), and state two correct characteristics of the sampling method (2 marks). I do not recommend using bullet points for this question as well and recommend mentioning an additional point to the two points that you have already made.

• The sampling method used is purposive sampling. Participants are chosen because they possess characteristics salient to the research study, in this case, those who have some experience with taking drugs. Participants are recruited through advertising in places where those with the selected criteria can be found. In this case, in school magazines, for instance. Finally, purposive sampling may include snowball sampling.

(c) Suggest an alternative or additional research method giving one reason for your choice.

For this question, I recommend suggesting an additional research method instead of an alternative one. This is because it is suggesting an additional research method is easier to justify instead of arguing for using an alternative method.

• An additional research method is to utilize a quantitative survey, in which objective questions are used to gain insights from respondents. Subsequently, interviews can be conducted with some randomly selected participants to gain deeper insight into their cultural identity. The survey would enable the researchers to design questions to elicit the required data. Moreover, it can be quantified to make comparisons and enables larger samples size compared to only 19 participants, as in the study.

N.B. Connect your answer to the stimulus material presented when you can.

  Q2 on the exam can be on any ONE of the two prompts (no choice) for a total of 6 marks.

  1. Describe the ethical considerations that were applied in the study and explain if further ethical considerations could be applied.

• You are required to write about six different ethical considerations (because question = 6 marks maximum)
• The question is divided into two parts: first, students need to write about the three ethical considerations that were taken in the study, and second, you must explain three additional ethical considerations that can be accounted for. Although the question mentions “ if further ethical considerations could be applied,” it is, in fact, not asking that. Instead, think of it as “ how further ethical considerations could be applied”
• Thus, this requires you to explain three additional ethical considerations which were not mentioned/taken in the stimulus material.
• Informed consent or parental consent (if the participant is a minor)
• Confidentiality
• Right to withdraw before the study, during the study, or after the study
• Any use of concealment or deception is justified
• Approval from an ethics review board
• Coercion (is there any pressure to participate?)

  2.Describe the ethical considerations in reporting the results and explain additional ethical considerations that could be taken into         account when applying the findings of the study.

• This question is different from the first one in that it specifically asks students to refer to ethical considerations that could be taken into account when applying the findings of the study and not general additional ethical considerations.
• Your answer can refer to any of the following:
• Anonymity/pseudo anonymization: involves removing names or using fake names respectively for the publication of results
• Right to withdraw
• Informed consent/parental consent: informing people how their data will be used
• Debriefing: informing people how the results will be reported/published

N.B. Connect your answer to the stimulus material presented, at least once.

  Q3 on the exam is on any ONE of the three prompts (no choice) for a total of 9 marks.

However, before discussing the questions and how to answer them, it is salient to note that the top most markband for P3 Q3 mentions the following:

• 7 – 9: The question is understood and answered in a focused and effective manner with an accurate argument that addresses the requirements of the question. The response contains accurate references to approaches to research with regard to the question, describing their strengths and limitations. The response makes effective use of the stimulus material.

From my research, there seems to be ambiguity regarding how exactly to earn the 9 points in Q3 and it is not as clear-cut as it is for the previous questions.  Therefore, from a quality answer key from Pamoja online IB psychology, I mention the principle characteristics of two answers for two prompts and direct you to a sample answer for the third one:

    1.Discuss how a researcher could ensure that the results of the study are credible.

• Begin by defining “credibility”
• Moreover, you can discuss the internal validity, member checking, peer debriefing, and triangulation. Provide a definition for each of this concept before discussing it and relating it to the stimulus material.

    2.Discuss how the researcher in the study could avoid bias.

• Start by defining “bias”
• Moreover, you can discuss sampling bias, researcher bias, participant bias, and triagulation. Similar to above, provide a definition for each of this concept before discussing it and relating it to the stimulus material.

        3.Discuss the possibility of generalizing/transfering the findings of the study. The relevant term, generalizing or transferability is used in exams depending on whether the question relates to a quantitative or qualitative study.

• When I was in the IB DP, Dixon’s IB psychology website and youtube videos were of huge help; hence, I suggest utilizing this resource. Scroll to the bottom of this blog to see an example answer for this question.

Time yourself

P3 is one hour. Thus, I recommended using 10 mins for Q1, 20 mins for Q2, and 25 mins for Q3, leaving the last 5 mins to proofread.

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AQA A-Level Psychology Past Papers With Answers

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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.

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Ch 2: Psychological Research Methods

Have you ever wondered whether the violence you see on television affects your behavior? Are you more likely to behave aggressively in real life after watching people behave violently in dramatic situations on the screen? Or, could seeing fictional violence actually get aggression out of your system, causing you to be more peaceful? How are children influenced by the media they are exposed to? A psychologist interested in the relationship between behavior and exposure to violent images might ask these very questions.

The topic of violence in the media today is contentious. Since ancient times, humans have been concerned about the effects of new technologies on our behaviors and thinking processes. The Greek philosopher Socrates, for example, worried that writing—a new technology at that time—would diminish people’s ability to remember because they could rely on written records rather than committing information to memory. In our world of quickly changing technologies, questions about the effects of media continue to emerge. Is it okay to talk on a cell phone while driving? Are headphones good to use in a car? What impact does text messaging have on reaction time while driving? These are types of questions that psychologist David Strayer asks in his lab.

Watch this short video to see how Strayer utilizes the scientific method to reach important conclusions regarding technology and driving safety.

You can view the transcript for “Understanding driver distraction” here (opens in new window) .

How can we go about finding answers that are supported not by mere opinion, but by evidence that we can all agree on? The findings of psychological research can help us navigate issues like this.

Introduction to the Scientific Method

Learning objectives.

• Explain the steps of the scientific method
• Describe why the scientific method is important to psychology
• Summarize the processes of informed consent and debriefing
• Explain how research involving humans or animals is regulated

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.

The Scientific Process

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 the 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 forming decisions in our personal lives and in the public domain.

Process of Scientific Research

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.

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.

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 5). 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.

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

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.

Ethics in Research

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 6). 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.

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, the 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 7). 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?

Learn more about the Tuskegee Syphilis Study on the CDC website .

Research Involving Animal Subjects

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.

Introduction to Approaches to Research

• Differentiate between descriptive, correlational, and experimental research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys
• Describe the strength and weaknesses of archival research
• Compare longitudinal and cross-sectional approaches to research
• Explain what a correlation coefficient tells us about the relationship between variables
• Describe why correlation does not mean causation
• Describe the experimental process, including ways to control for bias
• Identify and differentiate between independent and dependent variables

Psychologists use descriptive, experimental, and correlational methods to conduct research. Descriptive, or qualitative, methods include the case study, naturalistic observation, surveys, archival research, longitudinal research, and cross-sectional research.

Experiments are conducted in order to determine cause-and-effect relationships. In ideal experimental design, the only difference between the experimental and control groups is whether participants are exposed to the experimental manipulation. Each group goes through all phases of the experiment, but each group will experience a different level of the independent variable: the experimental group is exposed to the experimental manipulation, and the control group is not exposed to the experimental manipulation. The researcher then measures the changes that are produced in the dependent variable in each group. Once data is collected from both groups, it is analyzed statistically to determine if there are meaningful differences between the groups.

When scientists passively observe and measure phenomena it is called correlational research. Here, psychologists do not intervene and change behavior, as they do in experiments. In correlational research, they identify patterns of relationships, but usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

Watch It: More on Research

If you enjoy learning through lectures and want an interesting and comprehensive summary of this section, then click on the Youtube link to watch a lecture given by MIT Professor John Gabrieli . Start at the 30:45 minute mark  and watch through the end to hear examples of actual psychological studies and how they were analyzed. Listen for references to independent and dependent variables, experimenter bias, and double-blind studies. In the lecture, you’ll learn about breaking social norms, “WEIRD” research, why expectations matter, how a warm cup of coffee might make you nicer, why you should change your answer on a multiple choice test, and why praise for intelligence won’t make you any smarter.

You can view the transcript for “Lec 2 | MIT 9.00SC Introduction to Psychology, Spring 2011” here (opens in new window) .

Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research  goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are, naturalistic observation, case studies, and surveys.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 9).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 10). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize  the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 11). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

Archival research.

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research  is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research . In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of observing a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) (Figure 13).

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition  rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

Correlational Research

Did you know that as sales in ice cream increase, so does the overall rate of crime? Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone? There is no question that a relationship exists between ice cream and crime (e.g., Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur.

It is much more likely that both ice cream sales and crime rates are related to the temperature outside. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?

Correlation Does Not Indicate Causation

Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.

Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research , we would be overstepping our bounds by making this assumption.

Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, recent research found that people who eat cereal on a regular basis achieve healthier weights than those who rarely eat cereal (Frantzen, Treviño, Echon, Garcia-Dominic, & DiMarco, 2013; Barton et al., 2005). Guess how the cereal companies report this finding. Does eating cereal really cause an individual to maintain a healthy weight, or are there other possible explanations, such as, someone at a healthy weight is more likely to regularly eat a healthy breakfast than someone who is obese or someone who avoids meals in an attempt to diet (Figure 15)? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.

Watch this clip from Freakonomics for an example of how correlation does  not  indicate causation.

You can view the transcript for “Correlation vs. Causality: Freakonomics Movie” here (opens in new window) .

Illusory Correlations

The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations , or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full (Figure 16).

There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.

Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias . Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).

We all have a tendency to make illusory correlations from time to time. Try to think of an illusory correlation that is held by you, a family member, or a close friend. How do you think this illusory correlation came about and what can be done in the future to combat them?

Experiments

Causality: conducting experiments and using the data, experimental hypothesis.

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon (Figure 17).

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group  gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to precisely define, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts is recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 18).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 19). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants  are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 20). If possible, we should use a random sample   (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

Introduction to Statistical Thinking

Psychologists use statistics to assist them in analyzing data, and also to give more precise measurements to describe whether something is statistically significant. Analyzing data using statistics enables researchers to find patterns, make claims, and share their results with others. In this section, you’ll learn about some of the tools that psychologists use in statistical analysis.

• Define reliability and validity
• Describe the importance of distributional thinking and the role of p-values in statistical inference
• Describe the role of random sampling and random assignment in drawing cause-and-effect conclusions
• Describe the basic structure of a psychological research article

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Reporting Research

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.

A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study’s design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve a healthy weight without changing their diet. But if other scientists could not replicate the results, the original study’s claims would be questioned.

Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies

Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has suggested that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.

A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated (Figure 21). For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.

Reliability and Validity

Dig deeper:  everyday connection: how valid is the sat.

Standardized tests like the SAT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT for admission, this high degree of predictive validity might be comforting.

However, the emphasis placed on SAT scores in college admissions has generated some controversy on a number of fronts. For one, some researchers assert that the SAT is a biased test that places minority students at a disadvantage and unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of the SAT is grossly exaggerated in how well it is able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).

In 2014, College Board president David Coleman expressed his awareness of these problems, recognizing that college success is more accurately predicted by high school grades than by SAT scores. To address these concerns, he has called for significant changes to the SAT exam (Lewin, 2014).

