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Top 10 Challenges Faced by Researchers on the Path to Excellence

The pursuit of research excellence is not for the faint of heart; you have to overcome several challenges faced by researchers to map a path to excellence. From formulating research questions to gathering data, writing research papers, and getting them published, researchers grapple with complexities that demand their unwavering dedication and perseverance. By shedding light on the challenges faced by researchers , we aim to help academics navigating the path of knowledge and foster a deeper understanding of the challenges in conducting research .  

A career in scientific research can be highly rewarding and fulfilling for those who enjoy intellectual pursuits, value continuous learning, and want to make a positive impact on society. It offers exciting opportunities to unravel the mysteries of the world, push the boundaries of knowledge, and contribute to the collective understanding of humanity. However, one requires dedication, hard work, and perseverance to overcome the many research challenges you will encounter along the way.  

Table of Contents

Top 10 Challenges faced by researchers  

While each research journey is unique, the challenges faced by researchers share common threads that bind them together. By acknowledging and understanding these research challenges , academics can equip themselves with the knowledge and strategies needed to conduct research effectively.  

1. Slow and time-consuming processes

 The scientific process is often slow and iterative, and progress sometimes can only be measured in small increments over many months or years. Research takes time to execute, and oftentimes the results are uncertain. This can be frustrating for researchers who may have invested significant time, money, and effort into these projects. By being patient and persistent and inculcating a willingness to accept failure and setbacks, you can overcome this researcher challenge and make important contributions to your field.  

2. Difficulty in getting grants and funds

One of the biggest challenges faced by researchers is securing adequate funding for their work. Grants can be highly competitive, and the process of applying can be time-consuming and complex. To secure research funding for your project, you need to seek out a variety of funding sources, including government grants, private foundations, and industry partnerships, and learn what is needed to write a successful grant proposal . One of the most common research challenges is writing a clear, concise, convincing grant proposal that outlines the goals and significance of your research and why the funding agency should support your project.  

 3. Juggling activities to ensure better time management

Managing multiple projects and deadlines successfully is among the most common research challenges . Careful time management , prioritizing work, and setting realistic goals and deadlines are simple ways for researchers to manage their many responsibilities. Experts suggest using strategies like breaking larger projects into smaller tasks and scheduling regular breaks to avoid burnout, a very real challenges faced by researchers .  

4. Active networking and collaboration

Collaboration and networking is essential for research, but it can also be a big challenge faced by researchers . It can be difficult to build a network when you are just starting out, especially if you are unfamiliar with the language or uncomfortable with public speaking. It doesn’t get easier when it comes to collaborations, especially in interdisciplinary research projects. Researchers have different working styles or conflicting priorities, which can lead to tension and conflict when working with larger teams. To overcome this challenge in conducting research , scientists should set the right expectations from the start, establish clear communication channels, and be willing to work together to achieve shared goals.  

 5. Managing and evaluating huge amounts of information

Managing and analyzing large datasets can be a time-consuming and complex process. To overcome this research challenge , researchers should develop effective data management strategies, such as using cloud-based tools for storage and analysis and implementing best practices for data security and privacy. The collection, analysis and management of research data is critical to scientific study and career advancement, which makes it important to know how to develop an effective data management plan for researchers .   

 6. Successfully publishing in impactful journals

Publishing research in reputable journals is one of the biggest challenges faced by researchers globally. Researchers have to learn to navigate the peer-review process, respond to feedback, meet strict formatting and style guidelines, and develop strong academic writing and editing skills. Experts suggest seeking mentorship and guidance from senior researchers and overcoming this research challenge by using AI academic writing assistants like Paperpal, powered by Researcher Life, to ease the process.  

 7. Securing intellectual property

Intellectual property rights protect your research ideas and work from being used unfairly or incorrectly by others. However, protecting intellectual property can be a critical challenge faced by researchers , particularly those working in areas with high commercial potential. It’s important to know that different laws related to intellectual property rights can impact research collaboration across boundaries, so discuss this beforehand. One way to tackle this research challenge is to be aware of your rights and responsibilities regarding intellectual property and seek out legal advice and guidance as when required.  

 8. Understanding and following the nuances of academic and scientific ethics

Research ethics are among the top challenges faced by researchers . Plagiarism, falsification, fabrication, missing ethical declarations and non-compliance with standard ethical guidelines are considered inappropriate and can have serious consequences for researchers. Overcome this research challenge by following all ethical considerations in research ; this helps to maintain the high standards of science and research, ensure accountability, prioritize transparency, and ensure trust and integrity in your work.   

 9. Staying abreast of technological developments

Researchers must keep track of and use technology effectively, but the continually developing landscape can also be a source of frustration. Today, there are several online tools, software, and platforms to optimize your academic writing, research reading, science communication, and more. Oddly, the challenge faced by researchers here is keeping up with technology trends, finding AI tools that are tailored for academics, and effectively integrating them into their work.  

10. Balancing work and life

One of the biggest and most common challenges faced by researchers is balancing the demands of a research career with personal and family responsibilities. PhD students and researchers must learn how to achieve a healthy work-life balance , prioritize self-care, and set boundaries to avoid academic burnout. Seeking support from peers, friends, and family is a great way to manage the stress that comes with a career in scientific research. Effective time management, pursuing a hobby, and taking breaks are other ways to find ways to take care of your mental health as a researcher.   

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Identifying problems and solutions in scientific text

Kevin heffernan.

Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD UK

Simone Teufel

Research is often described as a problem-solving activity, and as a result, descriptions of problems and solutions are an essential part of the scientific discourse used to describe research activity. We present an automatic classifier that, given a phrase that may or may not be a description of a scientific problem or a solution, makes a binary decision about problemhood and solutionhood of that phrase. We recast the problem as a supervised machine learning problem, define a set of 15 features correlated with the target categories and use several machine learning algorithms on this task. We also create our own corpus of 2000 positive and negative examples of problems and solutions. We find that we can distinguish problems from non-problems with an accuracy of 82.3%, and solutions from non-solutions with an accuracy of 79.7%. Our three most helpful features for the task are syntactic information (POS tags), document and word embeddings.

Introduction

Problem solving is generally regarded as the most important cognitive activity in everyday and professional contexts (Jonassen 2000 ). Many studies on formalising the cognitive process behind problem-solving exist, for instance (Chandrasekaran 1983 ). Jordan ( 1980 ) argues that we all share knowledge of the thought/action problem-solution process involved in real life, and so our writings will often reflect this order. There is general agreement amongst theorists that state that the nature of the research process can be viewed as a problem-solving activity (Strübing 2007 ; Van Dijk 1980 ; Hutchins 1977 ; Grimes 1975 ).

One of the best-documented problem-solving patterns was established by Winter ( 1968 ). Winter analysed thousands of examples of technical texts, and noted that these texts can largely be described in terms of a four-part pattern consisting of Situation, Problem, Solution and Evaluation. This is very similar to the pattern described by Van Dijk ( 1980 ), which consists of Introduction-Theory, Problem-Experiment-Comment and Conclusion. The difference is that in Winter’s view, a solution only becomes a solution after it has been evaluated positively. Hoey changes Winter’s pattern by introducing the concept of Response in place of Solution (Hoey 2001 ). This seems to describe the situation in science better, where evaluation is mandatory for research solutions to be accepted by the community. In Hoey’s pattern, the Situation (which is generally treated as optional) provides background information; the Problem describes an issue which requires attention; the Response provides a way to deal with the issue, and the Evaluation assesses how effective the response is.

An example of this pattern in the context of the Goldilocks story can be seen in Fig.  1 . In this text, there is a preamble providing the setting of the story (i.e. Goldilocks is lost in the woods), which is called the Situation in Hoey’s system. A Problem in encountered when Goldilocks becomes hungry. Her first Response is to try the porridge in big bear’s bowl, but she gives this a negative Evaluation (“too hot!”) and so the pattern returns to the Problem. This continues in a cyclic fashion until the Problem is finally resolved by Goldilocks giving a particular Response a positive Evaluation of baby bear’s porridge (“it’s just right”).

