research that attempts to generate a new theory

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

4.1 Phenomena and Theories

Learning objectives.

Define the terms phenomenon and theory and distinguish clearly between them.
Explain the purposes of scientific theories.
Explain why there are usually many plausible theories for any set of phenomena.

A phenomenon (plural, phenomena ) is a general result that has been observed reliably in systematic empirical research. In essence, it is an established answer to a research question. Some phenomena we have encountered in this book are that expressive writing improves health, women do not talk more than men, and cell phone usage impairs driving ability. Some others are that dissociative identity disorder (formerly called multiple personality disorder) increased greatly in prevalence during the late 20th century, people perform better on easy tasks when they are being watched by others (and worse on difficult tasks), and people recall items presented at the beginning and end of a list better than items presented in the middle.

Some Famous Psychological Phenomena

Phenomena are often given names by their discoverers or other researchers, and these names can catch on and become widely known. The following list is a small sample of famous phenomena in psychology.

Blindsight. People with damage to their visual cortex are often able to respond to visual stimuli that they do not consciously see.
Bystander effect. The more people who are present at an emergency situation, the less likely it is that any one of them will help.
Fundamental attribution error. People tend to explain others’ behavior in terms of their personal characteristics as opposed to the situation they are in.
McGurk effect. When audio of a basic speech sound is combined with video of a person making mouth movements for a different speech sound, people often perceive a sound that is intermediate between the two. For a demonstration, see http://www.faculty.ucr.edu/~rosenblu/VSMcGurk.html .
Own-race effect. People recognize faces of people of their own race more accurately than faces of people of other races.
Placebo effect. Placebos (fake psychological or medical treatments) often lead to improvements in people’s symptoms and functioning.
Mere exposure effect. The more often people have been exposed to a stimulus, the more they like it—even when the stimulus is presented subliminally.
Serial position effect. Stimuli presented near the beginning and end of a list are remembered better than stimuli presented in the middle. For a demonstration, see http://cat.xula.edu/thinker/memory/working/serial .
Spontaneous recovery. A conditioned response that has been extinguished often returns with no further training after the passage of time.

Although an empirical result might be referred to as a phenomenon after being observed only once, this term is more likely to be used for results that have been replicated. Replication means conducting a study again—either exactly as it was originally conducted or with modifications—to be sure that it produces the same results. Individual researchers usually replicate their own studies before publishing them. Many empirical research reports include an initial study and then one or more follow-up studies that replicate the initial study with minor modifications. Particularly interesting results come to the attention of other researchers who conduct their own replications. The positive effect of expressive writing on health and the negative effect of cell phone usage on driving ability are examples of phenomena that have been replicated many times by many different researchers.

Sometimes a replication of a study produces results that differ from the results of the initial study. This could mean that the results of the initial study or the results of the replication were a fluke—they occurred by chance and do not reflect something that is generally true. In either case, additional replications would be likely to resolve this. A failure to produce the same results could also mean that the replication differed in some important way from the initial study. For example, early studies showed that people performed a variety of tasks better and faster when they were watched by others than when they were alone. Some later replications, however, showed that people performed worse when they were watched by others. Eventually researcher Robert Zajonc identified a key difference between the two types of studies. People seemed to perform better when being watched on highly practiced tasks but worse when being watched on relatively unpracticed tasks (Zajonc, 1965). These two phenomena have now come to be called social facilitation and social inhibition.

What Is a Theory?

A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

In addition to theory , researchers in psychology use several related terms to refer to their explanations and interpretations of phenomena. A perspective is a broad approach—more general than a theory—to explaining and interpreting phenomena. For example, researchers who take a biological perspective tend to explain phenomena in terms of genetics or nervous and endocrine system structures and processes, while researchers who take a behavioral perspective tend to explain phenomena in terms of reinforcement, punishment, and other external events. A model is a precise explanation or interpretation of a specific phenomenon—often expressed in terms of equations, computer programs, or biological structures and processes. A hypothesis can be an explanation that relies on just a few key concepts—although this term more commonly refers to a prediction about a new phenomenon based on a theory (see Section 4.3 “Using Theories in Psychological Research” ). Adding to the confusion is the fact that researchers often use these terms interchangeably. It would not be considered wrong to refer to the drive theory as the drive model or even the drive hypothesis. And the biopsychosocial model of health psychology—the general idea that health is determined by an interaction of biological, psychological, and social factors—is really more like a perspective as defined here. Keep in mind, however, that the most important distinction remains that between observations and interpretations.

What Are Theories For?

Of course, scientific theories are meant to provide accurate explanations or interpretations of phenomena. But there must be more to it than this. Consider that a theory can be accurate without being very useful. To say that expressive writing helps people “deal with their emotions” might be accurate as far as it goes, but it seems too vague to be of much use. Consider also that a theory can be useful without being entirely accurate. Figure 4.2 “Representation of the Multistore Model of Human Memory” is a representation of the classic multistore model of human memory, which is still cited by researchers and discussed in textbooks despite the fact that it is now known to be inaccurate in a number of ways (Izawa, 1999). These two examples suggest that theories have purposes other than simply providing accurate explanations or interpretations. Here we look at three additional purposes of theories: the organization of known phenomena, the prediction of outcomes in new situations, and the generation of new research.

Figure 4.2 Representation of the Multistore Model of Human Memory

In the multistore model of human memory, information from the environment passes through a sensory store on its way to a short-term store, where it can be rehearsed, and then to a long-term store, where it can be stored and retrieved much later. This theory has been extremely successful at organizing old phenomena and predicting new ones.

Organization

One important purpose of scientific theories is to organize phenomena in ways that help people think about them clearly and efficiently. The drive theory of social facilitation and social inhibition, for example, helps to organize and make sense of a large number of seemingly contradictory results. The multistore model of human memory efficiently summarizes many important phenomena: the limited capacity and short retention time of information that is attended to but not rehearsed, the importance of rehearsing information for long-term retention, the serial-position effect, and so on. Or consider a classic theory of intelligence represented by Figure 4.3 “Representation of One Theory of Intelligence” . According to this theory, intelligence consists of a general mental ability, g , plus a small number of more specific abilities that are influenced by g (Neisset et al., 1996). Although there are other theories of intelligence, this one does a good job of summarizing a large number of statistical relationships between tests of various mental abilities. This includes the fact that tests of all basic mental abilities tend to be somewhat positively correlated and the fact that certain subsets of mental abilities (e.g., reading comprehension and analogy completion) are more positively correlated than others (e.g., reading comprehension and arithmetic).

Figure 4.3 Representation of One Theory of Intelligence

In this theory of intelligence, a general mental ability ( g ) influences each of three more specific mental abilities. Theories of this type help to organize a large number of statistical relationships among tests of various mental abilities.

Thus theories are good or useful to the extent that they organize more phenomena with greater clarity and efficiency. Scientists generally follow the principle of parsimony , which holds that a theory should include only as many concepts as are necessary to explain or interpret the phenomena of interest. Simpler, more parsimonious theories organize phenomena more efficiently than more complex, less parsimonious theories.

A second purpose of theories is to allow researchers and others to make predictions about what will happen in new situations. For example, a gymnastics coach might wonder whether a student’s performance is likely to be better or worse during a competition than when practicing alone. Even if this particular question has never been studied empirically, Zajonc’s drive theory suggests an answer. If the student generally performs with no mistakes, she is likely to perform better during competition. If she generally performs with many mistakes, she is likely to perform worse.

In clinical psychology, treatment decisions are often guided by theories. Consider, for example, dissociative identity disorder (formerly called multiple personality disorder). The prevailing scientific theory of dissociative identity disorder is that people develop multiple personalities (also called alters) because they are familiar with this idea from popular portrayals (e.g., the movie Sybil ) and because they are unintentionally encouraged to do so by their clinicians (e.g., by asking to “meet” an alter). This theory implies that rather than encouraging patients to act out multiple personalities, treatment should involve discouraging them from doing this (Lilienfeld & Lynn, 2003).

Generation of New Research

A third purpose of theories is to generate new research by raising new questions. Consider, for example, the theory that people engage in self-injurious behavior such as cutting because it reduces negative emotions such as sadness, anxiety, and anger. This theory immediately suggests several new and interesting questions. Is there, in fact, a statistical relationship between cutting and the amount of negative emotions experienced? Is it causal? If so, what is it about cutting that has this effect? Is it the pain, the sight of the injury, or something else? Does cutting affect all negative emotions equally?

Notice that a theory does not have to be accurate to serve this purpose. Even an inaccurate theory can generate new and interesting research questions. Of course, if the theory is inaccurate, the answers to the new questions will tend to be inconsistent with the theory. This will lead researchers to reevaluate the theory and either revise it or abandon it for a new one. And this is how scientific theories become more detailed and accurate over time.

Multiple Theories

At any point in time, researchers are usually considering multiple theories for any set of phenomena. One reason is that because human behavior is extremely complex, it is always possible to look at it from different perspectives. For example, a biological theory of sexual orientation might focus on the role of sex hormones during critical periods of brain development, while a sociocultural theory might focus on cultural factors that influence how underlying biological tendencies are expressed. A second reason is that—even from the same perspective—there are usually different ways to “go beyond” the phenomena of interest. For example, in addition to the drive theory of social facilitation and social inhibition, there is another theory that explains them in terms of a construct called “evaluation apprehension”—anxiety about being evaluated by the audience. Both theories go beyond the phenomena to be interpreted, but they do so by proposing somewhat different underlying processes.

Different theories of the same set of phenomena can be complementary—with each one supplying one piece of a larger puzzle. A biological theory of sexual orientation and a sociocultural theory of sexual orientation might accurately describe different aspects of the same complex phenomenon. Similarly, social facilitation could be the result of both general physiological arousal and evaluation apprehension. But different theories of the same phenomena can also be competing in the sense that if one is accurate, the other is probably not. For example, an alternative theory of dissociative identity disorder—the posttraumatic theory—holds that alters are created unconsciously by the patient as a means of coping with sexual abuse or some other traumatic experience. Because the sociocognitive theory and the posttraumatic theories attribute dissociative identity disorder to fundamentally different processes, it seems unlikely that both can be accurate. See Note 4.10 “Where Do Multiple Personalities Come From?” for more on these competing theories.

The fact that there are multiple theories for any set of phenomena does not mean that any theory is as good as any other or that it is impossible to know whether a theory provides an accurate explanation or interpretation. On the contrary, scientists are continually comparing theories in terms of their ability to organize phenomena, predict outcomes in new situations, and generate research. Those that fare poorly are assumed to be less accurate and are abandoned, while those that fare well are assumed to be more accurate and are retained and compared with newer—and hopefully better—theories. Although scientists generally do not believe that their theories ever provide perfectly accurate descriptions of the world, they do assume that this process produces theories that come closer and closer to that ideal.

Where Do Multiple Personalities Come From?

The literature on dissociative identity disorder (DID) features two competing theories. The sociocognitive theory is that DID comes about because patients are aware of the disorder, know its characteristic features, and are encouraged to take on multiple personalities by their therapists. The posttraumatic theory is that multiple personalities develop as a way of coping with sexual abuse or some other trauma. There are now several lines of evidence that support the sociocognitive model over the posttraumatic model (Lilienfeld & Lynn, 2003).

Diagnosis of DID greatly increased after the release of the book and film Sybil —about a woman with DID—in the 1970s.
DID is extremely rare outside of North America.
A very small percentage of therapists are responsible for diagnosing the vast majority of cases of DID.
The literature on treating DID includes many practices that encourage patients to act out multiple personalities (e.g., having a bulletin board on which personalities can leave messages for each other).
Normal people can easily re-create the symptoms of DID with minimal suggestion in simulated clinical interviews.

Key Takeaways

Scientists distinguish between phenomena, which are their systematic observations, and theories, which are their explanations or interpretations of phenomena.
In addition to providing accurate explanations or interpretations, scientific theories have three basic purposes. They organize phenomena, allow people to predict what will happen in new situations, and help generate new research.
Researchers generally consider multiple theories for any set of phenomena. Different theories of the same set of phenomena can be complementary or competing.
Practice: Think of at least three different theories to explain the fact that married people tend to report greater levels of happiness than unmarried people.

Practice: Find a recent article in a professional journal and do two things:

Identify the primary phenomenon of interest.
Identify the theory or theories used to explain or interpret that phenomenon.
Discussion: Can a theory be useful even if it is inaccurate? How?

Izawa, C. (Ed.) (1999). On human memory: Evolution, progress, and reflections on the 30th anniversary of the Atkinson-Shiffrin model . Mahwah, NJ: Erlbaum.

Lilienfeld, S. O., & Lynn, S. J. (2003). Dissociative identity disorder: Multiplepersonalities, multiple controversies. In S. O. Lilienfeld, S. J. Lynn, & J. M. Lohr (Eds.), Science and pseudoscience in clinical psychology (pp. 109–142). New York, NY: Guilford Press.

Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci,…Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51 , 77–101.

Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Perspective
Published: 29 November 2022

The fundamental importance of method to theory

Rick Dale ORCID: orcid.org/0000-0001-7865-474X 1 ,
Anne S. Warlaumont ORCID: orcid.org/0000-0001-9450-1372 1 &
Kerri L. Johnson ORCID: orcid.org/0000-0002-1458-2019 1 , 2

Nature Reviews Psychology volume 2 , pages 55–66 ( 2023 ) Cite this article

732 Accesses

2 Citations

15 Altmetric

Metrics details

Communication
Human behaviour

Many domains of inquiry in psychology are concerned with rich and complex phenomena. At the same time, the field of psychology is grappling with how to improve research practices to address concerns with the scientific enterprise. In this Perspective, we argue that both of these challenges can be addressed by adopting a principle of methodological variety. According to this principle, developing a variety of methodological tools should be regarded as a scientific goal in itself, one that is critical for advancing scientific theory. To illustrate, we show how the study of language and communication requires varied methodologies, and that theory development proceeds, in part, by integrating disparate tools and designs. We argue that the importance of methodological variation and innovation runs deep, travelling alongside theory development to the core of the scientific enterprise. Finally, we highlight ongoing research agendas that might help to specify, quantify and model methodological variety and its implications.

This is a preview of subscription content, access via your institution

Access options

Subscribe to this journal

Receive 12 digital issues and online access to articles

55,14 € per year

only 4,60 € per issue

Buy this article

Purchase on Springer Link
Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

research that attempts to generate a new theory

Worldwide divergence of values

Joshua Conrad Jackson & Danila Medvedev

The language network as a natural kind within the broader landscape of the human brain

Evelina Fedorenko, Anna A. Ivanova & Tamar I. Regev

Persistent interaction patterns across social media platforms and over time

Michele Avalle, Niccolò Di Marco, … Walter Quattrociocchi

Hacking, I. Representing and Intervening: Introductory Topics in The Philosophy of Natural Science (Cambridge Univ. Press, 1983).

Mayo, D. G. in PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association Vol. 1994, 270–279 (Cambridge Univ. Press, 1994).

Ackermann, R. The new experimentalism. Brit. J. Philos. Sci. 40 , 185–190 (1989).

Article Google Scholar

Simons, M. & Vagelli, M. Were experiments ever neglected? Ian Hacking and the history of philosophy of experiment. Phil. Inq. 9 , 167–188 (2021).

Google Scholar

Nosek, B. A. et al. Promoting an open research culture. Science 348 , 1422–1425 (2015).

Richardson, D. C., Dale, R. & Tomlinson, J. M. Conversation, gaze coordination, and beliefs about visual context. Cogn. Sci. 33 , 1468–1482 (2009).

Laidlaw, K. E., Foulsham, T., Kuhn, G. & Kingstone, A. Potential social interactions are important to social attention. Proc. Natl Acad. Sci. USA 108 , 5548–5553 (2011).

Richardson, D. C. et al. Joint perception: gaze and social context. Front. Hum. Neurosci. 6 , 194 (2012).

