hypothesis based investigation titles

Tim van Gelder

Epistemology is everywhere.

ACH , Analysis of Competing Hypotheses , Hypothesis mapping , Hypothesis Testing

Hypothesis Investigation – overview

Hypothesis investigation (short for “hypothesis-based investigation”) is simply attempting to determine “what is going on” in some situation by assessing various hypotheses or “guesses”.  The goal is to determine which hypothesis is most likely to be true. 

Hypothesis investigation can concern

• Factual situations – e.g. what are current Saudi oil reserves?
• Causes – e.g. what killed the dinosaurs?
• Functions or roles – e.g. what was the Antikythera mechanism for?
• Future events – e.g. how will the economy be affected by Peak Oil?
• States of mind – e.g. what are the enemy planning to do?
• Perpetrators – e.g. Who murdered Professor Plum?

Most investigation is to some extent hypothesis-based.  The exception is situations where the outcome is pre-determined in some way (e.g., a political show trial) and the point of the investigation is simply to amass evidence supporting that determination. 

A related, though subtly different notion is that of “hypothesis driven investigation” (Rasiel, 1999), in which a single hypothesis is selected relatively early in the process, and most effort is then devoted to substantiating this hypothesis.   It is hypothesis-based investigation with all attention focused on one guess, at least while not forced to reject it and consider another. 

Hypothesis investigation is comprised of three main activities

• Hypothesis generation  – coming up with hypotheses;
• Hypothesis evaluation – assessing relative plausibility of hypotheses given the available evidence; and
• Hypothesis testing – seeking further evidence.

Traps in Hypothesis Investigation

Hypothesis investigation fails, at its simplest, when we get (take as true) the wrong hypothesis.  This can have dismal consequences if costly actions are then taken.  Hypothesis investigation also fails when

• there is misplaced or excessive confidence in a hypothesis (even if it happens to be correct);
•  no conclusion is reached, when more careful investigation might have revealed that one hypothesis was most plausible. 

There are three main traps leading to these failures.

Tunnel vision

Not considering the full range of reasonable hypotheses.   Lots of effort is put into investigating one or a few hypotheses, usually obvious ones, while other possibilities are not considered at all.  All too often one of those others is in fact the right one. 

Abusing the evidence

Here the evidence already at hand is not evaluated properly, leading to erroneous assessments of the plausibility of hypotheses.

A particular item of evidence might be regarded as stronger or more significant than it really is, especially if it appears to support your preferred hypothesis.  Conversely, a “negative” piece of evidence – one that directly undercuts your preferred hypothesis, or appears to strongly support another – is regarded as weak or worthless.    

Further, the whole body of evidence bearing upon a hypothesis might be mis-rated.  A few scraps of dismal evidence might be taken as collectively amounting to a strong case. 

Looking in the wrong places

When seeking additional evidence, you instinctively look for information that in fact is useless or at least not very helpful in terms of helping you determine the truth.

In particular we are prone to “confirmation bias,” which is seeking information that would lend weight to our favoured hypothesis.  We tend to think that by accumulating lots of such supporting evidence, we’re rigorously testing the hypothesis.  But this is a classic mistake. We need to know not only that there’s lots of evidence consistent with our favoured hypothesis, but also that there is evidence inconsistent with alternatives.   You need to seek the right kind of evidence in relation to your whole hypothesis set, rather than just lots of evidence consistent with one hypothesis.  

This can have two unfortunate consequences.  The search may be

• Ineffective – you never find evidnce which could have very strongly ruled one or more hypotheses “in” or “out”. 
• Inefficient – the hypothesis testing process may take much more time and resources than it really should have. 

We fall for these traps because of basic facts of human psychology, hard-wired “features” of our thinking tracing back to our evolutionary origins as hunter-gatherers in small tribal units: 

• We dislike disorder, confusion and uncertainty.  Our brains strive to find the simple pattern that makes sense of a complex or noisy reality. 
• We don’t like changing our minds.  We find it easier to stick with our current opinion than to upend things and take  Further, we have undue preference for hypotheses that are consistent with our general background beliefs, and so don’t force us to question or modify those beliefs.  
• We become emotionally engaged in the issues, and build affection for one hypothesis and loathing for others.   Hypothesis investigation becomes a matter of protecting one’s young rather than culling the pack (Chamberlin, 1965).
• Social pressure.  We become publicly committed to a position, and feel that changing our minds would mean losing face. 

And of course we are frequently under time pressure, exacerbating the above tendencies.    

General Guidelines for Good Hypothesis Investigation

Canvass a wide range of hypotheses.

Our natural tendency is to grab hold of the first plausible hypothesis that comes to mind and start shaking it hard.  This should be resisted.  From the outset you should canvass as wide a range of hypotheses as you reasonably can.  It is impossible to canvass all hypotheses and absurd to even try ( Maybe 9/11 was the work of the Jasper County Beekeepers! ).   But you can and should keep in mind a broad selection of hypotheses, including at least some “long shots.”   In generating this hypothesis set, diversity is at least as important as quantity.

You should continue seeking additional hypotheses throughout the investigation.   Incoming information can suggest interesting new possibilities, but only if you’re in a suitably “suggestible” state of mind.   

Actively investigate multiple hypotheses

At any given time you should keep a number of hypotheses “in play”.   In hypothesis testing, i.e. seeking new information, you should seek information which discriminates which will be “telling” in relation to multiple hypotheses at once. 

