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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

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

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

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

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• Published: 18 January 2024

Learning in hybrid classes: the role of off-task activities

• Carli Ochs 1 ,
• Caroline Gahrmann 2 &
• Andreas Sonderegger   ORCID: orcid.org/0000-0003-0054-0544 2 , 3  

Scientific Reports volume  14 , Article number:  1629 ( 2024 ) Cite this article

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• Human behaviour

Hybrid teaching (synchronous online and on-site teaching) offers many advantages (e.g., increased flexibility). However, previous research has suggested that students who join classes online suffer higher levels of distractibility, which might translate into students engaging in more off-task activities. This, in turn, can impair students’ learning performance. The following quasi-experimental field study investigated this specific link between teaching mode, engagement in off-task activities, and learning performance. We collected survey data from N  = 690 students in six hybrid classes ( N  = 254 online, N  = 436 on-site). Participants reported the amount of time they spent engaging in digital and non-digital off-task activities and responded to a quiz on the course material. Results revealed that online students spent more time engaging in off-task activities than on-site students. Further, results were consistent with our hypothesis that joining the class online is associated with lower learning performance via time spent on digital off-task activities.

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Introduction.

Since the start of the SARS COVID-19 pandemic, hybrid teaching has risen to an unprecedented level. Hybrid teaching is a teaching format where students can synchronously follow the class on-site or online 1 . This was especially useful in the later stages of the pandemic because it allowed for non-vaccinated or immuno-compromised students to follow classes from the safety of their homes 2 , 3 .

Overall, hybrid classes have been argued to augment the accessibility of classes 1 . The flexibility of such a format allows people who live further away or have other commitments to follow the same lectures as students who can physically join the class 1 , 4 . The hybrid format is easy to set up: it often consists of a live stream of the on-site class with more or less opportunities for online students to interact (e.g., commenting using the chat function, unmuting the microphone and asking questions or answering questions of the teacher). Because of its reputed benefits and its low cost, there is a high probability that universities and other institutions in the education system will continue to employ hybrid teaching even once there is no longer need for social distancing.

However, a major concern with hybrid teaching is whether the quality of learning is the same for online and on-site students 5 , 6 . Previous research has argued that online students might face more distractions and consequently engage in more off-task activities, which may lead to lower learning performance 5 , 6 . Yet, little research has addressed this question empirically, and scientists have called for further research in this field 5 , 6 . Therefore, in the following study, we investigated student behavior and learning in hybrid teaching, specifically the effect of teaching mode (online vs. on-site) on learning performance mediated via off-task activities.

Hybrid teaching: the difference between online and on-site learning

Hybrid classes allow online and on-site students to simultaneously follow the same class. However, while the class content is the same for both student groups, the learning environment is not. Online and on-site students’ environments may vary in terms of various physical, social and technological aspects, which might influence students’ learning engagement, experience of boredom, and distractions 7 , 8 , 9 , 10 . In the following paragraphs, we demonstrate how differences between the online and on-site environments may lead online students to be more vulnerable to distractions.

First, students following classes online might be in an overall more distracting environment. On-site students are in a classroom, (a supervised space dedicated to teaching and learning), while online students often are following their classes in a space that is cluttered, noisy and disturbing, hence less favorable to learning 11 , 12 .

Second, the social environment between both groups varies in terms of social control. On-site students are in a classroom where other students and the lecturer are physically present. This is not the case for online students. Indeed, students that were asked about their experience with online courses admitted that they miss the presence of peers and engage in more off-task activities because of a lack of social control 13 .

Third, computer-mediated communication may make it harder for online students to communicate with their teachers compared to on-site students 3 , 7 . For example, online students cannot discuss a matter face to face with a teacher after the course 7 . Furthermore, online students have reported feeling less comfortable asking questions due to slight delays caused by the online nature of the communication 3 . In addition, online students may face technical issues during class, such as internet connection and communication problems 7 , 14 , 15 . Both factors may lead online students to feel excluded or neglected 16 . Thus, students might feel disconnected from the class and, therefore, might be more vulnerable to distractions.

In summary, online students are likely to suffer from distraction due to an overall more distracting environment (cluttered, noisy and disturbing environment), reduced social control, and feelings of exclusion or neglect. All of these factors might impinge on students’ ability to concentrate and focus on learning content. Higher levels of distraction imply lower attention on the class.

Impact of off-task activities on learning

Research has shown that students' attention may fluctuate during a lesson 6 , 17 and that reduced attention may be associated with reduced learning performance 18 , 19 , 20 . This link has been established for various forms of attentional disengagement, such as mind wandering and engagement in digital as well as non-digital off-task activities 21 , 22 , 23 .

Mind wandering can be defined as “a situation in which executive control shifts away from a primary task to the processing of personal goals 24 ”, p. 946. Mind wandering is prevalent in teaching and learning situations 12 , 25 , 26 , 27 . Various studies adopting different methodological approaches (e.g., using probes of attention in recorded video lectures or live courses, self-reports, diary studies etc.) have shown that the occurrence of mind wandering is negatively linked to academic performance 6 , 17 , 23 , 27 , 28 . Interestingly, more recent work has indicated that the negative effect of mind wandering has less of an impact compared to other indicators of attentional disengagement, such as engagement in digital off-task activities 29 .

Research on engagement in digital off-task activities (also referred to as multimedia multitasking) has primarily focused on different forms of digital off-task activities 22 , 30 , 31 , such as computer use in the classroom, e.g., 32 , 33 , 34 , smartphone use in the classroom 19 , 35 , 36 , 37 , 38 , 39 , 40 , or the use of specific content such as social media 41 , 42 , 43 , 44 , 45 . Overall, empirical evidence mostly suggests that students regularly engage in digital off-task activities and that this negatively affects their learning 29 , 32 , 34 , 35 , 39 .

While the impact of digital off-task activities in the classroom have been studied quite intensely, there has been a relative lack of research on the impact of non-digital off-task activities 46 . One non-digital off-task activity that has received some attention is doodling. Doodling is defined as absentmindedly drawing symbols, figures, and patterns that are unrelated to the primary task (e.g., listening to the lecture). Results on doodling and learning performance are mixed. Doodling has been shown to positively influence the retention of information in an auditory task 47 , 48 , 49 but to negatively influence retention and learning in visual tasks 21 , 50 . There is only little empirical research addressing the consequence of other types of non-digital off-task activities. This could be related to the fact that the range of such activities is very broad and diverse. Moreover, specific non-digital off-task activities, such as ironing clothing, cleaning, cooking, and yoga, can only be carried out when participating in the lesson online. This last type of attentional disengagement might be particularly relevant in hybrid lessons as online students have a wider variability of potential non-digital off-task activities they could engage in (e.g., cleaning or taking a walk). In this regard, one experimental lab study addressing the effect of doing laundry while following a recorded online lecture has indicated negative consequences on learning 46 .

Concerning hybrid teaching, previous research on attentional disengagement in hybrid classes and their effect on performance is scarce, and researchers have called for further research in this field 5 , 6 . One study on attentional disengagement and learning in the hybrid context examined how mind wandering affected learning performance between students watching a class live on-site versus watching a video of the class in a lab 6 . This study found that mind wandering decreased in the on-site class condition but increased in the lab condition. Students in the video-lecture showed non-significantly higher learning scores than in the on-site session. The authors suspected this was due to the students' sterile settings while they viewed the lesson (e.g., in a lab without phones or computers). Contrary to natural settings, students in the classroom were exposed to more digital distractions than those watching the lesson online. This may have confounded the results, as digital distractions also lead to attentional disengagement and have been linked to lower learning performance 35 , 40 .