Example of problem-solving pattern when applied to the Goldilocks story.

Reproduced with permission from Hoey ( 2001 )

It would be attractive to detect problem and solution statements automatically in text. This holds true both from a theoretical and a practical viewpoint. Theoretically, we know that sentiment detection is related to problem-solving activity, because of the perception that “bad” situations are transformed into “better” ones via problem-solving. The exact mechanism of how this can be detected would advance the state of the art in text understanding. In terms of linguistic realisation, problem and solution statements come in many variants and reformulations, often in the form of positive or negated statements about the conditions, results and causes of problem–solution pairs. Detecting and interpreting those would give us a reasonably objective manner to test a system’s understanding capacity. Practically, being able to detect any mention of a problem is a first step towards detecting a paper’s specific research goal. Being able to do this has been a goal for scientific information retrieval for some time, and if successful, it would improve the effectiveness of scientific search immensely. Detecting problem and solution statements of papers would also enable us to compare similar papers and eventually even lead to automatic generation of review articles in a field.

There has been some computational effort on the task of identifying problem-solving patterns in text. However, most of the prior work has not gone beyond the usage of keyword analysis and some simple contextual examination of the pattern. Flowerdew ( 2008 ) presents a corpus-based analysis of lexio-grammatical patterns for problem and solution clauses using articles from professional and student reports. Problem and solution keywords were used to search their corpora, and each occurrence was analysed to determine grammatical usage of the keyword. More interestingly, the causal category associated with each keyword in their context was also analysed. For example, Reason–Result or Means-Purpose were common causal categories found to be associated with problem keywords.

The goal of the work by Scott ( 2001 ) was to determine words which are semantically similar to problem and solution, and to determine how these words are used to signal problem-solution patterns. However, their corpus-based analysis used articles from the Guardian newspaper. Since the domain of newspaper text is very different from that of scientific text, we decided not to consider those keywords associated with problem-solving patterns for use in our work.

Instead of a keyword-based approach, Charles ( 2011 ) used discourse markers to examine how the problem-solution pattern was signalled in text. In particular, they examined how adverbials associated with a result such as “thus, therefore, then, hence” are used to signal a problem-solving pattern.

Problem solving also has been studied in the framework of discourse theories such as Rhetorical Structure Theory (Mann and Thompson 1988 ) and Argumentative Zoning (Teufel et al. 2000 ). Problem- and solutionhood constitute two of the original 23 relations in RST (Mann and Thompson 1988 ). While we concentrate solely on this aspect, RST is a general theory of discourse structure which covers many intentional and informational relations. The relationship to Argumentative Zoning is more complicated. The status of certain statements as problem or solutions is one important dimension in the definitions of AZ categories. AZ additionally models dimensions other than problem-solution hood (such as who a scientific idea belongs to, or which intention the authors might have had in stating a particular negative or positive statement). When forming categories, AZ combines aspects of these dimensions, and “flattens” them out into only 7 categories. In AZ it is crucial who it is that experiences the problems or contributes a solution. For instance, the definition of category “CONTRAST” includes statements that some research runs into problems, but only if that research is previous work (i.e., not if it is the work contributed in the paper itself). Similarly, “BASIS” includes statements of successful problem-solving activities, but only if they are achieved by previous work that the current paper bases itself on. Our definition is simpler in that we are interested only in problem solution structure, not in the other dimensions covered in AZ. Our definition is also more far-reaching than AZ, in that we are interested in all problems mentioned in the text, no matter whose problems they are. Problem-solution recognition can therefore be seen as one aspect of AZ which can be independently modelled as a “service task”. This means that good problem solution structure recognition should theoretically improve AZ recognition.

In this work, we approach the task of identifying problem-solving patterns in scientific text. We choose to use the model of problem-solving described by Hoey ( 2001 ). This pattern comprises four parts: Situation, Problem, Response and Evaluation. The Situation element is considered optional to the pattern, and so our focus centres on the core pattern elements.

Goal statement and task

Many surface features in the text offer themselves up as potential signals for detecting problem-solving patterns in text. However, since Situation is an optional element, we decided to focus on either Problem or Response and Evaluation as signals of the pattern. Moreover, we decide to look for each type in isolation. Our reasons for this are as follows: It is quite rare for an author to introduce a problem without resolving it using some sort of response, and so this is a good starting point in identifying the pattern. There are exceptions to this, as authors will sometimes introduce a problem and then leave it to future work, but overall there should be enough signal in the Problem element to make our method of looking for it in isolation worthwhile. The second signal we look for is the use of Response and Evaluation within the same sentence. Similar to Problem elements, we hypothesise that this formulation is well enough signalled externally to help us in detecting the pattern. For example, consider the following Response and Evaluation: “One solution is to use smoothing”. In this statement, the author is explicitly stating that smoothing is a solution to a problem which must have been mentioned in a prior statement. In scientific text, we often observe that solutions implicitly contain both Response and Evaluation (positive) elements. Therefore, due to these reasons there should be sufficient external signals for the two pattern elements we concentrate on here.

When attempting to find Problem elements in text, we run into the issue that the word “problem” actually has at least two word senses that need to be distinguished. There is a word sense of “problem” that means something which must be undertaken (i.e. task), while another sense is the core sense of the word, something that is problematic and negative. Only the latter sense is aligned with our sense of problemhood. This is because the simple description of a task does not predispose problemhood, just a wish to perform some act. Consider the following examples, where the non-desired word sense is being used:

• “Das and Petrov (2011) also consider the problem of unsupervised bilingual POS induction”. (Chen et al. 2011 ).
• “In this paper, we describe advances on the problem of NER in Arabic Wikipedia”. (Mohit et al. 2012 ).

Here, the author explicitly states that the phrases in orange are problems, they align with our definition of research tasks and not with what we call here ‘problematic problems’. We will now give some examples from our corpus for the desired, core word sense:

• “The major limitation of supervised approaches is that they require annotations for example sentences.” (Poon and Domingos 2009 ).
• “To solve the problem of high dimensionality we use clustering to group the words present in the corpus into much smaller number of clusters”. (Saha et al. 2008 ).

When creating our corpus of positive and negative examples, we took care to select only problem strings that satisfy our definition of problemhood; “ Corpus creation ” section will explain how we did that.

Corpus creation

Our new corpus is a subset of the latest version of the ACL anthology released in March, 2016 1 which contains 22,878 articles in the form of PDFs and OCRed text. 2

The 2016 version was also parsed using ParsCit (Councill et al. 2008 ). ParsCit recognises not only document structure, but also bibliography lists as well as references within running text. A random subset of 2500 papers was collected covering the entire ACL timeline. In order to disregard non-article publications such as introductions to conference proceedings or letters to the editor, only documents containing abstracts were considered. The corpus was preprocessed using tokenisation, lemmatisation and dependency parsing with the Rasp Parser (Briscoe et al. 2006 ).

Definition of ground truth

Our goal was to define a ground truth for problem and solution strings, while covering as wide a range as possible of syntactic variations in which such strings naturally occur. We also want this ground truth to cover phenomena of problem and solution status which are applicable whether or not the problem or solution status is explicitly mentioned in the text.

To simplify the task, we only consider here problem and solution descriptions that are at most one sentence long. In reality, of course, many problem descriptions and solution descriptions go beyond single sentence, and require for instance an entire paragraph. However, we also know that short summaries of problems and solutions are very prevalent in science, and also that these tend to occur in the most prominent places in a paper. This is because scientists are trained to express their contribution and the obstacles possibly hindering their success, in an informative, succinct manner. That is the reason why we can afford to only look for shorter problem and solution descriptions, ignoring those that cross sentence boundaries.

