write a suitable operationalised non directional hypothesis for this investigation

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

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

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

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

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

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

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

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

or with a directional correlational hypothesis….

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

With a non-directional or  two tailed hypothesis…

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

or for a correlational …

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

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

Exam Techniques/Advice

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

Practice Questions:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Non Directional Hypothesis

In the realm of hypothesis formulation, non-directional hypotheses offer a distinct perspective. These hypotheses suggest a relationship between variables without specifying the nature or direction of that relationship. This guide delves into non-directional hypothesis examples across various fields, outlines a step-by-step approach to crafting them, and provides expert tips to ensure your non-directional hypotheses are robust and insightful. Explore the world of Thesis statement hypotheses that explore connections without predetermined expectations.

What is the Non-Directional Hypothesis? – Definition

A non-directional hypothesis, also known as a two tailed hypothesis , is a type of hypothesis that predicts a relationship between variables without specifying the direction of that relationship. Unlike directional hypotheses that predict a specific outcome, non-directional hypotheses simply suggest that a relationship exists without indicating whether one variable will increase or decrease in response to changes in the other variable.

What is an Example of a Non-Directional Hypothesis Statement?

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“An increase in exercise frequency is associated with changes in weight.”

In this non-directional hypothesis, the statement suggests that a relationship exists between exercise frequency and weight changes but doesn’t specify whether increased exercise will lead to weight loss or weight gain. It leaves the direction of the relationship open for empirical investigation and data analysis.

100 Non Directional Hypothesis Statement Examples

Non-directional hypotheses explore relationships between variables without predicting the specific outcome. These simple hypothesis offer flexibility, allowing researchers to uncover unforeseen connections. Discover a range of non-directional hypothesis examples that span disciplines, enabling empirical exploration and evidence-based conclusions.