In summary, mind wandering, and digital and non-digital off-task activities reflect attentional disengagement that can hinder learning. As mentioned in the preceding section, online students may be more distracted due to their learning environment and the lack of social control. There is little empirical evidence comparing digital and non-digital off-task activities in on-site and online classes and their effects on learning performance. Thus, we intend to fill this research gap by studying hybrid teaching and learning performance mediated by students' digital and non-digital off-task activities.

The present study

In the present quasi-experimental mixed-method field study, we investigated the mediating effect of attentional disengagement between teaching mode (online vs. on-site) and learning performance. Specifically, we assessed the digital and non-digital activities of students as well as their learning performance in classes that were taught in hybrid form (i.e., students following the same class either on-site in a lecture theatre or online in a location of their choice). We were particularly interested in these two forms of attentional disengagement for the following reasons. First, digital off-task activities have been shown to be worse for learning than mind wandering. Second, non-digital activities are of particular interest in hybrid classes as the possibility of activities for online students is vast.

Based on our review of the literature, we put forward the following hypotheses (a depiction of our proposed model can be found in Fig.  1 ):

Proposed conceptual model.

H1: Teaching Mode is associated with time spent on digital off-task activities. Specifically, we expect students who participate online to spend more time on digital off-task activities than students who participate on-site.

H2: Teaching Mode is associated with time spent on non-digital off-task activities. Specifically, we expect students who participate online to spend more time on non-digital activities than students who participate on-site.

H3: Time spent on digital off-task activities mediates the relationship between teaching mode and learning score, such that online students spend more time on digital off-task activities, which, in turn, leads to a decreased learning score.

H4: Time spent on non-digital off-task activities mediates the relationship between teaching mode and learning score, such that online students spend more time on non-digital off-task activities, which, in turn, leads to a decreased learning score.

Teaching mode was coded in the following manner: 0 = on-site; 1 = online.

Participants

We collected 690 responses to our survey (556 participants identified themselves as female, 129 as male, and 5 as other). The ages of participants ranged between 18 and 64 ( M  = 21.46; SD  = 4.42). Participants were recruited at the end of a ninety-minute course in six different psychology lectures (i.e., introduction to work and organizational psychology ( N  = 47), clinical psychology ( N  = 107), introduction to developmental psychology ( N  = 161), introduction to cognitive psychology ( N  = 188), research methods ( N  = 142), diagnostical tests ( N  = 45) held in hybrid teaching mode by six different teachers. For each course some participants (total N  = 436) were following a class on-site, and some participants (total N  = 254) were following the class online. The choice whether to participate online or on-site was at the student’s discretion. In total, 74% of the students from these classes took part in the survey. Detailed information on participation rates per class and teaching mode can be found in the supplementary materials (Table S1 ). Based on Schönbrodt and Perugini (2013), we consider the total sample size of 690 as large enough to generate stable correlation estimates and offer a solid foundation for the correlation based statistical approaches used.

Observations of two independent raters indicated that the lectures did not differ considerably with regard to number of questions asked by the teacher ( M  = 7.33, SD  = 6.57), online student interactions ( M  = 0.33, SD  = 0.82), on-site student interactions ( M  = 7.33, SD  = 5.49), technical problems ( M  = 0.25, SD  = 0.61) and break duration ( M  = 10.83, SD  = 5.15).

Ethical approval

This study and its experimental protocols were approved by the Internal Review Board of the Department of Psychology at the University of Fribourg. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants.

Self-report measures

Questions regarding digital off-task activities.

Participants were asked a series of questions about their computer/tablet and smartphone use during the lecture. These questions can be found in Table 1 . Second-order questions (e.g., 1a. How many notifications/messages did you receive on your computer or tablet during this lecture?) were only presented if participants answered yes to the first-order question (e.g., 1. Did you use your computer or tablet during this lecture?). Items 2.a, and 3.c left enough space to report up to 5 activities.

Questions regarding non-digital off-task activities

Participants were asked if they engaged in any off-task activity that was not performed on a digital device. If they did engage in such activities, they were asked to list the activities and estimate how much time they spent on each separate activity (cf. Table 2 ).

Context variables

To get a better understanding for the physical and mental context of participants, participants were asked to rate their experience on 5 items on a 7-point Likert scale (1 = totally disagree, 7 = totally agree) measuring their motivation ( During the class I felt motivated ), boredom ( During the class I felt bored. ), fatigue ( During the class I felt tired. ) as well as how engaged they perceived their teacher ( The teacher kept the subject interesting .) and how distracting they rated their environment ( During this class my environment was distracting .).

Learning score

Six multiple-choice questions on the content of the specific topic of the class were asked to assess students' learning scores. For each question, four answer options were provided, of which either one or two were correct. An example of a question can be found in Table 3 . The learning score was based on the number of correctly responded answer options. Each question had 4 answer options, allowing participants to score 4 points per question. Therefore, the maximum achievable score is 24 (based on the multiplication of the number of question (6) and the number of answer options (4)).

We implemented the exact same procedure for the six classes from which we recruited participants. Before each class, lecturers were contacted and asked for permission to perform recruitment in their class. Once they had agreed, we collaborated with them to create six exam questions on the course material for the upcoming class session. In each class, two researchers attended the course both online and on-site. Researchers sat at the back of the classroom and connected to the digital collaboration platform used to stream the class online (Microsoft Teams), where they listened with one earphone to the online class. With the other ear, they listened to the on-site class. This allowed them to code for aspects such as interactions on-site (questions from students) as well as interactions online (e.g., in chat) or issues with connections or lag.

The classes ended approximately 15 min before the scheduled time. The two researchers came to the front of the class and asked students to participate in the study. Students were told that the survey was on the effects of teaching mode (online/ on-site) on their learning. As incentives, participants were told that there would be a quiz on course material that might help them to prepare for the end-of-term exam, and participants were also rewarded points toward course credits. Great emphasis was given (orally and in written form on the first page of the survey) to the anonymous nature of the study and it was stressed that students should answer as honestly as possible. After the participants had confirmed their informed consent, they answered the questionnaire and responded to the quiz on the course material.

Qualitative data analysis

Participants' responses regarding their digital and non-digital off-task activities were analyzed using inductive coding. Eleven categories were identified for digital off-task activities, and eleven were identified for non-digital off-task activities (category descriptions can be found in supplementary materials Table S2 .). Activities were then coded into their respective categories by two coders. The inter-rater reliability was estimated using Cohen's kappa and showed to be satisfactory for digital off-task activities ( k  = 0.93) as well as for non-digital off-task activities ( k  = 0.97). The few differences in coding were then solved through discussion.

Quantitative data analysis

We performed a single-level mediated linear regression analysis via the R package lavaan 51 to probe our hypotheses. To ensure robust statistical inference while accounting for the nested structure of the data, we treated classrooms as fixed effects. This approach effectively addresses the issue of violating independence assumptions within clusters, ensuring the accuracy of our results without the inherent unreliability that would have ensued from a multilevel model with a small number of units at Level 2. Moreover, to account for the different learning tests employed across classes, we centered students' learning scores around their respective class mean.

Preceding the analysis, we checked for potential violations of relevant inference assumptions. We performed visual inspections of residuals. Our inspections did not reveal any apparent violations of the linearity assumption. However, residuals were non-normally distributed, thus violating the homoscedasticity assumption. We hence based our inference on bootstrapped confidence intervals (1.000 bootstrapped samples) to ensure robust estimation.

Descriptive data

Table 4 presents a comprehensive overview of participant enrollment, delineated by class and learning environment, alongside their corresponding average learning scores. The data discerns that, on the whole, the mean learning scores per class tended to be higher for students attending on-site, with the exception of one class, where scores were higher for online participants.

Table 5 reveals that a higher proportion of online students engaged in non-digital and digital activities compared to students that joined the class on-site. On average, online students also spent more time engaging in these activities. A table with the detailed summary of students’ percentage of activity and average time per specific activity can be found in supplementary materials (Table S3 ).

