operationalised directional hypothesis example

Directional Hypothesis: Definition and 10 Examples

Chris Drew (PhD)

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

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A directional hypothesis refers to a type of hypothesis used in statistical testing that predicts a particular direction of the expected relationship between two variables.

In simpler terms, a directional hypothesis is an educated, specific guess about the direction of an outcome—whether an increase, decrease, or a proclaimed difference in variable sets.

For example, in a study investigating the effects of sleep deprivation on cognitive performance, a directional hypothesis might state that as sleep deprivation (Independent Variable) increases, cognitive performance (Dependent Variable) decreases (Killgore, 2010). Such a hypothesis offers a clear, directional relationship whereby a specific increase or decrease is anticipated.

Global warming provides another notable example of a directional hypothesis. A researcher might hypothesize that as carbon dioxide (CO2) levels increase, global temperatures also increase (Thompson, 2010). In this instance, the hypothesis clearly articulates an upward trend for both variables. 

In any given circumstance, it’s imperative that a directional hypothesis is grounded on solid evidence. For instance, the CO2 and global temperature relationship is based on substantial scientific evidence, and not on a random guess or mere speculation (Florides & Christodoulides, 2009).

Directional vs Non-Directional vs Null Hypotheses

A directional hypothesis is generally contrasted to a non-directional hypothesis. Here’s how they compare:

• Directional hypothesis: A directional hypothesis provides a perspective of the expected relationship between variables, predicting the direction of that relationship (either positive, negative, or a specific difference). 
• Non-directional hypothesis: A non-directional hypothesis denotes the possibility of a relationship between two variables ( the independent and dependent variables ), although this hypothesis does not venture a prediction as to the direction of this relationship (Ali & Bhaskar, 2016). For example, a non-directional hypothesis might state that there exists a relationship between a person’s diet (independent variable) and their mood (dependent variable), without indicating whether improvement in diet enhances mood positively or negatively. Overall, the choice between a directional or non-directional hypothesis depends on the known or anticipated link between the variables under consideration in research studies.

Another very important type of hypothesis that we need to know about is a null hypothesis :

• Null hypothesis : The null hypothesis stands as a universality—the hypothesis that there is no observed effect in the population under study, meaning there is no association between variables (or that the differences are down to chance). For instance, a null hypothesis could be constructed around the idea that changing diet (independent variable) has no discernible effect on a person’s mood (dependent variable) (Yan & Su, 2016). This proposition is the one that we aim to disprove in an experiment.

While directional and non-directional hypotheses involve some integrated expectations about the outcomes (either distinct direction or a vague relationship), a null hypothesis operates on the premise of negating such relationships or effects.

The null hypotheses is typically proposed to be negated or disproved by statistical tests, paving way for the acceptance of an alternate hypothesis (either directional or non-directional).

Directional Hypothesis Examples

1. exercise and heart health.

Research suggests that as regular physical exercise (independent variable) increases, the risk of heart disease (dependent variable) decreases (Jakicic, Davis, Rogers, King, Marcus, Helsel, Rickman, Wahed, Belle, 2016). In this example, a directional hypothesis anticipates that the more individuals maintain routine workouts, the lesser would be their odds of developing heart-related disorders. This assumption is based on the underlying fact that routine exercise can help reduce harmful cholesterol levels, regulate blood pressure, and bring about overall health benefits. Thus, a direction – a decrease in heart disease – is expected in relation with an increase in exercise. 

2. Screen Time and Sleep Quality

Another classic instance of a directional hypothesis can be seen in the relationship between the independent variable, screen time (especially before bed), and the dependent variable, sleep quality. This hypothesis predicts that as screen time before bed increases, sleep quality decreases (Chang, Aeschbach, Duffy, Czeisler, 2015). The reasoning behind this hypothesis is the disruptive effect of artificial light (especially blue light from screens) on melatonin production, a hormone needed to regulate sleep. As individuals spend more time exposed to screens before bed, it is predictably hypothesized that their sleep quality worsens. 

3. Job Satisfaction and Employee Turnover

A typical scenario in organizational behavior research posits that as job satisfaction (independent variable) increases, the rate of employee turnover (dependent variable) decreases (Cheng, Jiang, & Riley, 2017). This directional hypothesis emphasizes that an increased level of job satisfaction would lead to a reduced rate of employees leaving the company. The theoretical basis for this hypothesis is that satisfied employees often tend to be more committed to the organization and are less likely to seek employment elsewhere, thus reducing turnover rates.

4. Healthy Eating and Body Weight

Healthy eating, as the independent variable, is commonly thought to influence body weight, the dependent variable, in a positive way. For example, the hypothesis might state that as consumption of healthy foods increases, an individual’s body weight decreases (Framson, Kristal, Schenk, Littman, Zeliadt, & Benitez, 2009). This projection is based on the premise that healthier foods, such as fruits and vegetables, are generally lower in calories than junk food, assisting in weight management.

5. Sun Exposure and Skin Health

The association between sun exposure (independent variable) and skin health (dependent variable) allows for a definitive hypothesis declaring that as sun exposure increases, the risk of skin damage or skin cancer increases (Whiteman, Whiteman, & Green, 2001). The premise aligns with the understanding that overexposure to the sun’s ultraviolet rays can deteriorate skin health, leading to conditions like sunburn or, in extreme cases, skin cancer.

6. Study Hours and Academic Performance

A regularly assessed relationship in academia suggests that as the number of study hours (independent variable) rises, so too does academic performance (dependent variable) (Nonis, Hudson, Logan, Ford, 2013). The hypothesis proposes a positive correlation , with an increase in study time expected to contribute to enhanced academic outcomes.

