what is meant by alignment of dissertation components

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Dissertation alignment made simple, published by nicholas tippins on february 25, 2019 february 25, 2019.

Last Updated on: 2nd February 2024, 05:26 am

In my years of editing dissertations, I found one concept that students struggle with the most: dissertation alignment. They would submit their proposals and, time after time, they would be rejected.

After seeing so many clients with the same issue, I developed materials that I used to explain alignment. These were usually accompanied by a phone conversation and a lightbulb moment.

What Is Dissertation Alignment?

Dissertation alignment is one of the most important concepts in a dissertation proposal. It means that each section, and especially the key sentences in those sections, are all focused on the same specific point.

At its most basic, dissertation alignment is about focus and consistency.

Focus means that you are being specific. For example, rather than researching “history teacher preparation,” you might research “high school history teacher preparation for the topic of Middle Eastern conflict.”

Consistency means that the focus is exactly the same throughout your paper. For example, a student may write that their focus is shareholder wealth maximization and then later state that the focus is on management impact on stock price. While these are related, they are not the same and are thus out of alignment.

How to Achieve Dissertation Alignment

1. narrow your focus.

Remember that the focus of a dissertation is extremely narrow, so broad or vague statements won’t cut it. This is where most students struggle–they have statements that are related but don’t have exactly the same minute focus.

Alignment may seem annoying while you’re writing it, but you’ll be grateful for the rigor of phrasing once you start your study, because it tells you exactly what to look for and pay attention to.

Creating a study about “cats” would be difficult, because that’s such a broad subject. But creating a study about “social behavior of cats weaned before 5 months of age” would be easier, because it’s specific. Now, that brings us to the next point.

2. Achieve Consistency

Consistency is the other important part of dissertation alignment. Over time, I’ve developed the idea of “Key Sentences” to support students in achieving consistency.  I’ve found this to be the most effective way of helping students grasp the concept of dissertation alignment and integrate it into their dissertation.

It’s this: your Title, Problem Statement, Purpose Statement, and Research Questions should all be variations of the same sentence.

I’ve found the most effective way is to focus on these sentences and build the rest of the paper around them.

Or, if you’ve already written your paper, focus on these sentences and then revise the surrounding text to align with them. Let’s go over the key sentences for each section:

Key Sentences

For the purpose of example, let’s say I’m writing a paper titled, “Employee Perceptions of the Effect of ABC Diversity Training Program on Reducing Racism in the Workplace in an Organization in Southwest Wisconsin” You’ll see how this aligns as you go through the other sections.

Problem Statement

The Problem Statement is made up of two primary sentences: the General Problem Statement and the Specific Problem Statement. Your key sentence should be the specific problem statement.

General problem statement. This statement should be one sentence indicating the general problem. This isn’t one of the key sentences, but I include it here so that you can see how it’s different from the specific problem statement. Using my example, that would sound something like:

“The general problem is that racism in the workplace is common in the US and leads to unsafe work environments, abusive employee relationships, wage disparity, and lower productivity.” This sentence is the second-most important part of your Problem Statement section.

Specific problem statement. This statement should be one sentence directly following the general problem statement. It is a precise sentence about what will be studied and leads directly to the Purpose Statement. This sentence should have almost the exact same wording as your Title and Purpose statement. In this case, it could be something like,

“The specific problem is that the influence of the ABC Diversity Training Program on reducing racism in the workplace is unknown.”

One very easy way to make sure your problem statement is in alignment is to write, “[your title] is unknown.” Notice how I did this above. You may have to adjust the wording slightly, but this will ensure your paper is in alignment. I’ve encouraged many clients to do it this way, and so far their chairs have always been happy with it.

Purpose Statement

The most important part of your Purpose Statement section is the one sentence designated as your “purpose statement.” Your purpose statement should align directly with your Title and specific Problem Statement. It should even have almost the exact same wording. Here is an example, based on the imaginary paper I’m writing:

“The purpose of this qualitative case study is to identify employee perceptions regarding the effects of the ABC Diversity Training Program on racism in the workplace in an organization in Southwest Wisconsin.”

See how I’ve taken the same sentence and changed the wording slightly to turn it from a Problem Statement to a Purpose Statement?

Note that quantitative studies should include the independent, dependent, and covariate variables in the purpose statement.

Research Question

Your overarching research question should sound very similar to your Title, Problem Statement, and Purpose Statement. They should also be in alignment with your chosen research method. For example, a phenomenological study’s research questions should have wording like, “What are the lived experiences of…” and a quantitative study might have wording like, “What is the relationship between…”

For this study, the research question might sound something like, “What are the perceptions of employees at an organization in Southwest Wisconsin regarding the effects of ABC Diversity Training Program on reducing racism in the workplace?

Look at Key Sentences First

When helping students bring their papers into alignment, the first thing I do is pull out the key sentences in their paper. This makes it easy to see what’s in alignment and what’s not. For example, the imaginary paper I’m writing would have the following key sentences:

Title : “Employee Perceptions of the Effect of ABC Diversity Training Program on Reducing Racism in the Workplace in an Organization in Southwest Wisconsin”

General: “The general problem is that racism in the workplace is common in the US and leads to unsafe work environments, abusive employee relationships, and lower productivity.”

Specific: “The specific problem is that the influence of the ABC Diversity Training Program on reducing racism in the workplace is unknown”

Purpose Statement: “The purpose of this qualitative case study is to identify employee perceptions regarding the effects of the ABC Diversity Training Program on racism in the workplace in an organization in Southwest Wisconsin.”

Research Question: “What are the perceptions of employees at an organization in Southwest Wisconsin regarding the effects of ABC Diversity Training Program on reducing racism in the workplace?

Dissertation Alignment Worksheet

Copy and paste the following text into a blank document and fill it in for yourself. Do your Title, Problem Statement, Purpose Statement, and Research Questions align? Are they all variations of the same sentence? If not, revise until they are. Note that depending on your method, your research questions may deviate slightly from this prescription, but it’s a good place to start.

Problem Statement:

Purpose Statement:

Overarching Research Question:

Aligning Your Whole Paper

If you haven’t written your paper yet, write the key sentences first, and use them to focus the surrounding paragraphs. If you do that, your paper should have no problem with alignment.

If you’ve already written your first chapter and had it returned due to issues with alignment, you may have to do some rewriting. Start by getting clear on your key sentences and then re-write the surrounding text in each section so that the focus is consistent.

While it’s useful to look at the key sentences, those alone won’t bring your paper into alignment. Remember, the same two rules apply for all of the text in your paper, not just your key sentences. Throughout the entire paper, your topic should be focused and consistent.

Final Thoughts on Dissertation Alignment

With proper alignment, you will be able to move forward in the dissertation process much more quickly and easily. Taking time to pay attention to focus and consistency will hone your research project, making it easier for you to stay on track and much easier for your committee to approve your proposal and final document.