Hypotheses testing

Table 6 shows the means, standard deviations, intra-class coefficients and intercorrelations of the study variables. All hypotheses were tested by means of a single level mediated linear regression model. Table 7 displays the results of this analysis.

We proposed a direct association between teaching mode and time spent on digital (H1) and non-digital (H2) off-task activities by students in hybrid classrooms. In line with H1, data analysis (see Table 7 ) revealed that teaching mode was associated with time spent on digital off-task activities, with students who participated online spending more time on digital off-task activities than students who participated on-site ( a1  = 3.76, 95% CI [0.99; 6.42]). Hypothesis 1 was thus supported. In line with H2, teaching mode was associated with time spent on non-digital off-task activities, with students who participated online spending more time on non-digital off-task activities than students who participated on-site ( a2  = 2.40, 95% CI [0.98; 3.75]). Hypothesis 2 was thus supported.

Test of indirect effects

We estimated the total effect of teaching mode on students' learning performance ( a1b1  +  a2b2  =  − 0.09, 95% CI [− 0.17; − 0.03]). This suggests that, on the learning performance scale ranging from 0 to 24 points, the expected difference in learning performance between a student attending online and one attending on-site is 0.09 points, with the online student being expected to score lower. Crucially, this total effect is attributed entirely to the indirect influences of teaching mode on learning outcomes through engagement in both digital and non-digital off-task activities. Specifically, we assumed time spent on digital (H3) and non-digital (H4) off-task activities to mediate the relationship between teaching mode and learning scores. Our results revealed an indirect effect of teaching mode via time spent on digital off-task activities on learning scores ( a1b1  =  − 0.07, 95% CI [− 0.12; − 0.02]), nominally accounting for 77.78% of the total effect of teaching mode on learning performance detailed above. Hypothesis 3 was thus supported. However, our results did not reveal an indirect effect of teaching mode via time spent on non-digital off-task activities on learning scores ( a2b2  =  − 0.02, 95% CI [− 0.09; 0.01]), nominally accounting for only 22.22% of the total effect of teaching mode on learning performance detailed above. Hypothesis 4 was thus not supported.

Analysis of context variables

The analysis of the context variables revealed very small, non-significant differences between online and on-site ratings of student motivation, experienced boredom and fatigue, as well as the evaluation of the teacher’s engagement. The environment however was rated as more distracting by students taking the class online compared to students participating on-site. A table summarizing the results can be found in the supplementary materials (see Table S4 ).

This study aimed to evaluate potential differences in levels of distractibility and learning performance between online and on-site students in hybrid classrooms. As predicted, we found that students following a class online spend more time on digital and non-digital off-task activities than on-site students. Results were consistent with our hypothesis that engagement in digital off-task activities mediates a detrimental effect of online teaching on learning. Notably, results were not consistent with our hypothesis that engagement in non-digital off-task activities mediates a detrimental effect of online teaching on learning.

The results regarding the effects of teaching mode on engagement in off-task activities corroborate the assumptions outlined in the introduction. The descriptive data revealed that online students did not only engage in more off-task activities overall, but they also engaged in these activities for longer compared to on-site students. Some of the off-task activities were exclusively carried out by online students. This might be because some activities can only be carried out at home (e.g., cat care) while others might be considered inappropriate in class (e.g., watching a video). These examples indicate that online students might be confronted with more potential distractors and might feel less restricted to engage in certain behaviors due to a lack of social control.

This pattern of results indicates that despite the important advantages of hybrid teaching (e.g., increased accessibility of teaching), online students are particularly vulnerable within this setting: They report a higher degree of distractibility that is linked with an increased engagement in off-task activities. It is important to note that the teachers participating in this study did not implement any measures to optimize online students' learning experiences such as regularly integrating online students into class discussions, or using online quizzes and other tools to generate an interactive learning environment 1 , 52 , 53 . The focus on on-site students and the absence of particular measures to improve online students’ experience observed in this study may be attributable to the particular context of the COVID-19 pandemic. These classes were held as a temporary solution, meaning that they were not originally conceived as hybrid classes and that teachers had not undergone training on how to ideally teach in such a format 54 . Therefore, future research should address this research question in courses that are more geared towards integrating online students. Investigating hybrid teaching during the pandemic is important since the experiences made during this period are bound to influence and shape future use and understanding of hybrid teaching 15 , 54 , 55 , 56 . For example, now that teachers and institutions have discovered how simple it is to live stream a class, they might do this more frequently without investigating any further into the potential pitfalls of this format.

Regarding the effect of off-task activities on learning, the question arises why students' engagement in non-digital off-task activities did not show the same negative effects as the engagement in digital off-task activities. This might be related to the nature of the activities students engaged in. Most of the reported non-digital activities are manual tasks (e.g., eating and drinking, drawing), while digital activities mainly require verbal and visual resources (e.g., writing text messages and reading). Listening to a class and learning are tasks that require verbal resources 46 . Wickens’ Multiple Resource Theory 57 states that individuals can engage concurrently in tasks that require different resources, such as verbal and manual resources, without negative consequences on task performance. In contrast, concurrent engagement in tasks that require similar resources is linked to reduced task performance. In the context of learning, such effects have been shown already in research on doodling, where positive effects of absentminded drawing (a task requiring some visual resources) on learning were reported for auditory tasks 47 , 48 , 49 , while negative effects were observed for visual tasks 21 , 50 . In this study, verbal non-digital off-task activities (e.g., social interactions or writing and reading) were less common than manual ones. In contrast, reported digital activities relied largely on students' verbal and visual resources, interfering with class support.

Our findings have several practical and scientific implications. First, we found that student, especially online students, in hybrid lessons engage in off-task activities. This shows the need of interactive and engaging teaching to keep students focused on class topic and away from off-task activities. Online students should be reminded to study in a distraction-free atmosphere because they are more susceptible to distraction. Second, this study also revealed a negative effect of digital off-task activities on learning performance. Therefore, it is important to consider measures such as warning students of the negative consequences of partaking in off-task activities as well as suggesting rules of conduct regarding digital devices during class. Third, concerning non-digital off-task activities, a negative effect on learning was not found as, most likely, most activities did not interfere with the resources required for learning. However, for future research it might be interesting to conduct a more fine-grained analysis of the specific off-task activities. Lastly, while literature on how to conduct good hybrid teaching was available, it was not used in the examined classes. This suggests a gap between practice and research and should stimulate reflection on how to effectively communicate this knowledge to practitioners.

There are limitations to the present study. First, due to the quasi-experimental nature of the study design, participants self-selected their teaching mode (online vs. on-site). A random assignment of students to the two teaching modes was not possible because of ethical and legal constraints. In other studies, this problem was circumvented by simulating a course in the laboratory 6 , 17 . However, this implies that the occurrence of off-task activities cannot be observed in a naturalistic environment, which severely undermines the ecological validity of any study. Therefore, we are convinced that this first limitation regarding randomization is outweighed by gains in ecological validity. Second, the assessment of participants' engagement and time spent on off-task activities was based on self-reports. This might be problematic for two reasons: (1) a social desirability bias, (2) memory distortions. To reduce the risk of (1) a social desirability bias, we highlighted the importance of answering the survey honestly and correctly and stressed the anonymity of the data. With regard to potential (2) memory distortions, results of a meta-analysis indicated that self-report data only moderately correlated with objective measures of technology use 58 . This meta-analysis, however, summarized data from studies comparing self-reports and objective technology use over multiple days and weeks, while this study recorded self-reports for the last 90 min. For such short time frames, previous research has indicated that self-report measures regarding technology use are reliable 59 .

