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1669 results in Psychology research methods and statistics

Page 43 of 84

  • 9 - Multilevel or Hierarchical Modeling

  • from Part II - Parameter Estimation
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 203-238

  • Summary

    In Chapter 5, we considered how best to account for data from multiple participants in our modeling. We proposed two solutions: At one end of the spectrum, fitting a model to individual participants avoids the averaging artifacts that we discussed in Section 5.2. At the other end of the spectrum, fitting aggregate data runs the risk of introducing artifacts, but it takes advantage of the stability introduced by averaging data. There is, however, a third approach that we are now ready to discuss. This approach is known as hierarchical or multilevel modeling (the terms are typically used interchangeably), and like fitting of aggregate data, it takes into account the data from all participants simultaneously. However, unlike fits of individual participants, we do not consider the data from different participants independently; instead, hierarchical models postulate – and exploit – some degree of dependence between participants. This chapter first conceptualizes hierarchical models at a general level before presenting three examples of Bayesian hierarchical models of cognition in some detail. We then briefly touch on maximum-likelihood techniques for hierarchical models before concluding with some recommendations.

    Conceptualizing Hierarchical Modeling

    The key aspect of a hierarchical model is that although it recognizes individual variation, it also assumes that there is an orderly distribution governing this variation. This distribution across individuals is frequently known as the parent distribution. The parent distribution characterizes the distribution of the parameters that determine the priors for each individual. For that reason, the parent distribution is also sometimes known as a “hyperprior distribution” (Gelman et al., 2013) because it determines the priors for each individual. A hierarchical model therefore always embodies a theory of individual differences, however rudimentary.

    Each participant's performance is described by whatever model is being considered – in principle, any cognitive model can be instantiated as a hierarchical model. When the model is fitted to data, the individual parameters are estimated for each subject together with the parameters of the parent distribution. The latter are sometimes called hyperhyperparameters (Gelman, 2006), although we find it simpler and more intuitive to refer to them as “parameters of the parent distribution.”

    The hierarchical approach necessarily creates a tension between fitting each participant as well as possible (by estimating optimal individual parameters) and fitting the group of participants as a whole (by seeking to minimize the variance of the parent distribution).

  • 5 - Combining Information from Multiple Participants

  • from Part II - Parameter Estimation
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 105-125

  • Summary

    Whether we use maximum likelihood estimation (Chapter 4) or some other method (Chapter 3) for fitting models to data, one issue is how to fit data from multiple units. Those units are typically individuals, but we can also observe higher-level clustering of individuals (e.g., students in different schools). One issue we already gave some discussion to in Chapter 4 is how to model the data from individual participants. We know from the previous chapter that we can obtain a joint log-likelihood for multiple participants by multiplying the individual likelihoods (or, equivalently, adding log-likelihoods), but this leaves open multiple ways of fitting the data from multiple individuals. This chapter addresses different ways of fitting multiple participants. We first highlight how the manner of obtaining an “ average” fit can affect the conclusions we draw from data, and then consider different ways in which multiple participants can be modelled. Later in the chapter we discuss ways of identifying and fitting clusters of participants, and discuss how individual differences can be accounted for in computational models.

    It Matters How You Combine Data from Multiple Units

    Suppose you are an affirmative-action officer at a major university and you learn that of the nearly 13,000 applicants to your institution's graduate programs, 8,442 were males and 4,321 were females. Suppose furthermore that 44% of the males but only 35% of the females were admitted. Red alert! Inscapes; this clear evidence of a gender bias in admissions? Your suspicions are confirmed when you conduct a statistical test on these data to detect whether there is a relationship between gender and admission rates and find χ2 (1) = 110.8, with a p-value that's nearly indistinguishable from zero. It seems obvious that the next action is to identify the culprit or culprits – that is, the departments that discriminate against women – so that corrective action can be taken. We did not make up these numbers; they represent the real admissions data of the University of California at Berkeley in 1973 (Bickel et al., 1975). And as you might expect, those data (quite justifiably) caused much concern and consternation, and the university embarked on an examination of the admission records of individual departments. A snapshot of the outcome of this further examination, taken from Freedman etal.