Statistical Significance

Does drinking coffee actually increase your life expectancy? A recent study (Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012) found that men who drank at least six cups of coffee a day also had a 10% lower chance of dying (women’s chances were 15% lower) than those who drank none. Does this mean you should pick up or increase your own coffee habit? We will explore these results in more depth in the next section about drawing conclusions from statistics. Modern society has become awash in studies such as this; you can read about several such studies in the news every day.

Conducting such a study well, and interpreting the results of such studies requires understanding basic ideas of statistics , the science of gaining insight from data. Key components to a statistical investigation are:

• Planning the study: Start by asking a testable research question and deciding how to collect data. For example, how long was the study period of the coffee study? How many people were recruited for the study, how were they recruited, and from where? How old were they? What other variables were recorded about the individuals? Were changes made to the participants’ coffee habits during the course of the study?
• Examining the data: What are appropriate ways to examine the data? What graphs are relevant, and what do they reveal? What descriptive statistics can be calculated to summarize relevant aspects of the data, and what do they reveal? What patterns do you see in the data? Are there any individual observations that deviate from the overall pattern, and what do they reveal? For example, in the coffee study, did the proportions differ when we compared the smokers to the non-smokers?
• Inferring from the data: What are valid statistical methods for drawing inferences “beyond” the data you collected? In the coffee study, is the 10%–15% reduction in risk of death something that could have happened just by chance?
• Drawing conclusions: Based on what you learned from your data, what conclusions can you draw? Who do you think these conclusions apply to? (Were the people in the coffee study older? Healthy? Living in cities?) Can you draw a cause-and-effect conclusion about your treatments? (Are scientists now saying that the coffee drinking is the cause of the decreased risk of death?)

Notice that the numerical analysis (“crunching numbers” on the computer) comprises only a small part of overall statistical investigation. In this section, you will see how we can answer some of these questions and what questions you should be asking about any statistical investigation you read about.

Distributional Thinking

When data are collected to address a particular question, an important first step is to think of meaningful ways to organize and examine the data. Let’s take a look at an example.

Example 1 : Researchers investigated whether cancer pamphlets are written at an appropriate level to be read and understood by cancer patients (Short, Moriarty, & Cooley, 1995). Tests of reading ability were given to 63 patients. In addition, readability level was determined for a sample of 30 pamphlets, based on characteristics such as the lengths of words and sentences in the pamphlet. The results, reported in terms of grade levels, are displayed in Figure 23.

• Data vary . More specifically, values of a variable (such as reading level of a cancer patient or readability level of a cancer pamphlet) vary.
• Analyzing the pattern of variation, called the distribution of the variable, often reveals insights.

Addressing the research question of whether the cancer pamphlets are written at appropriate levels for the cancer patients requires comparing the two distributions. A naïve comparison might focus only on the centers of the distributions. Both medians turn out to be ninth grade, but considering only medians ignores the variability and the overall distributions of these data. A more illuminating approach is to compare the entire distributions, for example with a graph, as in Figure 24.

Figure 24 makes clear that the two distributions are not well aligned at all. The most glaring discrepancy is that many patients (17/63, or 27%, to be precise) have a reading level below that of the most readable pamphlet. These patients will need help to understand the information provided in the cancer pamphlets. Notice that this conclusion follows from considering the distributions as a whole, not simply measures of center or variability, and that the graph contrasts those distributions more immediately than the frequency tables.

Finding Significance in Data

Even when we find patterns in data, often there is still uncertainty in various aspects of the data. For example, there may be potential for measurement errors (even your own body temperature can fluctuate by almost 1°F over the course of the day). Or we may only have a “snapshot” of observations from a more long-term process or only a small subset of individuals from the population of interest. In such cases, how can we determine whether patterns we see in our small set of data is convincing evidence of a systematic phenomenon in the larger process or population? Let’s take a look at another example.

Example 2 : In a study reported in the November 2007 issue of Nature , researchers investigated whether pre-verbal infants take into account an individual’s actions toward others in evaluating that individual as appealing or aversive (Hamlin, Wynn, & Bloom, 2007). In one component of the study, 10-month-old infants were shown a “climber” character (a piece of wood with “googly” eyes glued onto it) that could not make it up a hill in two tries. Then the infants were shown two scenarios for the climber’s next try, one where the climber was pushed to the top of the hill by another character (“helper”), and one where the climber was pushed back down the hill by another character (“hinderer”). The infant was alternately shown these two scenarios several times. Then the infant was presented with two pieces of wood (representing the helper and the hinderer characters) and asked to pick one to play with.

The researchers found that of the 16 infants who made a clear choice, 14 chose to play with the helper toy. One possible explanation for this clear majority result is that the helping behavior of the one toy increases the infants’ likelihood of choosing that toy. But are there other possible explanations? What about the color of the toy? Well, prior to collecting the data, the researchers arranged so that each color and shape (red square and blue circle) would be seen by the same number of infants. Or maybe the infants had right-handed tendencies and so picked whichever toy was closer to their right hand?

Well, prior to collecting the data, the researchers arranged it so half the infants saw the helper toy on the right and half on the left. Or, maybe the shapes of these wooden characters (square, triangle, circle) had an effect? Perhaps, but again, the researchers controlled for this by rotating which shape was the helper toy, the hinderer toy, and the climber. When designing experiments, it is important to control for as many variables as might affect the responses as possible. It is beginning to appear that the researchers accounted for all the other plausible explanations. But there is one more important consideration that cannot be controlled—if we did the study again with these 16 infants, they might not make the same choices. In other words, there is some randomness inherent in their selection process.

Maybe each infant had no genuine preference at all, and it was simply “random luck” that led to 14 infants picking the helper toy. Although this random component cannot be controlled, we can apply a probability model to investigate the pattern of results that would occur in the long run if random chance were the only factor.

If the infants were equally likely to pick between the two toys, then each infant had a 50% chance of picking the helper toy. It’s like each infant tossed a coin, and if it landed heads, the infant picked the helper toy. So if we tossed a coin 16 times, could it land heads 14 times? Sure, it’s possible, but it turns out to be very unlikely. Getting 14 (or more) heads in 16 tosses is about as likely as tossing a coin and getting 9 heads in a row. This probability is referred to as a p-value . The p-value represents the likelihood that experimental results happened by chance. Within psychology, the most common standard for p-values is “p < .05”. What this means is that there is less than a 5% probability that the results happened just by random chance, and therefore a 95% probability that the results reflect a meaningful pattern in human psychology. We call this statistical significance .

So, in the study above, if we assume that each infant was choosing equally, then the probability that 14 or more out of 16 infants would choose the helper toy is found to be 0.0021. We have only two logical possibilities: either the infants have a genuine preference for the helper toy, or the infants have no preference (50/50) and an outcome that would occur only 2 times in 1,000 iterations happened in this study. Because this p-value of 0.0021 is quite small, we conclude that the study provides very strong evidence that these infants have a genuine preference for the helper toy.

If we compare the p-value to some cut-off value, like 0.05, we see that the p=value is smaller. Because the p-value is smaller than that cut-off value, then we reject the hypothesis that only random chance was at play here. In this case, these researchers would conclude that significantly more than half of the infants in the study chose the helper toy, giving strong evidence of a genuine preference for the toy with the helping behavior.

Drawing Conclusions from Statistics

Generalizability.

One limitation to the study mentioned previously about the babies choosing the “helper” toy is that the conclusion only applies to the 16 infants in the study. We don’t know much about how those 16 infants were selected. Suppose we want to select a subset of individuals (a sample ) from a much larger group of individuals (the population ) in such a way that conclusions from the sample can be generalized to the larger population. This is the question faced by pollsters every day.

Example 3 : The General Social Survey (GSS) is a survey on societal trends conducted every other year in the United States. Based on a sample of about 2,000 adult Americans, researchers make claims about what percentage of the U.S. population consider themselves to be “liberal,” what percentage consider themselves “happy,” what percentage feel “rushed” in their daily lives, and many other issues. The key to making these claims about the larger population of all American adults lies in how the sample is selected. The goal is to select a sample that is representative of the population, and a common way to achieve this goal is to select a r andom sample  that gives every member of the population an equal chance of being selected for the sample. In its simplest form, random sampling involves numbering every member of the population and then using a computer to randomly select the subset to be surveyed. Most polls don’t operate exactly like this, but they do use probability-based sampling methods to select individuals from nationally representative panels.

In 2004, the GSS reported that 817 of 977 respondents (or 83.6%) indicated that they always or sometimes feel rushed. This is a clear majority, but we again need to consider variation due to random sampling . Fortunately, we can use the same probability model we did in the previous example to investigate the probable size of this error. (Note, we can use the coin-tossing model when the actual population size is much, much larger than the sample size, as then we can still consider the probability to be the same for every individual in the sample.) This probability model predicts that the sample result will be within 3 percentage points of the population value (roughly 1 over the square root of the sample size, the margin of error. A statistician would conclude, with 95% confidence, that between 80.6% and 86.6% of all adult Americans in 2004 would have responded that they sometimes or always feel rushed.

The key to the margin of error is that when we use a probability sampling method, we can make claims about how often (in the long run, with repeated random sampling) the sample result would fall within a certain distance from the unknown population value by chance (meaning by random sampling variation) alone. Conversely, non-random samples are often suspect to bias, meaning the sampling method systematically over-represents some segments of the population and under-represents others. We also still need to consider other sources of bias, such as individuals not responding honestly. These sources of error are not measured by the margin of error.

Cause and Effect

In many research studies, the primary question of interest concerns differences between groups. Then the question becomes how were the groups formed (e.g., selecting people who already drink coffee vs. those who don’t). In some studies, the researchers actively form the groups themselves. But then we have a similar question—could any differences we observe in the groups be an artifact of that group-formation process? Or maybe the difference we observe in the groups is so large that we can discount a “fluke” in the group-formation process as a reasonable explanation for what we find?

Example 4 : A psychology study investigated whether people tend to display more creativity when they are thinking about intrinsic (internal) or extrinsic (external) motivations (Ramsey & Schafer, 2002, based on a study by Amabile, 1985). The subjects were 47 people with extensive experience with creative writing. Subjects began by answering survey questions about either intrinsic motivations for writing (such as the pleasure of self-expression) or extrinsic motivations (such as public recognition). Then all subjects were instructed to write a haiku, and those poems were evaluated for creativity by a panel of judges. The researchers conjectured beforehand that subjects who were thinking about intrinsic motivations would display more creativity than subjects who were thinking about extrinsic motivations. The creativity scores from the 47 subjects in this study are displayed in Figure 26, where higher scores indicate more creativity.

In this example, the key question is whether the type of motivation affects creativity scores. In particular, do subjects who were asked about intrinsic motivations tend to have higher creativity scores than subjects who were asked about extrinsic motivations?

Figure 26 reveals that both motivation groups saw considerable variability in creativity scores, and these scores have considerable overlap between the groups. In other words, it’s certainly not always the case that those with extrinsic motivations have higher creativity than those with intrinsic motivations, but there may still be a statistical tendency in this direction. (Psychologist Keith Stanovich (2013) refers to people’s difficulties with thinking about such probabilistic tendencies as “the Achilles heel of human cognition.”)

