problem solver in research

Problem Solving

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• pp 2680–2683
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• David H. Jonassen 2 &
• Woei Hung 3  

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Cognition ; Problem typology ; Problem-based learning ; Problems ; Reasoning

Problem solving is the process of constructing and applying mental representations of problems to finding solutions to those problems that are encountered in nearly every context.

Theoretical Background

Problem solving is the process of articulating solutions to problems. Problems have two critical attributes. First, a problem is an unknown in some context. That is, there is a situation in which there is something that is unknown (the difference between a goal state and a current state). Those situations vary from algorithmic math problems to vexing and complex social problems, such as violence in society (see Problem Typology ). Second, finding or solving for the unknown must have some social, cultural, or intellectual value. That is, someone believes that it is worth finding the unknown. If no one perceives an unknown or a need to determine an unknown, there is no perceived problem. Finding...

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Bransford, J., & Stein, B. S. (1984). The IDEAL problem solver: A guide for improving thinking, learning, and creativity . New York: WH Freeman.

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Rumelhart, D. E., & Norman, D. A. (1988). Representation in memory. In R. C. Atkinson, R. J. Herrnstein, G. Lindzey, & R. D. Luce (Eds.), Steven’s handbook of experimental psychology (Learning and cognition 2nd ed., Vol. 2, pp. 511–587). New York: Wiley.

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Jonassen, D.H., Hung, W. (2012). Problem Solving. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_208

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The Research Problem & Statement

What they are & how to write them (with examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, you’re bound to encounter the concept of a “ research problem ” or “ problem statement ” fairly early in your learning journey. Having a good research problem is essential, as it provides a foundation for developing high-quality research, from relatively small research papers to a full-length PhD dissertations and theses.

In this post, we’ll unpack what a research problem is and how it’s related to a problem statement . We’ll also share some examples and provide a step-by-step process you can follow to identify and evaluate study-worthy research problems for your own project.

Overview: Research Problem 101

What is a research problem.

• What is a problem statement?

Where do research problems come from?

• How to find a suitable research problem
• Key takeaways

A research problem is, at the simplest level, the core issue that a study will try to solve or (at least) examine. In other words, it’s an explicit declaration about the problem that your dissertation, thesis or research paper will address. More technically, it identifies the research gap that the study will attempt to fill (more on that later).

Let’s look at an example to make the research problem a little more tangible.

To justify a hypothetical study, you might argue that there’s currently a lack of research regarding the challenges experienced by first-generation college students when writing their dissertations [ PROBLEM ] . As a result, these students struggle to successfully complete their dissertations, leading to higher-than-average dropout rates [ CONSEQUENCE ]. Therefore, your study will aim to address this lack of research – i.e., this research problem [ SOLUTION ].

A research problem can be theoretical in nature, focusing on an area of academic research that is lacking in some way. Alternatively, a research problem can be more applied in nature, focused on finding a practical solution to an established problem within an industry or an organisation. In other words, theoretical research problems are motivated by the desire to grow the overall body of knowledge , while applied research problems are motivated by the need to find practical solutions to current real-world problems (such as the one in the example above).

As you can probably see, the research problem acts as the driving force behind any study , as it directly shapes the research aims, objectives and research questions , as well as the research approach. Therefore, it’s really important to develop a very clearly articulated research problem before you even start your research proposal . A vague research problem will lead to unfocused, potentially conflicting research aims, objectives and research questions .

What is a research problem statement?

As the name suggests, a problem statement (within a research context, at least) is an explicit statement that clearly and concisely articulates the specific research problem your study will address. While your research problem can span over multiple paragraphs, your problem statement should be brief , ideally no longer than one paragraph . Importantly, it must clearly state what the problem is (whether theoretical or practical in nature) and how the study will address it.

Here’s an example of a statement of the problem in a research context:

Rural communities across Ghana lack access to clean water, leading to high rates of waterborne illnesses and infant mortality. Despite this, there is little research investigating the effectiveness of community-led water supply projects within the Ghanaian context. Therefore, this study aims to investigate the effectiveness of such projects in improving access to clean water and reducing rates of waterborne illnesses in these communities.

As you can see, this problem statement clearly and concisely identifies the issue that needs to be addressed (i.e., a lack of research regarding the effectiveness of community-led water supply projects) and the research question that the study aims to answer (i.e., are community-led water supply projects effective in reducing waterborne illnesses?), all within one short paragraph.

Need a helping hand?

Wherever there is a lack of well-established and agreed-upon academic literature , there is an opportunity for research problems to arise, since there is a paucity of (credible) knowledge. In other words, research problems are derived from research gaps . These gaps can arise from various sources, including the emergence of new frontiers or new contexts, as well as disagreements within the existing research.

Let’s look at each of these scenarios:

New frontiers – new technologies, discoveries or breakthroughs can open up entirely new frontiers where there is very little existing research, thereby creating fresh research gaps. For example, as generative AI technology became accessible to the general public in 2023, the full implications and knock-on effects of this were (or perhaps, still are) largely unknown and therefore present multiple avenues for researchers to explore.

New contexts – very often, existing research tends to be concentrated on specific contexts and geographies. Therefore, even within well-studied fields, there is often a lack of research within niche contexts. For example, just because a study finds certain results within a western context doesn’t mean that it would necessarily find the same within an eastern context. If there’s reason to believe that results may vary across these geographies, a potential research gap emerges.

Disagreements – within many areas of existing research, there are (quite naturally) conflicting views between researchers, where each side presents strong points that pull in opposing directions. In such cases, it’s still somewhat uncertain as to which viewpoint (if any) is more accurate. As a result, there is room for further research in an attempt to “settle” the debate.

Of course, many other potential scenarios can give rise to research gaps, and consequently, research problems, but these common ones are a useful starting point. If you’re interested in research gaps, you can learn more here .

How to find a research problem

Given that research problems flow from research gaps , finding a strong research problem for your research project means that you’ll need to first identify a clear research gap. Below, we’ll present a four-step process to help you find and evaluate potential research problems.

If you’ve read our other articles about finding a research topic , you’ll find the process below very familiar as the research problem is the foundation of any study . In other words, finding a research problem is much the same as finding a research topic.

Step 1 – Identify your area of interest

Naturally, the starting point is to first identify a general area of interest . Chances are you already have something in mind, but if not, have a look at past dissertations and theses within your institution to get some inspiration. These present a goldmine of information as they’ll not only give you ideas for your own research, but they’ll also help you see exactly what the norms and expectations are for these types of projects.

At this stage, you don’t need to get super specific. The objective is simply to identify a couple of potential research areas that interest you. For example, if you’re undertaking research as part of a business degree, you may be interested in social media marketing strategies for small businesses, leadership strategies for multinational companies, etc.

Depending on the type of project you’re undertaking, there may also be restrictions or requirements regarding what topic areas you’re allowed to investigate, what type of methodology you can utilise, etc. So, be sure to first familiarise yourself with your institution’s specific requirements and keep these front of mind as you explore potential research ideas.

Step 2 – Review the literature and develop a shortlist

Once you’ve decided on an area that interests you, it’s time to sink your teeth into the literature . In other words, you’ll need to familiarise yourself with the existing research regarding your interest area. Google Scholar is a good starting point for this, as you can simply enter a few keywords and quickly get a feel for what’s out there. Keep an eye out for recent literature reviews and systematic review-type journal articles, as these will provide a good overview of the current state of research.

At this stage, you don’t need to read every journal article from start to finish . A good strategy is to pay attention to the abstract, intro and conclusion , as together these provide a snapshot of the key takeaways. As you work your way through the literature, keep an eye out for what’s missing – in other words, what questions does the current research not answer adequately (or at all)? Importantly, pay attention to the section titled “ further research is needed ”, typically found towards the very end of each journal article. This section will specifically outline potential research gaps that you can explore, based on the current state of knowledge (provided the article you’re looking at is recent).

Take the time to engage with the literature and develop a big-picture understanding of the current state of knowledge. Reviewing the literature takes time and is an iterative process , but it’s an essential part of the research process, so don’t cut corners at this stage.

As you work through the review process, take note of any potential research gaps that are of interest to you. From there, develop a shortlist of potential research gaps (and resultant research problems) – ideally 3 – 5 options that interest you.

Step 3 – Evaluate your potential options

Once you’ve developed your shortlist, you’ll need to evaluate your options to identify a winner. There are many potential evaluation criteria that you can use, but we’ll outline three common ones here: value, practicality and personal appeal.

Value – a good research problem needs to create value when successfully addressed. Ask yourself:

• Who will this study benefit (e.g., practitioners, researchers, academia)?
• How will it benefit them specifically?
• How much will it benefit them?

Practicality – a good research problem needs to be manageable in light of your resources. Ask yourself:

• What data will I need access to?
• What knowledge and skills will I need to undertake the analysis?
• What equipment or software will I need to process and/or analyse the data?
• How much time will I need?
• What costs might I incur?

Personal appeal – a research project is a commitment, so the research problem that you choose needs to be genuinely attractive and interesting to you. Ask yourself:

• How appealing is the prospect of solving this research problem (on a scale of 1 – 10)?
• Why, specifically, is it attractive (or unattractive) to me?
• Does the research align with my longer-term goals (e.g., career goals, educational path, etc)?

Depending on how many potential options you have, you may want to consider creating a spreadsheet where you numerically rate each of the options in terms of these criteria. Remember to also include any criteria specified by your institution . From there, tally up the numbers and pick a winner.

Step 4 – Craft your problem statement

Once you’ve selected your research problem, the final step is to craft a problem statement. Remember, your problem statement needs to be a concise outline of what the core issue is and how your study will address it. Aim to fit this within one paragraph – don’t waffle on. Have a look at the problem statement example we mentioned earlier if you need some inspiration.

Key Takeaways

We’ve covered a lot of ground. Let’s do a quick recap of the key takeaways:

• A research problem is an explanation of the issue that your study will try to solve. This explanation needs to highlight the problem , the consequence and the solution or response.
• A problem statement is a clear and concise summary of the research problem , typically contained within one paragraph.
• Research problems emerge from research gaps , which themselves can emerge from multiple potential sources, including new frontiers, new contexts or disagreements within the existing literature.
• To find a research problem, you need to first identify your area of interest , then review the literature and develop a shortlist, after which you’ll evaluate your options, select a winner and craft a problem statement .

Psst... there’s more!

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I APPRECIATE YOUR CONCISE AND MIND-CAPTIVATING INSIGHTS ON THE STATEMENT OF PROBLEMS. PLEASE I STILL NEED SOME SAMPLES RELATED TO SUICIDES.

Very pleased and appreciate clear information.

Your videos and information have been a life saver for me throughout my dissertation journey. I wish I’d discovered them sooner. Thank you!

Very interesting. Thank you. Please I need a PhD topic in climate change in relation to health.

Your posts have provided a clear, easy to understand, motivating literature, mainly when these topics tend to be considered “boring” in some careers.

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What is a Problem Statement in Research? How to Write It with Examples

The question, “What is a research problem statement?” is usually followed by “Why should I care about problem statements, and how can it affect my research?” In this article, we will try to simplify the concept so that you not only grasp its meaning but internalize its importance and learn how to craft a problem statement.

To put it simply, a “problem statement” as the name implies is any statement that describes a problem in research. When you conduct a study, your aim as a researcher is to answer a query or resolve a problem. This learned information is then typically disseminated by writing a research paper that details the entire process for readers (both for experts and the general public). To better grasp this concept, we’ll try to explain what a research problem statement is from the viewpoint of a reader. For the purpose of clarity and brevity the topic is divided into subsections.

Table of Contents

What is a research problem?

A research problem is a clearly defined issue in a particular field of study that requires additional investigation and study to resolve. Once identified, the problem can be succinctly stated to highlight existing knowledge gaps, the importance of solving the research problem, and the difference between a current situation and an improved state.

But why is it important to have a research problem ready? Keep in mind that a good research problem helps you define the main concepts and terms of research that not only guide your study but help you add to or update existing literature. A research problem statement should ideally be clear, precise, and tangible enough to assist you in developing a framework for establishing the objectives, techniques, and analysis of the research project. Hence, any research project, if it is to be completed successfully,  must start with a well-defined research problem.

What is research problem statement?

A research problem statement in research writing is the most crucial component of any study, which the researcher must perfect for a variety of reasons, including to get funding and boost readership. We’ve already established that a research article’s “research problem” is a sentence that expresses the specific problem that the research is addressing. But first, let’s discuss the significance of the problem statement in research and how to formulate one, using a few examples.

Do you recall the thoughts that went through your head the last time you read a study article? Have you ever tried to quickly scan the introduction or background of the research article to get a sense of the context and the exact issue the authors were attempting to address through the study? Were you stuck attempting to pinpoint the key sentence(s) that encapsulates the background and context of the study, the motivation behind its initial conduct, and its goals? A research problem statement is the descriptive statement which conveys the issue a researcher is trying to address through the study with the aim of informing the reader the context and significance of performing the study at hand . The research problem statement is crucial for researchers to focus on a particular component of a vast field of study, and for readers to comprehend the significance of the research. A well-defined problem allows you to create a framework to develop research objectives or hypotheses.

Now that we are aware of the significance of a problem statement in research, we can concentrate on creating one that is compelling. Writing a problem statement is a fairly simple process; first, you select a broad topic or research area based on your expertise and the resources at your disposal. Then, you narrow it down to a specific research question or problem relevant to that area of research while keeping the gaps in existing knowledge in mind. To give you a step-by-step instruction on how to write a problem statement for research proposal we’ve broken the process down into sections discussing individual aspects.

When to write a problem statement?

The placement of the research problem in the research project is another crucial component when developing a problem statement. Since the research problem statement is fundamental to writing any research project, it is best to write it at the start of the research process, before experimental setup, data collection, and analysis. Without identifying a specific research problem, you don’t know what exactly you are trying to address through the research so it would not be possible for you to set up the right conditions and foundation for the research project.

It is important to describe the research problem statement at the beginning of the research process to guide the research design and methodology. Another benefit of having a clear and defined research problem early on is that it helps researchers stay on track and focus on the problem at hand without deviating into other trajectories. Writing down the research problem statement also ensures that the current study is relevant, fitting, and fills a knowledge gap. However, note that a research statement can be refined or modified as the research advances and new information becomes available. This could be anything from further deconstructing a specific query to posing a fresh query related to the selected topic area. In fact, it is common practice to revise the problem statement in research to maintain specificity and clarity and to allow room to reflect advancement in the research field.

Bonus point:

A well-defined research problem statement that is referenced in the proper position in the research proposal/article is crucial to effectively communicate the goal and significance of the study to all stakeholders concerned with the research. It piques the reader’s interest in the research area, which can advance the work in several ways and open up future partnerships and even employment opportunities for authors.

What does a research problem statement include?

If you have to create a problem statement from scratch, follow the steps/important aspects listed below to create a well-defined research problem statement.

• Describe the wide-ranging research topics

To put things in perspective, it is important to first describe the background of the research issue, which derives from a broad area of study or interest that the research project is concerned with.

• Talk about the research problem/issue

As mentioned earlier, it’s important to state the problem or issues that the research project seeks to address in a clear, succinct manner, preferably in a sentence or two to set the premise of the entire study.

• Emphasize the importance of the issue

After defining the problem your research will try to solve, explain why it’s significant in the larger context and how your study aims to close the knowledge gap between the current state of knowledge and the ideal scenario.