7. Screen Time and Eye Strain

It’s commonly hypothesized that as screen time (independent variable) increases, the likelihood of experiencing eye strain (dependent variable) also increases (Sheppard & Wolffsohn, 2018). This is based on the idea that prolonged engagement with digital screens—computers, tablets, or mobile phones—can cause discomfort or fatigue in the eyes, attributing to symptoms of eye strain.

8. Physical Activity and Stress Levels

In the sphere of mental health, it’s often proposed that as physical activity (independent variable) increases, levels of stress (dependent variable) decrease (Stonerock, Hoffman, Smith, Blumenthal, 2015). Regular exercise is known to stimulate the production of endorphins, the body’s natural mood elevators, helping to alleviate stress.

9. Water Consumption and Kidney Health

A common health-related hypothesis might predict that as water consumption (independent variable) increases, the risk of kidney stones (dependent variable) decreases (Curhan, Willett, Knight, & Stampfer, 2004). Here, an increase in water intake is inferred to reduce the risk of kidney stones by diluting the substances that lead to stone formation.

10. Traffic Noise and Sleep Quality

In urban planning research, it’s often supposed that as traffic noise (independent variable) increases, sleep quality (dependent variable) decreases (Muzet, 2007). Increased noise levels, particularly during the night, can result in sleep disruptions, thus, leading to poor sleep quality.

11. Sugar Consumption and Dental Health

In the field of dental health, an example might be stating as one’s sugar consumption (independent variable) increases, dental health (dependent variable) decreases (Sheiham, & James, 2014). This stems from the fact that sugar is a major factor in tooth decay, and increased consumption of sugary foods or drinks leads to a decline in dental health due to the high likelihood of cavities.

See 15 More Examples of Hypotheses Here

A directional hypothesis plays a critical role in research, paving the way for specific predicted outcomes based on the relationship between two variables. These hypotheses clearly illuminate the expected direction—the increase or decrease—of an effect. From predicting the impacts of healthy eating on body weight to forecasting the influence of screen time on sleep quality, directional hypotheses allow for targeted and strategic examination of phenomena. In essence, directional hypotheses provide the crucial path for inquiry, shaping the trajectory of research studies and ultimately aiding in the generation of insightful, relevant findings.

Ali, S., & Bhaskar, S. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60 (9), 662-669. doi: https://doi.org/10.4103%2F0019-5049.190623  

Chang, A. M., Aeschbach, D., Duffy, J. F., & Czeisler, C. A. (2015). Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proceeding of the National Academy of Sciences, 112 (4), 1232-1237. doi: https://doi.org/10.1073/pnas.1418490112  

Cheng, G. H. L., Jiang, D., & Riley, J. H. (2017). Organizational commitment and intrinsic motivation of regular and contractual primary school teachers in China. New Psychology, 19 (3), 316-326. Doi: https://doi.org/10.4103%2F2249-4863.184631  

Curhan, G. C., Willett, W. C., Knight, E. L., & Stampfer, M. J. (2004). Dietary factors and the risk of incident kidney stones in younger women: Nurses’ Health Study II. Archives of Internal Medicine, 164 (8), 885–891.

Florides, G. A., & Christodoulides, P. (2009). Global warming and carbon dioxide through sciences. Environment international , 35 (2), 390-401. doi: https://doi.org/10.1016/j.envint.2008.07.007

Framson, C., Kristal, A. R., Schenk, J. M., Littman, A. J., Zeliadt, S., & Benitez, D. (2009). Development and validation of the mindful eating questionnaire. Journal of the American Dietetic Association, 109 (8), 1439-1444. doi: https://doi.org/10.1016/j.jada.2009.05.006  

Jakicic, J. M., Davis, K. K., Rogers, R. J., King, W. C., Marcus, M. D., Helsel, D., … & Belle, S. H. (2016). Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial. JAMA, 316 (11), 1161-1171.

Khan, S., & Iqbal, N. (2013). Study of the relationship between study habits and academic achievement of students: A case of SPSS model. Higher Education Studies, 3 (1), 14-26.

Killgore, W. D. (2010). Effects of sleep deprivation on cognition. Progress in brain research , 185 , 105-129. doi: https://doi.org/10.1016/B978-0-444-53702-7.00007-5  

Marczinski, C. A., & Fillmore, M. T. (2014). Dissociative antagonistic effects of caffeine on alcohol-induced impairment of behavioral control. Experimental and Clinical Psychopharmacology, 22 (4), 298–311. doi: https://psycnet.apa.org/doi/10.1037/1064-1297.11.3.228  

Muzet, A. (2007). Environmental Noise, Sleep and Health. Sleep Medicine Reviews, 11 (2), 135-142. doi: https://doi.org/10.1016/j.smrv.2006.09.001  

Nonis, S. A., Hudson, G. I., Logan, L. B., & Ford, C. W. (2013). Influence of perceived control over time on college students’ stress and stress-related outcomes. Research in Higher Education, 54 (5), 536-552. doi: https://doi.org/10.1023/A:1018753706925  

Sheiham, A., & James, W. P. (2014). A new understanding of the relationship between sugars, dental caries and fluoride use: implications for limits on sugars consumption. Public health nutrition, 17 (10), 2176-2184. Doi: https://doi.org/10.1017/S136898001400113X  

Sheppard, A. L., & Wolffsohn, J. S. (2018). Digital eye strain: prevalence, measurement and amelioration. BMJ open ophthalmology , 3 (1), e000146. doi: http://dx.doi.org/10.1136/bmjophth-2018-000146

Stonerock, G. L., Hoffman, B. M., Smith, P. J., & Blumenthal, J. A. (2015). Exercise as Treatment for Anxiety: Systematic Review and Analysis. Annals of Behavioral Medicine, 49 (4), 542–556. doi: https://doi.org/10.1007/s12160-014-9685-9  

Thompson, L. G. (2010). Climate change: The evidence and our options. The Behavior Analyst , 33 , 153-170. Doi: https://doi.org/10.1007/BF03392211  

Whiteman, D. C., Whiteman, C. A., & Green, A. C. (2001). Childhood sun exposure as a risk factor for melanoma: a systematic review of epidemiologic studies. Cancer Causes & Control, 12 (1), 69-82. doi: https://doi.org/10.1023/A:1008980919928

Yan, X., & Su, X. (2009). Linear regression analysis: theory and computing . New Jersey: World Scientific.