Risko, E. F., Richardson, D. C. & Kingstone, A. Breaking the fourth wall of cognitive science: real-world social attention and the dual function of gaze. Curr. Dir. Psychol. Sci. 25 , 70–74 (2016).

Levin, I. P., Schneider, S. L. & Gaeth, G. J. All frames are not created equal: a typology and critical analysis of framing effects. Organ. Behav. Hum. Decis. Process. 76 , 149–188 (1998).

Pothos, E. M. & Busemeyer, J. R. A quantum probability explanation for violations of ‘rational’ decision theory. Proc. R. Soc. B 276 , 2171–2178 (2009).

Pärnamets, P. et al. Biasing moral decisions by exploiting the dynamics of eye gaze. Proc. Natl Acad. Sci. USA 112 , 4170–4175 (2015).

Vinson, D. W., Dale, R. & Jones, M. N. Decision contamination in the wild: sequential dependencies in online review ratings. Behav. Res. Methods 51 , 1477–1484 (2019).

Longino, H. E. Gender, politics, and the theoretical virtues. Synthese 104 , 383–397 (1995).

Hansson, S. O. in Stanford Encyclopedia of Philosophy (ed. Zalta, E. N.) https://plato.stanford.edu/archives/fall2021/entries/pseudo-science/ (Stanford Univ., 2015).

Dupré, J. The Disorder of Things: Metaphysical Foundations of the Disunity of Science (Harvard Univ. Press, 1993).

Cartwright, N. The Dappled World: A Study of the Boundaries of Science (Cambridge Univ. Press, 1999).

McCauley, R. N. & Bechtel, W. Explanatory pluralism and heuristic identity theory. Theory Psychol. 11 , 736–760 (2001).

Mitchell, S. D. Integrative pluralism. Biol. Phil. 17 , 55–70 (2002).

Abrahamsen, A. & Bechtel, W. in Contemporary Debates in Cognitive Science (ed. Stainton, R.) 159–187 (Blackwell, 2006).

Kellert, S. H., Longino, H. E. & Waters, C. K. Scientific Pluralism (Univ. Minnesota Press, 2006).

Dale, R., Dietrich, E. & Chemero, A. Explanatory pluralism in cognitive science. Cogn. Sci. 33 , 739–742 (2009).

Weisberg, M. & Muldoon, R. Epistemic landscapes and the division of cognitive labor. Phil. Sci. 76 , 225–252 (2009).

Zollman, K. J. S. The epistemic benefit of transient diversity. Erkenntnis 72 , 17–35 (2010).

Horst, S. Beyond reduction: from naturalism to cognitive pluralism. Mind Matter 12 , 197–244 (2014).

Open Science Collaboration. Estimating the reproducibility of psychological science. Science 349 , 943 (2015).

Lin, H., Werner, K. M. & Inzlicht, M. Promises and perils of experimentation: the mutual-internal-validity problem. Perspect. Psychol. Sci. 16 , 854–863 (2021).

van Rooij, I. & Baggio, G. Theory before the test: how to build high-verisimilitude explanatory theories in psychological science. Perspect. Psychol. Sci. 16 , 682–697 (2021).

MacLeod, C. M. The Stroop task: the gold standard of attentional measures. J. Exp. Psychol. Gen. 121 , 12–14 (1992).

Eriksen, C. W. The flankers task and response competition: a useful tool for investigating a variety of cognitive problems. Vis. Cogn. 2 , 101–118 (1995).

Dickter, C. L. & Bartholow, B. D. Ingroup categorization and response conflict: interactive effects of target race, flanker compatibility, and infrequency on N2 amplitude. Psychophysiology 47 , 596–601 (2010).

Parris, B. A., Hasshim, N., Wadsley, M., Augustinova, M. & Ferrand, L. The loci of Stroop effects: a critical review of methods and evidence for levels of processing contributing to color-word Stroop effects and the implications for the loci of attentional selection. Psychol. Res. 86 , 1029–1053 (2022).

Barzykowski, K., Wereszczyski, M., Hajdas, S. & Radel, R. Cognitive inhibition behavioral tasks in online and laboratory settings: data from Stroop, SART and Eriksen Flanker tasks. Data Brief. 43 , 108398 (2022).

Gobel, M. S., Kim, H. S. & Richardson, D. C. The dual function of social gaze. Cognition 136 , 359–364 (2015).

Gallup, A. C., Chong, A. & Couzin, I. D. The directional flow of visual information transfer between pedestrians. Biol. Lett. 8 , 520–522 (2012).

Lick, D. J. & Johnson, K. L. Straight until proven gay: a systematic bias toward straight categorizations in sexual orientation judgments. J. Pers. Soc. Psychol. 110 , 801 (2016).

Alt, N. P., Lick, D. J. & Johnson, K. L. The straight categorization bias: a motivated and altruistic reasoning account. J. Pers. Soc. Psychol. https://doi.org/10.1037/pspi0000232 (2020).

Popper, K. Conjectures and Refutations: The Growth of Scientific Knowledge (Routledge, 2002).

Heit, E. & Hahn, U. Diversity-based reasoning in children. Cogn. Psychol. 43 , 243–273 (2001).

Heit, E., Hahn, U. & Feeney, A. in Categorization Inside and Outside the Laboratory: Essays in Honor of Douglas L. Medin 87–99 (American Psychological Association, 2005).

MacWhinney, B. in The Handbook of Linguistics 2nd edn (eds. Aronoff, M. & Rees-Miller, J.) 397–413 (Wiley, 2017).

Stivers, T. et al. Universals and cultural variation in turn-taking in conversation. Proc. Natl Acad. Sci. USA 106 , 10587–10592 (2009).

Louwerse, M. M., Dale, R., Bard, E. G. & Jeuniaux, P. Behavior matching in multimodal communication is synchronized. Cogn. Sci. 36 , 1404–1426 (2012).

Fusaroli, R., Bjørndahl, J. S., Roepstorff, A. & Tylén, K. A heart for interaction: shared physiological dynamics and behavioral coordination in a collective, creative construction task. J. Exp. Psychol. Hum. Percept. Perform. 42 , 1297 (2016).

Rasenberg, M., Özyürek, A. & Dingemanse, M. Alignment in multimodal interaction: an integrative framework. Cogn. Sci. 44 , e12911 (2020).

Dunn, M., Greenhill, S. J., Levinson, S. C. & Gray, R. D. Evolved structure of language shows lineage-specific trends in word-order universals. Nature 473 , 79–82 (2011).

Hua, X., Greenhill, S. J., Cardillo, M., Schneemann, H. & Bromham, L. The ecological drivers of variation in global language diversity. Nat. Commun. 10 , 1–10 (2019).

Christiansen, M. H. & Chater, N. The now-or-never bottleneck: a fundamental constraint on language. Behav. Brain Sci. 39 , e62 (2016).

Fitch, W. T., De Boer, B., Mathur, N. & Ghazanfar, A. A. Monkey vocal tracts are speech-ready. Sci. Adv. 2 , e1600723 (2016).

Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: what is it, who has it, and how did it evolve? Science 298 , 1569–1579 (2002).

Cowley, S. J. Distributed Language (John Benjamins, 2011).

Samuels, R. Nativism in cognitive science. Mind Lang. 17 , 233–265 (2002).

Behme, C. & Deacon, S. H. Language learning in infancy: does the empirical evidence support a domain specific language acquisition device? Phil. Psychol. 21 , 641–671 (2008).

Chomsky, N. 4. A Review Of BF Skinner’s Verbal Behavior (Harvard Univ. Press, 2013).

Vihman, M. M. Phonological Development: The Origins of Language in the Child (Blackwell, 1996).

Oller, D. K. The Emergence of the Speech Capacity (Psychology Press, 2000).

Clark, E. V. & Casillas, M. First Language Acquisition (Routledge, 2015).

Goldstein, M. H., King, A. P. & West, M. J. Social interaction shapes babbling: testing parallels between birdsong and speech. Proc. Natl Acad. Sci. USA 100 , 8030–8035 (2003).

Warlaumont, A. S. Modeling the emergence of syllabic structure. J. Phonet. 53 , 61–65 (2015).

VanDam, M. et al. HomeBank: An online repository of daylong child-centered audio recordings. Semin. Speech Lang. 37 , 128–142 (2016).

Elmlinger, S. L., Schwade, J. A. & Goldstein, M. H. The ecology of prelinguistic vocal learning: parents simplify the structure of their speech in response to babbling. J. Child Lang. 46 , 998–1011 (2019).

Roy, B. C., Frank, M. C., DeCamp, P., Miller, M. & Roy, D. Predicting the birth of a spoken word. Proc. Natl Acad. Sci. USA 112 , 12663–12668 (2015).

McClelland, J. L. The place of modeling in cognitive science. Top. Cogn. Sci. 1 , 11–38 (2009).

Smaldino, P. E. in Computational Social Psychology (eds Vallacher’, R., Read, S. J. & Nowak, A.) 311–331 (Routledge, 2017).

Guest, O. & Martin, A. E. How computational modeling can force theory building in psychological science. Perspect. Psychol. Sci. 16 , 789–802 (2021).

Elman, J. L., Bates, E. A. & Johnson, M. H. Rethinking Innateness: A Connectionist Perspective on Development Vol. 10 (MIT Press, 1996).

Warlaumont, A. S., Westermann, G., Buder, E. H. & Oller, D. K. Prespeech motor learning in a neural network using reinforcement. Neural Netw. 38 , 64–75 (2013).

Warlaumont, A. S. & Finnegan, M. K. Learning to produce syllabic speech sounds via reward-modulated neural plasticity. PLoS One 11 , e0145096 (2016).

MacWhinney, B. & Snow, C. The child language data exchange system: an update. J. Child. Lang. 17 , 457–472 (1990).

MacWhinney, B. The CHILDES Project: Tools for Analyzing Talk 3rd edn (Psychology Press, 2014).

Kachergis, G., Marchman, V. A. & Frank, M. C. Toward a “standard model” of early language learning. Curr. Dir. Psychol. Sci. 31 , 20–27 (2022).

Lewis, J. D. & Elman, J. L. A connectionist investigation of linguistic arguments from the poverty of the stimulus: learning the unlearnable. In Proc. Annual Meeting of the Cognitive Science Society Vol. 23, 552–557 (eds. Moore, J. D. & Stenning, K.) (2001).

Regier, T. & Gahl, S. Learning the unlearnable: the role of missing evidence. Cognition 93 , 147–155 (2004).

Reali, F. & Christiansen, M. H. Uncovering the richness of the stimulus: structure dependence and indirect statistical evidence. Cogn. Sci. 29 , 1007–1028 (2005).

Foraker, S., Regier, T., Khetarpal, N., Perfors, A. & Tenenbaum, J. Indirect evidence and the poverty of the stimulus: the case of anaphoric one . Cognit. Sci. 33 , 287–300 (2009).

Saffran, J. R., Aslin, R. N. & Newport, E. L. Statistical learning by 8-month-old infants. Science 274 , 1926–1928 (1996).

McMurray, B. & Hollich, G. Core computational principles of language acquisition: can statistical learning do the job? Dev. Sci. 12 , 365–368 (2009).

Frost, R., Armstrong, B. C., Siegelman, N. & Christiansen, M. H. Domain generality versus modality specificity: the paradox of statistical learning. Trends Cogn. Sci. 19 , 117–125 (2015).

Isbilen, E. S., Frost, R. L. A., Monaghan, P. & Christiansen, M. H. Statistically based chunking of nonadjacent dependencies. J. Exp. Psychol. Gen. https://doi.org/10.1037/xge0001207 (2022).

Ruba, A. L., Pollak, S. D. & Saffran, J. R. Acquiring complex communicative systems: Statistical learning of language and emotion. Top. Cogn. Sci. https://doi.org/10.1111/tops.12612 (2022).

Abney, D. H., Warlaumont, A. S., Oller, D. K., Wallot, S. & Kello, C. T. Multiple coordination patterns in infant and adult vocalizations. Infancy 22 , 514–539 (2017).

Mendoza, J. K. & Fausey, C. M. Everyday music in infancy. Dev. Sci. 24 , e13122 (2019).

Ritwika, V. et al. Exploratory dynamics of vocal foraging during infant-caregiver communication. Sci. Rep. 10 , 10469 (2020).

Mendoza, J. K. & Fausey, C. M. Quantifying everyday ecologies: principles for manual annotation of many hours of infants’ lives. Front. Psychol. 12 , 710636 (2021).

Fernald, A., Zangl, R., Portillo, A. L. & Marchman, V. A. in Developmental Psycholinguistics: On-line Methods in Children’s Language Processing (eds. Sekerina, I. A., Fernández, E. M. & Clahsen, H.) Vol. 44, 97 (John Benjamins, 2008).

Weisleder, A. & Fernald, A. Talking to children matters: early language experience strengthens processing and builds vocabulary. Psychol. Sci. 24 , 2143–2152 (2013).

Bergelson, E. & Aslin, R. N. Nature and origins of the lexicon in 6-mo-olds. Proc. Natl Acad. Sci. USA 114 , 12916–12921 (2017).

Brennan, S. E., Galati, A. & Kuhlen, A. K. in Psychology of Learning and Motivation (ed. Ross, B. H.) Vol. 53, 301–344 (Elsevier, 2010).

Streeck, J., Goodwin, C. & LeBaron, C. Embodied Interaction: Language and Body in the Material World (Cambridge Univ. Press, 2011).

Goodwin, C. Co-operative Action (Cambridge Univ. Press, 2017).

Dale, R., Spivey, M. J. in Eye-Tracking In Interaction. Studies On The Role Of Eye Gaze In Dialogue (eds. Oben, B. and Brône, G.) 67–90 (John Benjamins, 2018).

Richardson, D. C. & Spivey, M. J. in Encyclopedia of Biomaterials and Biomedical Engineering 573–582 (CRC Press, 2004).

Tanenhaus, M. K., Spivey-Knowlton, M. J., Eberhard, K. M. & Sedivy, J. C. Integration of visual and linguistic information in spoken language comprehension. Science 268 , 1632–1634 (1995).

Spivey, M. J., Tanenhaus, M. K., Eberhard, K. M. & Sedivy, J. C. Eye movements and spoken language comprehension: effects of visual context on syntactic ambiguity resolution. Cogn. Psychol. 45 , 447–481 (2002).

Richardson, D. C., Dale, R. & Spivey, M. J. Eye movements in language and cognition. Methods Cogn. Linguist. 18 , 323–344 (2007).

Ferreira, F. & Clifton, C. Jr The independence of syntactic processing. J. Mem. Lang. 25 , 348–368 (1986).

Kamide, Y., Altmann, G. T. & Haywood, S. L. The time-course of prediction in incremental sentence processing: evidence from anticipatory eye movements. J. Mem. Lang. 49 , 133–156 (2003).

Coco, M. I., Keller, F. & Malcolm, G. L. Anticipation in real‐world scenes: the role of visual context and visual memory. Cogn. Sci. 40 , 1995–2024 (2016).

Coco, M. I. & Keller, F. Scan patterns predict sentence production in the cross‐modal processing of visual scenes. Cogn. Sci. 36 , 1204–1223 (2012).

Kieslich, P. J., Henninger, F., Wulff, D. U., Haslbeck, J. M. & Schulte-Mecklenbeck, M. in A Handbook of Process Tracing Methods 111–130 (Routledge, 2019).

Spivey, M. J., Grosjean, M. & Knoblich, G. Continuous attraction toward phonological competitors. Proc. Natl Acad. Sci. USA 102 , 10393–10398 (2005).

Freeman, J., Dale, R. & Farmer, T. Hand in motion reveals mind in motion. Front. Psychol. 2 , 59 (2011).

Freeman, J. B. & Johnson, K. L. More than meets the eye: split-second social perception. Trends Cogn. Sci. 20 , 362–374 (2016).