• Outline research questions to address the issue

Give a brief description of the list of research questions your study will use to solve the problem at hand and explain how these will address various components of the problem statement.

• Specify the key goals of the research project

Next, carefully define a set of specific and measurable research objectives that the research project aims to address.

• Describe the experimental setup

Be sure to include a description of the experimental design, including the intended sample (population/size), setting, or context in the problem statement.

• Discuss the theoretical framework

Mention the numerous theoretical ideas and precepts necessary to comprehend the study issue and guide the research activity in this section.

• Include the research methodology

To provide a clear and concise research framework, add a brief description of the research methodologies, including collection and analysis of data, which will be needed to address the research questions and objectives.

Characteristics of a research problem statement

It is essential for a research statement to be clear and concise so that it can guide the development of the research project. A good research statement also helps other stakeholders in comprehending the scope and relevance of the research, which could further lead to opportunities for collaboration or exploration. Here is a list of the key characteristics of a research problem that you should keep in mind when writing an effective research problem statement.

• The “need” to resolve the issue must be present.

It is not enough to choose a problem in your area of interest and expertise; the research problem should have larger implications for a population or a specific subset. Unless the significance of the research problem is elaborated in detail, the research is not deemed significant. Hence, mentioning the “need” to conduct the research in the context of the subject area and how it will create a difference is of utmost importance.

• The research problem needs to be presented rationally and clearly

The research statement must be written at the start and be simple enough for even researchers outside the subject area to understand. The two fundamental elements of a successful research problem statement are clarity and specificity. So, check and rewrite your research problem statement if your peers have trouble understanding it. Aim to write in a straightforward manner while addressing all relevant issues and coherent arguments.

• The research issue is supported by facts and evidence

Before you begin writing the problem statement, you must collect all relevant information available to gain a better understanding of the research topic and existing gaps. A thorough literature search will give you an idea about the current situation and the specific questions you need to ask to close any knowledge gaps. This will also prevent you from asking the questions or identifying issues that have already been addressed. Also, the problem statement should be based on facts and data and should not depend upon hypothetical events.

• The research problem should generate more research questions

Ideally, the research problem should be such that it helps advance research and encourage more questions. The new questions could be specific to the research that highlights different components or aspects of the problem. These questions must also aid in addressing the problem in a more comprehensive manner which provides a solid foundation for the research study.

• The research problem should be tangible

The research issue should be concrete, which means that the study project’s budget and time constraints should be met. The research problem should not call for any actions and experiments that are impractical or outside of your area of competence.

To summarize the main characteristics of a research problem statement, it must:

• Address the knowledge gap
• Be current and relevant
• Aids in advancing the field
• Support future research
• Be tangible and should suit researcher’s time and interest
• Be based on facts and data

  How to write a problem statement in research proposal

The format of a problem statement might vary based on the nature and subject of the research; there is no set format. It is typically written in clear, concise sentences and can range from a few sentences to a few pages. Three considerations must be made when formulating a problem statement for a research proposal:

• Context: The research problem statement needs to be created in the right setting with sufficient background information on the research topic. Context makes it easier to distinguish between the current state and the ideal one in which the issue would not exist. In this section, you can also include instances of any prior attempts and significant roadblocks to solving the problem.
• Relevance: The main goal of the researcher here is to highlight the relevance of the research study. Explain how the research problem affects society or the field of research and, if the study is conducted to mitigate the issue, what an ideal scenario would look like. Who your study will most affect if the issue is resolved and how it can impact future research are other arguments that might be made in this section.
• Strategy: Be sure to mention the goals and objectives of your research, and your approach to solve the problem. The purpose of this section is to lay out the research approach for tackling various parts of the research subject.

Examples of problem statement in research proposal

To put what we learned into practice, let’s look at an example of a problem statement in a research report. Suppose you decide to conduct a study on the topic of attention span of different generations. After a thorough literature search you concluded that the attention span of university students is reducing over generations compared to the previous one, even though there are many websites and apps to simplify tasks and make learning easy . This decrease in attention span is attributed to constant exposure to digital content and multiple screens.

In this scenario, the problem statement could be written as – “The problem this study addresses is the lack of regulative measures to control consumption of digital content by young university students, which negatively impacts their attention span”. The research’s goals and objectives, which may employ strategies to increase university students’ attention span by limiting their internet exposure, can then be described in more detail in subsequent paragraphs.

Frequently asked questions

What is a problem statement.

A problem statement is a succinct and unambiguous overview of the research issue that the study is trying to solve.

What is the difference between problem statement and thesis statement?

A problem statement is different from a thesis statement in that the former highlights the main points of a research paper while emphasizing the hypothesis, whilst the latter identifies the issue for which research is being done.

Why is a problem statement needed in a research proposal?

A problem statement identifies the specific problem that the researchers are trying to solve through their research. It is necessary to establish a framework for the project, focus the researcher’s attention, and inform stakeholders of the study’s importance.

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• How to Define a Research Problem | Ideas & Examples

How to Define a Research Problem | Ideas & Examples

Published on November 2, 2022 by Shona McCombes and Tegan George. Revised on May 31, 2023.

A research problem is a specific issue or gap in existing knowledge that you aim to address in your research. You may choose to look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge.

Some research will do both of these things, but usually the research problem focuses on one or the other. The type of research problem you choose depends on your broad topic of interest and the type of research you think will fit best.

This article helps you identify and refine a research problem. When writing your research proposal or introduction , formulate it as a problem statement and/or research questions .

Table of contents

Why is the research problem important, step 1: identify a broad problem area, step 2: learn more about the problem, other interesting articles, frequently asked questions about research problems.

Having an interesting topic isn’t a strong enough basis for academic research. Without a well-defined research problem, you are likely to end up with an unfocused and unmanageable project.

You might end up repeating what other people have already said, trying to say too much, or doing research without a clear purpose and justification. You need a clear problem in order to do research that contributes new and relevant insights.

Whether you’re planning your thesis , starting a research paper , or writing a research proposal , the research problem is the first step towards knowing exactly what you’ll do and why.

Prevent plagiarism. Run a free check.

As you read about your topic, look for under-explored aspects or areas of concern, conflict, or controversy. Your goal is to find a gap that your research project can fill.

Practical research problems

If you are doing practical research, you can identify a problem by reading reports, following up on previous research, or talking to people who work in the relevant field or organization. You might look for:

• Issues with performance or efficiency
• Processes that could be improved
• Areas of concern among practitioners
• Difficulties faced by specific groups of people

Examples of practical research problems

Voter turnout in New England has been decreasing, in contrast to the rest of the country.

The HR department of a local chain of restaurants has a high staff turnover rate.

A non-profit organization faces a funding gap that means some of its programs will have to be cut.

Theoretical research problems

If you are doing theoretical research, you can identify a research problem by reading existing research, theory, and debates on your topic to find a gap in what is currently known about it. You might look for:

• A phenomenon or context that has not been closely studied
• A contradiction between two or more perspectives
• A situation or relationship that is not well understood
• A troubling question that has yet to be resolved

Examples of theoretical research problems

The effects of long-term Vitamin D deficiency on cardiovascular health are not well understood.

The relationship between gender, race, and income inequality has yet to be closely studied in the context of the millennial gig economy.

Historians of Scottish nationalism disagree about the role of the British Empire in the development of Scotland’s national identity.

Next, you have to find out what is already known about the problem, and pinpoint the exact aspect that your research will address.

Context and background

• Who does the problem affect?
• Is it a newly-discovered problem, or a well-established one?
• What research has already been done?
• What, if any, solutions have been proposed?
• What are the current debates about the problem? What is missing from these debates?

Specificity and relevance

• What particular place, time, and/or group of people will you focus on?
• What aspects will you not be able to tackle?
• What will the consequences be if the problem is not resolved?

Example of a specific research problem

A local non-profit organization focused on alleviating food insecurity has always fundraised from its existing support base. It lacks understanding of how best to target potential new donors. To be able to continue its work, the organization requires research into more effective fundraising strategies.

Once you have narrowed down your research problem, the next step is to formulate a problem statement , as well as your research questions or hypotheses .

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

Methodology

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

 Statistics

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

Research bias

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

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

Research questions anchor your whole project, so it’s important to spend some time refining them.

In general, they should be:

• Focused and researchable
• Answerable using credible sources
• Complex and arguable
• Feasible and specific
• Relevant and original

Your research objectives indicate how you’ll try to address your research problem and should be specific:

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

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How to identify and resolve research problems

Updated August 23, 2024

In this article, we’re going to take you through one of the most pertinent parts of conducting research: a research problem (also known as a research problem statement).

When trying to formulate a good research statement, and understand how to solve it for complex projects, it can be difficult to know where to start.

Not only are there multiple perspectives (from stakeholders to project marketers who want answers), you have to consider the particular context of the research topic: is it timely, is it relevant and most importantly of all, is it valuable?

In other words: are you looking at a research worthy problem?

The fact is, a well-defined, precise, and goal-centric research problem will keep your researchers, stakeholders, and business-focused and your results actionable.

And when it works well, it's a powerful tool to identify practical solutions that can drive change and secure buy-in from your workforce.

Free eBook: The ultimate guide to market research

What is a research problem?

In social research methodology and behavioral sciences , a research problem establishes the direction of research, often relating to a specific topic or opportunity for discussion.

For example: climate change and sustainability, analyzing moral dilemmas or wage disparity amongst classes could all be areas that the research problem focuses on.

As well as outlining the topic and/or opportunity, a research problem will explain:

• why the area/issue needs to be addressed,
• why the area/issue is of importance,
• the parameters of the research study
• the research objective
• the reporting framework for the results and
• what the overall benefit of doing so will provide (whether to society as a whole or other researchers and projects).

Having identified the main topic or opportunity for discussion, you can then narrow it down into one or several specific questions that can be scrutinized and answered through the research process.

What are research questions?

Generating research questions underpinning your study usually starts with problems that require further research and understanding while fulfilling the objectives of the study.

A good problem statement begins by asking deeper questions to gain insights about a specific topic.

For example, using the problems above, our questions could be:

"How will climate change policies influence sustainability standards across specific geographies?"

"What measures can be taken to address wage disparity without increasing inflation?"

Developing a research worthy problem is the first step - and one of the most important - in any kind of research.

It’s also a task that will come up again and again because any business research process is cyclical. New questions arise as you iterate and progress through discovering, refining, and improving your products and processes. A research question can also be referred to as a "problem statement".

Note: good research supports multiple perspectives through empirical data. It’s focused on key concepts rather than a broad area, providing readily actionable insight and areas for further research.

Research question or research problem?

As we've highlighted, the terms “research question” and “research problem” are often used interchangeably, becoming a vague or broad proposition for many.

The term "problem statement" is far more representative, but finds little use among academics.

Instead, some researchers think in terms of a single research problem and several research questions that arise from it.

As mentioned above, the questions are lines of inquiry to explore in trying to solve the overarching research problem.

Ultimately, this provides a more meaningful understanding of a topic area.

It may be useful to think of questions and problems as coming out of your business data – that’s the O-data (otherwise known as operational data) like sales figures and website metrics.

What's an example of a research problem?

Your overall research problem could be: "How do we improve sales across EMEA and reduce lost deals?"

This research problem then has a subset of questions, such as:

"Why do sales peak at certain times of the day?"

"Why are customers abandoning their online carts at the point of sale?"

As well as helping you to solve business problems, research problems (and associated questions) help you to think critically about topics and/or issues (business or otherwise). You can also use your old research to aid future research -- a good example is laying the foundation for comparative trend reports or a complex research project.

(Also, if you want to see the bigger picture when it comes to research problems, why not check out our ultimate guide to market research? In it you'll find out: what effective market research looks like, the use cases for market research, carrying out a research study, and how to examine and action research findings).

The research process: why are research problems important?

A research problem has two essential roles in setting your research project on a course for success.

1. They set the scope

The research problem defines what problem or opportunity you’re looking at and what your research goals are. It stops you from getting side-tracked or allowing the scope of research to creep off-course .

Without a strong research problem or problem statement, your team could end up spending resources unnecessarily, or coming up with results that aren’t actionable - or worse, harmful to your business - because the field of study is too broad.

2. They tie your work to business goals and actions

To formulate a research problem in terms of business decisions means you always have clarity on what’s needed to make those decisions. You can show the effects of what you’ve studied using real outcomes.

Then, by focusing your research problem statement on a series of questions tied to business objectives, you can reduce the risk of the research being unactionable or inaccurate.

It's also worth examining research or other scholarly literature (you’ll find plenty of similar, pertinent research online) to see how others have explored specific topics and noting implications that could have for your research.

Four steps to defining your research problem

Image credit: http://myfreeschooltanzania.blogspot.com/2014/11/defining-research-problem.html

1. Observe and identify

Businesses today have so much data that it can be difficult to know which problems to address first. Researchers also have business stakeholders who come to them with problems they would like to have explored. A researcher’s job is to sift through these inputs and discover exactly what higher-level trends and key concepts are worth investing in.

This often means asking questions and doing some initial investigation to decide which avenues to pursue. This could mean gathering interdisciplinary perspectives identifying additional expertise and contextual information.

Sometimes, a small-scale preliminary study might be worth doing to help get a more comprehensive understanding of the business context and needs, and to make sure your research problem addresses the most critical questions.

This could take the form of qualitative research using a few in-depth interviews , an environmental scan, or reviewing relevant literature.

The sales manager of a sportswear company has a problem: sales of trail running shoes are down year-on-year and she isn’t sure why. She approaches the company’s research team for input and they begin asking questions within the company and reviewing their knowledge of the wider market.

2. Review the key factors involved

As a marketing researcher, you must work closely with your team of researchers to define and test the influencing factors and the wider context involved in your study. These might include demographic and economic trends or the business environment affecting the question at hand. This is referred to as a relational research problem.

To do this, you have to identify the factors that will affect the research and begin formulating different methods to control them.

You also need to consider the relationships between factors and the degree of control you have over them. For example, you may be able to control the loading speed of your website but you can’t control the fluctuations of the stock market.

Doing this will help you determine whether the findings of your project will produce enough information to be worth the cost.

You need to determine:

• which factors affect the solution to the research proposal.
• which ones can be controlled and used for the purposes of the company, and to what extent.
• the functional relationships between the factors.
• which ones are critical to the solution of the research study.

The research team at the running shoe company is hard at work. They explore the factors involved and the context of why YoY sales are down for trail shoes, including things like what the company’s competitors are doing, what the weather has been like – affecting outdoor exercise – and the relative spend on marketing for the brand from year to year.

The final factor is within the company’s control, although the first two are not. They check the figures and determine marketing spend has a significant impact on the company.

3. Prioritize

Once you and your research team have a few observations, prioritize them based on their business impact and importance. It may be that you can answer more than one question with a single study, but don’t do it at the risk of losing focus on your overarching research problem.

Questions to ask:

• Who? Who are the people with the problem? Are they end-users, stakeholders, teams within your business? Have you validated the information to see what the scale of the problem is?
• What? What is its nature and what is the supporting evidence?
• Why? What is the business case for solving the problem? How will it help?
• Where? How does the problem manifest and where is it observed?

To help you understand all dimensions, you might want to consider focus groups or preliminary interviews with external (including consumers and existing customers) and internal (salespeople, managers, and other stakeholders) parties to provide what is sometimes much-needed insight into a particular set of questions or problems.