• Impact of Stress on Sleep Quality : Stress levels are related to changes in sleep quality among college students.
• Relationship Between Social Media Use and Loneliness : Social media use is associated with variations in reported feelings of loneliness.
• Connection Between Parenting Styles and Adolescent Self-Esteem : Different parenting styles correlate with differences in adolescent self-esteem levels.
• Effects of Temperature on Productivity : Temperature variations affect productivity levels in office environments.
• Link Between Screen Time and Eye Strain : Screen time is related to variations in reported eye strain among digital device users.
• Influence of Study Techniques on Exam Performance : Study techniques correlate with differences in exam performance among students.
• Relationship Between Classroom Environment and Student Engagement : Classroom environment is associated with variations in student engagement levels.
• Impact of Music Tempo on Heart Rate : Music tempo relates to changes in heart rate during exercise.
• Connection Between Diet and Cholesterol Levels : Dietary choices are related to variations in cholesterol levels among adults.
• Effects of Outdoor Exposure on Mood : Outdoor exposure is associated with changes in reported mood among urban dwellers.
• Relationship Between Personality Traits and Leadership Styles : Personality traits are associated with differences in preferred leadership styles among professionals.
• Impact of Time Management Strategies on Academic Performance : Time management strategies correlate with variations in academic performance among college students.
• Connection Between Cultural Exposure and Empathy Levels : Cultural exposure relates to changes in reported empathy levels among individuals.
• Effects of Nutrition Education on Dietary Choices : Nutrition education is associated with variations in dietary choices among adolescents.
• Link Between Social Support and Stress Levels : Social support is related to differences in reported stress levels among working adults.
• Influence of Exercise Intensity on Mood : Exercise intensity correlates with variations in reported mood among fitness enthusiasts.
• Relationship Between Parental Involvement and Academic Achievement : Parental involvement is associated with differences in academic achievement among schoolchildren.
• Impact of Sleep Duration on Cognitive Function : Sleep duration is related to changes in cognitive function among older adults.
• Connection Between Environmental Factors and Creativity : Environmental factors correlate with variations in reported creative thinking abilities among artists.
• Effects of Communication Styles on Conflict Resolution : Communication styles are associated with differences in conflict resolution outcomes among couples.
• Relationship Between Social Interaction and Life Satisfaction : Social interaction is related to variations in reported life satisfaction among elderly individuals.
• Impact of Classroom Seating Arrangements on Participation : Classroom seating arrangements correlate with differences in student participation levels.
• Connection Between Smartphone Use and Sleep Quality : Smartphone use is associated with changes in reported sleep quality among young adults.
• Effects of Mindfulness Practices on Stress Reduction : Mindfulness practices relate to variations in reported stress levels among participants.
• Link Between Gender and Communication Styles : Gender is related to differences in communication styles among individuals in group discussions.
• Influence of Advertising Exposure on Purchase Decisions : Advertising exposure correlates with variations in reported purchase decisions among consumers.
• Relationship Between Job Satisfaction and Employee Productivity : Job satisfaction is associated with differences in employee productivity levels.
• Impact of Social Support on Coping Mechanisms : Social support relates to variations in reported coping mechanisms among individuals facing challenges.
• Connection Between Classroom Environment and Student Creativity : Classroom environment is related to changes in student creativity levels.
• Effects of Exercise on Mood : Exercise is associated with variations in reported mood levels among participants.
• Relationship Between Music Preferences and Stress Levels : Music preferences are related to variations in reported stress levels among individuals.
• Impact of Nutrition Education on Food Choices : Nutrition education correlates with differences in dietary food choices among adolescents.
• Connection Between Physical Activity and Cognitive Function : Physical activity is associated with changes in cognitive function among older adults.
• Effects of Color Exposure on Mood : Color exposure relates to variations in reported mood levels among participants.
• Link Between Personality Traits and Career Choice : Personality traits are related to differences in career choices among individuals.
• Influence of Outdoor Recreation on Mental Well-being : Outdoor recreation is associated with variations in reported mental well-being among participants.
• Relationship Between Social Media Use and Self-Esteem : Social media use correlates with changes in reported self-esteem levels among young adults.
• Impact of Parenting Styles on Adolescent Risk Behavior : Parenting styles are related to variations in reported risk behaviors among adolescents.
• Connection Between Sleep Quality and Cognitive Performance : Sleep quality relates to changes in cognitive performance among students.
• Effects of Art Exposure on Creativity : Art exposure is associated with differences in reported creative thinking abilities among participants.
• Relationship Between Social Support and Mental Health : Social support is related to variations in reported mental health outcomes among individuals.
• Impact of Technology Use on Interpersonal Communication : Technology use correlates with differences in reported interpersonal communication skills among individuals.
• Connection Between Parental Attachment and Romantic Relationships : Parental attachment is associated with variations in the quality of romantic relationships among adults.
• Effects of Environmental Noise on Concentration : Environmental noise relates to changes in reported concentration levels among students.
• Link Between Music Exposure and Memory Performance : Music exposure is related to differences in memory performance among participants.
• Influence of Nutrition on Physical Fitness : Nutrition choices correlate with variations in reported physical fitness levels among athletes.
• Relationship Between Stress and Health Outcomes : Stress levels are associated with changes in reported health outcomes among individuals.
• Impact of Workplace Environment on Job Satisfaction : Workplace environment relates to differences in reported job satisfaction among employees.
• Connection Between Humor and Stress Reduction : Humor is related to variations in reported stress reduction among participants.
• Effects of Social Interaction on Emotional Well-being : Social interaction correlates with changes in reported emotional well-being among participants.
• Relationship Between Cultural Exposure and Cognitive Flexibility : Cultural exposure is related to variations in reported cognitive flexibility among individuals.
• Impact of Parent-Child Communication on Academic Achievement : Parent-child communication correlates with differences in academic achievement levels among students.
