how to write a fully operationalised hypothesis

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Hypotheses; directional and non-directional, what is the difference between an experimental and an alternative hypothesis.

Nothing much! If the study is a laboratory experiment then we can call the hypothesis “an experimental hypothesis”, where we make a prediction about how the IV causes an effect on the DV. If we have a non-experimental design, i.e. we are not able to manipulate the IV as in a natural or quasi-experiment , or if some other research method has been used, then we call it an “alternativehypothesis”, alternative to the null.

Directional hypothesis: A directional (or one tailed hypothesis) states which way you think the results are going to go, for example in an experimental study we might say…”Participants who have been deprived of sleep for 24 hours will have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived”; the hypothesis compares the two groups/conditions and states which one will ….have more/less, be quicker/slower, etc.

If we had a correlational study, the directional hypothesis would state whether we expect a positive or a negative correlation, we are stating how the two variables will be related to each other, e.g. there will be a positive correlation between the number of stressful life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”. The directional hypothesis can also state a negative correlation, e.g. the higher the number of face-book friends, the lower the life satisfaction score “

Non-directional hypothesis: A non-directional (or two tailed hypothesis) simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc. Using our example above we would say “There will be a difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.”

When the study is correlational, we simply state that variables will be correlated but do not state whether the relationship will be positive or negative, e.g. there will be a significant correlation between variable A and variable B.

Null hypothesis The null hypothesis states that the alternative or experimental hypothesis is NOT the case, if your experimental hypothesis was directional you would say…

Participants who have been deprived of sleep for 24 hours will NOT have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived and any difference that does arise will be due to chance alone.

or with a directional correlational hypothesis….

There will NOT be a positive correlation between the number of stress life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”

With a non-directional or  two tailed hypothesis…

There will be NO difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.

or for a correlational …

there will be NO correlation between variable A and variable B.

When it comes to conducting an inferential stats test, if you have a directional hypothesis , you must do a one tailed test to find out whether your observed value is significant. If you have a non-directional hypothesis , you must do a two tailed test .

Exam Techniques/Advice

• Remember, a decent hypothesis will contain two variables, in the case of an experimental hypothesis there will be an IV and a DV; in a correlational hypothesis there will be two co-variables
• both variables need to be fully operationalised to score the marks, that is you need to be very clear and specific about what you mean by your IV and your DV; if someone wanted to repeat your study, they should be able to look at your hypothesis and know exactly what to change between the two groups/conditions and exactly what to measure (including any units/explanation of rating scales etc, e.g. “where 1 is low and 7 is high”)
• double check the question, did it ask for a directional or non-directional hypothesis?
• if you were asked for a null hypothesis, make sure you always include the phrase “and any difference/correlation (is your study experimental or correlational?) that does arise will be due to chance alone”

Practice Questions:

• Mr Faraz wants to compare the levels of attendance between his psychology group and those of Mr Simon, who teaches a different psychology group. Which of the following is a suitable directional (one tailed) hypothesis for Mr Faraz’s investigation?

A There will be a difference in the levels of attendance between the two psychology groups.

B Students’ level of attendance will be higher in Mr Faraz’s group than Mr Simon’s group.

C Any difference in the levels of attendance between the two psychology groups is due to chance.

D The level of attendance of the students will depend upon who is teaching the groups.

2. Tracy works for the local council. The council is thinking about reducing the number of people it employs to pick up litter from the street. Tracy has been asked to carry out a study to see if having the streets cleaned at less regular intervals will affect the amount of litter the public will drop. She studies a street to compare how much litter is dropped at two different times, once when it has just been cleaned and once after it has not been cleaned for a month.

Write a fully operationalised non-directional (two-tailed) hypothesis for Tracy’s study. (2)

3. Jamila is conducting a practical investigation to look at gender differences in carrying out visuo-spatial tasks. She decides to give males and females a jigsaw puzzle and will time them to see who completes it the fastest. She uses a random sample of pupils from a local school to get her participants.

(a) Write a fully operationalised directional (one tailed) hypothesis for Jamila’s study. (2) (b) Outline one strength and one weakness of the random sampling method. You may refer to Jamila’s use of this type of sampling in your answer. (4)

4. Which of the following is a non-directional (two tailed) hypothesis?

A There is a difference in driving ability with men being better drivers than women

B Women are better at concentrating on more than one thing at a time than men

C Women spend more time doing the cooking and cleaning than men

D There is a difference in the number of men and women who participate in sports

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Travis Dixon October 24, 2016 Assessment (IB) , Internal Assessment (IB) , Research Methodology

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Updated June 2020

Writing good hypotheses in IB Psychology IAs is something many students find challenging. After moderating another 175+ IA’s this year I could see some common errors students were making. This post hopes to give a clear explanation with examples to help with this tricky task. 

Null and Alternative Hypotheses

Null hypothesis (h0).