Seek disconfirming evidence       Instead of trying to prove that some hypothesis is correct, you should be trying to prove that it is false.   As philosopher Karl Popper famously observed, the best hypotheses are those that survive numerous attempts at refutation.   Ideally, you should seek to disconfirm multiple hypotheses at the same.   This can be easier if your hypothesis set is hierarchically organised, allowing you to seek evidence knocking out whole groups of hypotheses at a time.  

Instead of trying to prove that some hypothesis is correct, you should be trying to prove that it is false.   As philosopher Karl Popper famously observed, the best hypotheses are those that survive numerous attempts at refutation.  

Ideally, you should seek to disconfirm multiple hypotheses at the same.   This can be easier if your hypothesis set is hierarchically organised, allowing you to seek evidence knocking out whole groups of hypotheses at a time.  

Structured methodologies.

Some methodologies have been developed to help with hypothesis investigation.  The methodologies have some important advantages over proceeding in an “intuitive” or spontaneous fashion. 

• They are designed to help us avoid the traps, and do so by building in, to some extent, the general guidelines above.
• They provide distinctive external representations which help us organize and comprehend the hypothesis sets and the evidence.   These external representations reduce the cognitive load involved in keeping lots of information related in complex ways in our heads.

Some structured methodologies are:

• Analysis of Competing Hypotheses (Heuer, 1999), designed especially for intelligence analysis
• Hypothesis Mapping
• Root Cause Analysis

4 thoughts on “ Hypothesis Investigation – overview ”

Add Comment

The point there is misplaced or excessive confidence in a hypothesis (even if it happens to be correct) could do with a little extra explication–why is excessive confidence in a correct hypothesis a problem?

‘evidence’ is misspelled.

There are some words missing from We find it easier to stick with our current opinion than to upend things and take Further,

But it seems nice and clear otherwise.

It’s only peripherally related, but you might be interested in this latest twist on second-guessing yourself.

I like the title too: “You know more than you think” http://www.scientificamerican.com/article.cfm?id=you-know-more-than-you-think

cheers, RdR

I would argue for a activity between 2 and 3 above – “hypothesis framing”, in which the hypothesis is expressed in such a way that it can be tested.

Hi, nice job! Like the lack of jargon :)

Hope my comment does not lead you the other direction, but you may want to take a look at the literature on “abduction,” a term coined by philosopher Charles Pierce, if you have not already.

Abduction is defined by most as the process of generating hypotheses or generating and evaluating hypotheses.

There is a diverse set of academic literature that touches on abduction, including philosophy, the history of science (e.g., scientific discovery), management (e.g., product development), and healthcare (e.g., medical diagnosis).

I dug into this pretty deep so if you have any follow up questions, pls feel free to ping me.

Regards, Michael

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

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

Prevent plagiarism, run a free check.

Step 1: ask a question.

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

Step 2: Do some preliminary research

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

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Investigative journalism: Hypothesis-based investigations

What is a hypothesis-based investigation, how do you come up with one and how do you investigate and prove it? Part 2 of our series on investigative journalism

Coming up with hypotheses is one of the investigative journalist’s most important mental and organisational tools. 

The hypothesis is critical to how an investigation proceeds. And because of how critical it is, it cannot simply be cooked up out of nowhere. It must have a solid grounding in carefully acquired facts and draw on preliminary research showing that there is a story worth writing.

A hypothesis is made up of facts and assumptions. Facts here means solid, corroborated and carefully documented information, while assumptions means information that is yet to be corroborated, which the journalist works to prove or disprove. 

Investigative journalism, Part 1: What should you investigate?

Investigative journalism in the digital age

Investigative journalism, like science, is about coming up with hypotheses, testing them, and trying to prove them. The best examples of investigative journalism are rooted in a hypothesis that allows them to work out what happened, how it happened, and why it happened. 

In investigative journalism, a hypothesis is a proposed explanation that assesses a problem or issue in order to establish the truth of what happened by making connections between the facts - even if those facts are not yet entirely verified. It provides provisional answers on how an event might be connected to an actor (a perpetrator) and its victim and how big the problem might be. These are the basic elements of a hypothesis, as Figure 1 shows:

Hypotheses are important because:

• They make it easier to collect data, gather and organise new facts and evidence, and analyse it. 
• They help us keep control of the investigation and manage it effectively. 
• They help test the easiest and best methodology for establishing a hypothesis. 
• They help us to focus and be precise and to establish the boundaries and goals of the investigation. 
• They help us to more closely understand the issue that we are researching. 
• They help us to come up with solutions in the event that problems arise. 
• They are the cornerstone of a fully integrated investigation. 
• They help us to market the idea to others.
• They help us to set budgets and keep a tighter hold on time and resources. 
• They help us to establish the sources of the investigation. 

A hypothesis has the following characteristics: 

• It can be tested. 
• It is based on established and documented facts as well as uncorroborated information (assumptions). 
• It is concise. 
• It is coherent and based on facts that the journalist is looking to gather as well as information they already have. 
• It deals with a single problem.

Despite the centrality of the hypothesis to the investigative process, it can always be amended if new evidence and facts require. A good journalist should always be open to evidence that contradicts their hypothesis and work just as hard to disprove the hypothesis as to prove it - that is, they should make just as much effort to find evidence contradicting it as they do to find evidence supporting it. The hypothesis is not an end in itself but a means of getting to the truth. 