Goodale, B. M., Alt, N. P., Lick, D. J. & Johnson, K. L. Groups at a glance: perceivers infer social belonging in a group based on perceptual summaries of sex ratio. J. Exp. Psychol. Gen. 147 , 1660–1676 (2018).

Sneller, B. & Roberts, G. Why some behaviors spread while others don’t: a laboratory simulation of dialect contact. Cognition 170 , 298–311 (2018).

Atkinson, M., Mills, G. J. & Smith, K. Social group effects on the emergence of communicative conventions and language complexity. J. Lang. Evol. 4 , 1–18 (2019).

Raviv, L., Meyer, A. & Lev‐Ari, S. The role of social network structure in the emergence of linguistic structure. Cogn. Sci. 44 , e12876 (2020).

Lupyan, G. & Dale, R. Language structure is partly determined by social structure. PLoS One 5 , e8559 (2010).

Lupyan, G. & Dale, R. Why are there different languages? The role of adaptation in linguistic diversity. Trends Cogn. Sci. 20 , 649–660 (2016).

Wu, L., Waber, B. N., Aral, S., Brynjolfsson, E. & Pentland, A. Mining face-to-face interaction networks using sociometric badges: predicting productivity in an IT configuration task. Inf. Syst. Behav. Soc. Methods https://doi.org/10.2139/ssrn.1130251 (2008).

Paxton, A. & Dale, R. Argument disrupts interpersonal synchrony. Q. J. Exp. Psychol. 66 , 2092–2102 (2013).

Alviar, C., Dale, R. & Galati, A. Complex communication dynamics: exploring the structure of an academic talk. Cogn. Sci. 43 , e12718 (2019).

Joo, J., Bucy, E. P. & Seidel, C. Automated coding of televised leader displays: detecting nonverbal political behavior with computer vision and deep learning. Int. J. Commun. 13 , 4044–4066 (2019).

Metallinou, A. et al. The USC CreativeIT database of multimodal dyadic interactions: from speech and full body motion capture to continuous emotional annotations. Lang. Res. Eval. 50 , 497–521 (2016).

Pouw, W., Paxton, A., Harrison, S. J. & Dixon, J. A. Acoustic information about upper limb movement in voicing. Proc. Natl Acad. Sci. USA 117 , 11364–11367 (2020).

Enfield, N., Levinson, S. C., De Ruiter, J. P. & Stivers, T. in Field Manual Vol. 10, 96–99 (ed. Majid, A.) (Max Planck Institute for Psycholinguistics, 2007).

Enfield, N. & Sidnell, J. On the concept of action in the study of interaction. Discourse Stud. 19 , 515–535 (2017).

Duran, N. D., Paxton, A. & Fusaroli, R. ALIGN: analyzing linguistic interactions with generalizable techNiques — a Python library. Psychol. Methods 24 , 419 (2019).

Brennan, S. E. & Clark, H. H. Conceptual pacts and lexical choice in conversation. J. Exp. Psychol. Learn. Mem. Cogn. 22 , 1482–1493 (1996).

Hasson, U., Nir, Y., Levy, I., Fuhrmann, G. & Malach, R. Intersubject synchronization of cortical activity during natural vision. Science 303 , 1634–1640 (2004).

Huth, A. G., De Heer, W. A., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532 , 453–458 (2016).

Shain, C. et al. Robust effects of working memory demand during naturalistic language comprehension in language-selective cortex. J. Neurosci. 42 , 7412–7430 (2022).

Fedorenko, E., Blank, I. A., Siegelman, M. & Mineroff, Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition 203 , 104348 (2020).

Stephens, G. J., Silbert, L. J. & Hasson, U. Speaker–listener neural coupling underlies successful communication. Proc. Natl Acad. Sci. USA 107 , 14425–14430 (2010).

Schilbach, L. et al. Toward a second-person neuroscience. Behav. Brain Sci. 36 , 393–414 (2013).

Redcay, E. & Schilbach, L. Using second-person neuroscience to elucidate the mechanisms of social interaction. Nat. Rev. Neurosci. 20 , 495–505 (2019).

Riley, M. A., Richardson, M., Shockley, K. & Ramenzoni, V. C. Interpersonal synergies. Front. Psychol. 2 , 38 (2011).

Dale, R., Fusaroli, R., Duran, N. D. & Richardson, D. C. in Psychology of Learning and Motivation (ed. Ross, B. H.) Vol. 59, 43–95 (Elsevier, 2013).

Fusaroli, R., Rączaszek-Leonardi, J. & Tylén, K. Dialog as interpersonal synergy. N. Ideas Psychol. 32 , 147–157 (2014).

Hadley, L. V., Naylor, G. & Hamilton, A. F. D. C. A review of theories and methods in the science of face-to-face social interaction. Nat. Rev. Psychol. 1 , 42–54 (2022).

Cornejo, C., Cuadros, Z., Morales, R. & Paredes, J. Interpersonal coordination: methods achievements and challenges. Front. Psychol. https://doi.org/10.3389/fpsyg.2017.01685 (2017).

Smaldino, P. E. in Computational Social Psychology 311–331 (Routledge, 2017).

Devezer, B., Nardin, L. G., Baumgaertner, B. & Buzbas, E. O. Scientific discovery in a model-centric framework: reproducibility, innovation, and epistemic diversity. PLoS One 14 , e0216125 (2019).

Sulik, J., Bahrami, B. & Deroy, O. The diversity gap: when diversity matters for knowledge. Perspect. Psychol. Sci. 17 , 752–767 (2022).

O’Connor, C. & Bruner, J. Dynamics and diversity in epistemic communities. Erkenntnis 84 , 101–119 (2019).

Longino, H. in The Stanford Encyclopedia of Philosophy (ed. Zalta, E. N.) https://plato.stanford.edu/archives/sum2019/entries/scientific-knowledge-social/ (Stanford Univ., 2019).

Van Rooij, I. The tractable cognition thesis. Cogn. Sci. 32 , 939–984 (2008).

Kwisthout, J., Wareham, T. & Van Rooij, I. Bayesian intractability is not an ailment that approximation can cure. Cogn. Sci. 35 , 779–784 (2011).

Contreras Kallens, P. & Dale, R. Exploratory mapping of theoretical landscapes through word use in abstracts. Scientometrics 116 , 1641–1674 (2018).

Methods for methods’ sake. Nat. Methods https://doi.org/10.1038/nmeth1004-1 (2004).

Oberauer, K. & Lewandowsky, S. Addressing the theory crisis in psychology. Psychon. Bull. Rev. 26 , 1596–1618 (2019).

Meehl, P. E. Theory-testing in psychology and physics: a methodological paradox. Phil. Sci. 34 , 103–115 (1967).

Klein, O. et al. A practical guide for transparency in psychological science. Collabra Psychol. 4 , 20 (2018).

Muthukrishna, M. & Henrich, J. A problem in theory. Nat. Hum. Behav. 3 , 221–229 (2019).

Eronen, M. I. & Bringmann, L. F. The theory crisis in psychology: how to move forward. Perspect. Psychol. Sci. https://doi.org/10.1177/1745691620970586 (2021).

Borsboom, D., van der Maas, H. L. J., Dalege, J., Kievit, R. A. & Haig, B. D. Theory construction methodology: a practical framework for building theories in psychology. Perspect. Psychol. Sci. https://doi.org/10.1177/1745691620969647 (2021).

Kyvik, S. & Reymert, I. Research collaboration in groups and networks: differences across academic fields. Scientometrics 113 , 951–967 (2017).

Tebes, J. K. & Thai, N. D. Interdisciplinary team science and the public: steps toward a participatory team science. Am. Psychol. 73 , 549 (2018).

Falk-Krzesinski, H. J. et al. Mapping a research agenda for the science of team science. Res. Eval. 20 , 145–158 (2011).

da Silva, J. A. T. The Matthew effect impacts science and academic publishing by preferentially amplifying citations, metrics and status. Scientometrics 126 , 5373–5377 (2021).

Scheel, A. M., Tiokhin, L., Isager, P. M. & Lakens, D. Why hypothesis testers should spend less time testing hypotheses. Perspect. Psychol. Sci. 16 , 744–755 (2020).

Jones, M. N. Big Data in Cognitive Science (Psychology Press, 2016).

Paxton, A. & Griffiths, T. L. Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets. Behav. Res. Methods 49 , 1630–1638 (2017).

Lupyan, G. & Goldstone, R. L. Beyond the lab: using big data to discover principles of cognition. Behav Res. Methods , 51 , 1554–3528 (2019).

Haspelmath, M., Dryer, M. S., Gil, D. & Comrie, B. (eds) The World Atlas of Language Structures (Max Planck Digital Library, 2013).

Eberhard, D. M., Simons, G. F. & Fennig, C. D. Ethnologue: Languages of the World (SIL International, 2021).

MacCorquodale, K. & Meehl, P. E. On a distinction between hypothetical constructs and intervening variables. Psychol. Rev. 55 , 95–107 (1948).

van Rooij, I. & Baggio, G. Theory before the test: how to build high-verisimilitude explanatory theories in psychological science. Perspect. Psychol. Sci . https://doi.org/10.1177/1745691620970604 (2021).

Christiansen, M. H. & Chater, N. Creating Language: Integrating Evolution, Acquisition, and Processing (MIT Press, 2016).

Berwick, R. C. & Chomsky, N. Why Only Us: Language and Evolution (MIT Press, 2016).

Gopnik, A. Scientific thinking in young children: theoretical advances, empirical research, and policy implications. Science 337 , 1623–1627 (2012).

Pereira, A. F., James, K. H., Jones, S. S. & Smith, L. B. Early biases and developmental changes in self-generated object views. J. Vis. 10 , 22–22 (2010).

Fagan, M. K. & Iverson, J. M. The influence of mouthing on infant vocalization. Infancy 11 , 191–202 (2007).

Martin, J., Ruthven, M., Boubertakh, R. & Miquel, M. E. Realistic dynamic numerical phantom for MRI of the upper vocal tract. J. Imaging 6 , 86 (2020).

Spivey, M. J. & Dale, R. Continuous dynamics in real-time cognition. Curr. Dir. Psychol. Sci. 15 , 207–211 (2006).

Publication Manual of the American Psychological Association 3rd edn (American Psychological Association, 1983).

Publication Manual of the American Psychological Association 6th edn (American Psychological Association, 2010).

Ashby, W. R. An Introduction to Cybernetics (Martino, 1956).

de Raadt, J. D. R. Ashby’s law of requisite variety: an empirical study. Cybern. Syst. 18 , 517–536 (1987).

Ward, L. M. Dynamical Cognitive Science (MIT Press, 2002).

Regier, T., Carstensen, A. & Kemp, C. Languages support efficient communication about the environment: words for snow revisited. PLoS One 11 , e0151138 (2016).

Newell, A. Unified Theories of Cognition (Harvard Univ. Press, 1990).

Rich, P., de Haan, R., Wareham, T. & van Rooij, I. in Proc. Annual Meeting of the Cognitive Science Society Vol. 43, 3034–3040 (eds. Fitch, T., Lamm, C., Leder, H., and Teßmar-Raible, K.) (2021).

Potochnik, A. & Sanches de Oliveira, G. Patterns in cognitive phenomena and pluralism of explanatory styles. Top. Cogn. Sci. 12 , 1306–1320 (2020).

Leydesdorff, L. & Schank, T. Dynamic animations of journal maps: indicators of structural changes and interdisciplinary developments. J. Am. Soc. Inf. Sci. Technol. 59 , 1810–1818 (2008).

Leydesdorff, L. & Goldstone, R. L. Interdisciplinarity at the journal and specialty level: the changing knowledge bases of the journal Cognitive Science . J. Assoc. Inf. Sci. Technol. 65 , 164–177 (2014).

DeStefano, I., Oey, L. A., Brockbank, E. & Vul, E. Integration by parts: collaboration and topic structure in the CogSci community. Top. Cogn. Sci. 13 , 399–413 (2021).

Cummins, R. in Explanation And Cognition (eds Keil, F. C. & Wilson, R.) 117–144 (MIT Press, 2000).

Boyd, N. M. & Bogen, J. in Stanford Encyclopedia of Philosophy (ed. Zalta, E. N.) https://plato.stanford.edu/archives/win2021/entries/science-theory-observation/ (Stanford Univ., 2021).

Smaldino, P. E. How to build a strong theoretical foundation. Psychol. Inq. 31 , 297–301 (2020).

Chang, H. Inventing Temperature: Measurement and Scientific Progress (Oxford Univ. Press, 2004).

Download references

Acknowledgements

A.S.W. was supported by the National Science Foundation (grants 1529127 and 1539129/1827744) and by the James S. McDonnell Foundation ( https://doi.org/10.37717/220020507 ). K.L.J. was supported by the National Science Foundation (grant 2017245).

Author information

Authors and affiliations.

Department of Communication, University of California, Los Angeles, Los Angeles, CA, USA

Rick Dale, Anne S. Warlaumont & Kerri L. Johnson

Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA

Kerri L. Johnson

You can also search for this author in PubMed Google Scholar

Contributions

R.D. discussed the submission theme with the editor and wrote the first draft. A.S.W. and K.L.J. refined and added to this plan and contributed major new sections of writing and revision. All authors contributed to developing the figures.

Corresponding author

Correspondence to Rick Dale .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Reviews Psychology thanks Berna Devezer; Michael Frank, who co-reviewed with Anjie Cao; and Justin Sulik for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Dale, R., Warlaumont, A.S. & Johnson, K.L. The fundamental importance of method to theory. Nat Rev Psychol 2 , 55–66 (2023). https://doi.org/10.1038/s44159-022-00120-5

Download citation

Accepted : 22 September 2022

Published : 29 November 2022

Issue Date : January 2023

DOI : https://doi.org/10.1038/s44159-022-00120-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Chapter 4: Theory in Psychology

Phenomena and theories, learning objectives.

Define the terms phenomenon and theory and distinguish clearly between them.
Explain the purposes of scientific theories.
Explain why there are usually many plausible theories for any set of phenomena.

Some Famous Psychological Phenomena

Blindsight . People with damage to their visual cortex are often able to respond to visual stimuli that they do not consciously see.
Bystander effect . The more people who are present at an emergency situation, the less likely it is that any one of them will help.
Fundamental attribution error . People tend to explain others’ behaviour in terms of their personal characteristics as opposed to the situation they are in.
McGurk effect . When audio of a basic speech sound is combined with video of a person making mouth movements for a different speech sound, people often perceive a sound that is intermediate between the two. For a demonstration, see “The McGurk Effect .”
O ther -race effect . People recognize faces of people of their own race more accurately than faces of people of other races.
Placebo effect . Placebos (fake psychological or medical treatments) often lead to improvements in people’s symptoms and functioning.
Mere exposure effect . The more often people have been exposed to a stimulus, the more they like it—even when the stimulus is presented subliminally.
Serial position effect . Stimuli presented near the beginning and end of a list are remembered better than stimuli presented in the middle. For a demonstration, see “Serial Position Effect .”
Spontaneous recovery . A conditioned response that has been extinguished often returns with no further training after the passage of time.

Sometimes a replication of a study produces results that differ from the results of the initial study. This difference could mean that the results of the initial study or the results of the replication were a fluke—they occurred by chance and do not reflect something that is generally true. In either case, additional replications would be likely to resolve this discrepancy. A failure to produce the same results could also mean that the replication differed in some important way from the initial study. For example, early studies showed that people performed a variety of tasks better and faster when they were watched by others than when they were alone. Some later replications, however, showed that people performed worse when they were watched by others. Eventually researcher Robert Zajonc identified a key difference between the two types of studies. People seemed to perform better when being watched on highly practiced tasks but worse when being watched on relatively unpracticed tasks (Zajonc, 1965) [1] . These two phenomena have now come to be called social facilitation and social inhibition.