If you need support writing your dissertation, check out our dissertation coaching and dissertation editing services.

Nicholas Tippins

Nicholas has been a dissertation editor since 2015. He founded a dissertation editing firm that served clients around the world. Currently, he manages the editing team at Beyond PhD Coaching.

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ARTICLE: Achieving Alignment: How to Develop Research Alignment In A Dissertation Study

Research alignment is a relatively new concept with dissertations and research projects. Most researchers and dissertation chairs are not familiar with it. To achieve research alignment is very important and must be taught to novice and experienced researchers. The purpose of this article to discuss and illustrate how to achieve research alignment in studies. This article will provide a model and template for developing research alignment in a study. As basis for a research study, it is important the researcher and doctoral student understand the concept of alignment and know how to achieve it. First, this article discusses the definition of alignment. Second, it provides two models for developing alignment. Last, the article provides examples of research alignment with studies to illustrate it.

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Empirical research is all about trying to model and predict the world. In this article, I discuss how design-based research methods can help do this effectively. In particular, design-based research methods can help with the problem of methodological alignment: ensuring that the research methods we use actually test what we think they are testing. I argue that our current notions of rigor overemphasize certain types of rigor at the expense of others and that design-based research provides an opportunity to select different inferential trade-offs. I describe how 1 design-based research trajectory evolved over time in a way that helped ensure that the learning theories being studied were well represented by the planned interventions and that the interpretation of outcomes was grounded in an understanding of not only the research design, but how the research played out in practice when enacted in real classrooms.

This study examined previous research publication on constructive alignment. The purpose of this study is to present sources of information regarding constructive alignment especially on its relation to education. Several bibliometric indicators were used to trace research publications related to constructive alignment in education from Web of Science online database. The research areas, countries, publication years, source titles, authorship, and citations were examined and the results of these analyses were discussed and presented. It was eminent that constructive alignment was discussed widely in education rather than other fields.

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The main purpose of the guideline is to develop a basic academic research structure and format guiding principles for students with two specific objectives; to develop a coherent research structure and academic research format for the public universities in South Sudan. One hundred and seventeen dissertations were reviewed, and a total of 88 teaching staff were interviewed, giving 91% response rate. The dissertations were selected randomly, and teaching staff were selected purposively by virtue of their positions and availability. The validity and reliability of the instruments was ensured through pretesting, CVI, and triangulations. SPSS v22 was used to analyze data, both descriptive and thematic analysis were used. Permissions were sought from university administrations and one of the respondents before data collection. There is some significant variation on academic structure and format across the five universities in the republic of South Sudan. The research structural variations and inconsistencies were found in their contents and order of titles and subtitles of dissertations reviewed. In conclusion, this basic academic research guide is developed to set some standards and directions for students and their supervisors to complete the dissertation on time with less stress as it provides where to begin and the end. It will also enhance the objective grading of dissertations and evaluation levels of the university. The guideline will contribute to the development of consensus on academic research structure and format standards for students that will encourage young researchers to have an interest in research as a career.

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Degree projects (DP) are currently intensively focused in Sweden: The future national model for evaluation of higher education will place a major emphasis on the quality of degree projects as an indicator of the quality of the entire education, and their quality will influence the funding of a university. Moreover, DP:s are actively used in program development as a vehicle to develop not only in-depth subject matter knowledge but also professional skills such as planning and communication. Simultaneously, Constructive alignment (CA) is being widely applied as a general approach for improving educational quality. Potentially, CA might also contribute to improving the quality of degree projects. In this paper, we examine how CA can be applied to degree projects. We conclude that CA is indeed applicable to degree projects in the sense that intended learning outcomes as well as teaching and assessment activities can be identified and aligned. But objectives, activities and assessment ar...

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5 tips to fixing alignment in a dissertation proposal: what is it and how do you fix it.

By Mary Dereshiwsky, PhD

One of the most common reasons your dissertation may be sent back to you is for “alignment”. You may hear this repeatedly.. “your dissertation is not in alignment.” “Your problem statement and purpose are not in alignment.” It seems to be an endless battle! If you’ve ever put together a jigsaw puzzle, you know that feeling of accomplishment when all the pieces fit together perfectly so that you can see the big picture. This is also the idea behind alignment of the key components of a dissertation proposal.

Why is alignment important in research? When research components fit together, you can arrive more directly at the answers to your research questions. Your readers can follow along with you, step by step, to see what you studied, why and how, to get those answers.

Here are some specific ways to check for alignment in your dissertation proposal:

Tip 1: Does the purpose of your study map tightly to your preceding problem statement? The problem is where you point out a current need, gap, or lack of information. Someone needs to know something. But they don’t yet have the necessary information to solve a problem or answer a question. Your study purpose identifies what you will do or look at to solve this problem. It is the driving-force curiosity itself that you will research. If you have thought through the nature of the problem very specifically, the purpose should flow directly from it. If there is a shortage of literature in a specific topic area, your purpose should state how you will fill that shortage.

Tip 2: Does the literature synthesize “what we (think we) know so far” about your key concepts of focus? Be careful of accidentally meandering down rabbit holes when you search the literature. If your study purpose has to do with motivation, don’t accidentally get derailed into interesting but irrelevant literature on satisfaction. The internet has made it possible to access tons of information in a split second. This is helpful if you stay on track in specifying exactly the concepts you will be investigating when you search the literature. On the other hand, if you get distracted by literature that doesn’t directly map to your key concepts, your literature review won’t match the goals of your own study.

Tip 3: Does the ‘how’ of your study align with ‘what’ you want to research? Another term for ‘how’ in research is methodology and design. Let’s consider methodology as a first step. There are only three types of research methodologies: quantitative, qualitative, and mixed methods. Think of them as three trees in the forest. Every tree has branches emanating from it. Those branches will be the research designs aligned with each methodology. Will your study consist of data in numbers? If so, you want a quantitative methodology. Or will your data be in the form of words? If so, qualitative methodology aligns best with your research study goals. If you are planning to collect data in both numbers and words, you will have a mixed-methods methodology.

Tip 4: Next, does your curiosity (as contained with your study purpose) match to the best research design? If you are ‘identifying/what is/what are’ something(s), you have a descriptive design. If you are instead looking at relationships, associations, or predictions, you have a correlational design. Interested in determining what causes what? Your research will match the experimental design family. Or are you only looking at between-group differences on something? If so, you have a causal-comparative (also known as ex post facto) design. What about qualitative studies? If your intent is to capture your subjects’ lived experience in rich detail, your study aligns with a phenomenological design. Or you may instead be gathering and analyzing multiple forms of evidence on a ‘case’ (however you have defined it), which means you have a case study. Maybe instead you are immersed in a setting or situation and want to investigate that setting and subjects in depth. This means you have an ethnographic design. These are just a few possible examples of how to map your study goals to a related specific research design.

