2 IWO Psychology as a Science
2.1 Science
The online Oxford Dictionary of English (2025) has a good definition for science:
the systematic study of the structure and behavior of the physical and natural world through observation, experimentation, and the testing of theories against the evidence obtained:
“the world of science and technology” · “science laboratories”
- a particular area of science:
“veterinary science” · “the agricultural sciences”- a systematically organized body of knowledge on a particular subject:
“the science of criminology”
As you can see, the word science can refer to any and all of these:
- any (scientific) process for studying something (e.g., “IWO psychologists use science to study people in the context of work”);
- any topic that is studying via a scientific method (e.g., behavioral sciences);
- any collection of scientifically-derived knowledge on a topic (e.g., “the science of personality indicates that people are complicated…”).
What makes science so special is its epistemology. A good definition of epistemology is:
The theory of knowledge and understanding, esp. with regard to its methods, validity, and scope, and the distinction between justified belief and opinion; (as a count noun) a particular theory of knowledge and understanding.
(Oxford English Dictionary, 2025)
In a nutshell, there are many different epistemologies. For example, some people rely on others’ authority to figure out what’s true. Others may rely on consensus to figure out what’s true. Others may rely on intuition to figure out what’s true. And still others may rely on logic to figure out what’s true. However, scientific methods emphasize systematic empirical observations as an important basis to figure out what’s true. Scientists still use logic, consensus, intuition, and (sometimes) authority to figure out what’s true, but the feature that distinguishes science is systematic empirical observations.
An observations is empirical if it is based on and/or verifiable by experiences, rather than only theory or logic. Therefore, systematic empirical observations are repeatable, reasonable, logical, and they’re based on lived experiences.
Despite rigorous research methods, any scientifically-derived knowledge is almost always tentative and subject to revision based on new empirical results. In other words, scientifically derived-knowledge is still often fallible. Thus, researchers—including those in the behavioral sciences such as IWO psychology—tend to “hedge” their conclusions by using language such as: “the results suggest that…”, “the results may indicate that…” “the results provide support for…”.
2.2 IWO Psychology as a science
For each topic that is scientifically studied (e.g., biology, physics, psychology, etc.) the scientists who study those topics use special tools and methods—and the same is true for IWO psychology. To help contextualize the scientific approaches that are typically used in IWO psychology, I’ll now introduce a couple more -ologies: axiology and praxiology. Axiology is:
The study of the nature of value and valuation, and of the kinds of things that are valuable.
- A particular theory of axiology
(Oxford Dictionary of English, 2025)
When we talk about the axiology of IWO psychology, we’re talking about the IWO psychologists’ typical principles or beliefs about what they generally consider to be important. In other words, we’re talking about the values of IWO psychologists.
Relatedly, the praxiology (also spelled praxeology) of IWO psychology is the set of actions that IWO psychologists generally do in their praxis.
Praxis is also known as practice, but it doesn’t mean beginner training. Rather, praxis is like the word practice in medical practice, clinical practice, or IWO psychology practitioner. Scholars often use the word praxis instead of practice, to disambiguate between those two difference senses of practice. So when we say praxis, we’re referring to the application of some skill or ability, as a practitioner would.
Obviously, the axiology and praxiology of IWO psychology evolves over time, and it can never be described perfectly because it is a generalization about all IWO psychologists. However, in the following sections, we can try to describe its historical development and current state.
2.2.1 The axiology and praxiology of IWO psychology
There are several excellent summaries of the historical development of IWO psychology in the USA as a field of science and praxis. Consider reading Salas et al. (2017) and Porter and Schneider (2014). For the enthusiast, Cascio and Aguinis (2008) and Katzell and Austin (1992) are other good (though older) resources.
In a nutshell, IWO psychology in the USA has historically been dominated by an axiology that values quantitative rigor and a balance between science and praxis—hence the classic term: the scientist–practitioner approach in IWO psychology. Historically, the praxiology of IWO psychologists has prominently featured the study of people situated in real work-contexts (though laboratory studies have also played an important role). Such studies typically involve a hypothesis and a quantitative analysis of data in an attempt to evaluate the truth-value of the hypothesis via falsificationism, and hopefully benefit the company and workers being studied and/or other workers more broadly. All of this is quite standard among many scientific disciplines.