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Aims, Hypotheses & Sampling

Aims and hypotheses.

Each research study specifies aims and hypotheses. An aim is what it is trying to achieve, while a hypothesis is a specific prediction of what it will find.

• A researcher usually states the aim of their study.
• This involves saying what they are trying to achieve, or what the point of their study is.
• For example, a researcher may state that they aim to find out the effect of caffeine on sleep.

• A hypothesis is different from an aim.
• It involves making a specific prediction of what will be found, expressed in terms of a change in variables.
• For example, a researcher may state a hypothesis that consuming 200mg of caffeine will increase the length of time it takes people to fall asleep compared to having no caffeine.

Experimental vs alternative

• In an experiment, the researcher’s main hypothesis is known as an experimental hypothesis. It is also referred to as H1.
• In a non-experimental study, it is typically called an alternative hypothesis.

Null hypothesis

• Most studies also clearly state a null hypothesis (sometimes referred to as H0).
• This is a statement of what will be found if the experimental/alternative hypothesis is not supported by the results.

Directional hypothesis

• A directional or one-tailed hypothesis predicts the direction in which change is expected to occur.
• It is used when previous research has suggested the direction of change.
• e.g.Alcohol increases reaction times.

Non-directional hypothesis

• A non-directional or two-tailed hypothesis simply predicts change and does not specify direction.

It is used when there is no previous research. It is non-specific and uses words like: effect, change, difference etc.

• e.g. Alcohol will affect reaction times.

NB. All variables must be fully operationalised e.g. alcohol measured in units; reaction times measured in seconds.

Populations and Samples

Any research study needs a group of participants. These are called the sample, and they are drawn from a wider group called the target population.

• Sampling means selecting a group of participants who will take part in the study.

Populations

• A sample always comes from a broader population.
• This does not necessarily mean the whole population of a country, but could be a specific group.
• For example, all sixth-form school pupils in the country is an example of a target population, and a selection of 50 sixth-form school pupils is an example of a sample.

Representation

• A key aspect of sampling is that the sample should be representative of the target population.
• This means that they should have similar characteristics.
• Studying a representative sample allows the researcher to generalise the findings to the target population. This is a key aim of any research.

Sampling Techniques

There are multiple ways of obtaining a sample for a research study. Four major sampling techniques are opportunity sampling, systematic sampling, volunteer sampling and stratified sampling.

Opportunity sampling

• Examples of opportunity sampling include conducting research on the researcher’s own friends, classmates or students.
• Opportunity sampling is very prone to bias because the most easily available participants may not be representative of the target population.

Systematic sampling

• Examples of systematic sampling include picking every 50th person that walks along a corridor, or every 100th name in the phone book, or posting a questionnaire to every 10th house in a village.
• Systematic sampling reduces researcher bias, but some potential participants may be excluded e.g. because they are not in the phone book or do not live in a house. This leads to bias.

Volunteer sampling

• Eg. posting an advert on a school noticeboard, asking people to complete your online survey.
• One source of bias with volunteer sampling is that certain personalities are more likely than others to come forward and help the research. This may have affected classic research studies such as Milgram’s obedience research and Zimbardo’s Stanford Prison experiment.

Stratified sampling

• An example of stratified sampling would involve selecting people from different ethnic groups to create a sample with the same proportions as exist in the target population.
• This reduces bias by making the sample more representative, but before stratification can occur, participants must already have been selected using another sampling technique.

Random sampling

• In random sampling like the National Lottery, all members of the target population must stand an equal chance of being selected.
• E.g. putting the names of every member of the target population into a hat and pulling a sample out (without looking!).

Evaluation of random sampling

• If the sample is large enough, the rules of probability suggest that it should be representative.
• Participants may not be willing or able to take part in the research.
• Sample could still be biased in terms of variables such as gender, age, ethnicity etc.