  • 14 - Models of Choice Response Time

  • from Part IV - Models in Psychology
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 369-394

  • Summary

    Making speeded choices is one of the simplest yet most prevalent human cognitive activities. We decide whether a traffic light is green or red. We decide whether the mouse is closer to the cheese than the cat is to the mouse, or whether our plate contains more apples than oranges, or vice versa, in a seemingly effortless and rapid manner. We first explored how those decisions might be modeled with the simple random-walk model in Chapter 2. At the time, we already noted the importance of the modeler's decisions during the construction of a model, and we placed the random-walk idea into a broader context of sequential-sampling models – that is, models that stipulate that the cognitive system is sampling information from the displayed stimulus over time in order to reach a decision.

    We resume our exploration of response-time models by revisiting the taxonomy of models proposed by Ratcliff et al. (2016), shown again in Figure 14.1. At the time of this writing, the random-walk model from Chapter 2 is primarily of historical and pedagogical interest. Contemporary research interest is instead focused on the remaining models, including in particular the diffusion model of Ratcliff and colleagues and the linear ballistic accumulator (LBA) of Brown, Heathcote, and colleagues. We focus on those two particularly popular models.

    We first introduce the diffusion model and show how it can be applied to data using maximum likelihood estimation. We then explore how the power of this model derives from its ability to describe performance in a choice task across the main response measures, namely speed and accuracy, thereby resolving a long-standing dilemma concerning the relationship between accuracy and speed. We show that the model is nonetheless in principle falsifiable. We then turn to the LBA and show how it can provide an alternative account of the data.

    Ratcliff's Diffusion Model

    The first variant of the diffusion model in psychology was proposed nearly four decades ago (Ratcliff, 1978). Although it has evolved considerably since then (for a brief history, see Ratcliff et al., 2016), the fundamental premise of the model has proven resilient across that time.

  • 8 - Bayesian Parameter Estimation

  • from Part II - Parameter Estimation
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 172-202

  • Summary

    The goal of this chapter is to instantiate the theory from the previous chapter in some real-world models using the JAGS programming language (Plummer, 2003). The acronym JAGS stands for “Just Another Gibbs Sampler,” and JAGS is one of several modern computer packages that rely on a particular form of MCMC known as Gibbs sampling. JAGS is one of several existing packages that perfom MCMC for Bayesian modeling. Other packages include “WinBUGS” (Spiegelhalter et al., 2003) and “Stan” (Carpenter et al., 2016), which each have their own strengths and weaknesses. We chose JAGS because of the flexibility it offers for extension (Wabersich and Vandekerckhove, 2014) and because it is easily used from within R. Readers who want to build on the limited number of examples provided in this chapter may wish to consult the excellent book by Lee and Wagenmakers (2013), which contains numerous additional examples.

    Gibbs Sampling

    JAGS relies on a particular type of MCMC known as Gibbs sampling. Although it is named after the late 19th -century American physicist JosiahWillard Gibbs, the sampler was only invented 8 decades after his death and named in his honor because of the similarities between the sampler and Gibbs’ contributions to statistical theory (Geman and Geman, 1984). Gibbs sampling has much in common with the Metropolis-Hastings approach introduced in the previous chapter. Both algorithms involve a Markov Chain of samples and both converge on the target distribution when given a sufficiently large number of samples. There are however also some important differences. The key property of the Gibbs sampler is that it samples from conditional distributions, which are often known even in situations in which the joint density is not available for integration – as is required for computation of the marginal likelihood (or “evidence”) in the denominator of Equation 6.8. Specifically, whereas sampling from the joint posterior for all the parameters may be unachievable in many situations, we can often easily sample from the posterior for one parameter given knowledge of the other parameter values. By iterating through the parameters, sampling each conditional upon the others being constant, the Gibbs sampler manages to provide us with posterior distributions for each parameter.

  • 12 - Using Models in Psychology

  • from Part IV - Models in Psychology
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 311-333

  • Summary

    Many of the previous chapters have been necessarily technical in nature, describing specific techniques for estimating parameters and comparing models on their account of empirical data. Having estimated parameters of a model, or identified one model as being more likely than another, what have we learned about how people think and behave?

