preliminary design research paper

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Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Research guides, guide to research and writing for the academic study of religion.

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Developing a good research question is impossible without doing some preliminary research.  Preliminary research gives you background information on your topic, answering questions such as who, what, when and where.  This research will also help you determine controversies related to your topic and determine if there are enough sources available to cover the topic effectively.

 You will encounter and learn much more information than you will convey in your final paper. Background information will enrich your research paper but should not bog it down in trivia. For example, if you were doing a paper on Hildegaard of Bingen, you should know that she was born into a noble family in Germany in 1098 and entered a hermitage at the age of eight and became a Benedictine Abbess. This information will help you contextualize her work in your own mind but your research paper should not be a simple recitation of these facts. Your research question should take you beyond the common knowledge found in encyclopedias, but without that  common knowledge your research will lack a solid foundation.

What follows is a list of resources that you may find useful for doing preliminary research in the field of Religious Studies. Keep in mind the type of information that you will need based on your preliminary topic and where your topic falls in the topic pyramid. Remember that the pyramid is a continuum rather than a series of discrete stages, so your topic likely will draw on both columns for some resources.

Resources for Preliminary Research

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Article contents

Decision-making in preliminary engineering design.

Published online by Cambridge University Press:  27 February 2009

A designer often has to deal with complex and ill-structured situations during specification synthesis and preliminary engineering design. To assist in the development of computer-aided design systems, it is desirable to capture the designers decision-making process during these design states. The research presented in this paper is towards this direction. Based on the conceptual understanding of the process, three postulates are presented. The following two postulates; (1) the decisions are neither optimum nor just satisfying but retain certain characteristics of both, (2) the design is driven by the important objective(s) among all the specified objectives, at the preliminary design, although the remaining objectives do have a weak influence on the preliminary design; are used to develop a compensatory and a non-compensatory model of the decision-making. These models are formulated with the help of fuzzy set theory and they implicitly or explicitly follow the two postulates. These models are suitable for discrete decision situations where the above mentioned postulates apply. Examples of material selection during a preliminary structural design are used to illustrate the effectiveness of these models.

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• Volume 5, Issue 1
• S. P. Joshi (a1) , J. R. Umaretiya (a2) and Sanjay B. Joshi (a3)
• DOI: https://doi.org/10.1017/S0890060400002511

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Research output : Contribution to journal › Article › peer-review

A designer often has to deal with complex and ill-structured situations during specification synthesis and preliminary engineering design. To assist in the development of computer-aided design systems, it is desirable to capture the designers decision-making process during these design states. The research presented in this paper is towards this direction. Based on the conceptual understanding of the process, three postulates are presented. The following two postulates; (1) the decisions are neither optimum nor just satisfying but retain certain characteristics of both, (2) the design is driven by the important objective(s) among all the specified objectives, at the preliminary design, although the remaining objectives do have a weak influence on the preliminary design; are used to develop a compensatory and a non-compensatory model of the decision-making. These models are formulated with the help of fuzzy set theory and they implicitly or explicitly follow the two postulates. These models are suitable for discrete decision situations where the above mentioned postulates apply. Examples of material selection during a preliminary structural design are used to illustrate the effectiveness of these models.

All Science Journal Classification (ASJC) codes

• Industrial and Manufacturing Engineering
• Artificial Intelligence

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• 10.1017/S0890060400002511

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• Decision making Engineering & Materials Science 100%
• Fuzzy set theory Engineering & Materials Science 33%
• Structural design Engineering & Materials Science 26%
• Computer aided design Engineering & Materials Science 23%
• Specifications Engineering & Materials Science 17%

T1 - Decision-making in preliminary engineering design

AU - Joshi, S. P.

AU - Umaretiya, J. R.

AU - Joshi, Sanjay B.