After observing and investigating, the running shoe researchers come up with a few candidate questions, including:

• What is the relationship between US average temperatures and sales of our products year on year?
• At present, how does our customer base rank Competitor X and Competitor Y’s trail running shoe compared to our brand?
• What is the relationship between marketing spend and trail shoe product sales over the last 12 months?

They opt for the final question, because the variables involved are fully within the company’s control, and based on their initial research and stakeholder input, seem the most likely cause of the dive in sales. The research question is specific enough to keep the work on course towards an actionable result, but it allows for a few different avenues to be explored, such as the different budget allocations of offline and online marketing and the kinds of messaging used.

Get feedback from the key teams within your business to make sure everyone is aligned and has the same understanding of the research problem and questions, and the actions you hope to take based on the results. Now is also a good time to demonstrate the ROI of your research and lay out its potential benefits to your stakeholders.

Different groups may have different goals and perspectives on the issue. This step is vital for getting the necessary buy-in and pushing the project forward.

The running shoe company researchers now have everything they need to begin. They call a meeting with the sales manager and consult with the product team, marketing team, and C-suite to make sure everyone is aligned and has bought into the direction of the research topic. They identify and agree that the likely course of action will be a rethink of how marketing resources are allocated, and potentially testing out some new channels and messaging strategies .

Can you explore a broad area and is it practical to do so?

A broader research problem or report can be a great way to bring attention to prevalent issues, societal or otherwise, but are often undertaken by those with the resources to do so.

Take a typical government cybersecurity breach survey, for example. Most of these reports raise awareness of cybercrime, from the day-to-day threats businesses face to what security measures some organizations are taking. What these reports don't do, however, is provide actionable advice - mostly because every organization is different.

The point here is that while some researchers will explore a very complex issue in detail, others will provide only a snapshot to maintain interest and encourage further investigation. The "value" of the data is wholly determined by the recipients of it - and what information you choose to include.

To summarize, it can be practical to undertake a broader research problem, certainly, but it may not be possible to cover everything or provide the detail your audience needs. Likewise, a more systematic investigation of an issue or topic will be more valuable, but you may also find that you cover far less ground.

It's important to think about your research objectives and expected findings before going ahead.

Ensuring your research project is a success

A complex research project can be made significantly easier with clear research objectives, a descriptive research problem, and a central focus. All of which we've outlined in this article.

If you have previous research, even better. Use it as a benchmark

Remember: what separates a good research paper from an average one is actually very simple: valuable, empirical data that explores a prevalent societal or business issue and provides actionable insights.

And we can help.

Sophisticated research made simple with Qualtrics

Trusted by the world's best brands, our platform enables researchers from academic to corporate to tackle the hardest challenges and deliver the results that matter.

Our CoreXM platform supports the methods that define superior research and delivers insights in real-time. It's easy to use (thanks to drag-and-drop functionality) and requires no coding, meaning you'll be capturing data and gleaning insights in no time.

It also excels in flexibility; you can track consumer behavior across segments , benchmark your company versus competitors , carry out complex academic research, and do much more, all from one system.

It's one platform with endless applications, so no matter your research problem, we've got the tools to help you solve it. And if you don't have a team of research experts in-house, our market research team has the practical knowledge and tools to help design the surveys and find the respondents you need.

Of course, you may want to know where to begin with your own market research . If you're struggling, make sure to download our ultimate guide using the link below.

It's got everything you need and there’s always information in our research methods knowledge base.

Scott Smith

Scott Smith, Ph.D. is a contributor to the Qualtrics blog.

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Complex Problem Solving: What It Is and What It Is Not

Dietrich dörner.

1 Department of Psychology, University of Bamberg, Bamberg, Germany

Joachim Funke

2 Department of Psychology, Heidelberg University, Heidelberg, Germany

Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems. Psychometric issues such as reliable assessments and addressing correlations with other instruments have been in the foreground of these discussions and have left the content validity of complex problem solving in the background. In this paper, we return the focus to content issues and address the important features that define complex problems.

Succeeding in the 21st century requires many competencies, including creativity, life-long learning, and collaboration skills (e.g., National Research Council, 2011 ; Griffin and Care, 2015 ), to name only a few. One competence that seems to be of central importance is the ability to solve complex problems ( Mainzer, 2009 ). Mainzer quotes the Nobel prize winner Simon (1957) who wrote as early as 1957:

The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problem whose solution is required for objectively rational behavior in the real world or even for a reasonable approximation to such objective rationality. (p. 198)

The shift from well-defined to ill-defined problems came about as a result of a disillusion with the “general problem solver” ( Newell et al., 1959 ): The general problem solver was a computer software intended to solve all kind of problems that can be expressed through well-formed formulas. However, it soon became clear that this procedure was in fact a “special problem solver” that could only solve well-defined problems in a closed space. But real-world problems feature open boundaries and have no well-determined solution. In fact, the world is full of wicked problems and clumsy solutions ( Verweij and Thompson, 2006 ). As a result, solving well-defined problems and solving ill-defined problems requires different cognitive processes ( Schraw et al., 1995 ; but see Funke, 2010 ).

Well-defined problems have a clear set of means for reaching a precisely described goal state. For example: in a match-stick arithmetic problem, a person receives a false arithmetic expression constructed out of matchsticks (e.g., IV = III + III). According to the instructions, moving one of the matchsticks will make the equations true. Here, both the problem (find the appropriate stick to move) and the goal state (true arithmetic expression; solution is: VI = III + III) are defined clearly.

Ill-defined problems have no clear problem definition, their goal state is not defined clearly, and the means of moving towards the (diffusely described) goal state are not clear. For example: The goal state for solving the political conflict in the near-east conflict between Israel and Palestine is not clearly defined (living in peaceful harmony with each other?) and even if the conflict parties would agree on a two-state solution, this goal again leaves many issues unresolved. This type of problem is called a “complex problem” and is of central importance to this paper. All psychological processes that occur within individual persons and deal with the handling of such ill-defined complex problems will be subsumed under the umbrella term “complex problem solving” (CPS).

Systematic research on CPS started in the 1970s with observations of the behavior of participants who were confronted with computer simulated microworlds. For example, in one of those microworlds participants assumed the role of executives who were tasked to manage a company over a certain period of time (see Brehmer and Dörner, 1993 , for a discussion of this methodology). Today, CPS is an established concept and has even influenced large-scale assessments such as PISA (“Programme for International Student Assessment”), organized by the Organization for Economic Cooperation and Development ( OECD, 2014 ). According to the World Economic Forum, CPS is one of the most important competencies required in the future ( World Economic Forum, 2015 ). Numerous articles on the subject have been published in recent years, documenting the increasing research activity relating to this field. In the following collection of papers we list only those published in 2010 and later: theoretical papers ( Blech and Funke, 2010 ; Funke, 2010 ; Knauff and Wolf, 2010 ; Leutner et al., 2012 ; Selten et al., 2012 ; Wüstenberg et al., 2012 ; Greiff et al., 2013b ; Fischer and Neubert, 2015 ; Schoppek and Fischer, 2015 ), papers about measurement issues ( Danner et al., 2011a ; Greiff et al., 2012 , 2015a ; Alison et al., 2013 ; Gobert et al., 2015 ; Greiff and Fischer, 2013 ; Herde et al., 2016 ; Stadler et al., 2016 ), papers about applications ( Fischer and Neubert, 2015 ; Ederer et al., 2016 ; Tremblay et al., 2017 ), papers about differential effects ( Barth and Funke, 2010 ; Danner et al., 2011b ; Beckmann and Goode, 2014 ; Greiff and Neubert, 2014 ; Scherer et al., 2015 ; Meißner et al., 2016 ; Wüstenberg et al., 2016 ), one paper about developmental effects ( Frischkorn et al., 2014 ), one paper with a neuroscience background ( Osman, 2012 ) 1 , papers about cultural differences ( Güss and Dörner, 2011 ; Sonnleitner et al., 2014 ; Güss et al., 2015 ), papers about validity issues ( Goode and Beckmann, 2010 ; Greiff et al., 2013c ; Schweizer et al., 2013 ; Mainert et al., 2015 ; Funke et al., 2017 ; Greiff et al., 2017 , 2015b ; Kretzschmar et al., 2016 ; Kretzschmar, 2017 ), review papers and meta-analyses ( Osman, 2010 ; Stadler et al., 2015 ), and finally books ( Qudrat-Ullah, 2015 ; Csapó and Funke, 2017b ) and book chapters ( Funke, 2012 ; Hotaling et al., 2015 ; Funke and Greiff, 2017 ; Greiff and Funke, 2017 ; Csapó and Funke, 2017a ; Fischer et al., 2017 ; Molnàr et al., 2017 ; Tobinski and Fritz, 2017 ; Viehrig et al., 2017 ). In addition, a new “Journal of Dynamic Decision Making” (JDDM) has been launched ( Fischer et al., 2015 , 2016 ) to give the field an open-access outlet for research and discussion.

This paper aims to clarify aspects of validity: what should be meant by the term CPS and what not? This clarification seems necessary because misunderstandings in recent publications provide – from our point of view – a potentially misleading picture of the construct. We start this article with a historical review before attempting to systematize different positions. We conclude with a working definition.

Historical Review

The concept behind CPS goes back to the German phrase “komplexes Problemlösen” (CPS; the term “komplexes Problemlösen” was used as a book title by Funke, 1986 ). The concept was introduced in Germany by Dörner and colleagues in the mid-1970s (see Dörner et al., 1975 ; Dörner, 1975 ) for the first time. The German phrase was later translated to CPS in the titles of two edited volumes by Sternberg and Frensch (1991) and Frensch and Funke (1995a) that collected papers from different research traditions. Even though it looks as though the term was coined in the 1970s, Edwards (1962) used the term “dynamic decision making” to describe decisions that come in a sequence. He compared static with dynamic decision making, writing:

• simple  In dynamic situations, a new complication not found in the static situations arises. The environment in which the decision is set may be changing, either as a function of the sequence of decisions, or independently of them, or both. It is this possibility of an environment which changes while you collect information about it which makes the task of dynamic decision theory so difficult and so much fun. (p. 60)

The ability to solve complex problems is typically measured via dynamic systems that contain several interrelated variables that participants need to alter. Early work (see, e.g., Dörner, 1980 ) used a simulation scenario called “Lohhausen” that contained more than 2000 variables that represented the activities of a small town: Participants had to take over the role of a mayor for a simulated period of 10 years. The simulation condensed these ten years to ten hours in real time. Later, researchers used smaller dynamic systems as scenarios either based on linear equations (see, e.g., Funke, 1993 ) or on finite state automata (see, e.g., Buchner and Funke, 1993 ). In these contexts, CPS consisted of the identification and control of dynamic task environments that were previously unknown to the participants. Different task environments came along with different degrees of fidelity ( Gray, 2002 ).

According to Funke (2012) , the typical attributes of complex systems are (a) complexity of the problem situation which is usually represented by the sheer number of involved variables; (b) connectivity and mutual dependencies between involved variables; (c) dynamics of the situation, which reflects the role of time and developments within a system; (d) intransparency (in part or full) about the involved variables and their current values; and (e) polytely (greek term for “many goals”), representing goal conflicts on different levels of analysis. This mixture of features is similar to what is called VUCA (volatility, uncertainty, complexity, ambiguity) in modern approaches to management (e.g., Mack et al., 2016 ).

In his evaluation of the CPS movement, Sternberg (1995) compared (young) European approaches to CPS with (older) American research on expertise. His analysis of the differences between the European and American traditions shows advantages but also potential drawbacks for each side. He states (p. 301): “I believe that although there are problems with the European approach, it deals with some fundamental questions that American research scarcely addresses.” So, even though the echo of the European approach did not enjoy strong resonance in the US at that time, it was valued by scholars like Sternberg and others. Before attending to validity issues, we will first present a short review of different streams.

Different Approaches to CPS

In the short history of CPS research, different approaches can be identified ( Buchner, 1995 ; Fischer et al., 2017 ). To systematize, we differentiate between the following five lines of research:

• simple (a) The search for individual differences comprises studies identifying interindividual differences that affect the ability to solve complex problems. This line of research is reflected, for example, in the early work by Dörner et al. (1983) and their “Lohhausen” study. Here, naïve student participants took over the role of the mayor of a small simulated town named Lohhausen for a simulation period of ten years. According to the results of the authors, it is not intelligence (as measured by conventional IQ tests) that predicts performance, but it is the ability to stay calm in the face of a challenging situation and the ability to switch easily between an analytic mode of processing and a more holistic one.
• simple (b) The search for cognitive processes deals with the processes behind understanding complex dynamic systems. Representative of this line of research is, for example, Berry and Broadbent’s (1984) work on implicit and explicit learning processes when people interact with a dynamic system called “Sugar Production”. They found that those who perform best in controlling a dynamic system can do so implicitly, without explicit knowledge of details regarding the systems’ relations.
• simple (c) The search for system factors seeks to identify the aspects of dynamic systems that determine the difficulty of complex problems and make some problems harder than others. Representative of this line of research is, for example, work by Funke (1985) , who systematically varied the number of causal effects within a dynamic system or the presence/absence of eigendynamics. He found, for example, that solution quality decreases as the number of systems relations increases.
• simple (d) The psychometric approach develops measurement instruments that can be used as an alternative to classical IQ tests, as something that goes “beyond IQ”. The MicroDYN approach ( Wüstenberg et al., 2012 ) is representative for this line of research that presents an alternative to reasoning tests (like Raven matrices). These authors demonstrated that a small improvement in predicting school grade point average beyond reasoning is possible with MicroDYN tests.
• simple (e) The experimental approach explores CPS under different experimental conditions. This approach uses CPS assessment instruments to test hypotheses derived from psychological theories and is sometimes used in research about cognitive processes (see above). Exemplary for this line of research is the work by Rohe et al. (2016) , who test the usefulness of “motto goals” in the context of complex problems compared to more traditional learning and performance goals. Motto goals differ from pure performance goals by activating positive affect and should lead to better goal attainment especially in complex situations (the mentioned study found no effect).

To be clear: these five approaches are not mutually exclusive and do overlap. But the differentiation helps to identify different research communities and different traditions. These communities had different opinions about scaling complexity.

The Race for Complexity: Use of More and More Complex Systems

In the early years of CPS research, microworlds started with systems containing about 20 variables (“Tailorshop”), soon reached 60 variables (“Moro”), and culminated in systems with about 2000 variables (“Lohhausen”). This race for complexity ended with the introduction of the concept of “minimal complex systems” (MCS; Greiff and Funke, 2009 ; Funke and Greiff, 2017 ), which ushered in a search for the lower bound of complexity instead of the higher bound, which could not be defined as easily. The idea behind this concept was that whereas the upper limits of complexity are unbound, the lower limits might be identifiable. Imagine starting with a simple system containing two variables with a simple linear connection between them; then, step by step, increase the number of variables and/or the type of connections. One soon reaches a point where the system can no longer be considered simple and has become a “complex system”. This point represents a minimal complex system. Despite some research having been conducted in this direction, the point of transition from simple to complex has not been identified clearly as of yet.