    The last part of this book is dedicated to describing how models are typically used in psychology. This chapter aims to give some broad coverage of the use of models in psychology. We discuss the inferences psychologists can (and do) draw from modeling, and how we use models as tools to aid understanding. We finish the chapter with a discussion of reproducibility, and how our modeling tools can be shared so as to help others understand our models and how they apply to our data.

    Broad Overview of the Steps in Modeling

    To begin our discussion, it will help to step back and consider how the preceding chapters all fit together. Figure 12.1 shows a flowchart representing the relationships between models and data that were implicitly or explicitly covered in the preceding chapters. The boxes on the left represent psychological science as it is often conducted, without the aid of modeling. People generate data in experiments, and the data that people produce depend on the exact situation that we put them in (i.e., the experimental method). The data are only informative to the extent that they can be related to some theory. What Figure 12.1 does not show are the (usually vague) verbal theories that researchers hold when conducting experiments, and the predictions that might be loosely associated with those vague verbal theories.

    The boxes on the right of the figure show the mo del pathway. In Chapter 2 we talked about how our theoretical ideas can be turned into a computational model (for a similar demonstration for the domain of verbal working memory, see Lewandowsky and Farrell, 2011). Note that we distinguish between what a researcher might consider “core” assumptions, and more auxiliary assumptions that are not an essential part of the model but which need to be made in order to derive quantitative predictions from the model.

  • 7 - Bayesian Parameter Estimation

  • from Part II - Parameter Estimation
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 146-171

  • Summary

    The goal of this chapter is to introduce the methods that can be applied in cases when the posterior distribution is not analytically tractable. Consider a Beta distribution with parameters α and β. We know from Equation 6.13 that its mean is α/ (α +β). But what if we did not know that equation? We could still obtain the mean of a Beta distribution by drawing a huge sample from that distribution in R and computing its mean using the single command mean(rbeta(n,alpha,beta)), where n would be a suitably large number. As long as we can sample from a distribution, given a sufficiently large sample we can begin to understand many attributes of that distribution even if we do not have an explicit formula for it. This general sampling approach, where an unknown distribution is approximated by a large sample, underlies all Monte Carlo methods of Bayesian inference, although there is considerable variation between methods in assumptions about what we do and do not know about the underlying distributions. Table 7.1 summarizes and compares the main approaches to Bayesian inference and parameter estimation that are presented in this chapter. The figure clarifies that the methods from the previous chapter can only be applied with complete knowledge of all distributions involved. When that knowledge is limited, we can resort to the sampling methods outlined in this chapter.

    Markov Chain Monte Carlo Methods

    The basic idea is simple: we want to replace the unknown posterior distribution with a large sample whose properties mirror those of the posterior, and that we can conveniently interpret to answer questions about the model parameters. How do we obtain this sample if the posterior is not analytically tractable? The answer is embedded in Bayes’ rule provided by Equations 6.9 and 6.20 in the previous chapter. While we may be unable to write down an equation for the posterior and solve it for the parameters, θ, we still have access to the right-hand side of the equation. In particular, we necessarily have knowledge of the prior distribution of parameters because we commence our inference by formulating those assumptions. We also typically have knowledge of the likelihood of our observed data given the parameters (though Section 7.3 will relax even that).

Computational Modeling of Cognition and Behavior
  • Simon Farrell, Stephan Lewandowsky
  • Published online:
    05 February 2018
    Print publication:
    22 February 2018
  • Computational modeling is now ubiquitous in psychology, and researchers who are not modelers may find it increasingly difficult to follow the theoretical developments in their field. This book presents an integrated framework for the development and application of models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models. Both the development of models and key features of any model are covered, as are the applications of models in a variety of domains across the behavioural sciences. A number of chapters are devoted to fitting models using maximum likelihood and Bayesian estimation, including fitting hierarchical and mixture models. Model comparison is described as a core philosophy of scientific inference, and the use of models to understand theories and advance scientific discourse is explained.