• Connection Between Personality Traits and Prosocial Behavior : Personality traits are associated with variations in reported prosocial behaviors among individuals.
• Effects of Nature Exposure on Stress Reduction : Nature exposure relates to changes in reported stress reduction among participants.
• Link Between Sleep Duration and Cognitive Performance : Sleep duration is related to differences in cognitive performance among participants.
• Influence of Social Media Use on Body Image : Social media use correlates with variations in reported body image satisfaction among young adults.
• Relationship Between Exercise and Mental Well-being : Exercise levels are associated with changes in reported mental well-being among participants.
• Impact of Cultural Competency Training on Patient Care : Cultural competency training relates to differences in patient care outcomes among healthcare professionals.
• Connection Between Perceived Social Support and Resilience : Perceived social support is related to variations in reported resilience levels among individuals.
• Effects of Environmental Factors on Mood : Environmental factors correlate with changes in reported mood levels among participants.
• Relationship Between Cultural Diversity and Team Performance : Cultural diversity is related to variations in reported team performance outcomes among professionals.
• Impact of Parental Involvement on Academic Motivation : Parental involvement correlates with differences in academic motivation levels among schoolchildren.
• Connection Between Mindfulness Practices and Anxiety Reduction : Mindfulness practices are associated with changes in reported anxiety levels among participants.
• Effects of Nutrition Education on Eating Habits : Nutrition education relates to variations in dietary eating habits among adolescents.
• Link Between Personality Traits and Learning Styles : Personality traits are related to differences in preferred learning styles among students.
• Influence of Nature Exposure on Creativity : Nature exposure correlates with variations in reported creative thinking abilities among individuals.
• Relationship Between Extracurricular Activities and Social Skills : Extracurricular activities are associated with changes in reported social skills among adolescents.
• Impact of Cultural Awareness Training on Stereotypes : Cultural awareness training relates to differences in perceived stereotypes among participants.
• Connection Between Sleep Quality and Emotional Regulation : Sleep quality is related to variations in reported emotional regulation skills among individuals.
• Effects of Music Exposure on Mood : Music exposure correlates with changes in reported mood levels among participants.
• Relationship Between Cultural Sensitivity and Cross-Cultural Communication : Cultural sensitivity is related to variations in reported cross-cultural communication skills among professionals.
• Impact of Parent-Child Bonding on Emotional Well-being : Parent-child bonding correlates with differences in reported emotional well-being levels among individuals.
• Connection Between Personality Traits and Conflict Resolution Styles : Personality traits are associated with variations in preferred conflict resolution styles among individuals.
• Effects of Mindfulness Practices on Focus and Concentration : Mindfulness practices relate to changes in reported focus and concentration levels among participants.
• Link Between Gender Identity and Career Aspirations : Gender identity is related to differences in reported career aspirations among individuals.
• Influence of Art Exposure on Emotional Expression : Art exposure correlates with variations in reported emotional expression abilities among participants.
• Relationship Between Peer Influence and Risky Behavior : Peer influence is associated with changes in reported engagement in risky behaviors among adolescents.
• Impact of Diversity Training on Workplace Harmony : Diversity training relates to differences in perceived workplace harmony among employees.
• Connection Between Sleep Patterns and Cognitive Performance : Sleep patterns are related to variations in cognitive performance among students.
• Effects of Exercise on Self-Esteem : Exercise correlates with changes in reported self-esteem levels among participants.
• Relationship Between Social Interaction and Well-being : Social interaction is related to variations in reported well-being levels among individuals.
• Impact of Parenting Styles on Adolescent Peer Relationships : Parenting styles correlate with differences in peer relationship quality among adolescents.
• Connection Between Personality Traits and Communication Effectiveness : Personality traits are associated with variations in communication effectiveness among professionals.
• Effects of Outdoor Activities on Stress Reduction : Outdoor activities relate to changes in reported stress reduction among participants.
• Link Between Music Exposure and Emotional Regulation : Music exposure is related to differences in reported emotional regulation skills among individuals.
• Influence of Family Dynamics on Academic Achievement : Family dynamics correlate with variations in academic achievement levels among students.
• Relationship Between Cultural Engagement and Empathy : Cultural engagement is associated with changes in reported empathy levels among individuals.
• Impact of Conflict Resolution Strategies on Relationship Satisfaction : Conflict resolution strategies relate to differences in reported relationship satisfaction levels among couples.
• Connection Between Sleep Quality and Physical Health : Sleep quality is related to variations in reported physical health outcomes among individuals.
• Effects of Social Support on Coping with Stress : Social support correlates with changes in reported coping strategies for stress among participants.
• Relationship Between Cultural Sensitivity and Patient Care : Cultural sensitivity is related to variations in reported patient care outcomes among healthcare professionals.
• Impact of Family Communication on Adolescent Well-being : Family communication correlates with differences in reported well-being levels among adolescents.
• Connection Between Personality Traits and Leadership Styles : Personality traits are associated with variations in preferred leadership styles among professionals.
• Effects of Nature Exposure on Attention Span : Nature exposure relates to changes in reported attention span among participants.
• Link Between Music Preference and Emotional Expression : Music preference is related to differences in reported emotional expression abilities among individuals.
• Influence of Peer Support on Academic Success : Peer support correlates with variations in reported academic success levels among students.
• Relationship Between Cultural Engagement and Creativity : Cultural engagement is associated with changes in reported creative thinking abilities among individuals.
• Impact of Conflict Resolution Skills on Relationship Satisfaction : Conflict resolution skills relate to differences in reported relationship satisfaction levels among couples.
• Connection Between Sleep Patterns and Stress Levels : Sleep patterns are related to variations in reported stress levels among individuals.
• Effects of Social Interaction on Happiness : Social interaction correlates with changes in reported happiness levels among participants.