1 Social Influence

1.1 Social Influence

1.1.1 Conformity

1.1.2 Asch (1951)

1.1.3 Sherif (1935)

1.1.4 Conformity to Social Roles

1.1.5 BBC Prison Study

1.1.6 End of Topic Test - Conformity

1.1.7 Obedience

1.1.8 Analysing Milgram's Experiment

1.1.9 Agentic State & Legitimate Authority

1.1.10 Variables of Obedience

1.1.11 Resistance to Social Influence

1.1.12 Minority Influence & Social Change

1.1.13 Minority Influence & Social Impact Theory

1.1.14 End of Topic Test - Social Influences

1.1.15 Exam-Style Question - Conformity

1.1.16 Top Grade AO2/AO3 - Social Influence

2.1.1 Multi-Store Model of Memory

2.1.2 Short-Term vs Long-Term Memory

2.1.3 Long-Term Memory

2.1.4 Support for the Multi-Store Model of Memory

2.1.5 Duration Studies

2.1.6 Capacity Studies

2.1.7 Coding Studies

2.1.8 The Working Memory Model

2.1.9 The Working Memory Model 2

2.1.10 Support for the Working Memory Model

2.1.11 Explanations for Forgetting

2.1.12 Studies on Interference

2.1.13 Cue-Dependent Forgetting

2.1.14 Eye Witness Testimony - Loftus & Palmer

2.1.15 Eye Witness Testimony Loftus

2.1.16 Eyewitness Testimony - Post-Event Discussion

2.1.17 Eyewitness Testimony - Age & Misleading Questions

2.1.18 Cognitive Interview

2.1.19 Cognitive Interview - Geiselman & Fisher

2.1.20 End of Topic Test - Memory

2.1.21 Exam-Style Question - Memory

2.1.22 A-A* (AO3/4) - Memory

3 Attachment

3.1 Attachment

3.1.1 Caregiver-Infant Interaction

3.1.2 Condon & Sander (1974)

3.1.3 Schaffer & Emerson (1964)

3.1.4 Multiple Attachments

3.1.5 Studies on the Role of the Father

3.1.6 Animal Studies of Attachment

3.1.7 Explanations of Attachment

3.1.8 Attachment Types - Strange Situation

3.1.9 Cultural Differences in Attachment

3.1.10 Disruption of Attachment

3.1.11 Disruption of Attachment - Privation

3.1.12 Overcoming the Effects of Disruption

3.1.13 The Effects of Institutionalisation

3.1.14 Early Attachment

3.1.15 Critical Period of Attachment

3.1.16 End of Topic Test - Attachment

3.1.17 Exam-Style Question - Attachment

3.1.18 Top Grade AO2/AO3 - Attachment

4 Psychopathology

4.1 Psychopathology

4.1.1 Definitions of Abnormality

4.1.2 Definitions of Abnormality 2

4.1.3 Phobias, Depression & OCD

4.1.4 Phobias: Behavioural Approach

4.1.5 Evaluation of Behavioural Explanations of Phobias

4.1.6 Depression: Cognitive Approach

4.1.7 OCD: Biological Approach

4.1.8 Evidence for the Biological Approach

4.1.9 End of Topic Test - Psychopathy

4.1.10 Exam-Style Question - Phobias

4.1.11 Top Grade AO2/AO3 - Psychopathology

5 Approaches in Psychology

5.1 Approaches in Psychology

5.1.1 Psychology as a Science

5.1.2 Origins of Psychology

5.1.3 Reductionism & Problems with Introspection

5.1.4 The Behaviourist Approach - Classical Conditioning

5.1.5 Pavlov's Experiment

5.1.6 Little Albert Study

5.1.7 The Behaviourist Approach - Operant Conditioning

5.1.8 Social Learning Theory

5.1.9 The Cognitive Approach 1

5.1.10 The Cognitive Approach 2

5.1.11 The Biological Approach

5.1.12 Gottesman (1991) - Twin Studies

5.1.13 Brain Scanning

5.1.14 Structure of Personality & Little Hans

5.1.15 The Psychodynamic Approach (A2 only)

5.1.16 Humanistic Psychology (A2 only)

5.1.17 Aronoff (1957) (A2 Only)

5.1.18 Rogers' Client-Centred Therapy (A2 only)

5.1.19 End of Topic Test - Approaches in Psychology

5.1.20 Exam-Style Question - Approaches in Psychology

5.2 Comparison of Approaches (A2 only)

5.2.1 Psychodynamic Approach

5.2.2 Cognitive Approach

5.2.3 Biological Approach

5.2.4 Behavioural Approach

5.2.5 End of Topic Test - Comparison of Approaches

6 Biopsychology

6.1 Biopsychology

6.1.1 Nervous System Divisions

6.1.2 Neuron Structure & Function

6.1.3 Neurotransmitters

6.1.4 Endocrine System Function

6.1.5 Fight or Flight Response

6.1.6 The Brain (A2 only)

6.1.7 Localisation of Brain Function (A2 only)

6.1.8 Studying the Brain (A2 only)

6.1.9 CIMT (A2 Only) & Postmortem Examinations

6.1.10 Biological Rhythms (A2 only)

6.1.11 Studies on Biological Rhythms (A2 Only)

6.1.12 End of Topic Test - Biopsychology

6.1.13 Top Grade AO2/AO3 - Biopsychology

7 Research Methods

7.1 Research Methods

7.1.1 Experimental Method

7.1.2 Observational Techniques

7.1.3 Covert, Overt & Controlled Observation

7.1.4 Self-Report Techniques

7.1.5 Correlations

7.1.6 Exam-Style Question - Research Methods

7.1.7 End of Topic Test - Research Methods

7.2 Scientific Processes

7.2.1 Aims, Hypotheses & Sampling

7.2.2 Pilot Studies & Design

7.2.3 Questionnaires

7.2.4 Variables & Control

7.2.5 Demand Characteristics & Investigator Effects

7.2.6 Ethics

7.2.7 Limitations of Ethical Guidelines

7.2.8 Consent & Protection from Harm Studies

7.2.9 Peer Review & The Economy

7.2.10 Validity (A2 only)

7.2.11 Reliability (A2 only)

7.2.12 Features of Science (A2 only)

7.2.13 Paradigms & Falsifiability (A2 only)

7.2.14 Scientific Report (A2 only)

7.2.15 Scientific Report 2 (A2 only)