Physics has the laws of motions and chemistry has the law of conservation of mass. Unlike in other sciences, psychology does not have laws but rather effects. Laws imply that the phenomenon is universally true and rarely in psychology can you not find an exception. Even the effects that have been established are often culturally dependent. For example, the fundamental attribution error is committed more frequently in North America than in East Asia (Miyamoto & Kitayama, 2002) [2] .

What Is a Theory?

In addition to theory , researchers in psychology use several related terms to refer to their explanations and interpretations of phenomena. A perspective is a broad approach—more general than a theory—to explaining and interpreting phenomena. For example, researchers who take a biological perspective tend to explain phenomena in terms of genetics or nervous and endocrine system structures and processes, while researchers who take a behavioural perspective tend to explain phenomena in terms of reinforcement, punishment, and other external events. A model is a precise explanation or interpretation of a specific phenomenon—often expressed in terms of equations, computer programs, or biological structures and processes. A hypothesis can be an explanation that relies on just a few key concepts—although this term more commonly refers to a prediction about a new phenomenon based on a theory (see Section 4.3 “Using Theories in Psychological Research” ). A theoretical framework can be as broad as a perspective or a specific as a model, but it is the context applied to understanding a phenomenon. Adding to the confusion is the fact that researchers often use these terms interchangeably. It would not be considered wrong to refer to the drive theory as the drive model or even the drive hypothesis. And the biopsychosocial model of health psychology—the general idea that health is determined by an interaction of biological, psychological, and social factors—is really more like a perspective as defined here. Keep in mind, however, that the most important distinction remains that between observations and interpretations.

What Are Theories For?

Of course, scientific theories are meant to provide accurate explanations or interpretations of phenomena. But there must be more to it than this explanation. Consider that a theory can be accurate without being very useful. To say that expressive writing helps people “deal with their emotions” might be accurate as far as it goes, but it seems too vague to be of much use. Consider also that a theory can be useful without being entirely accurate. Figure 4.2 is a representation of the classic multistore model of human memory, which is still cited by researchers and discussed in textbooks despite the fact that it is now known to be inaccurate in a number of ways (Izawa, 1999) [3] . These two examples suggest that theories have purposes other than simply providing accurate explanations or interpretations. Here we look at three additional purposes of theories: the organization of known phenomena, the prediction of outcomes in new situations, and the generation of new research.

Flow Chart. Information from Environment leads to Sensory Store, which leads to Short-Term Store, which leads both directions to Long-Term Store. All lead to Forgetting.

Figure 4.1 Representation of the Multistore Model of Human Memory. In the multistore model of human memory, information from the environment passes through a sensory store on its way to a short-term store, where it can be rehearsed, and then to a long-term store, where it can be stored and retrieved much later. This theory has been extremely successful at organizing old phenomena and predicting new ones.

Organization

One important purpose of scientific theories is to organize phenomena in ways that help people think about them clearly and efficiently. The drive theory of social facilitation and social inhibition, for example, helps to organize and make sense of a large number of seemingly contradictory results. The multistore model of human memory efficiently summarizes many important phenomena: the limited capacity and short retention time of information that is attended to but not rehearsed, the importance of rehearsing information for long-term retention, the serial-position effect, and so on. Or consider a classic theory of intelligence represented by Figure 4.2 . According to this theory, intelligence consists of a general mental ability, g , plus a small number of more specific abilities that are influenced by g (Neisset et al., 1996) [4] . Although there are other theories of intelligence, this one does a good job of summarizing a large number of statistical relationships between tests of various mental abilities. This theory includes the fact that tests of all basic mental abilities tend to be somewhat positively correlated and the fact that certain subsets of mental abilities (e.g., reading comprehension and analogy completion) are more positively correlated than others (e.g., reading comprehension and arithmetic).

Flow chart: g leads to Numerical ability, spacial ability, and verbal ability

Figure 4.2 Representation of One Theory of Intelligence In this theory of intelligence, a general mental ability (g) influences each of three more specific mental abilities. Theories of this type help to organize a large number of statistical relationships among tests of various mental abilities.

Thus theories are good or useful to the extent that they organize more phenomena with greater clarity and efficiency. Scientists generally follow the principle of parsimony , also known as Occam’s razor , which holds that a theory should include only as many concepts as are necessary to explain or interpret the phenomena of interest. Simpler, more parsimonious theories organize phenomena more efficiently than more complex, less parsimonious theories.

Generation of New Research

A third purpose of theories is to generate new research by raising new questions. Consider, for example, the theory that people engage in self-injurious behaviour such as cutting because it reduces negative emotions such as sadness, anxiety, and anger. This theory immediately suggests several new and interesting questions. Is there, in fact, a statistical relationship between cutting and the amount of negative emotions experienced? Is it causal? If so, what is it about cutting that has this effect? Is it the pain, the sight of the injury, or something else? Does cutting affect all negative emotions equally?

Notice that a theory does not have to be accurate to serve this purpose. Even an inaccurate theory can generate new and interesting research questions. Of course, if the theory is inaccurate, the answers to the new questions will tend to be inconsistent with the theory. This new direction will lead researchers to reevaluate the theory and either revise it or abandon it for a new one. And this cycle of revising is how scientific theories become more detailed and accurate over time.

Multiple Theories

At any point in time, researchers are usually considering multiple theories for any set of phenomena. One reason is that because human behaviour is extremely complex, it is always possible to look at it from different perspectives. For example, a biological theory of sexual orientation might focus on the role of sex hormones during critical periods of brain development, while a sociocultural theory might focus on cultural factors that influence how underlying biological tendencies are expressed. A second reason is that—even from the same perspective—there are usually different ways to “go beyond” the phenomena of interest. For example, in addition to the drive theory of social facilitation and social inhibition, there is another theory that explains them in terms of a construct called “evaluation apprehension”—anxiety about being evaluated by the audience. Both theories go beyond the phenomena to be interpreted, but they do so by proposing somewhat different underlying processes.

Where Do Multiple Personalities Come From?

Diagnosis of DID greatly increased after the release of the book and film Sybil —about a woman with DID—in the 1970s.
DID is extremely rare outside of North America.
A very small percentage of therapists are responsible for diagnosing the vast majority of cases of DID.
The literature on treating DID includes many practices that encourage patients to act out multiple personalities (e.g., having a bulletin board on which personalities can leave messages for each other).
Normal people can easily re-create the symptoms of DID with minimal suggestion in simulated clinical interviews.

Key Takeaways

Scientists distinguish between phenomena, which are their systematic observations, and theories, which are their explanations or interpretations of phenomena.
In addition to providing accurate explanations or interpretations, scientific theories have three basic purposes. They organize phenomena, allow people to predict what will happen in new situations, and help generate new research.
Researchers generally consider multiple theories for any set of phenomena. Different theories of the same set of phenomena can be complementary or competing.
Practice: Think of at least three different theories to explain the fact that married people tend to report greater levels of happiness than unmarried people.
Identify the primary phenomenon of interest.
Identify the theory or theories used to explain or interpret that phenomenon.
Discussion: Can a theory be useful even if it is inaccurate? How?
Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274. ↵
Miyamoto, Y. & Kitayama, S. (2002). Cultural variation in correspondence bias: The critical role of attitude diagnosticity of socially constrained behavior. Journal of Personality and Social Psychology, 83 (5), 1239-1348. ↵
Izawa, C. (Ed.) (1999). On human memory: Evolution, progress, and reflections on the 30th anniversary of the Atkinson-Shiffrin model . Mahwah, NJ: Erlbaum. ↵
Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci,…Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51 , 77–101. ↵
Lilienfeld, S. O., & Lynn, S. J. (2003). Dissociative identity disorder: Multiple personalities, multiple controversies. In S. O. Lilienfeld, S. J. Lynn, & J. M. Lohr (Eds.), Science and pseudoscience in clinical psychology (pp. 109–142). New York, NY: Guilford Press. ↵
Lilienfeld, S. O., & Lynn, S. J. (2003). Dissociative identity disorder: Multiple personalities, multiple controversies. In S. O. Lilienfeld, S. J. Lynn, & J. M. Lohr (Eds.), Science and pseudoscience in clinical psychology (pp. 109–142). New York, NY: Guilford Press. ↵
Research Methods in Psychology. Authored by : Paul C. Price, Rajiv S. Jhangiani, and I-Chant A. Chiang. Provided by : BCCampus. Located at : https://opentextbc.ca/researchmethods/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
HHS Author Manuscripts

How Does Research Start?

How does research start.

Clinical research aims to deliver healthcare advancements that are safe, beneficial, and cost-effective ( Ford & Norrie, 2016 ). Research requires a methodical approach to develop studies that generate high-quality evidence to support changes in clinical practice. The method is a step-wise process that attempts to limit the chances of errors, random and systematic, which can compromise conclusions ( Cummings, 2013 ) and invalidate findings. As healthcare professionals, nurses need to be versed in understanding the vast amount of information and available research in their field ( Pollock & Berge, 2018 ) to find the best evidence to guide their clinical practice and/or to develop their research. However, to effectively use the literature, it is imperative to understand the principles of critical appraisal and basic study designs.

There are many roles for nurses in research. Nurses can be consumers of research, by staying abreast of the current issues and trends in their specialty area; a nurse champion initiating quality improvement projects guided by the best clinical evidence ( Luz, Shadmi, & Drach-Zahavy, 2019 ) ( White, 2011 ); a member of an interprofessional research team helping to address a complex health problem; or an independent nurse scientist developing their scientific inquiry. Regardless of the nurse’s role in research, a common goal of clinical research is to understand health and illness and, to discover novel methods to detect, diagnose, treat, and prevent disease ( NCI, 2018 ).

This column is the first in a series focusing on the concepts of clinical research using a step by step approach. Each column will build upon earlier columns to provide an overview of the essential components of clinical research. The goal of the columns is to discuss the concepts that underpin evidence-based practice from research designs to data interpretation. Each article can serve as a review of the elements used to develop clinical research. The focus of this inaugural column is how to start the research process, which involves the identification of the topic of interest and the development of a well-defined research question. This article also discusses methods of how to formulate quantitative and qualitative research questions.

The inspiration for the Topic

The motivation to explore an area of inquiry often starts from an observation that leads one to question why does that occur or what if we did this instead? Speaking to patients and hearing their concerns about managing specific conditions or symptoms is another way to get inspired. Exploring new technologies, successful techniques, and procedures from other fields or disciplines and adapting them in a different area could be another source for new insights and discoveries ( Cummings, 2013 ). For example, those working in a cardiac setting may take an interest in fitness watches to monitor adherence to a walking program to reduce blood pressure and body weight. The ease of use, cost, and availability of fitness watches may be the draw to this technology. Staying curious and willing to explore ideas to solve or understand clinical issues is vital in engaging in clinical research since the goal of research is to improve the lives of patients.

Developing a research project requires knowing in depth the chosen area of inquiry (i.e., etiology, and treatment of hypertension). Methods to get immersed in the topic of interest include speaking to experts in the field and conducting a comprehensive literature review. Reading narrative review (NR) articles is one approach for updates on the latest issues and trends in the area of interest. NRs can address clinical, background, or theoretical questions. It can also summarize current findings, identify the gaps in research, and provide suggestions for the next steps in research ( Ferrari, 2015 ). On the downside, NRs can be biased based on the author(s) experience and interpretation of findings ( Pae, 2015 ). Systematic reviews (SR), another summary paper, differs from NRs, in that it uses a systematic approach to select, appraise, and evaluate the published reports ( Armstrong, Hall, Doyle, & Waters, 2011 ).

SRs start with a defined clinical question that is answered during the review ( Hoffmann et al., 2017 ). SRs use specific strategies for the inclusion criteria of papers to include or not to include. SRs help to understand what works or do not work in terms of intervention based-research ( Uman, 2011 ). SRs are excellent resources if your area of inquiry is leading towards an intervention based project. (See Table 1 for Classifications of Interventions).

Classifications Interventional Studies (Clinical Trials)

Source: ( National Institutes of Health (NIH, 2019 )

Reviewing citations from published papers is another method to find relevant publications. Highly cited publications in a particular area could indicate a landmark paper, wherein the author(s) may have made an important discovery or identified a critical issue in the area. An essential goal of the literature review is to ensure that previously conducted studies are located and understood. Previous studies provide insight into recent discoveries, as well as dilemmas and challenges encountered in conducting the research.

The Research Question

The two branches of research methods are experimental and observational. Under the experimental methods, randomized controlled trials and non-randomized controlled trials belong in this category, while the observational methods include analytical studies with control groups and descriptive studies with no control groups. The analytical studies are cohort and case-control studies and descriptive studies are ecological, cross-sectional and case reports. Despite the differences in research methods, the common thread among the various types of research is the research question. The question helps guide the study design and is the foundation for developing the study. In the health sciences, the question needs to pass the “So what?” test. In that, is the issue relevant and lead to the advancement of the field and feasible in terms of conducting the study? Cummings and colleagues ( Cummings, 2013 ) use the mnemonic FINER (Feasible, Interesting, Novel, Ethical, Relevant) to define the characteristics of a good research question.

Feasibility

Feasibility is a critical element of research. Research questions must be answerable and focused on using methods to measure or quantify change or outcome. For example, assessing blood pressure for a study designed to lower hypertension is feasible, because methods to measure blood pressure and results associated with normal, and stages of hypertension are established. For studies requiring human study participants, approaches to recruiting and to enrolling them into the research need careful planning. Strategies must consider where and how to recruit the best study participants who fit the study population under investigation. An adequate number of study participants must be available to implement the study. The allotted timeframe to complete the study, the workforce to perform the study, and the budget to conduct the investigation must also be realistic. Research studies funded by private or public sponsors usually have timeframes to complete an investigation (2 years, three years). Funders can also request for a timeline showing when aspects of the research are achieved (institutional review board approval, recruitment of participants, data analysis).

Interesting

Several reasons may drive interest in an area of inquiry. Cummings and colleagues ( Cummings, 2013 ), use the term Interesting to refer to an area of importance for the investigator to examine. For some investigators, an experience or an observation drives them to evaluate the underpinnings of a situation or condition. While for some, obtaining financial support either through private or public funding is an important consideration, and for others, the research question is the logical next step in their program of research.

Novel research implies that new information contributes to or advances a field of inquiry. It can also mean that research confirms or refutes earlier results. Replicating past research is appropriate to validate scientific findings. When repeating studies, improving previously used research methods (i.e., increase sample size, outcome measures, increase follow-up period) can strengthen the project. For example, a study replicating a hypertension study may add a way to physiologically assess dietary sodium intake instead of only collecting dietary food records to determine sodium intake.

Ethical research is mandatory, from the protection of human and animal subjects to the data collection, storage, and reporting of research results ( Applebaum, 2005 ; Grady, 2015 ). Research studies must obtain institutional review board (IRB) approval before proceeding with the investigation. IRB is known as an ethics committee. The committee reviews the proposed research plan to ensure that it has adequate safeguards for the well-being of the study participants, as well as evaluates the risk-benefits of the proposed study. If the level of the risk outweighs the benefits of the outcome, the IRB may require changes to the research plan to improve the safety profile or reject the study. For example, an IRB will not approve a study proposing to use a placebo when well-established and effective treatments are available. The National Institutes of Health (NIH) offers an excellent educational resource, titled, Clinical Research Training . This training is a free online tutorial for ethics, patient safety, protocol implementation, and regulatory research ( https://crt.nihtraining.com/login.php ). Registration is required to enter the NIH portal, and the course takes approximately three-four hours to complete.

Relevant research questions address critical issues. It will add to the current knowledge in the field. It may also change clinical practice or influence policy. The questions must be timely and appropriate for the study population under investigation. In continuing the hypertension example from above, for individuals diagnosed with hypertension, it is recognized that reducing the dietary intake of sodium and increasing potassium can lower blood pressure and reduce the risk for heart disease and stroke ( McDonough, Veiras, Guevara, & Ralph, 2017 ). Therefore, an investigator should target both the dietary intakes of sodium and potassium if conducting a dietary study to reduce blood pressure. Focusing solely on lowering dietary sodium intake does not take into consideration the best available evidence in the field.