PY - 1991/2

Y1 - 1991/2

N2 - A designer often has to deal with complex and ill-structured situations during specification synthesis and preliminary engineering design. To assist in the development of computer-aided design systems, it is desirable to capture the designers decision-making process during these design states. The research presented in this paper is towards this direction. Based on the conceptual understanding of the process, three postulates are presented. The following two postulates; (1) the decisions are neither optimum nor just satisfying but retain certain characteristics of both, (2) the design is driven by the important objective(s) among all the specified objectives, at the preliminary design, although the remaining objectives do have a weak influence on the preliminary design; are used to develop a compensatory and a non-compensatory model of the decision-making. These models are formulated with the help of fuzzy set theory and they implicitly or explicitly follow the two postulates. These models are suitable for discrete decision situations where the above mentioned postulates apply. Examples of material selection during a preliminary structural design are used to illustrate the effectiveness of these models.

AB - A designer often has to deal with complex and ill-structured situations during specification synthesis and preliminary engineering design. To assist in the development of computer-aided design systems, it is desirable to capture the designers decision-making process during these design states. The research presented in this paper is towards this direction. Based on the conceptual understanding of the process, three postulates are presented. The following two postulates; (1) the decisions are neither optimum nor just satisfying but retain certain characteristics of both, (2) the design is driven by the important objective(s) among all the specified objectives, at the preliminary design, although the remaining objectives do have a weak influence on the preliminary design; are used to develop a compensatory and a non-compensatory model of the decision-making. These models are formulated with the help of fuzzy set theory and they implicitly or explicitly follow the two postulates. These models are suitable for discrete decision situations where the above mentioned postulates apply. Examples of material selection during a preliminary structural design are used to illustrate the effectiveness of these models.

UR - http://www.scopus.com/inward/record.url?scp=84972016385&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84972016385&partnerID=8YFLogxK

U2 - 10.1017/S0890060400002511

DO - 10.1017/S0890060400002511

M3 - Article

AN - SCOPUS:84972016385

SN - 0890-0604

JO - Artificial Intelligence for Engineering, Design, Analysis and Manufacturing

JF - Artificial Intelligence for Engineering, Design, Analysis and Manufacturing

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General Principles of Preclinical Study Design

Wenlong huang.

Institute of Medical Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK

Nathalie Percie du Sert

NC3Rs, London, UK

Preclinical studies using animals to study the potential of a therapeutic drug or strategy are important steps before translation to clinical trials. However, evidence has shown that poor quality in the design and conduct of these studies has not only impeded clinical translation but also led to significant waste of valuable research resources. It is clear that experimental biases are related to the poor quality seen with preclinical studies. In this chapter, we will focus on hypothesis testing type of preclinical studies and explain general concepts and principles in relation to the design of in vivo experiments, provide definitions of experimental biases and how to avoid them, and discuss major sources contributing to experimental biases and how to mitigate these sources. We will also explore the differences between confirmatory and exploratory studies, and discuss available guidelines on preclinical studies and how to use them. This chapter, together with relevant information in other chapters in the handbook, provides a powerful tool to enhance scientific rigour for preclinical studies without restricting creativity.

This chapter will give an overview of some generic concepts pertinent to the design of preclinical research. The emphasis is on the requirements of in vivo experiments which use experimental animals to discover and validate new clinical therapeutic approaches. However, these general principles are, by and large, generically relevant to all areas of preclinical research. The overarching requirement should be that preclinical research should only be conducted to answer an important question for which a robust scrutiny of the available evidence demonstrates that the answer is not already known. Furthermore, such experiments must be designed, conducted, analysed and reported to the highest levels of rigour and transparency. Assessments of research outputs should focus more on these factors and less on any apparent “novelty”.