Our teacher support pack has everything students and teachers need to get top marks in the IA. Download a Free preview from https://store.themantic-education.com/

The term “null” means having no value, significance or effect. It also refers to something associated with zero. A null hypothesis in a student’s IA, therefore, should state that there is (or will be) no effect of the IV on the DV. This is what we assume to be true until we have the evidence to suggest otherwise.

A common misconception is that the hypothesis is based on the sample in the study. Our hypotheses should actually be about the population from which we’ve drawn the sample, not the sample itself. Therefore, when writing our hypotheses we can use present tense instead of future tense (e.g. There is instead of There will be… ).

Having said that, in the IB Psych’ IA, the IB is apparently assuming the hypotheses are based on the sample (because variables need to be operationalized) so writing your hypotheses as predictions of what might happen in the experiment is fine (see below for examples).

IB Psych IA Tip: It’s fine (and even recommended) to state in your null hypotheses that there will be no significant difference between the two conditions in your experiment or any differences are due to chance (see footnote 1)

The Alternative Hypothesis (H1)

This is also referred to as the research hypothesis or the experimental hypothesis. It’s an alternative hypothesis to the null because if the null is not true, there must be an alternative explanation.

Generally speaking it’s not a prediction of what will happen in the study, but it’s an assumption about what is true for the population being studied. But, similar to the null hypothesis in the IB Psych IA you can (and should) write this about a prediction of what you think will happen in your study (see examples below).

This must be operationalized: it must be evident how the variables will be quantified, and may be either one- or two-tailed (directional or non-directional).

Read more: 

Operational Definitions

• Key Studies for the IA
• Lesson Idea: Inferential Statistics

To avoid issues with copying and plagiarism, the following examples are from studies that students cannot do for the internal assessment. Some are taken from this post on how to operationalize definitions of variables .

A Fictional Drug Trial

• H1: Taking Paroxetine  will decrease symptoms of PTSD.
• Ho: Taking paroxetine will not decrease symptoms of PTSD.

Operationalized (as if for an IB Psych IA):

• H1: The experimental group who take 20mg of Paroxetine (as a pill) every morning for 7 days will have a larger decrease in symptoms (as measured by the CAPs scale) when compared to the control group who will take an identical placebo pill every morning for 7 days.

A Fictional Study on Body Image*

• H1: Viewing media that portrays the thin ideal increases feelings of body image dissatisfaction.
• Ho: Types of media viewed does not affect body image dissatisfaction.
• H1: Watching a video portraying the thin ideal in a  Baywatch  film trailer will result in higher scores on the Body Shape Questionnaire (BSQ-34) compared with watching media with “normal” body types in the Grownups film trailer.

*This entire IA exemplar is included in the IA Teacher Support Pack.  

A fictional study on weight training.

• H1: Listening to music affects training performance.
• Ho: Music has no effect on training performance.
• H1:  Listening to heavy metal rock music (AC/DC songs) causes a difference in the number of push-ups performed compared to listening to classical music (Mozart’s symphony #41).

One vs. Two Tailed

It is important to know if your hypothesis is one or two-tailed. This will influence the type of inferential statistics test you use later. If you have a one-tailed hypotheses, you should use a one-tailed test. And if you have a two-tailed hypothesis? You guessed it – a two-tailed test.

The one vs two tailed debate still continues in Psychology ( read more ). The IB ignores this and makes it simple: one tailed hypotheses = one tailed test. No ifs, ands, or buts!

If you are predicting that one of your conditions in your experiment will have a higher value than the other, it’s one-tailed (because you know the direction of the effect – the IV is increasing the DV). Similarly, your hypothesis is one-tailed if you are predicting that manipulating the IV will cause a decrease in the DV.

However, if you think your IV will have an effect, but you’re not sure if it will increase  or  decrease it, this is two-tailed.

Of the three examples above, can you tell which one is two-tailed and which one is one-tailed?

Read more about operationally defining your variables in your hypotheses in this blog post .

Points to Remember

• Hypotheses are based on the population, not the sample, so you can write in present tense. However, the norm for IB Psych IA’s is to write in the future tense as a prediction of what will happen in your experiment.
• In IB IA’s, we’re hypothesizing about a causal relationship of an IV on a DV in a population – the hypotheses should reflect that causal relationship.
• Inferential tests are test of the null hypothesis (hence it’s called null hypothesis testing). We are conducting the tests to see the chances of obtaining our results even if the null is true (i.e. there is no effect).

Footnote 1: Saying “that there will be no significant difference between the two conditions in or any differences are due to chance” is technically an incorrect way to state a null hypothesis. That’s because when we conduct our inferential tests we’re seeing what the probability is of getting our results even if our null were true. So if we get a p value of say 0.10 (10%), according to the above null hypothesis we’re saying there is a 10% chance that there will be no significant difference between the two conditions, which isn’t actually accurate (don’t worry if I’ve lost you – it’s mind bending stuff). This is one of those instances where poor statistical practice has ingrained itself in IB assessment. But on the plus side it does make it easier for students (and not enough time is spent on this for the bad habits to be too ingrained anyway).

