three characteristics of a hypothesis

• Scientific Methods

What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.

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What is Hypothesis? Definition, Meaning, Characteristics, Sources

• Post last modified: 10 January 2022
• Reading time: 18 mins read
• Post category: Research Methodology

• What is Hypothesis?

Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.

As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.

In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.

Table of Content

• 1 What is Hypothesis?
• 2 Hypothesis Definition
• 3 Meaning of Hypothesis
• 4.1 Conceptual Clarity
• 4.2 Need of empirical referents
• 4.3 Hypothesis should be specific
• 4.4 Hypothesis should be within the ambit of the available research techniques
• 4.5 Hypothesis should be consistent with the theory
• 4.6 Hypothesis should be concerned with observable facts and empirical events
• 4.7 Hypothesis should be simple
• 5.1 Observation
• 5.2 Analogies
• 5.4 State of Knowledge
• 5.5 Culture
• 5.6 Continuity of Research
• 6.1 Null Hypothesis
• 6.2 Alternative Hypothesis

Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.

The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).

Hypothesis Definition

A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart

Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black

Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt

A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)

Meaning of Hypothesis

From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.

• At the primary level, a hypothesis is the possible and probable explanation of the sequence of happenings or data.
• Sometimes, hypothesis may emerge from an imagination, common sense or a sudden event.
• Hypothesis can be a probable answer to the research problem undertaken for study. 4. Hypothesis may not always be true. It can get disproven. In other words, hypothesis need not always be a true proposition.
• Hypothesis, in a sense, is an attempt to present the interrelations that exist in the available data or information.
• Hypothesis is not an individual opinion or community thought. Instead, it is a philosophical means which is to be used for research purpose. Hypothesis is not to be considered as the ultimate objective; rather it is to be taken as the means of explaining scientifically the prevailing situation.

The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.

Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.

Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.

• Population growth moderates with the rise in per capita income.
• Sales growth is positively linked with the availability of credit.
• Commerce education increases the employability of the graduate students.
• High rates of direct taxes prompt people to evade taxes.
• Good working conditions improve the productivity of employees.
• Advertising is the most effecting way of promoting sales than any other scheme.
• Higher Debt-Equity Ratio increases the probability of insolvency.
• Economic reforms in India have made the public sector banks more efficient and competent.
• Foreign direct investment in India has moved in those sectors which offer higher rate of profit.
• There is no significant association between credit rating and investment of fund.

Characteristics of Hypothesis

Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:

Conceptual Clarity

Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.

The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.

A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.

A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.

For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.

For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.

For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.

While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.

It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.

A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.

If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.

This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”

If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.

Sources of Hypothesis

Hypotheses can be derived from various sources. Some of the sources is given below:

Observation

State of knowledge, continuity of research.

Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.

Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.

This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.

An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.

Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.

The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.

Null and Alternative Hypothesis

Null hypothesis.

The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.

Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.

Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .

Alternative Hypothesis

Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.

As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .

Business Ethics

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• What is Process Audits?
• Six Sigma Implementation at Ford
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• Research Methodology
• What is Research?

Sampling Method

• Research Methods
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Hypothesis Testing

• Research Report
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• Prahalad and Gary Hammel
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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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What is a Hypothesis – Types, Examples and Writing Guide

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Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

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

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

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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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.

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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

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

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Developing a Hypothesis

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

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

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.3  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

• Zajonc, R. B. (1965). Social facilitation.  Science, 149 , 269–274 ↵
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Developing a Hypothesis Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

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I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

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It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

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It is really a useful for me Kindly give some examples of hypothesis

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5 Characteristics of a Good Hypothesis: A Guide for Researchers

• by Brian Thomas
• October 10, 2023

Are you a curious soul, always seeking answers to the whys and hows of the world? As a researcher, formulating a hypothesis is a crucial first step towards unraveling the mysteries of your study. A well-crafted hypothesis not only guides your research but also lays the foundation for drawing valid conclusions. But what exactly makes a hypothesis a good one? In this blog post, we will explore the five key characteristics of a good hypothesis that every researcher should know.

Here, we will delve into the world of hypotheses, covering everything from their types in research to understanding if they can be proven true. Whether you’re a seasoned researcher or just starting out, this blog post will provide valuable insights on how to craft a sound hypothesis for your study. So let’s dive in and uncover the secrets to formulating a hypothesis that stands strong amidst the scientific rigor!

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5 Characteristics of a Good Hypothesis

Clear and specific.

A good hypothesis is like a GPS that guides you to the right destination. It needs to be clear and specific so that you know exactly what you’re testing. Avoid vague statements or general ideas. Instead, focus on crafting a hypothesis that clearly states the relationship between variables and the expected outcome. Clarity is key, my friend!

Testable and Falsifiable

A hypothesis might sound great in theory, but if you can’t test it or prove it wrong, then it’s like chasing unicorns. A good hypothesis should be testable and falsifiable – meaning there should be a way to gather evidence to support or refute it. Don’t be afraid to challenge your hypothesis and put it to the test. Only when it can be proven false can it truly be considered a good hypothesis.

Based on Existing Knowledge

Imagine trying to build a Lego tower without any Lego bricks. That’s what it’s like to come up with a hypothesis that has no basis in existing knowledge. A good hypothesis is grounded in previous research, theories, or observations. It shows that you’ve done your homework and understand the current state of knowledge in your field. So, put on your research hat and gather those building blocks for a solid hypothesis!

Specific Predictions

No, we’re not talking about crystal ball predictions or psychic abilities here. A good hypothesis includes specific predictions about what you expect to happen. It’s like making an educated guess based on your understanding of the variables involved. These predictions help guide your research and give you something concrete to look for. So, put on those prediction goggles, my friend, and let’s get specific!

Relevant to the Research Question

A hypothesis is a road sign that points you in the right direction. But if it’s not relevant to your research question, then you might end up in a never-ending detour. A good hypothesis aligns with your research question and addresses the specific problem or phenomenon you’re investigating. Keep your focus on the main topic and avoid getting sidetracked by shiny distractions. Stay relevant, my friend, and you’ll find the answers you seek!

And there you have it: the five characteristics of a good hypothesis. Remember, a good hypothesis is clear, testable, based on existing knowledge, makes specific predictions, and is relevant to your research question. So go forth, my friend, and hypothesize your way to scientific discovery!

FAQs: Characteristics of a Good Hypothesis

In the realm of scientific research, a hypothesis plays a crucial role in formulating and testing ideas. A good hypothesis serves as the foundation for an experiment or study, guiding the researcher towards meaningful results. In this FAQ-style subsection, we’ll explore the characteristics of a good hypothesis, their types, formulation, and more. So let’s dive in and unravel the mysteries of hypothesis-making!

What Are Two Important Characteristics of a Good Hypothesis

A good hypothesis possesses two important characteristics:

Testability : A hypothesis must be testable to determine its validity. It should be formulated in a way that allows researchers to design and conduct experiments or gather data for analysis. For example, if we hypothesize that “drinking herbal tea reduces stress,” we can easily test it by conducting a study with a control group and a group drinking herbal tea.

Falsifiability : Falsifiability refers to the potential for a hypothesis to be proven wrong. A good hypothesis should make specific predictions that can be refuted or supported by evidence. This characteristic ensures that hypotheses are based on empirical observations rather than personal opinions. For instance, the hypothesis “all swans are white” can be falsified by discovering a single black swan.

What Are the Types of Hypothesis in Research

In research, there are three main types of hypotheses:

Null Hypothesis (H0) : The null hypothesis is a statement of no effect or relationship. It assumes that there is no significant difference between variables or no effect of a treatment. Researchers aim to reject the null hypothesis in favor of an alternative hypothesis.

Alternative Hypothesis (HA or H1) : The alternative hypothesis is the opposite of the null hypothesis. It asserts that there is a significant difference between variables or an effect of a treatment. Researchers seek evidence to support the alternative hypothesis.

Directional Hypothesis : A directional hypothesis predicts the specific direction of the relationship or difference between variables. For example, “increasing exercise duration will lead to greater weight loss.”

Can a Hypothesis Be Proven True

In scientific research, hypotheses are not proven true; they are supported or rejected based on empirical evidence . Even if a hypothesis is supported by multiple studies, new evidence could arise that contradicts it. Scientific knowledge is always subject to revision and refinement. Therefore, the goal is to gather enough evidence to either support or reject a hypothesis, rather than proving it absolutely true.

What Are the Six Parts of a Hypothesis

A hypothesis typically consists of six essential parts:

Research Question : A clear and concise question that the hypothesis seeks to answer.

Variables : Identification of the independent (manipulated) and dependent (measured) variables involved in the hypothesis.

Population : The specific group or individuals the hypothesis is concerned with.

Relationship or Comparison : The expected relationship or difference between variables, often indicated by directional terms like “more,” “less,” “higher,” or “lower.”

Predictability : A statement of the predicted outcome or result based on the relationship between variables.

Testability : The ability to design an experiment or gather data to support or reject the hypothesis.

How Do You Start a Hypothesis Sentence

When starting a hypothesis sentence, it is essential to use clear and concise language to express your ideas. A common approach is to use the phrase “If…then…” to establish the conditional relationship between variables. For example:

• If [independent variable], then [dependent variable] because [explanation of expected relationship].

This structure allows for a straightforward and logical formulation of the hypothesis.

What Are Examples of Hypotheses

Here are a few examples of well-formulated hypotheses:

If exposure to sunlight increases, then plants will grow taller because sunlight is necessary for photosynthesis.

If students receive praise for good grades, then their motivation to excel will increase because they seek recognition and approval.

If the dose of a painkiller is increased, then the relief from pain will last longer because a higher dosage has a prolonged effect.

What Are the Five Key Elements to a Good Hypothesis

A good hypothesis should include the following five key elements:

Clarity : The hypothesis should be clear and specific, leaving no room for interpretation.

Testability : It should be possible to test the hypothesis through experimentation or data collection.

Relevance : The hypothesis should be directly tied to the research question or problem being investigated.

Specificity : It must clearly state the relationship or difference between variables being studied.

Falsifiability : The hypothesis should make predictions that can be refuted or supported by empirical evidence.

What Makes a Good Hypothesis in a Research Paper

In a research paper, a good hypothesis should have the following characteristics:

Relevance : It must directly relate to the research topic and address the objectives of the study.

Clarity : The hypothesis should be concise and precisely worded to avoid confusion.

Unambiguous : It must leave no room for multiple interpretations or ambiguity.

Logic : The hypothesis should be based on rational and logical reasoning, considering existing theories and observations.

Empirical Support : Ideally, the hypothesis should be supported by prior empirical evidence or strong theoretical justifications.

Is a Hypothesis Always a Question

No, a hypothesis is not always in the form of a question. While some hypotheses can take the form of a question, others may be statements asserting a relationship or difference between variables. The form of a hypothesis depends on the research question being addressed and the researcher’s preferred style of expression.

What Are the Three Things Needed for a Good Hypothesis

For a hypothesis to be considered good, it must fulfill the following three criteria:

Testability : The hypothesis should be formulated in a way that allows for empirical testing through experimentation or data collection.

Falsifiability : It must make specific predictions that can be potentially refuted or supported by evidence.

Relevance : The hypothesis should directly address the research question or problem being investigated.

What Are the Four Components to a Good Hypothesis

A good hypothesis typically consists of four components:

Independent Variable : The variable being manipulated or controlled by the researcher.

Dependent Variable : The variable being measured or observed to determine the effect of the independent variable.

Directionality : The predicted relationship or difference between the independent and dependent variables.

Population : The specific group or individuals to which the hypothesis applies.

How Do You Formulate a Hypothesis

To formulate a hypothesis, follow these steps:

Identify the Research Topic : Clearly define the area or phenomenon you want to study.

Conduct Background Research : Review existing literature and research to gain knowledge about the topic.

Formulate a Research Question : Ask a clear and focused question that you want to answer through your hypothesis.

State the Null and Alternative Hypotheses : Develop a null hypothesis to assume no effect or relationship, and an alternative hypothesis to propose a significant effect or relationship.

Decide on Variables and Relationships : Determine the independent and dependent variables and the predicted relationship between them.

Refine and Test : Refine your hypothesis, ensuring it is clear, testable, and falsifiable. Then, design experiments or gather data to support or reject it.

What Is a Characteristic of a Hypothesis MCQ

Multiple-choice questions (MCQ) regarding the characteristics of a hypothesis often assess knowledge on the testability and falsifiability of hypotheses. They may ask about the criteria that distinguish a good hypothesis from a poor one or the importance of making specific predictions. Remember to choose answers that emphasize the empirical and testable nature of hypotheses.

What Five Criteria Must Be Satisfied for a Hypothesis to Be Scientific

For a hypothesis to be considered scientific, it must satisfy the following five criteria:

Testability : The hypothesis must be formulated in a way that allows it to be tested through experimentation or data collection.

Falsifiability : It should make specific predictions that can be potentially refuted or supported by empirical evidence.

Empirical Basis : The hypothesis should be based on empirical observations or existing theories and knowledge.

Relevance : It must directly address the research question or problem being investigated.

Objective : A scientific hypothesis should be free from personal biases or subjective opinions, focusing on objective observations and analysis.

What Are the Steps of Theory Development in Scientific Methods

In scientific methods, theory development typically involves the following steps:

Observation : Identifying a phenomenon or pattern worthy of investigation through observation or empirical data.

Formulation of a Hypothesis : Constructing a hypothesis that explains the observed phenomena or predicts a relationship between variables.

Data Collection : Gathering relevant data through experiments, surveys, observations, or other research methods.

Analysis : Analyzing the collected data to evaluate the hypothesis’s predictions and determine their validity.