7.2.16 End of Topic Test - Scientific Processes

7.3 Data Handling & Analysis

7.3.1 Types of Data

7.3.2 Descriptive Statistics

7.3.3 Correlation

7.3.4 Evaluation of Descriptive Statistics

7.3.5 Presentation & Display of Data

7.3.6 Levels of Measurement (A2 only)

7.3.7 Content Analysis (A2 only)

7.3.8 Case Studies (A2 only)

7.3.9 Thematic Analysis (A2 only)

7.3.10 End of Topic Test - Data Handling & Analysis

7.4 Inferential Testing

7.4.1 Introduction to Inferential Testing

7.4.2 Sign Test

7.4.3 Piaget Conservation Experiment

7.4.4 Non-Parametric Tests

8 Issues & Debates in Psychology (A2 only)

8.1 Issues & Debates in Psychology (A2 only)

8.1.1 Culture Bias

8.1.2 Sub-Culture Bias

8.1.3 Gender Bias

8.1.4 Ethnocentrism

8.1.5 Cross Cultural Research

8.1.6 Free Will & Determinism

8.1.7 Comparison of Free Will & Determinism

8.1.8 Reductionism & Holism

8.1.9 Reductionist & Holistic Approaches

8.1.10 Nature-Nurture Debate

8.1.11 Interactionist Approach

8.1.12 Nature-Nurture Methods

8.1.13 Nature-Nurture Approaches

8.1.14 Idiographic & Nomothetic Approaches

8.1.15 Socially Sensitive Research

8.1.16 End of Topic Test - Issues and Debates

9 Option 1: Relationships (A2 only)

9.1 Relationships: Sexual Relationships (A2 only)

9.1.1 Sexual Selection & Human Reproductive Behaviour

9.1.2 Intersexual & Intrasexual Selection

9.1.3 Evaluation of Sexual Selection Behaviour

9.1.4 Factors Affecting Attraction: Self-Disclosure

9.1.5 Evaluation of Self-Disclosure Theory

9.1.6 Self Disclosure in Computer Communication

9.1.7 Factors Affecting Attraction: Physical Attributes

9.1.8 Matching Hypothesis Studies

9.1.9 Factors Affecting Physical Attraction

9.1.10 Factors Affecting Attraction: Filter Theory 1

9.1.11 Factors Affecting Attraction: Filter Theory 2

9.1.12 Evaluation of Filter Theory

9.1.13 End of Topic Test - Sexual Relationships

9.2 Relationships: Romantic Relationships (A2 only)

9.2.1 Social Exchange Theory

9.2.2 Evaluation of Social Exchange Theory

9.2.3 Equity Theory

9.2.4 Evaluation of Equity Theory

9.2.5 Rusbult’s Investment Model

9.2.6 Evaluation of Rusbult's Investment Model

9.2.7 Relationship Breakdown

9.2.8 Studies on Relationship Breakdown

9.2.9 Evaluation of Relationship Breakdown

9.2.10 End of Topic Test - Romantic relationships

9.3 Relationships: Virtual & Parasocial (A2 only)

9.3.1 Virtual Relationships in Social Media

9.3.2 Evaluation of Reduced Cues & Hyperpersonal

9.3.3 Parasocial Relationships

9.3.4 Attachment Theory & Parasocial Relationships

9.3.5 Evaluation of Parasocial Relationship Theories

9.3.6 End of Topic Test - Virtual & Parasocial Realtions

10 Option 1: Gender (A2 only)

10.1 Gender (A2 only)

10.1.1 Sex, Gender & Androgyny

10.1.2 Gender Identity Disorder

10.1.3 Biological & Social Explanations of GID

10.1.4 Biological Influences on Gender

10.1.5 Effects of Hormones on Gender

10.1.6 End of Topic Test - Gender 1

10.1.7 Kohlberg’s Theory of Gender Constancy

10.1.8 Evaluation of Kohlberg's Theory

10.1.9 Gender Schema Theory

10.1.10 Psychodynamic Approach to Gender Development 1

10.1.11 Psychodynamic Approach to Gender Development 2

10.1.12 Social Approach to Gender Development

10.1.13 Criticisms of Social Theory

10.1.14 End of Topic Test - Gender 2

10.1.15 Media Influence on Gender Development

10.1.16 Cross Cultural Research

10.1.17 Childcare & Gender Roles

10.1.18 End of Topic Test - Gender 3

11 Option 1: Cognition & Development (A2 only)

11.1 Cognition & Development (A2 only)

11.1.1 Piaget’s Theory of Cognitive Development 1

11.1.2 Piaget's Theory of Cognitive Development 2

11.1.3 Schema Accommodation Assimilation & Equilibration

11.1.4 Piaget & Inhelder’s Three Mountains Task (1956)

11.1.5 Conservation & Class Inclusion

11.1.6 Evaluation of Piaget

11.1.7 End of Topic Test - Cognition & Development 1

11.1.8 Vygotsky

11.1.9 Evaluation of Vygotsky

11.1.10 Baillargeon

11.1.11 Baillargeon's studies

11.1.12 Evaluation of Baillargeon

11.1.13 End of Topic Test - Cognition & Development 2

11.1.14 Sense of Self & Theory of Mind

11.1.15 Baron-Cohen Studies

11.1.16 Selman’s Five Levels of Perspective Taking

11.1.17 Biological Basis of Social Cognition

11.1.18 Evaluation of Biological Basis of Social Cognition

11.1.19 Important Issues in Social Neuroscience

11.1.20 End of Topic Test - Cognition & Development 3

11.1.21 Top Grade AO2/AO3 - Cognition & Development

12 Option 2: Schizophrenia (A2 only)

12.1 Schizophrenia: Diagnosis (A2 only)

12.1.1 Classification & Diagnosis

12.1.2 Reliability & Validity of Diagnosis

12.1.3 Gender & Cultural Bias

12.1.4 Pinto (2017) & Copeland (1971)

12.1.5 End of Topic Test - Scizophrenia Diagnosis

12.2 Schizophrenia: Treatment (A2 only)

12.2.1 Family-Based Psychological Explanations

12.2.2 Evaluation of Family-Based Explanations

12.2.3 Cognitive Explanations

12.2.4 Drug Therapies

12.2.5 Evaluation of Drug Therapies

12.2.6 Biological Explanations for Schizophrenia