Let’s take the example of road accidents in Country X. Our journalist has conducted her initial investigation, and is now categorising the data and assumptions she has come up with: 

According to official reports that she has found, 10 main roads in Country X - all built between 2015 and 2019 - do not meet technical specifications. 

Technical reports and mapping of seventy different accidents on these roads between 2015 and 2019 show that engineering defects were the main reason in those accidents. 

There are twenty coroners’ reports showing deaths resulting from these accidents. 

There are 15 medical reports showing injuries resulting from these accidents. 

Data and information 

All tendering information on the construction of those roads, published in dailies and relevant websites. 

Our journalist has conducted interviews with experts and specialists who confirm that there are roads that do not meet the established technical standards. They say that the oversight committee takes money and gifts from contractors, and give plenty of examples - but no real evidence. 

All the annual reports of Country X’s Traffic Authority issued from 2015 to 2019. 

All the annual reports of the engineering bodies responsible for the construction of those roads during the same period. 

The names of all those responsible at the oversight committee. 

All the names of companies that won road construction tenders and the names of their owners and employees during this period. 

All the news items relevant to the roads, categorised by timeframe and information. 

Technical plans for the roads provided in the tendering documents before construction began. 

Construction and infrastructure legislation. 

Assumptions 

• The oversight committee is colluding with contractors. 
• The committee receives money and gifts from contractors. In exchange, they turn a blind eye to problems with the roads. 
• The contractors change the plans agreed on in contracts concluded with the government, without any justification, in order to cut costs. 
• Contractors cut corners on construction in order to save money. 
• The laws that regulate public infrastructure projects include loopholes that allow contracts to be circumvented. 
• There are serious shortcomings in government oversight of roads. There is both abuse of office and neglect taking place. 

Our journalist then takes all of these points together and formulates the following hypothesis: “There are injuries and deaths on the motorways in Country X because of construction defects caused by contractors cutting corners. The government committee responsible for oversight is colluding with the contractors.” 

This hypothesis is clearly based on the preliminary research and the information and data that it has provided. It is concise, coherent and consists of one or two sentences. It breaks down into a number of central points, and each of these points produces a series of questions, each of which can be answered by a source or sources (whether human or documentary). This allows our journalist to produce a clear and specific hypothesis which can be tested and proved or disproved. 

Note that she has looked at open sources. She has inspected the roads herself by looking at official reports and speaking to experts. She has contacted human sources. 

The key to all this was generating a series of questions through an organised brainstorming exercise, questions she then tried to find answers for. The hypothesis shows the links between the event, the agent and the victim. It provides a provisional answer to the problem that our journalist wants to investigate. 

We can break it down into its central elements as follows: 

• Two events. Firstly, contractors are cutting corners when building roads. Secondly, the government committee responsible for oversight is colluding with the contractors. 
• Two actors. The contractors and the committee. 
• The victim. Those killed and injured and their families. Also, the public purse, which is the common property of everyone. A hypothesis may involve one or more actors. It may also involve a single main event with several subsequent events. The same applies to victims.

Planning an investigation 

After coming up with a hypothesis, a journalist can work out what information they have already and what information they want to get access to. The best way to do this is to produce a written research plan. A systematic plan requires systematic thinking. To produce a plan, we think up questions about the facts, sources, opinions and analysis, the background, and anticipated obstacles. 

• The facts necessary to produce an investigation. 
• The questions that lead us to those facts. 
• The sources that answer these questions. 
• The methodology. 
• Acceptable standards for evidence. 

This division helps the journalist to prove the events took place and to gather evidence. Here we can use a schema like the one here: 

This schema will help our journalist to predict the results of the research plan. It will tell her where to start looking, and help her map out prospective and confirmed sources, establish sufficient standards of proof and decide on a methodology, budget and timeline for the investigation. 

When producing an investigation plan, we focus on proving that there has been logical, legal and ethical wrongdoing and establishing where this occurred, who is responsible and what their motives were (money, fear, coercion, maintaining the status quo, or neglect and incompetence). 

The schema answers the questions raised by the investigation plan: 

• What is going on? Why should the public be interested in this investigation? Who is the target audience? The greater the likely audience interest, the more important it will be to the journalist and the news outlet. 
• What is the mistake or wrongdoing that has taken place – that is, what is the event that we are focusing on? Is it a legal or an ethical violation? Does it make sense? How did this happen? Why did it happen? 
• Who is the main actor? Who else participated? How did they do it? Why did they do it? Who benefited? 
• What are the ramifications of this mistake? What are its effects? What are the likely consequences? 
• Who was harmed directly? Who was harmed indirectly? Who suffers from this mistake or violation? 
• Who benefits from publishing the investigation? Who will be hurt by its publication? Will it help enrich the public discussion

Proving your hypothesis 

In investigative journalism, evidence must be proven beyond all doubt, because doubt will be seen as a failure of the hypothesis. If an investigation presents possibilities rather than facts, then it is not yet fully formed. 

If a journalist is unable to provide proof of the incident and connect the perpetrator to it, it will undermine the hypothesis as a whole. The assumption is that the event did not take place and that the perpetrator did not cause it. Disproving this assumption requires solid, provable evidence collected by the journalist. 