Guidelines for Question Development: PICO, PEO

Guidelines are available to help frame the research question that clarifies the concepts of interest; common frameworks include PICO and PEO. PICO is best suited for quantitative studies, while PEO for qualitative studies ( Methley, Campbell, Chew-Graham, McNally, & Cheraghi-Sohi, 2014 ). Quantitative and qualitative methodologies view the research approach using different lenses. In quantitative research, numerical data is produced necessitating statistical analysis. While qualitative research generates themes using words, the outcome of interest for these studies is understanding phenomena and experiences. It is essential to recognize that some topics will not fit the PICO and PEO frameworks. Novice researchers should seek consultation from a mentor or academic research advisor to formulate the research question.

PICO incorporates the following components P opulation, I ntervention, C omparison, and O utcomes. Population considers the persons or community affected with a specific health condition or problem (i.e., middle-aged adults, aged 45-65 with stage 1 hypertension; older adults, aged 65 and older with stage 1 hypertension living in nursing homes). Intervention is the process or action under investigation in a clinical study. Interventions include pharmaceutical agents, devices, and procedures, such as education about diet or exercise. The intervention under study can be investigational or already available to consumers or healthcare professionals for use ( NLM, 2019 ). Comparison is the group assessed against the intervention (i.e., vegan diet versus the Mediterranean style diet). Outcome is the planned measure to determine the effect of an intervention on the population under study. Using the vegan versus Mediterranean style diet example, the Outcome of interest could be the percent of body weight loss and reduction of blood pressure.

PEO includes the following elements P opulation, E xposure, and O utcome. Population centers on those affected and their problems (i.e., middle-aged adults who smoke with hypertension). Exposure focuses on the area of interest (i.e., experience with smoking cessation programs; triggers of smoking). The Exposure viewpoint depends on the framing or wording of the research question and the goals of the project since qualitative studies can denote a broad area of research or specific sub-categories of topics ( Creswell, 2013 ). Outcome using the PEO model might examine a person’s experience with smoking cessation and the themes associated with quitting and relapsing. Since the PEO model is best suited for qualitative studies, Outcome tends to have elements of defining a person’s experiences or discovering processes that happen in specific locations or context ( Doody & Bailey, 2016 ). (See Table 2 for Sample Questions Using PICO and PEO).

Sample Questions Using PICO and PEO

To start in research, find an area of interest to study. For some, the inspiration for research comes from observations and experiences from the work-setting, colleagues, investigations from other fields, and past research. Before delving into developing a research protocol, master the subject of interest by speaking with experts, and understand the literature in the field. Use the FINER mnemonic as a guide to determine if the research question can pass the “So what?” test and use the PICO or PEO models to structure the research question. Formulating the appropriate research question is vital because the question is the starting point to select the design of the study, the population of interest, interventions, exposure, and outcomes.

Acknowledgments

This manuscript is supported in part by grant # UL1TR001866 from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program.

Applebaum M, Kalichman M, Plemmons D, Rook K (2005). Resources for Research Ethics Education . Retrieved from http://research-ethics.org/ [ Google Scholar ]
Armstrong R, Hall BJ, Doyle J, & Waters E (2011). ‘Scoping the scope’ of a Cochrane review . Journal of Public Health , 33 ( 1 ), 147–150. doi: 10.1093/pubmed/fdr015%J Journal of Public Health [ PubMed ] [ CrossRef ] [ Google Scholar ]
Creswell J (2013). Qualitative Inquiry & Research Design (Third ed.). Thousand Oaks, CA: Sage Publications, Inc. [ Google Scholar ]
Cummings SBHSR (Ed.) (2013). Designing Clinical Research (4th ed.). Philadelphia, PA: Wolters Kluwer/Lippincott Williams & Wilkins. [ Google Scholar ]
Doody O, & Bailey ME (2016). Setting a research question, aim, and objective . Nurse Researcher , 23 ( 4 ), 19–23. doi: 10.7748/nr.23.4.19.s5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ferrari R (2015). Writing narrative style literature . Medical Writing , 24 ( 4 ), 230–235. doi:DOI: 10.1179/2047480615Z.000000000329 [ CrossRef ] [ Google Scholar ]
Ford I, & Norrie J (2016). Pragmatic Trials . New England Journal of Medicine , 375 ( 5 ), 454–463. doi: 10.1056/NEJMra1510059 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Grady C (2015). Institutional Review Boards: Purpose and Challenges . Chest , 148 ( 5 ), 1148–1155. doi: 10.1378/chest.15-0706 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Heneghan C, & Badenoch D (2006). Evidence-Based Medicine Toolkit (2nd ed.). Malden, MA: Blackwell Publishing. [ Google Scholar ]
Hoffmann TC, Oxman AD, Ioannidis JP, Moher D, Lasserson TJ, Tovey DI, … Glasziou P (2017). Enhancing the usability of systematic reviews by improving the consideration and description of interventions . 358 , j2998. doi: 10.1136/bmj.j2998 %JBMJ [ PubMed ] [ CrossRef ] [ Google Scholar ]
Luz S, Shadmi E, & Drach-Zahavy A (2019). Nursing innovation: The joint effects of championship behaviors, project types, and initiation levels . Nursing Outlook . doi: 10.1016/j.outlook.2019.02.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
McDonough AA, Veiras LC, Guevara CA, & Ralph DL (2017). Cardiovascular benefits associated with higher dietary K+ vs. lower dietary Na+: evidence from population and mechanistic studies . American Journal of Physiology-Endocrinology and Metabolism , 312 ( 4 ), E348–E356. doi: 10.1152/ajpendo.00453.2016 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Methley AM, Campbell S, Chew-Graham C, McNally R, & Cheraghi-Sohi S (2014). PICO, PICOS, and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews . BMC Health Services Research , 14 ( 1 ), 579. doi: 10.1186/s12913-014-0579-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
NCI. (2018, September 13, 2018). NCI Dictionary of Cancer Terms . Retrieved from https://www.cancer.gov/publications/dictionaries/cancer-terms/search?contains=false&q=clinical+research
NIH. (2019, March 4, 2019). NIH Clinical Trials and You . Retrieved from https://clinicaltrials.gov/ct2/about-studies/glossary
NLM. (2019). Glossary of Common Site Terms . Retrieved from https://clinicaltrials.gov/ct2/about-studies/glossary
Pae C-U (2015). Why Systematic Review rather than Narrative Review? Psychiatry investigation , 12 ( 3 ), 417–419. doi: 10.4306/pi.2015.12.3.417 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Pollock A, & Berge E (2018). How to do a systematic review . International Journal of Stroke , 13 ( 2 ), 138–156. doi: 10.1177/1747493017743796 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Uman LS (2011). Systematic reviews and meta-analyses . Journal of the Canadian Academy of Child and Adolescent Psychiatry = Journal de l'Academie Canadienne de Psychiatrie de L'Enfant et de adolescent , 20 ( 1 ), 57–59. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/21286370 https://www.ncbi.nlm.nih.gov/pmc/PMC3024725/ [ PMC free article ] [ PubMed ] [ Google Scholar ]
White CL (2011). Nurse Champions: A Key Role in Bridging the Gap Between Research and Practice . Journal of Emergency Nursing , 37 ( 4 ), 386–387. doi: 10.1016/j.jen.2011.04.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

4 Theories in scientific research

As we know from previous chapters, science is knowledge represented as a collection of ‘theories’ derived using the scientific method. In this chapter, we will examine what a theory is, why we need theories in research, the building blocks of a theory, how to evaluate theories, how can we apply theories in research, and also present illustrative examples of five theories frequently used in social science research.

Theories are explanations of a natural or social behaviour, event, or phenomenon. More formally, a scientific theory is a system of constructs (concepts) and propositions (relationships between those constructs) that collectively presents a logical, systematic, and coherent explanation of a phenomenon of interest within some assumptions and boundary conditions (Bacharach 1989). [1]

Theories should explain why things happen, rather than just describe or predict. Note that it is possible to predict events or behaviours using a set of predictors, without necessarily explaining why such events are taking place. For instance, market analysts predict fluctuations in the stock market based on market announcements, earnings reports of major companies, and new data from the Federal Reserve and other agencies, based on previously observed correlations . Prediction requires only correlations. In contrast, explanations require causations , or understanding of cause-effect relationships. Establishing causation requires three conditions: one, correlations between two constructs, two, temporal precedence (the cause must precede the effect in time), and three, rejection of alternative hypotheses (through testing). Scientific theories are different from theological, philosophical, or other explanations in that scientific theories can be empirically tested using scientific methods.

Explanations can be idiographic or nomothetic. Idiographic explanations are those that explain a single situation or event in idiosyncratic detail. For example, you did poorly on an exam because: you forgot that you had an exam on that day, you arrived late to the exam due to a traffic jam, you panicked midway through the exam, you had to work late the previous evening and could not study for the exam, or even your dog ate your textbook. The explanations may be detailed, accurate, and valid, but they may not apply to other similar situations, even involving the same person, and are hence not generalisable. In contrast, nomothetic explanations seek to explain a class of situations or events rather than a specific situation or event. For example, students who do poorly in exams do so because they did not spend adequate time preparing for exams or because they suffer from nervousness, attention-deficit, or some other medical disorder. Because nomothetic explanations are designed to be generalisable across situations, events, or people, they tend to be less precise, less complete, and less detailed. However, they explain economically, using only a few explanatory variables. Because theories are also intended to serve as generalised explanations for patterns of events, behaviours, or phenomena, theoretical explanations are generally nomothetic in nature.

While understanding theories, it is also important to understand what theories are not. A theory is not data, facts, typologies, taxonomies, or empirical findings. A collection of facts is not a theory, just as a pile of stones is not a house. Likewise, a collection of constructs (e.g., a typology of constructs) is not a theory, because theories must go well beyond constructs to include propositions, explanations, and boundary conditions. Data, facts, and findings operate at the empirical or observational level, while theories operate at a conceptual level and are based on logic rather than observations.

There are many benefits to using theories in research. First, theories provide the underlying logic for the occurrence of natural or social phenomena by explaining the key drivers and outcomes of the target phenomenon, and the underlying processes responsible for driving that phenomenon. Second, they aid in sense-making by helping us synthesise prior empirical findings within a theoretical framework and reconcile contradictory findings by discovering contingent factors influencing the relationship between two constructs in different studies. Third, theories provide guidance for future research by helping identify constructs and relationships that are worthy of further research. Fourth, theories can contribute to cumulative knowledge building by bridging gaps between other theories and by causing existing theories to be re-evaluated in a new light.

However, theories can also have their own share of limitations. As simplified explanations of reality, theories may not always provide adequate explanations of the phenomenon of interest based on a limited set of constructs and relationships. Theories are designed to be simple and parsimonious explanations, while reality may be significantly more complex. Furthermore, theories may impose blinders or limit researchers’ ‘range of vision’, causing them to miss out on important concepts that are not defined by the theory.

Building blocks of a theory

David Whetten (1989) [2] suggests that there are four building blocks of a theory: constructs, propositions, logic, and boundary conditions/assumptions. Constructs capture the ‘what’ of theories (i.e., what concepts are important for explaining a phenomenon?), propositions capture the ‘how’ (i.e., how are these concepts related to each other?), logic represents the ‘why’ (i.e., why are these concepts related?), and boundary conditions/assumptions examines the ‘who, when, and where’ (i.e., under what circumstances will these concepts and relationships work?). Though constructs and propositions were previously discussed in Chapter 2, we describe them again here for the sake of completeness.

Constructs are abstract concepts specified at a high level of abstraction that are chosen specifically to explain the phenomenon of interest. Recall from Chapter 2 that constructs may be unidimensional (i.e., embody a single concept), such as weight or age, or multi-dimensional (i.e., embody multiple underlying concepts), such as personality or culture. While some constructs, such as age, education, and firm size, are easy to understand, others, such as creativity, prejudice, and organisational agility, may be more complex and abstruse, and still others such as trust, attitude, and learning may represent temporal tendencies rather than steady states. Nevertheless, all constructs must have clear and unambiguous operational definitions that should specify exactly how the construct will be measured and at what level of analysis (individual, group, organisational, etc.). Measurable representations of abstract constructs are called variables . For instance, IQ score is a variable that is purported to measure an abstract construct called ‘intelligence’. As noted earlier, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualised at the theoretical plane, while variables are operationalised and measured at the empirical (observational) plane. Furthermore, variables may be independent, dependent, mediating, or moderating, as discussed in Chapter 2. The distinction between constructs (conceptualised at the theoretical level) and variables (measured at the empirical level) is shown in Figure 4.1.

Distinction between theoretical and empirical concepts

Propositions are associations postulated between constructs based on deductive logic. Propositions are stated in declarative form and should ideally indicate a cause-effect relationship (e.g., if X occurs, then Y will follow). Note that propositions may be conjectural but must be testable, and should be rejected if they are not supported by empirical observations. However, like constructs, propositions are stated at the theoretical level, and they can only be tested by examining the corresponding relationship between measurable variables of those constructs. The empirical formulation of propositions, stated as relationships between variables, are called hypotheses . The distinction between propositions (formulated at the theoretical level) and hypotheses (tested at the empirical level) is depicted in Figure 4.1.

The third building block of a theory is the logic that provides the basis for justifying the propositions as postulated. Logic acts like a ‘glue’ that connects the theoretical constructs and provides meaning and relevance to the relationships between these constructs. Logic also represents the ‘explanation’ that lies at the core of a theory. Without logic, propositions will be ad hoc, arbitrary, and meaningless, and cannot be tied into the cohesive ‘system of propositions’ that is the heart of any theory.

Finally, all theories are constrained by assumptions about values, time, and space, and boundary conditions that govern where the theory can be applied and where it cannot be applied. For example, many economic theories assume that human beings are rational (or boundedly rational) and employ utility maximisation based on cost and benefit expectations as a way of understand human behaviour. In contrast, political science theories assume that people are more political than rational, and try to position themselves in their professional or personal environment in a way that maximises their power and control over others. Given the nature of their underlying assumptions, economic and political theories are not directly comparable, and researchers should not use economic theories if their objective is to understand the power structure or its evolution in an organisation. Likewise, theories may have implicit cultural assumptions (e.g., whether they apply to individualistic or collective cultures), temporal assumptions (e.g., whether they apply to early stages or later stages of human behaviour), and spatial assumptions (e.g., whether they apply to certain localities but not to others). If a theory is to be properly used or tested, all of the implicit assumptions that form the boundaries of that theory must be properly understood. Unfortunately, theorists rarely state their implicit assumptions clearly, which leads to frequent misapplications of theories to problem situations in research.

Attributes of a good theory

Theories are simplified and often partial explanations of complex social reality. As such, there can be good explanations or poor explanations, and consequently, there can be good theories or poor theories. How can we evaluate the ‘goodness’ of a given theory? Different criteria have been proposed by different researchers, the more important of which are listed below:

Logical consistency: Are the theoretical constructs, propositions, boundary conditions, and assumptions logically consistent with each other? If some of these ‘building blocks’ of a theory are inconsistent with each other (e.g., a theory assumes rationality, but some constructs represent non-rational concepts), then the theory is a poor theory.

Explanatory power: How much does a given theory explain (or predict) reality? Good theories obviously explain the target phenomenon better than rival theories, as often measured by variance explained (R-squared) value in regression equations.

Falsifiability: British philosopher Karl Popper stated in the 1940s that for theories to be valid, they must be falsifiable. Falsifiability ensures that the theory is potentially disprovable, if empirical data does not match with theoretical propositions, which allows for their empirical testing by researchers. In other words, theories cannot be theories unless they can be empirically testable. Tautological statements, such as ‘a day with high temperatures is a hot day’ are not empirically testable because a hot day is defined (and measured) as a day with high temperatures, and hence, such statements cannot be viewed as a theoretical proposition. Falsifiability requires the presence of rival explanations, it ensures that the constructs are adequately measurable, and so forth. However, note that saying that a theory is falsifiable is not the same as saying that a theory should be falsified. If a theory is indeed falsified based on empirical evidence, then it was probably a poor theory to begin with.