12.2.7 Dopamine Hypothesis

12.2.8 End of Topic Test - Schizoprenia Treatment 1

12.2.9 Psychological Therapies 1

12.2.10 Psychological Therapies 2

12.2.11 Evaluation of Psychological Therapies

12.2.12 Interactionist Approach - Diathesis-Stress Model

12.2.13 Interactionist Approach - Triggers & Treatment

12.2.14 Evaluation of the Interactionist Approach

12.2.15 End of Topic Test - Scizophrenia Treatments 2

13 Option 2: Eating Behaviour (A2 only)

13.1 Eating Behaviour (A2 only)

13.1.1 Explanations for Food Preferences

13.1.2 Birch et al (1987) & Lowe et al (2004)

13.1.3 Control of Eating Behaviours

13.1.4 Control of Eating Behaviour: Leptin

13.1.5 Biological Explanations for Anorexia Nervosa

13.1.6 Psychological Explanations: Family Systems Theory

13.1.7 Psychological Explanations: Social Learning Theory

13.1.8 Psychological Explanations: Cognitive Theory

13.1.9 Biological Explanations for Obesity

13.1.10 Biological Explanations: Studies

13.1.11 Psychological Explanations for Obesity

13.1.12 Psychological Explanations: Studies

13.1.13 End of Topic Test - Eating Behaviour

14 Option 2: Stress (A2 only)

14.1 Stress (A2 only)

14.1.1 Physiology of Stress

14.1.2 Role of Stress in Illness

14.1.3 Role of Stress in Illness: Studies

14.1.4 Social Readjustment Rating Scales

14.1.5 Hassles & Uplifts Scales

14.1.6 Stress, Workload & Control

14.1.7 Stress Level Studies

14.1.8 End of Topic Test - Stress 1

14.1.9 Physiological Measures of Stress

14.1.10 Individual Differences

14.1.11 Stress & Gender

14.1.12 Drug Therapy & Biofeedback for Stress

14.1.13 Stress Inoculation Therapy

14.1.14 Social Support & Stress

14.1.15 End of Topic Test - Stress 2

15 Option 3: Aggression (A2 only)

15.1 Aggression: Physiological (A2 only)

15.1.1 Neural Mechanisms

15.1.2 Serotonin

15.1.3 Hormonal Mechanisms

15.1.4 Genetic Factors

15.1.5 Genetic Factors 2

15.1.6 End of Topic Test - Aggression: Physiological 1

15.1.7 Ethological Explanation

15.1.8 Innate Releasing Mechanisms & Fixed Action Pattern

15.1.9 Evolutionary Explanations

15.1.10 Buss et al (1992) - Sex Differences in Jealousy

15.1.11 Evaluation of Evolutionary Explanations

15.1.12 End of Topic Test - Aggression: Physiological 2

15.2 Aggression: Social Psychological (A2 only)

15.2.1 Social Psychological Explanation

15.2.2 Buss (1963) - Frustration/Aggression

15.2.3 Social Psychological Explanation 2

15.2.4 Social Learning Theory (SLT) 1

15.2.5 Social Learning Theory (SLT) 2

15.2.6 Limitations of Social Learning Theory (SLT)

15.2.7 Deindividuation

15.2.8 Deindividuation 2

15.2.9 Deindividuation - Diener et al (1976)

15.2.10 End of Topic Test - Aggression: Social Psychology

15.2.11 Institutional Aggression: Prisons

15.2.12 Evaluation of Dispositional & Situational

15.2.13 Influence of Computer Games

15.2.14 Influence of Television

15.2.15 Evaluation of Studies on Media

15.2.16 Desensitisation & Disinhibition

15.2.17 Cognitive Priming

15.2.18 End of Topic Test - Aggression: Social Psychology

16 Option 3: Forensic Psychology (A2 only)

16.1 Forensic Psychology (A2 only)

16.1.1 Defining Crime

16.1.2 Measuring Crime

16.1.3 Offender Profiling

16.1.4 Evaluation of Offender Profiling

16.1.5 John Duffy Case Study

16.1.6 Biological Explanations 1

16.1.7 Biological Explanations 2

16.1.8 Evaluation of the Biological Explanation

16.1.9 Cognitive Explanations

16.1.10 Moral Reasoning

16.1.11 Psychodynamic Explanation 1

16.1.12 Psychodynamic Explanation 2

16.1.13 End of Topic Test - Forensic Psychology 1

16.1.14 Differential Association Theory

16.1.15 Custodial Sentencing

16.1.16 Effects of Prison

16.1.17 Evaluation of the Effects of Prison

16.1.18 Recidivism

16.1.19 Behavioural Treatments & Therapies

16.1.20 Effectiveness of Behavioural Treatments

16.1.21 Restorative Justice

16.1.22 End of Topic Test - Forensic Psychology 2

17 Option 3: Addiction (A2 only)

17.1 Addiction (A2 only)

17.1.1 Definition

17.1.2 Brain Neurochemistry Explanation

17.1.3 Learning Theory Explanation

17.1.4 Evaluation of a Learning Theory Explanation

17.1.5 Cognitive Bias

17.1.6 Griffiths on Cognitive Bias

17.1.7 Evaluation of Cognitive Theory (A2 only)

17.1.8 End of Topic Test - Addiction 1

17.1.9 Gambling Addiction & Learning Theory

17.1.10 Social Influences on Addiction 1

17.1.11 Social Influences on Addiction 2

17.1.12 Personal Influences on Addiction

17.1.13 Genetic Explanations of Addiction

17.1.14 End of Topic Test - Addiction 2

17.2 Treating Addiction (A2 only)

17.2.1 Drug Therapy

17.2.2 Behavioural Interventions

17.2.3 Cognitive Behavioural Therapy

17.2.4 Theory of Reasoned Action

17.2.5 Theory of Planned Behaviour

17.2.6 Six Stage Model of Behaviour Change

17.2.7 End of Topic Test - Treating Addiction

Jump to other topics

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End of Topic Test - Research Methods

Pilot Studies & Design

9990 Psychology AO2 Exercise 1 Activity 1

Topic outline.