Revision and Refinement : Based on the analysis, refining the hypothesis, modifying the theory, or formulating new hypotheses for further investigation.

Which of the Following Makes a Good Hypothesis

A good hypothesis is characterized by:

Testability : The ability to form experiments or gather data to support or refute the hypothesis.

Falsifiability : The potential for the hypothesis’s predictions to be proven wrong based on empirical evidence.

Clarity : A clear and concise statement or question that leaves no room for ambiguity.

Relevancy : Directly addressing the research question or problem at hand.

Remember, it is important to select the option that encompasses all these characteristics.

What Are the Characteristics of a Good Hypothesis

A good hypothesis possesses several characteristics, such as:

Testability : It should allow for empirical testing through experiments or data collection.

Falsifiability : The hypothesis should make specific predictions that can be potentially refuted or supported by evidence.

Clarity : It must be clearly and precisely formulated, leaving no room for ambiguity or multiple interpretations.

Relevance : The hypothesis should directly relate to the research question or problem being investigated.

What Is the Five-Step p-value Approach to Hypothesis Testing

The five-step p-value approach is a commonly used framework for hypothesis testing:

Step 1: Formulating the Hypotheses : The null hypothesis (H0) assumes no effect or relationship, while the alternative hypothesis (HA) proposes a significant effect or relationship.

Step 2: Setting the Significance Level : Decide on the level of significance (α), which represents the probability of rejecting the null hypothesis when it is true. The commonly used level is 0.05 (5%).

Step 3: Collecting Data and Performing the Test : Acquire and analyze the data, calculating the test statistic and the corresponding p-value.

Step 4: Comparing the p-value with the Significance Level : If the p-value is less than the significance level (α), reject the null hypothesis. Otherwise, fail to reject the null hypothesis.

Step 5: Drawing Conclusions : Based on the comparison in Step 4, interpret the results and draw conclusions about the hypothesis.

What Are the Stages of Hypothesis

The stages of hypothesis generally include:

Observation : Identifying a pattern, phenomenon, or research question that warrants investigation.

Formulation : Developing a hypothesis that explains or predicts the relationship or difference between variables.

Testing : Collecting data, designing experiments, or conducting studies to gather evidence supporting or refuting the hypothesis.

Analysis : Assessing the collected data to determine whether the results support or reject the hypothesis.

Conclusion : Drawing conclusions based on the analysis and making further iterations, refinements, or new hypotheses for future research.

What Is a Characteristic of a Good Hypothesis

A characteristic of a good hypothesis is its ability to make specific predictions about the relationship or difference between variables. Good hypotheses avoid vague statements and clearly articulate the expected outcomes. By doing so, researchers can design experiments or gather data that directly test the predictions, leading to meaningful results.

How Do You Write a Good Hypothesis Example

To write a good hypothesis example, follow these guidelines:

If possible, use the “If…then…” format to express a conditional relationship between variables.

Be clear and concise in stating the variables involved, the predicted relationship, and the expected outcome.

Ensure the hypothesis is testable, meaning it can be evaluated through experiments or data collection.

For instance, consider the following example:

If students study for longer periods of time, then their test scores will improve because increased study time allows for better retention of information and increased proficiency.

What Is the Difference Between Hypothesis and Hypotheses

The main difference between a hypothesis and hypotheses lies in their grammatical number. A hypothesis refers to a single statement or proposition that is formulated to explain or predict the relationship between variables. On the other hand, hypotheses is the plural form of the term hypothesis, commonly used when multiple statements or propositions are proposed and tested simultaneously.

What Is a Good Hypothesis Statement

A good hypothesis statement exhibits the following qualities:

Clarity : It is written in clear and concise language, leaving no room for confusion or ambiguity.

Testability : The hypothesis should be formulated in a way that enables testing through experiments or data collection.

Specificity : It must clearly state the predicted relationship or difference between variables.

By adhering to these criteria, a good hypothesis statement guides research efforts effectively.

What Is Not a Characteristic of a Good Hypothesis

A characteristic that does not align with a good hypothesis is subjectivity . A hypothesis should be objective, based on empirical observations or existing theories, and free from personal bias. While personal interpretations and opinions can inspire the formulation of a hypothesis, it must ultimately rely on objective observations and be open to empirical testing.

By now, you’ve gained insights into the characteristics of a good hypothesis, including testability, falsifiability, clarity,

• characteristics
• falsifiable
• good hypothesis
• hypothesis testing
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Brian Thomas

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2.1.3: The Research Hypothesis and the Null Hypothesis

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• Page ID 44856

• Michelle Oja
• Taft College

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Hypotheses are predictions of expected findings.

The Research Hypothesis

A research hypothesis is a mathematical way of stating a research question. A research hypothesis names the groups (we'll start with a sample and a population), what was measured, and which we think will have a higher mean. The last one gives the research hypothesis a direction. In other words, a research hypothesis should include:

• The name of the groups being compared. This is sometimes considered the IV.
• What was measured. This is the DV.
• Which group are we predicting will have the higher mean.

There are two types of research hypotheses related to sample means and population means: Directional Research Hypotheses and Non-Directional Research Hypotheses

Directional Research Hypothesis

If we expect our obtained sample mean to be above or below the other group's mean (the population mean, for example), we have a directional hypothesis. There are two options:

• Symbol: \( \displaystyle \bar{X} > \mu \)
• (The mean of the sample is greater than than the mean of the population.)
• Symbol: \( \displaystyle \bar{X} < \mu \)
• (The mean of the sample is less than than mean of the population.)

Example \(\PageIndex{1}\)

A study by Blackwell, Trzesniewski, and Dweck (2007) measured growth mindset and how long the junior high student participants spent on their math homework. What’s a directional hypothesis for how scoring higher on growth mindset (compared to the population of junior high students) would be related to how long students spent on their homework? Write this out in words and symbols.

Answer in Words: Students who scored high on growth mindset would spend more time on their homework than the population of junior high students.

Answer in Symbols: \( \displaystyle \bar{X} > \mu \)

Non-Directional Research Hypothesis

A non-directional hypothesis states that the means will be different, but does not specify which will be higher. In reality, there is rarely a situation in which we actually don't want one group to be higher than the other, so we will focus on directional research hypotheses. There is only one option for a non-directional research hypothesis: "The sample mean differs from the population mean." These types of research hypotheses don’t give a direction, the hypothesis doesn’t say which will be higher or lower.

A non-directional research hypothesis in symbols should look like this: \( \displaystyle \bar{X} \neq \mu \) (The mean of the sample is not equal to the mean of the population).

Exercise \(\PageIndex{1}\)

What’s a non-directional hypothesis for how scoring higher on growth mindset higher on growth mindset (compared to the population of junior high students) would be related to how long students spent on their homework (Blackwell, Trzesniewski, & Dweck, 2007)? Write this out in words and symbols.

Answer in Words: Students who scored high on growth mindset would spend a different amount of time on their homework than the population of junior high students.

Answer in Symbols: \( \displaystyle \bar{X} \neq \mu \)

See how a non-directional research hypothesis doesn't really make sense? The big issue is not if the two groups differ, but if one group seems to improve what was measured (if having a growth mindset leads to more time spent on math homework). This textbook will only use directional research hypotheses because researchers almost always have a predicted direction (meaning that we almost always know which group we think will score higher).

The Null Hypothesis

The hypothesis that an apparent effect is due to chance is called the null hypothesis, written \(H_0\) (“H-naught”). We usually test this through comparing an experimental group to a comparison (control) group. This null hypothesis can be written as:

\[\mathrm{H}_{0}: \bar{X} = \mu \nonumber \]

For most of this textbook, the null hypothesis is that the means of the two groups are similar. Much later, the null hypothesis will be that there is no relationship between the two groups. Either way, remember that a null hypothesis is always saying that nothing is different.

This is where descriptive statistics diverge from inferential statistics. We know what the value of \(\overline{\mathrm{X}}\) is – it’s not a mystery or a question, it is what we observed from the sample. What we are using inferential statistics to do is infer whether this sample's descriptive statistics probably represents the population's descriptive statistics. This is the null hypothesis, that the two groups are similar.

Keep in mind that the null hypothesis is typically the opposite of the research hypothesis. A research hypothesis for the ESP example is that those in my sample who say that they have ESP would get more correct answers than the population would get correct, while the null hypothesis is that the average number correct for the two groups will be similar.

In general, the null hypothesis is the idea that nothing is going on: there is no effect of our treatment, no relation between our variables, and no difference in our sample mean from what we expected about the population mean. This is always our baseline starting assumption, and it is what we seek to reject. If we are trying to treat depression, we want to find a difference in average symptoms between our treatment and control groups. If we are trying to predict job performance, we want to find a relation between conscientiousness and evaluation scores. However, until we have evidence against it, we must use the null hypothesis as our starting point.

In sum, the null hypothesis is always : There is no difference between the groups’ means OR There is no relationship between the variables .

In the next chapter, the null hypothesis is that there’s no difference between the sample mean and population mean. In other words:

• There is no mean difference between the sample and population.
• The mean of the sample is the same as the mean of a specific population.
• \(\mathrm{H}_{0}: \bar{X} = \mu \nonumber \)
• We expect our sample’s mean to be same as the population mean.

Exercise \(\PageIndex{2}\)

A study by Blackwell, Trzesniewski, and Dweck (2007) measured growth mindset and how long the junior high student participants spent on their math homework. What’s the null hypothesis for scoring higher on growth mindset (compared to the population of junior high students) and how long students spent on their homework? Write this out in words and symbols.

Answer in Words: Students who scored high on growth mindset would spend a similar amount of time on their homework as the population of junior high students.

Answer in Symbols: \( \bar{X} = \mu \)

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Characteristics Of A Good Hypothesis​

What exactly is a hypothesis.

A hypothesis is a conclusion reached after considering the evidence. This is the first step in any investigation, where the research questions are translated into a prediction. Variables, population, and the relationship between the variables are all included. A research hypothesis is a hypothesis that is tested to see if two or more variables have a relationship. Now let’s have a look at the characteristics of a  good hypothesis.

 Characteristics of

A good hypothesis has the following characteristics.

 Ability To Predict

Closest to things that can be seen, testability, relevant to the issue, techniques that are applicable, new discoveries have been made as a result of this ., harmony & consistency.

• The similarity between the two phenomena.
• Observations from previous studies, current experiences, and feedback from rivals.
• Theories based on science.
• People’s thinking processes are influenced by general patterns.
• A straightforward hypothesis
• Complex Hypothesis
• Hypothesis  with a certain direction
•  Non-direction Hypothesis
• Null Hypothesis
• Hypothesis of association and chance

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3.1.3: Developing Theories and Hypotheses

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2.5: Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

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

A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure \(\PageIndex{1}\) shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

• Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

What Is a Testable Hypothesis?

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A hypothesis is a tentative answer to a scientific question. A testable hypothesis is a  hypothesis that can be proved or disproved as a result of testing, data collection, or experience. Only testable hypotheses can be used to conceive and perform an experiment using the scientific method .

Requirements for a Testable Hypothesis

In order to be considered testable, two criteria must be met:

• It must be possible to prove that the hypothesis is true.
• It must be possible to prove that the hypothesis is false.
• It must be possible to reproduce the results of the hypothesis.

Examples of a Testable Hypothesis

All the following hypotheses are testable. It's important, however, to note that while it's possible to say that the hypothesis is correct, much more research would be required to answer the question " why is this hypothesis correct?" 

• Students who attend class have higher grades than students who skip class.  This is testable because it is possible to compare the grades of students who do and do not skip class and then analyze the resulting data. Another person could conduct the same research and come up with the same results.
• People exposed to high levels of ultraviolet light have a higher incidence of cancer than the norm.  This is testable because it is possible to find a group of people who have been exposed to high levels of ultraviolet light and compare their cancer rates to the average.
• If you put people in a dark room, then they will be unable to tell when an infrared light turns on.  This hypothesis is testable because it is possible to put a group of people into a dark room, turn on an infrared light, and ask the people in the room whether or not an infrared light has been turned on.

Examples of a Hypothesis Not Written in a Testable Form

• It doesn't matter whether or not you skip class.  This hypothesis can't be tested because it doesn't make any actual claim regarding the outcome of skipping class. "It doesn't matter" doesn't have any specific meaning, so it can't be tested.
• Ultraviolet light could cause cancer.  The word "could" makes a hypothesis extremely difficult to test because it is very vague. There "could," for example, be UFOs watching us at every moment, even though it's impossible to prove that they are there!
• Goldfish make better pets than guinea pigs.  This is not a hypothesis; it's a matter of opinion. There is no agreed-upon definition of what a "better" pet is, so while it is possible to argue the point, there is no way to prove it.

How to Propose a Testable Hypothesis

Now that you know what a testable hypothesis is, here are tips for proposing one.

• Try to write the hypothesis as an if-then statement. If you take an action, then a certain outcome is expected.
• Identify the independent and dependent variable in the hypothesis. The independent variable is what you are controlling or changing. You measure the effect this has on the dependent variable.
• Write the hypothesis in such a way that you can prove or disprove it. For example, a person has skin cancer, you can't prove they got it from being out in the sun. However, you can demonstrate a relationship between exposure to ultraviolet light and increased risk of skin cancer.
• Make sure you are proposing a hypothesis you can test with reproducible results. If your face breaks out, you can't prove the breakout was caused by the french fries you had for dinner last night. However, you can measure whether or not eating french fries is associated with breaking out. It's a matter of gathering enough data to be able to reproduce results and draw a conclusion.
• Null Hypothesis Examples
• Examples of Independent and Dependent Variables
• What Is the Visible Light Spectrum?
• What Glows Under Black Light?
• Difference Between Independent and Dependent Variables
• What Are Examples of a Hypothesis?
• What Is a Black Light?
• What Is a Hypothesis? (Science)
• Understanding Simple vs Controlled Experiments
• How To Design a Science Fair Experiment
• Scientific Method Vocabulary Terms
• Null Hypothesis Definition and Examples
• Theory Definition in Science
• Hypothesis, Model, Theory, and Law
• Six Steps of the Scientific Method

Hypothesis: Functions, Problems, Types, Characteristics, Examples

Basic Elements of the Scientific Method: Hypotheses

The Function of the Hypotheses

A hypothesis states what one is looking for in an experiment. When facts are assembled, ordered, and seen in a relationship, they build up to become a theory. This theory needs to be deduced for further confirmation of the facts, this formulation of the deductions constitutes of a hypothesis. As a theory states a logical relationship between facts and from this, the propositions which are deduced should be true. Hence, these deduced prepositions are called hypotheses.