1. An Overview

Broadly, preclinical research can be classified into two distinct categories depending on the aim and purpose of the experiment, namely, “hypothesis generating” (exploratory) and “hypothesis testing” (confirmatory) research ( Fig. 1 ). Hypothesis generating studies are often scientifically-informed, curiosity and intuition-driven explorations which may generate testable theories regarding the pathophysiology of disease and potential drug targets. The freedom of researchers to explore such innovative ideas is the lifeblood of preclinical science and should not be stifled by excessive constraints in terms of experimental design and conduct. Nevertheless, in order to subsequently assess the veracity of hypotheses generated in this way, and certainly to justify clinical development of a therapeutic target, hypothesis testing studies which seek to show reproducible intervention effects in relevant animal models must be designed, conducted, analysed and reported to the highest possible levels of rigour and transparency. This will also contribute to reducing research “waste” ( Ioannidis et al. 2014 ; Macleod et al. 2014 ). Chapter “Resolving the Tension Between Exploration and Confirmation in Preclinical Biomedical Research” of the handbook will deal with exploratory and confirmatory studies in details. This chapter will only focus on general design principles for hypothesis testing studies. We will address the issue of design principles for hypothesis-generating studies at the end of this chapter. We advise that when researchers design and conduct hypothesis testing in vivo studies, they should conform to the general principles for the major domains that are outlined in Sect. 4 of the chapter and incorporate these principles into a protocol that can be registered and published. The purpose of using these principles is to enhance scientific rigour without restricting creativity. It is advisable that sometimes there can be exploratory elements within the same hypothesis testing studies; therefore, extra care in terms of applying these principles to reduce experimental biases would be needed before the start of the studies. This chapter will not cover reporting, which will be detailed in chapters “Minimum Information and Quality Standards for Conducting, Reporting, and Organizing In Vitro Research”, “Minimum Information in In Vivo Research”, and “Quality Governance in Biomedical Research” of the handbook.

Comparison of exploratory (hypothesis generating) and confirmatory (hypothesis testing) preclinical studies. Descriptive statistics describes data and provides descriptions of the population, using numerical calculations, graphs, and tables. In contrast, inferential statistics predicts and infers about a population using a sample of data from the population, therefore one can take data from samples and make generalisation about a population

We would recommend that researchers who conduct hypothesis testing in vivo studies should prepare clear protocols, which include a statistical analysis plan, detailing how they are going to set up measures to address the major domains of experimental biases before the experiments start. Ideally, these protocols should be preregistered and/or published, so that the methods which will be used to reduce the impact of bias are documented in an a priori fashion. The process of peer review of a protocol prior to initiating experiments of course is a valuable opportunity for refinement and improvement. Registering protocols encourages rigour and transparency, even if the protocol is not peer-reviewed. Some journals are open to submissions of these types of protocols, such as BMJ Open Science, and many journals offer the Registered Reports format. In addition, there are online resources that allow researchers to preregister their experimental protocols, such as preclinical. eu and osf.io/registries.

2. General Scientific Methods for Designing In Vivo Experiments

Designing an in vivo experiment involves taking a number of decisions on different aspects of the experimental plan. Typically, a comparative experiment can be broken into several component parts.

2.1. Hypotheses and Effect Size

The objective is usually to test a hypothesis. On some occasions, two hypotheses may be postulated: the null hypothesis and the alternative hypothesis. The alternative hypothesis refers to the presumption that the experimental manipulation has an effect on the response measured; the null hypothesis is the hypothesis of no change, or no effect. In a statistical test, the p-value reports the probability of observing an effect as large or larger than the one being observed if the null hypothesis was true; the smaller the p -value, the least likely it is that the null hypothesis is true. The null hypothesis cannot be accepted or proven true. This also defines the effect of interest, i.e. the outcome that will be measured to test the hypothesis. The minimum effect size is the smallest effect the researcher designs the experiment to be able to detect and should be declared in the protocol; it is set up as the minimum difference which would be of biological relevance. The effect size is then used in the sample size calculation to ensure that the experiment is powered to detect only meaningful effects and does not generate statistically significant results that are not biologically relevant. In many cases, it will be hard to determine the minimum difference of biological relevance as for early stage experiments it might be completely unknown, or translatability between clinical relevance and experimental detection thresholds will be complex. There is no simple and easy answer to this question, but in general, a minimum effect size should be set so one can assume to have a beneficial effect for individuals rather than large cohorts, the difference must be experimentally testable and reasonable to achieve, and should have a rationale for translation into patients in the long run.