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

Operational Hypothesis

An Operational Hypothesis is a testable statement or prediction made in research that not only proposes a relationship between two or more variables but also clearly defines those variables in operational terms, meaning how they will be measured or manipulated within the study. It forms the basis of an experiment that seeks to prove or disprove the assumed relationship, thus helping to drive scientific research.

The Core Components of an Operational Hypothesis

Understanding an operational hypothesis involves identifying its key components and how they interact.

The Variables

An operational hypothesis must contain two or more variables — factors that can be manipulated, controlled, or measured in an experiment.

The Proposed Relationship

Beyond identifying the variables, an operational hypothesis specifies the type of relationship expected between them. This could be a correlation, a cause-and-effect relationship, or another type of association.

The Importance of Operationalizing Variables

Operationalizing variables — defining them in measurable terms — is a critical step in forming an operational hypothesis. This process ensures the variables are quantifiable, enhancing the reliability and validity of the research.

Constructing an Operational Hypothesis

Creating an operational hypothesis is a fundamental step in the scientific method and research process. It involves generating a precise, testable statement that predicts the outcome of a study based on the research question. An operational hypothesis must clearly identify and define the variables under study and describe the expected relationship between them. The process of creating an operational hypothesis involves several key steps:

Steps to Construct an Operational Hypothesis

• Define the Research Question : Start by clearly identifying the research question. This question should highlight the key aspect or phenomenon that the study aims to investigate.
• Identify the Variables : Next, identify the key variables in your study. Variables are elements that you will measure, control, or manipulate in your research. There are typically two types of variables in a hypothesis: the independent variable (the cause) and the dependent variable (the effect).
• Operationalize the Variables : Once you’ve identified the variables, you must operationalize them. This involves defining your variables in such a way that they can be easily measured, manipulated, or controlled during the experiment.
• Predict the Relationship : The final step involves predicting the relationship between the variables. This could be an increase, decrease, or any other type of correlation between the independent and dependent variables.

By following these steps, you will create an operational hypothesis that provides a clear direction for your research, ensuring that your study is grounded in a testable prediction.

Evaluating the Strength of an Operational Hypothesis

Not all operational hypotheses are created equal. The strength of an operational hypothesis can significantly influence the validity of a study. There are several key factors that contribute to the strength of an operational hypothesis:

• Clarity : A strong operational hypothesis is clear and unambiguous. It precisely defines all variables and the expected relationship between them.
• Testability : A key feature of an operational hypothesis is that it must be testable. That is, it should predict an outcome that can be observed and measured.
• Operationalization of Variables : The operationalization of variables contributes to the strength of an operational hypothesis. When variables are clearly defined in measurable terms, it enhances the reliability of the study.
• Alignment with Research : Finally, a strong operational hypothesis aligns closely with the research question and the overall goals of the study.

By carefully crafting and evaluating an operational hypothesis, researchers can ensure that their work provides valuable, valid, and actionable insights.

Examples of Operational Hypotheses

To illustrate the concept further, this section will provide examples of well-constructed operational hypotheses in various research fields.

The operational hypothesis is a fundamental component of scientific inquiry, guiding the research design and providing a clear framework for testing assumptions. By understanding how to construct and evaluate an operational hypothesis, we can ensure our research is both rigorous and meaningful.

Examples of Operational Hypothesis:

• In Education : An operational hypothesis in an educational study might be: “Students who receive tutoring (Independent Variable) will show a 20% improvement in standardized test scores (Dependent Variable) compared to students who did not receive tutoring.”
• In Psychology : In a psychological study, an operational hypothesis could be: “Individuals who meditate for 20 minutes each day (Independent Variable) will report a 15% decrease in self-reported stress levels (Dependent Variable) after eight weeks compared to those who do not meditate.”
• In Health Science : An operational hypothesis in a health science study might be: “Participants who drink eight glasses of water daily (Independent Variable) will show a 10% decrease in reported fatigue levels (Dependent Variable) after three weeks compared to those who drink four glasses of water daily.”
• In Environmental Science : In an environmental study, an operational hypothesis could be: “Cities that implement recycling programs (Independent Variable) will see a 25% reduction in landfill waste (Dependent Variable) after one year compared to cities without recycling programs.”

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

Related Articles

Research Methodology

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

Thematic Analysis: A Step by Step Guide

Metasynthesis Of Qualitative Research

Grounded Theory In Qualitative Research: A Practical Guide

9990 Psychology AO2 Exercise 1 Activity 1

Topic outline.