Problems in Formulating the Hypothesis

As difficult as the process may be, it is very essential to understand the need of a hypothesis. The research would be much unfocused and a random empirical wandering without it. The hypothesis provides a necessary link between the theory and investigation which often leads to the discovery of additions to knowledge.

There are three major difficulties in the formulation of a hypothesis, they are as follows:

• Absence of a clear theoretical framework
• Lack of ability to utilize that theoretical framework logically
• Failure to be acquainted with available research techniques so as to phrase the hypothesis properly.

Sometimes the deduction of a hypothesis may be difficult as there would be many variables and the necessity to take them all into consideration becomes a challenge. For instance, observing two cases:

• Principle: A socially recognized relationship with built-in strains also governed by the institutional controls has to ensure conformity of the participants with implicit or explicit norms.

Deduction: This situation holds much more sense to the people who are in professions such as psychotherapy, psychiatry and law to some extent. They possess a very intimate relationship with their clients, thus are more susceptible to issues regarding emotional strains in the client-practitioner relationship and more implicit and explicit controls over both participants in comparison to other professions.

The above-mentioned case has variable hypotheses, so the need is to break them down into sub hypotheses, they are as follows:

• Specification of the degree of difference
• Specification of profession and problem
• Specification of kinds of controls.

2. Principle: Extensive but relatively systematized data show the correlation between members of the upper occupational class and less unhappiness and worry. Also, they are subjected to more formal controls than members of the lower strata.

Deduction: There can numerous ways to approach this principle, one could go with the comparison applying to martial relationships of the members and further argue that such differential pressures could be observed through divorce rates. This hypothesis would show inverse correlations between class position and divorce rates. There would be a very strong need to define the terms carefully to show the deduction from the principle problem.

The reference of these examples showcases a major issue in the hypothesis formulations procedures. One needs to keep the lines set for the deductions and one should be focusing on having a hypothesis at the beginning of the experiment, that hypothesis may be subject to change in the later stages and it is referred to as a „working hypothesis. Hence, the devising and utilization of a hypothesis is essential for the success of the experiment.

Types of Hypothesis

There are many ways to classify hypotheses, but it seems adequate to distinguish to separate them on the basis of their level of abstraction. They can be divided into three broad levels which will be increasing in abstractness.

• The existence of empirical uniformities : These hypotheses are made from problems which usually have a very high percentage of representing scientific examination of common–sense proportions. These studies may show a variety of things such as the distribution of business establishments in a city, behavior patterns of specific groups, etc. and they tend to show no irregularities in their data collection or review. There have been arguments which say that these aren’t hypothesis as they represent what everyone knows. This can be counter argued on the basis of two things that, “what everyone knows” isn’t always in coherence with the framework of science and it may also be incorrect. Hence, testing these hypotheses is necessary too.
• Complex ideal types: These hypotheses aim at testing the existence of logically derived relationships between empirical uniformities. This can be understood with an example, to observe ecology one should take in many factors and see the relationship between and how they affect the greater issue. A theory by Ernest W. Burgess gave out the statement that concentric growth circles are the one which characterize the city. Hence, all issues such as land values, industrial growth, ethnic groups, etc. are needed to be analyzed for forming a correct and reasonable hypothesis.
• Relations of analytic variables: These hypotheses are a bit more complex as they focus on they lead to the formulation of a relationship between the changes in one property with respect to another. For instance, taking the example of human fertility in diverse regions, religions, wealth gap, etc. may not always affect the end result but it doesn’t mean that the variables need not be accounted for. This level of hypothesizing is one of the most effective and sophisticated and thus is only limited by theory itself.

Science and Hypothesis

“The general culture in which a science develops furnishes many of its basic hypotheses” holds true as science has developed more in the West and is no accident that it is a function of culture itself. This is quite evident with the culture of the West as they read for morals, science and happiness. After the examination of a bunch of variables, it is quite easy to say that the cultural emphasis upon happiness has been productive of an almost limitless range.

The hypotheses originate from science; a key example in the form of “socialization” may be taken. The socialization process in learning science involves a feedback mechanism between the scientist and the student. The student learns from the scientist and then tests for results with his own experience, and the scientist in turn has to do the same with his colleagues.

Analogies are a source of useful hypotheses but not without its dangers as all variables may not be accounted for it as no civilization has a perfect system.

Hypotheses are also the consequence of personal, idiosyncratic experience as the manner in which the individual reacts to the hypotheses is also important and should be accounted for in the experiment.

The Characteristics for Usable Hypotheses

The criteria for judging a hypothesis as mentioned below:

• Complete Clarity : A good hypothesis should have two main elements, the concepts should be clearly defined and they should be definitions which are communicable and accepted by a larger section of the public. A lot of sources may be used and fellow associates may be used to help with the cause.
• Empirical Referents : A great hypothesis should have scientific concepts with the ultimate empirical referent. It can‟t be based on moral judgment though it can explore them but the goal should be separated from moral preachment and the acceptance of values. A good start could be analyzing the concepts which express attitudes rather than describing or referring to empirical phenomena.
• Specific Goal : The goal and procedure of the hypothesis should be tangible as grand experiments are harder to carry out. All operations and predictions should be mapped and in turn the possibility of testing the hypothesis increases. This not only enables the conceptual clarity but also the description of any indexes used. These indexes are used as variables for testing hypotheses on a larger scale. A general prediction isn’t as reliable as a specific prediction as the specific prediction provides a better result.
• Relation to Available Techniques : The technique with which a hypothesis is tested is of the utmost importance and so thorough research should be carried out before the experiment in order to find the best possible way to go about it. The example of Karl Marx may be given regarding his renowned theories; he formulated his hypothesis by observing individuals and thus proving his hypothesis. So, finding the right technique may be the key to a successful test.
• Relation to a Body of Theory: Theories on social relations can never be developed in isolation but they are a further extension of already developed or developing theories. For instance, if the “intelligence quotient” of a member of the society is to be measured, certain variables such as caste, ethnicity, nationality, etc. are chosen thus deductions are made from time to time to eventually find out what is the factor that influences intelligence.

The Conclusion

The formulation of a hypothesis is probably the most necessary step in good research practice and it is very essential to get the thought process started. It helps the researcher to have a specific goal in mind and deduce the end result of an experiment with ease and efficiency. History is evident that asking the right questions always works out fine.

Also Read: Research Methods – Basics

Goode, W. E. and P. K. Hatt. 1952. Methods in Social Research.New York: McGraw Hill. Chapters 5 and 6. Pp. 41-73

Kartik is studying BA in International Relations at Amity and Dropped out of engineering from NIT Hamirpur and he lived in over 5 different countries.

Characteristics & Qualities of a Good Hypothesis

A good hypothesis possesses the following certain attributes.

Power of Prediction

One of the valuable attribute of a good hypothesis is to predict for future. It not only clears the present problematic situation but also predict for the future that what would be happened in the coming time. So, hypothesis is a best guide of research activity due to power of prediction.

Closest to observable things

A hypothesis must have close contact with observable things. It does not believe on air castles but it is based on observation. Those things and objects which we cannot observe, for that hypothesis cannot be formulated. The verification of a hypothesis is based on observable things.

A hypothesis should be so dabble to every layman, P.V young says, “A hypothesis wo0uld be simple, if a researcher has more in sight towards the problem”. W-ocean stated that, “A hypothesis should be as sharp as razor’s blade”. So, a good hypothesis must be simple and have no complexity.

A hypothesis must be conceptually clear. It should be clear from ambiguous information’s. The terminology used in it must be clear and acceptable to everyone.

Testability

A good hypothesis should be tested empirically. It should be stated and formulated after verification and deep observation. Thus testability is the primary feature of a good hypothesis.

Relevant to Problem

If a hypothesis is relevant to a particular problem, it would be considered as good one. A hypothesis is guidance for the identification and solution of the problem, so it must be accordance to the problem.

It should be formulated for a particular and specific problem. It should not include generalization. If generalization exists, then a hypothesis cannot reach to the correct conclusions.

Relevant to available Techniques

Hypothesis must be relevant to the techniques which is available for testing. A researcher must know about the workable techniques before formulating a hypothesis.

Fruitful for new Discoveries

It should be able to provide new suggestions and ways of knowledge. It must create new discoveries of knowledge J.S. Mill, one of the eminent researcher says that “Hypothesis is the best source of new knowledge it creates new ways of discoveries”.

Consistency & Harmony

Internal harmony and consistency is a major characteristic of good hypothesis. It should be out of contradictions and conflicts. There must be a close relationship between variables which one is dependent on other.

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Understanding Science

How science REALLY works...

• Understanding Science 101
• Misconceptions
• Testing ideas with evidence from the natural world is at the core of science.
• Scientific testing involves figuring out what we would  expect  to observe if an idea were correct and comparing that expectation to what we  actually  observe.
• Scientific arguments are built from an idea and the evidence relevant to that idea.
• Scientific arguments can be built in any order. Sometimes a scientific idea precedes any evidence relevant to it, and other times the evidence helps inspire the idea.

Misconception:  Science proves ideas.

Misconception:  Science can only disprove ideas.

Correction:  Science neither proves nor disproves. It accepts or rejects ideas based on supporting and refuting evidence, but may revise those conclusions if warranted by new evidence or perspectives.  Read more about it.

The core of science: Relating evidence and ideas

In this case, the term  argument  refers not to a disagreement between two people, but to an evidence-based line of reasoning — so scientific arguments are more like the closing argument in a court case (a logical description of what we think and why we think it) than they are like the fights you may have had with siblings. Scientific arguments involve three components: the idea (a  hypothesis  or theory), the  expectations  generated by that idea (frequently called predictions), and the actual observations relevant to those expectations (the evidence). These components are always related in the same logical way:

• What would we expect to see if this idea were true (i.e., what is our expected observation)?
• What do we actually observe?
• Do our expectations match our observations?

PREDICTIONS OR EXPECTATIONS?

When scientists describe their arguments, they frequently talk about their expectations in terms of what a hypothesis or theory predicts: “If it were the case that smoking causes lung cancer, then we’d  predict  that countries with higher rates of smoking would have higher rates of lung cancer.” At first, it might seem confusing to talk about a prediction that doesn’t deal with the future, but that refers to something going on right now or that may have already happened. In fact, this is just another way of discussing the expectations that the hypothesis or theory generates. So when a scientist talks about the  predicted  rates of lung cancer, he or she really means something like “the rates that we’d expect to see if our hypothesis were correct.”

If the idea generates expectations that hold true (are actually observed), then the idea is more likely to be accurate. If the idea generates expectations that don’t hold true (are not observed), then we are less likely to  accept  the idea. For example, consider the idea that cells are the building blocks of life. If that idea were true, we’d expect to see cells in all kinds of living tissues observed under a microscope — that’s our expected observation. In fact, we do observe this (our actual observation), so evidence supports the idea that living things are built from cells.

Though the structure of this argument is consistent (hypothesis, then expectation, then actual observation), its pieces may be assembled in different orders. For example, the first observations of cells were made in the 1600s, but cell theory was not postulated until 200 years later — so in this case, the evidence actually helped inspire the idea. Whether the idea comes first or the evidence comes first, the logic relating them remains the same.

Here, we’ll explore scientific arguments and how to build them. You can investigate:

Putting the pieces together: The hard work of building arguments

• Predicting the past
• Arguments with legs to stand on

Or just click the  Next  button to dive right in!

• Take a sidetrip
• Teaching resources

Scientific arguments rely on testable ideas. To learn what makes an idea testable, review our  Science Checklist .

• Forming hypotheses — scientific explanations — can be difficult for students. It is often easier for students to generate an expectation (what they think will happen or what they expect to observe) based on prior experience than to formulate a potential explanation for that phenomena. You can help students go beyond expectations to generate real, explanatory hypotheses by providing sentence stems for them to fill in: “I expect to observe A because B.” Once students have filled in this sentence you can explain that B is a hypothesis and A is the expectation generated by that hypothesis.
• You can help students learn to distinguish between hypotheses and the expectations generated by them by regularly asking students to analyze lecture material, text, or video. Students should try to figure out which aspects of the content were hypotheses and which were expectations.