Tip 5: Finally, do the tools of your (analysis) trade match your research goals? For quantitative studies, you want to ensure that the statistic you compute to test your hypotheses matches the study goals. A detailed discussion of this topic is beyond the scope of this blog. For now, I’ll highlight just a few. If you are looking at relationships between pairs of variables, you will want some form of a correlation coefficient. If your goal is to predict something from something(s), you will likely want to apply regression analysis. Between-group mean differences suggest some form of the t-test or analysis of variance (ANOVA). Again, this only scratches the surface of how to pick the best-fitting statistic for the goals of your study. It is important to ensure that the statistical method fits the questions you are trying to answer and the related hypotheses you are trying to test, as well as the scales of measure of your variables.

Keeping the above components of your research proposal in alignment will help ensure that you reach your goal of answering the question(s) you started with in your research study. This will also make the results of your study useful to readers who also have an interest in your research topic. They will be able to follow your train of thought and how you arrived at your answers. They can then decide on any actions to take related to your study: for example, applying your findings directly to their own professional situations. Or they can continue to help build knowledge in this topic area by designing and implementing their own follow-up study. Either way, you are being a contributing responsible citizen of the wider research community by sharing what you learned in a way that others can apply.

Mary Dereshiwsky, PhD | Mentor

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The Importance of Alignment in Your Dissertation Proposal

One common issue dissertation students face when writing their proposal is that the problem statement, the research questions, and the literature review fail to match up properly.

This is known as a lack of “alignment,” and it happens when students have so many ideas that the dissertation proposal becomes unfocused and rambling..

In this article, we’ll explore:

• What alignment is
• Why it matters, and
• How you can achieve it

What Do We Mean by Alignment?

A well-aligned dissertation proposal is one in which everything you present lines up clearly with your research questions..

In other words, the context should be the context of those questions, the problem statement should show why they are relevant questions, the literature review should establish they have not been adequately answered already, and so on.

Why is Alignment Important?

Dissertations need to be focused like a laser on a specific problem or topic. without alignment, however, this focus can get lost..

This makes it hard for a reader to follow the line of logic you are establishing.

By aligning all parts of your dissertation proposal to your research questions, you will help your reader recognize the research gap you are filling, follow your argument, and assess the viability of your study.

How to Achieve Dissertation Proposal Alignment

One easy way to make sure your dissertation proposal sections are aligned to your research questions is to use a chart when you are drafting..

First, create a table. Then, put your research questions in the first column, your objectives (if applicable) in the next column, different aspects of the problem in the next column, key lit review sources and gaps you have identified in the next, and so on. Then check—does each row make sense and line up properly?

If it does not, try to work out where you have gone off track. Maybe your objectives need rethinking, or you need to complete more literature research. Keep tweaking the project until the chart tells you everything lines up. Only then are you ready to start writing your proposal.

Need More Help?

Need help planning or writing your dissertation proposal? Get in touch to find out how dissertation coaching can help.

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A Framework for Evaluating and Enhancing Alignment in Self-Regulated Learning Research

Amy l. dent.

University of Nebraska - Lincoln

Rick H. Hoyle

Duke University

We discuss the articles of this special issue with reference to an important yet previously only implicit dimension of study quality: alignment across the theoretical and methodological decisions that collectively define an approach to self-regulated learning. Integrating and extending work by leaders in the field, we propose a framework for evaluating alignment in the way self-regulated learning research is both conducted and reported. Within this framework, the special issue articles provide a springboard for discussing methodological promises and pitfalls of increasingly sophisticated research on the dynamic, contingent, and contextualized features of self-regulated learning.

Contributors to this special issue pose important new questions about self-regulated learning as a dynamic, contingent, and deeply contextualized constellation of constructs that has proven challenging to conceptually and operationally define ( Pintrich, 2000 ; Winne & Perry, 2000 ; Zeidner, Boekaerts, & Pintrich, 2000 ). This special issue represents the latest addition to a growing literature tackling these challenges (e.g., Azevedo et al., 2010 ; Greene & Azevedo, 2010 ; Molenaar & Jarvela, 2014 ; Schraw, 2009 ; Veenman, Van Hout-Wolters, & Afflerbach, 2006 ), framed by three topical themes that are increasingly acknowledged if not empirically tested in self-regulated learning research (Ben-Eliyahu & Bernacki, this issue). Our commentary is thus organized around these themes, using the studies that address each as a springboard for exploring methodological promises and pitfalls of increasingly sophisticated self-regulated learning research.

In particular, we reflect on the articles and themes of this special issue through a framework for evaluating alignment in the way self-regulated learning research is both conducted and reported. That is, we propose interrelated recommendations that promote coherence or compatibility across the theoretical and methodological decisions that collectively define a scholar’s approach to self-regulated learning from the first through final stages of a study. The remainder of this introductory section is devoted to describing the evaluative framework, after which we apply its recommendations in the subsequent sections organized around each special issue theme: dynamic relations, contingencies, and context. We conclude with suggestions for integrating these themes through series of primary studies and meta-analysis.

The diverse literature on self-regulated learning is characterized by multiple theoretical perspectives (see Zimmerman, 2001 ), elaborate theoretical models (e.g., Efklides, 2011; Greene & Azevedo, 2007; Winne & Hadwin, 2008 ), different ways of defining or labeling their many constructs ( Dinsmore, Alexander, & Loughlin, 2008 ; Zeidner, Boekaerts, & Pintrich, 2000 ), and evolving views on measurement ( Azevedo, 2014 ; Hadwin et al., 2007 ; Winne & Perry, 2000 ; Zimmerman, 2008 ). Once implying a relatively stable aptitude, both conceptual and operational definitions of self-regulated learning now reflect an unfolding series of events (e.g., Azevedo, 2014 ; Winne, 2014 ) sensitive to contextual factors ranging from task structure (e.g., Lodewyk, Winne, & Jamieson-Noel, 2009 ; Malmberg, Järvelä & Kirschner, 2014 ) to societal stressors (e.g., Ben-Eliyahu & Bernacki, this issue). Conceived this way, self-regulated learning can be facilitated or constrained by several ecological levels that manifest in task, sample, and setting characteristics. As a result, these characteristics influence how self-regulated learning operates in a given study.

Taken together, features of both self-regulated learning and its diverse literature produce several critical decisions that collectively define a scholar’s approach to studying this complex phenomenon. From adopting a theoretical perspective to drawing inferences from findings, these decisions have assumptions and implications that reverberate throughout the research process. In this commentary, we integrate and extend work by leaders in the field (e.g., Azevedo, 2009 , 2014 ; Schunk, 2008 ; Winters, Greene, & Costich, 2008 ; Wampold, Davis, & Good, 1990 ; Winne & Perry, 2000 ; Zimmerman, 2001 ) to identify nine decisions that shape the approach to and results of a given study on self-regulated learning. These decisions include the theoretical perspective, theoretical model, construct label, construct definition, research question, certain characteristics of the research design (i.e., task, sample, setting), measurement strategy, approach to data analysis, and inferences from findings that together guide how the study is conducted from its first through final stages. We argue for careful planning and transparent reporting of these decisions to improve their alignment, which ultimately influences both the interpretation of study findings and evaluation of study quality.