The mean creativity score is 19.88 for the intrinsic group, compared to 15.74 for the extrinsic group, which supports the researchers’ conjecture. Yet comparing only the means of the two groups fails to consider the variability of creativity scores in the groups. We can measure variability with statistics using, for instance, the standard deviation: 5.25 for the extrinsic group and 4.40 for the intrinsic group. The standard deviations tell us that most of the creativity scores are within about 5 points of the mean score in each group. We see that the mean score for the intrinsic group lies within one standard deviation of the mean score for extrinsic group. So, although there is a tendency for the creativity scores to be higher in the intrinsic group, on average, the difference is not extremely large.

We again want to consider possible explanations for this difference. The study only involved individuals with extensive creative writing experience. Although this limits the population to which we can generalize, it does not explain why the mean creativity score was a bit larger for the intrinsic group than for the extrinsic group. Maybe women tend to receive higher creativity scores? Here is where we need to focus on how the individuals were assigned to the motivation groups. If only women were in the intrinsic motivation group and only men in the extrinsic group, then this would present a problem because we wouldn’t know if the intrinsic group did better because of the different type of motivation or because they were women. However, the researchers guarded against such a problem by randomly assigning the individuals to the motivation groups. Like flipping a coin, each individual was just as likely to be assigned to either type of motivation. Why is this helpful? Because this random assignment  tends to balance out all the variables related to creativity we can think of, and even those we don’t think of in advance, between the two groups. So we should have a similar male/female split between the two groups; we should have a similar age distribution between the two groups; we should have a similar distribution of educational background between the two groups; and so on. Random assignment should produce groups that are as similar as possible except for the type of motivation, which presumably eliminates all those other variables as possible explanations for the observed tendency for higher scores in the intrinsic group.

But does this always work? No, so by “luck of the draw” the groups may be a little different prior to answering the motivation survey. So then the question is, is it possible that an unlucky random assignment is responsible for the observed difference in creativity scores between the groups? In other words, suppose each individual’s poem was going to get the same creativity score no matter which group they were assigned to, that the type of motivation in no way impacted their score. Then how often would the random-assignment process alone lead to a difference in mean creativity scores as large (or larger) than 19.88 – 15.74 = 4.14 points?

We again want to apply to a probability model to approximate a p-value , but this time the model will be a bit different. Think of writing everyone’s creativity scores on an index card, shuffling up the index cards, and then dealing out 23 to the extrinsic motivation group and 24 to the intrinsic motivation group, and finding the difference in the group means. We (better yet, the computer) can repeat this process over and over to see how often, when the scores don’t change, random assignment leads to a difference in means at least as large as 4.41. Figure 27 shows the results from 1,000 such hypothetical random assignments for these scores.

Only 2 of the 1,000 simulated random assignments produced a difference in group means of 4.41 or larger. In other words, the approximate p-value is 2/1000 = 0.002. This small p-value indicates that it would be very surprising for the random assignment process alone to produce such a large difference in group means. Therefore, as with Example 2, we have strong evidence that focusing on intrinsic motivations tends to increase creativity scores, as compared to thinking about extrinsic motivations.

Notice that the previous statement implies a cause-and-effect relationship between motivation and creativity score; is such a strong conclusion justified? Yes, because of the random assignment used in the study. That should have balanced out any other variables between the two groups, so now that the small p-value convinces us that the higher mean in the intrinsic group wasn’t just a coincidence, the only reasonable explanation left is the difference in the type of motivation. Can we generalize this conclusion to everyone? Not necessarily—we could cautiously generalize this conclusion to individuals with extensive experience in creative writing similar the individuals in this study, but we would still want to know more about how these individuals were selected to participate.

Statistical thinking involves the careful design of a study to collect meaningful data to answer a focused research question, detailed analysis of patterns in the data, and drawing conclusions that go beyond the observed data. Random sampling is paramount to generalizing results from our sample to a larger population, and random assignment is key to drawing cause-and-effect conclusions. With both kinds of randomness, probability models help us assess how much random variation we can expect in our results, in order to determine whether our results could happen by chance alone and to estimate a margin of error.

So where does this leave us with regard to the coffee study mentioned previously (the Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012 found that men who drank at least six cups of coffee a day had a 10% lower chance of dying (women 15% lower) than those who drank none)? We can answer many of the questions:

• This was a 14-year study conducted by researchers at the National Cancer Institute.
• The results were published in the June issue of the New England Journal of Medicine , a respected, peer-reviewed journal.
• The study reviewed coffee habits of more than 402,000 people ages 50 to 71 from six states and two metropolitan areas. Those with cancer, heart disease, and stroke were excluded at the start of the study. Coffee consumption was assessed once at the start of the study.
• About 52,000 people died during the course of the study.
• People who drank between two and five cups of coffee daily showed a lower risk as well, but the amount of reduction increased for those drinking six or more cups.
• The sample sizes were fairly large and so the p-values are quite small, even though percent reduction in risk was not extremely large (dropping from a 12% chance to about 10%–11%).
• Whether coffee was caffeinated or decaffeinated did not appear to affect the results.
• This was an observational study, so no cause-and-effect conclusions can be drawn between coffee drinking and increased longevity, contrary to the impression conveyed by many news headlines about this study. In particular, it’s possible that those with chronic diseases don’t tend to drink coffee.

This study needs to be reviewed in the larger context of similar studies and consistency of results across studies, with the constant caution that this was not a randomized experiment. Whereas a statistical analysis can still “adjust” for other potential confounding variables, we are not yet convinced that researchers have identified them all or completely isolated why this decrease in death risk is evident. Researchers can now take the findings of this study and develop more focused studies that address new questions.

Explore these outside resources to learn more about applied statistics:

• Video about p-values:  P-Value Extravaganza
• Interactive web applets for teaching and learning statistics
• Inter-university Consortium for Political and Social Research  where you can find and analyze data.
• The Consortium for the Advancement of Undergraduate Statistics
• Find a recent research article in your field and answer the following: What was the primary research question? How were individuals selected to participate in the study? Were summary results provided? How strong is the evidence presented in favor or against the research question? Was random assignment used? Summarize the main conclusions from the study, addressing the issues of statistical significance, statistical confidence, generalizability, and cause and effect. Do you agree with the conclusions drawn from this study, based on the study design and the results presented?
• Is it reasonable to use a random sample of 1,000 individuals to draw conclusions about all U.S. adults? Explain why or why not.

How to Read Research

In this course and throughout your academic career, you’ll be reading journal articles (meaning they were published by experts in a peer-reviewed journal) and reports that explain psychological research. It’s important to understand the format of these articles so that you can read them strategically and understand the information presented. Scientific articles vary in content or structure, depending on the type of journal to which they will be submitted. Psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract, introduction, methods, results, discussion, and references.

• Abstract : the abstract is the concise summary of the article. It summarizes the most important features of the manuscript, providing the reader with a global first impression on the article. It is generally just one paragraph that explains the experiment as well as a short synopsis of the results.
• Introduction : this section provides background information about the origin and purpose of performing the experiment or study. It reviews previous research and presents existing theories on the topic.
• Method : this section covers the methodologies used to investigate the research question, including the identification of participants , procedures , and  materials  as well as a description of the actual procedure . It should be sufficiently detailed to allow for replication.
• Results : the results section presents key findings of the research, including reference to indicators of statistical significance.
• Discussion : this section provides an interpretation of the findings, states their significance for current research, and derives implications for theory and practice. Alternative interpretations for findings are also provided, particularly when it is not possible to conclude for the directionality of the effects. In the discussion, authors also acknowledge the strengths and limitations/weaknesses of the study and offer concrete directions about for future research.

Watch this 3-minute video for an explanation on how to read scholarly articles. Look closely at the example article shared just before the two minute mark.

https://digitalcommons.coastal.edu/kimbel-library-instructional-videos/9/

Practice identifying these key components in the following experiment: Food-Induced Emotional Resonance Improves Emotion Recognition.

In this chapter, you learned to

• define and apply the scientific method to psychology
• describe the strengths and weaknesses of descriptive, experimental, and correlational research
• define the basic elements of a statistical investigation

Putting It Together: Psychological Research

Psychologists use the scientific method to examine human behavior and mental processes. Some of the methods you learned about include descriptive, experimental, and correlational research designs.

Watch the CrashCourse video to review the material you learned, then read through the following examples and see if you can come up with your own design for each type of study.

You can view the transcript for “Psychological Research: Crash Course Psychology #2” here (opens in new window).

Case Study: a detailed analysis of a particular person, group, business, event, etc. This approach is commonly used to to learn more about rare examples with the goal of describing that particular thing.

• Ted Bundy was one of America’s most notorious serial killers who murdered at least 30 women and was executed in 1989. Dr. Al Carlisle evaluated Bundy when he was first arrested and conducted a psychological analysis of Bundy’s development of his sexual fantasies merging into reality (Ramsland, 2012). Carlisle believes that there was a gradual evolution of three processes that guided his actions: fantasy, dissociation, and compartmentalization (Ramsland, 2012). Read   Imagining Ted Bundy  (http://goo.gl/rGqcUv) for more information on this case study.

Naturalistic Observation : a researcher unobtrusively collects information without the participant’s awareness.

• Drain and Engelhardt (2013) observed six nonverbal children with autism’s evoked and spontaneous communicative acts. Each of the children attended a school for children with autism and were in different classes. They were observed for 30 minutes of each school day. By observing these children without them knowing, they were able to see true communicative acts without any external influences.

Survey : participants are asked to provide information or responses to questions on a survey or structure assessment.

• Educational psychologists can ask students to report their grade point average and what, if anything, they eat for breakfast on an average day. A healthy breakfast has been associated with better academic performance (Digangi’s 1999).
• Anderson (1987) tried to find the relationship between uncomfortably hot temperatures and aggressive behavior, which was then looked at with two studies done on violent and nonviolent crime. Based on previous research that had been done by Anderson and Anderson (1984), it was predicted that violent crimes would be more prevalent during the hotter time of year and the years in which it was hotter weather in general. The study confirmed this prediction.

Longitudinal Study: researchers   recruit a sample of participants and track them for an extended period of time.

• In a study of a representative sample of 856 children Eron and his colleagues (1972) found that a boy’s exposure to media violence at age eight was significantly related to his aggressive behavior ten years later, after he graduated from high school.

Cross-Sectional Study:  researchers gather participants from different groups (commonly different ages) and look for differences between the groups.

• In 1996, Russell surveyed people of varying age groups and found that people in their 20s tend to report being more lonely than people in their 70s.

Correlational Design:  two different variables are measured to determine whether there is a relationship between them.

• Thornhill et al. (2003) had people rate how physically attractive they found other people to be. They then had them separately smell t-shirts those people had worn (without knowing which clothes belonged to whom) and rate how good or bad their body oder was. They found that the more attractive someone was the more pleasant their body order was rated to be.
• Clinical psychologists can test a new pharmaceutical treatment for depression by giving some patients the new pill and others an already-tested one to see which is the more effective treatment.