Some years later, the original “minimal complex systems” approach ( Greiff and Funke, 2009 ) shifted to the “multiple complex systems” approach ( Greiff et al., 2013a ). This shift is more than a slight change in wording: it is important because it taps into the issue of validity directly. Minimal complex systems have been introduced in the context of challenges from large-scale assessments like PISA 2012 that measure new aspects of problem solving, namely interactive problems besides static problem solving ( Greiff and Funke, 2017 ). PISA 2012 required test developers to remain within testing time constraints (given by the school class schedule). Also, test developers needed a large item pool for the construction of a broad class of problem solving items. It was clear from the beginning that MCS deal with simple dynamic situations that require controlled interaction: the exploration and control of simple ticket machines, simple mobile phones, or simple MP3 players (all of these example domains were developed within PISA 2012) – rather than really complex situations like managerial or political decision making.

As a consequence of this subtle but important shift in interpreting the letters MCS, the definition of CPS became a subject of debate recently ( Funke, 2014a ; Greiff and Martin, 2014 ; Funke et al., 2017 ). In the words of Funke (2014b , p. 495):

• simple  It is funny that problems that nowadays come under the term ‘CPS’, are less complex (in terms of the previously described attributes of complex situations) than at the beginning of this new research tradition. The emphasis on psychometric qualities has led to a loss of variety. Systems thinking requires more than analyzing models with two or three linear equations – nonlinearity, cyclicity, rebound effects, etc. are inherent features of complex problems and should show up at least in some of the problems used for research and assessment purposes. Minimal complex systems run the danger of becoming minimal valid systems.

Searching for minimal complex systems is not the same as gaining insight into the way how humans deal with complexity and uncertainty. For psychometric purposes, it is appropriate to reduce complexity to a minimum; for understanding problem solving under conditions of overload, intransparency, and dynamics, it is necessary to realize those attributes with reasonable strength. This aspect is illustrated in the next section.

Importance of the Validity Issue

The most important reason for discussing the question of what complex problem solving is and what it is not stems from its phenomenology: if we lose sight of our phenomena, we are no longer doing good psychology. The relevant phenomena in the context of complex problems encompass many important aspects. In this section, we discuss four phenomena that are specific to complex problems. We consider these phenomena as critical for theory development and for the construction of assessment instruments (i.e., microworlds). These phenomena require theories for explaining them and they require assessment instruments eliciting them in a reliable way.

The first phenomenon is the emergency reaction of the intellectual system ( Dörner, 1980 ): When dealing with complex systems, actors tend to (a) reduce their intellectual level by decreasing self-reflections, by decreasing their intentions, by stereotyping, and by reducing their realization of intentions, (b) they show a tendency for fast action with increased readiness for risk, with increased violations of rules, and with increased tendency to escape the situation, and (c) they degenerate their hypotheses formation by construction of more global hypotheses and reduced tests of hypotheses, by increasing entrenchment, and by decontextualizing their goals. This phenomenon illustrates the strong connection between cognition, emotion, and motivation that has been emphasized by Dörner (see, e.g., Dörner and Güss, 2013 ) from the beginning of his research tradition; the emergency reaction reveals a shift in the mode of information processing under the pressure of complexity.

The second phenomenon comprises cross-cultural differences with respect to strategy use ( Strohschneider and Güss, 1999 ; Güss and Wiley, 2007 ; Güss et al., 2015 ). Results from complex task environments illustrate the strong influence of context and background knowledge to an extent that cannot be found for knowledge-poor problems. For example, in a comparison between Brazilian and German participants, it turned out that Brazilians accept the given problem descriptions and are more optimistic about the results of their efforts, whereas Germans tend to inquire more about the background of the problems and take a more active approach but are less optimistic (according to Strohschneider and Güss, 1998 , p. 695).

The third phenomenon relates to failures that occur during the planning and acting stages ( Jansson, 1994 ; Ramnarayan et al., 1997 ), illustrating that rational procedures seem to be unlikely to be used in complex situations. The potential for failures ( Dörner, 1996 ) rises with the complexity of the problem. Jansson (1994) presents seven major areas for failures with complex situations: acting directly on current feedback; insufficient systematization; insufficient control of hypotheses and strategies; lack of self-reflection; selective information gathering; selective decision making; and thematic vagabonding.

The fourth phenomenon describes (a lack of) training and transfer effects ( Kretzschmar and Süß, 2015 ), which again illustrates the context dependency of strategies and knowledge (i.e., there is no strategy that is so universal that it can be used in many different problem situations). In their own experiment, the authors could show training effects only for knowledge acquisition, not for knowledge application. Only with specific feedback, performance in complex environments can be increased ( Engelhart et al., 2017 ).

These four phenomena illustrate why the type of complexity (or degree of simplicity) used in research really matters. Furthermore, they demonstrate effects that are specific for complex problems, but not for toy problems. These phenomena direct the attention to the important question: does the stimulus material used (i.e., the computer-simulated microworld) tap and elicit the manifold of phenomena described above?

Dealing with partly unknown complex systems requires courage, wisdom, knowledge, grit, and creativity. In creativity research, “little c” and “BIG C” are used to differentiate between everyday creativity and eminent creativity ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). Everyday creativity is important for solving everyday problems (e.g., finding a clever fix for a broken spoke on my bicycle), eminent creativity changes the world (e.g., inventing solar cells for energy production). Maybe problem solving research should use a similar differentiation between “little p” and “BIG P” to mark toy problems on the one side and big societal challenges on the other. The question then remains: what can we learn about BIG P by studying little p? What phenomena are present in both types, and what phenomena are unique to each of the two extremes?

Discussing research on CPS requires reflecting on the field’s research methods. Even if the experimental approach has been successful for testing hypotheses (for an overview of older work, see Funke, 1995 ), other methods might provide additional and novel insights. Complex phenomena require complex approaches to understand them. The complex nature of complex systems imposes limitations on psychological experiments: The more complex the environments, the more difficult is it to keep conditions under experimental control. And if experiments have to be run in labs one should bring enough complexity into the lab to establish the phenomena mentioned, at least in part.

There are interesting options to be explored (again): think-aloud protocols , which have been discredited for many years ( Nisbett and Wilson, 1977 ) and yet are a valuable source for theory testing ( Ericsson and Simon, 1983 ); introspection ( Jäkel and Schreiber, 2013 ), which seems to be banned from psychological methods but nevertheless offers insights into thought processes; the use of life-streaming ( Wendt, 2017 ), a medium in which streamers generate a video stream of think-aloud data in computer-gaming; political decision-making ( Dhami et al., 2015 ) that demonstrates error-proneness in groups; historical case studies ( Dörner and Güss, 2011 ) that give insights into the thinking styles of political leaders; the use of the critical incident technique ( Reuschenbach, 2008 ) to construct complex scenarios; and simulations with different degrees of fidelity ( Gray, 2002 ).

The methods tool box is full of instruments that have to be explored more carefully before any individual instrument receives a ban or research narrows its focus to only one paradigm for data collection. Brehmer and Dörner (1993) discussed the tensions between “research in the laboratory and research in the field”, optimistically concluding “that the new methodology of computer-simulated microworlds will provide us with the means to bridge the gap between the laboratory and the field” (p. 183). The idea behind this optimism was that computer-simulated scenarios would bring more complexity from the outside world into the controlled lab environment. But this is not true for all simulated scenarios. In his paper on simulated environments, Gray (2002) differentiated computer-simulated environments with respect to three dimensions: (1) tractability (“the more training subjects require before they can use a simulated task environment, the less tractable it is”, p. 211), correspondence (“High correspondence simulated task environments simulate many aspects of one task environment. Low correspondence simulated task environments simulate one aspect of many task environments”, p. 214), and engagement (“A simulated task environment is engaging to the degree to which it involves and occupies the participants; that is, the degree to which they agree to take it seriously”, p. 217). But the mere fact that a task is called a “computer-simulated task environment” does not mean anything specific in terms of these three dimensions. This is one of several reasons why we should differentiate between those studies that do not address the core features of CPS and those that do.

What is not CPS?

Even though a growing number of references claiming to deal with complex problems exist (e.g., Greiff and Wüstenberg, 2015 ; Greiff et al., 2016 ), it would be better to label the requirements within these tasks “dynamic problem solving,” as it has been done adequately in earlier work ( Greiff et al., 2012 ). The dynamics behind on-off-switches ( Thimbleby, 2007 ) are remarkable but not really complex. Small nonlinear systems that exhibit stunningly complex and unstable behavior do exist – but they are not used in psychometric assessments of so-called CPS. There are other small systems (like MicroDYN scenarios: Greiff and Wüstenberg, 2014 ) that exhibit simple forms of system behavior that are completely predictable and stable. This type of simple systems is used frequently. It is even offered commercially as a complex problem-solving test called COMPRO ( Greiff and Wüstenberg, 2015 ) for business applications. But a closer look reveals that the label is not used correctly; within COMPRO, the used linear equations are far from being complex and the system can be handled properly by using only one strategy (see for more details Funke et al., 2017 ).

Why do simple linear systems not fall within CPS? At the surface, nonlinear and linear systems might appear similar because both only include 3–5 variables. But the difference is in terms of systems behavior as well as strategies and learning. If the behavior is simple (as in linear systems where more input is related to more output and vice versa), the system can be easily understood (participants in the MicroDYN world have 3 minutes to explore a complex system). If the behavior is complex (as in systems that contain strange attractors or negative feedback loops), things become more complicated and much more observation is needed to identify the hidden structure of the unknown system ( Berry and Broadbent, 1984 ; Hundertmark et al., 2015 ).

Another issue is learning. If tasks can be solved using a single (and not so complicated) strategy, steep learning curves are to be expected. The shift from problem solving to learned routine behavior occurs rapidly, as was demonstrated by Luchins (1942) . In his water jar experiments, participants quickly acquired a specific strategy (a mental set) for solving certain measurement problems that they later continued applying to problems that would have allowed for easier approaches. In the case of complex systems, learning can occur only on very general, abstract levels because it is difficult for human observers to make specific predictions. Routines dealing with complex systems are quite different from routines relating to linear systems.

What should not be studied under the label of CPS are pure learning effects, multiple-cue probability learning, or tasks that can be solved using a single strategy. This last issue is a problem for MicroDYN tasks that rely strongly on the VOTAT strategy (“vary one thing at a time”; see Tschirgi, 1980 ). In real-life, it is hard to imagine a business manager trying to solve her or his problems by means of VOTAT.

What is CPS?

In the early days of CPS research, planet Earth’s dynamics and complexities gained attention through such books as “The limits to growth” ( Meadows et al., 1972 ) and “Beyond the limits” ( Meadows et al., 1992 ). In the current decade, for example, the World Economic Forum (2016) attempts to identify the complexities and risks of our modern world. In order to understand the meaning of complexity and uncertainty, taking a look at the worlds’ most pressing issues is helpful. Searching for strategies to cope with these problems is a difficult task: surely there is no place for the simple principle of “vary-one-thing-at-a-time” (VOTAT) when it comes to global problems. The VOTAT strategy is helpful in the context of simple problems ( Wüstenberg et al., 2014 ); therefore, whether or not VOTAT is helpful in a given problem situation helps us distinguish simple from complex problems.

Because there exist no clear-cut strategies for complex problems, typical failures occur when dealing with uncertainty ( Dörner, 1996 ; Güss et al., 2015 ). Ramnarayan et al. (1997) put together a list of generic errors (e.g., not developing adequate action plans; lack of background control; learning from experience blocked by stereotype knowledge; reactive instead of proactive action) that are typical of knowledge-rich complex systems but cannot be found in simple problems.

Complex problem solving is not a one-dimensional, low-level construct. On the contrary, CPS is a multi-dimensional bundle of competencies existing at a high level of abstraction, similar to intelligence (but going beyond IQ). As Funke et al. (2018) state: “Assessment of transversal (in educational contexts: cross-curricular) competencies cannot be done with one or two types of assessment. The plurality of skills and competencies requires a plurality of assessment instruments.”

There are at least three different aspects of complex systems that are part of our understanding of a complex system: (1) a complex system can be described at different levels of abstraction; (2) a complex system develops over time, has a history, a current state, and a (potentially unpredictable) future; (3) a complex system is knowledge-rich and activates a large semantic network, together with a broad list of potential strategies (domain-specific as well as domain-general).

Complex problem solving is not only a cognitive process but is also an emotional one ( Spering et al., 2005 ; Barth and Funke, 2010 ) and strongly dependent on motivation (low-stakes versus high-stakes testing; see Hermes and Stelling, 2016 ).

Furthermore, CPS is a dynamic process unfolding over time, with different phases and with more differentiation than simply knowledge acquisition and knowledge application. Ideally, the process should entail identifying problems (see Dillon, 1982 ; Lee and Cho, 2007 ), even if in experimental settings, problems are provided to participants a priori . The more complex and open a given situation, the more options can be generated (T. S. Schweizer et al., 2016 ). In closed problems, these processes do not occur in the same way.

In analogy to the difference between formative (process-oriented) and summative (result-oriented) assessment ( Wiliam and Black, 1996 ; Bennett, 2011 ), CPS should not be reduced to the mere outcome of a solution process. The process leading up to the solution, including detours and errors made along the way, might provide a more differentiated impression of a person’s problem-solving abilities and competencies than the final result of such a process. This is one of the reasons why CPS environments are not, in fact, complex intelligence tests: research on CPS is not only about the outcome of the decision process, but it is also about the problem-solving process itself.

Complex problem solving is part of our daily life: finding the right person to share one’s life with, choosing a career that not only makes money, but that also makes us happy. Of course, CPS is not restricted to personal problems – life on Earth gives us many hard nuts to crack: climate change, population growth, the threat of war, the use and distribution of natural resources. In sum, many societal challenges can be seen as complex problems. To reduce that complexity to a one-hour lab activity on a random Friday afternoon puts it out of context and does not address CPS issues.

Theories about CPS should specify which populations they apply to. Across populations, one thing to consider is prior knowledge. CPS research with experts (e.g., Dew et al., 2009 ) is quite different from problem solving research using tasks that intentionally do not require any specific prior knowledge (see, e.g., Beckmann and Goode, 2014 ).

More than 20 years ago, Frensch and Funke (1995b) defined CPS as follows:

• simple  CPS occurs to overcome barriers between a given state and a desired goal state by means of behavioral and/or cognitive, multi-step activities. The given state, goal state, and barriers between given state and goal state are complex, change dynamically during problem solving, and are intransparent. The exact properties of the given state, goal state, and barriers are unknown to the solver at the outset. CPS implies the efficient interaction between a solver and the situational requirements of the task, and involves a solver’s cognitive, emotional, personal, and social abilities and knowledge. (p. 18)

The above definition is rather formal and does not account for content or relations between the simulation and the real world. In a sense, we need a new definition of CPS that addresses these issues. Based on our previous arguments, we propose the following working definition:

• simple  Complex problem solving is a collection of self-regulated psychological processes and activities necessary in dynamic environments to achieve ill-defined goals that cannot be reached by routine actions. Creative combinations of knowledge and a broad set of strategies are needed. Solutions are often more bricolage than perfect or optimal. The problem-solving process combines cognitive, emotional, and motivational aspects, particularly in high-stakes situations. Complex problems usually involve knowledge-rich requirements and collaboration among different persons.

The main differences to the older definition lie in the emphasis on (a) the self-regulation of processes, (b) creativity (as opposed to routine behavior), (c) the bricolage type of solution, and (d) the role of high-stakes challenges. Our new definition incorporates some aspects that have been discussed in this review but were not reflected in the 1995 definition, which focused on attributes of complex problems like dynamics or intransparency.

This leads us to the final reflection about the role of CPS for dealing with uncertainty and complexity in real life. We will distinguish thinking from reasoning and introduce the sense of possibility as an important aspect of validity.