An investigation begins with a hypothesis. A hypothesis starts as a suspicion that something has happened and that somebody is responsible. A good journalist works to develop this suspicion and possibly turn it into a certainty. If, after exhausting all your options, you still are not certain, then you may have produced decent journalism - but it will not be investigative journalism. 

Investigative journalism requires conviction, conviction requires certainty and certainty requires the elimination of any doubt. Doubt can only be eliminated by marshalling solid, reliable evidence. 

Suspicion is about events. Do the events make sense? Did they really happen as claimed? Is there evidence to support this? Is there evidence to suggest otherwise? What evidence? 

Are there official documents? Is there a single account or are there several logical accounts that can be woven together? 

Another kind of reasonable suspicion arises after study and inspection, after weighing up the evidence and deciding which is more solid and precise or free of problems. A good journalist looks for evidence refuting their hypothesis. This is the best way of avoiding guesswork. 

Contradictory evidence may demonstrate that the event did not happen as suggested in the hypothesis, and that the error was not deliberate - that it occurred because of a third party wanting it to. In this case you should be fair to the person and seek justice and precision.

Evidence may prove that the error was the result not of collusion and bad intentions but dereliction of duty or neglect. This is a very different story, even if the ultimate result is the same. 

This article has been adapted from the AJMI Investigative Journalism Handbook

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15 Hypothesis Examples

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

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

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

Hypothesis Examples

1. “inadequate sleep decreases memory retention”.

Field: Psychology

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

How to Test:

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

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

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

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

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

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

Field: Physics

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

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

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

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

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

Field: Psychology/Linguistics

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

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

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

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

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

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

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

Field: Medicine/Nutrition

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

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

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

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

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

5. “Gravity Influences the Directional Growth of Plants”

Field: Agronomy / Botany

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

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

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

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

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

Field: Education

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

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

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

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

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

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

7. “Mathematics Anxiety Negatively Affects Performance”

Field: Educational Psychology

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

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

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

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

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

Field: Organizational Psychology

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

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

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

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

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

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

Field: Health Psychology

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

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

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

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

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

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

10. “Regular Meditation Enhances Emotional Stability”

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

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

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

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

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

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

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

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

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

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

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

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

Field: Environmental Science

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

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

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

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

13. “Reduced Screen Time Improves Sleep Quality”

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

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

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

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

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

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

Field: Gerontology

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

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

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

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

15. Farming Practices Influence Soil Erosion Rates

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

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

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

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

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

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

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

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

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

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Decision Making in Title IX Investigations: A Hypothesis Testing Approach to Overcome Cognitive Bias

• First Online: 19 October 2019

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Title IX investigations have been controversial and much of the scrutiny has been on legal dimensions such as thresholds of proof or cross-examining witnesses. However, another important issue is the accurate and unbiased processing of the information. This chapter discusses a well-known heuristic error known as confirmation bias and presents a methodology for potentially controlling for this bias in Title IX investigations. A hypothesis testing model in which competing hypotheses are developed and disconfirming or confirming information is delineated antecedently is presented. This chapter also discussed the difficulty of making final judgments which is also complicated by the lack of development of clear markers of sexual misconduct or of false allegations.

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O’Donohue, W.T. (2019). Decision Making in Title IX Investigations: A Hypothesis Testing Approach to Overcome Cognitive Bias. In: O’Donohue, W.T., Schewe, P.A. (eds) Handbook of Sexual Assault and Sexual Assault Prevention. Springer, Cham. https://doi.org/10.1007/978-3-030-23645-8_37

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University of Kansas

Gregory P Hanley

The initial purpose of the present study was to replicate procedures for teaching preschool children to recruit attention at appropriate times by having an experimenter signal the availability and unavailability of attention (i.e., arrange a multiple schedule involving reinforcement and extinction; Tiger & Hanley, 2004 ). Following the development of discriminated social responding, the schedule-correlated stimuli were removed (i.e., a mixed schedule of reinforcement was arranged). However, discriminated responding continued during these conditions. Further evaluation suggested that stimulus control over children's social responding had transferred from the schedule-correlated stimuli to the delivery of reinforcement. The effect of a history of reinforcement under multiple-schedule conditions on performance under mixed schedules was then replicated with 2 participants in a reversal design. These findings suggest that following experience with schedule-correlated stimuli, these stimuli may be removed with only modest disruption to discriminated responding.

Advances in behavioral science emanate from two forms of research. In programmatic research, completion of one research project inevitably leads to the development of another project in a systematic line of inquiry. Progress is achieved in small increments as the believability of phenomena is established through both direct and systematic replications ( Sidman, 1960 ). However on some occasions, an unexpected or unintended event results in a novel inquiry, otherwise known as discovery research. Several major advances in behavior analysis are a result of serendipitous events that stimulated discovery research. Skinner (1956/1972) provided anecdotes of two such discoveries: The effects of intermittent schedules of reinforcement came about following a need to conserve grain, and the extinction curve was discovered only when an automatic feeder jammed. Pavlov developed the classical conditioning paradigm when his dogs unexpectedly began salivating when an experimenter entered the room (see Babkin, 1949 ). Although most applied behavior-analytic research is driven by problems of immediate social importance, unexpected, yet practical, benefits may result from following Skinner's adage, “When you run onto something interesting, drop everything else and study it” (p. 104).