Summing up the process

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Characteristics of the built environment in the Eastern Mediterranean and Middle East and related energy and climate policies

• Review Article
• Open access
• Published: 04 June 2024
• Volume 17 , article number  52 , ( 2024 )

Cite this article

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• Salvatore Carlucci   ORCID: orcid.org/0000-0002-4239-3039 1 ,
• Manfred A. Lange   ORCID: orcid.org/0000-0003-3345-4618 1 , 2 ,
• Georgios Artopoulos   ORCID: orcid.org/0000-0003-4500-1464 1 ,
• Hanan M. Albuflasa   ORCID: orcid.org/0000-0003-2141-1944 3 ,
• Margarita-Niki Assimakopoulos 4 ,
• Shady Attia   ORCID: orcid.org/0000-0002-9477-5098 5 ,
• Elie Azar 6 , 14 ,
• Erdem Cuce   ORCID: orcid.org/0000-0003-0150-4705 7 ,
• Ali Hajiah   ORCID: orcid.org/0000-0001-8815-2372 8 ,
• Isaac A. Meir   ORCID: orcid.org/0000-0001-8427-5789 9 ,
• Marina Neophytou 10 ,
• Melina Nicolaides 11 ,
• Despina Serghides   ORCID: orcid.org/0000-0002-4279-8327 1 ,
• Aaron Sprecher   ORCID: orcid.org/0000-0002-2621-7350 12 ,
• Muhieddin Tawalbeh 13 ,
• Stavroula Thravalou   ORCID: orcid.org/0000-0001-6001-0313 1 &
• Ioanna Kyprianou   ORCID: orcid.org/0000-0001-6375-588X 1  

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The Eastern Mediterranean and Middle East (EMME) region hosts some of the world’s most influential and troubled cities. It is also a hotspot of climate change and socio-economic and political turbulence, which inflate the already flammable conditions and reinforce existing local vulnerabilities. Some of the most arduous challenges of cities relate to the built environment – although vital for human well-being, buildings rarely offer both sufficient and affordable shelter to their inhabitants. With energy performance regulations coming into effect during the past three decades, a considerable proportion of the worldwide building stock had already been constructed and is now ageing and inefficient. Harmonising the energy performance of buildings at a sufficient level requires common objectives and priorities, and the EMME region consists of nations with different governance and regulations. Scarce literature exists on the existing operational frameworks, and this study aims to offer an overview of the built environment policy scene in the EMME region, identifying gaps, good practices and prospects. The study draws from scholarly literature, national and international regulations and other document sources, as well as local experts. This work finds that although most EMME countries participate in and embrace international agreements, they act individually and not collectively, confirming our hypothesis that the policy agenda reflects the diverse characteristics of the region. By recognising standing failings and strengths, moving forward becomes a possibility through the adoption of integrated governance, common policy agendas and financing mechanisms to create sustainable urban centres inhabited by resilient and equitable communities.

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Introduction

Places and people are transformed by the circumstances they are found in, the interplays between them and the force of unexpected externalities. The Eastern Mediterranean and Middle East (EMME) region is a conglomeration of distinct yet similar countries. There are countless differences in the evolution of nations in the EMME region, as well as in socio-economic conditions and geographic settings. Still, all EMME countries face similar challenges induced by climate change and its impacts (Lelieveld et al., 2012 ). The current state of the built environment and the struggle to create sustainable cities and societies are among the most compelling challenges affecting them. Millions of people inhabit these cities, and while some of them are adequately sheltered and can live a comfortably modern lifestyle, others live in informal settlements and struggle with access to a basic level of services, including healthcare, energy, sanitary water and housing (Kyprianou et al., 2022 ).

Politically and geographically, the EMME countries are diverse and multi-ethnic. In the definition adopted here, the region includes countries from Europe (Cyprus and Greece), North Africa (Egypt), the Middle East (Israel, Jordan, Lebanon, Palestine, the Syrian Arab Republic and Türkiye), the Arabian Peninsula (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates) and Western Asia (Iran and Iraq). The region’s climate is mostly classified as arid to semi-arid, comprising a variety of features, landscapes and vegetation covers. This includes extended mountain ranges and desert areas, as well as agriculturally utilised lowlands such as the Fertile Crescent between the Euphrates and Tigris rivers, known as Mesopotamia. Major rivers and their tributaries and floodplains also cross the EMME region, allowing for productive agriculture. The region’s coastlines extend along the Mediterranean Sea, the Persian Gulf and the Red Sea. Urban areas include the megacities of Cairo and Istanbul, with their larger metropolitan areas being populated by more than 20 and 15 million inhabitants, respectively, and the city of Tehran, with 9 million. The region’s human geography is characterised by a significant split between urban and rural lifestyles and living conditions. Prosperity, both at the individual and national level and economic performance, differs starkly between those countries with significant hydrocarbon resources and those without (Baysoy & Altug, 2021 ). Other challenges include exponential population growth, intense urbanisation, the uneven allocation of rudimental resources such as water and high military spending amid chronic regional conflicts. Meanwhile, efforts at greater integration into the global economy often aggravate long-standing inequalities and create further downstream challenges.

In terms of socio-economic conditions, after the long struggle endured in most countries of the region to gain independence from their colonial European rulers, the nationalist regimes that came to power tended to maintain significant control over their economies. In the early 20th century, the discovery of vast oil deposits in the Middle East coincided with increasing oil dependence in the West, creating ever-shifting global political dynamics (Freeman, 2021 ; Khatib, 2014 ; Khodjaeva et al., 2021 ). New opportunities arose in the EMME region, and since then, the build-up of the oil industry has created enormous opportunities for development in hydrocarbon-rich countries, particularly in Bahrain, Iran, Iraq, Kuwait, Qatar, Saudi Arabia and the United Arab Emirates.

Socio-economic conditions affect the buildings and other urban structures of the EMME region, and vice versa; advancement embraces new materials and habits, and the state of the built environment influences livelihoods. The EMME region is one of the most vulnerable to climate change and already faces numerous environmental stresses (Zittis et al., 2022 ). In the future, available water and arable land resources will diminish, and air and soil pollution, degradation of ecosystems and loss of biodiversity will escalate (Meir et al., 2012 ). The region anticipates further downstream consequences, such as food and potable water scarcity, ultimately leading to social unrest and local conflicts, as has already been documented (Aw-Hassan et al., 2014 ; De Châtel, 2014 ). In addition, the expected rapid increase in population and urban growth rates will amplify these environmental stresses (Hungate & Koch, 2015 ). Related challenges include the proliferation of urban sprawl and the growth of illegal dwellings (‘slums’), increasing population and building density and the ever-increasing demand for energy and water among city inhabitants. Moreover, a growing transport sector (including private cars, public transport and commercial vehicles) brings forward further consequences for urban infrastructure and public health [see, e.g., (Lange, 2019 ).

Another major challenge lies in the stark differences in income, personal wealth and lifestyles between rural and urban populations in most countries of the region. That is exacerbated in countries that rely heavily on foreign labour. Disparities can also be observed at the national level in terms of geographic and economic characteristics, as observed in Table 1 . For instance, in the region’s richest country per capita , Qatar, the 2020 gross domestic product (GDP) per person was 97 times higher than in the poorest country, Syria. At the same time, certain countries are experiencing rapid and intense population growth that is related to high levels of economic activity and urbanisation. For instance, the United Arab Emirates (UAE) has increased by almost 7000% since 1960, with 85% of the population living in cities; as a matter of comparison, the global population of the whole region has increased by less than 300% with an urbanisation rate of 61% (see Table 1 ).

Some countries experience very little fluctuations in the annual growth rates, but others sustain great ones; the trends in annual population change rate per country are observed in Fig. 1 . While the annual urban growth rate in Cyprus, Egypt, Greece and Israel never surpassed 2% (or decreased), the urban population in Qatar grew by 22% in just one year (2006–2007) and shrank by 3% later on (2020–2021). Moreover, there are countries with ad infinitum growing urban populations, but there are no countries with perpetually shrinking ones – even in nations going through violent conflicts, populations may briefly show signs of decrease and then return to increasing trends (e.g. Syria’s population diminished between 2012 and 2015 but started growing again from 2017 onwards). Two countries of the Gulf Cooperation Council (GCC), Qatar and UAE, experienced the most impressive population surges, starting from 2004 up to 2007. During the 20th century, urbanisation began due to the exploitation of oil reserves and continues in the 21st century through national development strategies, but the sudden influx of populations can be attributed to specific milestones. For instance, Doha hosted a mega-event in 2006 (the Asian Games), drawing not only visitors but also the workforce required to build infrastructure, and designated ‘investment zones’ were created in Abu Dhabi in 2005, generating critical opportunities for foreign investments that shifted the real estate market (Azzali, 2016 ; Samarrai, 2016 ). Likewise, the steep drop in the urban population of Syria between 2011 and 2014 can be attributed to the events surrounding the civil war (Deng et al., 2021 ).

Time series showing variation in urban populations for the years 2001–2022 (source: (Worldbank, World Bank Open Data, 2021 ))

About CO 2 emissions per capita , the highest polluting countries are associated with high GDP per capita . Most of the countries of the EMME region are highly urbanised (higher than 70%), and this exposes them to a multitude of climate change challenges and higher levels of polluting activities. Among the oil-rich countries, most enjoy affluent economies (Kuwait, Qatar, Saudi Arabia and UAE), while others (Iran, Iraq), affected by regional conflicts, have GDP per capita lower than the world average.

While a review of the history of the region’s architecture and buildings exceeds the scope of this work, we briefly overview the main characteristics of the building sector. Table 2 presents the predominant building load-bearing materials and degree of diversification, showing that the majority of the building stock is constructed with reinforced concrete, and another big part is built with masonry techniques. The rest of the building stock includes adobe and light wood frames, whereas tall buildings are constructed using a mix of steel and glass. There are three material diversification classes: low, intermediate and high, and since reinforced concrete prevails, it is used as the primary determinant for the classification. If the reinforced category is higher than 70%, the country’s diversification is deemed low; if it is between 70 and 50%, diversification is intermediate, and if it is lower than 50%, diversification is high. In the case of countries for which information is lacking or sparse, estimates were made based on alternative sources (e.g. (Facts and details, cities, towns and villages in the Arab-Muslim world, 2018 ) for Iran, (Hoare, 2020 ) for Israel and (Gunes, 2015 ) for Türkiye).

Reinforced concrete has become the predominant option (more than 50%) for all EMME countries except for Iran, Palestine and Syria, possibly reflecting on the lower levels of the economy in general. Low diversification mostly indicates heavy reliance on concrete, with countries such as the UAE championing modern architecture that promotes reinforced concrete, steel and glass. On the other hand, intermediate diversification is related to attempts for the preservation of vernacular architecture in booming economies where modern materials are also dominant. High diversification is encountered only on two occasions – in Greece, where masonry techniques with stone are mostly observed in rural parts of the country, and in Syria, where concrete-based construction has been extremely limited during the past decade, and a variety of alternative techniques is preferred. The choice of building materials, therefore, seems to march along the pace of the economy, with some countries partly retaining fragments of their cultural heritage and others completely opting for cutting-edge technologies for their urban fabric. Although new materials and construction methods aim at improving the living conditions and comfort of inhabitants, their adoption comes at a high cost. Low recycling rates in the building industry mean that construction and demolition waste continuously increases and new materials are continuously created, spending large amounts of energy and water and releasing greenhouse gases in the process (Kabirifar et al., 2020 ). The lesser material-intense principles of vernacular architecture can be studied to draw inspiration, all the while understanding its limitations (Beccali et al., 2018 ; Meir & Roaf, 2005 ; Oikonomou & Bougiatioti, 2011 ). All these issues must be seen in light of ongoing and anticipated climate change. The enhanced warming resulting from the urban heat island (UHI) effect will lead to additional challenges in the built environment [e.g. (Santamouris, 2007 ), especially if prioritisation is given to economic benefit and fast construction.

In line with the global community, countries in the EMME region recognise the urgent need to address regional and national climate change impacts, exhibiting a willingness to comply with international commitments stipulated by the United Nations’ Paris Agreement (Horowitz, 2016 ). However, keeping in line with the region’s diverse socio-economic conditions, diverse pathways towards the achievement of the Paris Agreement’s goals are expected. All of the included countries carry a rich cultural heritage and geopolitical priorities to their unique circumstances, often following predetermined paths operating based on singularity, rather than collectivity. We hypothesise that the climate change and energy policy landscape in the EMME region reflects the diversification discussed throughout Tables 1 and 2 in terms of scales of economies, urbanisation rates, carbon footprints and preferences for building practices and materials.

Several policy meta-analyses on European regulations indicate a mature state of policy implementation and assessment on energy and climate planning (Economidou et al., 2022 ; Economidou, Ringel, et al., 2020 ), energy efficiency in buildings (Economidou, Todeschi, et al., 2020 ) and nearly zero energy buildings (D’Agostino et al., 2021 ). The same cannot be said for other EMME countries, where research outcomes focus on case studies and policies are not discussed in depth. The most recent energy policy analysis is dated in 2010 and is focused only on six countries of the Gulf Cooperation Council (Reiche, 2010 ). In case studies, Alqahtani and Alareeni (2020) highlight the weaknesses in sustainable building design and integration of renewables in the Kingdom of Bahrain (Alqahtani & Alareeni, 2020 ) and Gamaleldine and Corvacho (2022) quantify the potential environmental impacts of building energy codes, in terms of energy savings and thermal comfort enhancement (Gamaleldine & Corvacho, 2022 ). Evidence-based work emerges in the fields of energy efficiency and savings, decentralised renewable systems and frameworks that promote and enhance sustainability (Abubakar & Dano, 2020 ; AlHashmi et al., 2021 ; Al-Homoud & Krarti, 2021 ; Almushaikah & Almasri, 2021 ; Balabel & Alwetaishi, 2021 ; Elshurafa & Muhsen, 2019 ; Krarti, 2019 ; Salah et al., 2021 ; Souayfane et al., 2023 ), most of them fixing on the developed countries of the region. Supporting this finding, a study on the landscape of research and development (R&D) in the Arab region shows that the UAE and Qatar are leading in competitiveness indices (Badran, 2018 ). On some occasions, research findings may create more questions than the ones they address, such as in the study of Al-Saidi, who argues that the Saudi energy transition is underway, juxtaposing the petrochemical industries with the integration of renewables in their facilities in one of the biggest carbon-fuel exporting countries of the region (Al-Saidi, 2022 ). Investigations across the EMME appear, therefore, uneven, and a research gap emerges in the field of energy policy studies dedicated to the built environment.