Non-Directional Hypothesis Statement Examples for Psychology

These examples pertain to psychological studies and cover various relationships between psychological hypothesis concepts. For instance, the first example suggests that attachment styles might be related to romantic satisfaction, but it doesn’t specify whether attachment styles would increase or decrease satisfaction.

• Relationship Between Attachment Styles and Romantic Satisfaction : Attachment styles are related to variations in reported romantic satisfaction levels among individuals in psychology studies.
• Impact of Personality Traits on Career Success : Personality traits correlate with differences in reported career success outcomes among psychology study participants.
• Connection Between Parenting Styles and Adolescent Self-Esteem : Parenting styles are associated with variations in reported self-esteem levels among adolescents in psychological research.
• Effects of Social Media Use on Body Image : Social media use relates to changes in reported body image satisfaction among young adults in psychology experiments.
• Link Between Sleep Patterns and Emotional Well-being : Sleep patterns are related to differences in reported emotional well-being levels among psychology research participants.
• Influence of Mindfulness Practices on Stress Reduction : Mindfulness practices correlate with variations in reported stress reduction among psychology study participants.
• Relationship Between Social Interaction and Mental Health : Social interaction is associated with changes in reported mental health outcomes among individuals in psychology studies.
• Impact of Parent-Child Bonding on Emotional Resilience : Parent-child bonding relates to differences in reported emotional resilience levels among psychology research participants.
• Connection Between Cultural Sensitivity and Empathy : Cultural sensitivity is related to variations in reported empathy levels among individuals in psychology experiments.
• Effects of Exercise on Mood : Exercise correlates with changes in reported mood levels among psychology study participants.

Non-Directional Hypothesis Statement Examples in Research

These research hypothesis examples focus on research studies in general, covering a wide range of topics and relationships. For instance, the second example suggests that employee training might be related to workplace productivity, without indicating whether the training would lead to higher or lower productivity.

• Relationship Between Time Management and Academic Performance : Time management is related to variations in academic performance levels among research participants.
• Impact of Employee Training on Workplace Productivity : Employee training correlates with differences in reported workplace productivity outcomes among research subjects.
• Connection Between Media Exposure and Political Knowledge : Media exposure is associated with variations in reported political knowledge levels among research participants.
• Effects of Environmental Factors on Children’s Cognitive Development : Environmental factors relate to changes in reported cognitive development among research subjects.
• Link Between Parental Involvement and Student Motivation : Parental involvement is related to differences in reported student motivation levels among research participants.
• Influence of Cultural Immersion on Language Proficiency : Cultural immersion correlates with variations in reported language proficiency levels among research subjects.
• Relationship Between Leadership Styles and Team Performance : Leadership styles are associated with changes in reported team performance outcomes among research participants.
• Impact of Financial Literacy Education on Savings Habits : Financial literacy education relates to differences in reported savings habits among research subjects.
• Connection Between Stress Levels and Physical Health : Stress levels are related to variations in reported physical health outcomes among research participants.
• Effects of Music Exposure on Concentration : Music exposure correlates with changes in reported concentration levels among research subjects.