To define our ground truth, we examined the parsed dependencies and looked for a target word (“problem/solution”) in subject position, and then chose its syntactic argument as our candidate problem or solution phrase. To increase the variation, i.e., to find as many different-worded problem and solution descriptions as possible, we additionally used semantically similar words (near-synonyms) of the target words “problem” or “solution” for the search. Semantic similarity was defined as cosine in a deep learning distributional vector space, trained using Word2Vec (Mikolov et al. 2013 ) on 18,753,472 sentences from a biomedical corpus based on all full-text Pubmed articles (McKeown et al. 2016 ). From the 200 words which were semantically closest to “problem”, we manually selected 28 clear synonyms. These are listed in Table  1 . From the 200 semantically closest words to “solution” we similarly chose 19 (Table  2 ). Of the sentences matching our dependency search, a subset of problem and solution candidate sentences were randomly selected.

Selected words for use in problem candidate phrase extraction

Selected words for use in solution candidate phrase extraction

An example of this is shown in Fig.  2 . Here, the target word “drawback” is in subject position (highlighted in red), and its clausal argument (ccomp) is “(that) it achieves low performance” (highlighted in purple). Examples of other arguments we searched for included copula constructions and direct/indirect objects.

Example of our extraction method for problems using dependencies. (Color figure online)

If more than one candidate was found in a sentence, one was chosen at random. Non-grammatical sentences were excluded; these might appear in the corpus as a result of its source being OCRed text.

800 candidates phrases expressing problems and solutions were automatically extracted (1600 total) and then independently checked for correctness by two annotators (the two authors of this paper). Both authors found the task simple and straightforward. Correctness was defined by two criteria:

• An unexplained phenomenon or a problematic state in science; or
• A research question; or
• An artifact that does not fulfil its stated specification.
• The phrase must not lexically give away its status as problem or solution phrase.

The second criterion saves us from machine learning cues that are too obvious. If for instance, the phrase itself contained the words “lack of” or “problematic” or “drawback”, our manual check rejected it, because it would be too easy for the machine learner to learn such cues, at the expense of many other, more generally occurring cues.

Sampling of negative examples

We next needed to find negative examples for both cases. We wanted them not to stand out on the surface as negative examples, so we chose them so as to mimic the obvious characteristics of the positive examples as closely as possible. We call the negative examples ‘non-problems’ and ‘non-solutions’ respectively. We wanted the only differences between problems and non-problems to be of a semantic nature, nothing that could be read off on the surface. We therefore sampled a population of phrases that obey the same statistical distribution as our problem and solution strings while making sure they really are negative examples. We started from sentences not containing any problem/solution words (i.e. those used as target words). From each such sentence, we at random selected one syntactic subtree contained in it. From these, we randomly selected a subset of negative examples of problems and solutions that satisfy the following conditions:

• The distribution of the head POS tags of the negative strings should perfectly match the head POS tags 3 of the positive strings. This has the purpose of achieving the same proportion of surface syntactic constructions as observed in the positive cases.
• The average lengths of the negative strings must be within a tolerance of the average length of their respective positive candidates e.g., non-solutions must have an average length very similar (i.e. + / -  small tolerance) to solutions. We chose a tolerance value of 3 characters.

Again, a human quality check was performed on non-problems and non-solutions. For each candidate non-problem statement, the candidate was accepted if it did not contain a phenomenon, a problematic state, a research question or a non-functioning artefact. If the string expressed a research task, without explicit statement that there was anything problematic about it (i.e., the ‘wrong’ sense of “problem”, as described above), it was allowed as a non-problem. A clause was confirmed as a non-solution if the string did not represent both a response and positive evaluation.

If the annotator found that the sentence had been slightly mis-parsed, but did contain a candidate, they were allowed to move the boundaries for the candidate clause. This resulted in cleaner text, e.g., in the frequent case of coordination, when non-relevant constituents could be removed.

From the set of sentences which passed the quality-test for both independent assessors, 500 instances of positive and negative problems/solutions were randomly chosen (i.e. 2000 instances in total). When checking for correctness we found that most of the automatically extracted phrases which did not pass the quality test for problem-/solution-hood were either due to obvious learning cues or instances where the sense of problem-hood used is relating to tasks (cf. “ Goal statement and task ” section).

Experimental design

In our experiments, we used three classifiers, namely Naïve Bayes, Logistic Regression and a Support Vector Machine. For all classifiers an implementation from the WEKA machine learning library (Hall et al. 2009 ) was chosen. Given that our dataset is small, tenfold cross-validation was used instead of a held out test set. All significance tests were conducted using the (two-tailed) Sign Test (Siegel 1956 ).

Linguistic correlates of problem- and solution-hood

We first define a set of features without taking the phrase’s context into account. This will tell us about the disambiguation ability of the problem/solution description’s semantics alone. In particular, we cut out the rest of the sentence other than the phrase and never use it for classification. This is done for similar reasons to excluding certain ‘give-away’ phrases inside the phrases themselves (as explained above). As the phrases were found using templates, we know that the machine learner would simply pick up on the semantics of the template, which always contains a synonym of “problem” or “solution”, thus drowning out the more hidden features hopefully inherent in the semantics of the phrases themselves. If we allowed the machine learner to use these stronger features, it would suffer in its ability to generalise to the real task.

ngrams Bags of words are traditionally successfully used for classification tasks in NLP, so we included bags of words (lemmas) within the candidate phrases as one of our features (and treat it as a baseline later on). We also include bigrams and trigrams as multi-word combinations can be indicative of problems and solutions e.g., “combinatorial explosion”.

Polarity Our second feature concerns the polarity of each word in the candidate strings. Consider the following example of a problem taken from our dataset: “very conservative approaches to exact and partial string matches overgenerate badly”. In this sentence, words such as “badly” will be associated with negative polarity, therefore being useful in determining problem-hood. Similarly, solutions will often be associated with a positive sentiment e.g. “smoothing is a good way to overcome data sparsity” . To do this, we perform word sense disambiguation of each word using the Lesk algorithm (Lesk 1986 ). The polarity of the resulting synset in SentiWordNet (Baccianella et al. 2010 ) was then looked up and used as a feature.

Syntax Next, a set of syntactic features were defined by using the presence of POS tags in each candidate. This feature could be helpful in finding syntactic patterns in problems and solutions. We were careful not to base the model directly on the head POS tag and the length of each candidate phrase, as these are defining characteristics used for determining the non-problem and non-solution candidate set.

Negation Negation is an important property that can often greatly affect the polarity of a phrase. For example, a phrase containing a keyword pertinent to solution-hood may be a good indicator but with the presence of negation may flip the polarity to problem-hood e.g., “this can’t work as a solution”. Therefore, presence of negation is determined.

Exemplification and contrast Problems and solutions are often found to be coupled with examples as they allow the author to elucidate their point. For instance, consider the following solution: “Once the translations are generated, an obvious solution is to pick the most fluent alternative, e.g., using an n-gram language model”. (Madnani et al. 2012 ). To acknowledge this, we check for presence of exemplification. In addition to examples, problems in particular are often found when contrast is signalled by the author (e.g. “however, “but”), therefore we also check for presence of contrast in the problem and non-problem candidates only.

Discourse Problems and solutions have also been found to have a correlation with discourse properties. For example, problem-solving patterns often occur in the background sections of a paper. The rationale behind this is that the author is conventionally asked to objectively criticise other work in the background (e.g. describing research gaps which motivate the current paper). To take this in account, we examine the context of each string and capture the section header under which it is contained (e.g. Introduction, Future work). In addition, problems and solutions are often found following the Situation element in the problem-solving pattern (cf. “ Introduction ” section). This preamble setting up the problem or solution means that these elements are likely not to be found occurring at the beginning of a section (i.e. it will usually take some sort of introduction to detail how something is problematic and why a solution is needed). Therefore we record the distance from the candidate string to the nearest section header.