2.2. Groups, Experimental Unit and Sample Size

In comparative experiments, animals are split into groups, and each group is subjected to different interventions, such as a drug or vehicle injection, or a surgical procedure. The sample size is the number of experimental units per group; identifying the experimental unit underpins the reliability of the experiment, but it is often incorrectly identified ( Lazic et al. 2018 ). The experimental unit is the entity subjected to an intervention independently of all other units; it must be possible to assign any two experimental units to different comparison groups. For example, if the treatment is applied to individual mice by injection, the experimental unit may be the animal, in which case the number of experimental units per group and the number of animals per group is the same. However, if there is any contamination between mice within a cage, the treatment given to one mouse might influence other mice in that cage, and it would be more appropriate to subject all mice in one cage to the same treatment and treat the cage as the experimental unit. In another example, if the treatment is added to the water in a fish tank, two fish in the same tank cannot receive different treatments; thus the experimental unit is the tank, and the sample size is the number of tanks per group. Once identified, experimental units are allocated to the different comparison groups of the desired sample size; this is done using an appropriate method of randomisation to prevent selection bias (see Sect. 3 ). Each comparison group will be subjected to different interventions, at least one of which will be a control. The purpose of the control group is to allow the researcher to investigate the effect of a treatment and distinguish it from other confounding experimental effects. It is therefore crucial that any control group is treated exactly in the same way as the other comparison groups. Types of control group to consider include negative control, vehicle control, positive control, sham control, comparative control and naïve control ( Bate and Clark 2014 ).

2.3. Measurements and Outcome Measures

Measurements are taken to assess the results; these are recorded as outcome measures (also known as dependent variable). A number of outcome measures can be recorded in a single experiment, for example, if burrowing behaviour is measured, the outcome measure might be the weight of gravel displaced, or if neuronal density is measured from histological brain slides, the outcome measure might be the neuron count. The primary outcome measure should be identified in the planning stage of the experiment and stated in the protocol; it is the outcome of greatest importance, which will answer the main experimental question. The number of animals in the experiment is determined by the power needed to detect a difference in the primary outcome measure. A hypothesis testing experiment may also include additional outcome measures, i.e. secondary outcome measures, which can be used to generate hypotheses for follow-up experiments. Secondary outcome measures cannot be used to draw conclusions about the experiment if the experiment was not powered to detect a minimum difference for these outcome measures.

For the purpose of the statistical analysis, outcome measures fall into two broad categories: continuous or categorical. Continuous measures are sometimes referred to as quantitative data and are measured on a numerical scale. Continuous measures include truly continuous data but also discrete data. Examples of true continuous data include bodyweight, body temperature, blood/CSF concentration or time to event, while examples of discrete data include litter size, number of correct response or clinical score. Categorical responses are measured on a nonnumerical scale; they can be ordinal (e.g. severity score, mild/moderate/severe), nominal (e.g. behavioural response, left/middle/right arm maze) or binary (e.g. disease state, present/absent). Continuous responses may take longer to measure, but they contain more information. If possible, it is preferable to measure a continuous rather than categorical response because continuous data can be analysed using the parametric analyses, which have higher power; this reduces the sample size needed ( Bate and Clark 2014 ).

2.4. Independent Variables and Analysis

There are many ways to analyse data from in vivo experiments; the first step in devising the analysis plan is to identify the independent variables. There can be two broad types: independent variables of interest which the researcher specifically manipulates to test the hypothesis, for example, a drug with different doses, and nuisance variables, which are other sources of variability that may impact on the outcome measure, but are not of direct interest to the researcher. Examples of nuisance variables could be the day of the experiment, if animals used on different days, or baseline body weight or locomotor activity. Every experiment has nuisance variables. Identifying them at the protocol stage and accounting for them in the design and the analysis, for example, as blocking factors, or co-variables, increase the sensitivity of the experiment to detect changes induced by the independent variable(s) of interest. The analysis plan should be established before the experiment starts and any data is collected; it should also be included in the protocol. Additional analyses can be performed on the data, but if an analysis was not planned before the data was collected, it should be clearly reported as a post hoc or exploratory analysis. Exploratory analyses are at greater risk of yielding false positive results.