A recent publication by Roane, Fisher, and McDonough (2003) provides an example of this approach. The authors began a single-subject evaluation of the overjustification effect by identifying the baseline rate at which an individual with multiple disabilities completed a sorting task. A putative reinforcement contingency was then arranged by providing access to toys following engagement in the sorting task. However, sorting decreased under this contingency and subsequently increased when the reward contingency was removed (i.e., the opposite of what would be expected given both a reinforcement effect and subsequent overjustification effect). The authors then sought to determine why a reward contingency would result in decreased sorting. Several evaluations determined that sorting was a more preferred activity than toy play, and therefore arranging toy play to follow sorting approximated a punishment contingency.

Similar to Roane et al. (2004), the initial purpose of the current investigation was to follow a line of programmatic research. However, an unexpected finding led to the development of additional procedures to investigate these results. Initially we attempted to teach preschool children to recruit teacher attention at appropriate times by arranging the availability of attention into a multiple schedule of reinforcement. This procedure involved programming regular periods in which attention would and would not be available, providing children with continuous and distinct signals during both of these periods, and describing the different consequences for recruiting teacher attention (reinforcement or extinction) correlated with each stimulus ( Tiger & Hanley, 2004 ). In an attempt to demonstrate functional control over the effects of the multiple-schedule arrangement on social approach responses, we eventually removed the schedule-correlated stimuli, in essence creating a mixed schedule of reinforcement (i.e., alternation of two different schedules in the absence of correlated stimuli). In contrast to the results of Tiger and Hanley, however, discriminated social responding persisted for both participants when the availability of teacher attention was seemingly unpredictable.

Based on this unanticipated finding, we chose to forgo the original question and attempt to understand the variables that influence mixed-schedule performance. Although it is difficult to determine when a researcher should “drop everything” and attempt to understand an unexpected relation, we thought this was an important area of inquiry because our results were inconsistent with previously published research ( Hanley, Iwata, & Thompson, 2001 ; Tiger & Hanley, 2004 ). Also, identifying the relevant histories or present contingencies that result in discriminated responding in the absence of overt schedule-correlated stimuli may have practical implications. Finally, one of the later goals of our programmatic research, targeted to increase the social acceptability of procedures described by Tiger and Hanley (2004) , was to eliminate the artificial signals by transferring stimulus control to events in the natural environment.

Participants and setting

Two preschool-aged children who were enrolled in a full-day, university-based inclusive preschool participated. Dena was a typically developing 4-year-old girl. Chad was a 5-year-old boy who had been diagnosed with nonspecified developmental delays. Dena and Chad were nominated for participation by their teachers for engaging in either poorly timed (Dena) or excessive (Chad) social approaches in the classroom. All sessions were conducted in a room (5 m by 5 m) arranged to emulate classroom periods in which a teacher provided instruction to 2 children simultaneously.

Response Measurement and Interobserver Agreement

The number of social approach responses, defined as any vocal (e.g., saying, “Look what I built”) or nonvocal (e.g., handing a toy to the experimenter) behavior directed toward the experimenter; and the number of attention deliveries, defined as any vocal (e.g., saying, “Wow, that is a great tower”) or nonvocal (e.g., giving the child a high-five) behavior directed toward the child, were recorded within 10-s intervals. An occurrence was scored following a 2-s pause between responses. Data were recorded using handheld computers and are reported as a response rate during reinforcement and extinction components.

Interobserver agreement was assessed by having a second observer simultaneously but independently score responses during at least 25% of sessions in all conditions for each participant. Agreement coefficients were determined by partitioning sessions into 72 10-s intervals and dividing the smaller number of responses by the larger number within each interval, and averaging these scores across intervals. Agreement for social approaches averaged 91% (range, 76% to 100%) for Dena and 89% (range, 78% to 100%) for Chad. Agreement for attention delivery averaged 96% (range, 86% to 100%) to Dena and 96% (range, 90% to 100%) for Chad.

Treatment Integrity

We assessed the integrity of implementation of the independent variable by determining the correspondence between the number of social approaches and the number of instances of attention delivery within reinforcement and extinction components of the mixed- and multiple-schedule arrangements. During reinforcement components, the smaller number of the two measures was divided by the larger number (components with zero social approaches and zero instances of attention were scored as agreements). During extinction components, this fraction was then subtracted from one. All measures were then multiplied by 100% to yield a treatment integrity score. For example, if three approaches were emitted during a reinforcement component and two instances of attention were scored, this would yield an integrity score of 67% for that component. However, if the same amount of each response was scored during an extinction component, this would yield a score of 33% for that component. These percentages were then averaged across sessions to yield a treatment integrity score of 98% for Dena and 96% for Chad.

Sessions were similar to those described by Tiger and Hanley (2004) . Children had access to academic materials (e.g., blocks, string beads) at individual tables across from and facing the experimenter. The experimenter looked down except when delivering contingent attention. There were three components in each session, one in which Chad's approach responses produced reinforcement (SR+) while Dena's were exposed to extinction (EXT 1 ), one in which Dena's approach responses produced reinforcement while Chad's were exposed to extinction, and one in which both children's approach responses were exposed to extinction (EXT 2 ). Each of the three components was presented twice for 1 min and once for 2 min (i.e., each component was scheduled for a total of 4 min) for a total session duration of 12 min. The order of components was randomly determined prior to sessions.