Non-Directional Hypothesis Statement Examples for Research Methodology

These examples are specific to the methods used in conducting research. The eighth example states that randomization might relate to group equivalence, but it doesn’t specify whether randomization would lead to more equivalent or less equivalent groups.

• Relationship Between Sampling Techniques and Research Validity : Sampling techniques are related to variations in research validity outcomes in studies of research methodology.
• Impact of Data Collection Methods on Data Accuracy : Data collection methods correlate with differences in reported data accuracy in research methodology experiments.
• Connection Between Research Design and Study Reproducibility : Research design is associated with variations in reported study reproducibility in research methodology studies.
• Effects of Questionnaire Format on Response Consistency : Questionnaire format relates to changes in reported response consistency in research methodology research.
• Link Between Ethical Considerations and Research Credibility : Ethical considerations are related to differences in reported research credibility in studies of research methodology.
• Influence of Measurement Scales on Data Precision : Measurement scales correlate with variations in reported data precision in research methodology experiments.
• Relationship Between Experimental Controls and Internal Validity : Experimental controls are associated with changes in internal validity outcomes in research methodology studies.
• Impact of Randomization on Group Equivalence : Randomization relates to differences in reported group equivalence in research methodology research.
• Connection Between Qualitative Data Analysis Methods and Data Richness : Qualitative data analysis methods are related to variations in reported data richness in studies of research methodology.
• Effects of Hypothesis Formulation on Research Focus : Hypothesis formulation correlates with changes in reported research focus in research methodology experiments.

These non-directional hypothesis statement examples offer insights into the diverse array of relationships explored in psychology, research, and research methodology studies, fostering empirical discovery and contributing to the advancement of knowledge across various fields.

Difference between Directional & Non-Directional Hypothesis

Directional and non-directional hypotheses are distinct approaches used in formulating hypotheses for research studies. Understanding the differences between them is essential for researchers to choose the appropriate type of causal hypothesis based on their study’s goals and prior knowledge.

• Direction: Directional hypotheses predict a specific relationship direction, while non-directional hypotheses do not specify a direction.
• Specificity: Directional hypotheses are more specific, while non-directional hypotheses are more general.
• Flexibility: Non-directional hypotheses allow for open-ended exploration, while directional hypotheses focus on confirming or refuting specific expectations.

How to Write a Non-Directional Hypothesis Statement – Step by Step Guide

• Identify Variables: Clearly define the variables you’re investigating—usually, an independent variable (the one manipulated) and a dependent variable (the one measured).
• Indicate Relationship: State that a relationship exists between the variables without predicting a specific direction.
• Use General Language: Craft the statement in a way that encompasses various possible outcomes.
• Avoid Biased Language: Do not include words that suggest a stronger effect or specific outcome for either variable.
• Connect to Research: If applicable, link the hypothesis to existing research or theories that justify exploring the relationship.

Tips for Writing a Non-Directional Hypothesis

• Start with Inquiry: Frame your hypothesis as an answer to a research question.
• Embrace Openness: Non-directional hypotheses are ideal when no strong expectation exists.
• Be Succinct: Keep the hypothesis statement concise and clear.
• Stay Neutral: Avoid implying that one variable will have a stronger impact.
• Allow Exploration: Leave room for various potential outcomes without preconceived notions.
• Tailor to Context: Ensure the hypothesis aligns with your research context and goals.

Non-directional hypotheses are particularly useful in exploratory research, where researchers aim to discover relationships without imposing specific expectations. They allow for unbiased investigation and the potential to uncover unexpected patterns or connections.