  • Chapter 10 - Quasi-Experimental Designs

  • Wendy Heath, Rider University, New Jersey
  • Book:
    Psychology Research Methods
    Published online:
    01 July 2021
    Print publication:
    11 January 2018, pp 241-262

  • Summary

    Would you be more likely to lie for a friend than a stranger? That was one of the questions asked recently by Marion and Burke (2015). They had pairs of friends and pairs of strangers come to their lab to work on a series of tasks. Shortly after their arrival, the two participants were separated and one was recruited to be a confederate (the other remained naïve to the true purpose of the study). Then the two participants came together to work on a task. The researcher left them alone while they were working, saying she was briefly leaving the building. Before completing the collaborative task, the confederate briefly left the testing room “to use the restroom.” The confederate returned and the two participants finished the task.

    The researcher returned shortly thereafter and said some money had been taken from an adjoining room. She asked the participants whether they had seen or heard anyone enter that room. As pre-arranged, the confederate claimed they were both in the testing room with the door closed during the entire session. This, of course, was a lie. The confederate had left the room. Each naïve participant was later questioned separately about whether they had both been in the room the whole time. The question of interest was: Would naïve participants be equally likely to lie for a friend as for a stranger? Would the naïve participant say the other participant had never left the room?

    Marion and Burke found that those who were friends with the confederate were significantly more likely to lie (41%) than those who were not (18%). They cannot conclude that the reason for this result was solely friendship however. As Marion and Burke acknowledge, what is missing from their design is random assignment to groups. They chose to work with students who were already friends and with students who had never met. They did not randomly assign people to groups and say, “Forget whether you know each other or not; for the purpose of this research, you will be friends” or “you will be strangers.” The independent variable of relationship (friends, strangers) was out of the researchers’ control. Thus, Marion and Burke needed to use a quasi-experimental design. Since the groups are not considered equivalent, something inherently different about the groups (beyond the independent variable) could be responsible for the results.

  • Chapter 12 - External Validity

  • Wendy Heath, Rider University, New Jersey
  • Book:
    Psychology Research Methods
    Published online:
    01 July 2021
    Print publication:
    11 January 2018, pp 283-304

  • Summary

    I'd like to start this chapter by describing an experiment designed to investigate whether drinking alcohol affects self-disclosure. To do this Sayette (1994) brought male social drinkers to his lab and, given their informed consent, gave them either enough alcohol to be legally drunk or just a placebo (those who drank the placebo thought they were drinking alcohol). Then he asked each participant to speak for 3 minutes on the following topic: “What I like and dislike about my body and physical appearance” (p. 129). After that, undergraduates working for the experimenter listened to audiotapes of these speeches. They counted the number of negative, positive, and neutral items in each speech as well as the amount of time each speaker spent presenting each type of information.

    What did Sayette find? The participants who were intoxicated disclosed fewer negative pieces of information about themselves than those who were sober. The intoxicated participants also spent marginally less time disclosing negative information than those who were sober. On the other hand, there was no difference in the amount of positive information participants in the two groups disclosed about themselves.

    So what does this mean? Does Sayette's research suggest that a drunk person you meet in a bar is likely to disclose less negative information than a sober person? In other words, “Do people behave in real life as they behave in our experimental laboratories?” (Bem & Lord, 1979, p. 833). This question is an example of those we ask when we are concerned with external validity, and that's the topic of this chapter.

    What Is External Validity?

    When we want to assess external validity, we ask, “Will the results generalize?” More specifically, as noted in Chapter 1, external validity is the extent to which results of a study (experimental or non-experimental research) can be generalized to other people, places, and times. Considering the issue of external validity is often but not always important. It depends on what your goals are for your research (Mook, 1983).

  • Chapter 7 - Survey Research

  • Wendy Heath, Rider University, New Jersey
  • Book:
    Psychology Research Methods
    Published online:
    01 July 2021
    Print publication:
    11 January 2018, pp 157-182

  • Summary

    It may not surprise you to learn that researchers have recently reported that a substantial percentage of college students do not sleep well. How do we know this? Lund, Reider, Whiting, and Prichard (2010) conducted a survey; they asked over a thousand students at a midwestern university questions about their sleep habits.

    When you conduct a survey, you are attempting to estimate the opinions, characteristics, and/ or behaviors of a particular population by asking a sample to respond to questions. A population (sometimes called a target population or a population of interest) is a defined group of individuals, and it can be very large (such as all American adults) or it can be more specific (such as all female Democrats in the Princeton, NJ area). A sample is a subset of individuals chosen from a population.