Subcategorisation and adverbials Solutions often involve an activity (e.g. a task). We also model the subcategorisation properties of the verbs involved. Our intuition was that since problematic situations are often described as non-actions, then these are more likely to be intransitive. Conversely solutions are often actions and are likely to have at least one argument. This feature was calculated by running the C&C parser (Curran et al. 2007 ) on each sentence. C&C is a supertagger and parser that has access to subcategorisation information. Solutions are also associated with resultative adverbial modification (e.g. “thus, therefore, consequently”) as it expresses the solutionhood relation between the problem and the solution. It has been seen to occur frequently in problem-solving patterns, as studied by Charles ( 2011 ). Therefore, we check for presence of resultative adverbial modification in the solution and non-solution candidate only.

Embeddings We also wanted to add more information using word embeddings. This was done in two different ways. Firstly, we created a Doc2Vec model (Le and Mikolov 2014 ), which was trained on  ∼  19  million sentences from scientific text (no overlap with our data set). An embedding was created for each candidate sentence. Secondly, word embeddings were calculated using the Word2Vec model (cf. “ Corpus creation ” section). For each candidate head, the full word embedding was included as a feature. Lastly, when creating our polarity feature we query SentiWordNet using synsets assigned by the Lesk algorithm. However, not all words are assigned a sense by Lesk, so we need to take care when that happens. In those cases, the distributional semantic similarity of the word is compared to two words with a known polarity, namely “poor” and “excellent”. These particular words have traditionally been consistently good indicators of polarity status in many studies (Turney 2002 ; Mullen and Collier 2004 ). Semantic similarity was defined as cosine similarity on the embeddings of the Word2Vec model (cf. “ Corpus creation ” section).

Modality Responses to problems in scientific writing often express possibility and necessity, and so have a close connection with modality. Modality can be broken into three main categories, as described by Kratzer ( 1991 ), namely epistemic (possibility), deontic (permission / request / wish) and dynamic (expressing ability).

Problems have a strong relationship to modality within scientific writing. Often, this is due to a tactic called “hedging” (Medlock and Briscoe 2007 ) where the author uses speculative language, often using Epistemic modality, in an attempt to make either noncommital or vague statements. This has the effect of allowing the author to distance themselves from the statement, and is often employed when discussing negative or problematic topics. Consider the following example of Epistemic modality from Nakov and Hearst ( 2008 ): “A potential drawback is that it might not work well for low-frequency words”.

To take this linguistic correlate into account as a feature, we replicated a modality classifier as described by (Ruppenhofer and Rehbein 2012 ). More sophisticated modality classifiers have been recently introduced, for instance using a wide range of features and convolutional neural networks, e.g, (Zhou et al. 2015 ; Marasović and Frank 2016 ). However, we wanted to check the effect of a simpler method of modality classification on the final outcome first before investing heavily into their implementation. We trained three classifiers using the subset of features which Ruppenhofer et al. reported as performing best, and evaluated them on the gold standard dataset provided by the authors 4 . The results of the are shown in Table  3 . The dataset contains annotations of English modal verbs on the 535 documents of the first MPQA corpus release (Wiebe et al. 2005 ).

Modality classifier results (precision/recall/f-measure) using Naïve Bayes (NB), logistic regression, and a support vector machine (SVM)

Italicized results reflect highest f-measure reported per modal category

Logistic Regression performed best overall and so this model was chosen for our upcoming experiments. With regards to the optative and concessive modal categories, they can be seen to perform extremely poorly, with the optative category receiving a null score across all three classifiers. This is due to a limitation in the dataset, which is unbalanced and contains very few instances of these two categories. This unbalanced data also is the reason behind our decision of reporting results in terms of recall, precision and f-measure in Table  3 .

The modality classifier was then retrained on the entirety of the dataset used by Ruppenhofer and Rehbein ( 2012 ) using the best performing model from training (Logistic Regression). This new model was then used in the upcoming experiment to predict modality labels for each instance in our dataset.

As can be seen from Table  4 , we are able to achieve good results for distinguishing a problematic statement from non-problematic one. The bag-of-words baseline achieves a very good performance of 71.0% for the Logistic Regression classifier, showing that there is enough signal in the candidate phrases alone to distinguish them much better than random chance.

Results distinguishing problems from non-problems using Naïve Bayes (NB), logistic regression (LR) and a support vector machine (SVM)

Each feature set’s performance is shown in isolation followed by combinations with other features. Tenfold stratified cross-validation was used across all experiments. Statistical significance with respect to the baseline at the p  < 0.05 , 0.01, 0.001 levels is denoted by *, ** and *** respectively

Taking a look at Table  5 , which shows the information gain for the top lemmas,

Information gain (IG) in bits of top lemmas from the bag-of-words baseline in Table  4

we can see that the top lemmas are indeed indicative of problemhood (e.g. “limit”,“explosion”). Bigrams achieved good performance on their own (as did negation and discourse) but unfortunately performance deteriorated when using trigrams, particularly with the SVM and LR. The subcategorisation feature was the worst performing feature in isolation. Upon taking a closer look at our data, we saw that our hypothesis that intransitive verbs are commonly used in problematic statements was true, with over 30% of our problems (153) using them. However, due to our sampling method for the negative cases we also picked up many intransitive verbs (163). This explains the almost random chance performance (i.e.  50%) given that the distribution of intransitive verbs amongst the positive and negative candidates was almost even.

The modality feature was the most expensive to produce, but also didn’t perform very well is isolation. This surprising result may be partly due to a data sparsity issue

where only a small portion (169) of our instances contained modal verbs. The breakdown of how many types of modal senses which occurred is displayed in Table  6 . The most dominant modal sense was epistemic. This is a good indicator of problemhood (e.g. hedging, cf. “ Linguistic correlates of problem- and solution-hood ” section) but if the accumulation of additional data was possible, we think that this feature may have the potential to be much more valuable in determining problemhood. Another reason for the performance may be domain dependence of the classifier since it was trained on text from different domains (e.g. news). Additionally, modality has also shown to be helpful in determining contextual polarity (Wilson et al. 2005 ) and argumentation (Becker et al. 2016 ), so using the output from this modality classifier may also prove useful for further feature engineering taking this into account in future work.

Number of instances of modal senses

Polarity managed to perform well but not as good as we hoped. However, this feature also suffers from a sparsity issue resulting from cases where the Lesk algorithm (Lesk 1986 ) is not able to resolve the synset of the syntactic head.

Knowledge of syntax provides a big improvement with a significant increase over the baseline results from two of the classifiers.

Examining this in greater detail, POS tags with high information gain mostly included tags from open classes (i.e. VB-, JJ-, NN- and RB-). These tags are often more associated with determining polarity status than tags such as prepositions and conjunctions (i.e. adverbs and adjectives are more likely to be describing something with a non-neutral viewpoint).

The embeddings from Doc2Vec allowed us to obtain another significant increase in performance (72.9% with Naïve Bayes) over the baseline and polarity using Word2Vec provided the best individual feature result (77.2% with SVM).

Combining all features together, each classifier managed to achieve a significant result over the baseline with the best result coming from the SVM (81.8%). Problems were also better classified than non-problems as shown in the confusion matrix in Table  7 . The addition of the Word2Vec vectors may be seen as a form of smoothing in cases where previous linguistic features had a sparsity issue i.e., instead of a NULL entry, the embeddings provide some sort of value for each candidate. Particularly wrt. the polarity feature, cases where Lesk was unable to resolve a synset meant that a ZERO entry was added to the vector supplied to the machine learner. Amongst the possible combinations, the best subset of features was found by combining all features with the exception of bigrams, trigrams, subcategorisation and modality. This subset of features managed to improve results in both the Naïve Bayes and SVM classifiers with the highest overall result coming from the SVM (82.3%).