Other, more objective ways to assess students’ off-task activities in class would imply installing tracking software on students’ devices or using thought-probe techniques, all of which would reveal the nature of the study to the students. Because previous research has shown that knowledge about the objective of a study can influence participants’ behavior during the study 60 , it was important to us that the participants have no previous knowledge about our research objectives. Other researchers have made similar methodological choices and used self-reports for their studies in this context as well 35 , 59 , 61 , 62 . Third, we only recruited participants in psychology classes. For generalization purposes it would be ideal to conduct such a study in classes of other fields of study. Lastly, we note that teaching mode and off-task activities explained a relatively small portion of variance in students' learning scores overall. While learning performance represents a particularly important objective teaching outcome, it is endogenous to many contextual and student-specific factors 63 . Considering any one factor is therefore bound to explain relatively limited proportions of variance.

In conclusion, while hybrid teaching might be a useful tool to make classes more accessible, it does not guarantee equal opportunities in terms of learning performance. To ensure that students have an equal opportunity to achieve important learning objectives, instructors must be aware of the negative consequences of off-task activities, especially digital off-task activities for students participating online. In addition, they must adapt to the specific requirements of hybrid teaching in order to create optimal learning conditions for both on-site and online students. At the same time, students need to be made aware of creating remote learning conditions that offer as few distractions as possible and enable concentrated and focused learning.

Taking these measures into account is extremely important. Indeed, the past pandemic has shown that individuals with special needs, such as those who are immunocompromised or have physical disabilities, and those with family obligations benefit from being able to participate in courses online. Therefore, it is an ethical and social necessity to create learning environments that do not disadvantage them and enable them to succeed in their studies.

Data availability

The dataset is available on OSF ( https://osf.io/q4vf5/?view_only=21662f508cf44f68be78794d55f83be2 ).

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Acknowledgements

The authors thank Cindy Niclasse, Noemie Stadelmann, Valentine Boillat and Viviane Gaillard for their support in data collection. They also want to thank Anne-Raphaëlle Caldara, Valérie Camos, Alain Chavaillaz, Viktoria Gochmann, Pascal Gygax and Chantal Martin Sölch for their support and for providing access to their classes.

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10 learning hypothesis – Activity 8 H817

Rough notes

Very little academic literature on the theory of eLearning. It tends to be pragmatic and based on experience. Surprising considering the volume of literature on the subject.

What is a theory? “A theory can be described as a set of hypotheses that apply to all instances of a particular phenomenon, assisting in decision-making, philosophy of practice and effective implementation through practice”  interestingly……. “Theory can only be effectively communicated if a common set of terms is used and if their meaning is popularly adhered to”

Terms defined: Online learning, eLearning, learning Object, LMS, Interactive, pedagogy and Mixed-mode/blended/resource-based learning

Ten hypothesis for learning: (Direct from the article)

• eLearning is a means of implementing education that can be applied within varying education models (for example, face to face or distance education) and educational philosophies (for example behaviourism and constructivism).
• eLearning enables unique forms of education that fits within the existing paradigms of face to face and distance education.
• The choice of eLearning tools should reflect rather than determine the pedagogy of a course; how technology is used is more important than which technology is used.
• eLearning advances primarily through the successful implementation of pedagogical innovation.
• eLearning can be used in two major ways; the presentation of education content, and the facilitation of education processes.
• eLearning tools are best made to operate within a carefully selected and optimally integrated course design model.
• eLearning tools and techniques should be used only after consideration has been given to online vs offline trade-offs.
• Effective eLearning practice considers the ways in which end-users will engage with the learning opportunities provided to them.
• The overall aim of education, that is, the development of the learner in the context of a predetermined curriculum or set of learning objectives, does not change when eLearning is applied.
• Only pedagogical advantages will provide a lasting rationale for implementing eLearning approaches.

In order to eLearning to develop the theoretical underpinnings must be made explicit and available for critique. Ravenscroft (2001:150)   “given that the pace of change of educational technology is unlikely to slow down, the need for relatively more stable and theoretically founded interaction models is becoming increasingly important.”

Activity questions

Q1 – with which hypotheses do you agree.

As there is no one confined way of using eLearning, it fits into a broad array of forms and can be adapted into various educational philosophies. An example is   Second Life , its use a platform for learning fits many different ways of creating learning and engaging with students: From one on one conversations, presentations, simulations, group discussions and activities. It hosts regular “ inworld ” conferences to innovate in eLearning. The next one being March 9th to 12th 2016 if you fancy popping along.

Image from page promoting Second Life Education Conference

2.  eLearning enables unique forms of education that fits within the existing paradigms of face to face and distance education.

Similar to the previous point, learning is not binary “traditional” or “learning” but rather a continuum of learning processes that are more or less supported by technology. As an example of this I really like this diagram that Phillippa Cleaves has put together,  as it shows a myriad of ways technology can intertwine with “traditional” education:

Phillippa Cleaves Technology Continuum

3 The choice of eLearning tools should reflect rather than determine the pedagogy of a course; how technology is used is more important than which technology is used.

This is the same for eLearning tools as with any other tool. Appropriate use of ANY tool is essential, even books, pen & paper, flip charts, whiteboards etc. from a learning design perspective I see the flow go something like: Overall learning goals > Specific learning goals > Appropriate interactions / learning processes > Appropriate tools to be used > Design of materials / activities

The selection tools used is, for the most part, the penultimate part of the learning design process.

8 – Effective eLearning practice considers the ways in which end-users will engage with the learning opportunities provided to them

This hypothesis is common sense for using any tool or resource. Having said that, practitioners should also consider and be open to unplanned ways resources may be used.

9 – The overall aim of education, that is, the development of the learner in the context of a predetermined curriculum or set of learning objectives, does not change when eLearning is applied

No, but the process of education may be expanded as learners use and master the toolset provided to support the curriculum.

Unsure about:

4 – eLearning advances primarily through the successful implementation of pedagogical innovation.

“Advances” is a very wooly term. I am sure eLearning has advanced without “successful implementation” as the pace of the industry is quicker than the capacity to research and draw results together of pedagogical success. Innovation in eLearning like all innovation is often “push” driven off the back of companies developing new technologies / services rather than “pull” from practitioners.

5 – eLearning can be used in two major ways; the presentation of education content, and the facilitation of education processes.

eLearning provides a whole range of ways its support learning, from tools to design, through to delivery. A lot of the advances in the eLearning areas are in the creation and sharing of curriculum success as OER. An example of that is Cloudworks , a collaboration between the Open University and JISC (Joint Information Systems Committee):

Cloudworks – a resource for learning practitioners to share   ideas and resources

6 – eLearning tools are best made to operate within a carefully selected and optimally integrated course design model.

I am unsure of this, simply due to the creative nature of designing and developing learning. Without experimentation and play, new ways of using technologies or even the development of doing technologies would not happen. An example of this is Twitter as a tool for learning. Originally not conceived as a carefully selected and optimally integrated tool as part of a course design model, it works well for learners and practitioners in a myriad of ways. One example being #edchat , an award winning, weekly chat about education, developed on the twitter platform. Another perspective if that of learning theories and constructivism and connectivism rely on social interaction and this in itself can create innovation around what technologies work best for the learners in their learning process. H817 itself is designed around a forum focused VLE, yet students are self organising to use Twitter as a collaboration platform.

7 – eLearning tools and techniques should be used only after consideration has been given to online vs offline trade-offs.

The perspective of this point is very much from a hypothesis driven from a classroom delivery perspective. Online learning, supported by self study,  is sometimes the only option to learners in rural or hard to reach areas. The UNESCO  and many others have a done a great deal of work in this area to consider how to support learning in areas where even classroom learning is a challenge.

10 –  Only pedagogical advantages will provide a lasting rationale for implementing eLearning approaches.

Unsure about this as many tools, techniques and technologies get taken up without a deep consideration of the advantages. A challenge here is that it is assumed that tools will work well in all settings. The way the eLearning is introduced and supported has such a critical impact. A great example are the ideas in THIS paper: “Success and Failure of e-Learning Projects: Alignment of Vision and Reality, Change and Culture”. There are clearly advantages to eLearning but there are as many, if not more barriers.