My goal in mentioning all of that is to present to you a vocabulary that can enable you to think about and talk about the way IWO psychologists do their work today. Equipped with such language, you can have greater acuity in your analyses of the work of other IWO psychologists, and you can have greater self-awareness of your tendencies and preferences in doing IWO psychology work.
Stance can be a good word to describe an overall orientation or approach for inquiry. For example, someone could have a critical stance, or a practical stance, or a combination of several stances. For an excellent introduction to the typical stances that researchers adopt, see Craig (2009). For brevity, here are some of the most commonly discussed stances:
- Empirical-explanatory stance: This stance is probably what you were taught in science class in grade school. In this stance, researchers typically seek to explain phenomena in an objective manner that is devoid of judgment about whether those phenomena are “good or bad”, and they typically do that by creating testable hypotheses and analyzing empirical data.
- Practical stance: The main distinguishing features of the practical stance are its axiology and praxiology. It seeks to not only understand and explain phenomena, but also to take action to improve the human condition by solving concrete problems. One popular method is known as participatory action research, in which the intended beneficiaries of some solution are involved as partners in co-creating those solutions with the researchers. The practical stance can accommodate a wide variety of epistemologies (e.g., quantitative, qualitative, deductive, inductive, etc.).
- Critical stance: The main distinguishing features of the critical stance are its axiology and praxiology. A core aim of the critical stance is to emancipate society from conditions that perpetuate injustice (e.g., unjust power relations)—in other words, justice is a common feature of their axiology. Persons operating in the tradition of the critical stance tend to be politically active (i.e., that’s a common feature of their praxiology). The critical stance can accommodate a wide variety of epistemologies (e.g., quantitative, qualitative, deductive, inductive, etc.).
- Interpretive stance: According to this stance, it is important that the significance of human action be interpreted from the meaning that humans attribute to their own actions. Thus, the focus isn’t so much on general cause-mechanism theories that are devoid of rich context. A prominent feature of its axiology is the intrinsic worth of understanding the culture and history of the persons being studied.
Traditionally, the field of IWO psychology has operated with a blend of the empirical-explanatory stance (to understand what’s going on) and the practical stance (to help do something good about what’s going on). However, this was primarily due to the culture of the researchers that dominated the field of IWO psychology in its early days in the early-to-mid 1900s. Often, their goal was to understand humans in the context of work, so that their performance could be enhanced (typically for the benefit of the company). Compared to those early days, today there are many more IWO psychologists that skillfully adopt a critical stance and/or interpretive stance, and other stances. Likewise, many IWO psychologists today have a much broader set of topics they focus on, including the wellbeing of workers for its own sake (not simply as a stepping stone toward greater profits), and the social responsibilities of companies.
I encourage you to explore these and other stances for inquiry, and discover which stance(s) resonate with you, and have confidence in the power of being flexible to adopt different stances when they are warranted.
2.2.2 The epistemology of IWO psychology
Most likely, when you were in grade school, you were taught in science class to specify a hypothesis and then test it via an empirical study. That approach is known as hypothetico-deductivism. Deductivism comes from the word deductive, which is often used to describe a type of reasoning known as deductive reasoning: any process of reasoning in which an inference is made about a particular instance of something, based on general principles that transcend that instance. In contrast, inductive reasoning is any process of reasoning in which an inference is made about general principles, based on particular instances. Deductive and inductive reasoning is perhaps easiest to explain via examples, which I’ve provided in Table tbl-deductive_vs_inductive.