Confusion matrix for problems

The results for disambiguation of solutions from non-solutions can be seen in Table  8 . The bag-of-words baseline performs much better than random, with the performance being quite high with regard to the SVM (this result was also higher than any of the baseline performances from the problem classifiers). As shown in Table  9 , the top ranked lemmas from the best performing model (using information gain) included “use” and “method”. These lemmas are very indicative of solutionhood and so give some insight into the high baseline returned from the machine learners. Subcategorisation and the result adverbials were the two worst performing features. However, the low performance for subcategorisation is due to the sampling of the non-solutions (the same reason for the low performance of the problem transitivity feature). When fitting the POS-tag distribution for the negative samples, we noticed that over 80% of the head POS-tags were verbs (much higher than the problem heads). The most frequent verb type being the infinite form.

Results distinguishing solutions from non-solutions using Naïve Bayes (NB), logistic regression (LR) and a support vector machine (SVM)

Each feature set’s performance is shown in isolation followed by combinations with other features. Tenfold stratified cross-validation was used across all experiments

Information gain (IG) in bits of top lemmas from the bag-of-words baseline in Table  8

This is not surprising given that a very common formulation to describe a solution is to use the infinitive “TO” since it often describes a task e.g., “One solution is to find the singletons and remove them”. Therefore, since the head POS tags of the non-solutions had to match this high distribution of infinitive verbs present in the solution, the subcategorisation feature is not particularly discriminatory. Polarity, negation, exemplification and syntactic features were slightly more discriminate and provided comparable results. However, similar to the problem experiment, the embeddings from Word2Vec and Doc2Vec proved to be the best features, with polarity using Word2Vec providing the best individual result (73.4% with SVM).

Combining all features together managed to improve over each feature in isolation and beat the baseline using all three classifiers. Furthermore, when looking at the confusion matrix in Table  10 the solutions were classified more accurately than the non-solutions. The best subset of features was found by combining all features without adverbial of result, bigrams, exemplification, negation, polarity and subcategorisation. The best result using this subset of features was achieved by the SVM with 79.7%. It managed to greatly improve upon the baseline but was just shy of achieving statistical significance ( p = 0.057 ).

Confusion matrix for solutions

In this work, we have presented new supervised classifiers for the task of identifying problem and solution statements in scientific text. We have also introduced a new corpus for this task and used it for evaluating our classifiers. Great care was taken in constructing the corpus by ensuring that the negative and positive samples were closely matched in terms of syntactic shape. If we had simply selected random subtrees for negative samples without regard for any syntactic similarity with our positive samples, the machine learner may have found easy signals such as sentence length. Additionally, since we did not allow the machine learner to see the surroundings of the candidate string within the sentence, this made our task even harder. Our performance on the corpus shows promise for this task, and proves that there are strong signals for determining both the problem and solution parts of the problem-solving pattern independently.

With regard to classifying problems from non-problems, features such as the POS tag, document and word embeddings provide the best features, with polarity using the Word2Vec embeddings achieving the highest feature performance. The best overall result was achieved using an SVM with a subset of features (82.3%). Classifying solutions from non-solutions also performs well using the embedding features, with the best feature also being polarity using the Word2Vec embeddings, and the highest result also coming from the SVM with a feature subset (79.7%).

In future work, we plan to link problem and solution statements which were found independently during our corpus creation. Given that our classifiers were trained on data solely from the ACL anthology, we also hope to investigate the domain specificity of our classifiers and see how well they can generalise to domains other than ACL (e.g. bioinformatics). Since we took great care at removing the knowledge our classifiers have of the explicit statements of problem and solution (i.e. the classifiers were trained only on the syntactic argument of the explicit statement of problem-/solution-hood), our classifiers should in principle be in a good position to generalise, i.e., find implicit statements too. In future work, we will measure to which degree this is the case.

To facilitate further research on this topic, all code and data used in our experiments can be found here: www.cl.cam.ac.uk/~kh562/identifying-problems-and-solutions.html

Acknowledgements

The first author has been supported by an EPSRC studentship (Award Ref: 1641528). We thank the reviewers for their helpful comments.

1 http://acl-arc.comp.nus.edu.sg/ .

2 The corpus comprises 3,391,198 sentences, 71,149,169 words and 451,996,332 characters.

3 The head POS tags were found using a modification of the Collins’ Head Finder. This modified algorithm addresses some of the limitations of the head finding heuristics described by Collins ( 2003 ) and can be found here: http://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/trees/ModCollinsHeadFinder.html .

4 https://www.uni-hildesheim.de/ruppenhofer/data/modalia_release1.0.tgz.

Contributor Information

Kevin Heffernan, Email: [email protected] .

Simone Teufel, Email: [email protected] .

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How to Write a Problem Statement | Guide & Examples

Published on November 6, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

A problem statement is a concise and concrete summary of the research problem you seek to address. It should:

• Contextualize the problem. What do we already know?
• Describe the exact issue your research will address. What do we still need to know?
• Show the relevance of the problem. Why do we need to know more about this?
• Set the objectives of the research. What will you do to find out more?

Table of contents

When should you write a problem statement, step 1: contextualize the problem, step 2: show why it matters, step 3: set your aims and objectives.

Problem statement example

Other interesting articles

Frequently asked questions about problem statements.

There are various situations in which you might have to write a problem statement.

In the business world, writing a problem statement is often the first step in kicking off an improvement project. In this case, the problem statement is usually a stand-alone document.

In academic research, writing a problem statement can help you contextualize and understand the significance of your research problem. It is often several paragraphs long, and serves as the basis for your research proposal . Alternatively, it can be condensed into just a few sentences in your introduction .

A problem statement looks different depending on whether you’re dealing with a practical, real-world problem or a theoretical issue. Regardless, all problem statements follow a similar process.

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The problem statement should frame your research problem, giving some background on what is already known.

Practical research problems

For practical research, focus on the concrete details of the situation:

• Where and when does the problem arise?
• Who does the problem affect?
• What attempts have been made to solve the problem?

Theoretical research problems

For theoretical research, think about the scientific, social, geographical and/or historical background:

• What is already known about the problem?
• Is the problem limited to a certain time period or geographical area?
• How has the problem been defined and debated in the scholarly literature?

The problem statement should also address the relevance of the research. Why is it important that the problem is addressed?

Don’t worry, this doesn’t mean you have to do something groundbreaking or world-changing. It’s more important that the problem is researchable, feasible, and clearly addresses a relevant issue in your field.

Practical research is directly relevant to a specific problem that affects an organization, institution, social group, or society more broadly. To make it clear why your research problem matters, you can ask yourself:

• What will happen if the problem is not solved?
• Who will feel the consequences?
• Does the problem have wider relevance? Are similar issues found in other contexts?

Sometimes theoretical issues have clear practical consequences, but sometimes their relevance is less immediately obvious. To identify why the problem matters, ask:

• How will resolving the problem advance understanding of the topic?
• What benefits will it have for future research?
• Does the problem have direct or indirect consequences for society?

Finally, the problem statement should frame how you intend to address the problem. Your goal here should not be to find a conclusive solution, but rather to propose more effective approaches to tackling or understanding it.

The research aim is the overall purpose of your research. It is generally written in the infinitive form:

• The aim of this study is to determine …
• This project aims to explore …
• This research aims to investigate …

The research objectives are the concrete steps you will take to achieve the aim:

• Qualitative methods will be used to identify …
• This work will use surveys to collect …
• Using statistical analysis, the research will measure …

The aims and objectives should lead directly to your research questions.

Learn how to formulate research questions

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You can use these steps to write your own problem statement, like the example below.

Step 1: Contextualize the problem A family-owned shoe manufacturer has been in business in New England for several generations, employing thousands of local workers in a variety of roles, from assembly to supply-chain to customer service and retail. Employee tenure in the past always had an upward trend, with the average employee staying at the company for 10+ years. However, in the past decade, the trend has reversed, with some employees lasting only a few months, and others leaving abruptly after many years.