3. Experimental Biases: Definitions and Methods to Reduce Them

For any researcher who intends to carry out preclinical in vivo studies, it is important to understand what experimental biases are. First, we need to know the definition of bias. It is the inadequacies in the design, conduct, analysis or reporting of an experiment that cause systematic distortion of the estimated intervention effect away from the “truth” ( Altman et al. 2001 ; van der Worp et al. 2010 ), and it will significantly confound in vivo studies and reduce their internal validity. Sources of bias are multiple and in many cases context dependant. In this overview chapter, it is not possible to give an exhaustive list of potential sources of bias, and it behoves the researcher to systematically identify all potential significant sources of bias for the particular experiment being in planned and to design appropriate mitigation tactics into the protocol. Major known types of biases include selection bias, performance bias, detection bias, and attrition bias. Table 1 gives the definition of each type of bias and describe the methods to reduce them.

Researchers who conduct hypothesis testing in vivo animal work should understand the importance of limiting the impact of experimental biases in the design, conduct, analysis and reporting of in vivo experiments. Experimental biases can cause significant weakness in the design, conduct and analysis of in vivo animal studies, which can produce misleading results and waste valuable resources. In biomedical research, many effects of interventions are fairly small, and small effects therefore are difficult to distinguish from experimental biases ( Ioannidis et al. 2014 ). Evidence (1960–2012 from PubMed) shows that adequate steps to reduce biases, e.g. blinded assessment of outcome and randomisation, have not been taken in more than 20% and 50% of biomedical studies, respectively, leading to inflated estimates of effectiveness, e.g. in the fields of preclinical stroke, multiple sclerosis, Parkinson’s disease, bone cancer pain and myocardial infarction research ( Currie et al. 2013 ; Macleod et al. 2008 ; Rooke et al. 2011 ; Sena et al. 2007 ; van Hout et al. 2016 ; Vesterinen et al. 2010 ) and consequently significant research waste ( Ioannidis et al. 2014 ; Macleod et al. 2014 , 2015 ). Therefore, it is imperative that biomedical researchers should spend efforts on improvements in the quality of their studies using the methods described in this chapter to reduce experimental biases which will lead to increased effect-to-bias ratio.

However, it is worth pointing out that the notion that experimental biases could significantly impact on in vivo animal studies is often assumed because they are believed to be important in clinical research. Therefore, such an assumption may be flawed, as the body of evidence showing the importance of bias-reducing methods such as randomisation, blinding, etc. for animal studies is still limited and most of the evidence is indirect. Furthermore, there may also be sources of bias which impact on preclinical studies which are currently unknown. Thus, systematic review and metaanalysis of in vivo studies have shown that papers that do not report bias-reducing methods report larger effect sizes ( Vesterinen et al. 2010 ). However, these studies are based on reported data alone, and therefore there might be a difference between what researchers do and what they report in their publications ( Reichlin et al. 2016 ). Reporting of the precise details of bias reduction methods is often scanty, and therefore accurate assessment of the precise method and rigour of such procedures is challenging. Moreover, those papers that do not report one bias-reducing method, e.g. randomisation, also tend to not report other bias-reducing methods, e.g. blinding and sample size calculation, suggesting that there could be interactions between these methods.