Learners are required to write and apply knowledge of null hypotheses and alternative directional (one-tailed) and non-directional (two-tailed) hypotheses.

It is important that they can distinguish between the different types of hypotheses as well as write their own, fully operationalised hypotheses for novel scenarios.

Use Worksheet 1: Hypothesis writing to help learners practise differentiating between the types of hypothesis.

Lead a feedback session using Worksheet 1: Hypothesis writing answers , to go through the answers making sure any misconceptions are addressed. 

Ask learners to practise writing their own hypothesis using the stems provided as structured support.

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• Operationalisation | A Guide with Examples, Pros & Cons

Operationalisation | A Guide with Examples, Pros & Cons

Published on 6 May 2022 by Pritha Bhandari . Revised on 10 October 2022.

Operationalisation means turning abstract concepts into measurable observations. Although some concepts, like height or age, are easily measured, others, like spirituality or anxiety, are not.

Through operationalisation, you can systematically collect data on processes and phenomena that aren’t directly observable.

• Self-rating scores on a social anxiety scale
• Number of recent behavioural incidents of avoidance of crowded places
• Intensity of physical anxiety symptoms in social situations

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Table of contents

Why operationalisation matters, how to operationalise concepts, strengths of operationalisation, limitations of operationalisation, frequently asked questions about operationalisation.

In quantitative research , it’s important to precisely define the variables that you want to study.

Without transparent and specific operational definitions, researchers may measure irrelevant concepts or inconsistently apply methods. Operationalisation reduces subjectivity and increases the reliability  of your study.

Your choice of operational definition can sometimes affect your results. For example, an experimental intervention for social anxiety may reduce self-rating anxiety scores but not behavioural avoidance of crowded places. This means that your results are context-specific and may not generalise to different real-life settings.

Generally, abstract concepts can be operationalised in many different ways. These differences mean that you may actually measure slightly different aspects of a concept, so it’s important to be specific about what you are measuring.

If you test a hypothesis using multiple operationalisations of a concept, you can check whether your results depend on the type of measure that you use. If your results don’t vary when you use different measures, then they are said to be ‘robust’.

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There are three main steps for operationalisation:

• Identify the main concepts you are interested in studying.
• Choose a variable to represent each of the concepts.
• Select indicators for each of your variables.

Step 1: Identify the main concepts you are interested in studying

Based on your research interests and goals, define your topic and come up with an initial research question .

There are two main concepts in your research question:

• Social media behaviour

Step 2: Choose a variable to represent each of the concepts

Your main concepts may each have many variables , or properties, that you can measure.

For instance, are you going to measure the  amount of sleep or the  quality of sleep? And are you going to measure  how often teenagers use social media,  which social media they use, or when they use it?

• Alternate hypothesis: Lower quality of sleep is related to higher night-time social media use in teenagers.
• Null hypothesis: There is no relation between quality of sleep and night-time social media use in teenagers.

Step 3: Select indicators for each of your variables

To measure your variables, decide on indicators that can represent them numerically.

Sometimes these indicators will be obvious: for example, the amount of sleep is represented by the number of hours per night. But a variable like sleep quality is harder to measure.

You can come up with practical ideas for how to measure variables based on previously published studies. These may include established scales or questionnaires that you can distribute to your participants. If none are available that are appropriate for your sample, you can develop your own scales or questionnaires.

• To measure sleep quality, you give participants wristbands that track sleep phases.
• To measure night-time social media use, you create a questionnaire that asks participants to track how much time they spend using social media in bed.

After operationalising your concepts, it’s important to report your study variables and indicators when writing up your methodology section. You can evaluate how your choice of operationalisation may have affected your results or interpretations in the discussion section.

Operationalisation makes it possible to consistently measure variables across different contexts.

Scientific research is based on observable and measurable findings. Operational definitions break down intangible concepts into recordable characteristics.

Objectivity

A standardised approach for collecting data leaves little room for subjective or biased personal interpretations of observations.

Reliability

A good operationalisation can be used consistently by other researchers. If other people measure the same thing using your operational definition, they should all get the same results.

Operational definitions of concepts can sometimes be problematic.

Underdetermination

Many concepts vary across different time periods and social settings.

For example, poverty is a worldwide phenomenon, but the exact income level that determines poverty can differ significantly across countries.

Reductiveness

Operational definitions can easily miss meaningful and subjective perceptions of concepts by trying to reduce complex concepts to numbers.

For example, asking consumers to rate their satisfaction with a service on a 5-point scale will tell you nothing about why they felt that way.

Lack of universality

Context-specific operationalisations help preserve real-life experiences, but make it hard to compare studies if the measures differ significantly.

For example, corruption can be operationalised in a wide range of ways (e.g., perceptions of corrupt business practices, or frequency of bribe requests from public officials), but the measures may not consistently reflect the same concept.