Step 2: Show why it matters As the perceived loyalty of their employees has long been a source of pride for the company, they employed an outside consultant firm to see why there was so much turnover. The firm focused on the new hires, concluding that a rival shoe company located in the next town offered higher hourly wages and better “perks”, such as pizza parties. They claimed this was what was leading employees to switch. However, to gain a fuller understanding of why the turnover persists even after the consultant study, in-depth qualitative research focused on long-term employees is also needed. Focusing on why established workers leave can help develop a more telling reason why turnover is so high, rather than just due to salaries. It can also potentially identify points of change or conflict in the company’s culture that may cause workers to leave.

Step 3: Set your aims and objectives This project aims to better understand why established workers choose to leave the company. Qualitative methods such as surveys and interviews will be conducted comparing the views of those who have worked 10+ years at the company and chose to stay, compared with those who chose to leave.

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

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

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 objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

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

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How to Write a Statement of the Problem in Research

Table of Contents

The problem statement is a foundation of academic research writing , providing a precise representation of an existing gap or issue in a particular field of study.

Crafting a sharp and focused problem statement lays the groundwork for your research project.

• It highlights the research's significance .
• Emphasizes its potential to influence the broader academic community.
• Represents the initial step for you to make a meaningful contribution to your discipline.

Therefore, in this article, we will discuss what is a statement of the problem in research and how to craft a compelling research problem statement.

What is a research problem statement?

A research problem statement is a concise, clear, and specific articulation of a gap in current knowledge that your research aims to bridge. It not only sets forth the scope and direction of your research but also establishes its relevance and significance.

Your problem statement in your research paper aims to:

• Define the gap : Clearly identify and articulate a specific gap or issue in the existing knowledge.
• Provide direction : Serve as a roadmap, guiding the course of your research and ensuring you remain focused.
• Establish relevance : Highlight the importance and significance of the problem in the context of your field or the broader world.
• Guide inquiry :  Formulate the research questions or hypotheses you'll explore.
• Communicate intent : Succinctly convey the core purpose of your research to stakeholders, peers, and any audience.
• Set boundaries : Clearly define the scope of your research to ensure it's focused and achievable.

When should you write a problem statement in research?

Initiate your research by crafting a clear problem statement. This should be done before any data collection or analysis, serving as a foundational anchor that clearly identifies the specific issue you aim to address.

By establishing this early on, you shape the direction of your research, ensuring it targets a genuine knowledge gap.

Furthermore, an effective and a concise statement of the problem in research attracts collaborators, funders, and supporters, resonating with its clarity and purpose. Remember, as your research unfolds, the statement might evolve, reflecting new insights and staying pertinent.

But how do you distinguish between a well-crafted problem statement and one that falls short?

Effective vs. ineffective research problem statements

Imagine a scenario where medical researchers aim to tackle a new strain of virus. Their effective problem statement wouldn't merely state the existence of the virus. Instead, it would delve into the specifics — the regions most affected, the demographics most vulnerable, and the current limitations in medical interventions.

Whereas an ineffective research problem statement is vague, overly broad, or ambiguous, failing to provide a clear direction for the research. It may not be rooted in existing literature, might lack clarity on its significance, or could be framed in a way that makes the research objectives unachievable or irrelevant.

To understand it better, let's consider the topic of “Remote work and employee productivity.”

Effective problem statement

“Over the past decade, there has been a 70% increase in organizations adopting remote work policies. While some studies suggest remote work enhances employee productivity, others indicate potential declines due to distractions at home.

However, there’s a lack of comprehensive research examining the specific factors in a remote environment that influence productivity. This study aims to identify and analyze these factors, providing organizations with actionable insights to optimize remote work policies.”

Why is this statement of a problem in research effective?

• Specificity : The statement provides a clear percentage to highlight the rise in remote work.
• Context : It acknowledges existing research and the conflicting findings.
• Clear gap identification : It points out the lack of comprehensive research on specific factors affecting productivity in remote work.
• Purpose : The statement concludes with a clear aim for the research.

Ineffective problem statement

"People are working from home a lot now, especially since there are so many internet tools. Some say it's good; others say it's not that great. This research will just look into the whole work-from-home thing and see what's up."

Why is this statement of a problem in research ineffective?

• Informal language : Phrases like "what's up" and "the whole work-from-home thing" are not suitable for academic writing.
• Vagueness : The statement doesn't provide any specific data or context about the rise of remote work.
• Lack of clear focus : It's unclear what aspect of remote work the research will address.
• Ambiguous purpose : The statement doesn't specify the research's objectives or expected outcomes.

After gaining an understanding of what an effective research problem statement looks like, let's dive deeper into how to write one.

How to write a problem statement in research?

Drafting your research problem statement at the onset of your research journey ensures that your research remains anchored. That means by defining and articulating the main issue or challenge you intend to address at the very beginning of your research process; you provide a clear focus and direction for the entire study.

Here's a detailed guide to how you can write an effective statement of the problem in research.

Identify the research area : Before addressing a specific problem, you need to know the broader domain or field of your study. This helps in contextualizing your research and ensuring it aligns with existing academic disciplines.

Example: If you're curious about the effects of digital technology on human behavior, your broader research area might be Digital Sociology or Media Studies.

Conduct preliminary literature review : Familiarize yourself with existing research related to your topic. This will help you understand what's already known and, more importantly, identify gaps or unresolved questions in the existing knowledge. This step also ensures you're advancing upon existing work rather than replicating it.

Example: Upon reviewing literature on digital technology and behavior, you find many studies on social media's impact on youth but fewer on its effects on the elderly.

Read how to conduct an effective literature review .

Define the specific problem : After thoroughly reviewing the literature, pinpoint a particular issue that your research will address. Ensure that this chosen issue is not only of substantial importance in its field but also realistically approachable given your resources and expertise. To define it precisely, you might consider:

• Highlighting discrepancies or contradictions in existing literature.
• Emphasizing the real-world implications of this gap.
• Assessing the feasibility of exploring this issue within your means and timeframe.

Example: You decide to investigate how digital technology, especially social media, affects the mental well-being of the elderly, given the limited research in this area.

Articulate clearly and concisely : Your problem statement should be straightforward and devoid of jargon. It needs to convey the essence of your research issue in a manner that's understandable to both experts and non-experts.

Example: " The impact of social media on the mental well-being of elderly individuals remains underexplored, despite the growing adoption of digital technology in this age group. "

Highlight the significance : Explain why your chosen research problem matters. This could be due to its real-world implications, its potential to fill a knowledge gap or its relevance to current events or trends.

Example: As the elderly population grows and becomes more digitally connected, understanding the psychological effects of social media on this demographic could inform digital literacy programs and mental health interventions.

Ensure feasibility : Your research problem should be something you can realistically study, given your resources, timeframe, and expertise. It's essential to ensure that you can gather data, conduct experiments, or access necessary materials or participants.

Example: You plan to survey elderly individuals in local community centers about their social media usage and perceived mental well-being, ensuring you have the means to reach this demographic.

Seek feedback : Discuss your preliminary problem statement with peers, mentors, or experts in the field. They can provide insights, point out potential pitfalls, or suggest refinements.

Example: After discussing with a gerontologist, you decide to also consider the role of digital training in moderating the effects of social media on the elderly.

Refine and Revise : Based on feedback and further reflection, revise and improve your problem statement. This iterative process ensures clarity, relevance, and precision.

Example: Your refined statement reads: Despite the increasing digital connectivity of the elderly, the effects of social media on their mental well-being, especially in the context of digital training, remain underexplored.

By following these detailed steps, you can craft a research problem statement that is both compelling and academically rigorous.

Having explored the details of crafting a research problem statement, it's crucial to distinguish it from another fundamental element in academic research: the thesis statement.

Difference between a thesis statement and a problem statement

While both terms are central to research, a thesis statement presents your primary claim or argument, whereas a problem statement describes the specific issue your research aims to address.