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grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing

well-developed set of ideas that propose an explanation for observed phenomena

(plural: hypotheses) tentative and testable statement about the relationship between two or more variables

an experiment must be replicable by another researcher

implies that a theory should enable us to make predictions about future events

able to be disproven by experimental results

implies that all data must be considered when evaluating a hypothesis

committee of administrators, scientists, and community members that reviews proposals for research involving human participants

process of informing a research participant about what to expect during an experiment, any risks involved, and the implications of the research, and then obtaining the person’s consent to participate

purposely misleading experiment participants in order to maintain the integrity of the experiment

when an experiment involved deception, participants are told complete and truthful information about the experiment at its conclusion

committee of administrators, scientists, veterinarians, and community members that reviews proposals for research involving non-human animals

research studies that do not test specific relationships between variables

research investigating the relationship between two or more variables

research method that uses hypothesis testing to make inferences about how one variable impacts and causes another

observation of behavior in its natural setting

inferring that the results for a sample apply to the larger population

when observations may be skewed to align with observer expectations

measure of agreement among observers on how they record and classify a particular event

observational research study focusing on one or a few people

list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

subset of individuals selected from the larger population

overall group of individuals that the researchers are interested in

method of research using past records or data sets to answer various research questions, or to search for interesting patterns or relationships

studies in which the same group of individuals is surveyed or measured repeatedly over an extended period of time

compares multiple segments of a population at a single time

reduction in number of research participants as some drop out of the study over time

relationship between two or more variables; when two variables are correlated, one variable changes as the other does

number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by r

two variables change in the same direction, both becoming either larger or smaller

two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation

changes in one variable cause the changes in the other variable; can be determined only through an experimental research design

unanticipated outside factor that affects both variables of interest, often giving the false impression that changes in one variable causes changes in the other variable, when, in actuality, the outside factor causes changes in both variables

seeing relationships between two things when in reality no such relationship exists

tendency to ignore evidence that disproves ideas or beliefs

group designed to answer the research question; experimental manipulation is the only difference between the experimental and control groups, so any differences between the two are due to experimental manipulation rather than chance

serves as a basis for comparison and controls for chance factors that might influence the results of the study—by holding such factors constant across groups so that the experimental manipulation is the only difference between groups

description of what actions and operations will be used to measure the dependent variables and manipulate the independent variables

researcher expectations skew the results of the study

experiment in which the researcher knows which participants are in the experimental group and which are in the control group

experiment in which both the researchers and the participants are blind to group assignments

people's expectations or beliefs influencing or determining their experience in a given situation

variable that is influenced or controlled by the experimenter; in a sound experimental study, the independent variable is the only important difference between the experimental and control group

variable that the researcher measures to see how much effect the independent variable had

subjects of psychological research

subset of a larger population in which every member of the population has an equal chance of being selected

method of experimental group assignment in which all participants have an equal chance of being assigned to either group

consistency and reproducibility of a given result

accuracy of a given result in measuring what it is designed to measure

determines how likely any difference between experimental groups is due to chance

statistical probability that represents the likelihood that experimental results happened by chance

Psychological Science is the scientific study of mind, brain, and behavior. We will explore what it means to be human in this class. It has never been more important for us to understand what makes people tick, how to evaluate information critically, and the importance of history. Psychology can also help you in your future career; indeed, there are very little jobs out there with no human interaction!

Because psychology is a science, we analyze human behavior through the scientific method. There are several ways to investigate human phenomena, such as observation, experiments, and more. We will discuss the basics, pros and cons of each! We will also dig deeper into the important ethical guidelines that psychologists must follow in order to do research. Lastly, we will briefly introduce ourselves to statistics, the language of scientific research. While reading the content in these chapters, try to find examples of material that can fit with the themes of the course.

To get us started:

• The study of the mind moved away Introspection to reaction time studies as we learned more about empiricism
• Psychologists work in careers outside of the typical "clinician" role. We advise in human factors, education, policy, and more!
• While completing an observation study, psychologists will work to aggregate common themes to explain the behavior of the group (sample) as a whole. In doing so, we still allow for normal variation from the group!
• The IRB and IACUC are important in ensuring ethics are maintained for both human and animal subjects

Psychological Science: Understanding Human Behavior Copyright © by Karenna Malavanti is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How to answer Paper 3, Question 1 (a, b & c)

Travis Dixon October 7, 2021 IB Psychology , Revision and Exam Preparation

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The following has been adapted from our textbook “ IB Psychology: A Revision Guide .” (Now available as an online textbook ).

All HL students should score 100% (9/9) for the first questions in Paper 3 because it’s so easy. But most don’t because they make basic mistakes. Let’s look at the best and simplest way to answer these three questions.

IB Psychology Guide, pg 32.

We know exactly what the Paper 3 questions will be. What we don’t know is the study because in Paper 3 students read a study they’ve never seen before and answer questions (scroll down for examples).

The first three questions (Question 1a, 1b, and 1c) are as follows:

I) Identify the method used and outline two characteristics of the method. (3 marks)

II) Describe the sampling method used in the study. (3 marks)

III) Suggest an alternative or additional research method giving one reason for your choice. (3 marks)

Example from May, 2019. These first three questions are always the same.

Let’s look at how to answer these with example answers based on the practice Paper 3 (on power posing) included in the revision textbook.

1a. Identify the method

You need to be able to state the research method used in the stimulus (1 mark) and outline two characteristics of the method (2 marks). The methods you need to know about are in the guide.

Example:  If a study used a true experiment, your answer might look like this:

This study was an experiment (true/laboratory). A true experiment tests causal relationships between and IV and a DV (the IV in this study was the type of posing and there were three DVs, including sense of power, hormone levels and confidence). They often take place in controlled environments and random allocation is possible (random allocation was used in this study).

1b Describe the sampling method

Answer this in the exact same way as 1b, but for the sampling method. The sampling method will be one of the following:

• volunteer/self-selected
• convenience/opportunity
• snowball sampling

Example:  If a study used a self-selected sample, your answer could look like this (bullet points are acceptable):

Sampling method = self-selected (volunteer) sampling. Two characteristics are:

• Participants are the ones that approach the researchers to volunteer to participate in the study
• There is typically some form of marketing that calls for volunteers. In this case, they were responding to an advertisement in a school magazine

1c Suggest another method

You need to state the alternative or additional research method (1 mark) and explain how and why the method could be used (2 marks). Include at least one characteristic of the method and apply details from the stimulus to support your answer.

Mr Dixon’s Top Tip: It will be easier to explain an additional method, rather than an alternative one. Explaining how an additional qualitative method (e.g. interview) is often much easier than explaining an alternative quantitative method.

Here’s an example answer:

A semi-structured interview could be used to follow-up the experimental results. A semi-structured interview has a list of open and closed questions with the researcher free to deviate as the interview unfolds. Researchers could ask questions about how powerful and confident the participants felt before and after doing the posing. This helps to triangulate findings and gathering qualitative data might help get a deeper understanding of the psychological effects of power posing, not just the behavioural and biological effects.

How to Prepare

Use flashcards to review two characteristics of each of the research methods and sampling methods. Use the past papers (find some linked above) to practice using this information in response to stimulus papers. Make sure you can answer all three questions in about 20 minutes or less.

• Paper 3 Practice: Individualism and Happiness in a Japanese Workplace
• Paper 3 Practice: Trauma and the September 11 Attacks
• Paper 3 Practice: Observation of Hospitals
• Paper 3 Practice: Smartphones and sleep
• Be concise:  State the method and get straight into the characteristics.
• Use one detail from the stimulus:  This shows you’re doing more than just memorizing.
• Pre-plan for 1c:  This is the toughest question to answer so have a generic response ready to go in case you get stuck on exam day.
• Triangulation : Is a good reason to use any additional research method. Hint hint!

Tricky Bits: Experiments

This is extremely frustrating but in Paper One and Paper Two the IB assessment team considers “experiments” as  one  method. However, this same team considers them unique in Paper 3. In the May 2019 Paper 3, the correct answer for 1a was a “lab/true experiment.” This was quite controversial as many, including myself, could see how it would be a field experiment. However, zero marks were awarded if a student wrote “…field experiment, natural experiment, quasi experiment, field study.”  BUT… “Stating ‘experiment’ without specification (was) acceptable.

So, if you know it’s an experiment but you don’t know what type, just state experiment. The characteristics of any experiment is that (a) they have independent and dependent variables and (b) researchers try to control extraneous variables so they don’t confound the results, which allows them to draw conclusions about cause and effect.

Travis Dixon is an IB Psychology teacher, author, workshop leader, examiner and IA moderator.

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Understanding Methods for Research in Psychology

A Psychology Research Methods Study Guide

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

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Types of Research in Psychology

• Cross-Sectional vs. Longitudinal Research
• Reliability and Validity

Glossary of Terms

Research in psychology focuses on a variety of topics , ranging from the development of infants to the behavior of social groups. Psychologists use the scientific method to investigate questions both systematically and empirically.

Research in psychology is important because it provides us with valuable information that helps to improve human lives. By learning more about the brain, cognition, behavior, and mental health conditions, researchers are able to solve real-world problems that affect our day-to-day lives.

At a Glance

Knowing more about how research in psychology is conducted can give you a better understanding of what those findings might mean to you. Psychology experiments can range from simple to complex, but there are some basic terms and concepts that all psychology students should understand.

Start your studies by learning more about the different types of research, the basics of experimental design, and the relationships between variables.

Research in Psychology: The Basics

The first step in your review should include a basic introduction to psychology research methods . Psychology research can have a variety of goals. What researchers learn can be used to describe, explain, predict, or change human behavior.

Psychologists use the scientific method to conduct studies and research in psychology. The basic process of conducting psychology research involves asking a question, designing a study, collecting data, analyzing results, reaching conclusions, and sharing the findings.

The Scientific Method in Psychology Research

The steps of the scientific method in psychology research are:

• Make an observation
• Ask a research question and make predictions about what you expect to find
• Test your hypothesis and gather data
• Examine the results and form conclusions
• Report your findings

Research in psychology can take several different forms. It can describe a phenomenon, explore the causes of a phenomenon, or look at relationships between one or more variables. Three of the main types of psychological research focus on:

Descriptive Studies

This type of research can tell us more about what is happening in a specific population. It relies on techniques such as observation, surveys, and case studies.

Correlational Studies

Correlational research is frequently used in psychology to look for relationships between variables. While research look at how variables are related, they do not manipulate any of the variables.

While correlational studies can suggest a relationship between two variables, finding a correlation does not prove that one variable causes a change in another. In other words, correlation does not equal causation.

Experimental Research Methods

Experiments are a research method that can look at whether changes in one variable cause changes in another. The simple experiment is one of the most basic methods of determining if there is a cause-and-effect relationship between two variables.

A simple experiment utilizes a control group of participants who receive no treatment and an experimental group of participants who receive the treatment.

Experimenters then compare the results of the two groups to determine if the treatment had an effect.

Cross-Sectional vs. Longitudinal Research in Psychology

Research in psychology can also involve collecting data at a single point in time, or gathering information at several points over a period of time.

Cross-Sectional Research

In a cross-sectional study , researchers collect data from participants at a single point in time. These are descriptive type of research and cannot be used to determine cause and effect because researchers do not manipulate the independent variables.

However, cross-sectional research does allow researchers to look at the characteristics of the population and explore relationships between different variables at a single point in time.

Longitudinal Research

A longitudinal study is a type of research in psychology that involves looking at the same group of participants over a period of time. Researchers start by collecting initial data that serves as a baseline, and then collect follow-up data at certain intervals. These studies can last days, months, or years. 