Q2  – Consider hypothesis 4 that ‘elearning advances primarily through the successful implementation of pedagogical innovation’.

As I posted above: “Advances” is a very wooly term. I am sure eLearning has advanced without “successful implementation” as the pace of the industry is quicker than the capacity to research and draw results together of pedagogical success. Innovation in eLearning like all innovation is often “push” driven off the back of companies developing new technologies / services rather than “pull” from practitioners.

References:

I tend to link, rather than churn out here lots of references:

Nichols, M. (2003) ‘A theory for elearning’, Educational Technology & Society , vol. 6, no. 2, pp. 1–10; also available online at http://ifets.ieee.org/ discussions/ discuss_march2003.html (last accessed 29 January 2016)).

Ravenscroft, A. (2001). “Designing E-learning Interactions in the 21st Century: revisiting and rethinking the role of theory.” European Journal of Education 36(2), pp.133-156

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How to Write a Hypothesis? Types and Examples 

All research studies involve the use of the scientific method, which is a mathematical and experimental technique used to conduct experiments by developing and testing a hypothesis or a prediction about an outcome. Simply put, a hypothesis is a suggested solution to a problem. It includes elements that are expressed in terms of relationships with each other to explain a condition or an assumption that hasn’t been verified using facts. 1 The typical steps in a scientific method include developing such a hypothesis, testing it through various methods, and then modifying it based on the outcomes of the experiments.  

A research hypothesis can be defined as a specific, testable prediction about the anticipated results of a study. 2 Hypotheses help guide the research process and supplement the aim of the study. After several rounds of testing, hypotheses can help develop scientific theories. 3 Hypotheses are often written as if-then statements. 

Here are two hypothesis examples: 

Dandelions growing in nitrogen-rich soils for two weeks develop larger leaves than those in nitrogen-poor soils because nitrogen stimulates vegetative growth. 4  

If a company offers flexible work hours, then their employees will be happier at work. 5  

Table of Contents

• What is a hypothesis? 
• Types of hypotheses 
• Characteristics of a hypothesis 
• Functions of a hypothesis 
• How to write a hypothesis 
• Hypothesis examples 
• Frequently asked questions 

What is a hypothesis?

A hypothesis expresses an expected relationship between variables in a study and is developed before conducting any research. Hypotheses are not opinions but rather are expected relationships based on facts and observations. They help support scientific research and expand existing knowledge. An incorrectly formulated hypothesis can affect the entire experiment leading to errors in the results so it’s important to know how to formulate a hypothesis and develop it carefully.

A few sources of a hypothesis include observations from prior studies, current research and experiences, competitors, scientific theories, and general conditions that can influence people. Figure 1 depicts the different steps in a research design and shows where exactly in the process a hypothesis is developed. 4  

There are seven different types of hypotheses—simple, complex, directional, nondirectional, associative and causal, null, and alternative. 

Types of hypotheses

The seven types of hypotheses are listed below: 5 , 6,7  

• Simple : Predicts the relationship between a single dependent variable and a single independent variable. 

Example: Exercising in the morning every day will increase your productivity.  

• Complex : Predicts the relationship between two or more variables. 

Example: Spending three hours or more on social media daily will negatively affect children’s mental health and productivity, more than that of adults.  

• Directional : Specifies the expected direction to be followed and uses terms like increase, decrease, positive, negative, more, or less. 

Example: The inclusion of intervention X decreases infant mortality compared to the original treatment.  

• Non-directional : Does not predict the exact direction, nature, or magnitude of the relationship between two variables but rather states the existence of a relationship. This hypothesis may be used when there is no underlying theory or if findings contradict prior research. 

Example: Cats and dogs differ in the amount of affection they express.  

• Associative and causal : An associative hypothesis suggests an interdependency between variables, that is, how a change in one variable changes the other.  

Example: There is a positive association between physical activity levels and overall health.  

A causal hypothesis, on the other hand, expresses a cause-and-effect association between variables. 

Example: Long-term alcohol use causes liver damage.  

• Null : Claims that the original hypothesis is false by showing that there is no relationship between the variables. 

Example: Sleep duration does not have any effect on productivity.  

• Alternative : States the opposite of the null hypothesis, that is, a relationship exists between two variables. 

Example: Sleep duration affects productivity.  

Characteristics of a hypothesis

So, what makes a good hypothesis? Here are some important characteristics of a hypothesis. 8,9  

• Testable : You must be able to test the hypothesis using scientific methods to either accept or reject the prediction. 
• Falsifiable : It should be possible to collect data that reject rather than support the hypothesis. 
• Logical : Hypotheses shouldn’t be a random guess but rather should be based on previous theories, observations, prior research, and logical reasoning. 
• Positive : The hypothesis statement about the existence of an association should be positive, that is, it should not suggest that an association does not exist. Therefore, the language used and knowing how to phrase a hypothesis is very important. 
• Clear and accurate : The language used should be easily comprehensible and use correct terminology. 
• Relevant : The hypothesis should be relevant and specific to the research question. 
• Structure : Should include all the elements that make a good hypothesis: variables, relationship, and outcome. 

Functions of a hypothesis

The following list mentions some important functions of a hypothesis: 1  

• Maintains the direction and progress of the research. 
• Expresses the important assumptions underlying the proposition in a single statement. 
• Establishes a suitable context for researchers to begin their investigation and for readers who are referring to the final report. 
• Provides an explanation for the occurrence of a specific phenomenon. 
• Ensures selection of appropriate and accurate facts necessary and relevant to the research subject. 

To summarize, a hypothesis provides the conceptual elements that complete the known data, conceptual relationships that systematize unordered elements, and conceptual meanings and interpretations that explain the unknown phenomena. 1  

How to write a hypothesis

Listed below are the main steps explaining how to write a hypothesis. 2,4,5  

• Make an observation and identify variables : Observe the subject in question and try to recognize a pattern or a relationship between the variables involved. This step provides essential background information to begin your research.  

For example, if you notice that an office’s vending machine frequently runs out of a specific snack, you may predict that more people in the office choose that snack over another. 

• Identify the main research question : After identifying a subject and recognizing a pattern, the next step is to ask a question that your hypothesis will answer.  

For example, after observing employees’ break times at work, you could ask “why do more employees take breaks in the morning rather than in the afternoon?” 

• Conduct some preliminary research to ensure originality and novelty : Your initial answer, which is your hypothesis, to the question is based on some pre-existing information about the subject. However, to ensure that your hypothesis has not been asked before or that it has been asked but rejected by other researchers you would need to gather additional information.  

For example, based on your observations you might state a hypothesis that employees work more efficiently when the air conditioning in the office is set at a lower temperature. However, during your preliminary research you find that this hypothesis was proven incorrect by a prior study. 

• Develop a general statement : After your preliminary research has confirmed the originality of your proposed answer, draft a general statement that includes all variables, subjects, and predicted outcome. The statement could be if/then or declarative.  
• Finalize the hypothesis statement : Use the PICOT model, which clarifies how to word a hypothesis effectively, when finalizing the statement. This model lists the important components required to write a hypothesis. 

P opulation: The specific group or individual who is the main subject of the research 

I nterest: The main concern of the study/research question 

C omparison: The main alternative group 

O utcome: The expected results  

T ime: Duration of the experiment 

Once you’ve finalized your hypothesis statement you would need to conduct experiments to test whether the hypothesis is true or false. 

Hypothesis examples

The following table provides examples of different types of hypotheses. 10 ,11  

Key takeaways  

Here’s a summary of all the key points discussed in this article about how to write a hypothesis. 