4. Experimental Biases: Major Domains and General Principles

In this section, we will describe the major domains, in other words, sources that could contribute to experimental bias if not carefully considered and if mitigating tactics are not included in the design of hypothesis testing experiments before data collection starts. These include sample size estimation, randomisation, allocation concealment, blinding, primary and secondary outcome measures and inclusion/exclusion criteria. General descriptions for these domains ( Macleod et al. 2009 ; Rice et al. 2008 ; Rice 2010 ; van der Worp et al. 2010 ) are shown in the following Table 2 . It is important to note that these domains are key things to be included in a protocol as mentioned in Sect. 1 .

General principles to reduce experimental bias in each of the above-mentioned domains ( Andrews et al. 2016 ; Knopp et al. 2015 ) are outlined in the following Table 3 .

5. Existing Guidelines and How to Use Them

There are resources to assist investigators in designing rigorous protocols and identify sources of bias. Cross-referencing to experimental reporting guidelines and checklists (e.g. ARRIVE (NC3Rs 2018a) , the NIH guidelines ( NIH 2018a ) and the Nature reporting of animal studies checklist ( Nature 2013 )) can be informative and helpful when planning an experimental protocol. However, it is important to bear in mind that these are primarily designed for reporting purposes and are not specifically designed for use in assisting with experimental design. There are more comprehensive planning guidelines specifically aiming at early experimental design stage. Henderson et al. identified 26 guidelines for in vivo experiments in animals in 2012 ( Henderson et al. 2013 ) (and a few more have been published since, like PREPARE ( Smith et al. 2018 ), developed by the NORECEPA (Norway’s National Consensus Platform for the advancement of the 3Rs), and PPRECISE for the field of pain research ( Andrews et al. 2016 )). Most of them have been developed for a specific research field but carry ideas and principles that can be transferred to all forms of in vivo experiments. Notable are, for example, the very detailed Lambeth Conventions ( Curtis et al. 2013 ) (developed for cardiac arrhythmia research), from Alzheimer’s research recommendations by Shineman et al. (2011) and generally applicable call by Landis et al. (2012) .

The authors of many of these guidelines state that their list might need adaption to the specific experiment. This is pointing out the general shortcoming that a fixed-item list can hardly foresee and account for any possible experimental situation and a blind ticking of boxes ticking of boxes is unlikely to improve experimental design. Such guidelines rather serve an educational purpose of making researchers aware of possible pitfalls and biases before the experimental conduct.

Two examples for a more adaptive and reactive way to serve a similar purpose should be stated: the NIH pages on rigour and reproducibility ( NIH 2018b ) provide in-depth information and collect important publications and workshop updates on these topics and have a funding scheme specifically for rigour and reproducibility. Second, using the Experimental Design Assistant (EDA) ( NC3Rs 2018b ; Percie du Sert et al. 2017 ) developed by the UK’s National Centre for the 3Rs (NC3Rs), a free to use online platform guiding researchers through experimental planning will give researchers the opportunity to adopt guideline and rigour principles precisely to their needs. The researcher creates a flow diagram of their experimental set-up grouped in three domains: the experiment (general questions on hypotheses and aims, animals used, animal strains, etc.), the practical steps (experimental conduct, assessment, etc.) and the analysis stage (e.g. outcome measures, statistical methods, data processing). Unlike a fixed checklist, the EDA checks the specific design as presented by the experimenter within the tool using logic algorithms. The user is then faced with the flaws the EDA identified and can adjust their design accordingly. This process can go through multiple rounds, by that forming a dynamic feedback loop educating the researcher and providing more nuanced assistance than a static checklist can.

While this process, however valid, might take time, the following steps of the EDA actively guide researchers through crucial and complex questions of the experiment, by suggesting fitting methods of statistical analyses of the experiment and subsequently carrying out sample size calculations. The EDA can then also generate a randomization sequence or compile a report of the planned experiment that can, e.g. be part of a preregistration of the experimental protocol.