The longest longitudinal study in psychology was started in 1921 and the study is planned to continue until the last participant dies or withdraws. As of 2003, more than 200 of the partipants were still alive.

The Reliability and Validity of Research in Psychology

Reliability and validity are two concepts that are also critical in psychology research. In order to trust the results, we need to know if the findings are consistent (reliability) and that we are actually measuring what we think we are measuring (validity).

Reliability

Reliability is a vital component of a valid psychological test. What is reliability? How do we measure it? Simply put, reliability refers to the consistency of a measure. A test is considered reliable if we get the same result repeatedly.

When determining the merits of a psychological test, validity is one of the most important factors to consider. What exactly is validity? One of the greatest concerns when creating a psychological test is whether or not it actually measures what we think it is measuring.

For example, a test might be designed to measure a stable personality trait but instead measures transitory emotions generated by situational or environmental conditions. A valid test ensures that the results accurately reflect the dimension undergoing assessment.

Review some of the key terms that you should know and understand about psychology research methods. Spend some time studying these terms and definitions before your exam. Some key terms that you should know include:

• Correlation
• Demand characteristic
• Dependent variable
• Hawthorne effect
• Independent variable
• Naturalistic observation
• Placebo effect
• Random assignment
• Replication
• Selective attrition

Erol A.  How to conduct scientific research ?  Noro Psikiyatr Ars . 2017;54(2):97-98. doi:10.5152/npa.2017.0120102

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research .  Nurse Res . 2016;23(6):20-25. doi:10.7748/nr.2016.e1382

Wang X, Cheng Z. Cross-sectional studies: Strengths, weaknesses, and recommendations .  Chest . 2020;158(1S):S65-S71. doi:10.1016/j.chest.2020.03.012

Caruana EJ, Roman M, Hernández-Sánchez J, Solli P. Longitudinal studies .  J Thorac Dis . 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

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

PSYCH 3002 - Research Methods in Psychology

• APA PsycInfo
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APA 7th --- from the APA (American Psychological Association)

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APA 7th (Plan B - Purdue OWL also offers APA guidance and examples)

• Citing Journal Articles (Purdue OWL)
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• In-Text Citations - APA 7th - Basics (Purdue OWL)
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Finding the DOI with CrossRef

Finding the DOI with Crossref

For journals with Direct Object Identifiers you place the DOI at the end of the citation.

APA recommends that the DOI should be in the form https://doi.org/10.1037/arc0000014

Since few citation generators provide the DOI (or at least the DOI in this format) you should copy the title of the article and paste it in a Crossref search - https://search.crossref.org/

If Crossref contains the article you will find the DOI at the bottom of the entry for that article.

So for the article:

Reppy, D., & Larwin, K. H. (2020). The association between perception of caring and intrinsic motivation: A study of urban middle school students. Journal of Education , 200 (1), 48-61. 

We need to find the DOI for this article.

So take the title “The association between perception of caring and intrinsic motivation: A study of urban middle school students” … and plug this into Crossref .

The DOI found there is https://doi.org/10.1177/0022057419875123

So place DOI at the end of the article as follows:

Reppy, D., & Larwin, K. H. (2020). The association between perception of caring and intrinsic motivation: A study of urban middle school students. Journal of Education , 200 (1), 48-61. https://doi.org/10.1177/0022057419875123

What about a journal article that DOESN’T have a DOI ?

If you check your article for a DOI in Crossref and it doesn’t appear than you’ll need to use the web address (URL) that is associated with the article (note APA 7 no longer requires the words "Retrieved from" to precede a non-DOI URL).    

Suppose you want to place the following article in your “References.”

Tarc, P., & Beatty, L. (2012). The emergence of the International Baccalaureate diploma in Ontario: Diffusion, pilot study and prospective research. Canadian Journal of Education , 35 (4).

A search of “The emergence of the International Baccalaureate diploma in Ontario: Diffusion, pilot study and prospective research.” in Crossref does not yield a DOI . So instead you should search for this title in Google Scholar which indicates this article is available via JSTOR at …

https://www.jstor.org/stable/pdf/canajeducrevucan.35.4.341.pdf?acceptTC=true&coverpage=false&casa_token=Zbn7TCBmz8UAAAAA:MyLNfy72auHvOJMfzyB7-V31tfu2ZL83J1IIPY3pFxL9Hlvq6UwfvY-yri13a1I15jJkUE_CRqu-m9y2xeUCL7sLUTyosAlg_ sjZoHO4ft0o3RVB59A

APA 7th now permits shortening of URLs so the first part of the URL - https://www.jstor.org/stable/pdf/canajeducrevucan.35.4.341.pdf - which also points to the citation for this article could be used instead.  

So the final citation should be:

Tarc, P., & Beatty, L. (2012). The emergence of the International Baccalaureate diploma in Ontario: Diffusion, pilot study and prospective research. Canadian Journal of Education , 35 (4). https://www.jstor.org/stable/pdf/ canajeducrevucan.35.4.341.pdf

Examples of APA Citations for Journal Articles

Citing journal articles in APA 7th with a Direct Object Identifier (DOI).

(Reminder - find the DOI with CrossRef ).

Alkharusi, H. (2017). Predicting students’ academic achievement: Contributions of perceptions of classroom assessment tasks and motivated learning strategies. Electronic Journal of Research in Education Psychology 14 (40) 515-533. https://doi.org/10.14204/ejrep.40.15177

Kjeldsen, T. H., & Blomhøj, M. (2012). Beyond motivation: history as a method for learning meta-discursive rules in mathematics. Educational Studies in Mathematics , 80 (3), 327-349. https://doi.org/10.1007/s10649-011-9352-z

Weidinger, A. F., Steinmayr, R., & Spinath, B. (2017). Math grades and intrinsic motivation in elementary school: A longitudinal investigation of their association. British Journal of Educational Psychology , 87 (2), 187-204. https://doi.org/10.1111/bjep.12143

For articles without a Direct Object Identifier ( CrossRef does not find a DOI for this article) simply use the web address (URL) associated with the article.

Pourdavood, R. G., Grob, S., Clark, J., & Orr, H. (1999). Discourse and professional growth: processes, relationships, dilemmas, and hope. School community journal , 9 (1), 33-48. http://www.adi.org/journal/ss99/PourdavoodGrobClarkOrrSpring1999.pdf

Examples of APA 7th Citations for Books and Book Chapters

Citing Books and Chapters of Books

APA 7th guidelines:

Treat the authors, date, and title as before (with the title in italics).

APA 7th requires an edition number after the title e.g. (4th ed) but this is not in italics.

APA 7th no longer requires a “place of publication” - just the name of the publisher.

APA 7th - For ebooks only use a DOI if it exists. Only use a URL if:

• the book does not have a DOI 
• the URL is NOT from an academic research database . 

For an entire book in print cite as follows:

Carr, M. (1996). Motivation in mathematics . Hampton Press.

For a chapter from a book in print cite as follows:

Dorfler, W. (1999). Mathematics provides tools for thinking and communicating. In C. Hoyles, C. Morgan, & G. Woodhouse (Eds.), Rethinking the mathematics curriculum (pp. 75-86). Falmer Press. 

For an entire ebook (that does not have a DOI) cite as follows:

Hall, N.C. & Goetz, T. (2013). Emotion, motivation, and self-regulation: A handbook for teachers . Emerald Group Publishing.

For a chapter from an ebook that has a DOI cite as follows:

Harackiewicz, J., Tibbetts, Y., Canning, E., & Hyde, J. (2014). Harnessing values to promote motivation in education. In S. Karabenick & T. Urdan (Eds.), Motivational interventions (pp. 71-105). Emerald Group Publishing. https://doi.org/10.1108/s0749-742320140000018002

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APA Handbook of Research Methods in Psychology

Available formats.

• Table of contents
• Contributor bios
• Book details

With significant new and updated content across dozens of chapters, this second edition  presents the most exhaustive treatment available of the techniques psychologists and others have developed to help them pursue a shared understanding of why humans think, feel, and behave the way they do.

The initial chapters in this indispensable three-volume handbook address broad, crosscutting issues faced by researchers: the philosophical, ethical, and societal underpinnings of psychological research. Next, chapters detail the research planning process, describe the range of measurement techniques that psychologists most often use to collect data, consider how to determine the best measurement techniques for a particular purpose, and examine ways to assess the trustworthiness of measures.

Additional chapters cover various aspects of quantitative, qualitative, neuropsychological, and biological research designs, presenting an array of options and their nuanced distinctions. Chapters on techniques for data analysis follow, and important issues in writing up research to share with the community of psychologists are discussed in the handbook’s concluding chapters.

Among the newly written chapters in the second edition, the handbook’s stellar roster of authors cover literature searching, workflow and reproducibility, research funding, neuroimaging, various facets of a wide range of research designs and data analysis methods, and updated information on the publication process, including research data management and sharing, questionable practices in statistical analysis, and ethical issues in manuscript preparation and authorship.

Volume 1. Foundations, Planning, Measures, and Psychometrics

Editorial Board

About the Editors

Contributors

A Note from the Publisher

Introduction: Objectives of Psychological Research and Their Relations to Research Methods

Part I. Philosophical, Ethical, and Societal Underpinnings of Psychological Research

• Chapter 1. Perspectives on the Epistemological Bases for Qualitative Research Carla Willig
• Chapter 2. Frameworks for Causal Inference in Psychological Science Peter M. Steiner, William R. Shadish, and Kristynn J. Sullivan
• Chapter 3. Ethics in Psychological Research: Guidelines and Regulations Adam L. Fried and Kate L. Jansen
• Chapter 4. Ethics and Regulation of Research With Nonhuman Animals Sangeeta Panicker, Chana K. Akins, and Beth Ann Rice
• Chapter 5. Cross-Cultural Research Methods David Masumoto and Fons J. R. van de Vijver
• Chapter 6.Research With Populations that Experience Marginalization George P. Knight, Rebecca M. B. White, Stefanie Martinez-Fuentes, Mark W. Roosa, and Adriana J. Umaña-Taylor

Part II. Planning Research

• Chapter 7. Developing Testable and Important Research Questions Frederick T. L. Leong, Neal Schmitt, and Brent J. Lyons
• Chapter 8. Searching With a Purpose: How to Use Literature Searching to Support Your Research Diana Ramirez and Margaret J. Foster
• Chapter 9. Psychological Measurement: Scaling and Analysis Heather Hayes and Susan E. Embretson
• Chapter 10. Sample Size Planning Ken Kelley, Samantha F. Anderson, and Scott E. Maxwell
• Chapter 11. Workflow and Reproducibility Oliver Kirchkamp
• Chapter 12. Obtaining and Evaluating Research Funding Jonathan S. Comer and Amanda L. Sanchez