• A hypothesis is an assumption about an association between variables made based on limited evidence, which should be tested. 
• A hypothesis has four parts—the research question, independent variable, dependent variable, and the proposed relationship between the variables.   
• The statement should be clear, concise, testable, logical, and falsifiable. 
• There are seven types of hypotheses—simple, complex, directional, non-directional, associative and causal, null, and alternative. 
• A hypothesis provides a focus and direction for the research to progress. 
• A hypothesis plays an important role in the scientific method by helping to create an appropriate experimental design. 

Frequently asked questions

Hypotheses and research questions have different objectives and structure. The following table lists some major differences between the two. 9  

Here are a few examples to differentiate between a research question and hypothesis. 

Yes, here’s a simple checklist to help you gauge the effectiveness of your hypothesis. 9   1. When writing a hypothesis statement, check if it:  2. Predicts the relationship between the stated variables and the expected outcome.  3. Uses simple and concise language and is not wordy.  4. Does not assume readers’ knowledge about the subject.  5. Has observable, falsifiable, and testable results. 

As mentioned earlier in this article, a hypothesis is an assumption or prediction about an association between variables based on observations and simple evidence. These statements are usually generic. Research objectives, on the other hand, are more specific and dictated by hypotheses. The same hypothesis can be tested using different methods and the research objectives could be different in each case.     For example, Louis Pasteur observed that food lasts longer at higher altitudes, reasoned that it could be because the air at higher altitudes is cleaner (with fewer or no germs), and tested the hypothesis by exposing food to air cleaned in the laboratory. 12 Thus, a hypothesis is predictive—if the reasoning is correct, X will lead to Y—and research objectives are developed to test these predictions. 

Null hypothesis testing is a method to decide between two assumptions or predictions between variables (null and alternative hypotheses) in a statistical relationship in a sample. The null hypothesis, denoted as H 0 , claims that no relationship exists between variables in a population and any relationship in the sample reflects a sampling error or occurrence by chance. The alternative hypothesis, denoted as H 1 , claims that there is a relationship in the population. In every study, researchers need to decide whether the relationship in a sample occurred by chance or reflects a relationship in the population. This is done by hypothesis testing using the following steps: 13   1. Assume that the null hypothesis is true.  2. Determine how likely the sample relationship would be if the null hypothesis were true. This probability is called the p value.  3. If the sample relationship would be extremely unlikely, reject the null hypothesis and accept the alternative hypothesis. If the relationship would not be unlikely, accept the null hypothesis. 

To summarize, researchers should know how to write a good hypothesis to ensure that their research progresses in the required direction. A hypothesis is a testable prediction about any behavior or relationship between variables, usually based on facts and observation, and states an expected outcome.  

We hope this article has provided you with essential insight into the different types of hypotheses and their functions so that you can use them appropriately in your next research project. 

References  

• Dalen, DVV. The function of hypotheses in research. Proquest website. Accessed April 8, 2024. https://www.proquest.com/docview/1437933010?pq-origsite=gscholar&fromopenview=true&sourcetype=Scholarly%20Journals&imgSeq=1  
• McLeod S. Research hypothesis in psychology: Types & examples. SimplyPsychology website. Updated December 13, 2023. Accessed April 9, 2024. https://www.simplypsychology.org/what-is-a-hypotheses.html  
• Scientific method. Britannica website. Updated March 14, 2024. Accessed April 9, 2024. https://www.britannica.com/science/scientific-method  
• The hypothesis in science writing. Accessed April 10, 2024. https://berks.psu.edu/sites/berks/files/campus/HypothesisHandout_Final.pdf  
• How to develop a hypothesis (with elements, types, and examples). Indeed.com website. Updated February 3, 2023. Accessed April 10, 2024. https://www.indeed.com/career-advice/career-development/how-to-write-a-hypothesis  
• Types of research hypotheses. Excelsior online writing lab. Accessed April 11, 2024. https://owl.excelsior.edu/research/research-hypotheses/types-of-research-hypotheses/  
• What is a research hypothesis: how to write it, types, and examples. Researcher.life website. Published February 8, 2023. Accessed April 11, 2024. https://researcher.life/blog/article/how-to-write-a-research-hypothesis-definition-types-examples/  
• Developing a hypothesis. Pressbooks website. Accessed April 12, 2024. https://opentext.wsu.edu/carriecuttler/chapter/developing-a-hypothesis/  
• What is and how to write a good hypothesis in research. Elsevier author services website. Accessed April 12, 2024. https://scientific-publishing.webshop.elsevier.com/manuscript-preparation/what-how-write-good-hypothesis-research/  
• How to write a great hypothesis. Verywellmind website. Updated March 12, 2023. Accessed April 13, 2024. https://www.verywellmind.com/what-is-a-hypothesis-2795239  
• 15 Hypothesis examples. Helpfulprofessor.com Published September 8, 2023. Accessed March 14, 2024. https://helpfulprofessor.com/hypothesis-examples/ 
• Editage insights. What is the interconnectivity between research objectives and hypothesis? Published February 24, 2021. Accessed April 13, 2024. https://www.editage.com/insights/what-is-the-interconnectivity-between-research-objectives-and-hypothesis  
• Understanding null hypothesis testing. BCCampus open publishing. Accessed April 16, 2024. https://opentextbc.ca/researchmethods/chapter/understanding-null-hypothesis-testing/#:~:text=In%20null%20hypothesis%20testing%2C%20this,said%20to%20be%20statistically%20significant  

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15 Hypothesis Examples

A hypothesis is defined as a testable prediction , and is used primarily in scientific experiments as a potential or predicted outcome that scientists attempt to prove or disprove (Atkinson et al., 2021; Tan, 2022).

In my types of hypothesis article, I outlined 13 different hypotheses, including the directional hypothesis (which makes a prediction about an effect of a treatment will be positive or negative) and the associative hypothesis (which makes a prediction about the association between two variables).

This article will dive into some interesting examples of hypotheses and examine potential ways you might test each one.

Hypothesis Examples

1. “inadequate sleep decreases memory retention”.

Field: Psychology

Type: Causal Hypothesis A causal hypothesis explores the effect of one variable on another. This example posits that a lack of adequate sleep causes decreased memory retention. In other words, if you are not getting enough sleep, your ability to remember and recall information may suffer.

How to Test:

To test this hypothesis, you might devise an experiment whereby your participants are divided into two groups: one receives an average of 8 hours of sleep per night for a week, while the other gets less than the recommended sleep amount.

During this time, all participants would daily study and recall new, specific information. You’d then measure memory retention of this information for both groups using standard memory tests and compare the results.

Should the group with less sleep have statistically significant poorer memory scores, the hypothesis would be supported.

Ensuring the integrity of the experiment requires taking into account factors such as individual health differences, stress levels, and daily nutrition.

Relevant Study: Sleep loss, learning capacity and academic performance (Curcio, Ferrara & De Gennaro, 2006)

2. “Increase in Temperature Leads to Increase in Kinetic Energy”

Field: Physics

Type: Deductive Hypothesis The deductive hypothesis applies the logic of deductive reasoning – it moves from a general premise to a more specific conclusion. This specific hypothesis assumes that as temperature increases, the kinetic energy of particles also increases – that is, when you heat something up, its particles move around more rapidly.

This hypothesis could be examined by heating a gas in a controlled environment and capturing the movement of its particles as a function of temperature.

You’d gradually increase the temperature and measure the kinetic energy of the gas particles with each increment. If the kinetic energy consistently rises with the temperature, your hypothesis gets supporting evidence.

Variables such as pressure and volume of the gas would need to be held constant to ensure validity of results.

3. “Children Raised in Bilingual Homes Develop Better Cognitive Skills”

Field: Psychology/Linguistics

Type: Comparative Hypothesis The comparative hypothesis posits a difference between two or more groups based on certain variables. In this context, you might propose that children raised in bilingual homes have superior cognitive skills compared to those raised in monolingual homes.

Testing this hypothesis could involve identifying two groups of children: those raised in bilingual homes, and those raised in monolingual homes.