CPS as Combining Reasoning and Thinking in an Uncertain Reality

Leading up to the Battle of Borodino in Leo Tolstoy’s novel “War and Peace”, Prince Andrei Bolkonsky explains the concept of war to his friend Pierre. Pierre expects war to resemble a game of chess: You position the troops and attempt to defeat your opponent by moving them in different directions.

“Far from it!”, Andrei responds. “In chess, you know the knight and his moves, you know the pawn and his combat strength. While in war, a battalion is sometimes stronger than a division and sometimes weaker than a company; it all depends on circumstances that can never be known. In war, you do not know the position of your enemy; some things you might be able to observe, some things you have to divine (but that depends on your ability to do so!) and many things cannot even be guessed at. In chess, you can see all of your opponent’s possible moves. In war, that is impossible. If you decide to attack, you cannot know whether the necessary conditions are met for you to succeed. Many a time, you cannot even know whether your troops will follow your orders…”

In essence, war is characterized by a high degree of uncertainty. A good commander (or politician) can add to that what he or she sees, tentatively fill in the blanks – and not just by means of logical deduction but also by intelligently bridging missing links. A bad commander extrapolates from what he sees and thus arrives at improper conclusions.

Many languages differentiate between two modes of mentalizing; for instance, the English language distinguishes between ‘thinking’ and ‘reasoning’. Reasoning denotes acute and exact mentalizing involving logical deductions. Such deductions are usually based on evidence and counterevidence. Thinking, however, is what is required to write novels. It is the construction of an initially unknown reality. But it is not a pipe dream, an unfounded process of fabrication. Rather, thinking asks us to imagine reality (“Wirklichkeitsfantasie”). In other words, a novelist has to possess a “sense of possibility” (“Möglichkeitssinn”, Robert Musil; in German, sense of possibility is often used synonymously with imagination even though imagination is not the same as sense of possibility, for imagination also encapsulates the impossible). This sense of possibility entails knowing the whole (or several wholes) or being able to construe an unknown whole that could accommodate a known part. The whole has to align with sociological and geographical givens, with the mentality of certain peoples or groups, and with the laws of physics and chemistry. Otherwise, the entire venture is ill-founded. A sense of possibility does not aim for the moon but imagines something that might be possible but has not been considered possible or even potentially possible so far.

Thinking is a means to eliminate uncertainty. This process requires both of the modes of thinking we have discussed thus far. Economic, political, or ecological decisions require us to first consider the situation at hand. Though certain situational aspects can be known, but many cannot. In fact, von Clausewitz (1832) posits that only about 25% of the necessary information is available when a military decision needs to be made. Even then, there is no way to guarantee that whatever information is available is also correct: Even if a piece of information was completely accurate yesterday, it might no longer apply today.

Once our sense of possibility has helped grasping a situation, problem solvers need to call on their reasoning skills. Not every situation requires the same action, and we may want to act this way or another to reach this or that goal. This appears logical, but it is a logic based on constantly shifting grounds: We cannot know whether necessary conditions are met, sometimes the assumptions we have made later turn out to be incorrect, and sometimes we have to revise our assumptions or make completely new ones. It is necessary to constantly switch between our sense of possibility and our sense of reality, that is, to switch between thinking and reasoning. It is an arduous process, and some people handle it well, while others do not.

If we are to believe Tuchman’s (1984) book, “The March of Folly”, most politicians and commanders are fools. According to Tuchman, not much has changed in the 3300 years that have elapsed since the misguided Trojans decided to welcome the left-behind wooden horse into their city that would end up dismantling Troy’s defensive walls. The Trojans, too, had been warned, but decided not to heed the warning. Although Laocoön had revealed the horse’s true nature to them by attacking it with a spear, making the weapons inside the horse ring, the Trojans refused to see the forest for the trees. They did not want to listen, they wanted the war to be over, and this desire ended up shaping their perception.

The objective of psychology is to predict and explain human actions and behavior as accurately as possible. However, thinking cannot be investigated by limiting its study to neatly confined fractions of reality such as the realms of propositional logic, chess, Go tasks, the Tower of Hanoi, and so forth. Within these systems, there is little need for a sense of possibility. But a sense of possibility – the ability to divine and construe an unknown reality – is at least as important as logical reasoning skills. Not researching the sense of possibility limits the validity of psychological research. All economic and political decision making draws upon this sense of possibility. By not exploring it, psychological research dedicated to the study of thinking cannot further the understanding of politicians’ competence and the reasons that underlie political mistakes. Christopher Clark identifies European diplomats’, politicians’, and commanders’ inability to form an accurate representation of reality as a reason for the outbreak of World War I. According to Clark’s (2012) book, “The Sleepwalkers”, the politicians of the time lived in their own make-believe world, wrongfully assuming that it was the same world everyone else inhabited. If CPS research wants to make significant contributions to the world, it has to acknowledge complexity and uncertainty as important aspects of it.

For more than 40 years, CPS has been a new subject of psychological research. During this time period, the initial emphasis on analyzing how humans deal with complex, dynamic, and uncertain situations has been lost. What is subsumed under the heading of CPS in modern research has lost the original complexities of real-life problems. From our point of view, the challenges of the 21st century require a return to the origins of this research tradition. We would encourage researchers in the field of problem solving to come back to the original ideas. There is enough complexity and uncertainty in the world to be studied. Improving our understanding of how humans deal with these global and pressing problems would be a worthwhile enterprise.

Author Contributions

JF drafted a first version of the manuscript, DD added further text and commented on the draft. JF finalized the manuscript.

Authors Note

After more than 40 years of controversial discussions between both authors, this is the first joint paper. We are happy to have done this now! We have found common ground!

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank the Deutsche Forschungsgemeinschaft (DFG) for the continuous support of their research over many years. Thanks to Daniel Holt for his comments on validity issues, thanks to Julia Nolte who helped us by translating German text excerpts into readable English and helped us, together with Keri Hartman, to improve our style and grammar – thanks for that! We also thank the two reviewers for their helpful critical comments on earlier versions of this manuscript. Finally, we acknowledge financial support by Deutsche Forschungsgemeinschaft and Ruprecht-Karls-Universität Heidelberg within their funding programme Open Access Publishing .

1 The fMRI-paper from Anderson (2012) uses the term “complex problem solving” for tasks that do not fall in our understanding of CPS and is therefore excluded from this list.

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Problem solving articles from across Nature Portfolio

Problem solving is the mental process of analyzing a situation, learning what options are available, and then choosing the alternative that will result in the desired outcome or some other selected goal.

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A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question. In the social and behavioral sciences, studies are most often framed around examining a problem that needs to be understood and resolved in order to improve society and the human condition.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Guba, Egon G., and Yvonna S. Lincoln. “Competing Paradigms in Qualitative Research.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, editors. (Thousand Oaks, CA: Sage, 1994), pp. 105-117; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

Importance of...

The purpose of a problem statement is to:

• Introduce the reader to the importance of the topic being studied . The reader is oriented to the significance of the study.
• Anchors the research questions, hypotheses, or assumptions to follow . It offers a concise statement about the purpose of your paper.
• Place the topic into a particular context that defines the parameters of what is to be investigated.
• Provide the framework for reporting the results and indicates what is probably necessary to conduct the study and explain how the findings will present this information.

In the social sciences, the research problem establishes the means by which you must answer the "So What?" question. This declarative question refers to a research problem surviving the relevancy test [the quality of a measurement procedure that provides repeatability and accuracy]. Note that answering the "So What?" question requires a commitment on your part to not only show that you have reviewed the literature, but that you have thoroughly considered the significance of the research problem and its implications applied to creating new knowledge and understanding or informing practice.

To survive the "So What" question, problem statements should possess the following attributes:

• Clarity and precision [a well-written statement does not make sweeping generalizations and irresponsible pronouncements; it also does include unspecific determinates like "very" or "giant"],
• Demonstrate a researchable topic or issue [i.e., feasibility of conducting the study is based upon access to information that can be effectively acquired, gathered, interpreted, synthesized, and understood],
• Identification of what would be studied, while avoiding the use of value-laden words and terms,
• Identification of an overarching question or small set of questions accompanied by key factors or variables,
• Identification of key concepts and terms,
• Articulation of the study's conceptual boundaries or parameters or limitations,
• Some generalizability in regards to applicability and bringing results into general use,
• Conveyance of the study's importance, benefits, and justification [i.e., regardless of the type of research, it is important to demonstrate that the research is not trivial],
• Does not have unnecessary jargon or overly complex sentence constructions; and,
• Conveyance of more than the mere gathering of descriptive data providing only a snapshot of the issue or phenomenon under investigation.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Brown, Perry J., Allen Dyer, and Ross S. Whaley. "Recreation Research—So What?" Journal of Leisure Research 5 (1973): 16-24; Castellanos, Susie. Critical Writing and Thinking. The Writing Center. Dean of the College. Brown University; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Selwyn, Neil. "‘So What?’…A Question that Every Journal Article Needs to Answer." Learning, Media, and Technology 39 (2014): 1-5; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518.

Structure and Writing Style

I.  Types and Content

There are four general conceptualizations of a research problem in the social sciences:

• Casuist Research Problem -- this type of problem relates to the determination of right and wrong in questions of conduct or conscience by analyzing moral dilemmas through the application of general rules and the careful distinction of special cases.
• Difference Research Problem -- typically asks the question, “Is there a difference between two or more groups or treatments?” This type of problem statement is used when the researcher compares or contrasts two or more phenomena. This a common approach to defining a problem in the clinical social sciences or behavioral sciences.
• Descriptive Research Problem -- typically asks the question, "what is...?" with the underlying purpose to describe the significance of a situation, state, or existence of a specific phenomenon. This problem is often associated with revealing hidden or understudied issues.
• Relational Research Problem -- suggests a relationship of some sort between two or more variables to be investigated. The underlying purpose is to investigate specific qualities or characteristics that may be connected in some way.

A problem statement in the social sciences should contain :

• A lead-in that helps ensure the reader will maintain interest over the study,
• A declaration of originality [e.g., mentioning a knowledge void or a lack of clarity about a topic that will be revealed in the literature review of prior research],
• An indication of the central focus of the study [establishing the boundaries of analysis], and
• An explanation of the study's significance or the benefits to be derived from investigating the research problem.

NOTE:   A statement describing the research problem of your paper should not be viewed as a thesis statement that you may be familiar with from high school. Given the content listed above, a description of the research problem is usually a short paragraph in length.

II.  Sources of Problems for Investigation

The identification of a problem to study can be challenging, not because there's a lack of issues that could be investigated, but due to the challenge of formulating an academically relevant and researchable problem which is unique and does not simply duplicate the work of others. To facilitate how you might select a problem from which to build a research study, consider these sources of inspiration:

Deductions from Theory This relates to deductions made from social philosophy or generalizations embodied in life and in society that the researcher is familiar with. These deductions from human behavior are then placed within an empirical frame of reference through research. From a theory, the researcher can formulate a research problem or hypothesis stating the expected findings in certain empirical situations. The research asks the question: “What relationship between variables will be observed if theory aptly summarizes the state of affairs?” One can then design and carry out a systematic investigation to assess whether empirical data confirm or reject the hypothesis, and hence, the theory.

Interdisciplinary Perspectives Identifying a problem that forms the basis for a research study can come from academic movements and scholarship originating in disciplines outside of your primary area of study. This can be an intellectually stimulating exercise. A review of pertinent literature should include examining research from related disciplines that can reveal new avenues of exploration and analysis. An interdisciplinary approach to selecting a research problem offers an opportunity to construct a more comprehensive understanding of a very complex issue that any single discipline may be able to provide.

Interviewing Practitioners The identification of research problems about particular topics can arise from formal interviews or informal discussions with practitioners who provide insight into new directions for future research and how to make research findings more relevant to practice. Discussions with experts in the field, such as, teachers, social workers, health care providers, lawyers, business leaders, etc., offers the chance to identify practical, “real world” problems that may be understudied or ignored within academic circles. This approach also provides some practical knowledge which may help in the process of designing and conducting your study.

Personal Experience Don't undervalue your everyday experiences or encounters as worthwhile problems for investigation. Think critically about your own experiences and/or frustrations with an issue facing society or related to your community, your neighborhood, your family, or your personal life. This can be derived, for example, from deliberate observations of certain relationships for which there is no clear explanation or witnessing an event that appears harmful to a person or group or that is out of the ordinary.

Relevant Literature The selection of a research problem can be derived from a thorough review of pertinent research associated with your overall area of interest. This may reveal where gaps exist in understanding a topic or where an issue has been understudied. Research may be conducted to: 1) fill such gaps in knowledge; 2) evaluate if the methodologies employed in prior studies can be adapted to solve other problems; or, 3) determine if a similar study could be conducted in a different subject area or applied in a different context or to different study sample [i.e., different setting or different group of people]. Also, authors frequently conclude their studies by noting implications for further research; read the conclusion of pertinent studies because statements about further research can be a valuable source for identifying new problems to investigate. The fact that a researcher has identified a topic worthy of further exploration validates the fact it is worth pursuing.

III.  What Makes a Good Research Statement?

A good problem statement begins by introducing the broad area in which your research is centered, gradually leading the reader to the more specific issues you are investigating. The statement need not be lengthy, but a good research problem should incorporate the following features:

1.  Compelling Topic The problem chosen should be one that motivates you to address it but simple curiosity is not a good enough reason to pursue a research study because this does not indicate significance. The problem that you choose to explore must be important to you, but it must also be viewed as important by your readers and to a the larger academic and/or social community that could be impacted by the results of your study. 2.  Supports Multiple Perspectives The problem must be phrased in a way that avoids dichotomies and instead supports the generation and exploration of multiple perspectives. A general rule of thumb in the social sciences is that a good research problem is one that would generate a variety of viewpoints from a composite audience made up of reasonable people. 3.  Researchability This isn't a real word but it represents an important aspect of creating a good research statement. It seems a bit obvious, but you don't want to find yourself in the midst of investigating a complex research project and realize that you don't have enough prior research to draw from for your analysis. There's nothing inherently wrong with original research, but you must choose research problems that can be supported, in some way, by the resources available to you. If you are not sure if something is researchable, don't assume that it isn't if you don't find information right away--seek help from a librarian !

NOTE:   Do not confuse a research problem with a research topic. A topic is something to read and obtain information about, whereas a problem is something to be solved or framed as a question raised for inquiry, consideration, or solution, or explained as a source of perplexity, distress, or vexation. In short, a research topic is something to be understood; a research problem is something that needs to be investigated.

IV.  Asking Analytical Questions about the Research Problem

Research problems in the social and behavioral sciences are often analyzed around critical questions that must be investigated. These questions can be explicitly listed in the introduction [i.e., "This study addresses three research questions about women's psychological recovery from domestic abuse in multi-generational home settings..."], or, the questions are implied in the text as specific areas of study related to the research problem. Explicitly listing your research questions at the end of your introduction can help in designing a clear roadmap of what you plan to address in your study, whereas, implicitly integrating them into the text of the introduction allows you to create a more compelling narrative around the key issues under investigation. Either approach is appropriate.

The number of questions you attempt to address should be based on the complexity of the problem you are investigating and what areas of inquiry you find most critical to study. Practical considerations, such as, the length of the paper you are writing or the availability of resources to analyze the issue can also factor in how many questions to ask. In general, however, there should be no more than four research questions underpinning a single research problem.