During SR+ components, approximately 5 s of attention was provided following each social approach response. While the SR+ component was arranged for 1 child, the other child's responding did not result in attention from the experimenter (EXT 1 component). This arrangement approximated classroom conditions in which a teacher would be able to attend to only 1 child. Attention was not available to either child during EXT 2 . This component approximated classroom conditions in which the teacher would not be available to provide attention to any child (e.g., when talking with a parent). Thus, in the arranged conditions, attention could be available to Dena only, to Chad only, or to neither child, but attention was never available to both children simultaneously.

Mixed Schedule (MIX)

SR+, EXT 1 , and EXT 2 components rotated on a time-based schedule, as described above, and component changes were unsignaled (i.e., no schedule-correlated stimuli were present). This condition served as a baseline from which the influence of schedule-correlated stimuli on children's social approaches would be assessed.

Multiple Schedule (MULT)

This condition was arranged similar to the MIX condition, except that a red, white, or blue floral lei was paired with each component. For example, when attention was available for 1 child, the experimenter wore the red lei; when attention was available for the other child, the experimenter wore the blue lei; and when attention was not available for either child, the experimenter wore the white lei. Floral leis were selected as schedule-correlated stimuli because they were portable, salient, and could be viewed by a child at any angle. The purpose of the MULT condition was to determine if correlating stimuli with the availability (SR+) and unavailability (EXT 1 and EXT 2 ) of attention was sufficient to bring children's social approaches under stimulus control.

Multiple Schedule Plus Rules (MULT + rules)

This condition was arranged similar to the MULT condition except that prior to each session the experimenter presented each lei and described the associated contingency. The contingencies were described as follows:

When I am wearing the red lei, it is your time. I can answer your questions and look at your work. When I am wearing the blue lei, it is [other child's name] time. I can't answer your questions or look at your work. When I am wearing the white lei, it is my time. I can't answer either of your questions or look at either of your work.

Prior to each session, each participant was prompted to respond twice in the presence of each lei and to contact the contingencies associated with each. The purpose of this condition was to determine if providing rules prior to sessions would facilitate the stimulus control of social approach responses.

Variable Interval (VI)

Based on the obtained data, we hypothesized that the delivery of reinforcement for a social approach during the MIX condition would signal the availability of reinforcement for subsequent approach responses, until the participants encountered a nonreinforced approach response (i.e., the onset of extinction). To control for the putative discriminative aspect of reinforcement delivery, attention was arranged into separate VI schedules of reinforcement for each participant. Interval lengths were based on the mean interreinforcement duration from all sessions in the previous mixed schedule phase, which resulted in a VI 28-s schedule for Dena and a VI 26-s schedule for Chad. Reinforcement intervals were programmed into a desktop computer using a VI timer software program that signaled the experimenter when a reinforcer was arranged in each VI schedule. The computer monitor was placed to the side of the experimenter, facing away from both participants. In contrast to the MULT and MIX schedules, the programmed delivery of reinforcement during the VI schedule was not correlated with the availability of subsequent reinforcement.

The data for Dena and Chad are shown in Figure 1 . During the initial MIX condition, both participants engaged in similar levels of responding across all three components ( M s  =  0.3 responses per minute during SR+, 0.9 during EXT 1 , and 0.2 during EXT 2 for Dena and 3.1 during SR+, 3 during EXT 1 , and 2.7 during EXT 2 for Chad). Following the introduction of schedule-correlated stimuli (i.e., MULT), Dena's responding increased similarly across all three components ( M s  =  2.5 responses per minute during SR+, 1.9 during EXT 1 , and 1.4 during EXT 2 ). Chad's responding accelerated most during SR+ components ( M  =  8.2 responses per minute), although elevated levels of responding persisted during both EXT 1 ( M  =  3.8) and EXT 2 ( M  =  3.4) components. Because highly discriminated performance was not produced by the addition of schedule-correlated stimuli, presession rules were provided. Under these conditions, both participants responded at high rates during SR+ ( M s  =  9.3 responses per minute for Dena and 9 for Chad) and at lower rates during EXT 1 ( M s  =  0.3 responses per minute for Dena and 1.5 for Chad) and EXT 2 ( M s  =  0.1 for Dena and 1.5 for Chad).

Data for Dena's Session 32 were lost due to an equipment failure.

When we attempted to demonstrate functional control of the effects of contingency-specifying and schedule-correlated stimuli by returning to the MIX condition, both Dena and Chad continued to respond at high rates during SR+ ( M s  =  6.9 responses per minute for Dena and 7.3 for Chad) with lower rate responding during EXT 1 ( M s  =  0.6 for Dena and 3.1 for Chad) and EXT 2 ( M s  =  1.6 for Dena and 3.4 for Chad). This was surprising, because in previous research ( Hanley, Thompson, & Iwata, 2001 ; Tiger & Hanley, 2004 ) discriminated social approaches failed to be maintained in the absence of programmed schedule-correlated stimuli.