Cognitive skills in both groups would be evaluated using a standard cognitive ability test at different stages of development. The examination would be repeated over a significant time period for consistency.

If the group raised in bilingual homes persistently scores higher than the other, the hypothesis would thereby be supported.

The challenge for the researcher would be controlling for other variables that could impact cognitive development, such as socio-economic status, education level of parents, and parenting styles.

Relevant Study: The cognitive benefits of being bilingual (Marian & Shook, 2012)

4. “High-Fiber Diet Leads to Lower Incidences of Cardiovascular Diseases”

Field: Medicine/Nutrition

Type: Alternative Hypothesis The alternative hypothesis suggests an alternative to a null hypothesis. In this context, the implied null hypothesis could be that diet has no effect on cardiovascular health, which the alternative hypothesis contradicts by suggesting that a high-fiber diet leads to fewer instances of cardiovascular diseases.

To test this hypothesis, a longitudinal study could be conducted on two groups of participants; one adheres to a high-fiber diet, while the other follows a diet low in fiber.

After a fixed period, the cardiovascular health of participants in both groups could be analyzed and compared. If the group following a high-fiber diet has a lower number of recorded cases of cardiovascular diseases, it would provide evidence supporting the hypothesis.

Control measures should be implemented to exclude the influence of other lifestyle and genetic factors that contribute to cardiovascular health.

Relevant Study: Dietary fiber, inflammation, and cardiovascular disease (King, 2005)

5. “Gravity Influences the Directional Growth of Plants”

Field: Agronomy / Botany

Type: Explanatory Hypothesis An explanatory hypothesis attempts to explain a phenomenon. In this case, the hypothesis proposes that gravity affects how plants direct their growth – both above-ground (toward sunlight) and below-ground (towards water and other resources).

The testing could be conducted by growing plants in a rotating cylinder to create artificial gravity.

Observations on the direction of growth, over a specified period, can provide insights into the influencing factors. If plants consistently direct their growth in a manner that indicates the influence of gravitational pull, the hypothesis is substantiated.

It is crucial to ensure that other growth-influencing factors, such as light and water, are uniformly distributed so that only gravity influences the directional growth.

6. “The Implementation of Gamified Learning Improves Students’ Motivation”

Field: Education

Type: Relational Hypothesis The relational hypothesis describes the relation between two variables. Here, the hypothesis is that the implementation of gamified learning has a positive effect on the motivation of students.

To validate this proposition, two sets of classes could be compared: one that implements a learning approach with game-based elements, and another that follows a traditional learning approach.

The students’ motivation levels could be gauged by monitoring their engagement, performance, and feedback over a considerable timeframe.

If the students engaged in the gamified learning context present higher levels of motivation and achievement, the hypothesis would be supported.

Control measures ought to be put into place to account for individual differences, including prior knowledge and attitudes towards learning.

Relevant Study: Does educational gamification improve students’ motivation? (Chapman & Rich, 2018)

7. “Mathematics Anxiety Negatively Affects Performance”

Field: Educational Psychology

Type: Research Hypothesis The research hypothesis involves making a prediction that will be tested. In this case, the hypothesis proposes that a student’s anxiety about math can negatively influence their performance in math-related tasks.

To assess this hypothesis, researchers must first measure the mathematics anxiety levels of a sample of students using a validated instrument, such as the Mathematics Anxiety Rating Scale.

Then, the students’ performance in mathematics would be evaluated through standard testing. If there’s a negative correlation between the levels of math anxiety and math performance (meaning as anxiety increases, performance decreases), the hypothesis would be supported.

It would be crucial to control for relevant factors such as overall academic performance and previous mathematical achievement.

8. “Disruption of Natural Sleep Cycle Impairs Worker Productivity”

Field: Organizational Psychology

Type: Operational Hypothesis The operational hypothesis involves defining the variables in measurable terms. In this example, the hypothesis posits that disrupting the natural sleep cycle, for instance through shift work or irregular working hours, can lessen productivity among workers.

To test this hypothesis, you could collect data from workers who maintain regular working hours and those with irregular schedules.

Measuring productivity could involve examining the worker’s ability to complete tasks, the quality of their work, and their efficiency.

If workers with interrupted sleep cycles demonstrate lower productivity compared to those with regular sleep patterns, it would lend support to the hypothesis.

Consideration should be given to potential confounding variables such as job type, worker age, and overall health.

9. “Regular Physical Activity Reduces the Risk of Depression”

Field: Health Psychology

Type: Predictive Hypothesis A predictive hypothesis involves making a prediction about the outcome of a study based on the observed relationship between variables. In this case, it is hypothesized that individuals who engage in regular physical activity are less likely to suffer from depression.

Longitudinal studies would suit to test this hypothesis, tracking participants’ levels of physical activity and their mental health status over time.

The level of physical activity could be self-reported or monitored, while mental health status could be assessed using standard diagnostic tools or surveys.

If data analysis shows that participants maintaining regular physical activity have a lower incidence of depression, this would endorse the hypothesis.

However, care should be taken to control other lifestyle and behavioral factors that could intervene with the results.

Relevant Study: Regular physical exercise and its association with depression (Kim, 2022)

10. “Regular Meditation Enhances Emotional Stability”

Type: Empirical Hypothesis In the empirical hypothesis, predictions are based on amassed empirical evidence . This particular hypothesis theorizes that frequent meditation leads to improved emotional stability, resonating with numerous studies linking meditation to a variety of psychological benefits.

Earlier studies reported some correlations, but to test this hypothesis directly, you’d organize an experiment where one group meditates regularly over a set period while a control group doesn’t.

Both groups’ emotional stability levels would be measured at the start and end of the experiment using a validated emotional stability assessment.

If regular meditators display noticeable improvements in emotional stability compared to the control group, the hypothesis gains credit.

You’d have to ensure a similar emotional baseline for all participants at the start to avoid skewed results.

11. “Children Exposed to Reading at an Early Age Show Superior Academic Progress”

Type: Directional Hypothesis The directional hypothesis predicts the direction of an expected relationship between variables. Here, the hypothesis anticipates that early exposure to reading positively affects a child’s academic advancement.

A longitudinal study tracking children’s reading habits from an early age and their consequent academic performance could validate this hypothesis.

Parents could report their children’s exposure to reading at home, while standardized school exam results would provide a measure of academic achievement.

If the children exposed to early reading consistently perform better acadically, it gives weight to the hypothesis.

However, it would be important to control for variables that might impact academic performance, such as socioeconomic background, parental education level, and school quality.

12. “Adopting Energy-efficient Technologies Reduces Carbon Footprint of Industries”

Field: Environmental Science

Type: Descriptive Hypothesis A descriptive hypothesis predicts the existence of an association or pattern related to variables. In this scenario, the hypothesis suggests that industries adopting energy-efficient technologies will resultantly show a reduced carbon footprint.

Global industries making use of energy-efficient technologies could track their carbon emissions over time. At the same time, others not implementing such technologies continue their regular tracking.

After a defined time, the carbon emission data of both groups could be compared. If industries that adopted energy-efficient technologies demonstrate a notable reduction in their carbon footprints, the hypothesis would hold strong.

In the experiment, you would exclude variations brought by factors such as industry type, size, and location.

13. “Reduced Screen Time Improves Sleep Quality”

Type: Simple Hypothesis The simple hypothesis is a prediction about the relationship between two variables, excluding any other variables from consideration. This example posits that by reducing time spent on devices like smartphones and computers, an individual should experience improved sleep quality.

A sample group would need to reduce their daily screen time for a pre-determined period. Sleep quality before and after the reduction could be measured using self-report sleep diaries and objective measures like actigraphy, monitoring movement and wakefulness during sleep.

If the data shows that sleep quality improved post the screen time reduction, the hypothesis would be validated.