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Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

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Conceptualizing and operationalizing (and sometimes hypothesizing)

Research questions are an essential starting point, but they tend to be too abstract. If we’re ultimately about making observations, we need to know more specifically what to observe. Conceptualization is a step in that direction. In this stage of the research process, we specify what concepts and what relationships among those concepts we need to observe. My research question might be How does government funding affect nonprofit organizations? This is fine, but I need to identify what I want to observe much more specifically. Theory (like the crowding out theory I referred to before) and previous research help me identify a set of concepts that I need to consider: different types of government funding, the amount of funding, effects on fundraising, effects on operations management, managerial capacity, donor attitudes, policies of intermediary funding agencies, and so on. It’s helpful at this stage to write what are called nominal definitions of the concepts that are central to my study. These are definitions like what you’d find in a dictionary, but tailored to your study; a nominal definition of government subsidy would describe what I mean in this study when I use the term.

After identifying and defining concepts, we’re ready to operationalize them. To operationalize a concept is to describe how to measure it. (Some authors refer to this as the operational definition , which I find confuses students since it doesn’t necessarily look like a definition.) Operationalization is where we get quite concrete: To operationalize the concept revenue of a nonprofit organization , we might record the dollar amount entered in line 12 of their most recent Form 990 (a financial statement nonprofit organizations must file with the IRS annually). This dollar amount will be my measure of nonprofit revenue.

Sometimes, the way we operationalize a concept is more indirect. Public support for nonprofit organizations, for example, is more of a challenge to operationalize. We might write a nominal definition for public support that describes it as having something to do with the sum of individuals’ active, tangible support of a nonprofit organization’s mission. We might operationalize this concept by recording the amount of direct charitable contributions, indirect charitable contributions, revenue from fundraising events, and the number of volunteer hours entered in the respective Form 990 lines.

Note that when we operationalized nonprofit revenue, the operationalization yielded a single measure. When we operationalized public support, however, the operationalization yielded multiple measures. Public support is a broader, more complex concept, and it’s hard to think of just one measure that would convincingly represent it. Also, when we’re using measures that measure the concept more indirectly, like our measures for public support, we’ll sometimes use the word indicator instead of measure . The term indicator can be more accurate. We know that measuring something as abstract as public support would be impossible; it is, after all, a social construct, not something concrete. Our measures, then, indicate the level of public support more than actually measure it.

I just slipped in that term, social construct , so we should go ahead and face an issue we’ve been sidestepping so far: Many concepts we’re interested in aren’t observable in the sense that they can’t be seen, felt, heard, tasted, or smelled. But aren’t we supposed to be building knowledge based on observations? Are unobservable concepts off limits for empirical social researchers? Let’s hope not! Lots of important concepts (maybe all the most important concepts) are social constructs, terms that don’t have meaning apart from the meaning we, collectively, assign to them. Consider political literacy, racial prejudice, voter intent, employee motivation, issue saliency, self-esteem, managerial capacity, fundraising effectiveness, introversion, and Constitutional ideology. These terms are a shorthand for sets of characteristics that we all more or less agree “belong” to the concepts they name. Can we observe political ideology? Not directly, but we can pretty much agree on what observations serve as indicators for political ideology. We can observe behaviors, like putting bumper stickers on cars, we can see how people respond to survey items, and we can hear how people respond to interview questions. We know we’re not directly measuring political ideology (which is impossible, after all, since it’s a social construct), but we can persuade each other that our measures of political ideology make sense (which seems fitting, since, again, it’s a social construct).

Each indicator or measure—each observation we repeat over and over again—yields a variable . The term variable is one of these terms that’s easier to learn by example than by definition. The definition, though, is something like “a logical grouping of attributes.” (Not very helpful!) Think of the various attributes that could be used to describe you and your friends: brown hair, green eyes, 6’2” tall, brown eyes, black hair, 19 years old, 5’8” tall, blue eyes, and so on. Obviously, some of these attributes go together, like green eyes, brown eyes, and blue eyes. We can group these attributes together and give them a label: eye color. Eye color, then, is a variable. In this example, the variable eye color takes on the values green, brown, and blue. In many research designs, our goal in making observations is to assign values to variables for cases . Cases are the things—here, you and your friends—that we’re observing and to which we’re assigning values. In social science research, cases are often individuals (like individual voters or individual respondents to a survey) or groups of people (like families or organizations), but cases can also be court rulings, elections, states, committee meetings, and an infinite number of other things that can be observed. The term unit of analysis is used to describe cases, too, but it’s a more general term; if your cases are firefighters, then your unit of analysis is the individual.

Getting this terminology—cases, variables, values—is essential. Here are some examples of cases, variables, and values . . .