Parsimony: Parsimony examines how much of a phenomenon is explained with how few variables. The concept is attributed to fourteenth century English logician Father William of Ockham (and hence called ‘Ockham’s razor’ or ‘Occam’s razor’), which states that among competing explanations that sufficiently explain the observed evidence, the simplest theory (i.e., one that uses the smallest number of variables or makes the fewest assumptions) is the best. Explanation of a complex social phenomenon can always be increased by adding more and more constructs. However, such an approach defeats the purpose of having a theory, which is intended to be a ‘simplified’ and generalisable explanation of reality. Parsimony relates to the degrees of freedom in a given theory. Parsimonious theories have higher degrees of freedom, which allow them to be more easily generalised to other contexts, settings, and populations.

Approaches to theorising

How do researchers build theories? Steinfeld and Fulk (1990) [3] recommend four such approaches. The first approach is to build theories inductively based on observed patterns of events or behaviours. Such an approach is often called ‘grounded theory building’, because the theory is grounded in empirical observations. This technique is heavily dependent on the observational and interpretive abilities of the researcher, and the resulting theory may be subjective and non-confirmable. Furthermore, observing certain patterns of events will not necessarily make a theory, unless the researcher is able to provide consistent explanations for the observed patterns. We will discuss the grounded theory approach in a later chapter on qualitative research.

The second approach to theory building is to conduct a bottom-up conceptual analysis to identify different sets of predictors relevant to the phenomenon of interest using a predefined framework. One such framework may be a simple input-process-output framework, where the researcher may look for different categories of inputs, such as individual, organisational, and/or technological factors potentially related to the phenomenon of interest (the output), and describe the underlying processes that link these factors to the target phenomenon. This is also an inductive approach that relies heavily on the inductive abilities of the researcher, and interpretation may be biased by researcher’s prior knowledge of the phenomenon being studied.

The third approach to theorising is to extend or modify existing theories to explain a new context, such as by extending theories of individual learning to explain organisational learning. While making such an extension, certain concepts, propositions, and/or boundary conditions of the old theory may be retained and others modified to fit the new context. This deductive approach leverages the rich inventory of social science theories developed by prior theoreticians, and is an efficient way of building new theories by expanding on existing ones.

The fourth approach is to apply existing theories in entirely new contexts by drawing upon the structural similarities between the two contexts. This approach relies on reasoning by analogy, and is probably the most creative way of theorising using a deductive approach. For instance, Markus (1987) [4] used analogic similarities between a nuclear explosion and uncontrolled growth of networks or network-based businesses to propose a critical mass theory of network growth. Just as a nuclear explosion requires a critical mass of radioactive material to sustain a nuclear explosion, Markus suggested that a network requires a critical mass of users to sustain its growth, and without such critical mass, users may leave the network, causing an eventual demise of the network.

Examples of social science theories

In this section, we present brief overviews of a few illustrative theories from different social science disciplines. These theories explain different types of social behaviors, using a set of constructs, propositions, boundary conditions, assumptions, and underlying logic. Note that the following represents just a simplistic introduction to these theories. Readers are advised to consult the original sources of these theories for more details and insights on each theory.

Agency theory. Agency theory (also called principal-agent theory), a classic theory in the organisational economics literature, was originally proposed by Ross (1973) [5] to explain two-party relationships—such as those between an employer and its employees, between organisational executives and shareholders, and between buyers and sellers—whose goals are not congruent with each other. The goal of agency theory is to specify optimal contracts and the conditions under which such contracts may help minimise the effect of goal incongruence. The core assumptions of this theory are that human beings are self-interested individuals, boundedly rational, and risk-averse, and the theory can be applied at the individual or organisational level.

The two parties in this theory are the principal and the agent—the principal employs the agent to perform certain tasks on its behalf. While the principal’s goal is quick and effective completion of the assigned task, the agent’s goal may be working at its own pace, avoiding risks, and seeking self-interest—such as personal pay—over corporate interests, hence, the goal incongruence. Compounding the nature of the problem may be information asymmetry problems caused by the principal’s inability to adequately observe the agent’s behaviour or accurately evaluate the agent’s skill sets. Such asymmetry may lead to agency problems where the agent may not put forth the effort needed to get the task done (the moral hazard problem) or may misrepresent its expertise or skills to get the job but not perform as expected (the adverse selection problem). Typical contracts that are behaviour-based, such as a monthly salary, cannot overcome these problems. Hence, agency theory recommends using outcome-based contracts, such as commissions or a fee payable upon task completion, or mixed contracts that combine behaviour-based and outcome-based incentives. An employee stock option plan is an example of an outcome-based contract, while employee pay is a behaviour-based contract. Agency theory also recommends tools that principals may employ to improve the efficacy of behaviour-based contracts, such as investing in monitoring mechanisms—e.g. hiring supervisors—to counter the information asymmetry caused by moral hazard, designing renewable contracts contingent on the agent’s performance (performance assessment makes the contract partially outcome-based), or by improving the structure of the assigned task to make it more programmable and therefore more observable.

Theory of planned behaviour. Postulated by Azjen (1991), [6] the theory of planned behaviour (TPB) is a generalised theory of human behaviour in social psychology literature that can be used to study a wide range of individual behaviours. It presumes that individual behaviour represents conscious reasoned choice, and is shaped by cognitive thinking and social pressures. The theory postulates that behaviours are based on one’s intention regarding that behaviour, which in turn is a function of the person’s attitude toward the behaviour, subjective norm regarding that behaviour, and perception of control over that behaviour (see Figure 4.2). Attitude is defined as the individual’s overall positive or negative feelings about performing the behaviour in question, which may be assessed as a summation of one’s beliefs regarding the different consequences of that behaviour, weighted by the desirability of those consequences. Subjective norm refers to one’s perception of whether people important to that person expect the person to perform the intended behaviour, and is represented as a weighted combination of the expected norms of different referent groups such as friends, colleagues, or supervisors at work. Behavioural control is one’s perception of internal or external controls constraining the behaviour in question. Internal controls may include the person’s ability to perform the intended behaviour (self-efficacy), while external control refers to the availability of external resources needed to perform that behaviour (facilitating conditions). TPB also suggests that sometimes people may intend to perform a given behaviour but lack the resources needed to do so, and therefore posits that behavioural control can have a direct effect on behaviour, in addition to the indirect effect mediated by intention.

TPB is an extension of an earlier theory called the theory of reasoned action, which included attitude and subjective norm as key drivers of intention, but not behavioural control. The latter construct was added by Ajzen in TPB to account for circumstances when people may have incomplete control over their own behaviours (such as not having high-speed Internet access for web surfing).

Innovation diffusion theory. Innovation diffusion theory (IDT) is a seminal theory in the communications literature that explains how innovations are adopted within a population of potential adopters. The concept was first studied by French sociologist Gabriel Tarde, but the theory was developed by Everett Rogers in 1962 based on observations of 508 diffusion studies. The four key elements in this theory are: innovation, communication channels, time, and social system. Innovations may include new technologies, new practices, or new ideas, and adopters may be individuals or organisations. At the macro (population) level, IDT views innovation diffusion as a process of communication where people in a social system learn about a new innovation and its potential benefits through communication channels—such as mass media or prior adopters— and are persuaded to adopt it. Diffusion is a temporal process—the diffusion process starts off slow among a few early adopters, then picks up speed as the innovation is adopted by the mainstream population, and finally slows down as the adopter population reaches saturation. The cumulative adoption pattern is therefore an s-shaped curve, as shown in Figure 4.3, and the adopter distribution represents a normal distribution. All adopters are not identical, and adopters can be classified into innovators, early adopters, early majority, late majority, and laggards based on the time of their adoption. The rate of diffusion also depends on characteristics of the social system such as the presence of opinion leaders (experts whose opinions are valued by others) and change agents (people who influence others’ behaviours).

At the micro (adopter) level, Rogers (1995) [7] suggests that innovation adoption is a process consisting of five stages: one, knowledge : when adopters first learn about an innovation from mass-media or interpersonal channels, two, persuasion : when they are persuaded by prior adopters to try the innovation, three, decision : their decision to accept or reject the innovation, four,: their initial utilisation of the innovation, and five, confirmation : their decision to continue using it to its fullest potential (see Figure 4.4). Five innovation characteristics are presumed to shape adopters’ innovation adoption decisions: one, relative advantage : the expected benefits of an innovation relative to prior innovations, two, compatibility : the extent to which the innovation fits with the adopter’s work habits, beliefs, and values, three, complexity : the extent to which the innovation is difficult to learn and use, four, trialability : the extent to which the innovation can be tested on a trial basis, and five, observability : the extent to which the results of using the innovation can be clearly observed. The last two characteristics have since been dropped from many innovation studies. Complexity is negatively correlated to innovation adoption, while the other four factors are positively correlated. Innovation adoption also depends on personal factors such as the adopter’s risk-taking propensity, education level, cosmopolitanism, and communication influence. Early adopters are venturesome, well educated, and rely more on mass media for information about the innovation, while later adopters rely more on interpersonal sources—such as friends and family—as their primary source of information. IDT has been criticised for having a ‘pro-innovation bias’—that is for presuming that all innovations are beneficial and will be eventually diffused across the entire population, and because it does not allow for inefficient innovations such as fads or fashions to die off quickly without being adopted by the entire population or being replaced by better innovations.

Elaboration likelihood model . Developed by Petty and Cacioppo (1986), [8] the elaboration likelihood model (ELM) is a dual-process theory of attitude formation or change in psychology literature. It explains how individuals can be influenced to change their attitude toward a certain object, event, or behaviour and the relative efficacy of such change strategies. The ELM posits that one’s attitude may be shaped by two ‘routes’ of influence: the central route and the peripheral route, which differ in the amount of thoughtful information processing or ‘elaboration required of people (see Figure 4.5). The central route requires a person to think about issue-related arguments in an informational message and carefully scrutinise the merits and relevance of those arguments, before forming an informed judgment about the target object. In the peripheral route, subjects rely on external ‘cues’ such as number of prior users, endorsements from experts, or likeability of the endorser, rather than on the quality of arguments, in framing their attitude towards the target object. The latter route is less cognitively demanding, and the routes of attitude change are typically operationalised in the ELM using the argument quality and peripheral cues constructs respectively.

Whether people will be influenced by the central or peripheral routes depends upon their ability and motivation to elaborate the central merits of an argument. This ability and motivation to elaborate is called elaboration likelihood . People in a state of high elaboration likelihood (high ability and high motivation) are more likely to thoughtfully process the information presented and are therefore more influenced by argument quality, while those in the low elaboration likelihood state are more motivated by peripheral cues. Elaboration likelihood is a situational characteristic and not a personal trait. For instance, a doctor may employ the central route for diagnosing and treating a medical ailment (by virtue of his or her expertise of the subject), but may rely on peripheral cues from auto mechanics to understand the problems with his car. As such, the theory has widespread implications about how to enact attitude change toward new products or ideas and even social change.

General deterrence theory. Two utilitarian philosophers of the eighteenth century, Cesare Beccaria and Jeremy Bentham, formulated general deterrence theory (GDT) as both an explanation of crime and a method for reducing it. GDT examines why certain individuals engage in deviant, anti-social, or criminal behaviours. This theory holds that people are fundamentally rational (for both conforming and deviant behaviours), and that they freely choose deviant behaviours based on a rational cost-benefit calculation. Because people naturally choose utility-maximising behaviours, deviant choices that engender personal gain or pleasure can be controlled by increasing the costs of such behaviours in the form of punishments (countermeasures) as well as increasing the probability of apprehension. Swiftness, severity, and certainty of punishments are the key constructs in GDT.

While classical positivist research in criminology seeks generalised causes of criminal behaviours, such as poverty, lack of education, psychological conditions, and recommends strategies to rehabilitate criminals, such as by providing them job training and medical treatment, GDT focuses on the criminal decision-making process and situational factors that influence that process. Hence, a criminal’s personal situation—such as his personal values, his affluence, and his need for money—and the environmental context—such as how protected the target is, how efficient the local police are, how likely criminals are to be apprehended—play key roles in this decision-making process. The focus of GDT is not how to rehabilitate criminals and avert future criminal behaviours, but how to make criminal activities less attractive and therefore prevent crimes. To that end, ‘target hardening’ such as installing deadbolts and building self-defence skills, legal deterrents such as eliminating parole for certain crimes, ‘three strikes law’ (mandatory incarceration for three offences, even if the offences are minor and not worth imprisonment), and the death penalty, increasing the chances of apprehension using means such as neighbourhood watch programs, special task forces on drugs or gang-related crimes, and increased police patrols, and educational programs such as highly visible notices such as ‘Trespassers will be prosecuted’ are effective in preventing crimes. This theory has interesting implications not only for traditional crimes, but also for contemporary white-collar crimes such as insider trading, software piracy, and illegal sharing of music.

Bacharach, S.B. (1989). Organizational theories: some criteria for evaluation. Academy of Management Review , 14(4), 496-515. ↵
Whetten, D. (1989). What constitutes a theoretical contribution? Academy of Management Review , 14(4), 490-495. ↵
Steinfield, C.W. and Fulk, J. (1990). The theory imperative. In J. Fulk & C.W. (Eds.), Organizations and communications technology (pp. 13–26). Newsburt Park, CA: Sage Publications. ↵
Markus, M.L. (1987). Toward a ‘critical mass’ theory of interactive media: universal access, interdependence and diffusion. Communication Research , 14(5), 491-511. ↵
Ross, S.A. (1973). The economic theory of agency: The principal’s problem. American Economic , 63(2), 134-139 ↵
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes , (50), 179–211. ↵
Rogers, E. (1995). Diffusion of innovations (4th ed.). New York: Free Press. ↵
Petty, R.E. and Cacioppo, J.T. (1986). C ommunication and persuasion: Central and peripheral routes to attitude change . New York: Springer-Verlag. ↵

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Buy Me a Coffee

Home » Theory – Definition, Types and Examples

Theory – Definition, Types and Examples

Table of Contents

Definition:

Theory is a set of ideas or principles used to explain or describe a particular phenomenon or set of phenomena. The term “theory” is commonly used in the scientific context to refer to a well-substantiated explanation of some aspect of the natural world that is based on empirical evidence and rigorous testing.

Types of Theories

Types of Theories are as follows:

Scientific Theories

These are theories that explain natural phenomena and are based on empirical evidence. Examples include the theory of evolution, the germ theory of disease, and the theory of relativity.

Social Theories

These are theories that attempt to explain social phenomena, such as human behavior, culture, and society. Examples include social learning theory, structural functionalism, and feminist theory.

Psychological Theories

These are theories that attempt to explain human behavior and mental processes. Examples include behaviorism, cognitive psychology, and psychoanalysis.

Economic Theories

These are theories that attempt to explain economic phenomena, such as the behavior of markets, businesses, and consumers. Examples include supply and demand theory, Keynesian economics, and game theory.

Political Theories

These are theories that attempt to explain political phenomena, such as the behavior of governments, political systems, and international relations. Examples include liberalism, conservatism, and Marxism.

Philosophical Theories

These are theories that attempt to explain fundamental concepts, such as the nature of reality, knowledge, and morality. Examples include existentialism, utilitarianism, and metaphysics.

Mathematical Theories

These are theories that use mathematical concepts and models to explain phenomena in various fields, such as physics, economics, and computer science. Examples include set theory, probability theory, and game theory.

Communication Theories

These are theories that attempt to explain the processes and effects of communication, such as the transmission of information, the influence of media, and the development of language. Examples include social penetration theory, media effects theory, and speech act theory.