Other aspects affecting sleep quality, like caffeine intake, should be controlled during the experiment.

Relevant Study: Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep (Waller et al., 2021)

14. Engaging in Brain-Training Games Improves Cognitive Functioning in Elderly

Field: Gerontology

Type: Inductive Hypothesis Inductive hypotheses are based on observations leading to broader generalizations and theories. In this context, the hypothesis deduces from observed instances that engaging in brain-training games can help improve cognitive functioning in the elderly.

A longitudinal study could be conducted where an experimental group of elderly people partakes in regular brain-training games.

Their cognitive functioning could be assessed at the start of the study and at regular intervals using standard neuropsychological tests.

If the group engaging in brain-training games shows better cognitive functioning scores over time compared to a control group not playing these games, the hypothesis would be supported.

15. Farming Practices Influence Soil Erosion Rates

Type: Null Hypothesis A null hypothesis is a negative statement assuming no relationship or difference between variables. The hypothesis in this context asserts there’s no effect of different farming practices on the rates of soil erosion.

Comparing soil erosion rates in areas with different farming practices over a considerable timeframe could help test this hypothesis.

If, statistically, the farming practices do not lead to differences in soil erosion rates, the null hypothesis is accepted.

However, if marked variation appears, the null hypothesis is rejected, meaning farming practices do influence soil erosion rates. It would be crucial to control for external factors like weather, soil type, and natural vegetation.

The variety of hypotheses mentioned above underscores the diversity of research constructs inherent in different fields, each with its unique purpose and way of testing.

While researchers may develop hypotheses primarily as tools to define and narrow the focus of the study, these hypotheses also serve as valuable guiding forces for the data collection and analysis procedures, making the research process more efficient and direction-focused.

Hypotheses serve as a compass for any form of academic research. The diverse examples provided, from Psychology to Educational Studies, Environmental Science to Gerontology, clearly demonstrate how certain hypotheses suit specific fields more aptly than others.

It is important to underline that although these varied hypotheses differ in their structure and methods of testing, each endorses the fundamental value of empiricism in research. Evidence-based decision making remains at the heart of scholarly inquiry, regardless of the research field, thus aligning all hypotheses to the core purpose of scientific investigation.

Testing hypotheses is an essential part of the scientific method . By doing so, researchers can either confirm their predictions, giving further validity to an existing theory, or they might uncover new insights that could potentially shift the field’s understanding of a particular phenomenon. In either case, hypotheses serve as the stepping stones for scientific exploration and discovery.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021).  SAGE research methods foundations . SAGE Publications Ltd.

Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic performance.  Sleep medicine reviews ,  10 (5), 323-337.

Kim, J. H. (2022). Regular physical exercise and its association with depression: A population-based study short title: Exercise and depression.  Psychiatry Research ,  309 , 114406.

King, D. E. (2005). Dietary fiber, inflammation, and cardiovascular disease.  Molecular nutrition & food research ,  49 (6), 594-600.

Marian, V., & Shook, A. (2012, September). The cognitive benefits of being bilingual. In Cerebrum: the Dana forum on brain science (Vol. 2012). Dana Foundation.

Tan, W. C. K. (2022). Research Methods: A Practical Guide For Students And Researchers (Second Edition) . World Scientific Publishing Company.

Waller, N. A., Zhang, N., Cocci, A. H., D’Agostino, C., Wesolek‐Greenson, S., Wheelock, K., … & Resnicow, K. (2021). Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep. Child: care, health and development, 47 (5), 618-626.

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 5 Top Tips for Succeeding at University
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 50 Durable Goods Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 100 Consumer Goods Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 30 Globalization Pros and Cons

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April 30, 2024

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Should online educational platforms offer courses following a schedule or release them on demand?

by Marilyn Stone, American Marketing Association

Researchers from Carnegie Mellon University and University of Pennsylvania have published a new Journal of Marketing study that examines online educational platforms and the question of whether they should release content through a scheduled format that resembles a traditional university course or use an on-demand release strategy.

The study is titled "More Likely to Pay but Less Engaged: The Effects of Switching Online Courses from Scheduled to On-Demand Release on User Behavior" and is authored by Joy Lu, Eric T. Bradlow, and J. Wesley Hutchinson.

In 2011, the online education industry catered to around 300,000 consumers. In 2021, it served 220 million, thanks in part to increased enrollment during the COVID-19 pandemic. Traditional universities and institutions are increasingly adopting hybrid course formats. For example, the number of full-time online MBA students surpassed in-person MBA students for the first time in the 2020-21 academic year.

Today, online educational platforms like Coursera and edX offer a range of flexible course content, but these firms are faced with a tricky question: Should they release content through a scheduled format that resembles a traditional university course with a subset of lectures and quizzes available at the start of each week, or should they follow in the footsteps of Netflix and Hulu with an on-demand release strategy where all the material is immediately available upon registration?

This new article finds that the choice of format for content release not only impacts overall user engagement and firm revenue but also user performance and learning outcomes.

The researchers studied over 67,000 users taking an introductory marketing course on Coursera consisting of 32 short lecture videos and four quizzes. The study took advantage of a natural experiment policy change where the platform switched the course from a scheduled format to an on-demand release format while keeping the actual content the same.

The scheduled format closely resembled a traditional university course, with some of the study material available at the beginning of each week for four weeks. In the on-demand format, all four weeks of content was made available upon registration. All users could take the course for free or opt into paying for a completion certificate, either as a one-time fee in the scheduled format or a monthly subscription in the on-demand format.

More users, less engagement

The study's findings show that the switch to on-demand content doubled the percentage of paying users from 14% to 28%. Lu explains that "the on-demand format was successful in increasing short-term firm revenue by bringing in more paying users. On the downside, the switch resulted in significantly lower lecture completion rates and lower quiz performance."

The on-demand format also negatively impacted downstream platform engagement. The marketing course was promoted in a "Business Foundations" set with three other courses on operations, accounting, and finance.

"Compared to users in the scheduled format, those in the on-demand format ended up taking one or two fewer additional courses six months after the focal marketing course," says Bradlow.

Analysis of user activity reveals two new learning patterns:

• A subset (13%) of users in the on-demand format continued to return and take quizzes well beyond the recommended four-week course period. The greater flexibility in the on-demand content release and payment structure likely enabled these users to "stretch out" their consumption.
• The on-demand format increased the practice of binging—with user activity being clumped together (i.e., more binging) as compared to being evenly spaced out (i.e., less binging). In the scheduled format, binging was negatively related to course performance, which is consistent with the intuition that binging reflects procrastination or cramming. However, in the on-demand format, binging was positively related to performance, suggesting that on-demand users may binge as a form of strategic time management by setting aside time to consume in spurts.

Real-world implications

This study offers vital lessons for chief marketing officers in the online education space:

• The switch to the on-demand format attracted a set of users who were more likely to pay, but were less engaged in the course. On-demand content is potentially helpful at bringing in a new user segment or expanding the current user base, similar to universities offering concurrent hybrid MBAs that cater to busy students with full-time jobs. Managers must consider the trade-off between offering structure versus flexibility and may even consider offering different content release options simultaneously but at different price points by emphasizing their unique features.
• Platforms may need to adapt their content to account for users who binge on content and others who space it out over time. For example, firms can include more recaps or reviews to reduce frustration resulting from users forgetting content. It may even be a viable strategy to embrace the prevalence of binging among users by highlighting or designing sets of lectures that are "bingeable" versus more modular.
• Many online platforms offer episodic content that may be released in installments and thus need to make decisions regarding the content release format.

"Our study provides insights that help managers anticipate the potential consequences of such decisions," says Hutchinson. "On-demand content offers clear short-term benefits in terms of increased revenue but potentially long-term costs in terms of decreased engagement and new challenges in maintaining user engagement."

Journal information: Journal of Marketing

Provided by American Marketing Association

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