• Cases : undergraduate college students; variable : classification; values : Freshmen, Sophomore, Junior, Senior;
• Cases : states; variable : whether or not citizen referenda are permitted; values : yes, no;
• Cases : counties; variable : type of voting equipment; values : manual mark, punch card, optical scan, electronic;
• Cases : clients; variable : length of time it took them to see a counselor; values : any number of minutes;
• Cases : Supreme Court dissenting opinions; variable : number of signatories; values : a number from 0 to 4;
• Cases : criminology majors; variable : GPA; values : any number from 0 to 4.0.

Researchers have a language for describing variables. A variable’s level of measurement describes the structure of the values it can take on, whether nominal, ordinal, interval, or ratio. Nominal and ordinal variables are the categorical variables ; their values divide up cases into distinct categories. The values of nominal-level variables have no inherent order. The variable sex can take on the values male and female; eye color—brown, blue, and green eyes; major— political science, sociology, biology, etc. Placing these values in one order—brown, blue, green— makes just as much sense as any other—blue, green, brown. The values of ordinal-level variables , though, have an inherent order. Classification—freshmen, sophomore, junior, senior; love of research methods—low, medium, high; class rank—first, second, . . . , 998th. These values can be placed in an order that makes sense—first to last (or last to first), least to most, best to worst, and so on. A point of confusion to be avoided: When we collect and record data, sometimes we assign numbers to values of categorical variables (like brown hair equals 1), but that’s just for the sake of convenience. Those numbers are just placeholders for the actual values, which remain categorical.

When values take on actual numeric values, the variables they belong to are numeric variables . If a numeric variable takes on the value 28 , it means there are actually 28 of something—28 degrees, 28 votes, 28 pounds, 28 percentage points. It makes sense to add and subtract these values. If one state has a 12% unemployment rate, that’s 3 more points than a state with a 9% unemployment rate. Numeric variables can be either interval-level variables or ratio-level variables. When ratio-level variables take on the value zero, zero means zero—it means nothing of whatever we’re measuring. Zero votes means no votes; zero senators means no senators. Most numeric variables we use in social research are ratio-level. (Note that many ratio-level variables, like height, age, states’ number of senators, would never actually take on the value zero, but if they did, zero would mean zero.) Occasionally, zero means something else besides nothing of something, and variables that take on these odd zeroes are interval-level variables. Zero degrees means—well, not “no degrees,” which doesn’t make sense. Year zero doesn’t mean the year that wasn’t. We can add and subtract the values of interval-level variables, but we cannot multiply and divide them. Someone born in 996 is not half the age of someone born in 1992, and 90 degrees is not twice as hot as 45.

We can sometimes choose the level of measurement when constructing a variable. We could measure age with a ratio-level variable (the number of times you’ve gone around the sun) or with an ordinal-level variable (check whether you’re 0-10, 11-20, 21-30, or over 30). We should make this choice intentionally because it will determine what kinds of statistical analysis we can do with our data later. If our data are ratio-level, we can do any statistical analysis we want, but our choices are more limited with interval-level data, still more limited with ordinal-level data, and most limited with nominal-level data. (See Appendix E on equity in research for an explanation of how dummy coding can be used to helpfully transform categorical variables to ratio-level variables.)

Variables can also be described as being either continuous or discrete . Just like with the level of measurement, we look at the variable’s values to determine whether it’s a continuous or discrete variable. All categorical variables are discrete, meaning their variables can only take on specific, discrete values. This is in contrast to some (but not all!) numeric variables. Take temperature, for example. For any two values of the variable temperature , we can always imagine a case with a value in between them. If Monday’s high is 62.5 degrees and Tuesday’s high is 63.0 degrees, Wednesday’s high could be 62.75 degrees. Temperature, then, measured in degrees, is a continuous variable. Other numeric variables are discrete variables, though. Any variable that is a count of things is discrete. For the variable number of siblings , Anna has two siblings and Henry has three siblings. We cannot imagine a person with any number of siblings between two and three—nobody could have 2.5 siblings. Number of siblings , then, is a discrete variable. (Note: Some textbooks and websites incorrectly state that all numeric variables are continuous. Do not be misled.)

If we’re engaging in causal research, we can also describe our variables in terms of their role in causal explanation. The “cause” variable is the independent variable . The “effect” variable is the dependent variable. If you’re interested in determining the effect of level of education on political party identification, level of education is the independent variable, and political party identification is the dependent variable.