When combined, these decisions give rise to an interrelated set of recommendations for conducting and reporting self-regulated learning research that focuses on alignment across them. We argue that the degree of alignment across decisions is one dimension along which research on self-regulated learning should be evaluated, especially as increasingly sophisticated ways of modeling and measuring it render alignment more difficult to achieve. Therefore, we evaluate each study in the special issue through this framework for two main reasons. First, applying its recommendations to each article allows us to explain and exemplify them in action. Second, doing so provides a springboard for discussing methodological considerations when studying the dynamic, contingent, or contextualized features of self-regulated learning.

We propose two complementary levels of alignment that together produce a single evaluative framework for conducting and reporting research on self-regulated learning, among other constructs within or even beyond psychology. The first, broader level is comprised of three decisions for which alignment could be achieved in any behavioral science: the research question(s), approach to generating data, and approach to analyzing data ( Figure 1 ). Reflecting its more universal application in behavioral science, this level of alignment is not unlike hypothesis validity in clinical research ( Wampold, Davis, & Good, 1990 ). The second, narrower level of alignment is comprised of several decisions specific to the complexities of self-regulated learning and its diverse literature. These decisions include the theoretical perspective, theoretical model, construct definition(s), construct label(s), construct measure(s), and certain features of the research design (i.e., task, sample, setting). The two levels of alignment share a common component, namely the approach to gathering data about focal constructs that we refer to as the measurement strategy in a study (e.g., retrospective self-report, think-aloud protocol). The shared and unique components of alignment at each level also influence a final decision in self-regulated learning research, namely inferences from findings.

Level of alignment applicable to research across the behavioral sciences. Vertical bars indicate location along the spectrum of research questions, measurement strategies, and approaches to data analysis for a hypothetical study displaying partial alignment across them. Misalignment producing the above location of vertical bars could arise from interrelated research questions proposing multiple mediators tested through structural equation modeling based on summative measures administered at two time points. For example, self-regulated learning processes (e.g., planning, metacognitive monitoring) are proposed as mediators of the relation between academic motivation constructs (e.g., goal orientations) and achievement in math or science. Self-regulated learning and academic motivation are then measured at the beginning of a semester through self-report questionnaires, while achievement is measured at the end of a semester through average course grades. In this hypothetical study, the location of research questions along their spectrum from a general focus on relations to a specific focus on mechanisms reflects an attempt to identify SRL processes that explain the relation between two other academic variables (i.e., motivation and achievement). The location of the data generating approach along its spectrum reflects the summative, retrospective nature of each measure and their administration at two time points (i.e., self-regulated learning and academic motivation at the beginning of a semester and achievement at the end). The location of the data analysis approach closely tracks the research questions by modeling multiple correlated mediators of two multidimensional constructs through statistical tests that can accommodate both. In this hypothetical study, alignment could be improved by measuring students’ academic motivation for a task then their SRL processes during and performance on it. Capturing self-regulated learning in real time would preserve its contingent and contextualized features while more closely tracking the complex research questions tested through structural equation modeling. Alignment in this and many cases is improved by critiquing the assumptions underlying theoretical or methodological decisions that shape how a study is conducted from its first through final stages.

As a result, our evaluative framework reflects nine components of alignment that together underlie an important yet previously only implicit dimension of study quality. Alignment at the broader level can be deceptively difficult given the proliferation of sophisticated statistical options, each with its own nuanced implications for precisely how a hypothesis is tested. Alignment at the narrower level is essential to ensure results are interpreted correctly and theory is revised appropriately, especially as models and measures of self-regulated learning evolve to more accurately reflect its dynamic, contingent, and contextualized processes. For these reasons, there is perhaps no better time or forum to focus a methodological lens on both levels of alignment within self-regulated learning research.

Our evaluative framework thus emphasizes alignment in how self-regulated learning research is conducted and reported, recognizing that determining how well the former has been achieved hinges on precision and transparency in the latter. Therefore, we argue that demonstrating the degree of alignment across theoretical and methodological decisions should be a new reporting standard in self-regulated learning research. To facilitate adoption of this standard, recommendations for enhancing and evaluating alignment are provided in Table 1 . Although this table focuses on reporting, it could be consulted when planning and conducting studies on self-regulated learning to promote alignment at these pivotal stages of the research process. Reviewers could also consult Table 1 when evaluating alignment as a new dimension of study quality in order to critique empirical manuscripts or proposals through consistent and comparable standards. We explain and exemplify recommendations in Table 1 within the next sections, which are organized around the themes of this special issue.

Summary of Recommendations to Improve Alignment When Conducting and Reporting Self-Regulated Learning Research

Dynamic Relations

Although long considered a defining feature of self-regulated learning (e.g., Zimmerman, 1989 ), dynamic relations among its phases or processes have only recently gained empirical traction with the emergence of sophisticated measures that can capture them in real time. Bernacki, Nokes-Malach, and Aleven (this issue) build a compelling case for the dynamic interplay among motivational precursors, metacognitive processes, and academic products of self-regulated learning during a task, doing so with a creative methodological approach capable of validly testing their theoretical argument. This argument gives rise to novel research questions that underscore both the empirical importance and scholarly value of considering the assumptions underlying how self-regulated learning has been conceptually and operationally defined.

Considering these assumptions is critical because alignment among theoretical and methodological decisions often depends on them. For example, Bernacki et al. argue that measures of self-efficacy have historically portrayed this motivational process as relatively stable during self-regulated learning despite both classic and modern conceptualizations implying sensitivity to contextual or cognitive cues of task competence. The theoretical assumption underlying this centerpiece of prominent self-regulated learning models (e.g., Zimmerman, 1989 , 2008 ) is thus misaligned with the assumptions underlying how it is often measured. This misalignment produces inferences about self-regulated learning that may misrepresent the dynamic relations among its defining motivational or metacognitive processes, reinforcing the empirical importance of alignment among decisions and their assumptions. Ensuring this alignment enhanced both the hypothesis validity and scholarly value of Bernacki et al.’s study.

Their study also highlights how misalignment among decisions may only be uncovered when theoretical or methodological assumptions are specified and critiqued. As a result, we recommend transparent reporting of assumptions that underlie how self-regulated learning is conceptually and operationally defined in a study. Researchers should supplement the reporting of relevant assumptions with an argument for their degree of alignment to further the goals of both enhancing and evaluating it.