Biological Theories

These are theories that attempt to explain biological phenomena, such as the functioning of the human body, genetics, and evolution. Examples include the theory of natural selection, the germ theory of disease, and the central dogma of molecular biology.

Environmental Theories

These are theories that attempt to explain the interactions between humans and the natural environment, including the effects of human activities on the environment and the impact of environmental changes on human society. Examples include ecological systems theory, environmental determinism, and sustainability theory.

Educational Theories

These are theories that attempt to explain the processes and effects of learning and education. Examples include behaviorism, constructivism, and social learning theory.

Cultural Theories

These are theories that attempt to explain cultural phenomena, such as the formation and transmission of cultural values, norms, and beliefs. Examples include cultural studies, postcolonial theory, and critical race theory.

Examples of Theories

There are many theories in various fields of study. Here are some examples of theories in different areas:

Evolutionary Theory: The theory of evolution by natural selection, proposed by Charles Darwin, explains how species change over time in response to their environment.
Quantum Theory : Quantum theory is the branch of physics that describes the behavior of matter and energy on a very small scale.
Social Learning Theory: Social learning theory suggests that people learn by observing and imitating the behaviors of others.
Chaos Theory: Chaos theory is a branch of mathematics that studies complex systems and how they can exhibit unpredictable behavior.
Cognitive Dissonance Theory : This theory explains how people often experience discomfort or tension when their beliefs, attitudes, and behaviors are inconsistent with each other.
Attachment Theory: Attachment theory explains how early relationships between infants and their caregivers can shape their emotional and social development later in life.
General Relativity: General relativity is a theory of gravitation that explains how the force of gravity arises from the curvature of spacetime caused by massive objects.
Game Theory: Game theory is a mathematical approach used to model and analyze the strategic interactions between individuals or groups.
Self-Determination Theory: This theory suggests that people are motivated by three fundamental needs: autonomy, competence, and relatedness.
Systems Theory: Systems theory is a framework for understanding complex systems that emphasizes their interdependence, feedback loops, and dynamic behavior.

Applications of Theories

Applications of Theories are as follows:

Science : Scientific theories are used to develop new technologies, create new medicines, and explore the natural world. For example, the theory of evolution by natural selection is used to understand the diversity of life on Earth, while the theory of relativity is used to develop new technologies such as GPS.
Psychology : Theories of psychology are used to understand human behavior and to develop effective therapies. For example, the theory of cognitive dissonance helps us to understand why people resist changing their beliefs, while the theory of operant conditioning is used to help people change their behavior.
Sociology : Sociological theories are used to understand social structures, institutions, and relationships. For example, the theory of social capital helps us to understand the importance of social networks in promoting economic and social development, while the theory of cultural capital explains how cultural knowledge and practices contribute to social inequality.
Economics : Economic theories are used to understand markets, trade, and economic growth. For example, the theory of comparative advantage helps to explain why countries specialize in certain goods and services, while the theory of supply and demand helps us to understand the behavior of consumers and producers.
Education : Theories of learning and teaching are used to develop effective educational practices. For example, the theory of constructivism emphasizes the importance of students constructing their own knowledge, while the theory of multiple intelligences suggests that students have different types of intelligence that should be recognized and nurtured.

Purpose of Theory

The purpose of a theory is to provide a framework or explanation for observed phenomena in a particular field of study. Theories are developed through a process of observation, experimentation, and analysis, and they are used to explain how and why things happen the way they do.

In science, theories are used to describe and predict natural phenomena, while in social sciences, theories are used to explain human behavior and social interactions. Theories can be tested through further observation and experimentation, and they can be modified or discarded if new evidence contradicts them.

Characteristics of Theory

Explanation : Theories provide an explanation of a phenomenon or event. They identify the causes and underlying mechanisms that contribute to the observed outcomes.
Predictive power: Theories have the ability to predict future outcomes or behaviors based on the identified causes and mechanisms.
Testable: Theories are testable through empirical research. They can be subjected to observation, experimentation, and analysis to determine their validity and accuracy.
Falsifiability : Theories can be falsified if they are found to be inconsistent with empirical evidence. This means that they can be proven to be false if the evidence does not support them.
Generalizability : Theories are generalizable to other contexts and situations beyond the original research setting. They are not specific to a particular time or place.
Organizing framework : Theories provide an organizing framework for understanding and interpreting information. They help researchers organize their observations and make sense of complex phenomena.
Parsimony: Theories are typically simple and concise. They strive to explain phenomena using the fewest number of assumptions or variables possible.

Advantages of Theory

Framework for research: Theories provide a framework for research by guiding the development of hypotheses and research questions.
Organizing information: Theories help researchers organize their observations and make sense of complex phenomena. They provide a structure for understanding and interpreting information.
Prediction: Theories can predict future outcomes or behaviors based on the identified causes and mechanisms.
Understanding causality: Theories help researchers understand the causal relationships between variables and events.
Integration of knowledge: Theories integrate existing knowledge and provide a foundation for new discoveries.
Application : Theories can be applied to real-world problems to develop interventions and policies that address social issues.
Communication: Theories provide a common language and understanding for researchers, which facilitates communication and collaboration.

Disadvantages of Theory

Limited scope: Theories are limited by the scope of their research and the context in which they were developed. They may not be applicable to other contexts or situations beyond the original research setting.
Simplification : Theories often simplify complex phenomena and may oversimplify or exclude important aspects of the phenomenon being studied.
Bias : Theories can be influenced by researcher bias, which can affect the development and interpretation of the theory.
Difficulty in testing: Some theories may be difficult to test empirically, making it challenging to determine their validity and accuracy.
Incomplete understanding : Theories may provide an incomplete understanding of a phenomenon, as they are based on limited research and knowledge.
Resistance to change : Theories can be resistant to change, making it challenging to update or revise them in light of new evidence.
Inconsistency: Different theories within the same field may conflict with each other or present different explanations for the same phenomenon, leading to inconsistencies and confusion.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

What is Art – Definition, Types, Examples

What is Anthropology – Definition and Overview

What is Literature – Definition, Types, Examples

Economist – Definition, Types, Work Area

Anthropologist – Definition, Types, Work Area

What is History – Definitions, Periods, Methods

Research and Hypothesis Testing: Moving from Theory to Experiment

First Online: 14 November 2019

Cite this chapter

Mark W. Scerbo 6 ,
Aaron W. Calhoun 7 &
Joshua Hui 8

2281 Accesses

In this chapter, we discuss the theoretical foundation for research and why theory is important for conducting experiments. We begin with a brief discussion of theory and its role in research. Next, we address the relationship between theory and hypotheses and distinguish between research questions and hypotheses. We then discuss theoretical constructs and how operational definitions make the constructs measurable. Next, we address the experiment and its role in establishing a plan to test the hypothesis. Finally, we offer an example from the literature of an experiment grounded in theory, the hypothesis that was tested, and the conclusions the authors were able to draw based on the hypothesis. We conclude by emphasizing that theory development and refinement does not result from a single experiment, but instead requires a process of research that takes time and commitment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Available as PDF
Read on any device
Instant download
Own it forever
Available as EPUB and PDF
Compact, lightweight edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Badia P, Runyon RP. Fundamentals of behavioral research. Reading: Addison-Wesley; 1982.

Google Scholar

Roeckelein JE. Elsevier’s dictionary of psychological theories. Amsterdam: Elsevier Science; 2006.

Maxwell SE, Delaney HD. Designing experiments and analyzing data: a model comparison perspective. 2nd ed. Mahwah: Erlbaum; 2004.

Graziano AM, Raulin ML. Research methods: a process of inquiry. 4th ed. Boston: Allyn and Bacon; 2000.

Passer MW. Research methods: concepts and connections. New York: Worth; 2014.

Weinger MB, Herndon OW, Paulus MP, Gaba D, Zornow MH, Dallen LD. An objective methodology for task analysis and workload assessment in anesthesia providers. Anesthesiology. 1994;80:77–92.

Article CAS Google Scholar

Warm JS. An introduction to vigilance. In: Warm JS, editor. Sustained attention in human performance. Chichester: Wiley; 1984. p. 1–14.

Freud S. The standard edition of the complete psychological works of Sigmund Freud. Volume XIX (1923–1926) The ego and the id and other works. Strachey, James, Freud, Anna, 1895–1982, Rothgeb, Carrie Lee, 1925-, Richards, Angela, Scientific literature corporation. London: Hogarth Press; 1978.

Mill JS. A system of logic, vol. 1. Honolulu: University Press of the Pacific; 2002. p. 1843.

Ericsson KA, Krampe RT, Tesch-Romer C. The role of deliberate practice in the acquisition of expert performance. Psychol Rev. 1993;100:363–406.

Article Google Scholar

Darian S. Understanding the language of science. Austin: University of Texas Press; 2003.

Lazarus RS, Folkman S. Stress, appraisal, and coping. New York: Springer Publishing Company; 1984.

Calhoun AW, Gaba DM. Live or let die: new developments in the ongoing debate over mannequin death. Simul Healthc. 2017;12:279–81.

Goldberg A, et al. Exposure to simulated mortality affects resident performance during assessment scenarios. Simul Healthc. 2017;12:282–8.

Robinson KA, Saldanha IJ, Mckoy NA. Frameworks for determining research gaps during systematic reviews. Report No.: 11-EHC043-EF. Rockville: Agency for Healthcare Research and Quality (US); 2011.

O’Sullivan D, Wilk S, Michalowski W, Farion K. Using PICO to align medical evidence with MDs decision making models. Stud Health Technol Inform. 2013;192:1057.

PubMed Google Scholar

Christensen LB, Johnson RB, Turner LA. Research methods: design and analysis. 12th ed. Boston: Pearson; 2014.

Keppel G. Design and analysis: a researcher’s handbook. 2nd ed. Englewood Cliffs: Prentice-Hall; 1982.

Spielberger CD, Sydeman SJ. State-trait anxiety inventory and state-trait anger expression inventory. In: Maruish ME, editor. The use of psychological testing for treatment planning and outcome assessment. Hillsdale: Lawrence Erlbaum Associates; 1994. p. 292–321.

Cheng A, et al. Reporting guidelines for health care simulation research: extensions to the CONSORT and STROBE statements. Simul Healthc. 2016;11(4):238–48.

Turner TR, Scerbo MW, Gliva-McConvey G, Wallace AM. Standardized patient encounters: periodic versus postencounter evaluation of nontechnical clinical performance. Simul Healthc. 2016;11:174–2.

Baddeley AD, Hitch GJ. Working memory. In: Bower GH, editor. The psychology of learning and motivation: advances in research and theory. 8th ed. New York: Academic; 1974. p. 47–89.

Kuhn TS. The structure of scientific revolutions. Chicago: University of Chicago Press; 1962.

Newton-Smith WH. The rationality of science. London: Routledge & Keegan Paul; 1981.

Book Google Scholar

Download references

Author information

Authors and affiliations.

Department of Psychology, Old Dominion University, Norfolk, VA, USA

Mark W. Scerbo

Department of Pediatrics, University of Louisville School of Medicine, Louisville, KY, USA

Aaron W. Calhoun

Emergency Medicine, Kaiser Permanente, Los Angeles Medical Center, Los Angeles, CA, USA

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark W. Scerbo .

Editor information

Editors and affiliations.

Monash Institute for Health and Clinical Education, Monash University, Clayton, VIC, Australia

Debra Nestel

Department of Surgery, University of Maryland, Baltimore, Baltimore, MD, USA

Kevin Kunkler

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Scerbo, M.W., Calhoun, A.W., Hui, J. (2019). Research and Hypothesis Testing: Moving from Theory to Experiment. In: Nestel, D., Hui, J., Kunkler, K., Scerbo, M., Calhoun, A. (eds) Healthcare Simulation Research. Springer, Cham. https://doi.org/10.1007/978-3-030-26837-4_22

Download citation

DOI : https://doi.org/10.1007/978-3-030-26837-4_22

Published : 14 November 2019

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-26836-7

Online ISBN : 978-3-030-26837-4

eBook Packages : Biomedical and Life Sciences Biomedical and Life Sciences (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

View all MCQs in

No comments yet

Related MCQs

Quantitative research is based on ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐.
Which of the following terms best describes data that were originally collected at an earlier time by originally collected at an earlier time by different person for a different purpose?
Which of the following includes examples of quantitative variables?
Motivation Research is a type of …………… research
After formulating the research problem the research will prepare ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐.
A concept which an quantitative values is called ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐.
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ concern with the quantitative, & statistical aspects of the design such as technique of study, the methods of model building etc.
Which of the following is the first step in starting the research process?
Which of the following refers to research supported by measurable evidence?
If a researcher is studying the effect of using laptops in his classroom to ascertain their merit and worth, he is likely conducting which of the following types of research?

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 4: Theory in Psychology

Phenomena and Theories

Learning Objectives

Define the terms phenomenon and theory and distinguish clearly between them.
Explain the purposes of scientific theories.
Explain why there are usually many plausible theories for any set of phenomena.

Some Famous Psychological Phenomena

Blindsight . People with damage to their visual cortex are often able to respond to visual stimuli that they do not consciously see.
Bystander effect . The more people who are present at an emergency situation, the less likely it is that any one of them will help.
Fundamental attribution error . People tend to explain others’ behaviour in terms of their personal characteristics as opposed to the situation they are in.
McGurk effect . When audio of a basic speech sound is combined with video of a person making mouth movements for a different speech sound, people often perceive a sound that is intermediate between the two. See a demonstration here: The McGurk Effect
O ther -race effect . People recognize faces of people of their own race more accurately than faces of people of other races.
Placebo effect . Placebos (fake psychological or medical treatments) often lead to improvements in people’s symptoms and functioning.
Mere exposure effect . The more often people have been exposed to a stimulus, the more they like it—even when the stimulus is presented subliminally.
Serial position effect . Stimuli presented near the beginning and end of a list are remembered better than stimuli presented in the middle. See a demonstration here: Serial Position Effect
Spontaneous recovery . A conditioned response that has been extinguished often returns with no further training after the passage of time.

Sometimes a replication of a study produces results that differ from the results of the initial study. This difference could mean that the results of the initial study or the results of the replication were a fluke—they occurred by chance and do not reflect something that is generally true. In either case, additional replications would be likely to resolve this discrepancy. A failure to produce the same results could also mean that the replication differed in some important way from the initial study. For example, early studies showed that people performed a variety of tasks better and faster when they were watched by others than when they were alone. Some later replications, however, showed that people performed worse when they were watched by others. Eventually researcher Robert Zajonc identified a key difference between the two types of studies. People seemed to perform better when being watched on highly practiced tasks but worse when being watched on relatively unpracticed tasks (Zajonc, 1965) [1] .These two phenomena have now come to be called social facilitation and social inhibition.

What Is a Theory?

In addition to theory , researchers in psychology use several related terms to refer to their explanations and interpretations of phenomena. A perspective is a broad approach—more general than a theory—to explaining and interpreting phenomena. For example, researchers who take a biological perspective tend to explain phenomena in terms of genetics or nervous and endocrine system structures and processes, while researchers who take a behavioural perspective tend to explain phenomena in terms of reinforcement, punishment, and other external events. A model is a precise explanation or interpretation of a specific phenomenon—often expressed in terms of equations, computer programs, or biological structures and processes. A hypothesis can be an explanation that relies on just a few key concepts—although this term more commonly refers to a prediction about a new phenomenon based on a theory (see Section 4.3 “Using Theories in Psychological Research” ). A theoretical framework can be as broad as a perspective or a specific as a model, but it is the context applied to understanding a phenomenon. Adding to the confusion is the fact that researchers often use these terms interchangeably. It would not be considered wrong to refer to the drive theory as the drive model or even the drive hypothesis. And the biopsychosocial model of health psychology—the general idea that health is determined by an interaction of biological, psychological, and social factors—is really more like a perspective as defined here. Keep in mind, however, that the most important distinction remains that between observations and interpretations.