| Deductive reasoning | Inductive reasoning |
|---|---|
| We’re fairly certain that flexible scheduling reduces turnover across the organization; therefore, any department that switches to flexible schedules will likely see a reduction in voluntary turnover. | In three departments that switched to flexible schedules, voluntary turnover fell noticeably; therefore, flexible scheduling likely reduces turnover across the organization. |
| We’re fairly certain that structured interviews produce performance-ratings that are more accurate at our company; therefore, for the next hiring season, structured interviews should result in higher-accuracy supervisor-rated performance than unstructured ones. | During this hiring season, structured interviews resulted in higher-accuracy supervisor-rated performance than the unstructured ones; thus, structured interviews likely have better predictive validity in our company. |
Hypothetico-deductivism is the research approach in which the first step is to specify a theory about how stuff works or what stuff is like. Then, from that theory, we use deductive reasoning to create an empirically-testable proposition, which we call a hypothesis, to test our theory. Then, we analyze some empirically-derived data to test the hypothesis, as a way to test our theory. Technically speaking, there is often some inductive reasoning involved when you use your analyses to make an inference about whether your hypothesis and/or theory is true—but we still call the entire approach hypothetico-deductivism because of the focal step of deductively creating a hypothesis from a theory.
In subsequent lectures in this course, I will guide you through a more formal definition of theory.
Another important way to think about the epistemology of IWO psychology is in terms of confirmatory versus exploratory modes of research. In confirmatory research, we seek to test a theory, to figure out whether it is true or not. In exploratory research, we seek to identify patterns in the data, without focusing on testing any specified theory. Both confirmatory and exploratory modes of research are useful, although you may notice a lot of IWO psychology research has traditionally been confirmatory (probably because of how those researchers were taught to do research).
2.2.2.1 Falsificationism
Scientists typically seek to make universal statements that are true. A universal statement is any expression that says a specified property is true for all elements in a specified set. For example, each of the statements in Table tbl-universal_statements is a universal statement because they each make a claim about all elements in a specified set.
A set is any collection of things (e.g., a set of tangible objects, a set of emotions, a set of abstract ideas, etc.). The things inside a set are called the elements of that set.
| Universal statement | The (implied) universal set |
|---|---|
| Dogs are better than cats. | All dogs and all cats. |
| My dogs are better than my cats. | All my dogs and all my cats |
| Workers who are at or higher than 5/10 on the conscientiousness scale have higher salaries than those who are below 5/10. | All workers. |
| Engineers at Company ABC who are at or higher than 5/10 on the conscientiousness scale have higher salaries than those who are below 5/10. | All engineers at Company ABC. |
As you can see from the first two examples in Table tbl-universal_statements, not all universal statements are useful, nor true. Scientists typically seek to make universal statements that are true, and some scientists seek to make universal statements that are true and useful at the same time.
The theories and hypotheses of IWO psychologists are typically universal statements. We test universal statements by trying to find systematic empirical evidence that disproves them. Why? It’s because universal statements are often difficult or impossible to prove true, but they can be quite easy to disprove (i.e., prove them false). The process of trying to disprove a statement is called falsification, and falsificationism is the epistemological belief that the best way to evaluate the truthiness (or, more formally, the truth-value) of a universal statement is to seek and evaluate evidence that seems to disprove it.
Specifically, most of the universal statements that scientists seek to make are about sets that contain such a large number of elements that it would be practically and/or literally impossible to observe every element of those sets to verify the truth of any universal statement about those sets. For example, the third statement in Table tbl-universal_statements is about all workers. To prove that the universal statement is true for all workers, we would need to empirically observe the conscientiousness and salaries of all workers. Every worker that has ever lived in the entire world. Obviously, that’s impossible. However, we can very easily disprove the universal statement if we find at least one worker with conscientiousness below 5/10 whose salary is higher than another worker’s whose conscientiousness is at or above 5/10. Obviously, this example is a bit simple and therefore a bit silly, but this powerful principle is still true for universal statements that are much more realistic and useful, such as this one: “A worker’s conscientiousness tends to correlate moderately with their salary”. If that statement is truly seeking to make a claim about all workers everywhere, a good way to evaluate its truth-value is to try to disprove it.
Obviously, if we try our best to falsify a universal statement, but all of those attempts fail to disprove it (i.e., the universal statement appears to be true via all of those tests), we haven’t proven the statement true (unless we were somehow able to test all elements in the universal set). Therefore, when all of our tests fail to disprove a universal statement, all of that evidence merely lends more support and/or credibility to the belief that the statement is actually true. Thus, when we specify a hypothesis in the form of a universal statement, and our best empirical methods failed to falsify the hypothesis, we don’t say the theory was proven true. Rather, we say things like: “the results suggest that the theory is true…”, or “the results indicate the theory may be true…”, or “the results provide support for the theory…”.