Bernacki et al.’s study illustrates the importance of another reporting standard for evaluating alignment between methodological decisions and inferences in self-regulated learning research. The increased popularity and prevalence of real time measures has been met with concerns that they disrupt how self-regulated learning unfolds during an academic task (see Binbasaran-Tuysuzoglu & Greene, this issue). Although these concerns have been successfully addressed (e.g., Ericsson & Simon, 1993 , 1998 ; Greene, Robertson, & Costa, 2011 ), implications of the task through which self-regulated learning is measured have received relatively less attention. Researchers agree that self-regulated learning can be facilitated or constrained by characteristics of an academic task ( Lodewyk, Winne, & Jamieson-Noel, 2009 ) and even domain ( Wolters & Pintrich, 1998 ), with more structure (e.g., detailed instructions, sequential steps) providing fewer affordances or demands for students to engage in this dynamic process. As a result, task characteristics such as the degree of structure influence how students self-regulate their learning ( Lodewyk, Winne, & Jamieson-Noel, 2009 ; Malmberg, Järvelä & Kirschner, 2014 ).

This sensitivity of self-regulated learning to contextual factors may limit the generalizability of findings to tasks with similar characteristics as the one used to measure it in real time ( Winne & Perry, 2000 ). Therefore, misalignment could occur between the academic task used in a study and the breadth of inferences about self-regulated learning drawn from it. For example, the Cognitive Tutor Algebra (Carnegie Learning, 2011; Bernacki et al., this issue ) provides an enriched learning experience during which the computer program scaffolds some self-regulated learning processes (e.g., knowledge judgments via the skillometer , metacognitive monitoring via embedded feedback) while students solve math problems. Taken together, the relatively well-structured academic task and domain in Bernacki et al. may constrain students’ need or opportunity to engage the full array of self-regulated learning processes. As a result, inferences about specific processes should be qualified if the academic task may alter how they operate or appear in a study. As this example illustrates, researchers should report the generalizability of task characteristics when interpreting findings and revising theory based on data from real time measures of self-regulated learning.

Despite distinctive task characteristics that may limit the generalizability of findings beyond intelligent tutoring systems or math, their combination in this study provides an ideal proving ground for Bernacki et al.’s hypotheses about dynamic relations during self-regulated learning. For example, observable traces of metacognitive processes were recorded in a way that preserves their temporal order and spacing. Moreover, automated prompts were frequent enough to capture meaningful fluctuations in self-efficacy without disrupting students’ progress or self-regulation through the math problems. Although commendable, this methodological approach also reveals considerations for future research on dynamic relations.

Given their focus on fluctuations in students’ self-efficacy during an academic task, the reliability of a single item to capture this construct deserves further attention. From an analytic perspective, the concern is how to distinguish valid sensitivity to change from unreliability of measurement. Bernacki et al. note that a high correlation (e.g., r = .90) between pairs of self-efficacy judgments would suggest test-retest reliability (i.e., temporal stability), yet their range of observed correlations did not reach this threshold. The authors conclude that a single item measure is suitable for detecting within-person variability because it failed to demonstrate test-retest reliability, a reflection of construct stability. However, this argument is compelling only if self-efficacy can be established as actually fluctuating between real time measures of it. Bernacki et al. address this concern by separately estimating reliable change (beyond that induced by measurement error) and stability of self-efficacy judgments while focusing predictive models on the former. In doing so, the authors illustrate a successful approach to capturing meaningful change in process data.

Another consideration arising in longitudinal research on processes assumed to be autoregressive, including those in this study, is the appropriate frequency of measuring focal constructs (see Azevedo, 2014 for complementary discussion). Bernacki et al. assessed self-efficacy after every fourth math problem, raising the question of whether efficacy judgments influenced by performance four problems prior provide an optimal view of variability and causal influence of those judgments. If more recent efficacy judgments supersede ones four problems prior, then their findings underestimate the influence of prior performance and motivation on subsequent performance. A related concern is what constitutes a complete cycle, or instance, of self-regulated learning. Do students progress through its loosely-sequenced phases from forethought through reflection ( Pintrich, 2000 ) within each new math problem or across an entire set of them? Does the duration of self-regulated learning cycles vary from student to student, or problem to problem? These questions, and evidence of their answers, are important to consider when interpreting findings from academic tasks with multiple problems or stages.

Implications of design characteristics, such as the academic task through which self-regulated learning is measured, are thoroughly reported by Lichtinger and Kaplan (this issue). Doing so was facilitated by their rich qualitative account of self-regulated learning across relatively diverse tasks, a methodological approach revealing complex ways motivation and metacognition can be dynamically related during self-regulated learning. Intensively focusing on a small sample underrepresented in this growing area of research, Lichtinger and Kaplan painstakingly describe potential challenges participants face in both reporting and undertaking self-regulated learning. Coupled with the authors’ research questions, these challenges limit how self-regulated learning should be measured. Lichtinger and Kaplan make this connection transparent, transforming it into a noteworthy strength of their approach. Doing so highlights another important yet often overlooked aspect of alignment, namely between sample characteristics and measurement choices. Ensuring this methodological alignment enhances the scholarly value and hypothesis validity of self-regulated learning research, as both contributions to the dynamic relations theme of the special issue demonstrate.

However, alignment can be obscured when constructs are labeled or defined in a way that belies their continuity with prevailing or historical trends in the literature. The literature on self-regulated learning is characterized by variation in labels and definitions ( Dinsmore, Alexander, & Loughlin, 2008 ; Zeidner, Boekaerts, & Pintrich, 2000 ) that can often be traced to different theoretical models, theoretical perspectives, or empirical traditions. Common themes across them have been catalogued by several researchers (e.g., Pintrich, 2000 ; Puustinen & Pulkkinen, 2001 ; Zimmerman, 2001 ), providing integrative definitions and frameworks that can orient future research on self-regulated learning. Prominent among these contributions is Pintrich’s (2000) working definition, which unites the assumptions underlying many models of self-regulated learning and reflects their shared metacognitive processes (e.g., monitoring, control). This definition is featured by authors in the special issue, including Lichtinger and Kaplan. While situating their study in the self-regulated learning literature, the broader construct label self-regulation was often applied. This apparent misalignment highlights the importance of precise construct specification to ensure its consistency with current conceptualizations and nomenclatures. For example, we encourage careful delineation between self-regulation and self-regulated learning when labeling and defining these elusive constructs. Doing so will become particularly important as the relatively distinct areas of research increasingly cross-pollinate as authors have advocated in this special issue (e.g., Ben-Eliyahu & Bernacki, this issue; Ben-Eliyahu & Linnenbrink-Garcia, this issue). Although theoretical misalignment (e.g., between construct labels and definitions) may not undermine methodological decisions, it can obscure the overall approach to self-regulated learning within a study and the implications of findings beyond it.