Given this, well-developed analytical questions can focus on any of the following:

• Highlights a genuine dilemma, area of ambiguity, or point of confusion about a topic open to interpretation by your readers;
• Yields an answer that is unexpected and not obvious rather than inevitable and self-evident;
• Provokes meaningful thought or discussion;
• Raises the visibility of the key ideas or concepts that may be understudied or hidden;
• Suggests the need for complex analysis or argument rather than a basic description or summary; and,
• Offers a specific path of inquiry that avoids eliciting generalizations about the problem.

NOTE:   Questions of how and why concerning a research problem often require more analysis than questions about who, what, where, and when. You should still ask yourself these latter questions, however. Thinking introspectively about the who, what, where, and when of a research problem can help ensure that you have thoroughly considered all aspects of the problem under investigation and helps define the scope of the study in relation to the problem.

V.  Mistakes to Avoid

Beware of circular reasoning! Do not state the research problem as simply the absence of the thing you are suggesting. For example, if you propose the following, "The problem in this community is that there is no hospital," this only leads to a research problem where:

• The need is for a hospital
• The objective is to create a hospital
• The method is to plan for building a hospital, and
• The evaluation is to measure if there is a hospital or not.

This is an example of a research problem that fails the "So What?" test . In this example, the problem does not reveal the relevance of why you are investigating the fact there is no hospital in the community [e.g., perhaps there's a hospital in the community ten miles away]; it does not elucidate the significance of why one should study the fact there is no hospital in the community [e.g., that hospital in the community ten miles away has no emergency room]; the research problem does not offer an intellectual pathway towards adding new knowledge or clarifying prior knowledge [e.g., the county in which there is no hospital already conducted a study about the need for a hospital, but it was conducted ten years ago]; and, the problem does not offer meaningful outcomes that lead to recommendations that can be generalized for other situations or that could suggest areas for further research [e.g., the challenges of building a new hospital serves as a case study for other communities].

Alvesson, Mats and Jörgen Sandberg. “Generating Research Questions Through Problematization.” Academy of Management Review 36 (April 2011): 247-271 ; Choosing and Refining Topics. Writing@CSU. Colorado State University; D'Souza, Victor S. "Use of Induction and Deduction in Research in Social Sciences: An Illustration." Journal of the Indian Law Institute 24 (1982): 655-661; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); How to Write a Research Question. The Writing Center. George Mason University; Invention: Developing a Thesis Statement. The Reading/Writing Center. Hunter College; Problem Statements PowerPoint Presentation. The Writing Lab and The OWL. Purdue University; Procter, Margaret. Using Thesis Statements. University College Writing Centre. University of Toronto; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518; Trochim, William M.K. Problem Formulation. Research Methods Knowledge Base. 2006; Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Walk, Kerry. Asking an Analytical Question. [Class handout or worksheet]. Princeton University; White, Patrick. Developing Research Questions: A Guide for Social Scientists . New York: Palgrave McMillan, 2009; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

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Home » Research Problem – Examples, Types and Guide

Research Problem – Examples, Types and Guide

Table of Contents

Research Problem

Definition:

Research problem is a specific and well-defined issue or question that a researcher seeks to investigate through research. It is the starting point of any research project, as it sets the direction, scope, and purpose of the study.

Types of Research Problems

Types of Research Problems are as follows:

Descriptive problems

These problems involve describing or documenting a particular phenomenon, event, or situation. For example, a researcher might investigate the demographics of a particular population, such as their age, gender, income, and education.

Exploratory problems

These problems are designed to explore a particular topic or issue in depth, often with the goal of generating new ideas or hypotheses. For example, a researcher might explore the factors that contribute to job satisfaction among employees in a particular industry.

Explanatory Problems

These problems seek to explain why a particular phenomenon or event occurs, and they typically involve testing hypotheses or theories. For example, a researcher might investigate the relationship between exercise and mental health, with the goal of determining whether exercise has a causal effect on mental health.

Predictive Problems

These problems involve making predictions or forecasts about future events or trends. For example, a researcher might investigate the factors that predict future success in a particular field or industry.

Evaluative Problems

These problems involve assessing the effectiveness of a particular intervention, program, or policy. For example, a researcher might evaluate the impact of a new teaching method on student learning outcomes.

How to Define a Research Problem

Defining a research problem involves identifying a specific question or issue that a researcher seeks to address through a research study. Here are the steps to follow when defining a research problem:

• Identify a broad research topic : Start by identifying a broad topic that you are interested in researching. This could be based on your personal interests, observations, or gaps in the existing literature.
• Conduct a literature review : Once you have identified a broad topic, conduct a thorough literature review to identify the current state of knowledge in the field. This will help you identify gaps or inconsistencies in the existing research that can be addressed through your study.
• Refine the research question: Based on the gaps or inconsistencies identified in the literature review, refine your research question to a specific, clear, and well-defined problem statement. Your research question should be feasible, relevant, and important to the field of study.
• Develop a hypothesis: Based on the research question, develop a hypothesis that states the expected relationship between variables.
• Define the scope and limitations: Clearly define the scope and limitations of your research problem. This will help you focus your study and ensure that your research objectives are achievable.
• Get feedback: Get feedback from your advisor or colleagues to ensure that your research problem is clear, feasible, and relevant to the field of study.

Components of a Research Problem

The components of a research problem typically include the following:

• Topic : The general subject or area of interest that the research will explore.
• Research Question : A clear and specific question that the research seeks to answer or investigate.
• Objective : A statement that describes the purpose of the research, what it aims to achieve, and the expected outcomes.
• Hypothesis : An educated guess or prediction about the relationship between variables, which is tested during the research.
• Variables : The factors or elements that are being studied, measured, or manipulated in the research.
• Methodology : The overall approach and methods that will be used to conduct the research.
• Scope and Limitations : A description of the boundaries and parameters of the research, including what will be included and excluded, and any potential constraints or limitations.
• Significance: A statement that explains the potential value or impact of the research, its contribution to the field of study, and how it will add to the existing knowledge.

Research Problem Examples

Following are some Research Problem Examples:

Research Problem Examples in Psychology are as follows:

• Exploring the impact of social media on adolescent mental health.
• Investigating the effectiveness of cognitive-behavioral therapy for treating anxiety disorders.
• Studying the impact of prenatal stress on child development outcomes.
• Analyzing the factors that contribute to addiction and relapse in substance abuse treatment.
• Examining the impact of personality traits on romantic relationships.

Research Problem Examples in Sociology are as follows:

• Investigating the relationship between social support and mental health outcomes in marginalized communities.
• Studying the impact of globalization on labor markets and employment opportunities.
• Analyzing the causes and consequences of gentrification in urban neighborhoods.
• Investigating the impact of family structure on social mobility and economic outcomes.
• Examining the effects of social capital on community development and resilience.

Research Problem Examples in Economics are as follows:

• Studying the effects of trade policies on economic growth and development.
• Analyzing the impact of automation and artificial intelligence on labor markets and employment opportunities.
• Investigating the factors that contribute to economic inequality and poverty.
• Examining the impact of fiscal and monetary policies on inflation and economic stability.
• Studying the relationship between education and economic outcomes, such as income and employment.

Political Science

Research Problem Examples in Political Science are as follows:

• Analyzing the causes and consequences of political polarization and partisan behavior.
• Investigating the impact of social movements on political change and policymaking.
• Studying the role of media and communication in shaping public opinion and political discourse.
• Examining the effectiveness of electoral systems in promoting democratic governance and representation.
• Investigating the impact of international organizations and agreements on global governance and security.

Environmental Science

Research Problem Examples in Environmental Science are as follows:

• Studying the impact of air pollution on human health and well-being.
• Investigating the effects of deforestation on climate change and biodiversity loss.
• Analyzing the impact of ocean acidification on marine ecosystems and food webs.
• Studying the relationship between urban development and ecological resilience.
• Examining the effectiveness of environmental policies and regulations in promoting sustainability and conservation.

Research Problem Examples in Education are as follows:

• Investigating the impact of teacher training and professional development on student learning outcomes.
• Studying the effectiveness of technology-enhanced learning in promoting student engagement and achievement.
• Analyzing the factors that contribute to achievement gaps and educational inequality.
• Examining the impact of parental involvement on student motivation and achievement.
• Studying the effectiveness of alternative educational models, such as homeschooling and online learning.

Research Problem Examples in History are as follows:

• Analyzing the social and economic factors that contributed to the rise and fall of ancient civilizations.
• Investigating the impact of colonialism on indigenous societies and cultures.
• Studying the role of religion in shaping political and social movements throughout history.
• Analyzing the impact of the Industrial Revolution on economic and social structures.
• Examining the causes and consequences of global conflicts, such as World War I and II.

Research Problem Examples in Business are as follows:

• Studying the impact of corporate social responsibility on brand reputation and consumer behavior.
• Investigating the effectiveness of leadership development programs in improving organizational performance and employee satisfaction.
• Analyzing the factors that contribute to successful entrepreneurship and small business development.
• Examining the impact of mergers and acquisitions on market competition and consumer welfare.
• Studying the effectiveness of marketing strategies and advertising campaigns in promoting brand awareness and sales.

Research Problem Example for Students

An Example of a Research Problem for Students could be:

“How does social media usage affect the academic performance of high school students?”

This research problem is specific, measurable, and relevant. It is specific because it focuses on a particular area of interest, which is the impact of social media on academic performance. It is measurable because the researcher can collect data on social media usage and academic performance to evaluate the relationship between the two variables. It is relevant because it addresses a current and important issue that affects high school students.

To conduct research on this problem, the researcher could use various methods, such as surveys, interviews, and statistical analysis of academic records. The results of the study could provide insights into the relationship between social media usage and academic performance, which could help educators and parents develop effective strategies for managing social media use among students.

Another example of a research problem for students:

“Does participation in extracurricular activities impact the academic performance of middle school students?”

This research problem is also specific, measurable, and relevant. It is specific because it focuses on a particular type of activity, extracurricular activities, and its impact on academic performance. It is measurable because the researcher can collect data on students’ participation in extracurricular activities and their academic performance to evaluate the relationship between the two variables. It is relevant because extracurricular activities are an essential part of the middle school experience, and their impact on academic performance is a topic of interest to educators and parents.

To conduct research on this problem, the researcher could use surveys, interviews, and academic records analysis. The results of the study could provide insights into the relationship between extracurricular activities and academic performance, which could help educators and parents make informed decisions about the types of activities that are most beneficial for middle school students.

Applications of Research Problem

Applications of Research Problem are as follows:

• Academic research: Research problems are used to guide academic research in various fields, including social sciences, natural sciences, humanities, and engineering. Researchers use research problems to identify gaps in knowledge, address theoretical or practical problems, and explore new areas of study.
• Business research : Research problems are used to guide business research, including market research, consumer behavior research, and organizational research. Researchers use research problems to identify business challenges, explore opportunities, and develop strategies for business growth and success.
• Healthcare research : Research problems are used to guide healthcare research, including medical research, clinical research, and health services research. Researchers use research problems to identify healthcare challenges, develop new treatments and interventions, and improve healthcare delivery and outcomes.
• Public policy research : Research problems are used to guide public policy research, including policy analysis, program evaluation, and policy development. Researchers use research problems to identify social issues, assess the effectiveness of existing policies and programs, and develop new policies and programs to address societal challenges.
• Environmental research : Research problems are used to guide environmental research, including environmental science, ecology, and environmental management. Researchers use research problems to identify environmental challenges, assess the impact of human activities on the environment, and develop sustainable solutions to protect the environment.

Purpose of Research Problems

The purpose of research problems is to identify an area of study that requires further investigation and to formulate a clear, concise and specific research question. A research problem defines the specific issue or problem that needs to be addressed and serves as the foundation for the research project.

Identifying a research problem is important because it helps to establish the direction of the research and sets the stage for the research design, methods, and analysis. It also ensures that the research is relevant and contributes to the existing body of knowledge in the field.

A well-formulated research problem should:

• Clearly define the specific issue or problem that needs to be investigated
• Be specific and narrow enough to be manageable in terms of time, resources, and scope
• Be relevant to the field of study and contribute to the existing body of knowledge
• Be feasible and realistic in terms of available data, resources, and research methods
• Be interesting and intellectually stimulating for the researcher and potential readers or audiences.

Characteristics of Research Problem

The characteristics of a research problem refer to the specific features that a problem must possess to qualify as a suitable research topic. Some of the key characteristics of a research problem are:

• Clarity : A research problem should be clearly defined and stated in a way that it is easily understood by the researcher and other readers. The problem should be specific, unambiguous, and easy to comprehend.
• Relevance : A research problem should be relevant to the field of study, and it should contribute to the existing body of knowledge. The problem should address a gap in knowledge, a theoretical or practical problem, or a real-world issue that requires further investigation.
• Feasibility : A research problem should be feasible in terms of the availability of data, resources, and research methods. It should be realistic and practical to conduct the study within the available time, budget, and resources.
• Novelty : A research problem should be novel or original in some way. It should represent a new or innovative perspective on an existing problem, or it should explore a new area of study or apply an existing theory to a new context.
• Importance : A research problem should be important or significant in terms of its potential impact on the field or society. It should have the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Manageability : A research problem should be manageable in terms of its scope and complexity. It should be specific enough to be investigated within the available time and resources, and it should be broad enough to provide meaningful results.

Advantages of Research Problem

The advantages of a well-defined research problem are as follows:

• Focus : A research problem provides a clear and focused direction for the research study. It ensures that the study stays on track and does not deviate from the research question.
• Clarity : A research problem provides clarity and specificity to the research question. It ensures that the research is not too broad or too narrow and that the research objectives are clearly defined.
• Relevance : A research problem ensures that the research study is relevant to the field of study and contributes to the existing body of knowledge. It addresses gaps in knowledge, theoretical or practical problems, or real-world issues that require further investigation.
• Feasibility : A research problem ensures that the research study is feasible in terms of the availability of data, resources, and research methods. It ensures that the research is realistic and practical to conduct within the available time, budget, and resources.
• Novelty : A research problem ensures that the research study is original and innovative. It represents a new or unique perspective on an existing problem, explores a new area of study, or applies an existing theory to a new context.
• Importance : A research problem ensures that the research study is important and significant in terms of its potential impact on the field or society. It has the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Rigor : A research problem ensures that the research study is rigorous and follows established research methods and practices. It ensures that the research is conducted in a systematic, objective, and unbiased manner.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Solve Problems

• Laura Amico

To bring the best ideas forward, teams must build psychological safety.

Teams today aren’t just asked to execute tasks: They’re called upon to solve problems. You’d think that many brains working together would mean better solutions, but the reality is that too often problem-solving teams fall victim to inefficiency, conflict, and cautious conclusions. The two charts below will help your team think about how to collaborate better and come up with the best solutions for the thorniest challenges.

• Laura Amico is a former senior editor at Harvard Business Review.

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What Are Problem-Solving Skills? Definition and Examples

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Forage puts students first. Our blog articles are written independently by our editorial team. They have not been paid for or sponsored by our partners. See our full  editorial guidelines .

Why do employers hire employees? To help them solve problems. Whether you’re a financial analyst deciding where to invest your firm’s money, or a marketer trying to figure out which channel to direct your efforts, companies hire people to help them find solutions. Problem-solving is an essential and marketable soft skill in the workplace. 