What Are Theories For?

Of course, scientific theories are meant to provide accurate explanations or interpretations of phenomena. But there must be more to it than this explanation. Consider that a theory can be accurate without being very useful. To say that expressive writing helps people “deal with their emotions” might be accurate as far as it goes, but it seems too vague to be of much use. Consider also that a theory can be useful without being entirely accurate. Figure 4.1 is a representation of the classic multistore model of human memory, which is still cited by researchers and discussed in textbooks despite the fact that it is now known to be inaccurate in a number of ways (Izawa, 1999). [3] These two examples suggest that theories have purposes other than simply providing accurate explanations or interpretations. Here we look at three additional purposes of theories: the organization of known phenomena, the prediction of outcomes in new situations, and the generation of new research.

Organization

One important purpose of scientific theories is to organize phenomena in ways that help people think about them clearly and efficiently. The drive theory of social facilitation and social inhibition, for example, helps to organize and make sense of a large number of seemingly contradictory results. The multistore model of human memory efficiently summarizes many important phenomena: the limited capacity and short retention time of information that is attended to but not rehearsed, the importance of rehearsing information for long-term retention, the serial-position effect, and so on. Or consider a classic theory of intelligence represented by Figure 4.2. According to this theory, intelligence consists of a general mental ability, g, plus a small number of more specific abilities that are influenced by g (Neisset et al., 1996) [4] . Although there are other theories of intelligence, this one does a good job of summarizing a large number of statistical relationships between tests of various mental abilities. This theory includes the fact that tests of all basic mental abilities tend to be somewhat positively correlated and the fact that certain subsets of mental abilities (e.g., reading comprehension and analogy completion) are more positively correlated than others (e.g., reading comprehension and arithmetic).

Thus theories are good or useful to the extent that they organize more phenomena with greater clarity and efficiency. Scientists generally follow the principle of parsimony , also known as Occam's razor , which holds that a theory should include only as many concepts as are necessary to explain or interpret the phenomena of interest. Simpler, more parsimonious theories organize phenomena more efficiently than more complex, less parsimonious theories.

In clinical psychology, treatment decisions are often guided by theories. Consider, for example, dissociative identity disorder (formerly called multiple personality disorder). The prevailing scientific theory of dissociative identity disorder is that people develop multiple personalities (also called alters) because they are familiar with this idea from popular portrayals (e.g., the movie Sybil) and because they are unintentionally encouraged to do so by their clinicians (e.g., by asking to “meet” an alter). This theory implies that rather than encouraging patients to act out multiple personalities, treatment should involve discouraging them from doing this role playing (Lilienfeld & Lynn, 2003) [5] .

Generation of New Research

Notice that a theory does not have to be accurate to serve this purpose. Even an inaccurate theory can generate new and interesting research questions. Of course, if the theory is inaccurate, the answers to the new questions will tend to be inconsistent with the theory. This new direction will lead researchers to reevaluate the theory and either revise it or abandon it for a new one. And this cycle of revising is how scientific theories become more detailed and accurate over time.

Multiple Theories

Where Do Multiple Personalities Come From?

The literature on dissociative identity disorder (DID) features two competing theories. The sociocognitive theory is that DID comes about because patients are aware of the disorder, know its characteristic features, and are encouraged to take on multiple personalities by their therapists. The post-traumatic theory is that multiple personalities develop as a way of coping with sexual abuse or some other trauma. There are now several lines of evidence that support the sociocognitive model over the post-traumatic model (Lilienfeld & Lynn, 2003 [6] ).

Diagnosis of DID greatly increased after the release of the book and film Sybil —about a woman with DID—in the 1970s.
DID is extremely rare outside of North America.
A very small percentage of therapists are responsible for diagnosing the vast majority of cases of DID.
The literature on treating DID includes many practices that encourage patients to act out multiple personalities (e.g., having a bulletin board on which personalities can leave messages for each other).
Normal people can easily re-create the symptoms of DID with minimal suggestion in simulated clinical interviews.

Key Takeaways

Scientists distinguish between phenomena, which are their systematic observations, and theories, which are their explanations or interpretations of phenomena.
In addition to providing accurate explanations or interpretations, scientific theories have three basic purposes. They organize phenomena, allow people to predict what will happen in new situations, and help generate new research.
Researchers generally consider multiple theories for any set of phenomena. Different theories of the same set of phenomena can be complementary or competing.
Practice: Think of at least three different theories to explain the fact that married people tend to report greater levels of happiness than unmarried people.
Identify the primary phenomenon of interest.
Identify the theory or theories used to explain or interpret that phenomenon.
Discussion: Can a theory be useful even if it is inaccurate? How?
Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274. ↵
Miyamoto, Y. & Kitayama, S. (2002). Cultural variation in correspondence bias: The critical role of attitude diagnosticity of socially constrained behavior. Journal of Personality and Social Psychology, 83 (5), 1239-1348. ↵
Izawa, C. (Ed.) (1999). On human memory: Evolution, progress, and reflections on the 30th anniversary of the Atkinson-Shiffrin model . Mahwah, NJ: Erlbaum. ↵
Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci,…Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51 , 77–101. ↵
Lilienfeld, S. O., & Lynn, S. J. (2003). Dissociative identity disorder: Multiple personalities, multiple controversies. In S. O. Lilienfeld, S. J. Lynn, & J. M. Lohr (Eds.), Science and pseudoscience in clinical psychology (pp. 109–142). New York, NY: Guilford Press. ↵
Lilienfeld, S. O., & Lynn, S. J. (2003). Dissociative identity disorder: Multiple personalities, multiple controversies. In S. O. Lilienfeld, S. J. Lynn, & J. M. Lohr (Eds.), Science and pseudoscience in clinical psychology (pp. 109–142). New York, NY: Guilford Press. ↵

A general result that has been observed reliably in systematic empirical research.

People with damage to their visual cortex are often able to respond to visual stimuli that they do not consciously see.

The more people who are present at an emergency situation, the less likely it is that any one of them will help.

People tend to explain others’ behaviour in terms of their personal characteristics as opposed to the situation they are in.

When audio of a basic speech sound is combined with video of a person making mouth movements for a different speech sound, people often perceive a sound that is intermediate between the two.

People recognize faces of people of their own race more accurately than faces of people of other races.

A positive effect of a treatment that lacks any active ingredient or element to make it effective.

The more often people have been exposed to a stimulus, the more they like it—even when the stimulus is presented subliminally.

Stimuli presented near the beginning and end of a list are remembered better than stimuli presented in the middle.

A conditioned response that has been extinguished often returns with no further training after the passage of time.

Conducting a study again, either exactly as was originally conducted or with modifications, to ensure that it will produce the same results.

A coherent explanation or interpretation of one or more phenomena.

A broad approach to explaining and interpreting phenomena.

A precise explanation or interpretation of a specific phenomenon; expressed in terms of equations, computer programs, or biological structures and processes.

A prediction about a new phenomenon based on a theory; can also be an explanation that relies on just a few key concepts.

The established context applied to understanding a phenomenon.

A principle which holds that a theory should include only as many concepts as are necessary to explain or interpret the phenomena of interest.

Another term for parsimony (see definition for parsimony).

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

IMAGES

How to generate research hypotheses?
Research Hypothesis: Definition, Types, Examples and Quick Tips
PPT
Step 3: Generate a Research Hypothesis
Assembling a Cabinet of Curiousities: Using Participatory Action
What Is Theoretical Framework In Research Methodology

VIDEO

Theory Building in Research Methodology, Part-1
Overview of the Spin-Oscillating Field Generator (Rotofluctuator)
Can Physics Predict Evolution?
Novelty in Research
How Scientists make Theories?
Transforming Theory of Change into an Actionable Strategy for Social Impact

COMMENTS

The Central Role of Theory in Qualitative Research
There are at least three primary applications of theory in qualitative research: (1) theory of research paradigm and method (Glesne, 2011), (2) theory building as a result of data collection (Jaccard & Jacoby, 2010), and (3) theory as a framework to guide the study (Anfara & Mertz, 2015). Differentiation and clarification between these ...
4.1 Phenomena and Theories
Generation of New Research. A third purpose of theories is to generate new research by raising new questions. Consider, for example, the theory that people engage in self-injurious behavior such as cutting because it reduces negative emotions such as sadness, anxiety, and anger. This theory immediately suggests several new and interesting ...
Chapter 4 Theories in Scientific Research
Chapter 4 Theories in Scientific Research. As we know from previous chapters, science is knowledge represented as a collection of "theories" derived using the scientific method. In this chapter, we will examine what is a theory, why do we need theories in research, what are the building blocks of a theory, how to evaluate theories, how can ...
Types of Research within Qualitative and Quantitative
Make your observations about something that is unknown, unexplained, or new. Investigate current theory surrounding your problem or issue. Hypothesize an explanation for those observations. ... Correlational research attempts to determine the extent of a relationship between two or more variables using statistical data. In this type of design ...
Developing a Theory in Academic Research: A Review of Experts' Advice
This paper reviews and collates the views of scholars on what a theory is and how a good theory can be developed. It explains the concept of a theory, and the different components that make up a ...
The use of theory in qualitative research: Challenges, development of a
Under- use of theory is an issue related to the poor establishment of methodology and rigour of qualitative research (Kelly, 2010). Over- reliance on a theory can reduce the develop-ment of new knowledge that is critically important to the progress of a profession (Meleis, 2012). An extensive review of the use of theory in published qual ...
Theory-generating design science research
A frequently mentioned challenge in design science research (DSR) is the generation of novel theory above and beyond information technology artefacts. This article analyzes the DSR process and extends established frameworks for theory generation to exemplify improvements to theory generation through methods of grounded theory development. On a conceptual base, we developed a theory-generating ...
(PDF) Theory-Generating Design Science Research
2. Theory-Generating Design Science Research. Abstract. A frequently mentioned challenge in design science resear ch (DSR) is the generation of novel theor y above and. beyond information ...
The fundamental importance of method to theory
The new experimentalists were, in part, reacting to a famous edict in the philosophy of science that observation is theory-laden: that when taking measurements in a particular context, the manner ...
Models of the Development of Scientific Theories
The three basic kinds of theory development are expansion, contraction and revision by empirical evidence (Sect. 25.1).Under empiricist assumptions, the history of scientific evidence can be represented by a sequence of true and cumulatively increasing evidence sets which in the limit determine the complete structure of the world (Sect. 25.2).Under these assumptions it turns out that purely ...
Researching and Developing Models, Theories and Approaches ...
Figure 5.2 depicts the new framework created to organise considerations for researching and developing models, theories and approaches. The framework comprises three phases that are situated within the context for the contribution and the knowledge paradigm that best represents the work:. Context—indicates the situations in which the contribution is to be developed and used, including ...
Phenomena and Theories
Generation of New Research. A third purpose of theories is to generate new research by raising new questions. Consider, for example, the theory that people engage in self-injurious behaviour such as cutting because it reduces negative emotions such as sadness, anxiety, and anger. This theory immediately suggests several new and interesting ...
PDF How a New Learning Theory Can Benefit Transformative Learning Research
theoretical basis for this new theory of learning, known as Frontiers in Education|www.frontiersin.org 2 June2022|Volume 7|Article 857091 feduc-07-857091 May 26, 2022 Time: 17:42 # 3
How Does Research Start?
Clinical research aims to deliver healthcare advancements that are safe, beneficial, and cost-effective ( Ford & Norrie, 2016 ). Research requires a methodical approach to develop studies that generate high-quality evidence to support changes in clinical practice. The method is a step-wise process that attempts to limit the chances of errors ...
Theories in scientific research
As we know from previous chapters, science is knowledge represented as a collection of 'theories' derived using the scientific method. In this chapter, we will examine what a theory is, why we need theories in research, the building blocks of a theory, how to evaluate theories, how can we apply theories in research, and also present illustrative examples of five theories frequently used in ...
Theory
Systems Theory: Systems theory is a framework for understanding complex systems that emphasizes their interdependence, feedback loops, and dynamic behavior. Applications of Theories. Applications of Theories are as follows: Science: Scientific theories are used to develop new technologies, create new medicines, and explore the natural world ...
MCQ Flashcards
A way of conducting research that is not grounded in theory. b. A framework for every stage of the collection and analysis of data. c. The choice between using qualitative or quantitative methods. ... of nonnumerical data b. an attempt to confirm the researcher's hypotheses c. research that is exploratory d. research that attempts to generate a ...
Research and Hypothesis Testing: Moving from Theory to Experiment
The theory is what provides the conceptual structure to a collection of facts, laws, and/or models. Theories can be used to explain facts and laws and to predict new laws or phenomena. However, most often we rely upon theory to guide research. A theory should suggest many possible tests.
Research Methods MCQs
a. quantitative research b. qualitative research c. mixed research d. none of the above. Which of the following best describes quantitative research? a. the collection of nonnumerical data b. an attempt to confirm the researcher's hypotheses c. research that is exploratory d. research that attempts to generate a new theory
Quiz Chapter 3 Flashcards
Azande. In __________ research, sociologists observe events as they actually occur. survey. O field. secondary analysis. historical. A theory is a set of. findings taken from other social research to explain social behavior. O statements that attempt to predict or explain some aspect of social life.
Solved 1. The feasibility of a research study should be
2. Which of the following best describes quantitative research? a) Research that attempts to generate a new theory b) Research that is exploratory c) An attempt to confirm the researcher's hypotheses d) The collection of non-numerical data 3. Which of the following is NOT a form of measurement validity? a) Convergent validity b) Face validity
Which of the following best describes quantitative research?
Which of the following best describes quantitative research? The collection of non numerical data. An attempt to confirm the researchers hypothesis. Research that is exploratory. research the attempts to generate a new theory. Answer» B. An attempt to confirm the researchers hypothesis.
Phenomena and Theories
Generation of New Research. A third purpose of theories is to generate new research by raising new questions. Consider, for example, the theory that people engage in self-injurious behaviour such as cutting because it reduces negative emotions such as sadness, anxiety, and anger. This theory immediately suggests several new and interesting ...

4.1 Phenomena and Theories

Some Famous Psychological Phenomena

What Is a Theory?

What Are Theories For?

Organization

Generation of New Research

Multiple Theories

Where Do Multiple Personalities Come From?

Key Takeaways

The fundamental importance of method to theory

Access options

Similar content being viewed by others

Worldwide divergence of values

The language network as a natural kind within the broader landscape of the human brain

Persistent interaction patterns across social media platforms and over time

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Peer review

Additional information

Rights and permissions

About this article

Share this article

Quick links

Chapter 4: Theory in Psychology

Some Famous Psychological Phenomena

What Is a Theory?

What Are Theories For?

Organization

Generation of New Research

Multiple Theories

Where Do Multiple Personalities Come From?

Key Takeaways

How Does Research Start?

The inspiration for the Topic

The Research Question

Feasibility

Interesting

Guidelines for Question Development: PICO, PEO

Acknowledgments

4 Theories in scientific research

Building blocks of a theory

Attributes of a good theory

Approaches to theorising

Examples of social science theories

Share This Book

Theory – Definition, Types and Examples

Types of Theories

Scientific Theories

Social Theories

Psychological Theories

Economic Theories

Political Theories

Philosophical Theories

Mathematical Theories

Communication Theories

Biological Theories

Environmental Theories

Educational Theories

Cultural Theories

Examples of Theories

Applications of Theories

Purpose of Theory

Characteristics of Theory

Advantages of Theory

Disadvantages of Theory

About the author

Muhammad Hassan

You may also like

What is Art – Definition, Types, Examples

What is Anthropology – Definition and Overview

What is Literature – Definition, Types, Examples

Economist – Definition, Types, Work Area

Anthropologist – Definition, Types, Work Area

What is History – Definitions, Periods, Methods

Research and Hypothesis Testing: Moving from Theory to Experiment

Cite this chapter

Access this chapter