This study provides an overview of the policy landscape in building performance regulation in the EMME region, and its specific objectives are to identify gaps, good practices and prospects towards reaching international sustainability goals. The research question addressed here is ‘ What does the built environment policy landscape look like in the Eastern Mediterranean and Middle East and how are countries addressing the threats of climate change, individually and collectively? ’

At the moment, the literature examining the diverse policies of this region is limited and main policy documents are only available in the local languages; therefore, the present work fills in a considerable gap. The structure of this article continues with a review of the international policy scene, followed by policies and regulations in the EMME region, organised in the sub-regions of North Africa, the Middle East, the Arabian Peninsula, Western Asia and members of the European Union (EU). Matrix analysis of currently implemented and pledged climate change policies is then employed to identify open gaps and future directions, ending the study with conclusions and a distillation of good practices.

Policy landscape

This section outlines major policy initiatives across international, regional and national scales, highlighting the most recent developments in governance within the building sectors for each investigated area.

International policies on climate change adaptation and mitigation

The adoption of international agreements, led by the United Nations in 2015, introduced new agendas for climate change adaptation for urban regions, considerably altering the policy landscape. These policies, plans and measures are intended to reinforce one another and hopefully foster synergies among stakeholders. The Paris Agreement, agreed upon and adopted in 2015, detects the urgency for adaptation and mitigation actions in response to climate change, highlighting that local action is needed within the international cooperation framework (Magnan & Ribera, 2016 ). Prior to the adoption of the Paris Agreement, the UN’s Sustainable Development Goals (SDGs) were established within the 2030 Agenda for Sustainable Development. This initiative forms the groundwork to ‘ make cities and human settlements inclusive, safe, resilient and sustainable ’ (UN, 2015a ), aiming to significantly increase resilient cities globally (UN, 2015b ). The unprecedented pledge of global leaders to participate in this initiative and take urgent action has been welcomed by the scientific community and the public, with specific objectives being detailed and ratified in the New Urban Agenda in late 2016 (UN-Habitat, 2016 ). The world leaders agreed to develop investigations of urban vulnerability and adaptation actions at the city level, integrating facets of climate change into their planning processes. The Global State of National Urban Policy (UN-Habitat, 2018 ) first monitored and evaluated national urban policies (NUPs) from 150 countries. The report builds on previous work by the UN-Habitat and the Organisation for Economic Cooperation and Development (OECD) and defines a common methodology. It is a noteworthy contribution to the monitoring and implementation of the SDGs and the New Urban Agenda that represents an attempt to conceive better and more sustainable cities where all citizens ‘ have equal rights and access to the benefits and opportunities that cities can offer ’ (UN-Habitat, 2016 ). Moreover, it contributes to the National Urban Policy Programme (NUPP), a global initiative launched by the UN-Habitat, OECD and Cities Alliance at the Habitat III Conference in 2016, which aims to expedite the development of NUPs across the globe. While scientists and the public welcome the pledge of world leaders to urgently address the challenges of climate change, reservations accompany such promises, as adopting an international framework cannot automatically translate to action. That is evident by findings of an analysis on the development of 147 local adaptation plans in Europe, indicating that the UNFCCC process had prompted the realisation of only 21 of these (Aguiar et al., 2018 ). Moreover, until recently, in the EMME region, several countries lacked mandatory building energy codes, signalling the build-up of legacy infrastructure that did not meet any minimum performance requirements (IEA, 2022 ). To meet the conditions of the SDGs by 2030, mandatory building energy codes must be enforced in all countries, new construction should be highly energy and resource-efficient, and existing stock should undergo deep energy renovation to achieve at least a 30–50% improvement in required energy intensity (IEA, 2020a ). Nevertheless, this is a challenging task on the legislative, implementation and monitoring fronts, as well as the behaviours of investors and consumers.

Policies and regulations in North Africa

In 2012, Egypt adopted the National Energy Efficiency Action Plan for the electricity sector for the period 2012-2015. The plan was updated between 2018 and 2020 in the context of the Integrated Sustainable Energy Strategy 2035, with energy efficiency and renewable energy standing out as the two pivotal components (IRENA, 2018 ). The new strategy reinforces existing energy efficiency standards, expands appliance labelling and promotes the application of building energy performance codes and energy-efficient lighting. Green building standards and codes to secure long-term energy conservation across residential, commercial and public buildings have already been developed, but no concrete policies and measures exist to enforce these (Bampou, 2016 ). Fuel poverty, institutional barriers, economic constraints, an underdeveloped market and local governance weaknesses are hurdles to achieving indoor thermal quality and exploiting the potential of energy conservation. Egypt’s Third National Communication under the UNFCCC (Egyptian Environmental Affairs Agency (EEAA), 2016 ) presents actions and policy instruments for mitigating greenhouse gas (GHG) emissions in the building sector and revises building codes and infrastructure standards, but mentions no rules, regulations or laws enabling such measures. Additional measures are also presented in Egypt’s National Strategy for Adaptation to Climate Change and Disaster Risk Reduction (Egypt’s Cabinet Information and Decision Support Centre (IDSC), 2011 ).

Policies and regulations in the Middle East

Israel has paid significant attention to GHG emissions from the built environment, which is responsible for 60% of Israeli electricity consumption and 33% of total GHG emissions (Ministry of Environmental Protection, 2020 ). In 2010, the government issued the first National Plan for the Reduction of GHG emissions in the context of Government Resolution 2508 (Ministry of Environmental Protection, 2019 ; The Government Secretariat, 2010 ). Meanwhile, Government Decision 1403, issued in April 2016 (Ministry of Environmental Protection, 2019 ), formulated the long-term goals of reducing energy consumption in buildings and GHG emissions through energy regulations and the labelling of new buildings as well as existing ones that had been maintained and renovated. It also promoted mechanisms and the steps required to meet the green building standards and discussed the feasibility of creating best-practice examples of green buildings in the educational and public sectors (Ministry of the Environmental Protection, 2018 ). Green buildings have been one of the main pillars of Israel’s policies to reduce GHG emissions and raise energy efficiencies in the building sector. They feature prominently in the Third National Communication on Climate Change (Ministry of Environmental Protection, 2018 ), which states that the potential reduction in GHG emissions by decreasing electricity consumption in buildings and industries is estimated at 7.1 million metric tonnes of carbon dioxide equivalents (tCO 2- eq) relative to the 2030 business-as-usual scenario. That would correspond to 29% of the total reduction needed to comply with the target, also bringing about significant direct savings to the economy (Ministry of Environmental Protection, 2018 ). Israel’s National Plan for implementing the Paris Agreement (Proaktor et al., 2016 ) refers to Government Decision 1403 and the Green Building Standards promoting energy efficiency in the building sector. Israel’s main green building standards are SI 5281 about sustainable buildings, SI 5282 about the energy rating of buildings and SI 1045 on thermal insulation of buildings. They establish minimum requirements for various green building components to be considered in the design and the choice of construction materials and active heating and cooling systems. In 2011 and 2016, standard SI 5281 was revised, motivated by the fact that while the Israeli standard had become increasingly established, it was not uncommon for buildings to seek certification from other international rating systems, particularly the US Green Building Council’s Leadership in Energy & Environmental Design (LEED) (Ben-Hur, 2014 ; Ministry of Environmental Protection, 2020 ).

Jordan faces two main challenges regarding its energy situation: the growing energy demand and the very limited domestic resources to fulfil it (energypedia, 2018a ). In the construction sector, several building codes have been developed by the Royal Scientific Society under the authority of the Jordan National Building Council since 2010 (Awadallah et al., 2009 ), several of which now relate to the energy efficiency of buildings. Some examples include the Thermal Insulation Code and Manual, Jordan Green Building Guide, Natural Ventilation Code, Energy Efficient Buildings Code and Manual and Solar Energy Code and Manual (Royal Scientific Society, 2020 ). In terms of climate change action, in Jordan’s Third National Communication on Climate Change, energy use and GHG emissions in the building sector are summarised under ‘other sectors’ (Ministry of Environment and United Nations Development Programme, 2014 ). Emissions from the ‘other sectors’ category account for 13.8% and 10% of the energy-related GHG emissions and Jordan’s total GHG emissions, respectively. Although this represents a moderate contribution to the national GHG emissions, the building sector accounts for more than 60% of total electricity consumption. Moreover, Jordan is highly reliant on imports of energy resources (more than 96%), with very little deployment of renewables (less than 1% of electricity generation). Energy is central to the growth of the Jordanian economy, but its reliance on energy imports strains the economy and poses energy supply security risks. These vulnerabilities drove the development of the 2007–2020 Master Energy Strategy, which called for greater utilisation of domestic resources, including renewable energy. The share of electricity from renewables in Jordan grew from 0.7% in 2014 to over 14% in 2020. The updated 2020-2030 Master Strategy for the Energy Sector calls for a sustainable future energy supply, diversification of the national energy mix and increased dependency on domestic energy resources (Chang., 2021 ). The strategy targets a 31% share of renewables in total power generation capacity and 14% of the total energy mix by 2030. Regarding mitigation strategies, a proposed energy conservation project foresees the insulation of walls and roofs in 35,000 new houses (Ministry of Environment and United Nations Development Programme, 2014 ). However, these are just general policy recommendations without any specific requirements. The Second National Energy Efficiency Action Plan for Jordan addressed six sectors and included more than 30 energy efficiency measures, such as the installation of 30,000 solar water heaters, improving energy efficiency in the water sector, street lighting, transport, roof insulation and others. In summary, the energy efficiency challenges in Jordan’s building sector are mainly related to unclear responsibilities in the implementation of these measures, and a lack of capacity building and compliance measures for the existing building codes (meetMED, 2020a ).

Energy efficiency in light of GHG mitigation was addressed in Lebanon’s Second National Communication to the UNFCCC (Ministry of Environment, 2011 ), referring to the standards for energy-efficient buildings as outlined in the ‘Capacity Building for the Adoption and Application of Thermal Standards for Buildings’ project. This was initiated in 2005 by the General Directorate of Urban Planning and the United Nations Development Programme, but these standards are not mandatory. The report also states that full implementation would lead to substantial energy savings estimated at around 7 million tCO 2 -eq between 2010 and 2029, or around 343,500 tCO 2 -eq per year (Ministry of Environment, 2011 ). Retrofitting the existing building stock has also been identified as a priority. The second National Energy Efficiency Action Plan (NEEAP) states that the share of energy consumption of residential buildings in the total final energy consumption was estimated to be less than 25% in 2010 (Lebanese Center for Energy Conservation (LCEC), 2016 ). The Lebanese Standards Institution has issued several guidelines on the thermal performance of buildings (mainly related to the demand for space cooling during summer months) and thermal insulation, as well as the calculation methodology of building components and elements (Lebanese Center for Energy Conservation (LCEC), 2016 ). Another proposed measure is the implementation of a dual-purpose testing facility: to test the thermal properties of different components of a building, offer certification and promote research and development of novel materials with higher energy efficiencies (Lebanese Center for Energy Conservation (LCEC), 2016 ). Moreover, several measures have been proposed in the 2016–2020 NEEAP (Lebanese Center for Energy Conservation (LCEC), 2016 ), including the drafting and application of a building code, the extensive use of energy-efficient equipment in buildings and the introduction of an Energy Performance Certificate for buildings. Further measures include conducting energy audits and implementing energy efficiency measures in public buildings, conducting a pilot project in energy efficiency measures and enhancing capacity building for refurbishments. Regarding end-use measures in the public sector, the 2016–2020 NEEAP measures recommend the adoption of green procurement for new and existing public buildings to reduce their energy consumption through increased uptake of energy-efficient products (Lebanese Center for Energy Conservation (LCEC), 2016 ).

Palestine represents a very complex area divided into two administrative regions, with a population of 4.7 million inhabitants, which causes various limitations to the development of infrastructures and policies in the energy sector. In 2018, Palestinian households consumed about 45% of the country’s final energy consumption, which included energy use for space heating and cooling (meetMED, 2020b ). The energy sector in Palestine depends almost entirely on energy imports, with 89% of the total electricity supply coming from Israel and 3% from Egypt and Jordan. Energy capacity is essentially fossil-fuel-based; however, Palestine strives to achieve the target of 12% domestic electricity generation from renewable energy sources by 2030 (meetMED, 2019 ). In 2012, the European project MED-ENC developed the first NEEAP for Palestine, listing actions such as the preparation of national green building guidelines and associated codes and building a national awareness programme on energy efficiency and the use of renewables in buildings (Khatib & Becker, 2012 ). It also proposes the promotion of energy efficiency and renewable energies in buildings through Energy Performance Certificates and human resources capacity building in energy efficiency in the building sector. In the same year, the Palestinian Energy and Natural Resources Authority launched the Palestine Solar Initiative to promote the installation of photovoltaic panels and to install on-grid residential rooftop solar systems with a nominal installed capacity between 1 and 5 kW p in 1000 houses. While there are plans to build an energy-efficient demonstration building near Bethlehem, at present, there are no energy-efficient buildings to be found in Palestine (meetMED, 2020b ). A second NEEAP was adopted in 2016 with an ambitious energy efficiency target to reduce total electricity consumption by 500 MWh per year (Worldbank, West Bank, & Gaza Energy Efficiency Action Plan for 2020-2030, 2016 ). There is currently no independent entity responsible for defining and implementing energy efficiency measures in buildings. Specifying such measures is hampered by limited data availability and the absence of a dedicated survey that can collect information on the status of energy efficiency in buildings and their current requirements. Moreover, implementing a follow-up and monitoring mechanism to evaluate the impact of these measures is largely missing (meetMED, 2020a ). Mitigation measures addressing the energy consumption in the built environment are scarce and include only strategies oriented to ‘implement energy efficiency measures to reduce consumption and hence imported energy’ (Smithers et al., 2016 ). The energy dependency on neighbouring countries, the weak enforcement of existing regulations and the low knowledge and capacity of public and private stakeholders are some of the main barriers Palestine faces to successfully promoting energy efficiency in the building sector (meetMED, 2020a ). At the moment, the humanitarian and housing crises caused by the Israel-Palestine conflicts overshadow the challenges towards energy efficiency and adaptation to climate change, with consequences yet to be assessed.