So, how can you improve your problem-solving and show employers you have this valuable skill? In this guide, we’ll cover:

Problem-Solving Skills Definition

Why are problem-solving skills important, problem-solving skills examples, how to include problem-solving skills in a job application, how to improve problem-solving skills, problem-solving: the bottom line.

Problem-solving skills are the ability to identify problems, brainstorm and analyze answers, and implement the best solutions. An employee with good problem-solving skills is both a self-starter and a collaborative teammate; they are proactive in understanding the root of a problem and work with others to consider a wide range of solutions before deciding how to move forward. 

Examples of using problem-solving skills in the workplace include:

• Researching patterns to understand why revenue decreased last quarter
• Experimenting with a new marketing channel to increase website sign-ups
• Brainstorming content types to share with potential customers
• Testing calls to action to see which ones drive the most product sales
• Implementing a new workflow to automate a team process and increase productivity

Problem-solving skills are the most sought-after soft skill of 2022. In fact, 86% of employers look for problem-solving skills on student resumes, according to the National Association of Colleges and Employers Job Outlook 2022 survey . 

It’s unsurprising why employers are looking for this skill: companies will always need people to help them find solutions to their problems. Someone proactive and successful at problem-solving is valuable to any team.

“Employers are looking for employees who can make decisions independently, especially with the prevalence of remote/hybrid work and the need to communicate asynchronously,” Eric Mochnacz, senior HR consultant at Red Clover, says. “Employers want to see individuals who can make well-informed decisions that mitigate risk, and they can do so without suffering from analysis paralysis.”

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Problem-solving includes three main parts: identifying the problem, analyzing possible solutions, and deciding on the best course of action.

>>MORE: Discover the right career for you based on your skills with a career aptitude test .

Research is the first step of problem-solving because it helps you understand the context of a problem. Researching a problem enables you to learn why the problem is happening. For example, is revenue down because of a new sales tactic? Or because of seasonality? Is there a problem with who the sales team is reaching out to? 

Research broadens your scope to all possible reasons why the problem could be happening. Then once you figure it out, it helps you narrow your scope to start solving it. 

Analysis is the next step of problem-solving. Now that you’ve identified the problem, analytical skills help you look at what potential solutions there might be.

“The goal of analysis isn’t to solve a problem, actually — it’s to better understand it because that’s where the real solution will be found,” Gretchen Skalka, owner of Career Insights Consulting, says. “Looking at a problem through the lens of impartiality is the only way to get a true understanding of it from all angles.”

Decision-Making

Once you’ve figured out where the problem is coming from and what solutions are, it’s time to decide on the best way to go forth. Decision-making skills help you determine what resources are available, what a feasible action plan entails, and what solution is likely to lead to success.

On a Resume

Employers looking for problem-solving skills might include the word “problem-solving” or other synonyms like “ critical thinking ” or “analytical skills” in the job description.

“I would add ‘buzzwords’ you can find from the job descriptions or LinkedIn endorsements section to filter into your resume to comply with the ATS,” Matthew Warzel, CPRW resume writer, advises. Warzel recommends including these skills on your resume but warns to “leave the soft skills as adjectives in the summary section. That is the only place soft skills should be mentioned.”

On the other hand, you can list hard skills separately in a skills section on your resume .

Forage Resume Writing Masterclass

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Skills you’ll build: Resume writing, professional brand, professional summary, narrative, transferable skills, industry keywords, illustrating your impact, standing out

In a Cover Letter or an Interview

Explaining your problem-solving skills in an interview can seem daunting. You’re required to expand on your process — how you identified a problem, analyzed potential solutions, and made a choice. As long as you can explain your approach, it’s okay if that solution didn’t come from a professional work experience.

“Young professionals shortchange themselves by thinking only paid-for solutions matter to employers,” Skalka says. “People at the genesis of their careers don’t have a wealth of professional experience to pull from, but they do have relevant experience to share.”

Aaron Case, career counselor and CPRW at Resume Genius, agrees and encourages early professionals to share this skill. “If you don’t have any relevant work experience yet, you can still highlight your problem-solving skills in your cover letter,” he says. “Just showcase examples of problems you solved while completing your degree, working at internships, or volunteering. You can even pull examples from completely unrelated part-time jobs, as long as you make it clear how your problem-solving ability transfers to your new line of work.”

Learn How to Identify Problems

Problem-solving doesn’t just require finding solutions to problems that are already there. It’s also about being proactive when something isn’t working as you hoped it would. Practice questioning and getting curious about processes and activities in your everyday life. What could you improve? What would you do if you had more resources for this process? If you had fewer? Challenge yourself to challenge the world around you.

Think Digitally

“Employers in the modern workplace value digital problem-solving skills, like being able to find a technology solution to a traditional issue,” Case says. “For example, when I first started working as a marketing writer, my department didn’t have the budget to hire a professional voice actor for marketing video voiceovers. But I found a perfect solution to the problem with an AI voiceover service that cost a fraction of the price of an actor.”

Being comfortable with new technology — even ones you haven’t used before — is a valuable skill in an increasingly hybrid and remote world. Don’t be afraid to research new and innovative technologies to help automate processes or find a more efficient technological solution.

Collaborate

Problem-solving isn’t done in a silo, and it shouldn’t be. Use your collaboration skills to gather multiple perspectives, help eliminate bias, and listen to alternative solutions. Ask others where they think the problem is coming from and what solutions would help them with your workflow. From there, try to compromise on a solution that can benefit everyone.

If we’ve learned anything from the past few years, it’s that the world of work is constantly changing — which means it’s crucial to know how to adapt . Be comfortable narrowing down a solution, then changing your direction when a colleague provides a new piece of information. Challenge yourself to get out of your comfort zone, whether with your personal routine or trying a new system at work.

Put Yourself in the Middle of Tough Moments

Just like adapting requires you to challenge your routine and tradition, good problem-solving requires you to put yourself in challenging situations — especially ones where you don’t have relevant experience or expertise to find a solution. Because you won’t know how to tackle the problem, you’ll learn new problem-solving skills and how to navigate new challenges. Ask your manager or a peer if you can help them work on a complicated problem, and be proactive about asking them questions along the way.

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Companies always need people to help them find solutions — especially proactive employees who have practical analytical skills and can collaborate to decide the best way to move forward. Whether or not you have experience solving problems in a professional workplace, illustrate your problem-solving skills by describing your research, analysis, and decision-making process — and make it clear that you’re the solution to the employer’s current problems. 

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What great problem solvers do differently.

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Most people assume that what it takes to be a great problem solver is simply raw intelligence. However, while intelligence helps, it’s not the only important element. Research done in the 1990s at Bell Labs, one of the most innovative research institutions on the planet, showcases this principle clearly. They identified the most productive scientists in the labs, whom they called “Stars,” and looked at what differentiated them from other scientists. Through the course of this research, Robert Kelley and Janet Caplan revealed a fascinating fact about the most productive scientists. “As it develops, academic type success was not a good predictor of on job productivity nor was IQ discovered to be a factor.” It’s likely that IQ was determined to not be a factor because everyone employed by Bell Labs already had a baseline skill of raw intelligence. (Robert Kelley and Janet Caplan, “How Bell Labs Creates Star Performers,” Harvard Business Review,  July–August 1993)

If intelligence alone isn’t enough, then what makes the difference? To answer the question of what great problem solvers do, my colleague Jack Zenger and I examined two datasets. One focused on individual contributors (6,000 people) and the other focused on managers and executives (76,000 people). Five skills emerged that enabled people to be great problem solvers. You will note that raw intelligence in not one of the skills; however, one should always assume that it is a base skill of these top problem solvers.

1. Deep technical expertise and experience. The best problem solvers either had deep expertise and experience themselves or access to others with that expertise and experience. Steve Jobs is a great example of someone who relied on the deep expertise of others. He had the vision and ideas, but lacked the deep technical expertise make his ideas come to life. In Apple’s early days it was Steve Wozniak who had the engineering skills to build the machines—but it was Jobs’ vision that pushed the boundaries of what could be done.

A significant problem that gets in the way of intelligent individuals is their desire to be the only source of expertise and knowledge. Today’s problems are more complex, more difficult to solve, and often involve multiple disciplines. They require group expertise and knowledge. Being open to others’ expertise increases your ability to be a great problem solver.

2. Ability to innovate, challenge, change, and push boundaries . Problems get solved when we look at them from a different direction, challenge standard assumptions, and/or push boundaries. Our educational experiences often teach standard approaches and algorithms to solving problems—but new problems require new approaches. Often our first assumption is to follow a pattern that worked in the past. The best problem solvers look for innovative solutions, different approaches, and unique perspectives. Excellent innovators often apply approaches from other disciplines with surprising success.

3. Broad/strategic focus rather than narrow focus . We often assume the best person to solve a problem would be the expert with a laser focus in that particular area. While these people can be very helpful, our data suggests the best problem solvers have a broad focus, with an excellent understanding of organizational strategy and direction. Laser-focused experts can resolve problems but too often the solution is at odds with the strategic direction of the organization.

4. Drive / Push . Ninety-nine percent  of problem solving is hard work. As an inventor, Edison made 1,000 unsuccessful attempts at inventing the light bulb. New drug therapies go through thousands of cycles to uncover a drug—clinical tries with thousands of animals and then people to prove safety—only to go back and try again. The best problem solvers drive hard, work hard, push hard, and then do it all again the next day.

5. Excellent interpersonal skills . We have this image of the great problem solver who isolates themselves from others; a loner who never talks to others but eventually comes up with a brilliant idea. This is almost never the case. The most productive scientists at Bell Labs were excellent at networking with others and had strong communication skills. Great solutions come from integrating your ideas with those of others to find a better solution. Excellent problem solvers build networks and know how to collaborate with other people and teams. They are skilled in bringing people together and sharing knowledge and information. A key skill for great problem solvers is that they are trusted by others. Those hermits that work along are never trusted and so information is never shared.

To be hero, solve a problem. Your ability to solve problems will greatly improve when you utilize these five skills. Keep in mind that problem solving is best done as a team sport, so you may have to share credit—but doing so will also raise others’ perception of your ability to problem solve as well.

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Problem-Solving Strategies and Obstacles

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From deciding what to eat for dinner to considering whether it's the right time to buy a house, problem-solving is a large part of our daily lives. Learn some of the problem-solving strategies that exist and how to use them in real life, along with ways to overcome obstacles that are making it harder to resolve the issues you face.

What Is Problem-Solving?

In cognitive psychology , the term 'problem-solving' refers to the mental process that people go through to discover, analyze, and solve problems.

A problem exists when there is a goal that we want to achieve but the process by which we will achieve it is not obvious to us. Put another way, there is something that we want to occur in our life, yet we are not immediately certain how to make it happen.

Maybe you want a better relationship with your spouse or another family member but you're not sure how to improve it. Or you want to start a business but are unsure what steps to take. Problem-solving helps you figure out how to achieve these desires.

The problem-solving process involves:

• Discovery of the problem
• Deciding to tackle the issue
• Seeking to understand the problem more fully
• Researching available options or solutions
• Taking action to resolve the issue

Before problem-solving can occur, it is important to first understand the exact nature of the problem itself. If your understanding of the issue is faulty, your attempts to resolve it will also be incorrect or flawed.

Problem-Solving Mental Processes

Several mental processes are at work during problem-solving. Among them are:

• Perceptually recognizing the problem
• Representing the problem in memory
• Considering relevant information that applies to the problem
• Identifying different aspects of the problem
• Labeling and describing the problem

Problem-Solving Strategies

There are many ways to go about solving a problem. Some of these strategies might be used on their own, or you may decide to employ multiple approaches when working to figure out and fix a problem.

An algorithm is a step-by-step procedure that, by following certain "rules" produces a solution. Algorithms are commonly used in mathematics to solve division or multiplication problems. But they can be used in other fields as well.

In psychology, algorithms can be used to help identify individuals with a greater risk of mental health issues. For instance, research suggests that certain algorithms might help us recognize children with an elevated risk of suicide or self-harm.

One benefit of algorithms is that they guarantee an accurate answer. However, they aren't always the best approach to problem-solving, in part because detecting patterns can be incredibly time-consuming.

There are also concerns when machine learning is involved—also known as artificial intelligence (AI)—such as whether they can accurately predict human behaviors.

Heuristics are shortcut strategies that people can use to solve a problem at hand. These "rule of thumb" approaches allow you to simplify complex problems, reducing the total number of possible solutions to a more manageable set.

If you find yourself sitting in a traffic jam, for example, you may quickly consider other routes, taking one to get moving once again. When shopping for a new car, you might think back to a prior experience when negotiating got you a lower price, then employ the same tactics.

While heuristics may be helpful when facing smaller issues, major decisions shouldn't necessarily be made using a shortcut approach. Heuristics also don't guarantee an effective solution, such as when trying to drive around a traffic jam only to find yourself on an equally crowded route.

Trial and Error

A trial-and-error approach to problem-solving involves trying a number of potential solutions to a particular issue, then ruling out those that do not work. If you're not sure whether to buy a shirt in blue or green, for instance, you may try on each before deciding which one to purchase.

This can be a good strategy to use if you have a limited number of solutions available. But if there are many different choices available, narrowing down the possible options using another problem-solving technique can be helpful before attempting trial and error.

In some cases, the solution to a problem can appear as a sudden insight. You are facing an issue in a relationship or your career when, out of nowhere, the solution appears in your mind and you know exactly what to do.

Insight can occur when the problem in front of you is similar to an issue that you've dealt with in the past. Although, you may not recognize what is occurring since the underlying mental processes that lead to insight often happen outside of conscious awareness .

Research indicates that insight is most likely to occur during times when you are alone—such as when going on a walk by yourself, when you're in the shower, or when lying in bed after waking up.

How to Apply Problem-Solving Strategies in Real Life

If you're facing a problem, you can implement one or more of these strategies to find a potential solution. Here's how to use them in real life:

• Create a flow chart . If you have time, you can take advantage of the algorithm approach to problem-solving by sitting down and making a flow chart of each potential solution, its consequences, and what happens next.
• Recall your past experiences . When a problem needs to be solved fairly quickly, heuristics may be a better approach. Think back to when you faced a similar issue, then use your knowledge and experience to choose the best option possible.
• Start trying potential solutions . If your options are limited, start trying them one by one to see which solution is best for achieving your desired goal. If a particular solution doesn't work, move on to the next.
• Take some time alone . Since insight is often achieved when you're alone, carve out time to be by yourself for a while. The answer to your problem may come to you, seemingly out of the blue, if you spend some time away from others.

Obstacles to Problem-Solving

Problem-solving is not a flawless process as there are a number of obstacles that can interfere with our ability to solve a problem quickly and efficiently. These obstacles include:

• Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options.
• Functional fixedness : This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution.
• Irrelevant or misleading information: When trying to solve a problem, it's important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. The more complex the problem, the easier it is to focus on misleading or irrelevant information.
• Mental set: A mental set is a tendency to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions.