6. Exploratory and Confirmatory Research

It is necessary to understand that there are in general two types of preclinical research, namely, exploratory and confirmatory research, respectively. Figure 1 shows that exploratory studies mainly aim to produce theories regarding the pathophysiology of disease (hypothesis generating), while confirmatory studies seek to reproduce exploratory findings as clearly defined intervention effects in relevant animal models (hypothesis testing). The next chapter will deal with exploratory and confirmatory studies in details. Similar standards of rigour are advisable for both forms of studies; this may be achieved by conforming to the general principles for the major domains that are outlined in Table 2 and incorporating these principles into a protocol that can be registered and published. It is important to note that both exploratory and confirmatory research can be closely linked: sometimes there can be exploratory and confirmatory components within the same studies. For example, a newly generated knockout mouse model is used to examine the effect of knockout on one specific phenotype (hypothesis testing–confirmatory) but may also describe a variety of other phenotypic characteristics as well (hypothesis generating–exploratory). Therefore, extra care in terms of applying these principles to reduce experimental bias would be needed before the commence of the studies. It also worth noting that sometimes it might not be compulsory or necessary to use some of the principles during exploratory studies such as sample size estimation and blinding which are albeit of highest importance in confirmatory research.

However, it is necessary to recognise how hypothesis confirming and hypothesis generating research relate to each other: while confirmatory research can turn into exploratory (e.g. if the findings are contrary to the hypothesis, this can lead to a new hypothesis that can be tested in a separate experiment), under no circumstances exploratory findings should be disseminated as the result of hypothesis confirming research by fitting a hypothesis to your results, i.e. to your p -values (often called HARKing = hypothesising after results are known or p -hacking = sifting through a multitude of p -values to find one below 0.05).

In conclusion, this chapter provides general concepts and principles that are important for the design and conduct of preclinical in vivo experiments, including experimental biases and how to reduce these biases in order to achieve the highest levels of rigour for hypothesis generating research using animals. The chapter should be used in conjunction with other relevant chapters in the handbook such as chapters “Blinding and Randomization”, “Minimum Information and Quality Standards for Conducting, Reporting, and Organizing In Vitro Research”, “Minimum Information in In Vivo Research”, “A Reckless Guide to P -Values: Local Evidence, Global Errors”, and “Quality Governance in Biomedical Research”.

Contributor Information

Wenlong Huang, Institute of Medical Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.

Nathalie Percie du Sert, NC3Rs, London, UK.