The Syrian Arab Republic

While currently undergoing major transitions in the context of the ongoing armed conflict, the Syrian Arab Republic issued its first regulations towards energy efficiency in the built environment before these challenging times (Meslmani, 2010 ). The country identified several measures to address the impacts of climate change on energy needs and the improvement of energy efficiencies in the built environment (Meslmani, 2010 ). These include conservation measures in all residential areas, including behavioural changes, development of alternative heating devices (e.g. solar water heating systems), increase in the share of solar energy for water and residential heating, reflective roofs for buildings and use of efficient lighting. Other priorities include thermal insulation in buildings to reduce energy consumption for space heating and cooling and improve the efficiency of air conditioners and refrigerators. However, due to the devastating war in the country, little progress has been made, and information on ongoing measures and activities enhancing energy efficiency is limited. The contribution of renewables to the energy supply remains minimal, while fossil fuels provide the major share (IEA, 2020b ).

Türkiye introduced plans for new building sector regulations in 2010 (Ministry of Environment and Urbanisation, 2023 ), introducing an ‘Energy Identity Certificate’ for new buildings, creating the infrastructure for the introduction of a similar certificate for existing buildings and deploying thermal isolation and other energy efficiency measures. Energy management in compliance with international standards was also introduced, both for the industrial and building sectors, carried out by certified energy managers. In terms of sectoral energy consumption, the buildings and services sector accounts for 37% of the total energy consumption in Türkiye (Ministry of Environment and Urbanisation, 2013 ). This high consumption is primarily attributed to high space heating and cooling loads since 90% of the buildings in Türkiye lack sufficient thermal insulation (energypedia, 2018b ). Regulations and laws about energy efficiency standards are the standard TS 825, which manages the reduction of energy needed for space heating and cooling by thermal insulation of housing and commercial buildings, and the Regulation on Energy Performance in Buildings. The latter states that buildings with more than 2000 m 2 of usable floor area will have to be equipped with a central space heating system, while for buildings having more than 20,000 m 2 floor area, space heating and cooling will have to be driven through renewable energies and co-generation facilities (energypedia, 2018b ). Regulations have been specified in the 2011–2023 National Climate Change Action Plan of the Ministry of Environment and Urbanisation (Ministry of Environment and Urbanisation, 2011 ), with several additional regulations being specified in the 2012–2023 Energy Efficiency Strategy Paper. Despite these laws and regulations, there are presently no incentives to support energy efficiency measures in buildings. Since fossil fuels provide most of the energy requirements in the building sector (Ministry of Environment and Urbanisation, 2011 ), it is not surprising that Türkiye’s Nationally Determined Contributions are classified as ‘critically insufficient’ in the context of the UNFCCC’s Paris Agreement (Climate Action Tracker, 2021 ).

Policies and regulations in the Arabian Peninsula

The Kingdom of Bahrain is the smallest oil producer among all the members of the Gulf Cooperation Council, and its oil and natural gas resources are governed by the National Oil and Gas Authority (U.S. Energy Information Administration, Analysis, 2020 ). Oil comprises about 85% of Bahrain’s revenues. Bahrain employs nearly 4 MW of installed electricity generating capacity, consisting mainly of five relatively efficient natural gas-fired units. Electricity demand is fast growing, fuelled mainly by population growth, the need for electricity for seawater desalination and the expansion of the industrial sector. The annual per capita electricity consumption is one of the highest in the world and is expected to rise, while it also has one of the highest population densities globally (U.S. Energy Information Administration, Analysis, 2020 ;Kingdom of Bahrain, 2012 ; Kingdom of Bahrain, 2020 ). Meanwhile, total GHG emissions rose from 22,374 GtCO 2 -eq in 2000 to 29,153 GtCO 2 -eq in 2006, with the energy sector accounting for a major share, about 77% and 67% in 2000 and 2006, respectively (Kingdom of Bahrain, 2012 ; Kingdom of Bahrain, 2020 ). Bahrain holds one of the highest per capita GHG emissions globally, with projections for continuous upward trends. In 2013, Bahrain had double the per capita GHG emissions relative to that of high-income countries and approximately five-fold emissions relative to the world average (Kingdom of Bahrain, 2017 ). Most of Bahrain’s housing stock is comprised of residences (76%), with commercial buildings accounting for around 17%. More than half of the residential sector’s annual electricity consumption is related to space cooling, evident by the significant increase in electricity consumption during the summer months, a phenomenon typically observed in the entire EMME region (Kingdom of Bahrain, 2017 ). This increase in electricity use during extreme summer temperatures also reflects the inadequacy of building envelopes to reduce the intake of heat into the interior of a building. In November 2014, the government established the Sustainable Energy Unit under the Minister of Energy with the support of the United Nations Development Programme. The Sustainable Energy Unit launched two major initiatives: the National Renewable Energy Action Plan (NREAP) and the National Energy Efficiency Action Plan (NEEAP) in 2017. The NEEAP identifies 22 new initiatives across all sectors to achieve a 6% reduction in energy use by 2025, relative to the average energy use over 2009–2013 (Kingdom of Bahrain, 2017 ). Thermal insulation regulations were introduced in 1999 with Ministerial Order No. 8, mandating all new construction over four stories to be insulated and providing minimum energy efficiency requirements for the envelopes of residential and commercial buildings, which were later expanded to include all buildings with the Ministerial Order No. 63 in 2012. The NEEAP introduced seven initiatives to improve energy efficiency in the residential and commercial sectors, including green building initiatives targeting the reduction of building energy demand and Bahrain’s Building Energy Efficiency Code initiative, which was responsible for evolving the existing regulations on thermal insulation and introducing additional requirements for various systems. The NEEAP also contains the Renewable Energy Mandate for New Buildings, the National Renewable Energy Action Plan, the Building Energy Labelling Initiative and the Green Building Certification initiative, a formal certification scheme to promote the construction of resource-efficient buildings (Kingdom of Bahrain, 2017 ).

Kuwait is a global leader in the production of petroleum and oil products among the members of the Organization of the Petroleum Exporting Countries. It relies heavily on oil for electricity generation and less so on natural gas (U.S. Energy Information Administration, Analysis, 2020 ), with buildings being the biggest consumers of primary energy and electricity. While Kuwait had an installed electricity generation capacity of 15.7 GW, this was insufficient to meet the high summer demand for extensive space cooling in residential and public buildings. Multiple factors, such as the growing population and GDP levels, as well as low energy prices, are responsible for a rise in electricity demand within the residential sector. As a result, in 2013, Kuwait was the world’s sixth-largest electricity consumer on a per capita basis (U.S. Energy Information Administration, Analysis, 2020 ). Furthermore, the country’s electricity consumption has tripled in the past 30 years and is expected to rise by 20% up to 2027 and double by 2040, with most of the demand coming from space cooling (Alajmi, 2019 ). Due to regional conflicts, the period 1991–2008 is marked by very little progress in terms of the development of sustainable energy construction strategies, something which may be evident in the high energy demands and GHG emissions of the state (Alsayegh et al., 2018 ). For example, the Building Energy Conservation code was first released in 1983, and a revision with updated standards was issued in 2010 (Krarti, 2015 ). The update included new minimum requirements for the design and construction of new energy-efficient buildings, as well as new portions of existing buildings, including specifications for insulation of the building envelope, lighting systems, fenestration and heating, ventilation and air-conditioning (HVAC) systems (Ministry of Electricity and Water, 2016 ). The enforcement of the code is being carried out by the Ministry of Electricity and Water, the Kuwait Municipality and the Ministry of Public Works (Ministry of Electricity and Water, 2016 ).

Oman is the largest oil and natural gas producer in the Middle East (EIA, 2019 ), relying exclusively on diesel oil and natural gas, with electricity being generated predominantly by natural gas-fired power plants (about 97%). Although Oman has abundant potential for renewable energy generation and ambitious targets, currently, its energy portfolio includes little renewable generation and no nuclear resources. Moreover, as a result of considerable subsidisation by the government, knowledge of electricity conservation practices by consumers is very limited (Amoatey et al., 2022 ). Urbanisation, mainly along the coast, has increased dramatically over the past five decades in Oman. The urbanisation rate was 78.1% in 2016 (Sultanate of Oman, 2019 ), and electricity consumption between 2006 and 2016 grew at a fast rate, approximately tripling from 10 to 29 TWh. The peak in power demand is observed during the summertime, coinciding with extreme heat and increased space cooling demand (Charabi et al., 2014 ). Air conditioning makes up about 75% of the total annual energy end use in residential buildings, highlighting the need for space cooling and heating through mechanical means (Krarti & Dubey, 2017 ). The sultanate adopted a comprehensive National Strategy for Adaptation and Mitigation of Climate Change in October 2019 (MECA, 2019 ). The potential of energy efficiency measures, including improved energy management, labelling systems and strict building codes, has been investigated; nevertheless, no building codes are currently in implementation. A scenario analysis for the period 2010–2035 shows that such measures can lead to substantial electricity savings amounting to roughly 25% of electricity consumption (Sultanate of Oman, 2019 ). Depending on the level of measures and investments applied savings of up to 6000 GWh/a in electricity consumption and up to 1300 MW in peak power demand could be achieved (Krarti & Dubey, 2017 ).

Qatar is another country that is rich in fossil fuels, considered to be the largest exporter of liquefied natural gas (LNG) globally, obtaining significant revenues from exports of petroleum products (EIA, 2015 ). The expansion of LNG production and economic growth have resulted in steadily rising electricity demand (U.S. Energy Information Administration, Analysis, 2020 ). Although the Qatar National Plan for Energy Efficiency and Resource Utilisation was launched in 2011, it said little about achieving energy efficiency in the built environment and focused more on the gas and oil industry (The State of Quatar - Ministry of Environment, 2011 ). A strong increase in energy demand is largely driven by population growth, changes in lifestyle and low electricity tariffs. Especially the residential sector requires substantial amounts of electricity for space cooling and appliances (The State of Quatar - Ministry of Environment, 2011 ), with air conditioning accounting for up to 80% of buildings’ energy bills. The overall strategies to enhance energy efficiency in the country have been presented in two documents: the Qatar National Vision 2030 and, in more detail, the Qatar National Development Strategy, but only a few mandatory energy efficiency regulations in the building sector have been implemented (Meier et al., 2013 ). The more recent Qatar Second Development Strategy 2018–2022 (Planning and Statistics Authority, 2018 ) states that the enforcement of the Green Building Code by the end of 2022 will significantly reduce the per capita and household energy consumption. This is expected to enhance energy efficiency in the built environment (Planning and Statistics Authority, 2018 ), but only limited progress has been made so far. Nonetheless, in 2007, Qatar introduced the Global Sustainability Assessment System (GSAS), leading the way in the Middle East and North Africa (MENA) region for performance-based assessments and the rating of green buildings and their related infrastructure. GSAS sets out to create a sustainable built environment that minimises ecological impacts and the consumption of resources while addressing the local needs and environmental conditions specific to the region. It addresses five major environmental challenges for the countries of the Gulf Cooperation Council: (i) climate change and air pollution, (ii) fossil fuel depletion, (iii) material depletion and land contamination and (iv) water pollution and depletion (GORD, 2020 ). The system adopts an integrated life-cycle approach for the assessment of the built environment, and its application has addressed several significant environmental challenges in the Gulf Cooperation Council countries. On successful completion of the two stages of design and certification, the project qualifies for the final GSAS certificate. However, Ferwati et al. (Ferwati et al., 2019 ) claimed that the GSAS needed to be extended to the neighbourhood level in urban areas, which led to the introduction of the Qatar Sustainability Assessment System–Neighbourhood Development (QSAS-ND) assessment model. Overall, Qatar presents a state determined to introduce regulations, identify weaknesses and attempt to improve the landscape of energy-efficient buildings nationally and at the local level.