    Many of us have been exposed to surveys. For example, during political campaigns, you may have been asked for your views on the candidates. According to one Gallup poll, shortly before the 2008 election, almost 40% of 18-to 29-year-olds had been contacted by either the Obama or the McCain campaign (Newport, Jones, & Saad, 2008).

    The same Gallup poll asked respondents whether they were registered to vote, whether they were giving a lot of thought to the election, and what was their chance of voting. These measures were used as voter turn-out indicators. With these numbers in hand, researchers were able to predict the likely turn-out of the youngest US voters on election day.

    To survey a population, you need to know what kinds of questions to ask, how to write effective ones, and how to obtain and contact your participant sample. These topics will each be covered in this chapter.

    Survey Questions

    In this section we will discuss different types of question content (demographic versus topical) as well as different types of question formats (openversus closed-ended questions).

  • Chapter 11 - Small-N Designs

  • Wendy Heath, Rider University, New Jersey
  • Book:
    Psychology Research Methods
    Published online:
    01 July 2021
    Print publication:
    11 January 2018, pp 263-282

  • Summary

    Do you ever have trouble getting to class on time or even at all? Some students certainly do, and, perhaps not surprisingly, they may find this difficulty affects their grade (Friedman, Rodriguez, & McComb, 2001). The reason I bring this up is that a group of researchers, Bicard, Lott, Mills, Bicard, and Baylot-Casey (2012), set out to help four college athletes attend class and get there on time. These particular students were in danger of failing out of school, so there was a real need to curb their problem behavior.

    What did Bicard et al. do to help? Before they could try to fix the problem, they had to find out just how bad it was. They needed to know how often these students were missing class and how often they were late. As we'll discuss below, this is called establishing the baseline. Once the baseline has been established, then it is time to provide an intervention to attempt to change the behavior. Bicard et al. instructed the students to text their academic counselor right before they entered the classroom each day. This requirement improved attendance and punctuality for all the students. In fact, as a test of how well it worked, Bicard et al. stopped requiring the texts at the start of a new semester, and the students tended to revert back to their old ways, missing class or arriving late. Once the texting requirement was put back into place, they once again showed improved behavior.

    What I have just described was the use of a small-N design. In this chapter we explore what small-N designs are, their history, and how they work.

    What Are Small- N Designs?

    Small-N designs, also referred to as single-participant or single-subject designs, are designed to aid participants in changing behavior. We call them small-N designs because they use small sample sizes (N refers to the size of your sample); only one participant or just a few participants are tested. We'll talk about how this works in a moment, but first note that in almost all the research we've discussed thus far in this textbook (except the case study technique), we've compared the performances of groups to each other.

  • Chapter 9 - Factorial Designs: Experiments with More than One Independent Variable

  • Wendy Heath, Rider University, New Jersey
  • Book:
    Psychology Research Methods
    Published online:
    01 July 2021
    Print publication:
    11 January 2018, pp 211-240

  • Summary

    Have you ever been bumped from an overbooked flight? Typically, when a flight is overbooked, the airline will offer passengers a monetary incentive and a ticket for a later flight. But what happens when no one volunteers to take the airline up on the offer? United Airlines was recently in the news because of the way their employees handled this circumstance. When no one accepted an offer of a $1,000 voucher and a ticket for a later flight, United Airline representatives selected passengers to remove from the flight (four United employees needed the seats). One of the chosen passengers refused to leave the plane, and thus he was forcibly dragged off, while videotaped by fellow passengers (Victor & Stevens, 2017).

    Overbooking a flight is a common phenomenon and you can be removed from a flight without your approval; however airlines set their own policies regarding who gets bumped (for example, some airlines use the order in which the passengers checked in as a guide for who to bump). United Airlines was heavily criticized for the particular way they handled the removal of this passenger. People were upset not only about the physical manner in which this passenger was removed, but also that United chose to bump passengers after they boarded the plane, an unusual occurrence (Horowitz & Ostrower, 2017).