Data obtained for both participants provided evidence that the delivery of reinforcement set the occasion for more responding during the unsignaled SR+ components. Figure 2 shows the mean interresponse times (IRTs) during MIX and VI conditions. During the MIX condition that followed the MULT + rules condition, if a response was followed by attention, Dena typically responded again after a relatively short pause (IRT  =  6 s). If a response was not followed by attention, however, she typically responded again after a relatively long pause (IRT  =  56.8 s). Similarly, if a response from Chad was followed by attention, he typically responded again after a short pause (IRT  =  7.4 s) and, if a response was not followed by attention, he typically paused for a longer time before his next response (IRT  =  19.4 s). In addition, the standard deviation was considerably smaller given a reinforced response relative to a nonreinforced response for both participants ( SD  =  4.8 and 22.9, respectively, for Dena and 2.7 and 56.3, respectively, for Chad). These data suggest that the delivery of reinforcement affected responding under MIX conditions, in that responding differed following the delivery or nondelivery of attention. That is, it appeared that reinforcer delivery was discriminative for further responding.

To disrupt the potential discriminative control of reinforcement delivery, we programmed independent VI schedules for each child. Both participants' response rates gradually slowed ( M s  =  1.9 responses per minute for Dena and Chad, Figure 1 ). Analysis of the IRTs during the VI condition showed similar pause durations for Dena when a response did (IRT  =  30.8 s) or did not (IRT  =  27.9 s) result in the delivery of attention ( Figure 2 ). Chad actually paused longer following a response that resulted in attention (IRT  =  38.2 s) than following a response that did not result in attention (IRT  =  22.6 s; Figure 2 ). These data also suggest that the discriminative properties of the delivery of reinforcement were disrupted by the VI schedules.

Following the VI condition, attention was again arranged into MIX conditions (sixth phase of Figure 1 ); however, this time both participants responded at low and indiscriminate rates across all three components ( M s  =  0.4 responses per minute during SR+, 0.4 during EXT 1 , and 0.2 during EXT 2 for Dena and 0.1 during SR+, 0.4 during EXT 1 , and 0.1 during EXT 2 for Chad). To replicate the previously observed discriminated performance under MIX conditions, the MULT + rules condition was reimplemented. Dena again responded at high rates during SR+ ( M  =  10.2) and at low rates during EXT 1 ( M  =  0.4) and EXT 2 ( M  =  0.3). Chad responded at lower rates than observed previously during SR+ ( M  =  2.3), but higher than in EXT 1 ( M  =  1) and EXT 2 ( M  =  0.6). When the MIX condition was reinstated, Dena responded at high rates during SR+ ( M  =  6.2) and low rates during EXT 1 ( M  =  1.2) and EXT 2 ( M  =  1.7). Chad similarly responded at higher rates during SR+ ( M  =  3.5) than during EXT 1 ( M  =  1) and EXT 2 ( M  =  1.3), although this effect was less clear than with Dena. Nevertheless, these patterns were similar to those observed earlier in the analysis and suggested that discriminated responding under a mixed schedule of reinforcement developed only following an immediate history of discriminated performance under MULT + rules conditions.

The necessity of the MULT + rules history on the development of discriminated performance under MIX conditions is highlighted by the discrimination indexes presented in Figure 3 , which were calculated by summing the number of responses emitted during SR+ components and dividing these by the total number of responses emitted across all components. Given that reinforcement was available for one third of each session, indiscriminate responding (i.e., that occurring at chance levels; dotted lines on Figure 3 ) would result in an index close to .33. By contrast, perfect discriminated responding would result in an index of 1. During the initial MIX condition (i.e., that which preceded MULT + rules) both participants responded at near-chance levels (indexes of .18 for Dena and .32 for Chad). However, in the MIX condition that followed the MULT + rules condition, both participants responded at above-chance levels (.77 for Dena and .52 for Chad). Once the discrimination had been disrupted by the VI history, both participants responded at near-chance levels during the subsequent MIX conditions (.42 for Dena and .17 for Chad). Under MIX conditions that followed MULT + rules, both participants responded at above-chance levels (.68 for Dena and .59 for Chad). It should be noted that these indexes were somewhat lower than those achieved under the MULT + rules arrangement (.94 for Dena and .75 for Chad; dashed lines on Figure 3 ).

The current study replicated the multiple-schedule procedures used by Tiger and Hanley (2004) , and the results demonstrated that discriminated performance failed to develop under a multiple-schedule arrangement until contingency-specifying statements were provided. However, these results differed from those of Hanley et al. (2001) and Tiger and Hanley in that discriminated performance persisted in the absence of schedule-correlated stimuli when the multiple-schedule intervention was removed. The results of subsequent analyses suggested that this responding was occasioned by the delivery of attention.

Previous research has demonstrated that the delivery of reinforcement may serve as a discriminative stimulus for behavior that is historically maintained by its delivery. For example, Spradlin, Girardeau, and Hom (1966) trained individuals with mental retardation to pull a lever to earn tokens. Lever pulling was placed on extinction after an initial reinforcement effect was observed. Following a period of nonresponding during extinction, tokens were occasionally delivered. For some participants, lever pulling increased following the delivery of the tokens relative to periods in which tokens were not delivered. Thompson, Iwata, Hanley, Dozier and Samaha (2003) reported similar results when they compared extinction, differential reinforcement of other behavior (i.e., reinforcement is delivered contingent on not engaging in the target response), and noncontingent reinforcement (i.e., reinforcement is delivered independent of responding) as control procedures for simple appetitive responses in adults with developmental disabilities. Extinction resulted in the most rapid reduction in responding, in that the delivery of reinforcement under the two other conditions continued to occasion responding.