Saudi Arabia

The Kingdom of Saudi Arabia is the most extended country of the Arabian Peninsula, with copious oil and gas deposits, relying mainly on fossil fuels for energy. According to expectations, renewables will develop rapidly to support the total energy mix and provide half the nation with clean energy by 2030 (Samargandi et al., 2023 ). While the present per capita energy consumption in the Kingdom is already higher than most industrial and developed nations, the residential sector accounts for 68% of its total consumption (Al-Douri et al., 2019 ). Similarly to other EMME countries, HVAC systems consume the most energy in buildings in Saudi Arabia (Babelli, 2012 ). The government announcement on the Intended Nationally Determined Contributions to be submitted to the UNFCCC identified the buildings sector as one of the three predominant ones, collectively accounting for over 90% of the energy demand in the Kingdom (Kingdom of Saudi Arabia, 2015 ). The Saudi Energy Efficiency Program (SEEP) includes updated standards for thermal insulation products, the development of regulations for thermal insulation in new buildings, better control over the implementation process, updated efficiency standards for small air conditioners to match those of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) and the development of efficiency standards for larger-capacity air conditioners (Saudi Energy Efficiency Center, 2015 ). SEEP defines a three-pronged approach for the erection of new buildings (Saudi Energy Efficiency Center, 2015 ): it specifies building material standards, defines requirements for buildings that are achievable and applicable in the Saudi context and establishes short- and long-term enforcement mechanisms employing current capabilities and international best practices. New regulations specify the minimum thermal insulation requirements for new low-rise residential buildings, defined for three distinct climatic zones in Saudi Arabia (Saudi Energy Efficiency Center, 2015 ). Even though SEEP measures were introduced in the early 2000s (Alyousef & Varnham, 2010 ), concrete progress is still limited. Mosly (Mosly, 2015 ) identified 14 obstacles to the development of green buildings in Saudi Arabia, including a lack of skilled personnel and unaccommodating government policies and regulations. A previous study found that the Saudi building industry has yet to understand the importance of sustainability, largely due to insufficient education and training in the construction sector, leading to a lack of appreciation for sustainability practices (Alrashed & Asif, 2014 ).

United Arab Emirates

The United Arab Emirates (UAE) is also one of the largest producers of petroleum and oil products globally, with almost all electricity generated in 2018 (98%) coming from natural gas (U.S. Energy Information Administration, Analysis, 2020 ). The building sector contributes significantly to national energy demand. In particular, residential and commercial buildings consume 65% of the generated electricity, resulting in a significant carbon footprint and economic implications (IEA, 2021 ). The rapid demographic and economic growth pushed the electricity grid to its limits, and the installed generation capacity continues to increase to meet the high demand. The UAE’s Energy Strategy 2050, launched in January 2017, aims to diversify the country’s energy mix to include clean coal (12%), natural gas (38%), nuclear energy (6%) and renewable energy (solar power, wind power and biofuels, amounting to 44%). The strategy includes an ambitious target of a 40% reduction in energy consumption across three main sectors, namely, the built environment, transportation and industry (United Arab Emirates, 2018 ). The UAE took critical steps to reduce the energy intensity of its building stock, such as promoting green construction practices through local green building certifications in Abu Dhabi and Dubai. The National Green Building Codes set out specific regulations that address minimum envelope performance requirements. These include external walls, roofs and floors, glazed elements (e.g. fenestration), air conditioning design parameters (including the outdoor and indoor condition of the building), air loss from entrance and exit, air leakage, as well as energy efficiency measures for HVAC equipment and systems (Government of Dubai, 2020 ). Key pilot projects combining several efficiency measures in building design and landscape architecture include Masdar City in Abu Dhabi and the Sustainable City in Dubai. In 2008, the Abu Dhabi Urban Planning Council started an initiative called ‘ Estidama ’, which is the Arabic translation for ‘sustainability’. It aims to promote sustainable communities and cities by balancing four main pillars: environment, economy, culture and society. In 2010, Estidama released its Pearl Rating, a green building rating system that considers projects at different life-cycle stages: design, construction and operation (Abu Dhabi urban planning council, 2010 ). It offers three types of certifications depending on the nature of the project and builds on a five-level system, or ‘pearls’, modelled on the LEED certification scheme. The accumulated number of points earned determines the rating: Pearl 1 represents the minimum requirements, leading up to Pearl 5, which is the highest achievable level (Abu Dhabi urban planning council, 2010 ). According to an executive order, all new buildings in Abu Dhabi, built after 2010, must meet the minimum Pearl 1 rating, and all government buildings must meet the Pearl 2 rating (Meltzer et al., 2014 ). Meanwhile, the Dubai Integrated Energy Strategy 2030 intends to decrease energy and water use by 30% by 2030 – by 2017, its implementation had shrunk per capita electricity consumption by 9% (Dubai Supreme Council of Energy, 2017 ). Addressing the inefficient operation of existing buildings, the Dubai government sets out to retrofit 25% of its building stock by 2030; starting in 2017, it commenced the retrofit programme for about 5000 government buildings (United Arab Emirates, 2018 ). In 2010, the Dubai Municipality issued the Dubai Green Building Regulations and Specification, mandating minimal building efficiency requirements and an update followed in 2016, issuing the ‘ Al’Safat ’ green building rating system for residential, commercial and public buildings and industrial facilities. In contrast to Estidama’s Pearl Rating System, Al Sa’fat is not a point-based system, but a building receives certification upon fulfilling the requirements of one of its four possible levels: bronze (minimum requirements), silver, gold and platinum (highest achievable level) (Dubai Municipality, 2016 ). Today, both the Pearl Rating System and the Al Sa’fat system are mandatory (at different levels) for all newly constructed buildings, but the actual performance of structures certified with one of these systems is not publicly available. Such a step would help illustrate the benefits of green rating mechanisms in the region, showcasing the UAE’s dedication towards sustainable cities in the EMME and encouraging other countries to follow suit.

Policies and regulations in Western Asia

Iran holds some of the world’s largest proven deposits of oil and natural gas reserves and consumed more than 270 million tonnes of oil equivalent (toe) of primary energy in 2016. Natural gas is the primary fuel source for electricity generation (70% of total generation). The total GHG emissions for all sub-sectors roughly doubled in the period 2000–2010, with the energy sector accounting for roughly 80% of the emissions (Islamic Republic of Iran, 2003 ; Islamic Republic of Iran, 2010 ; Islamic Republic of Iran, 2017 ). Iran’s energy intensity exceeds that of the MENA region and low-income countries and is almost twice as high as that of the European Union (Moshiri & Lechtenböhmer, 2015 ). Energy expenditure increased rapidly over the past decades, with an annual growth rate of about 4% in energy use per capita for the period 2001-2010 (Moshiri & Lechtenböhmer, 2015 ). Subsidisation policies, as well as the availability of vast energy resources, drive the alarming rise in energy consumption, with very little consideration given to matters of energy efficiency and environmental impacts. The building sector consumes about 35% of the energy used in Iran (Khodamoradi & Sojdei, 2017 ), and the average energy use in buildings amounts to more than twice the one in developed countries. Natural gas satisfies most household energy demands (46%), with electricity (28%) and oil products (20%) following suit. Households mainly consume fuel for space heating (71%), water heating (22%) and cooking (7%). As a result of the government’s policy of substituting natural gas for oil products, the energy mix of households in Iran has changed considerably since 1990 (Moshiri & Lechtenböhmer, 2015 ). Energy-efficient heating systems and proper insulation techniques and materials, both for new and existing buildings, present potential areas for considerable energy savings (Moshiri & Lechtenböhmer, 2015 ) – proposed mitigation measures include these as well as energy efficiency standards and labelling programmes (Islamic Republic of Iran, 2017 ). The General Policies of Consumption Reform (2011) represents one of the most important, but aborted, initiatives introduced by the Government of Iran (Khodamoradi & Sojdei, 2017 ). It aimed to halve energy intensity by the year 2021 but failed to achieve its objective due to economic difficulties, including sanctions and a lack of finance, technology and expertise. The National Regulations for Buildings passed in 1991 and amended in 2000 and 2014 forms yet another legislative instrument to introduce energy savings in buildings (Moshiri & Lechtenböhmer, 2015 ; Omrany & Marsono, 2016 ), backed by the Iran Energy Efficiency Organisation (SABA) and the Iranian Fuel Conservation Organisation (IFCO), founded to promote education and training and raise public awareness. Efforts to introduce appropriate regulations and laws have had, so far, only limited success, and relatively small agencies such as the SABA and IFCO, with restricted authority and a tight budget, are unable to cope with the scale of Iran’s energy efficiency problems. The potential to save almost half of the current energy consumption in buildings exists and can be materialised, if the appropriate central authorities deploy considerable efforts (Khodamoradi & Sojdei, 2017 ).

The Federal Iraq and Kurdistan Regional Government holds some of the world’s largest proven crude oil reserves, and its economy depends heavily on export revenues from it; in 2018, they amounted to more than 90% of total government revenues. Between 2008 and 2018, Iraq’s electricity generation grew by an average of about 8% annually, reaching an estimated 78 billion kWh, of which more than 97% came from oil and natural gas (U.S. Energy Information Administration, Analysis, 2020 ). Iraq’s initial Nationally Determined Contribution to the UNFCCC and National Environmental Strategy and Action Plan for Iraq (2013–2017) mentioned energy efficiency, in general, and in the built environment, in particular (Republic of Iraq, 2012 ; Republic of Iraq, 2016 ), as part of its national mitigation strategy, but with far less attention than supply-side issues. The government’s limited efforts to improve energy efficiency seem inadequate to alleviate the severe power crisis Iraq has been facing since 2003 (Istepanian, 2020 ). Technical and commercial losses of energy exceed half of the generated power, with most of them stemming from the residential sector, which is the greatest energy consumer on the demand side. Considerable potential to improve energy efficiency in Iraq exists (about 210 GWh per year by 2025), mostly in the electricity sector (World Bank, 2016 ). The government holds a pivotal role in developing an effective strategy to reduce energy consumption and lessen the country’s dependence on fossil fuel power generation, but up to now, it has not produced a building code regulating the energy efficiency of the design, construction and operation of new and existing buildings (Istepanian, 2020 ). Eliminating barriers to improved energy efficiency starts with reforms of the current electricity tariffs and energy subsidy systems to provide new incentives to implement and practice energy efficiency measures in the building sector (Istepanian, 2020 ).

Policies and regulations in European countries

Unlike the rest of the country agglomerations presented here, European Union (EU) law governs Cyprus and Greece, requiring them to implement directives related to energy efficiency in buildings in their national laws and regulations. Specifically, two directives address the building sector: Directive 2010/31/EU on the energy performance of buildings (EPBD) and Directive 2012/27/EU on energy efficiency. Recently, these two directives were amended as part of the Clean Energy for all Europeans package (Directorate-General for Energy, 2019 ) in 2019 and later in October 2020 as a part of the Renovation Wave strategy (European Commission, 2020 ) included in the European Green Deal (European Commission, 2019 ). In 2021, the EU adopted Regulation 2021/1119/EU (European Climate Law), establishing a framework for achieving climate neutrality by all European Member States in 2050. In early 2023, a recast of the EPBD released as part of the ‘Fit-for-55’ package proposes a minimum 55% reduction in GHG emissions by 2030 and reinforces safeguards for renters and flexibilities for European countries and building owners (European Parliament, 2023 ).

Cyprus transposed both European Directives related to the energy performance of buildings mentioned above (Department of Environment, 2018 ) and has prepared a National Energy Efficiency Programme to achieve certain energy-saving targets. The programme includes measures relevant to the built environment, such as an increased annual renovation rate of 3% of the surface of mechanically ventilated public buildings, energy refurbishment of existing residential and commercial buildings in compliance with the minimum requirements, and the promotion of roof thermal insulation on dwellings. More recently, Cyprus adopted Law 155 (I) on the Regulation of the Energy Efficiency of Buildings, which governs the regulation of new constructions and renovation of existing ones according to the updated Directive 2018/844 (Ministry of Energy Commerce and Industry, 2020 ). To adhere to the European Climate law, the Republic of Cyprus submitted to the European Commission in 2022 its Long-Term Strategy for Low Greenhouse Gas Emission Development which is currently under revision and update. Several schemes have been announced at regular intervals, promoting subsidised energy refurbishments for entire buildings or individual elements such as roof insulation and renewable energy technologies (Republic of Cyprus, 2020 ). Early findings on the implementation of EPBD in Cyprus showed that energy reduction by up to 60% can be achieved in ageing, inefficient housing stock (Fokaides et al., 2017 ). As part of the EPBD, an energy certification scheme stipulates that any building up for sale or rent should hold an energy performance certificate, informing the prospective buyers/owners of the energy performance of the dwelling. In Cyprus, the relatively transposed legislation applies to new and existing buildings, with varying levels of minimum energy performance depending on the type and status of buildings (Energy Service, 2021 ). According to the most recent data available, approximately 15% of all buildings (residential or not) hold an energy performance certificate, most of which are new constructions (Energy Service, 2020 ; Energy Service, 2021 ). This means that the existing building stock of the country remains highly energy inefficient, without considerable renovation. Regarding clean energy in Cyprus, electricity represents the predominant energy carrier, as a natural gas grid is lacking. Electric energy is generated almost entirely through the combustion of oil products (more than 90% of total consumption), and the rest comes from renewables (Mesimeris et al., 2020 ). Nevertheless, according to recent statistics, renewables contributed by more than 18% in gross final energy consumption in 2022, close to the European average (21.8%) and on track to reach the 2030 goal of 23% (Eurostat, 2023 ; Mesimeris et al., 2020 ). Solar water heaters exist in almost all buildings, boosting the country’s ranking in the production of direct-use solar thermal energy (European Commision, 2019 ). PV-generated electricity experienced a steady increase over the past decade, and the government aims to install ‘photovoltaics on every roof’, although considerable deficits in energy storage infrastructure caused wastage of about 20% of the generated renewable energy (Agapiou, 2023 ; Eracleous, 2023 ; Statista, 2023 ). The EPBD, energy efficiency schemes and clean energy actions fall under the authority of the Ministry of Energy, Commerce and Industry, which moves forward with the transition to clean energy but still faces many institutional and legacy obstacles.