2.3 A brief introduction to the types of scholarly works in IWO psychology
In subsequent lectures in this course, we will dive into understanding the different types of scholarly works that IWO psychologists produce. At this point in this course, it would be useful for you to have a basic familiarity with these types:
- empirical study: any study that involves the analysis of empirical data.
- theory paper: any written work that focuses on theories, rather than analyzing any empirical data. For example, it may describe, compare, and/or critique one or more theories.
- lab study: any study that involves the analysis of empirical data that was collected in a laboratory setting (e.g., a IWO psychologists research lab at a university).
- field study: any study that involves the analysis of empirical data that was collected about people behaving in their natural settings, such as a real workplace, rather than in a laboratory. Even if a laboratory is decorated to look like a natural setting, it’s not considered a field study.
- literature-review study: any study that focuses on analyzing multiple other published studies, typically to summarize and synthesize the current state of the science on some topic, but without doing a formal analysis of the aggregated data from those other studies. Typically, a literature-review study will culminate in a literature-review paper.
- meta-analysis study: similar to a literature review study, except a meta-analysis must involve a systematic approach for identifying the other studies that will be included or excluded from the meta-analysis, and the meta-analysis must also involve a systematic analysis of the aggregated data across all of the studies that were included in the meta-analysis. Most often, meta-analyses are quantitative, but they can also be qualitative, and/or mixed-methods.
2.4 A brief note on pre-registration
Pre-registration is a process or result of producing publicly verifiable time-stamped evidence related to a research endeavor (e.g., “she is pre-registering her hypothesis”, “I’m reading his pre-registered plan of analysis”). The most commonly pre-registered items are hypotheses and plans for how the data will be analyzed (but you could theoretically pre-register anything you want, such as a written statement of your underlying theories and/or your expected implications of your study).
One of the primary purposes of pre-registration is to prevent some types of HARKing: Hypothesizing After the Results are Known. HARKing is the act of creating a hypothesis after you’ve already analyzed the data and seen the results about that hypothesis. For example, suppose I collect some data about worker motivation and salary, and then I analyze the data and find that motivation is positively correlated with salary in my dataset. At that point, if I hypothesize that motivation is positively correlated with salary, I’ve just HARKed.
Scholarly discussions about HARKing distinguish between SHARKing—Secretly HARKing—versus THARKing—Transparently HARKing (Hollenbeck & Wright, 2017). HARKing isn’t necessarily a problem. THARKing is definitely not a problem, and in many cases it is arguably unethical not to THARK (Hollenbeck & Wright, 2017). But SHARKing is definitely unethical. For a scholarly analysis of the various problems caused by SHARKing, see Aguinis and Murphy (2019), Hollenbeck and Wright (2017), and Kerr (1998).
Pre-registering your hypotheses and other aspects of your research plan is a great way to establish evidence that you’re being honest and transparent. That’s important for increasing other people’s trust in science—especially for gaining the trust of the general population that might be skeptical about science. Many scholarly journals are increasingly requiring pre-registered study materials for papers that wish to be published in those journals.
In a subsequent lecture in this course, I will describe a popular website for pre-registering your studies, called the Open Science Framework https://osf.io.
Almost all theories and their subsequent hypotheses are based on our observations of the world around us. Therefore, you might be thinking: “aren’t all hypotheses ultimately created after we already know some results about the world around us?” This brings up an important distinction. Let’s suppose you analyze a dataset containing data about two variables: worker motivation and salary. If you analyze the data and find that motivation is positively correlated with salary—before you ever made any hypothesis about their correlation—you would be SHARKing if you pretend that you hypothesized a positive correlation before analyzing the data. However, if you see the correlation is positive in the first dataset and then you hypothesize that the positive correlation will occur in a new dataset from a different group of workers, and then you collect that new data and find the correlation is also positive in that dataset, then that’s good science! The difference is we used a different set of data to test our hypothesis that the correlation would be positive among other workers.
2.5 Suggested Readings
The only readings I suggest on the topics in this lecture are the ones I’ve already cited in these lecture notes, which you can see in the References section immediately below. All of those are available in our shared Zotero Group Library for this course.