I’m being a bit loose in using “cause” and “effect” here. Recall the concept of underlying causal mechanism. We may identify independent and dependent variables that really represent a much more complex underlying causal mechanism. Why, for example, do people make charitable contributions? At least four studies have asked whether people are more likely to make a contribution when the person asking for it is dressed nicely. (See the examples cited in Bekkers and Wiepking’s 2010 “A Literature Review of Empirical Studies of Philanthropy,” Nonprofit and Voluntary Sector Quarterly , volume 40, p. 924, which I also recommend for its many examples of how social research explores questions of causality.) Do these researchers believe the quality of stitching might affect altruism? Sort of, but not exactly. More likely, they believe potential donors’ perceptions of charitable solicitors may shape their attitudes toward the requests, which will make them more or less likely to respond positively. It’s a bit reductionist to say charitable solicitors’ clothing “causes” people to make charitable donations, but we still use the language of independent variables and dependent variables as labels for the quality of the solicitors’ clothing and the solicitees’ likelihood of making charitable donations, respectively. Think carefully about how this might apply anytime an independent variable—sometimes more helpfully called an explanatory variable—is a demographic characteristic. Women, on average, make lower salaries than men. Does sex “cause” salary? Not exactly, though we would rightly label sex as an independent variable and salary as a dependent variable. Underlying this simple dyad of variables is a set of complex, interacting, causal factors—gender socialization, discrimination, occupational preferences, economic systems’ valuing of different jobs, family leave policies, time in labor market—that more fully explain this causal relationship.

Identifying independent variables (IVs) and dependent variables (DVs) is often challenging for students at first. If you’re unsure which is which, try plugging your variables into the following phrases to see what makes sense:

• IV causes DV
• Change in IV causes change in DV
• IV affects DV
• DV is partially determined by IV
• A change in IV predicts a change in DV
• DV can be partially explained by IV
• DV depends on IV

In the later section on formal research designs, we’ll learn about control variables, another type of variable in causal studies often used in conjunction with independent and dependent variables.

Sometimes, especially if we’re collecting quantitative data and planning to conduct inferential statistical analysis, we’ll specify hypotheses at this point in the research process as well. A hypothesis is a statement of the expected relationship between two or more variables. Like operationalizing a concept, constructing a hypothesis requires getting specific. A good hypothesis will not just predict that two (or more) variables are related, but how. So, not Political science majors’ amount of volunteer experience will be related to their choice of courses, but Political science majors with more volunteer experience will be more likely to enroll in the public policy, public administration, and nonprofit management courses . Note that you may have to infer the actual variables; hypotheses often refer only to specific values of the variables. Here, public policy, public administration, and nonprofit management courses are values of the implied variable, types of courses .

A quick, somewhat easy-to-read introduction to empirical social science research methods Copyright © 2022 by Christopher S. Horne is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Hypothesis ( AQA A Level Psychology )

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Psychology Content Creator

• A hypothesis is a testable statement written as a prediction of what the researcher expects to find as a result of their experiment
• A hypothesis should be no more than one sentence long
• The hypothesis needs to include the independent variable (IV) and the dependent variable (DV)
• For example - stating that you will measure ‘aggression’ is not enough ('aggression' has not been operationalised)
• by exposing some children to an aggressive adult model whilst other children are not exposed to an aggressive adult model (operationalisation of the IV) 
• number of imitative and non-imitative acts of aggression performed by the child (operationalisation of the DV)

The Experimental Hypothesis

• Children who are exposed to an aggressive adult model will perform more acts of imitative and non-imitative aggression than children who have not been exposed to an aggressive adult model
• The experimental hypothesis can be written as a  directional hypothesis or as a non-directional hypothesis

The Experimental Hypothesis: Directional 

• A directional experimental hypothesis (also known as one-tailed)  predicts the direction of the change/difference (it anticipates more specifically what might happen)
• A directional hypothesis is usually used when there is previous research which support a particular theory or outcome i.e. what a researcher might expect to happen
• Participants who drink 200ml of an energy drink 5 minutes before running 100m will be faster (in seconds) than participants who drink 200ml of water 5 minutes before running 100m
• Participants who learn a poem in a room in which loud music is playing will recall less of the poem's content than participants who learn the same poem in a silent room

 The Experimental Hypothesis: Non-Directional 

• A non-directional experimental hypothesis (also known as two -tailed) does not predict the direction of the change/difference (it is an 'open goal' i.e. anything could happen)
• A non-directional hypothesis is usually used when there is either no or little previous research which support a particular theory or outcome i.e. what the researcher cannot be confident as to what will happen
• There will be a difference in time taken (in seconds) to run 100m depending on whether participants have drunk 200ml of an energy drink or 200ml of water 5 minutes before running 
• There will be a difference in recall of a poem depending on whether participants learn the poem in a room in which loud music is playing or in a silent room

The Null Hypothesis

• All published psychology research must include the null hypothesis
• There will be no difference in children's acts of imitative and non-imitative aggression depending on whether they have observed an aggressive adult model or a non-aggressive adult model
• The null hypothesis has to begin with the idea that the IV will have no effect on the DV  because until the experiment is run and the results are analysed it is impossible to state anything else! 
• To put this in 'laymen's terms: if you bought a lottery ticket you could not predict that you are going to win the jackpot: you have to wait for the results to find out (spoiler alert: the chances of this happening are soooo low that you might as well save your cash!)
• There will be no difference in time taken (in seconds) to run 100m depending on whether participants have drunk 200ml of an energy drink or 200ml of water 5 minutes before running 
• There will be no difference in recall of a poem depending on whether participants learn the poem in a room in which loud music is playing or in a silent room
• (NB this is not quite so slick and easy with a directional hypothesis as this sort of hypothesis will never begin with 'There will be a difference')
• this is why the null hypothesis is so important - it tells the researcher whether or not their experiment has shown a difference in conditions (which is generally what they want to see, otherwise it's back to the drawing board...)