Contingencies

Like dynamic relations, contingencies among metacognitive processes have been an essential if elusive feature of self-regulated learning since its first theoretical models (e.g., Winne, 1997 ). Validly capturing contingencies lagged behind conceptualizations of them, with think-aloud protocols and other real time measures reinventing how researchers do so. Binbasaran-Tuysuzoglu and Greene (this issue) capitalize on this development within a study exemplifying consistency across the theoretical and methodological decisions that collectively define an approach to self-regulated learning. Therefore, it serves as a flexible template for how to report these decisions and indirectly demonstrate alignment among them.

Like many recent studies (e.g., Lichtinger & Kaplan, this issue), Binbasaran-Tuysuzoglu and Greene is framed by Pintrich’s (2000) integrative definition. This definition is compatible with most theoretical perspectives of self-regulated learning (see Zimmerman, 2001 ), sharing common assumptions about its dynamic, contingent, and contextualized features ( Pintrich, 2000 ). This connection is noted by Binbasaran-Tuysuzoglu and Greene, who adopt a theoretical model ( Winne & Hadwin, 2008 ) consistent with Pintrich’s definition that is particularly suited to their research questions. These questions were answered through a think-aloud method capable of capturing metacognitive contingencies in real time as the theoretical model both predicts and requires. Rich qualitative data were collected during an academic task for which the authors thoroughly describe its self-regulatory implications, aligning them with the breadth of inferences from findings. Findings were derived from an analytic approach that mirrors the complexity of their research questions based on quantitative data generated by a theoretically grounded coding scheme ( Azevedo, 2005 ; Azevedo et al., 2004 ). Taken together, these decisions create a coherent approach to self-regulated learning that demonstrates alignment in how the study was both conducted and reported. Although often demonstrated indirectly, we recommend making connections among theoretical and methodological decisions explicit to both encourage and evaluate their alignment.

The measurement strategy in Binbasaran-Tuysuzoglu and Greene reveals an additional methodological consideration in Lichtinger and Kaplan. Although both studies richly capture self-regulated learning in real time, Binbasaran-Tuysuzoglu and Greene do so with an a priori protocol validated and refined in several recent studies (e.g., Azevedo & Cromley, 2004 ; Greene et al., 2010 ; Greene et al., 2013 ). Rather than evoking verbalizations during an academic task, which would pose a challenge for their sample, Lichtinger and Kaplan recorded both behavioral manifestations and observations of self-regulated learning (e.g., students’ outlining and rereading instructions, respectively). Piecing this real time data together with students’ retrospective explanations of their behavior during the academic task, Lichtinger and Kaplan create a complex picture of situated self-regulated learning that answers a common call for triangulation among measures (e.g., Schraw, 2009 ; Winters, Greene, & Costich, 2008 ). Although coding of their qualitative data did not appear to follow from a priori categories grounded in self-regulated learning theory, doing so improves alignment within a study and confidence in its findings. As a result, we recommend that researchers report how their coding protocol reflects a theoretical perspective or model of self-regulated learning along with any implications for interpreting results.

Studies demonstrating the special issue themes of dynamic and contingent relations among self-regulated learning processes did so through real time measures embedded within distinctive academic contexts. These contexts are defined by both immediate and indirect factors ranging from task structure (e.g., Lodewyk, Winne, & Jamieson-Noel, 2009 ; Malmberg, Järvelä & Kirschner, 2014 ) to societal stressors (e.g., Ben-Eliyahu & Bernacki, this issue). As a result, real time measures are inherently and perhaps inextricably contextualized. Yet studies representing this third theme of the special issue approach self-regulated learning differently, relying on students’ retrospective reports to capture the influence of novel contextual factors. Although doing so raises questions about alignment, both studies make important theoretical or methodological contributions to the self-regulated learning literature.

Ben-Eliyahu and Linnenbrink-Garcia (this issue) propose an innovative integration of self-regulated learning and self-regulation, supplementing existing models of the former with somewhat overlapping strategies for the latter (e.g., planning and environment structuring appear in both literatures). In their conceptualization, self-regulation of domains beyond learning (e.g., emotion) sets the psychosocial stage for learning strategies to operate and function optimally. Ben-Eliyahu and Linnenbrink-Garcia then test whether students’ enactment of learning strategies mediates the relation between self-regulation and achievement across two important yet previously unexplored contexts: favorite or least favorite courses. The influence of these novel contexts was tested among both high school and college students, representing different developmental periods and academic environments that together shape self-regulated learning.

In the study, retrospective self-reports aggregated across tasks within courses that could differ from student to student (i.e., one student’s favorite subject may be another student’s least). These tasks and domains likely vary in structure, among other characteristics that could have facilitated or constrained students’ situated use of learning strategies. Therefore, even small effects from summative measures may provide (attenuated) evidence of the general predictions tested through structural equation modeling. However, fluctuating demands on self-regulation and affordances for learning strategies are inevitably lost in summative measures. As a result, they cannot fully capture complex relations implicit in the theoretical model (iSRL; Ben-Eliyahu & Bernacki, this issue) adopted by Ben-Eliyahu and Linnenbrink-Garcia. However, future research could test more precise predictions from the model as its defining processes dynamically unfold in different academic contexts, including favorite or least favorite classes. For example, ecological momentary assessment ( Shiffman, Stone, & Hufford, 2008 ) could capture students’ self-regulatory state during class or while studying and their contingent use of learning strategies as a result. Suggestions such as this for improving alignment between measures and models should be provided when they do not capture self-regulated learning within the same time scale or grain size.

Time scale and grain size are only two considerations in the ongoing debate over the relative merits of different self-regulated learning measures. McCardle and Hadwin (this issue) contribute to this debate by arguing for an often overlooked benefit of self-report, namely its unique opportunity to capture students’ perceptions that presumably influence their self-regulated learning. McCardle and Hadwin propose purely methodological hypotheses about two relatively new options for measuring self-regulated learning retrospectively that overcome some of its strongest criticisms. In particular, prompting students to reflect on a single type of academic task (e.g., exam preparation) at a single time point within a certain course defines the contextualized nature of responses more clearly and narrowly compared to traditional self-report scales (see Winne & Perry, 2000 ).

This measurement strategy also enhances the specification of context we recommend reporting in self-regulated learning research. As conceptualizations of this elusive construct increasingly acknowledge its sensitivity to contextual factors, inferences from findings should follow suit. That is, researchers should take into account design features of their study (e.g., task, setting, sample characteristics) when drawing inferences from its results and generalizing to other instances of self-regulated learning ( Winne & Perry, 2000 ). Although doing so likely limits the scope of inferences, it produces a systematic opportunity to empirically test whether results are robust across contextual factors as described in our next section. Like construct specification, context specification improves alignment between methodological decisions and the interpretation of findings from increasingly sophisticated self-regulated learning research.