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

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

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

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

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

Directional and non-directional hypothesis: A Comprehensive Guide

Karolina Konopka

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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’.

Prevent plagiarism, run a free check.

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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Pritha Bhandari

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

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Updated June 2020

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

Null and Alternative Hypotheses

Null hypothesis (h0).

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

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

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

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

The Alternative Hypothesis (H1)

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

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

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

Read more: 

Operational Definitions

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

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

A Fictional Drug Trial

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

Operationalized (as if for an IB Psych IA):

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

A Fictional Study on Body Image*

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

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

A fictional study on weight training.

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

One vs. Two Tailed

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

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

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

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

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

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

Points to Remember

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

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

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

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.

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Directional Hypothesis

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

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• Operationalization | A Guide with Examples, Pros & Cons

Operationalization | A Guide with Examples, Pros & Cons

Published on May 6, 2022 by Pritha Bhandari . Revised on June 22, 2023.

Operationalization 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 operationalization, 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 behavioral incidents of avoidance of crowded places
• intensity of physical anxiety symptoms in social situations

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Table of contents

Why operationalization matters, how to operationalize concepts, strengths of operationalization, limitations of operationalization, other interesting articles, frequently asked questions about operationalization.

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

Without transparent and specific operational definitions, researchers may measure irrelevant concepts or inconsistently apply methods. Operationalization reduces subjectivity, minimizes the potential for research bias , 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 behavioral avoidance of crowded places. This means that your results are context-specific, and may not generalize to different real-life settings.

Generally, abstract concepts can be operationalized 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 operationalizations 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 3 main steps for operationalization:

• 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.

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 behavior

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 (H a or H 1 ): Lower quality of sleep is related to higher night-time social media use in teenagers.
• Null hypothesis (H 0 ): There is no relation between quality of sleep and night-time social media use in teenagers.

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 (e.g., Likert 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 operationalizing 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 operationalization may have affected your results or interpretations in the discussion section .

Operationalization 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 standardized approach for collecting data leaves little room for subjective or biased personal interpretations of observations .

Reliability

A good operationalization can be used consistently by other researchers (high replicability ). 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 operationalizations help preserve real-life experiences, but make it hard to compare studies if the measures differ significantly.

For example, corruption can be operationalized 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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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize 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 operationalization .

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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• Formulation of Hypothesis

Children who spend more time playing outside are more likely to be imaginative. What do you think this statement is an example of in terms of scientific research ? If you guessed a hypothesis, then you'd be correct. The formulation of hypotheses is a fundamental step in psychology research.

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What type of hypothesis matches the following definition. A hypothesis that states that the IV will influence the DV, and states how it will influence the DV. 

Which type of hypothesis is also known as a two-tailed hypothesis? 

What type of hypothesis matches the following definition. A predictive statement that researchers use when it is thought that the IV will not influence the DV.

What type of hypothesis is the following example. There will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

Is the following example a falsifiable hypothesis, "leprechauns always find the pot of gold at the end of the rainbow".

What type of hypothesis is the following example. There will be an observed difference in scores from a memory performance task between people with high- or low-depressive scores.

Is memory an operationalised variable that could be used in a good hypothesis? 

What type of hypothesis is the following example. People with low depressive scores will perform better in the memory performance task than people who score higher in depressive symptoms.  

What type of hypothesis matches the following definition. A hypothesis that states that the IV will influence the DV. But, the hypothesis does not state how the IV will influence the DV. 

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Jump to a key chapter

• First, we will discuss the importance of hypotheses in research.
• We will then cover formulating hypotheses in research, including the steps in the formulation of hypotheses in research methodology.
• We will provide examples of hypotheses in research throughout the explanation.
• Finally, we will delve into the different types of hypotheses in research.

What is a Hypothesis?

The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research . To be classed as scientific research , it must be observable, valid, reliable and follow a standardised procedure.

One of the important steps in scientific research is to formulate a hypothesis before starting the study procedure.

The hypothesis is a predictive, testable statement predicting the outcome and the results the researcher expects to find.

The hypothesis provides a summary of what direction, if any, is taken to investigate a theory.

In scientific research, there is a criterion that hypotheses need to be met to be regarded as acceptable.

If a hypothesis is disregarded, the research may be rejected by the community of psychology researchers.

Importance of Hypothesis in Research

The purpose of including hypotheses in psychology research is:

• To provide a summary of the research, how it will be investigated, and what is expected to be found.
• To provide an answer to the research question.

When carrying out research, researchers first investigate the research area they are interested in. From this, researchers are required to identify a gap in the literature.

Filling the gap essentially means finding what previous work has not been explained yet, investigated to a sufficient degree, or simply expanding or further investigating a theory if doubt exists.

The researcher then forms a research question that the researcher will attempt to answer in their study.

Remember, the hypothesis is a predictive statement of what is expected to happen when testing the research question.

The hypothesis can be used for later data analysis. This includes inferential tests such as hypothesis testing and identifying if statistical findings are significant.

Steps in the Formulation of Hypothesis in Research Methodology

Researchers must follow certain steps to formulate testable hypotheses when conducting research.