Remember that whether you choose a directional or non-directional hypothesis, both play critical roles in shaping the research process, guiding study design, data collection, and analysis. The choice depends on the research’s nature, goals, and existing knowledge in the field.  You may also be interested in our  science hypothesis .

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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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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

Directional and non-directional hypothesis: A Comprehensive Guide

Karolina Konopka

Customer support manager

In the world of research and statistical analysis, hypotheses play a crucial role in formulating and testing scientific claims. Understanding the differences between directional and non-directional hypothesis is essential for designing sound experiments and drawing accurate conclusions. Whether you’re a student, researcher, or simply curious about the foundations of hypothesis testing, this guide will equip you with the knowledge and tools to navigate this fundamental aspect of scientific inquiry.

Understanding Directional Hypothesis

Understanding directional hypotheses is crucial for conducting hypothesis-driven research, as they guide the selection of appropriate statistical tests and aid in the interpretation of results. By incorporating directional hypotheses, researchers can make more precise predictions, contribute to scientific knowledge, and advance their fields of study.

Definition of directional hypothesis

Directional hypotheses, also known as one-tailed hypotheses, are statements in research that make specific predictions about the direction of a relationship or difference between variables. Unlike non-directional hypotheses, which simply state that there is a relationship or difference without specifying its direction, directional hypotheses provide a focused and precise expectation.

A directional hypothesis predicts either a positive or negative relationship between variables or predicts that one group will perform better than another. It asserts a specific direction of effect or outcome. For example, a directional hypothesis could state that “increased exposure to sunlight will lead to an improvement in mood” or “participants who receive the experimental treatment will exhibit higher levels of cognitive performance compared to the control group.”

Directional hypotheses are formulated based on existing theory, prior research, or logical reasoning, and they guide the researcher’s expectations and analysis. They allow for more targeted predictions and enable researchers to test specific hypotheses using appropriate statistical tests.

The role of directional hypothesis in research

Directional hypotheses also play a significant role in research surveys. Let’s explore their role specifically in the context of survey research:

• Objective-driven surveys : Directional hypotheses help align survey research with specific objectives. By formulating directional hypotheses, researchers can focus on gathering data that directly addresses the predicted relationship or difference between variables of interest.
• Question design and measurement : Directional hypotheses guide the design of survey question types and the selection of appropriate measurement scales. They ensure that the questions are tailored to capture the specific aspects related to the predicted direction, enabling researchers to obtain more targeted and relevant data from survey respondents.
• Data analysis and interpretation : Directional hypotheses assist in data analysis by directing researchers towards appropriate statistical tests and methods. Researchers can analyze the survey data to specifically test the predicted relationship or difference, enhancing the accuracy and reliability of their findings. The results can then be interpreted within the context of the directional hypothesis, providing more meaningful insights.
• Practical implications and decision-making : Directional hypotheses in surveys often have practical implications. When the predicted relationship or difference is confirmed, it informs decision-making processes, program development, or interventions. The survey findings based on directional hypotheses can guide organizations, policymakers, or practitioners in making informed choices to achieve desired outcomes.
• Replication and further research : Directional hypotheses in survey research contribute to the replication and extension of studies. Researchers can replicate the survey with different populations or contexts to assess the generalizability of the predicted relationships. Furthermore, if the directional hypothesis is supported, it encourages further research to explore underlying mechanisms or boundary conditions.

By incorporating directional hypotheses in survey research, researchers can align their objectives, design effective surveys, conduct focused data analysis, and derive practical insights. They provide a framework for organizing survey research and contribute to the accumulation of knowledge in the field.