Through modern statistical methods, this research is capable of testing precise theoretical predictions about dynamic and contingent relations during self-regulated learning or its complex change over time. McCardle and Hadwin conduct exploratory analyses to capture change across a semester-long course, doing so through latent class analysis that revealed four groups of students with different patterns of improvement in self-regulatory skills the course promoted 1 . Although promising, these results are not tied to a priori predictions about how self-regulated learning may improve or dynamically unfold over time in this unique context. Absent theoretical predictions or a demonstrated need for data of the kind produced by their new measure, it is an innovative methodological approach in search of research questions.

Yet purely methodological questions about self-regulated learning are not without merit, especially during an empirical era where measurement and statistical advances threaten to outpace the theoretical refinements they can inform. McCardle and Hadwin reinforce the value of stepping back to appraise and compare new measures, whether capturing self-regulated learning in real time or retrospectively. Their approach to doing so could serve as a template for future research testing the congruence of different established measures, including real time options competing for status as the gold standard.

Like measures and models of self-regulated learning, the behavioral sciences more generally have reached a golden era of empirical advances in many respects. Theories of social, emotional, and cognitive processes implicated in behavior have evolved into sophisticated accounts of what shapes and underlies it. Rapid technological progress has transformed data collection, allowing for the acquisition of detailed information about those processes either in summary or as they unfold in real time. New statistical methods (e.g., latent growth curve modeling) and accessible software for applying them (e.g., M plus ) enable nuanced analyses that richly capture the complexities of processes implicated in self-regulated learning, among other constructs of considerable interest in the behavioral sciences. As a result, sophisticated theoretical accounts of behavioral phenomena can be evaluated more thoroughly and rigorously than ever before. The special issue catalogues and embodies this exciting progress in self-regulated learning research.

Moreover, innovative methods of data collection and analysis that facilitate this progress also suggest creative new ways to think about patterns of change and relations between variables that can lead to greater specificity in theoretical accounts. For example, ecological momentary assessment and other intensive repeated measurement strategies yield detailed information about personal experience close in time and context. Measuring self-regulated learning in real time can also produce analytic possibilities for which suitable data are rarely available, as two studies of the special issue demonstrate. In Bernacki et al., the data allow for estimating individual trajectories of self-regulatory processes across the entire academic task through which they were measured. In Binbasaran-Tuysuzoglu and Greene, several additional questions about contingencies could be addressed from their think-aloud data. Do students differ in how frequently or when they follow negative judgments of learning with strategy change? Do adaptive changes early in the academic task influence the timing or frequency of negative judgments as it unfolds? Under what conditions is strategy change more or less adaptive? Although these additional questions were not raised by the researchers, data were collected in a way that they could be addressed.

As these exciting possibilities illustrate, empirically testing theoretical predictions about dynamic and contingent relations that have come to define most models of self-regulated learning is accomplished through real time measures that richly capture metacognitive or motivational processes while they occur. These processes occur within a specific context defined by task characteristics, among many other nested factors that influence how self-regulated learning operates in a given study and for a given student (Ben-Eliyahu & Bernacki, this issue). As a result, findings from real time measures are inherently and perhaps inextricably tied to tasks, domains, settings, and even students exhibiting a similar profile of characteristics. Therefore, we encourage qualified inferences based on the generalizability of salient contextual factors (e.g., structure of the academic task or domain, computerized vs. classroom setting) that may facilitate or constrain students’ self-regulated learning. Yet the effect of contextual factors has received increasing empirical attention (e.g. Ben-Eliyahu & Bernacki, this issue; Hadwin et al., 2001 ; Lodewyk, Winne, & Jamieson-Noel, 2009 ; Malmberg, Järvelä & Kirschner, 2014 ; Wolters & Pintrich, 1998 ) as prevailing views of self-regulated learning converge on its sensitivity to them, producing a new frontier for research represented in the special issue as its third theme.

This confluence of theoretical and methodological advances finds research on self-regulated learning at a crossroads. How can studies accurately reflect the dynamic and contingent nature of self-regulated learning while testing contextual influences on it? This is a deceptively difficult question to answer in a single, feasible study. Although real time measures are often necessary to capture self-regulated learning as most theoretical models and definitions now conceive it, they are embedded within tasks, domains, and settings that exclude these contextual factors as testable moderators. Despite the strengths of real time measures diversely represented in this special issue, practical limitations (e.g., prohibitive cost) of intensive data collection and coding can limit the number of students sampled. As a result, sociocultural or demographic differences among them likely cannot be tested as moderators given underpowered or impossible statistical approaches within relatively small samples. This paradoxical challenge leaves series of relatively small studies that vary in their task, domain, setting, and sample characteristics to uncover contextual moderators of how self-regulated learning operates. Findings or even data from these studies can then be combined through meta-analysis to systematically identify moderators of how self-regulated learning operates or its relation with other academic variables (e.g., achievement).

However, combining studies through meta-analysis or evaluating their quality requires transparent reporting of theoretical and methodological decisions that collectively define an approach to self-regulated learning. These are two of the many reasons we propose a framework for conducting and reporting self-regulated learning research that focuses on alignment from its first through final stages. Although even perfect alignment does not ensure the impact of research, it is a goal worth pursuing perhaps now more than ever.

Acknowledgments

During preparation of this manuscript, the second author was supported by National Institute on Drug Abuse (NIDA) Grant P30 DA023026.

1 An alternative and perhaps more appropriate statistical approach to capture variation in patterns of improvement within this context is factor mixture modeling ( Lubke & Muthén, 2005 ). In particular, factor mixture modeling permits the possibility that the marginal fit of the measurement model could be attributed to nonequivalence of the latent structure over time or across subgroups within the sample.

The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of NIDA.

Contributor Information

Amy L. Dent, University of Nebraska - Lincoln.

Rick H. Hoyle, Duke University.

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• Chapter 1: Home
• Narrowing Your Topic
• Problem Statement
• Purpose Statement

Alignment of Problem, Purpose, and Questions

Alignment of the quantitative research components, the quantitative general and specific problem, alignment of the qualitative research components, the qualitative phenomenon and specific problem.

• Conceptual Framework
• Theoretical Framework
• Quantitative Research Questions This link opens in a new window
• Qualitative Research Questions This link opens in a new window
• Qualitative & Quantitative Research Support with the ASC This link opens in a new window
• Library Research Consultations This link opens in a new window

In a dissertation, alignment of problem, purpose, and questions is key.  To help check alignment, some students find the following activity to be helpful.

• Activity - Aligning Problem, Purpose, and Questions Download this activity to check the alignment of your problem, purpose, and questions.

Instructions for completing the activity:

• Copy each segment of your specific problem statement into a cell in the first column.
• Then copy the corresponding segment of your purpose statement into the second column.
• Finally, copy the related questions into the third column.
• Read across to note any discrepancies.