How to Improve Your Problem-Solving Skills

In the end, if your goal is to become a better problem-solver, it's helpful to remember that this is a process. Thus, if you want to improve your problem-solving skills, following these steps can help lead you to your solution:

• Recognize that a problem exists . If you are facing a problem, there are generally signs. For instance, if you have a mental illness , you may experience excessive fear or sadness, mood changes, and changes in sleeping or eating habits. Recognizing these signs can help you realize that an issue exists.
• Decide to solve the problem . Make a conscious decision to solve the issue at hand. Commit to yourself that you will go through the steps necessary to find a solution.
• Seek to fully understand the issue . Analyze the problem you face, looking at it from all sides. If your problem is relationship-related, for instance, ask yourself how the other person may be interpreting the issue. You might also consider how your actions might be contributing to the situation.
• Research potential options . Using the problem-solving strategies mentioned, research potential solutions. Make a list of options, then consider each one individually. What are some pros and cons of taking the available routes? What would you need to do to make them happen?
• Take action . Select the best solution possible and take action. Action is one of the steps required for change . So, go through the motions needed to resolve the issue.
• Try another option, if needed . If the solution you chose didn't work, don't give up. Either go through the problem-solving process again or simply try another option.

You can find a way to solve your problems as long as you keep working toward this goal—even if the best solution is simply to let go because no other good solution exists.

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Dunbar K. Problem solving . A Companion to Cognitive Science . 2017. doi:10.1002/9781405164535.ch20

Stewart SL, Celebre A, Hirdes JP, Poss JW. Risk of suicide and self-harm in kids: The development of an algorithm to identify high-risk individuals within the children's mental health system . Child Psychiat Human Develop . 2020;51:913-924. doi:10.1007/s10578-020-00968-9

Rosenbusch H, Soldner F, Evans AM, Zeelenberg M. Supervised machine learning methods in psychology: A practical introduction with annotated R code . Soc Personal Psychol Compass . 2021;15(2):e12579. doi:10.1111/spc3.12579

Mishra S. Decision-making under risk: Integrating perspectives from biology, economics, and psychology . Personal Soc Psychol Rev . 2014;18(3):280-307. doi:10.1177/1088868314530517

Csikszentmihalyi M, Sawyer K. Creative insight: The social dimension of a solitary moment . In: The Systems Model of Creativity . 2015:73-98. doi:10.1007/978-94-017-9085-7_7

Chrysikou EG, Motyka K, Nigro C, Yang SI, Thompson-Schill SL. Functional fixedness in creative thinking tasks depends on stimulus modality .  Psychol Aesthet Creat Arts . 2016;10(4):425‐435. doi:10.1037/aca0000050

Huang F, Tang S, Hu Z. Unconditional perseveration of the short-term mental set in chunk decomposition .  Front Psychol . 2018;9:2568. doi:10.3389/fpsyg.2018.02568

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Are operations backed by best practices in American problem-solving courts?

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How to Solve the Homeless Problem: Give Them a Camel

“When I hear a person talking about political solutions, I know I am not listening to a serious person.” —George Carlin

The late, great standup comic had a point: Many of the political issues we argue most vehemently about — crime, the economy, the environment, education, national defense — aren’t problems that we can ever solve. There’s no finish line to reach. Instead, it’s more akin to pain management for an achy, middle-aged back: It’s always going to be an issue, but if you stay ahead of it, you can still enjoy a pretty good quality of life.

Homelessness is, was, and will forever be an emotionally gut-wrenching societal tragedy. When you’re a child, losing the safety and security of your home — or by extension, your parents — is probably your most instinctive phobia. Other than starving to death (and/or being devoured by monsters), you couldn’t imagine anything worse. When I was a little kid, homelessness was one of the first social problems I was aware of. It was terrifying.

Rush Limbaugh noted that the homelessness crisis comes and goes depending on who’s in office: When the GOP’s in charge, it’s a major issue; when Democrats take over, it sort of gets swept under the rug. That’s because these poor, downtrodden (and often mentally ill) street people serve a higher purpose for the mainstream media: They’re flesh-and-blood props to advance the liberal agenda.

Furthermore, the more dire you perceive their plight, the more sympathetic you’ll be to a Big Government solution.

“Homeless advocates aren’t [just] satisfied with comparing the homeless to the Virgin Mary,” Limbaugh wrote in his best-selling book, “The Way Things Ought to Be.” “They want to use them as vehicles in their effort to portray America as an uncaring country.”

Still, it’s always sad when people struggle. Props or not, every single man, woman, and child living on the streets is deserving of basic human dignity. Images of large, sprawling homeless camps are just heartbreaking. (Dangerous, too — especially for women and children.)

But the problem remains unresolved: There were homeless families then, there are homeless families now, and there will undoubtedly be homeless families in the future. Clearly, none of the prior solutions were effective. And since we’re all out of good ideas, I’d like to throw my idea into the mix: Give each homeless person a camel.

First of all, camels are probably good for companionship. Everyone needs a friend. (Plus, it’ll be a funny conversation opener on Hump Days.)

Secondly — and far more importantly — this way, the entire homeless problem would vanish overnight. Seriously: Just like that!

Because instead of being homeless, now they’re nomads.

Isn’t that nice? Simply by redefining words and meanings, we can solve the ongoing, never-ending, heartbreaking problem of homelessness. 

(Hey, if we can redefine gender, we can certainly redefine nomads.)

This way, whenever we encounter a homeless person on the street, we no longer need to look away, averting our eyes in sadness and shame. Instead, we can point him out to our kids, using him as a positive example: “See that man, Timmy? That’s a nomad. They’re a noble breed, proud and true. Where he goes, nobody knows! Now let’s get some pie.”

Look, we’re well beyond the point of rational solutions. It’s time to try something new, bold, and (highly) experimental. So if you truly care about the homeless people, please write to your local congressman ASAP: Tell your representative to support camels for the homeless. Those poor people have suffered enough; give ‘em a camel, for crying out loud.

Besides, what’s the worst thing that could happen?

Even if the program’s a total failure, at least the homeless could eat the camel and get a good meal out of it.

Scott Pinsker is a Public Relations, Branding and Crisis Communications Expert in Tampa Bay, Florida.  His novel, "The Second Coming: A Love Story," is available for purchase on Amazon.

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Study: For Afghanistan veterans, anger, not PTSD, is the main problem

by Steinar Brandslet, Norwegian University of Science and Technology

From 2001 to 2021, about 9,200 Norwegian soldiers served in Afghanistan. The vast majority of them have managed well in the years that have followed. According to a new survey conducted by the Norwegian Armed Forces Joint Medical Services, however, a significant number of veterans struggle with mental health issues.

"All Norwegian veterans who served in Afghanistan were invited to participate in a large health survey in 2020," says Associate Professor Andreas Espetvedt Nordstrand at NTNU's Department of Psychology. He is also a senior researcher and a lieutenant commander in the Norwegian Armed Forces.

Veterans tend to be highly disciplined people, so it isn't a surprise that the response rate was so high, with 6205, or 68%, responding to the survey. This means that the results are credible and likely to be representative for the entire veteran population.

"One topic in this survey was 'problematic anger ,'" says Associate Professor Nordstrand.

The results are published in the journal BMC Psychology .

More anger than PTSD

Only in recent years has "problematic anger" become an important part of research regarding veterans and trauma. Researchers have often chosen to concentrate on Post-Traumatic Stress Disorder (PTSD).

"Over the years, we have understood that PTSD is just one of several possible trauma-related mental disorders," says Nordstrand.

PTSD is a so-called "fear dysregulation disorder." It is often a consequence of exposure to life-threatening events.

"In recent years, we have seen that factors other than fear often contribute to health problems among veterans. These factors include witnessing suffering or death in others, or exposure to serious moral transgressions. Trauma-related disorders can manifest in various ways beyond just fear dysregulation. For example, they can trigger alcohol problems , depression or insomnia," says Nordstrand.

Studying anger for the first time

Trauma research has rarely addressed the connection between trauma exposure and anger issues. Nordstand believes the reason for this is probably that researchers have concentrated on fear dysregulation instead.

"No one has studied anger among Nordic veterans before. Anger among veterans has received little attention internationally too, and leading military psychology researchers have gone so far as to call anger 'the forgotten emotion' in veteran research.

"Anger issues, also referred to as 'problematic anger,' occur when a person becomes so frequently, intensely, and persistently angry that it significantly disrupts their functioning, social life, and quality of life. So this is a problem we must take seriously," Nordstrand said.

High numbers

The researchers used a standard instrument to measure anger—the Dimensions of Anger Reactions Scale (DAR-5). They consider it a useful tool for identifying clinical anger problems, i.e. problems so severe that individuals require treatment.

Among the many findings are the following:

• 8.4% of Norwegian Afghanistan veterans have clinical anger problems, i.e., problematic anger. This makes anger by far the largest single mental health problem among these veterans.
• Anger problems are about three times as common as classic PTSD, which affects 2.9% of these veterans.
• The second most common mental health problem is insomnia, which affects 5.1% of the veterans.

It is clear from these findings that mental health problems are quite common among veterans.

"If we combine all types of mental illness, 10.4% of all Afghanistan veterans suffer from mental health problems—without including problematic anger, meaning we have potentially underestimated how many veterans are struggling with mental health problems after serving in Afghanistan," says Nordstrand.

In other words, anger problems are in addition to everything else.

"The veterans who struggle with problematic anger often have several other mental health problems as well," Nordstrand adds.

Some of the most resilient people to begin with

The numbers are particularly high when we take into consideration the fact that these veterans were handpicked to serve in Afghanistan because they can withstand more than most people.

"We need to remember that all Afghanistan veterans have undergone multiple rounds of medical and psychological selection, so they are presumed to represent the healthiest segment of the Norwegian population," Nordstrand points out.

In other words, it takes a lot before these people start to struggle. While they are generally more resilient than the average person, many nevertheless struggle because the pressure becomes so overwhelming.

"Problematic anger is closely associated with feeling guilt and shame related to things you saw or did during overseas service," says Nordstrand.

Problematic anger is also closely associated with having chronic pain problems after serving in Afghanistan.

"This means that we probably need to adopt a more holistic approach to treating veterans who are struggling than previously. Anger and chronic pain can quickly lead to a vicious circle where these two issues mutually exacerbate each other," says Nordstrand.

People who leave the military struggle the most

"We also see that people who leave the Armed Forces after having served in Afghanistan are much more likely to develop anger problems than people who continue to serve," says Nordstrand.

However, staying in touch with their former employer seems to help.

"For the individuals who leave the military, maintaining a connection to the Armed Forces, such as through service in the Home Guard, provides nearly the same level of protection against anger issues as continuing active duty.

"We need to be very cautious about assuming cause and effect here, but previous veteran research indicates that the transition from military to civilian life is a vulnerable phase for veterans.

"As civilians, veterans lose the social identity of being a soldier or officer, and this identity often provides significant protection when dealing with military trauma. In addition, many veterans feel alienated from civilians as they attempt to reintegrate into civilian life ," says Nordstrand.

The Norwegian Armed Forces needs veterans

This is often a bigger problem for people who have had many difficult experiences during their overseas service.

"Our findings must be interpreted in the context of this knowledge, and be applied to suggest possible ways to prevent anger problems. Perhaps it is a good idea to remain in the military for a while after completing overseas service. If they don't have the opportunity to do so, the new findings show that service in the Home Guard can be a good option.

"The Norwegian Armed Forces always needs experienced people to serve in the Home Guard, so this could be a win-win solution," says Nordstrand.

The researchers added that further studies should address problematic anger over time, ways to prevent such problems from escalating, and problematic anger in other high-risk professions.

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Oracle Offers Powerful Generative AI RAG Agent and Enhanced AI Services to Help Customers Solve Business Problems

Organizations benefit from a fully managed RAG service with support for Oracle Database 23ai AI Vector Search without manual integration

OCI Generative AI now provides access to the Meta Llama 3.1 models in addition to Cohere Command R and Command R+ models

Oracle today announced the general availability of Oracle Cloud Infrastructure (OCI) Generative AI (GenAI) Agents with retrieval-augmented generation (RAG) capabilities and enhanced Oracle AI innovations that help customers turn their data into a competitive advantage by making it easier to apply AI to real-world business operations.

The first of a series of OCI GenAI Agents, the RAG Agent provides out-of-the-box RAG capabilities, enabling customers to get started while avoiding manual processes such as agent planning, retrieval, reranking, generation, and integration. It also provides a self-check to reduce hallucinations, enabling customers to adopt RAG technologies to streamline their business processes without spending cycles on research and development. OCI GenAI Agents enable customers to access Oracle Database 23ai AI Vector Search and run fast similarity queries on enterprise data stored in the database. For customers who have a subscription to Oracle Database 23ai on OCI, the GenAI Agents service adds an automation layer to execute RAG and similarity search functions without having to move data to a separate vector database.

For organizations looking for open source solutions for their generative AI workloads, OCI GenAI Agents service also supports OCI Search with OpenSearch.

“AI is driving breakthroughs and efficiencies at an unprecedented pace, leading to new business models, applications, and innovation across industries,” said Greg Pavlik, executive vice president, AI and Data Management Services, Oracle Cloud Infrastructure. “With the OCI Generative AI Agents service, Oracle Database 23ai customers on OCI can subscribe to a fully managed RAG service that enables them to talk to their data. It’s AI designed for companies looking to put AI into their real-world production environments quickly—by bringing AI to their data.”

Key use cases for OCI GenAI Agents include:

• Call center optimization: Helps operators increase customer satisfaction through more accurate responses and a higher volume of query resolution.
• Expedite legal research: Helps researchers find answers faster by conversing with AI rather than manually searching court record databases.
• Revenue intelligence: Helps finance teams understand customer purchase history and trends by asking natural language questions instead of running reports.
• Recruit qualified job candidates: Helps recruiters source potential new hires more easily by typing in natural language rather than constructing a database query.

To provide versatile and accessible AI solutions for developers and business users, Oracle is also introducing AI innovations to the following:

• OCI Generative AI: Helps users seamlessly integrate versatile language models into a wide range of use cases, including writing assistance, summarization, analysis, and chat. To enable customers to harness the latest open source AI technology, OCI Generative AI now provides access to Meta’s Llama 3.1 models in sizes ranging from 405 billion parameters for AI use cases that require state-of-the-art capabilities to 70 billion parameters for more targeted workloads at a lower price. In addition, OCI Generative AI supports the Cohere Command R, Command R+, and Embed models.
• OCI Data Science: Helps teams of data scientists access open source tools to build, train, deploy, and manage AI models. OCI Data Science provides several new updates to the no-code AI Quick Actions feature, including OCI Ampere A1 shape support and the ability to bring any model from Hugging Face into OCI Data Science with just a few clicks.
• OCI Language: Helps developers perform sophisticated text analysis and machine translation at scale. New multilingual models are designed to seamlessly support over 100 languages, while a new Protected Health Information (PHI) feature extends the current Personally Identifying Information (PII) service to detect PHI entities and redact them from textual data.
• OCI Document Understanding: Helps developers automate manual business processing tasks with pre-built AI models. With newly trained models for the healthcare field, developers are able to identify and extract important data on medical identification cards while increasing efficiency and reducing manual errors.
• OCI Vision: Helps developers analyze videos frame-by-frame to identify objects, labels, text, and faces. It provides data about detection timestamps and bounding box coordinates for objects to support use cases, including digital asset management, visual anomaly detection, safety and surveillance, and ad tracking and placement.
• OCI Speech: Helps users transcribe speech to text and synthesizes speech from text with natural voices and a new real-time transcription capability that includes custom vocabularies support.
• Oracle Code Assist : Will help developers boost velocity by providing intelligent suggestions to help them build and optimize applications written in modern programming languages, including Java, Python, JavaScript, SuiteScript, Rust, Ruby, Go, PL/SQL, C#, and C.

Additional Resources

• Read the OCI Generative AI blog
• Learn more about Oracle AI

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