Overall, the researcher has to consider the direction of the research, i.e. will it be looking for a difference caused by independent variables ? Or will it be more concerned with the correlation between variables?

All researchers will likely complete the following.

• Investigating background research in the area of interest.
• Formulating or investigating a theory.
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works.

The above steps are used to formulate testable hypotheses.

The Formulation of Testable Hypotheses

The hypothesis is important in research as it indicates what and how a variable will be investigated.

The hypothesis essentially summarises what and how something will be investigated. This is important as it ensures that the researcher has carefully planned how the research will be done, as the researchers have to follow a set procedure to conduct research.

This is known as the scientific method.

Formulating Hypotheses in Research

When formulating hypotheses, things that researchers should consider are:

Types of Hypotheses in Research

Researchers can propose different types of hypotheses when carrying out research.

The following research scenario will be discussed to show examples of each type of hypothesis that the researchers could use. "A research team was investigating whether memory performance is affected by depression ."

The identified independent variable is the severity of depression scores, and the dependent variable is the scores from a memory performance task.

The null hypothesis predicts that the results will show no or little effect. The null hypothesis is a predictive statement that researchers use when it is thought that the IV will not influence the DV.

In this case, the null hypothesis would be there will be no difference in memory scores on the MMSE test of those who are diagnosed with depression and those who are not.

An alternative hypothesis is a predictive statement used when it is thought that the IV will influence the DV. The alternative hypothesis is also called a non-directional, two-tailed hypothesis, as it predicts the results can go either way, e.g. increase or decrease.

The example in this scenario is there will be an observed difference in scores from a memory performance task between people with high- or low-depressive scores.

The directional alternative hypothesis states how the IV will influence the DV, identifying a specific direction, such as if there will be an increase or decrease in the observed results.

The example in this scenario is people with low depressive scores will perform better in the memory performance task than people who score higher in depressive symptoms.

Example Hypothesis in Research

To summarise, let's look at an example of a straightforward hypothesis that indicates the relationship between two variables: the independent and the dependent.

If you stay up late, you will feel tired the following day; the more caffeine you drink, the harder you find it to fall asleep, or the more sunlight plants get, the taller they will grow.

Formulation of Hypothesis - Key Takeaways

• The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research. One of the important steps in scientific research is to create a hypothesis.
• The hypothesis is a predictive, testable statement concerning the outcome/results that the researcher expects to find.
• Hypotheses are needed in research to provide a summary of what the research is, how to investigate a theory and what is expected to be found, and to provide an answer to the research question so that the hypothesis can be used for later data analysis.
• There are requirements for the formulation of testable hypotheses. The hypotheses should identify and operationalise the IV and DV. In addition, they should describe the nature of the relationship between the IV and DV.
• There are different types of hypotheses: Null hypothesis, Alternative hypothesis (this is also known as the non-directional, two-tailed hypothesis), and Directional hypothesis (this is also known as the one-tailed hypothesis).

Flashcards in Formulation of Hypothesis 9

Directional, alternative hypothesis 

Alternative hypothesis 

Null hypothesis 

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Frequently Asked Questions about Formulation of Hypothesis

What are the 3 types of hypotheses?

The three types of hypotheses are:

• Null hypothesis 
• Alternative hypothesis 
• Directional/non-directional hypothesis 

What is an example of a hypothesis in psychology?

An example of a null hypothesis in psychology is, there will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

What are the steps in formulating a hypothesis?

All researchers will likely complete the following

• Investigating background research in the area of interest 
• Formulating or investigating a theory 
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works 

What is formulation of hypothesis in research? 

The formulation of a hypothesis in research is when the researcher formulates a predictive statement of what is expected to happen when testing the research question based on background research.

How to formulate  null and alternative hypothesis?

When formulating a null hypothesis the researcher would state a prediction that they expect to see no difference in the dependent variable when the independent variable changes or is manipulated. Whereas, when using an alternative hypothesis then it would be predicted that there will be a change in the dependent variable. The researcher can state in which direction they expect the results to go. 

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Operational Hypothesis

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

The Core Components of an Operational Hypothesis

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

The Variables

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

The Proposed Relationship

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

The Importance of Operationalizing Variables

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

Constructing an Operational Hypothesis

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

Steps to Construct an Operational Hypothesis

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

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

Evaluating the Strength of an Operational Hypothesis

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

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

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

Examples of Operational Hypotheses

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

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

Examples of Operational Hypothesis:

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

psychologyrocks

Cognitive: methods, designing and conducting experiments, inc. field, lab, independent and dependent variables, experimental and null hypotheses, directional (one-tailed) and non-directional (two-tailed) tests and hypotheses, experimental and research designs: repeated measures, independent groups and matched pairs, operationalisation of variables, extraneous variables and confounding variables, counterbalancing, randomisation and order effects, situational and participant variables, objectivity, reliability and validity (internal, predictive and ecological), experimenter effects, demand characteristics and control issues, quantitative data analysis, decision making and interpretation of inferential statistics, case study of brain-damaged patients, including henry molaison (hm) and the use of qualitative data, including strengths and weaknesses of the case study.

Assessment Questions:

• One research method commonly used in the Cognitive Approach is the laboratory experiment. Describe the main features of the laboratory experiment as a research method.

2. Jared decided to investigate how many household objects could be recalled by participants when rehearsal was prevented. He conducted a laboratory experiment where he displayed 25 household objects to the participants for one minute. Jared then asked the participants to count backwards from 20 before they attempted to recall as many household objects as they could.

a. State a fully operationalised directional (one-tailed) hypothesis for Jared’s experiment. (3)

b. Describe an appropriate participant design that Jared could use for this experiment. (3)

c. Stats Question – insert a file here AS Paper 1 2015; cant copy as its a table

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