Activity example:

For information: Please visit the NU ASC website and view the resources on constructing a problem statement. 

The problem of your study can be determined by gaps in the literature; HOWEVER, a gap in the literature is not the problem. A problem is a clear and distinct problem that can be empirically verified and has a consequence. NOTE: A problem statement does not suggest any action to be taken nor does it ask a question. 

Example: “My car has a flat tire, so I cannot go to work and my livelihood is affected.” (This is a statement of fact and can be verified.)

As soon as  an action is noted, it becomes a purpose statement – “I need to investigate why I have a flat tire.”

If you ask a question, it is no longer a problem statement either – “How does my flat tire affect my livelihood?”   

Your general and specific problem statements should have at least two to three current (within three years) citations.

An example problem statement format is provided below. Please use the information and templates below to construct each component based on the quantitative research design selected earlier.

Constructing the General problem and Specific Problem Statements using the Funnel Approach

The premise is that the “funnel” approach to constructing the problem statement funnels from a general problem to a specific one. 

The general problem statement. Using the funnel approach to write a problem statement, the first component developed is the general problem. The general problem represents a situation that exists that can be directly attributed to a specific problem that is the focus of the dissertation. 

Exercise #1.

Based on the type of problem addressed by the dissertation, write the general problem statement below.

"The general problem is (describe the situation linked to the negative outcome) (two-three citations)."

The Specific Problem Statement

Once again, using the funnel approach to write a problem statement (see Problem Statement webinar on the NU ASC website at http://www.viddler.com/v/a70ecc81), the second  component developed is the “specific problem.” The specific problem represents an undesirable or negative outcome that can be researched, and is directly attributable to the general problem.

Exercise #2

With the type of problem in mind, write the specific problem addressed by the proposed project below.

"The specific problem to be studied is when the (study population/site/program) experience/results in/causes (the general problem), (state the negative outcome) (two to three citations."

Following the Problem Statement is the Purpose Statement. The purpose should directly align with the problem.

The Purpose Statement.

The purpose statement describes the aim of the dissertation and includes the project design, method, and variables. 

Based on the design the purpose statement can be constructed slightly differently.

Correlational Design Purpose Statement

The purpose of this quantitative correlational Dissertation is to examine if there is a relationship between (variable 1) and (variable 2). 

Causal Comparative Design Purpose Statement

The purpose of this quantitative causal-comparative dissertation is to examine the difference in (dependent variable) between (group 1) and (group2). 

NOTE:  The groups represent the independent variable. For example, you could be investigating the difference between high school and college students, so the independent variable is education level.

Exercise #3

Based on the design write the purpose statement for the proposed dissertation below.

"The purpose of this quantitative (design) dissertation is to examine (connection) of (variables)."

Dissertation Research Questions

The type and number of research questions are dependent upon the design and purpose of the dissertation.

Visit the following site to identify the appropriate structure for the proposed project: http://dissertation.laerd.com/how-to-structure-quantitative-research-questions.php

• Causal Comparative Research Questions

RQ1.  What is the difference in (dependent variable) between (group 1)? (group 2), (group…n)? OR RQ1. How are/is (group 1) different from (group 2) in terms of (dependent variable) for (participants) at (research location)?

• Correlational Research Questions

Q1. What is the relationship of (variable 1)to ( variable 2) for (participants) at (research location)?

Exercise #4

Write the appropriate number research question(s) based on the project design and purpose of the proposed Dissertation.

Research Question: RQ1. (see examples above to complete)

Hypotheses For each research question there should be a null and alternative hypothesis.

Causal Comparative Hypotheses H10. There is no difference in (dependent variable between (group 1) and (group 2). H1A. There is a statistically significant difference in (dependent variable between (group 1) and (group 2). Correlational Hypotheses H10. There is no relationship between (variable 1) and (variable 2). H1A. There is a relationship between (variable 1) and (variable 2).

Visit Please review the following site to properly construct hypotheses: https://statistics.laerd.com/statistical-guides/hypothesis-testing-3.php

Exercise #6.

Write the appropriate hypotheses for the proposed dissertation below.

H10. (see examples above to complete)     H1A. (see examples above to complete)

For information: Please visit the NU ASC website and view the webinar about constructing a problem statement. 

As soon as an action is noted, it becomes a purpose statement – “I need to investigate why I have a flat tire.”

If you ask a question, it is no longer a problem statement either – “How does my flat tire affect my livelihood?”  

In qualitative studies, the problem is the phenomenon under study.

The General Problem Statement

Using the funnel approach, i.e., moving from a general to a specific problem, to write a problem statement (see Problem Statement webinar, on the NU ASC website at http://www.viddler.com/v/a70ecc81, the first component developed is the “phenomenon,” also known as the general problem. The phenomenon represents a situation that exists that can be directly attributed to a specific problem that is the focus of the proposed Dissertation.

Exercise #1

Use the script below by replacing the italicized text with the appropriate information to write a one-sentence statement representing the phenomenon, and include at least two to three current (within three years) citations to support the statement.

"The general problem is that (describe the phenomenon) (two to three current citations)."

Once again, using the funnel approach to write a problem statement (see Problem Statement webinar on the NU ASC website at http://www.viddler.com/v/a70ecc81), the second component developed is the “specific problem.” The specific problem represents an undesirable or negative outcome that can be researched, and is directly attributable to the phenomenon of the proposed dissertation.

Use the script below by replacing the italicized text with the appropriate information to write a one-sentence statement representing the specific problem, and include at least two to three current (within three years) citations to support the statement.

"The specific problem is when the (dissertation participants) (experience the phenomenon), (negative/undesirable outcome) (two to three current citations)."

Often, it may be more effective to write one overarching problem statement that includes both the general and specific problems.

The Purpose Statement

The purpose statement describes the aim of the proposed dissertation and includes the research methodology and design, phenomenon, and project participants.

Use the script below by replacing the italicized text with the appropriate information to write a one-sentence statement representing the purpose statement.

"The purpose of this qualitative (design) study is to explore (the phenomenon), (as perceived by dissertation participants)."

Research Questions

Often, one question is designed to explore the barriers or challenges related to the phenomenon, and the second question asks about how to improve the phenomenon. However, there can be more than two research questions. The questions can be constructed in several different ways; a few examples are shown in RQ1. And RQ2. The questions should always include the phenomenon and dissertation participants and ask the “How,” “What,” or “Why,” as related to the phenomenon.

Use the script below by replacing the italicized text with the appropriate information to write two one-sentence research questions that together explore the phenomenon as it is perceived by the  (dissertation participants). 

"RQ1.  What are the challenges of the (phenomenon) from the perspectives of the dissertation participants)?" "RQ2.  How can the (phenomenon) be improved, as perceived by the (dissertation participants)?"

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• Last Updated: Apr 24, 2024 2:48 PM
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