During the MIX conditions of the present study, the delivery of attention following a social approach was highly predictive of continued availability of attention, and a nonreinforced response was highly predictive of continued unavailability of attention. For instance, SR+ components lasted for either 1 or 2 min each, such that if attention was delivered early in the component, it was available for at least another minute. This predictability was eliminated by programming attention on a VI schedule of reinforcement, in which the delivery (or nondelivery) of attention was no longer predictive of its subsequent availability. The lack of predictability was evident in the data, in that both participants responded similarly following a reinforced or nonreinforced social approach under VI conditions. Following additional MULT + rules training, the predictability of reinforcer delivery again controlled children's social approaches, in that discriminated responding persisted under MIX conditions.

In addition to the discriminative properties of attention delivery, it is possible that the rules provided during the MULT + rules arrangement may have continued to influence children's social approaches under the mixed schedule. However, the rules described the consequences for responding in the presence of each of the three floral leis, and in the absence of the leis in the MIX condition, the rules were no longer relevant (i.e., the rule, “I can answer you when the red lei is present,” would not control behavior in sessions in which no leis were present). Thus, the hypothesis that the saliency of reinforcer delivery (or nondelivery) during the MULT + rules condition served as the discriminative stimulus for continued responding (or pausing) during subsequent MIX conditions seems to be a more parsimonious explanation for the obtained results. It is also possible that the consequences for the other child's responding may have signaled the differential availability of reinforcement. That is, the experimenter responding to Chad's social approaches could have signaled the unavailability of attention to Dena (i.e., when the SR+ component was scheduled for Chad, the EXT 1 component was scheduled for Dena). Similarly, the nondelivery of attention following a social approach from Chad could have signaled the potential availability of attention for Dena (i.e., when extinction was arranged for Chad, either the SR+ or EXT 2 component was arranged for Dena). There is some evidence that the consequences for Chad's responding served as discriminative stimuli for Dena. For example, when Chad engaged in high-rate responding during the second and fourth MIX conditions, Dena approached the experimenter more frequently during EXT 2 components (i.e., when Chad's responding was not reinforced) than during her EXT 1 components (i.e., when Chad's responding was reinforced). However, these differences were not large or stable, and Chad's data did not reveal the same pattern.

Because we followed an unexpected result in this study, we discovered a means of achieving discriminated social responding during periods in which the availability of social reinforcement was seemingly unpredictable. However, this finding may be an artifact of the particular procedures we employed and the effect may be short-lived, in that the effects with Chad were not robust and decreased over time. Likewise, the availability of teacher attention may fluctuate more rapidly in the natural environment than was programmed in our arrangement. These more complex unsignaled reinforcement schedules in classrooms would require children to respond more frequently to come under the control of the operating contingency. Finally, these results may have limited generality in that discriminated performances observed in the current study were not observed in other investigations that employed similar conditions ( Hanley et al. 2001 ; Tiger & Hanley, 2004 ). Therefore, future research should continue to identify relevant histories that will result in desirable patterns of social approach responding in the classroom in the absence of highly predictive cues.

There are many stimuli in the natural environment that may signal the unavailability of teachers' attention (e.g., when a teacher is engaged with other students, on the phone with a parent, collecting data) that may require an extensive history of unsuccessful or punished responding for behavior to come under the control of all these signals. Transferring the control of behavior from artificial signals (e.g., a floral lei) to those that occur more naturally in the environment may then be a desirable long-term goal (see Cuvo, Davis, O'Reilly, Mooney, & Crowley, 1992 , for an example). One strategy for transferring stimulus control might involve initially presenting the artificial signals continuously, as was done during the MULT + rules phases of the current study, while pairing them with more natural cues, such as body posture or a vocal invitation (e.g., saying, “What can I do for you?” while wearing a lei). The presentation of the artificial signals could be gradually reduced in duration such that the more natural vocal signal could eventually be provided at the onset of the period in which reinforcement is available, and a different vocal signal could be provided at the onset of an extinction period (e.g., “Everyone, I am going to work with Billy now”). Transferring stimulus control to more natural and brief cues may make such procedures more manageable for teachers and perhaps promote their adoption.

In addition to practical implications for promoting discriminated social responding of preschoolers, the present study also contributes to the small body of applied research that has demonstrated the effects of idiosyncratic histories of reinforcement on the effectiveness of behavioral interventions. That is, historical contingencies of reinforcement may come to control behavior to the exclusion of a prevailing set of contingencies that operate in the individual's environment. For example, Progar et al. (2001) conducted a functional analysis of aggression with an institutionalized adult who was transferred from another facility. This individual engaged in escape-maintained aggression during sessions conducted by familiar therapists (i.e., those who had historically provided escape following aggression) but not during sessions conducted by novel therapists. In the present study, Dena and Chad initially did not engage in discriminated social approaches under MIX conditions. However, once they had experienced a history of reinforcement under the MULT + rules conditions, the schedule of reinforcement continued to control responding in the absence of the schedule-correlated stimuli (i.e., when the MIX condition was reinstated). Future research should continue to determine the conditions under which different histories enhance or compromise the influence of prevailing contingencies (see also Ringdahl, Vollmer, Borrero, & Connell, 2001 ).

Acknowledgments

We thank Emma Hernandez, Jill White, Katie Zerr, and Amanda Perez for their assistance with data collection, Joseph Spradlin and Einar Ingvarsson for their thoughtful comments during the conduct of this study, and Troy Zarcone for providing the VI timer software.

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