Worked example

Jim wants to test the theory that chocolate helps your ability to solve word-search puzzles

He believes that sugar helps memory as he has read some research on this in a text book

He puts up a poster in his sixth-form common room asking for people to take part after school one day and explains that they will be required to play two memory games, where eating chocolate will be involved

(a)  Should Jim use a directional hypothesis in this study? Explain your answer (2 marks)

(b)  Write a suitable hypothesis for this study. (4 marks)

a) Jim should use a directional hypothesis (1 mark)

    because previous research exists that states what might happen (2 nd mark)

b)  'Participants will remember more items from a shopping list in a memory game within the hour after eating 50g of chocolate, compared to when they have not consumed any chocolate'

• 1 st mark for directional
• 2 nd mark for IV- eating chocolate
• 3 rd mark for DV- number of items remembered
• 4 th mark for operationalising both IV & DV
• If you write a non-directional or null hypothesis the mark is 0
• If you do not get the direction correct the mark is zero
• Remember to operationalise the IV & DV

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Author: Claire Neeson

Claire has been teaching for 34 years, in the UK and overseas. She has taught GCSE, A-level and IB Psychology which has been a lot of fun and extremely exhausting! Claire is now a freelance Psychology teacher and content creator, producing textbooks, revision notes and (hopefully) exciting and interactive teaching materials for use in the classroom and for exam prep. Her passion (apart from Psychology of course) is roller skating and when she is not working (or watching 'Coronation Street') she can be found busting some impressive moves on her local roller rink.

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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Prevent plagiarism. Run a free check.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Theory, hypothesis, and operationalization

Approach, theory, model.

First, you have to determine the general state of knowledge (or state of the art) as regards a certain objective. Are there already relevant attempts of explanation (models, theories, approaches, debates)? Many times there are theories already existing that provide a basis for discussing or looking at a certain problem.

When choosing a certain approach to explain complex circumstances, specific aspects of your problem area will be highlighted more prominently. Deciding on an approach means considering which questions can then be answered best. After choosing an approach it is necessary to use its related methods consequently.

Examples for approaches: «Education is an important prerequisite for a society's economic development» or «Earnings from tourism support national economy.»

Hypotheses and presumptions

Hypotheses are assumptions that could explain reality or - in other words - that could be the answer to your question. Such an assumption is based on the current state of research; it therefore delivers an answer that is theoretically possible («proposed solution») and applies at least to some extent to the question posed. When dealing with complex topics it is sometimes easier to develop a number of subordinate working hypotheses from just a few main hypotheses.

Example for a hypothesis: «Tourism offers children the possibility to earn money instead of going to school» or «The more tourists the fewer the children are going to school.»

Not all research projects are conducted by means of methods to test hypotheses. In social research, for example, there are reconstructive or interpretive methods as well. Here you try to explain and understand people's actions based on their interpretation of certain issues ( Bohnsack 2000: 12–13). However, also with such an approach researchers use hypotheses or presumptions to structure their work. The point is not to finally acknowledge or reject those hypotheses. You rather search for explanations that are plausible and comprehensible.

Example for a presumption: «In developing countries parents are skeptical about their children working for the tourism industry.»

However, most of the time one again acts on theses or presumptions. The point is not to finally acknowledge or reject those assumptions. One rather searches for explanations that are plausible and comprehensible.

Example for an explanation: «Parents don't worry about their children not going to school; they are afraid of losing their status when earning less than their children.»

Operationalization

It is necessary to operationalize the terms used in scientific research (that means particularly the central terms of a hypothesis). In order to guarantee the viability of a research method you have to define first which data will be collected by means of which methods. Research operations have to be specified to comprehend a subject matter in the first place ( Bopp 2000: 21). In order to turn the operationalized term into something manageable you determine its exact meaning during a research process.

Example for an operationalization: «When compared to other areas, tourist destinations are areas where children are less likely to go to school.»

Online Guidelines for Academic Research and Writing : The academic research process : Theory, hypothesis, and operationalization

Update: 28.10.2021 ( eLML ) - Contact - Print (PDF) - © OLwA 2011 (Creative Commons)

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Research Methods: Writing Hypothesis (Identifying and Operationalising Variables)

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22 February 2018

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Directional Hypothesis

A directional hypothesis is a one-tailed hypothesis that states the direction of the difference or relationship (e.g. boys are more helpful than girls).

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