Similarly to Cyprus, Greece adopted several laws and regulations based on the current European Directives. The 2019 Integrated National Energy and Climate Plan (NECP) addresses national energy and climate objectives for 2030 and sets ambitious objectives for increasing the overall share of renewable energy sources (Hellenic Republic, 2019 ). According to recent reports, renewables covered approximately 22% of the gross final energy consumption in Greece in 2021 (Eurostat, 2022 ), while the NECP targets raising this share to 35% by 2030. The portion of electricity from renewables grew from 26% in 2017 to almost 36% in 2021, which is slightly below the corresponding average among the EU countries (37.5%) (Eurostat, 2023 ). According to the NECP goals, this should rise to at least 60% by 2030: the share of renewables in the building sector (space heating and cooling) should rise to 42.5% from 30.6% in 2020, and in the transport sector, it should rise from 6.6% in 2020 to 19% in 2023 (Hellenic Republic, 2019 ). The NECP also sets out the timeframe for putting a complete end to the use of lignite for power generation in Greece by 2028. The new Climate Act15 (L.4936/22), adopted in May 2022, obliges all municipalities, including island ones, to produce local plans for GHG emission reduction in line with the NECP. It also forbade the use of oil for power generation on the islands (unless at times of critical risk for energy supply security) starting from the 1st of January 2030. Greece supports solar photovoltaic, onshore wind power and hydropower, with a focus on hybrid plants for non-interconnected islands. Incentives exist in the heating and cooling sector, solar thermal, biomass, aerothermal, geothermal and combined heat and power (CHP) plants for self-consumption (tax relief). Renewable energy source (RES) plants below 400 kW on interconnected islands and all RES on non-interconnected islands are eligible for a Feed-In tariff, while the remuneration for the electricity generated through RES and injected into the grid is regulated by Law 4964/2022 (Rakocevic et al., 2022 ).

Regarding the building sector, the residential and tertiary sectors consumed 44% of the final energy in 2019 (Dascalaki et al., 2016 ), showing great potential for energy savings, also highlighted by the fact that more than half of the existing buildings were constructed before the adoption of the first thermal insulation regulation in 1980 (Karakosta & Papapostolou, 2023 ). The Greek Parliament adopted Directive 2010/31/EU in 2013 (Law 4122/2013), and since 2017, an Energy Performance Study has been obligatory for issuing building permits for new buildings or extensively renovated ones (Law 4495/2017, article 2, par. 25). The national plan aiming to promote nearly zero energy buildings (issued in August 2018) defined, among others, that a new building may be characterised as a nearly zero energy building if it falls at least under energy class A, while an existing building when it falls at least under energy class B+ (Androutsopoulos & Giakoumi, 2020 ). The ‘Energy Savings in Households II ( Eksikonomisi kat’ Oikon II )’ programme, launched in 2018, provides financial incentives for implementing energy renovation measures in households. The ‘ELEKTRA’ programme strengthens the energy upgrading of public buildings and the participation of Energy Service Companies, while the Green Pilot Urban Neighbourhood programme addresses energy refurbishment of social housing dwellings (EPAH, 2021 ).

Identified open gaps and future research directions

States of the EMME region other than the European Union’s Member States lack governance by a single common framework, and each one has developed its own sets of regulations and standards applicable to the building sector. Given the diversity of the portfolio of Middle Eastern, Arabic, Western Asian and European countries, finding common grounds for the whole portfolio is a difficult task. To provide a systematic framework of analysis for currently implemented policies and initiatives, as well as pledges for future climate change mitigation, a matrix is presented in Table 3 . Moreover, the overall performance of each country is assessed based on the policies regarding the following themes:

policy on building energy performance standards,

Energy Performance Certificate issued (all new buildings),

zero energy building (any stage of implementation),

policy pledge on renewable energy,

policy pledge on GHG emissions reduction,

ratification of the Paris Agreement,

policy pledge on building renovations, and

research and development (R&D) in energy efficiency, clean energy and building technologies.

This policy summary highlights that the current degree of implementation of measures varies when it comes to addressing climate change in national legislation and building regulations. There are a few countries with high standardisation of the built sector and commitments towards climate change mitigation, several characterised by moderate efforts and others taking minimal action, indicating a lack of regulations and concrete commitments. The frontrunners are Cyprus and Greece, both governed by EU law, but also Türkiye and the UAE. In the case of Türkiye, it is a country that has matched all the policies and commitments made by the EU countries, although not a full member. The UAE is leading by its own example and prioritising sustainability and addressing the impacts of climate change, keeping in pace with the EU but on its terms (CAT, 2023a ).

In the middle ranges, Israel is marking positively all of the fields except for the policy pledge on building renovations, indicating a high level of commitment to progress in the built environment, but not holistically considering the existing building stock. Moreover, Saudi Arabia, Bahrain, Egypt, Jordan, Kuwait and Qatar have enforced mandatory energy efficiency guidelines in the construction sector; however, most are lacking in the rest of the regulations and initiatives relative to the sector. The energy performance certificates, zero energy buildings and building renovation policies are the most often weak points for these countries. Finally, Lebanon, Palestine, Oman, Iran, Iraq and Syria are showing the least amount of commitment, largely characterised by the absence of mandatory building energy codes (except for Iran). Moreover, signs of readiness to improve this are lacking, evident by the absence of energy performance certification schemes, zero energy buildings, building renovation policies and R&D relative to the built environment. From this group, the countries marked with the least progress are those involved in ongoing or prolonged conflict, with Syria and Iraq being the worst cases; the consequences of the war between Palestine and Israel remain to be determined in the coming years. This serves as a warning sign for countries about to, or already, experiencing transient conflicts that could become chronic (Bou Sanayeh & El Chamieh, 2023 ). It is noteworthy that despite the diverse status quo encountered in the EMME region, all countries have expressed their pledges towards greenhouse gas emissions reduction and have signed the Paris agreement, although only one has not backed it. Iran signed the Paris agreement but has not ratified it, pledging only a moderate reduction that is deemed critically insufficient (CAT, 2023b ). The reason behind this may be explained by the recent claims that the agreement will be signed if sanctions against Iran are lifted (McGrath, 2021 ).

Nevertheless, even among the countries that have enforced strict rules and opted for ambitious goals, the lived reality may be a different story. For instance, the catastrophic Türkiye-Syria earthquake of early 2023 has led to the loss of more than 44,000 human lives and the collapse of more than 100,000 buildings, with heavy criticism on the non-implementation of modern building codes that firstly ensure safety and then energy efficiency. Instead of building up to standard, Turkish contractors were asked to pay a fine in exchange for inadequate structural quality (Azak, 2023 ; Bilginsoy & Fraser, 2023 ). On the Syrian side, renovation works are delayed or abandoned – a result of the existing neglectful construction sector which has been exploiting granted permits to build additional storeys or intervene without the appropriate considerations for safety and regulations (Housing Land & Property Rights, 2023 ). Another very recent example of life-threatening and infrastructure-damaging conditions is met in Greece, which has suffered devastating damages due to extreme weather conditions. Nature was once again the driving factor for this disaster, and as in the case of the Türkiye-Syria earthquake, appropriate implementation of policies and preparedness could have possibly mitigated damages (Elissaiou, 2023 ). On an analogous negligent tone, although creating less precarious situations, construction practices in Cyprus promote low-cost and resource-heavy solutions rather than systemic, sustainable ones. In this case, renovated buildings may be equipped with more insulation material to meet minimum energy performance standards rather than integrate renewables (MECI, 2021 ). Three of the best performers in terms of climate change policies are therefore found laden with flaws and faults, and it becomes evident that while commitments are a solid first step, they must be followed by actions. On the offside, Lebanon stands out with a policy response that embraces renewables and promotes energy independence, resuscitating the country’s ailing electricity sector. The destructive explosion in Beirut in the summer of 2020 led to diminished power supply capacity, which still has not been recovered. In December 2023, the Lebanese government passed the Decentralized Renewable Energy Law, allowing peer-to-peer renewable energy trading and boosting energy security through the uptake of renewables (Lebanese Center for Energy Conservation (LCEC), 2023 ). This initiative comes from a country ranked in the lower classes of international commitment in the context of this study, highlighting that action can be a more robust solution than commitment.

Although this study focuses on selected climate change policies related to the built environment, it would be heedless to disregard the protracted refugee – and consequently – housing crisis that hinders equity and development of a sustainable built environment in the EMME region. Up to now, this is especially the case for Syria, as well as the countries that are receiving Syrian refugees, primarily Türkiye. By 2022, over 6.5 million Syrians fled the country and sought asylum in other parts of the world, creating a dual calamity: deserted homes in Syria and inadequate new housing elsewhere (WorldBank, 2023b ). Syria alone represents over 25% of the global refugee population and has the lowest GDP per capita of all the studied EMME countries, at 537 USD (the world average is around 11,000 USD) (WorldBank, 2023a ). On the other hand, Türkiye, Jordan, Palestine and Lebanon are being asked to provide adequate temporary shelter and possibly permanent housing for approximately 39% of the global refugee population (WorldBank, 2023c ). These countries, too, have low GDP per capita , below the world average (WorldBank, 2023a ). In addition to the extreme threats against human well-being, such significant numbers of transient populations create impactful disturbances to countries of origin and asylum alike. The new humanitarian and housing crisis caused by the Israel-Palestine conflict is expected to further exacerbate systemic societal disruptions in the area.

Synthesising the information collected in this study, the EMME region appears as a fragmented multi-ethnic agglomeration of countries; their efforts in climate change mitigation show different trajectories but a common goal. Even tormented states such as Syria and Palestine have recognised the urgency for coordinated long-term efforts to tackle climate change and its negative impacts. Nevertheless, the fact remains that each state operates within its capabilities and constraints. Warfare has, on occasion, hindered progress, whereas, on others, economic growth has accelerated it in an unsustainable manner. Leaders of EMME states should consider the development of unified strategies that ensure a baseline in terms of regulations over the built environment, flexible enough to accommodate both the slower and faster pacers of the region. Taking an example from European governance and Directives related to energy efficiency and building performance, a framework of building energy codes would ensure a minimum level of efficacy. Moving on from the policy scene, this would allow for research also to advance and deliver meta-analyses on the assessment of policies, as has been done for the European legislation.

Conclusions and good practices

Although most countries of the Eastern Mediterranean and Middle East (EMME) participate in and embrace international agreements, they act individually and not collectively. The energy and climate change policy landscape of this region is disorganised and lacks a backbone, confirming the initial hypothesis that the policy agenda reflects the diverse characteristics of the region. Intense urbanisation and climate change in the EMME region put millions of people at risk, with the most vulnerable having to endure the greatest impacts. Geographic, socio-economic and socio-political conditions affect the vulnerability of residents, whether that is in remote areas or dense urban centres. This paper identifies political and geographical features unique to the EMME region, linking them to socio-economic conditions. There is a stark contradiction in individual and national prosperity between the oil-rich countries of the Middle East and those with no significant hydrocarbon reserves. Cultural diversity in terms of ethnicity, religion and language often results in warfare, although competition for natural resources might be one of the underlying causes of conflict.

The diverse built environment across the region is often influenced by externalities, as well as internal prioritisation of other sectors of the economy. The new norm reflects profuse urbanisation and little attention to vernacular architecture and preservation of tangible cultural heritage – over 80% of the population lives in carbon-intensive cities in 10 out of the 17 EMME countries. Keeping up with this demand requires new urban constructions of high standards, leaving behind legacy dwellings of higher traditional value unattended. Drained pools of traditional technique builders and increasing ones comprising experts in cement, glass and steel signal the abandonment of heritage dwellings and indigenous building methods. Countries wishing to reclaim them should respond quickly to the altered built landscape and skill capacities through financing the renovation of such buildings, enriching service provision in remote areas and offering capacity building for craftsmen. Moreover, energy efficiency in new and existing buildings should assume greater importance, possibly drawing inspiration from traditional building practices rather than amplifying the embedded energy of buildings and their systems.

In the policy landscape, international efforts such as the Paris Agreement or the UN’s 2030 Agenda for Sustainable Development act as roadmaps for adaptation and mitigation actions. However, national and local governments hold the responsibility for the adoption and proper implementation of these proposed strategies. A quarter of the region lacks mandatory building energy codes in 2023, reflecting the poor transposition of international agreements into national laws and policies in the building sector. At the national level, each country advances on its own terms, but lacking or unevenly reported information makes comparative approaches impossible. International cooperation and common frameworks and ambitions can tackle the evident lack of homogeneity in the greater EMME region, and long-term planning and future development of the built environment can strengthen the currently weak universally pledged commitments. Whether caused by prioritisation of wealth accumulation, increased levels of competitiveness or conflict, the response of the EMME countries against the threat of climate change is inadequate. To address this chasm, a proposed framework of governance similar to the EU’s directives and regulations can level the field to a minimum amount of action and allow for individual countries to move even further than the margins.

The EMME region is a work in progress in multiple facets. One is the policy landscape on the regulation of performance in buildings, with each country showing advancement at its own pace and path. Having officially recognised the criticality of climate change impacts, a set of similar objectives will ensure as much uniformity as possible while retaining the unique specificities characterising each country. The region presents unique opportunities for innovative design approaches and the application of cutting-edge building technologies merged with abiding customary practices. However, any progress should account for the well-being of the entire population. Concurrent energy, economic and humanitarian crises in the EMME and globally block inhabitable and inclusive growth and while energy sufficiency and sustainable cities are integral aspirations for the EMME region, these should not come at the expense of the voiceless.

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This review was motivated by the Cyprus Government Initiative for Coordinating Climate Change Action in the Eastern Mediterranean and Middle East ( https://www.cyi.ac.cy/index.php/cyi/international-collaborations/cyprus-government-initiative-for-coordinating-climate-change-action-in-the-eastern-mediterranean-and-middle-east.html ).

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Carlucci, S., Lange, M.A., Artopoulos, G. et al. Characteristics of the built environment in the Eastern Mediterranean and Middle East and related energy and climate policies. Energy Efficiency 17 , 52 (2024). https://doi.org/10.1007/s12053-024-10217-w

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