Think of the thesis statement as the conclusion you're driving towards, while the problem statement identifies a specific gap in current knowledge.

For instance, a problem statement might highlight the rising mental health issues among teenagers, while the thesis statement could propose that increased screen time is a significant contributor.

Refer to the comparison table between what is a thesis and a problem statement in the research below:

Common mistakes to avoid in writing statement of the problem in research

Mistakes in the research problem statement can lead to a domino effect, causing misalignment in research objectives, wasted resources, and even inconclusive or irrelevant results.

Recognizing and avoiding these pitfalls not only strengthens the foundation of your research but also ensures that your efforts concede impactful insights.

Here's a detailed exploration of frequent subjective, qualitative, quantitative and measurable mistakes and how you can sidestep them.

Being too broad or too narrow

A problem statement that's too broad can lack focus, making it challenging to derive specific research questions or objectives. Conversely, a statement that's too narrow might limit the scope of your research or make it too trivial.

Example of mistake: "Studying the effects of diet on health" is too broad, while "Studying the effects of eating green apples at 3 pm on heart health" is overly narrow.

You can refine the scope based on preliminary research. The correct way to write this problem statement will be "Studying the effects of a high-fiber diet on heart health in adults over 50." This statement is neither too broad nor too narrow, and it provides a clear direction for the research.

Using unnecessary jargon or technical language

While academic writing often involves academic terms, overloading your problem statement with jargon can alienate readers and obscure the actual problem.

Example of Mistake: "Examining the diurnal variations in macronutrient ingestion vis-à-vis metabolic homeostasis."

To ensure it’s not complicated, you can simplify and clarify. "Examining how daily changes in nutrient intake affect metabolic balance" conveys the same idea more accessible.

Not emphasizing the "Why" of the problem

It's not enough to state a problem; you must also convey its significance. Why does this problem matter? What are the implications of not addressing it?

Example of Mistake: "Many students are not engaging with online learning platforms."

You can proceed with the approach of highlighting the significance here. "Many students are not engaging with online learning platforms, leading to decreased academic performance and widening educational disparities."

Circular reasoning and lack of relevance

Your problem statement should be grounded in existing research or observed phenomena. Avoid statements that assume what they set out to prove or lack a clear basis in current knowledge.

Example of Mistake: "We need to study X because not enough research has been done on X."

Instead, try grounding your statement based on already-known facts. "While several studies have explored Y, the specific impact of X remains unclear, necessitating further research."

Being overly ambitious

While it's commendable to aim high, your problem statement should reflect a challenge that's achievable within your means, timeframe, and resources.

Example of Mistake: "This research will solve world hunger."

Here, you need to be realistic and focused. "This research aims to develop sustainable agricultural techniques to increase crop yields in arid regions."

By being mindful of these common mistakes, you can craft a problem statement that is clear, relevant and sets a solid foundation for your research.

Over-reliance on outdated data

Using data that is no longer relevant can mislead the direction of your research. It's essential to ensure that the statistics or findings you reference are current and pertinent to the present scenario.

Example of Mistake: "According to a 1995 study, only 5% of the population uses the internet for daily tasks."

You always cross-check the dates and relevance of the data you're using. For a contemporary study on internet usage, you'd want to reference more recent statistics.

Not specifying the sample size or demographic

A problem statement should be clear about the population or sample size being studied, especially when making generalizations or claims.

Example of Mistake: "People prefer online shopping to in-store shopping."

Here, you would benefit from specifying the demographic or sample size when presenting data to avoid overgeneralization. " In a survey of 1,000 urban residents aged 18-35, 70% expressed a preference for online shopping over in-store shopping. "

Ignoring conflicting data

Cherry-picking data that supports your hypothesis while ignoring conflicting data can lead to a biased problem statement.

Example of Mistake: "Research shows that all students benefit from online learning."

You’ve to ensure a balanced view by considering all relevant data, even if it contradicts your hypothesis. " While many studies highlight the advantages of online learning for students, some research points to challenges such as decreased motivation and lack of face-to-face interaction. "

Making unsubstantiated predictions

Projecting future trends without solid data can weaken the credibility of your problem statement.

Example of Mistake: "The demand for electric cars will increase by 500% in the next year."

Base your predictions on current trends and reliable data sources, avoiding hyperbolic or unsupported claims. " With the current growth rate and recent advancements in battery technology, there's potential for a significant rise in the demand for electric cars. "

Wrapping Up

A well-crafted problem statement ensures that your research is focused, relevant, and contributes meaningfully to the broader academic community.

However, the consequences of an incorrect or poorly constructed problem statement can be severe. It can lead to misdirected research efforts, wasted resources, compromised credibility, and even ethical concerns. Such pitfalls underscore the importance of dedicating time and effort to craft a precise and impactful problem statement.

So, as you start your research journey , remember that a well-defined problem statement is not just a starting point; it guides your entire research journey, ensuring clarity, relevance, and meaningful contributions to your field.

Frequently Asked Questions

A problem statement is a clear, concise and specific articulation of a gap in current knowledge that your research aims to bridge.

The Problem Statement should highlight existing gaps in current knowledge and also the significance of the research. It should also include the research question and purpose of the research.

Clear articulation of the problem and establishing relevance; Working thesis (methods to solve the problem); Purpose and scope of study — are the 3 parts of the problem statement.

While the statement of the problem articulates and delineates a particular research problem, Objectives designates the aims, purpose and strategies to address the particular problem.

Here’s an example — “The study aims to identify and analyze the specific factors that impact employee productivity, providing organizations with actionable insights to optimize remote work policies.”

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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.

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• How to Define a Research Problem | Ideas & Examples

How to Define a Research Problem | Ideas & Examples

Published on 8 November 2022 by Shona McCombes and Tegan George.

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, 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 organisation. 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 organisation 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 organisation 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 organisation 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 .

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement.

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis – a prediction that will be confirmed or disproved by your research.

Research objectives describe what you intend your research project to accomplish.

They summarise the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

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The conclusion is intended to help the reader understand why your research should matter to them after they have finished reading the paper. A conclusion is not merely a summary of the main topics covered or a re-statement of your research problem, but a synthesis of key points and, if applicable, where you recommend new areas for future research. For most college-level research papers, one or two well-developed paragraphs is sufficient for a conclusion, although in some cases, more paragraphs may be required in summarizing key findings and their significance.

Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University.

Importance of a Good Conclusion

A well-written conclusion provides you with important opportunities to demonstrate to the reader your understanding of the research problem. These include:

• Presenting the last word on the issues you raised in your paper . Just as the introduction gives a first impression to your reader, the conclusion offers a chance to leave a lasting impression. Do this, for example, by highlighting key findings in your analysis that advance new understanding about the research problem, that are unusual or unexpected, or that have important implications applied to practice.
• Summarizing your thoughts and conveying the larger significance of your study . The conclusion is an opportunity to succinctly re-emphasize  the "So What?" question by placing the study within the context of how your research advances past research about the topic.
• Identifying how a gap in the literature has been addressed . The conclusion can be where you describe how a previously identified gap in the literature [described in your literature review section] has been filled by your research.
• Demonstrating the importance of your ideas . Don't be shy. The conclusion offers you the opportunity to elaborate on the impact and significance of your findings. This is particularly important if your study approached examining the research problem from an unusual or innovative perspective.
• Introducing possible new or expanded ways of thinking about the research problem . This does not refer to introducing new information [which should be avoided], but to offer new insight and creative approaches for framing or contextualizing the research problem based on the results of your study.