Examples of research questions for directional hypothesis

Here are some examples of research questions that lend themselves to directional hypotheses:

• Does increased daily exercise lead to a decrease in body weight among sedentary adults?
• Is there a positive relationship between study hours and academic performance among college students?
• Does exposure to violent video games result in an increase in aggressive behavior among adolescents?
• Does the implementation of a mindfulness-based intervention lead to a reduction in stress levels among working professionals?
• Is there a difference in customer satisfaction between Product A and Product B, with Product A expected to have higher satisfaction ratings?
• Does the use of social media influence self-esteem levels, with higher social media usage associated with lower self-esteem?
• Is there a negative relationship between job satisfaction and employee turnover, indicating that lower job satisfaction leads to higher turnover rates?
• Does the administration of a specific medication result in a decrease in symptoms among individuals with a particular medical condition?
• Does increased access to early childhood education lead to improved cognitive development in preschool-aged children?
• Is there a difference in purchase intention between advertisements with celebrity endorsements and advertisements without, with celebrity endorsements expected to have a higher impact?

These research questions generate specific predictions about the direction of the relationship or difference between variables and can be tested using appropriate research methods and statistical analyses.

Definition of non-directional hypothesis

Non-directional hypotheses, also known as two-tailed hypotheses, are statements in research that indicate the presence of a relationship or difference between variables without specifying the direction of the effect. Instead of making predictions about the specific direction of the relationship or difference, non-directional hypotheses simply state that there is an association or distinction between the variables of interest.

Non-directional hypotheses are often used when there is no prior theoretical basis or clear expectation about the direction of the relationship. They leave the possibility open for either a positive or negative relationship, or for both groups to differ in some way without specifying which group will perform better or worse.

Advantages and utility of non-directional hypothesis

Non-directional hypotheses in survey s offer several advantages and utilities, providing flexibility and comprehensive analysis of survey data. Here are some of the key advantages and utilities of using non-directional hypotheses in surveys:

• Exploration of Relationships : Non-directional hypotheses allow researchers to explore and examine relationships between variables without assuming a specific direction. This is particularly useful in surveys where the relationship between variables may not be well-known or there may be conflicting evidence regarding the direction of the effect.
• Flexibility in Question Design : With non-directional hypotheses, survey questions can be designed to measure the relationship between variables without being biased towards a particular outcome. This flexibility allows researchers to collect data and analyze the results more objectively.
• Open to Unexpected Findings : Non-directional hypotheses enable researchers to be open to unexpected or surprising findings in survey data. By not committing to a specific direction of the effect, researchers can identify and explore relationships that may not have been initially anticipated, leading to new insights and discoveries.
• Comprehensive Analysis : Non-directional hypotheses promote comprehensive analysis of survey data by considering the possibility of an effect in either direction. Researchers can assess the magnitude and significance of relationships without limiting their analysis to only one possible outcome.
• S tatistical Validity : Non-directional hypotheses in surveys allow for the use of two-tailed statistical tests, which provide a more conservative and robust assessment of significance. Two-tailed tests consider both positive and negative deviations from the null hypothesis, ensuring accurate and reliable statistical analysis of survey data.
• Exploratory Research : Non-directional hypotheses are particularly useful in exploratory research, where the goal is to gather initial insights and generate hypotheses. Surveys with non-directional hypotheses can help researchers explore various relationships and identify patterns that can guide further research or hypothesis development.

It is worth noting that the choice between directional and non-directional hypotheses in surveys depends on the research objectives, existing knowledge, and the specific variables being investigated. Researchers should carefully consider the advantages and limitations of each approach and select the one that aligns best with their research goals and survey design.

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• Operationalisation | A Guide with Examples, Pros & Cons

Operationalisation | A Guide with Examples, Pros & Cons

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

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

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

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

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Table of contents

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

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

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

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

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

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

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There are three main steps for operationalisation:

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

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

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

There are two main concepts in your research question:

• Social media behaviour

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

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

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

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

Step 3: Select indicators for each of your variables

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

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

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

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

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

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

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

Objectivity

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

Reliability

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

Operational definitions of concepts can sometimes be problematic.

Underdetermination

Many concepts vary across different time periods and social settings.

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

Reductiveness

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

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

Lack of universality

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

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

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Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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

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

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

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Non-Directional Hypothesis

A non-directional hypothesis is a two-tailed hypothesis that does not predict the direction of the difference or relationship (e.g. girls and boys are different in terms of helpfulness).

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