Part III. Measurement Methods

• Chapter 13. Behavioral Observation Roger Bakeman and Vicenç Quera
• Chapter 14. Question Order Effects Lisa Lee, Parvati Krishnamurty, and Struther Van Horn
• Chapter 15. Interviews and Interviewing Techniques Anna Madill
• Chapter 16. Using Intensive Longitudinal Methods in Psychological Research Masumi Iida, Patrick E. Shrout, Jean-Philippe Laurenceau, and Niall Bolger
• Chapter 17. Automated Analyses of Natural Language in Psychological Research Laura K. Allen, Arthur C. Graesser, and Danielle S. McNamara
• Chapter 18. Objective Tests as Instruments of Psychological Theory and Research David Watson
• Chapter 19. Norm- and Criterion-Referenced Testing Kurt F. Geisinger
• Chapter 20. The Current Status of "Projective" "Tests" Robert E. McGrath, Alec Twibell, and Elizabeth J. Carroll
• Chapter 21. Brief Instruments and Short Forms Emily A. Atkinson, Carolyn M. Pearson Carter, Jessica L. Combs Rohr, and Gregory T. Smith
• Chapter 22. Eye Movements, Pupillometry, and Cognitive Processes Simon P. Liversedge, Sara V. Milledge, and Hazel I. Blythe
• Chapter 23. Response Times Roger Ratcliff
• Chapter 24. Psychophysics: Concepts, Methods, and Frontiers Allie C. Hexley, Takuma Morimoto, and Manuel Spitschan
• Chapter 25. The Perimetric Physiological Measurement of Psychological Constructs Louis G. Tassinary, Ursula Hess, Luis M. Carcoba, and Joseph M. Orr
• Chapter 26. Salivary Hormone Assays Linda Becker, Nicholas Rohleder, and Oliver C. Schultheiss
• Chapter 27. Electro- and Magnetoencephalographic Methods in Psychology Eddie Harmon-Jones, David M. Amodio, Philip A. Gable, and Suzanne Dikker
• Chapter 28. Event-Related Potentials Steven J. Luck
• Chapter 29. Functional Neuroimaging Megan T. deBettencourt, Wilma A. Bainbridge, Monica D. Rosenberg
• Chapter 30. Noninvasive Stimulation of the Cerebral Cortex Dennis J. L. G. Schutter
• Chapter 31. Combined Neuroimaging Methods Marius Moisa and Christian C. Ruff
• Chapter 32. Neuroimaging Analysis Methods Yanyu Xiong and Sharlene D. Newman

Part IV. Psychometrics

• Chapter 33. Reliability Sean P. Lane, Elizabeth N. Aslinger, and Patrick E. Shrout
• Chapter 34. Generalizability Theory Xiaohong Gao and Deborah J. Harris
• Chapter 35. Construct Validity Kevin J. Grimm and Keith F. Widaman
• Chapter 36. Item-Level Factor Nisha C. Gottfredson, Brian D. Stucky, and A. T. Panter
• Chapter 37. Item Response Theory Steven P. Reise and Tyler M. Moore
• Chapter 38. Measuring Test Performance With Signal Detection Theory Techniques Teresa A. Treat and Richard J. Viken

Volume 2. Research Designs: Quantitative, Qualitative, Neuropsychological, and Biological

Part I. Qualitative Research Methods

• Chapter 1. Developments in Qualitative Inquiry Sarah Riley and Andrea LaMarre
• Chapter 2. Metasynthesis of Qualitative Research Sally Thorne
• Chapter 3. Grounded Theory and Psychological Research Robert Thornberg, Elaine Keane, and Malgorzata Wójcik
• Chapter 4. Thematic Analysis Virginia Braun and Victoria Clarke
• Chapter 5. Phenomenological Methodology, Methods, and Procedures for Research in Psychology Frederick J. Wertz
• Chapter 6. Narrative Analysis Javier Monforte and Brett Smith
• Chapter 7. Ethnomethodology and Conversation Analysis Paul ten Have
• Chapter 8. Discourse Analysis and Discursive Psychology Chris McVittie and Andy McKinlay
• Chapter 9. Ethnography in Psychological Research Elizabeth Fein and Jonathan Yahalom
• Chapter 10. Visual Research in Psychology Paula Reavey, Jon Prosser, and Steven D. Brown
• Chapter 11. Researching the Temporal Karen Henwood and Fiona Shirani

Part II. Working Across Epistemologies, Methodologies, and Methods

• Chapter 12. Mixed Methods Research in Psychology Timothy C. Guetterman and Analay Perez
• Chapter 13. The "Cases Within Trials" (CWT) Method: An Example of a Mixed-Methods Research Design Daniel B. Fishman
• Chapter 14. Researching With American Indian and Alaska Native Communities: Pursuing Partnerships for Psychological Inquiry in Service to Indigenous Futurity Joseph P. Gone
• Chapter 15. Participatory Action Research as Movement Toward Radical Relationality, Epistemic Justice, and Transformative Intervention: A Multivocal Reflection Urmitapa Dutta, Jesica Siham Fernández, Anne Galletta, and Regina Day Langhout

Part III. Sampling Across People and Time

• Chapter 16. Introduction to Survey Sampling Roger Tourangeau and Ting Yan
• Chapter 17. Epidemiology Rumi Kato Price and Heidi H. Tastet
• Chapter 18. Collecting Longitudinal Data: Present Issues and Future Challenges Simran K. Johal, Rohit Batra, and Emilio Ferrer
• Chapter 19. Using the Internet to Collect Data Ulf-Dietrich Reips

Part IV. Building and Testing Models

• Chapter 20. Statistical Mediation Analysis David P. MacKinnon, Jeewon Cheong, Angela G. Pirlott, and Heather L. Smyth
• Chapter 21. Structural Equation Modeling with Latent Variables Rick H. Hoyle and Nisha C. Gottfredson
• Chapter 22. Mathematical Psychology Parker Smith, Yanjun Liu, James T. Townsend, and Trisha Van Zandt
• Chapter 23. Computational Modeling Adele Diederich
• Chapter 24. Fundamentals of Bootstrapping and Monte Carlo Methods William Howard Beasley, Patrick O'Keefe, and Joseph Lee Rodgers
• Chapter 25. Designing Simulation Studies Xitao Fan
• Chapter 26. Bayesian Modeling for Psychologists: An Applied Approach Fred M. Feinberg and Richard Gonzalez

Part V. Designs Involving Experimental Manipulations

• Chapter 27. Randomized Designs in Psychological Research Larry Christensen, Lisa A. Turner, and R. Burke Johnson
• Chapter 28. Nonequivalent Comparison Group Designs Henry May and Zachary K. Collier
• Chapter 29. Regression Discontinuity Designs Charles S. Reichardt and Gary T. Henry
• Chapter 30. Treatment Validity for Intervention Studies Dianne L. Chambless and Steven D. Hollon
• Chapter 31. Translational Research Michael T. Bardo, Christopher Cappelli, and Mary Ann Pentz
• Chapter 32. Program Evaluation: Outcomes and Costs of Putting Psychology to Work Brian T. Yates

Part VI. Quantitative Research Designs Involving Single Participants or Units

• Chapter 33. Single-Case Experimental Design John M. Ferron, Megan Kirby, and Lodi Lipien
• Chapter 34. Time Series Designs Bradley J. Bartos, Richard McCleary, and David McDowall

Part VII. Designs in Neuropsychology and Biological Psychology

• Chapter 35. Case Studies in Neuropsychology Randi C. Martin, Simon Fischer-Baum, and Corinne M. Pettigrew
• Chapter 36. Group Studies in Experimental Neuropsychology Avinash R Vaidya, Maia Pujara, and Lesley K. Fellows
• Chapter 37. Genetic Methods in Psychology Terrell A. Hicks, Daniel Bustamante, Karestan C. Koenen, Nicole R. Nugent, and Ananda B. Amstadter
• Chapter 38. Human Genetic Epidemiology Floris Huider, Lannie Ligthart, Yuri Milaneschi, Brenda W. J. H. Penninx, and Dorret I. Boomsma

Volume 3. Data Analysis and Research Publication

Part I. Quantitative Data Analysis

• Chapter 1. Methods for Dealing With Bad Data and Inadequate Models: Distributions, Linear Models, and Beyond Rand R. Wilcox and Guillaume A. Rousselet
• Chapter 2. Maximum Likelihood and Multiple Imputation Missing Data Handling: How They Work, and How to Make Them Work in Practice Timothy Hayes and Craig K. Enders
• Chapter 3. Exploratory Data Analysis Paul F. Velleman and David C. Hoaglin
• Chapter 4. Graphic Displays of Data Leland Wilkinson
• Chapter 5. Estimating and Visualizing Interactions in Moderated Multiple Regression Connor J. McCabe and Kevin M. King
• Chapter 6. Effect Size Estimation Michael Borenstein
• Chapter 7. Measures of Clinically Significant Change Russell J. Bailey, Benjamin M. Ogles, and Michael J. Lambert
• Chapter 8. Analysis of Variance and the General Linear Model James Jaccard and Ai Bo
• Chapter 9. Generalized Linear Models David Rindskopf
• Chapter 10. Multilevel Modeling for Psychologists John B. Nezlek
• Chapter 11. Longitudinal Data Analysis Andrew K. Littlefield
• Chapter 12. Event History Analysis Fetene B. Tekle and Jeroen K. Vermunt
• Chapter 13. Latent State-Trait Models Rolf Steyer, Christian Geiser, and Christiane Loß​nitzer
• Chapter 14. Latent Variable Modeling of Continuous Growth David A. Cole, Jeffrey A. Ciesla, and Qimin Liu
• Chapter 15. Dynamical Systems and Differential Equation Models of Change Steven M. Boker and Robert G. Moulder
• Chapter 16. A Multivariate Growth Curve Model for Three-Level Data Patrick J. Curran, Chris L. Strauss, Ethan M. McCormick, and James S. McGinley
• Chapter 17. Exploratory Factor Analysis and Confirmatory Factor Analysis Keith F. Widaman and Jonathan Lee Helm
• Chapter 18. Latent Class and Latent Profile Models Brian P. Flaherty, Liying Wang, and Cara J. Kiff
• Chapter 19. Decision Trees and Ensemble Methods in the Behavioral Sciences Kevin J. Grimm, Ross Jacobucci, and John J. McArdle
• Chapter 20. Using the Social Relations Model to Understand Interpersonal Perception and Behavior P. Niels Christensen, Deborah A. Kashy, and Katelin E. Leahy
• Chapter 21. Dyadic Data Analysis Richard Gonzalez and Dale Griffin
• Chapter 22. The Data of Others: New and Old Faces of Archival Research Sophie Pychlau and David T. Wagner
• Chapter 23. Social Network Analysis in Psychology: Recent Breakthroughs in Methods and Theories Wei Wang, Tobias Stark, James D. Westaby, Adam K. Parr, and Daniel A. Newman
• Chapter 24. Meta-Analysis Jeffrey C. Valentine, Therese D. Pigott, and Joseph Morris

Part II. Publishing and the Publication Process

• Chapter 25. Research Data Management and Sharing Katherine G. Akers and John A. Borghi
• Chapter 26. Questionable Practices in Statistical Analysis Rex B. Kline
• Chapter 27. Ethical Issues in Manuscript Preparation and Authorship Jennifer Crocker

Harris Cooper, PhD, is the Hugo L. Blomquist professor, emeritus, in the Department of Psychology and Neuroscience at Duke University. His research interests concern research synthesis and research methodology, and he also studies the application of social and developmental psychology to education policy. His book Research Synthesis and Meta-Analysis: A Step-by-Step Approach (2017) is in its fifth edition. He is the coeditor of the Handbook of Research Synthesis and Meta-Analysis (3 rd ed. 2019).