    After videos of this event went viral, some competing airlines put forth ads reminding potential passengers of what happened on this United Airlines flight while promoting their own cause. For example, Emirates Airlines tweeted “Fly the Friendly Skies … THIS TIME FOR REAL.” Royal Jordanian Airlines tweeted “We would like to remind you that drags on our flights are strictly prohibited by passengers and crew” (“drags” here refer to both the Airline's non-smoking policy and what had occurred on that United flight). Spirit Airlines took a different approach with an ad that emphasized how gentle they are with your bags, and then proclaiming, “Here at Spirit, we treat your bags as well as we treat you” (Shropshire & Channick, 2017).

  • Preface

  • Wendy Heath, Rider University, New Jersey
  • Book:
    Psychology Research Methods
    Published online:
    01 July 2021
    Print publication:
    11 January 2018, pp xvi-xviii

  • Summary

    Why I Wrote This Textbook

    Every year one of my social psychology lectures holds my students’ attention more than any other. My students are rapt, at times laughing, at times seemingly astounded at the story I am telling. What is this lecture about? The topic is Clark and Hatfield's (1989) article on receptivity to sexual offers. In this experiment, Clark and Hatfield had research assistants approach students of the opposite sex on campus and randomly ask one of three questions: “Would you go out tonight?” “Will you come over to my apartment?” or “Would you go to bed with me?” Clark and Hatfield found that a majority of men but not a single woman agreed to have a “sexual liaison” with the person who approached them (p. 39). I will admit that my students do not act in quite the same way for all my lectures. Why is this lecture different? Developing a relationship is something that virtually all have experienced or want to experience. This research is relevant to the students’ lives, and it is exciting. I found myself wondering: Why not make all of this early research exposure relevant and exciting?

    Thus, this textbook uses research relevant to students’ lives to illustrate research methodology. Every major research approach in psychology is covered. Students will learn how to evaluate and use different varieties of descriptive research and experimental research methods. They will learn all steps of the research process from coming up with a research idea to writing about and presenting what they did. Through it all, the one constant is that information is presented within the confines of the familiar: the students’ own lives.

    This textbook is likely to be of interest to instructors who teach undergraduate research methodology courses that are not discipline-specific. The students and instructor potentially will have a range of discipline interests, but all of the students likely have the “student life” interest in common. This textbook could also be of interest to instructors in research methods courses that focus on social psychology, for many of the research examples come from the social psychology discipline.

  • Chapter 14 - Writing About and Presenting Your Research

  • Wendy Heath, Rider University, New Jersey
  • Book:
    Psychology Research Methods
    Published online:
    01 July 2021
    Print publication:
    11 January 2018, pp 337-369

  • Summary

    If a tree falls in the forest and no one is around to hear it, does it make a sound? I never liked that question, but I do like an analogous one: If you do research and you never tell anyone about it, have you contributed to science? I know the answer to that one! The answer is no. We have a responsibility to disseminate our research, to tell others what we have done and what we have found, so that together we can move toward finding answers to our questions.

    There are a few different ways to disseminate information about our research. We can present a paper or a poster at a conference, and we can publish our work. We'll cover all these approaches in this chapter.

    The sixth edition of the Publication Manual of the American Psychological Association (APA, 2010) governs the way we present our work (see Figure 14.1), and so we will refer to the relevant sections of the APA Publication Manual throughout this chapter. If you have questions regarding APA style that are not addressed here, you can consult the current Publication Manual or the APA style blog (blog.apastyle. org). Tutorials showing the basics of APA style are also available at the APA website (www.apa.org).

    We'll start by looking at how a psychology research paper is organized.

    The Organization of a Psychology Research Paper

    A typical psychology research paper will contain the following sections:

  • 1. Title page

  • 2. Abstract

  • 3. Introduction

  • 4. Method

  • 5. Results

  • 6. Discussion

  • 7. References

  • 8. Tables and figures (these are optional).

    • As we progress through this chapter, I will cover the kind of information contained in each section. Later, we will talk about the specific formatting of each section, and you'll see examples of the different sections of a research paper, which will be annotated so you can see exactly where and how different information is represented. At this stage, while you are writing the paper, it's called a manuscript. Writing in APA style is a learned skill that takes practice. Reading through this chapter and referring to the sample manuscript in Appendix A will help you write your own manuscript.

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