Bunton, David. “The Structure of PhD Conclusion Chapters.” Journal of English for Academic Purposes 4 (July 2005): 207–224; Conclusions. The Writing Center. University of North Carolina; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Conclusions. The Writing Lab and The OWL. Purdue University; Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Structure and Writing Style

I.  General Rules

The function of your paper's conclusion is to restate the main argument . It reminds the reader of the strengths of your main argument(s) and reiterates the most important evidence supporting those argument(s). Do this by stating clearly the context, background, and necessity of pursuing the research problem you investigated in relation to an issue, controversy, or a gap found in the literature. Make sure, however, that your conclusion is not simply a repetitive summary of the findings. This reduces the impact of the argument(s) you have developed in your essay.

When writing the conclusion to your paper, follow these general rules:

• Present your conclusions in clear, simple language. Re-state the purpose of your study, then describe how your findings differ or support those of other studies and why [i.e., what were the unique or new contributions your study made to the overall research about your topic?].
• Do not simply reiterate your findings or the discussion of your results. Provide a synthesis of arguments presented in the paper to show how these converge to address the research problem and the overall objectives of your study.
• Indicate opportunities for future research if you haven't already done so in the discussion section of your paper. Highlighting the need for further research provides the reader with evidence that you have an in-depth awareness of the research problem and that further investigations should take place.

Consider the following points to help ensure your conclusion is presented well:

• If the argument or purpose of your paper is complex, you may need to summarize the argument for your reader.
• If, prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the end of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration that returns the topic to the context provided by the introduction or within a new context that emerges from the data. 

The conclusion also provides a place for you to persuasively and succinctly restate the research problem, given that the reader has now been presented with all the information about the topic . Depending on the discipline you are writing in, the concluding paragraph may contain your reflections on the evidence presented. However, the nature of being introspective about the research you have conducted will depend on the topic and whether your professor wants you to express your observations in this way.

NOTE : If asked to think introspectively about the topics, do not delve into idle speculation. Being introspective means looking within yourself as an author to try and understand an issue more deeply, not to guess at possible outcomes or make up scenarios not supported by the evidence.

II.  Developing a Compelling Conclusion

Although an effective conclusion needs to be clear and succinct, it does not need to be written passively or lack a compelling narrative. Strategies to help you move beyond merely summarizing the key points of your research paper may include any of the following strategies:

• If your essay deals with a critical, contemporary problem, warn readers of the possible consequences of not attending to the problem proactively.
• Recommend a specific course or courses of action that, if adopted, could address a specific problem in practice or in the development of new knowledge.
• Cite a relevant quotation or expert opinion already noted in your paper in order to lend authority and support to the conclusion(s) you have reached [a good place to look is research from your literature review].
• Explain the consequences of your research in a way that elicits action or demonstrates urgency in seeking change.
• Restate a key statistic, fact, or visual image to emphasize the most important finding of your paper.
• If your discipline encourages personal reflection, illustrate your concluding point by drawing from your own life experiences.
• Return to an anecdote, an example, or a quotation that you presented in your introduction, but add further insight derived from the findings of your study; use your interpretation of results to recast it in new or important ways.
• Provide a "take-home" message in the form of a succinct, declarative statement that you want the reader to remember about your study.

III. Problems to Avoid

Failure to be concise Your conclusion section should be concise and to the point. Conclusions that are too lengthy often have unnecessary information in them. The conclusion is not the place for details about your methodology or results. Although you should give a summary of what was learned from your research, this summary should be relatively brief, since the emphasis in the conclusion is on the implications, evaluations, insights, and other forms of analysis that you make. Strategies for writing concisely can be found here .

Failure to comment on larger, more significant issues In the introduction, your task was to move from the general [the field of study] to the specific [the research problem]. However, in the conclusion, your task is to move from a specific discussion [your research problem] back to a general discussion [i.e., how your research contributes new understanding or fills an important gap in the literature]. In short, the conclusion is where you should place your research within a larger context [visualize your paper as an hourglass--start with a broad introduction and review of the literature, move to the specific analysis and discussion, conclude with a broad summary of the study's implications and significance].

Failure to reveal problems and negative results Negative aspects of the research process should never be ignored. These are problems, deficiencies, or challenges encountered during your study should be summarized as a way of qualifying your overall conclusions. If you encountered negative or unintended results [i.e., findings that are validated outside the research context in which they were generated], you must report them in the results section and discuss their implications in the discussion section of your paper. In the conclusion, use your summary of the negative results as an opportunity to explain their possible significance and/or how they may form the basis for future research.

Failure to provide a clear summary of what was learned In order to be able to discuss how your research fits within your field of study [and possibly the world at large], you need to summarize briefly and succinctly how it contributes to new knowledge or a new understanding about the research problem. This element of your conclusion may be only a few sentences long.

Failure to match the objectives of your research Often research objectives in the social sciences change while the research is being carried out. This is not a problem unless you forget to go back and refine the original objectives in your introduction. As these changes emerge they must be documented so that they accurately reflect what you were trying to accomplish in your research [not what you thought you might accomplish when you began].

Resist the urge to apologize If you've immersed yourself in studying the research problem, you presumably should know a good deal about it [perhaps even more than your professor!]. Nevertheless, by the time you have finished writing, you may be having some doubts about what you have produced. Repress those doubts! Don't undermine your authority by saying something like, "This is just one approach to examining this problem; there may be other, much better approaches that...." The overall tone of your conclusion should convey confidence to the reader.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Concluding Paragraphs. College Writing Center at Meramec. St. Louis Community College; Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University; Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. The Lab Report. University College Writing Centre. University of Toronto; Leibensperger, Summer. Draft Your Conclusion. Academic Center, the University of Houston-Victoria, 2003; Make Your Last Words Count. The Writer’s Handbook. Writing Center. University of Wisconsin Madison; Miquel, Fuster-Marquez and Carmen Gregori-Signes. “Chapter Six: ‘Last but Not Least:’ Writing the Conclusion of Your Paper.” In Writing an Applied Linguistics Thesis or Dissertation: A Guide to Presenting Empirical Research . John Bitchener, editor. (Basingstoke,UK: Palgrave Macmillan, 2010), pp. 93-105; Tips for Writing a Good Conclusion. Writing@CSU. Colorado State University; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Writing Conclusions. Writing Tutorial Services, Center for Innovative Teaching and Learning. Indiana University; Writing: Considering Structure and Organization. Institute for Writing Rhetoric. Dartmouth College.

Writing Tip

Don't Belabor the Obvious!

Avoid phrases like "in conclusion...," "in summary...," or "in closing...." These phrases can be useful, even welcome, in oral presentations. But readers can see by the tell-tale section heading and number of pages remaining to read, when an essay is about to end. You'll irritate your readers if you belabor the obvious.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Another Writing Tip

New Insight, Not New Information!

Don't surprise the reader with new information in your conclusion that was never referenced anywhere else in the paper and, as such, the conclusion rarely has citations to sources. If you have new information to present, add it to the discussion or other appropriate section of the paper. Note that, although no actual new information is introduced, the conclusion, along with the discussion section, is where you offer your most "original" contributions in the paper; the conclusion is where you describe the value of your research, demonstrate that you understand the material that you’ve presented, and locate your findings within the larger context of scholarship on the topic, including describing how your research contributes new insights or valuable insight to that scholarship.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Conclusions. The Writing Center. University of North Carolina.

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Problems and Strategies in Developing Writing Undergraduate Thesis: A Study in EFL Students

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The research aimed at finding out the problems faced by undergraduate students and strategies they used to overcome problems during the process of research and writing thesis. This research generally employed a qualitative approach, but some data were analyzed quantitatively. The sample of the research involved the eighth and tenth semester students of English Education Department at Islamic State University of Sultan Maulana Hasanuddin Banten. The data were obtained through questionnaire and interview. The result revealed that most of the students got problems in how to begin the research while only few students got difficulty in working with the advisors. In addition, strategies mostly used by the students to overcome the problems were reading many books, journals and articles related to the topic whereas the strategy of asking somebody to analyze the data was at the lowest rate. Therefore, it could be concluded that the students could be able to solve their problems they faced du...

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