In 2007, Dr. Cooper was the recipient of the Frederick Mosteller Award for Contributions to Research Synthesis Methodology, and in 2008 he received the Ingram Olkin Award for Distinguished Lifetime Contribution to Research Synthesis from the Society for Research Synthesis Methodology.

He served as the chair of the Department of Psychology and Neuroscience at Duke University from 2009 to 2014, and from 2017 to 2018 he served as the dean of social science at Duke. Dr. Cooper chaired the first APA committee that developed guidelines for information about research that should be included in manuscripts submitted to APA journals. He currently serves as the editor of American Psychologist, the flagship journal of APA.

Marc N. Coutanche, PhD, is an associate professor of psychology and research scientist in the Learning Research and Development Center at the University of Pittsburgh. Dr. Coutanche directs a program of cognitive neuroscience research and develops and tests new computational techniques to identify and understand the neural information present within neuroimaging data.

His work has been funded by the National Institutes of Health, National Science Foundation, American Psychological Foundation, and other organizations, and he has published in a variety of journals.

Dr. Coutanche received his PhD from the University of Pennsylvania, and conducted postdoctoral training at Yale University. He received a Howard Hughes Medical Institute International Student Research Fellowship and Ruth L. Kirschstein Postdoctoral National Research Service Award, and was named a 2019 Rising Star by the Association for Psychological Science.

Linda M. McMullen, PhD, is professor emerita of psychology at the University of Saskatchewan, Canada. Over her career, she has contributed to the development of qualitative inquiry in psychology through teaching, curriculum development, and pedagogical scholarship; original research; and service to the qualitative research community.

Dr. McMullen introduced qualitative inquiry into both the graduate and undergraduate curriculum in her home department, taught courses at both levels for many years, and has published articles, coedited special issues, and written a book ( Essentials of Discursive Psychology ) that is part of APA’s series on qualitative methodologies, among other works. She has been engaged with building the Society for Qualitative Inquiry in Psychology (SQIP; a section of Division 5 of the APA) into a vibrant scholarly society since its earliest days, and took on many leadership roles while working as a university professor.

Dr. McMullen’s contributions have been recognized by Division 5 of the APA, the Canadian Psychological Association, and the Saskatchewan Psychological Association.

Abigail Panter, PhD, is the senior associate dean for undergraduate education and a professor of psychology in the L. L. Thurstone Psychometric Laboratory at University of North Carolina at Chapel Hill. She is past president of APA’s Division 5, Quantitative and Qualitative Methods.

As a quantitative psychologist, she develops instruments, research designs and data-analytic strategies for applied research questions in higher education, personality, and health. She serves as a program evaluator for UNC’s Chancellor’s Science Scholars Program, and was also principal investigator for The Finish Line Project, a $3 million grant from the U.S. Department of Education that systematically investigated new supports and academic initiatives, especially for first-generation college students.

Her books include the  APA Dictionary of Statistics and Research Methods  (2014), the APA Handbook of Research Methods in Psychology  (first edition; 2012), the Handbook of Ethics in Quantitative Methodology  (2011), and the SAGE Handbook of Methods in Social Psychology (2004), among others.

David Rindskopf, PhD, is distinguished professor at the City University of New York Graduate Center, specializing in research methodology and statistics. His main interests are in Bayesian statistics, causal inference, categorical data analysis, meta-analysis, and latent variable models.

He is a fellow of the American Statistical Association and the American Educational Research Association, and is past president of the Society of Multivariate Experimental Psychology and the New York Chapter of the American Statistical Association.

Kenneth J. Sher, PhD, is chancellor’s professor and curators’ distinguished professor of psychological sciences, emeritus, at the University of Missouri. He received his PhD in clinical psychology from Indiana University (1980) and his clinical internship training at Brown University (1981).

His primary areas of research focus on etiological processes in the development of alcohol dependence, factors that affect the course of drinking and alcohol use disorders throughout adulthood, longitudinal research methodology, psychiatric comorbidity, and nosology. At the University of Missouri he directed the predoctoral and postdoctoral training program in alcohol studies, and his research has been continually funded by the National Institute on Alcohol Abuse and Alcoholism for more than 35 years.

Dr. Sher’s research contributions have been recognized by professional societies including the Research Society on Alcoholism and APA, and throughout his career, he has been heavily involved in service to professional societies and scholarly publications.

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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.

Back to top  

• 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

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• Knowledge Base
• APA Style 7th edition
• How to write an APA methods section

How to Write an APA Methods Section | With Examples

Published on February 5, 2021 by Pritha Bhandari . Revised on June 22, 2023.

The methods section of an APA style paper is where you report in detail how you performed your study. Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods .

In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample , measures, and procedures used.

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Table of contents

Structuring an apa methods section.

Participants

Example of an APA methods section

Other interesting articles, frequently asked questions about writing an apa methods section.

The main heading of “Methods” should be centered, boldfaced, and capitalized. Subheadings within this section are left-aligned, boldfaced, and in title case. You can also add lower level headings within these subsections, as long as they follow APA heading styles .

To structure your methods section, you can use the subheadings of “Participants,” “Materials,” and “Procedures.” These headings are not mandatory—aim to organize your methods section using subheadings that make sense for your specific study.

Note that not all of these topics will necessarily be relevant for your study. For example, if you didn’t need to consider outlier removal or ways of assigning participants to different conditions, you don’t have to report these steps.

The APA also provides specific reporting guidelines for different types of research design. These tell you exactly what you need to report for longitudinal designs , replication studies, experimental designs , and so on. If your study uses a combination design, consult APA guidelines for mixed methods studies.

Detailed descriptions of procedures that don’t fit into your main text can be placed in supplemental materials (for example, the exact instructions and tasks given to participants, the full analytical strategy including software code, or additional figures and tables).

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Begin the methods section by reporting sample characteristics, sampling procedures, and the sample size.

Participant or subject characteristics

When discussing people who participate in research, descriptive terms like “participants,” “subjects” and “respondents” can be used. For non-human animal research, “subjects” is more appropriate.

Specify all relevant demographic characteristics of your participants. This may include their age, sex, ethnic or racial group, gender identity, education level, and socioeconomic status. Depending on your study topic, other characteristics like educational or immigration status or language preference may also be relevant.

Be sure to report these characteristics as precisely as possible. This helps the reader understand how far your results may be generalized to other people.

The APA guidelines emphasize writing about participants using bias-free language , so it’s necessary to use inclusive and appropriate terms.

Sampling procedures

Outline how the participants were selected and all inclusion and exclusion criteria applied. Appropriately identify the sampling procedure used. For example, you should only label a sample as random  if you had access to every member of the relevant population.

Of all the people invited to participate in your study, note the percentage that actually did (if you have this data). Additionally, report whether participants were self-selected, either by themselves or by their institutions (e.g., schools may submit student data for research purposes).

Identify any compensation (e.g., course credits or money) that was provided to participants, and mention any institutional review board approvals and ethical standards followed.

Sample size and power

Detail the sample size (per condition) and statistical power that you hoped to achieve, as well as any analyses you performed to determine these numbers.

It’s important to show that your study had enough statistical power to find effects if there were any to be found.

Additionally, state whether your final sample differed from the intended sample. Your interpretations of the study outcomes should be based only on your final sample rather than your intended sample.

Write up the tools and techniques that you used to measure relevant variables. Be as thorough as possible for a complete picture of your techniques.

Primary and secondary measures

Define the primary and secondary outcome measures that will help you answer your primary and secondary research questions.

Specify all instruments used in gathering these measurements and the construct that they measure. These instruments may include hardware, software, or tests, scales, and inventories.

• To cite hardware, indicate the model number and manufacturer.
• To cite common software (e.g., Qualtrics), state the full name along with the version number or the website URL .
• To cite tests, scales or inventories, reference its manual or the article it was published in. It’s also helpful to state the number of items and provide one or two example items.

Make sure to report the settings of (e.g., screen resolution) any specialized apparatus used.

For each instrument used, report measures of the following:

• Reliability : how consistently the method measures something, in terms of internal consistency or test-retest reliability.
• Validity : how precisely the method measures something, in terms of construct validity  or criterion validity .

Giving an example item or two for tests, questionnaires , and interviews is also helpful.

Describe any covariates—these are any additional variables that may explain or predict the outcomes.

Quality of measurements

Review all methods you used to assure the quality of your measurements.

These may include:

• training researchers to collect data reliably,
• using multiple people to assess (e.g., observe or code) the data,
• translation and back-translation of research materials,
• using pilot studies to test your materials on unrelated samples.

For data that’s subjectively coded (for example, classifying open-ended responses), report interrater reliability scores. This tells the reader how similarly each response was rated by multiple raters.

Report all of the procedures applied for administering the study, processing the data, and for planned data analyses.

Data collection methods and research design

Data collection methods refers to the general mode of the instruments: surveys, interviews, observations, focus groups, neuroimaging, cognitive tests, and so on. Summarize exactly how you collected the necessary data.

Describe all procedures you applied in administering surveys, tests, physical recordings, or imaging devices, with enough detail so that someone else can replicate your techniques. If your procedures are very complicated and require long descriptions (e.g., in neuroimaging studies), place these details in supplementary materials.

To report research design, note your overall framework for data collection and analysis. State whether you used an experimental, quasi-experimental, descriptive (observational), correlational, and/or longitudinal design. Also note whether a between-subjects or a within-subjects design was used.

For multi-group studies, report the following design and procedural details as well:

• how participants were assigned to different conditions (e.g., randomization),
• instructions given to the participants in each group,
• interventions for each group,
• the setting and length of each session(s).

Describe whether any masking was used to hide the condition assignment (e.g., placebo or medication condition) from participants or research administrators. Using masking in a multi-group study ensures internal validity by reducing research bias . Explain how this masking was applied and whether its effectiveness was assessed.

Participants were randomly assigned to a control or experimental condition. The survey was administered using Qualtrics (https://www.qualtrics.com). To begin, all participants were given the AAI and a demographics questionnaire to complete, followed by an unrelated filler task. In the control condition , participants completed a short general knowledge test immediately after the filler task. In the experimental condition, participants were asked to visualize themselves taking the test for 3 minutes before they actually did. For more details on the exact instructions and tasks given, see supplementary materials.

Data diagnostics

Outline all steps taken to scrutinize or process the data after collection.

This includes the following:

• Procedures for identifying and removing outliers
• Data transformations to normalize distributions
• Compensation strategies for overcoming missing values

To ensure high validity, you should provide enough detail for your reader to understand how and why you processed or transformed your raw data in these specific ways.

Analytic strategies

The methods section is also where you describe your statistical analysis procedures, but not their outcomes. Their outcomes are reported in the results section.

These procedures should be stated for all primary, secondary, and exploratory hypotheses. While primary and secondary hypotheses are based on a theoretical framework or past studies, exploratory hypotheses are guided by the data you’ve just collected.

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This annotated example reports methods for a descriptive correlational survey on the relationship between religiosity and trust in science in the US. Hover over each part for explanation of what is included.

The sample included 879 adults aged between 18 and 28. More than half of the participants were women (56%), and all participants had completed at least 12 years of education. Ethics approval was obtained from the university board before recruitment began. Participants were recruited online through Amazon Mechanical Turk (MTurk; www.mturk.com). We selected for a geographically diverse sample within the Midwest of the US through an initial screening survey. Participants were paid USD $5 upon completion of the study.

A sample size of at least 783 was deemed necessary for detecting a correlation coefficient of ±.1, with a power level of 80% and a significance level of .05, using a sample size calculator (www.sample-size.net/correlation-sample-size/).

The primary outcome measures were the levels of religiosity and trust in science. Religiosity refers to involvement and belief in religious traditions, while trust in science represents confidence in scientists and scientific research outcomes. The secondary outcome measures were gender and parental education levels of participants and whether these characteristics predicted religiosity levels.

Religiosity

Religiosity was measured using the Centrality of Religiosity scale (Huber, 2003). The Likert scale is made up of 15 questions with five subscales of ideology, experience, intellect, public practice, and private practice. An example item is “How often do you experience situations in which you have the feeling that God or something divine intervenes in your life?” Participants were asked to indicate frequency of occurrence by selecting a response ranging from 1 (very often) to 5 (never). The internal consistency of the instrument is .83 (Huber & Huber, 2012).

Trust in Science

Trust in science was assessed using the General Trust in Science index (McCright, Dentzman, Charters & Dietz, 2013). Four Likert scale items were assessed on a scale from 1 (completely distrust) to 5 (completely trust). An example question asks “How much do you distrust or trust scientists to create knowledge that is unbiased and accurate?” Internal consistency was .8.

Potential participants were invited to participate in the survey online using Qualtrics (www.qualtrics.com). The survey consisted of multiple choice questions regarding demographic characteristics, the Centrality of Religiosity scale, an unrelated filler anagram task, and finally the General Trust in Science index. The filler task was included to avoid priming or demand characteristics, and an attention check was embedded within the religiosity scale. For full instructions and details of tasks, see supplementary materials.

For this correlational study , we assessed our primary hypothesis of a relationship between religiosity and trust in science using Pearson moment correlation coefficient. The statistical significance of the correlation coefficient was assessed using a t test. To test our secondary hypothesis of parental education levels and gender as predictors of religiosity, multiple linear regression analysis was used.

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

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

In your APA methods section , you should report detailed information on the participants, materials, and procedures used.

• Describe all relevant participant or subject characteristics, the sampling procedures used and the sample size and power .
• Define all primary and secondary measures and discuss the quality of measurements.
• Specify the data collection methods, the research design and data analysis strategy, including any steps taken to transform the data and statistical analyses.

You should report methods using the past tense , even if you haven’t completed your study at the time of writing. That’s because the methods section is intended to describe completed actions or research.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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Psychological Research Methods: Types and Tips

Categories Research Methods

Psychological research methods are the techniques used by scientists and researchers to study human behavior and mental processes. These methods are used to gather empirical evidence.

The goal of psychological research methods is to obtain objective and verifiable data collected through scientific experimentation and observation. 

The research methods that are used in psychology are crucial for understanding how and why people behave the way they do, as well as for developing and testing theories about human behavior.

Table of Contents

Reasons to Learn More About Psychological Research Methods

One of the key goals of psychological research is to make sure that the data collected is reliable and valid.

• Reliability means that the data is consistent and can be replicated
• Validity refers to the accuracy of the data collected

Researchers must take great care to ensure that their research methods are reliable and valid, as this is essential for drawing accurate conclusions and making valid claims about human behavior.

High school and college students who are interested in psychology can benefit greatly from learning about research methods. Understanding how psychologists study human behavior and mental processes can help students develop critical thinking skills and a deeper appreciation for the complexity of human behavior.

Having an understanding of these research methods can prepare students for future coursework in psychology, as well as for potential careers in the field.

Quantitative vs. Qualitative Psychological Research Methods

Psychological research methods can be broadly divided into two main types: quantitative and qualitative. These two methods differ in their approach to data collection and analysis.

Quantitative Research Methods

Quantitative research methods involve collecting numerical data through controlled experiments, surveys, and other objective measures.

The goal of quantitative research is to identify patterns and relationships in the data that can be analyzed statistically.

Researchers use statistical methods to test hypotheses, identify significant differences between groups, and make predictions about future behavior.

Qualitative Research Methods

Qualitative research methods, on the other hand, involve collecting non-numerical data through open-ended interviews, observations, and other subjective measures.

Qualitative research aims to understand the subjective experiences and perspectives of individuals and groups.

Researchers use methods such as content analysis and thematic analysis to identify themes and patterns in the data and to develop rich descriptions of the phenomenon under study.

How Quantitative and Qualitative Methods Are Used

While quantitative and qualitative research methods differ in their approach to data collection and analysis, they are often used together to gain a more complete understanding of complex phenomena.

For example, a researcher studying the impact of social media on mental health might use a quantitative survey to gather numerical data on social media use and a qualitative interview to gain insight into participants’ subjective experiences with social media.

Types of Psychological Research Methods

There are several types of research methods used in psychology, including experiments, surveys, case studies, and observational studies. Each method has its strengths and weaknesses, and researchers must choose the most appropriate method based on their research question and the data they hope to collect.

Case Studies

A case study is a research method used in psychology to investigate an individual, group, or event in great detail. In a case study, the researcher gathers information from a variety of sources, including:

• Observation
• Document analysis

These methods allow researchers to gain an in-depth understanding of the case being studied.

Case studies are particularly useful when the phenomenon under investigation is rare or complex, and when it is difficult to replicate in a laboratory setting.

Surveys are a commonly used research method in psychology that involve gathering data from a large number of people about their thoughts, feelings, behaviors, and attitudes.

Surveys can be conducted in a variety of ways, including:

• In-person interviews
• Online questionnaires
• Paper-and-pencil surveys

Surveys are particularly useful when researchers want to study attitudes or behaviors that are difficult to observe directly or when they want to generalize their findings to a larger population.

Experimental Psychological Research Methods

Experimental studies are a research method commonly used in psychology to investigate cause-and-effect relationships between variables. In an experimental study, the researcher manipulates one or more variables to see how they affect another variable, while controlling for other factors that may influence the outcome.

Experimental studies are considered the gold standard for establishing cause-and-effect relationships, as they allow researchers to control for potential confounding variables and to manipulate variables in a systematic way.

Correlational Psychological Research Methods

Correlational research is a research method used in psychology to investigate the relationship between two or more variables without manipulating them. The goal of correlational research is to determine the extent to which changes in one variable are associated with changes in another variable.

In other words, correlational research aims to establish the direction and strength of the relationship between two or more variables.

Naturalistic Observation

Naturalistic observation is a research method used in psychology to study behavior in natural settings, without any interference or manipulation from the researcher.

The goal of naturalistic observation is to gain insight into how people or animals behave in their natural environment without the influence of laboratory conditions.

Meta-Analysis

A meta-analysis is a research method commonly used in psychology to combine and analyze the results of multiple studies on a particular topic.

The goal of a meta-analysis is to provide a comprehensive and quantitative summary of the existing research on a topic, in order to identify patterns and relationships that may not be apparent in individual studies.

Tips for Using Psychological Research Methods

Here are some tips for high school and college students who are interested in using psychological research methods:

Understand the different types of research methods: 

Before conducting any research, it is important to understand the different types of research methods that are available, such as surveys, case studies, experiments, and naturalistic observation.

Each method has its strengths and limitations, and selecting the appropriate method depends on the research question and variables being investigated.

Develop a clear research question: 

A good research question is essential for guiding the research process. It should be specific, clear, and relevant to the field of psychology. It is also important to consider ethical considerations when developing a research question.

Use proper sampling techniques: 

Sampling is the process of selecting participants for a study. It is important to use proper sampling techniques to ensure that the sample is representative of the population being studied.

Random sampling is considered the gold standard for sampling, but other techniques, such as convenience sampling, may also be used depending on the research question.

Use reliable and valid measures:

It is important to use reliable and valid measures to ensure the data collected is accurate and meaningful. This may involve using established measures or developing new measures and testing their reliability and validity.

Consider ethical issues:

It is important to consider ethical considerations when conducting psychological research, such as obtaining informed consent from participants, maintaining confidentiality, and minimizing any potential harm to participants.

In many cases, you will need to submit your study proposal to your school’s institutional review board for approval.

Analyze and interpret the data appropriately : 

After collecting the data, it is important to analyze and interpret the data appropriately. This may involve using statistical techniques to identify patterns and relationships between variables, and using appropriate software tools for analysis.

Communicate findings clearly: 

Finally, it is important to communicate the findings clearly in a way that is understandable to others. This may involve writing a research report, giving a presentation, or publishing a paper in a scholarly journal.

Clear communication is essential for advancing the field of psychology and informing future research.

Frequently Asked Questions

What are the 5 methods of psychological research.

The five main methods of psychological research are:

• Experimental research : This method involves manipulating one or more independent variables to observe their effect on one or more dependent variables while controlling for other variables. The goal is to establish cause-and-effect relationships between variables.
• Correlational research : This method involves examining the relationship between two or more variables, without manipulating them. The goal is to determine whether there is a relationship between the variables and the strength and direction of that relationship.
• Survey research : This method involves gathering information from a sample of participants using questionnaires or interviews. The goal is to collect data on attitudes, opinions, behaviors, or other variables of interest.
• Case study research : This method involves an in-depth analysis of a single individual, group, or event. The goal is to gain insight into specific behaviors, attitudes, or phenomena.
• Naturalistic observation research : This method involves observing and recording behavior in natural settings without any manipulation or interference from the researcher. The goal is to gain insight into how people or animals behave in their natural environment.

What is the most commonly used psychological research method?

The most common research method used in psychology varies depending on the research question and the variables being investigated. However, correlational research is one of the most frequently used methods in psychology.

This is likely because correlational research is useful in studying a wide range of psychological phenomena, and it can be used to examine the relationships between variables that cannot be manipulated or controlled, such as age, gender, and personality traits. 

Experimental research is also a widely used method in psychology, particularly in the areas of cognitive psychology , social psychology , and developmental psychology .

Other methods, such as survey research, case study research, and naturalistic observation, are also commonly used in psychology research, depending on the research question and the variables being studied.

How do you know which research method to use?

Deciding which type of research method to use depends on the research question, the variables being studied, and the practical considerations involved. Here are some general guidelines to help students decide which research method to use:

• Identify the research question : The first step is to clearly define the research question. What are you trying to study? What is the hypothesis you want to test? Answering these questions will help you determine which research method is best suited for your study.
• Choose your variables : Identify the independent and dependent variables involved in your research question. This will help you determine whether an experimental or correlational research method is most appropriate.
• Consider your resources : Think about the time, resources, and ethical considerations involved in conducting the research. For example, if you are working on a tight budget, a survey or correlational research method may be more feasible than an experimental study.
• Review existing literature : Conducting a literature review of previous studies on the topic can help you identify the most appropriate research method. This can also help you identify gaps in the literature that your study can fill.
• Consult with a mentor or advisor : If you are still unsure which research method to use, consult with a mentor or advisor who has experience in conducting research in your area of interest. They can provide guidance and help you make an informed decision.

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Palinkas LA. Qualitative and mixed methods in mental health services and implementation research . J Clin Child Adolesc Psychol . 2014;43(6):851-861. doi:10.1080/15374416.2014.910791

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011;11(1):100. doi:10.1186/1471-2288-11-100