• Altman DG, Schulz KF, Moher D, Egger M, Davidoff F, Elbourne D, Gotzsche PC, Lang T, Consort G The revised CONSORT statement for reporting randomized trials: explanation and elaboration. Ann Intern Med. 2001; 134 :663–694. [ PubMed ] [ Google Scholar ]
• Andrews NA, Latremoliere A, Basbaum AI, Mogil JS, Porreca F, Rice AS, Woolf CJ, Currie GL, Dworkin RH, Eisenach JC, Evans S, et al. Ensuring transparency and minimization of methodologic bias in preclinical pain research: PPRECISE considerations. Pain. 2016; 157 :901–909. doi: 10.1097/j.pain.0000000000000458. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baastrup C, Maersk-Moller CC, Nyengaard JR, Jensen TS, Finnerup NB. Spinal-, brainstem- and cerebrally mediated responses at- and below-level of a spinal cord contusion in rats: evaluation of pain-like behavior. Pain. 2010; 151 :670–679. doi: 10.1016/j.pain.2010.08.024. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bate ST, Clark RA. The design and statistical analysis of animal experiments. Cambridge University Press; Cambridge: 2014. [ Google Scholar ]
• Currie GL, Delaney A, Bennett MI, Dickenson AH, Egan KJ, Vesterinen HM, Sena ES, Macleod MR, Colvin LA, Fallon MT. Animal models of bone cancer pain: systematic review and meta-analyses. Pain. 2013; 154 :917–926. doi: 10.1016/j.pain.2013.02.033. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Curtis MJ, Hancox JC, Farkas A, Wainwright CL, Stables CL, Saint DA, Clements-Jewery H, Lambiase PD, Billman GE, Janse MJ, Pugsley MK, et al. The Lambeth Conventions (II): guidelines for the study of animal and human ventricular and supraventricular arrhythmias. Pharmacol Ther. 2013; 139 :213–248. doi: 10.1016/j.pharmthera.2013.04.008. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Henderson VC, Kimmelman J, Fergusson D, Grimshaw JM, Hackam DG. Threats to validity in the design and conduct of preclinical efficacy studies: a systematic review of guidelines for in vivo animal experiments. PLoS Med. 2013; 10 :e1001489. doi: 10.1371/journal.pmed.1001489. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Higgins J, Altman DG, Sterne J. Assessing risk of bias in included studies. In: Higgins J, Green S, editors. Cochrane handbook for systematic reviews of interventions. Wiley; Hoboken: 2011. [ Google Scholar ]
• Huang W, Calvo M, Karu K, Olausen HR, Bathgate G, Okuse K, Bennett DL, Rice AS. A clinically relevant rodent model of the HIV antiretroviral drug stavudine induced painful peripheral neuropathy. Pain. 2013; 154 :560–575. doi: 10.1016/j.pain.2012.12.023. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ioannidis JP, Greenland S, Hlatky MA, Khoury MJ, Macleod MR, Moher D, Schulz KF, Tibshirani R. Increasing value and reducing waste in research design, conduct, and analysis. Lancet. 2014; 383 :166–175. doi: 10.1016/S0140-6736(13)62227-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Knopp KL, Stenfors C, Baastrup C, Bannon AW, Calvo M, Caspani O, Currie G, Finnerup NB, Huang W, Kennedy JD, Lefevre I, et al. Experimental design and reporting standards for improving the internal validity of pre-clinical studies in the field of pain: consensus of the IMI-Europain Consortium. Scand J Pain. 2015; 7 :58–70. doi: 10.1016/j.sjpain.2015.01.006. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Landis SC, Amara SG, Asadullah K, Austin CP, Blumenstein R, Bradley EW, Crystal RG, Darnell RB, Ferrante RJ, Fillit H, Finkelstein R, et al. A call for transparent reporting to optimize the predictive value of preclinical research. Nature. 2012; 490 :187–191. doi: 10.1038/nature11556. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lazic SE, Clarke-Williams CJ, Munafo MR. What exactly is ‘N’ in cell culture and animal experiments? PLoS Biol. 2018; 16 :e2005282. doi: 10.1371/journal.pbio.2005282. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Macleod MR, van der Worp HB, Sena ES, Howells DW, Dirnagl U, Donnan GA. Evidence for the efficacy of NXY-059 in experimental focal cerebral ischaemia is confounded by study quality. Stroke. 2008; 39 :2824–2829. doi: 10.1161/STROKEAHA.108.515957. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Macleod MR, Fisher M, O’Collins V, Sena ES, Dirnagl U, Bath PM, Buchan A, van der Worp HB, Traystman R, Minematsu K, Donnan GA, et al. Good laboratory practice: preventing introduction of bias at the bench. Stroke. 2009; 40 :e50–e52. doi: 10.1161/STROKEAHA.108.525386. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Macleod MR, Michie S, Roberts I, Dirnagl U, Chalmers I, Ioannidis JP, Al-Shahi Salman R, Chan AW, Glasziou P. Biomedical research: increasing value, reducing waste. Lancet. 2014; 383 :101–104. doi: 10.1016/S0140-6736(13)62329-6. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Macleod MR, Lawson McLean A, Kyriakopoulou A, Serghiou S, de Wilde A, Sherratt N, Hirst T, Hemblade R, Bahor Z, Nunes-Fonseca C, Potluru A, et al. Risk of bias in reports of in vivo research: a focus for improvement. PLoS Biol. 2015; 13 :e1002273. doi: 10.1371/journal.pbio.1002273. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nature. Enhancing reproducibility. Nat Methods. 2013; 10 (5):367. https://www.nature.com/articles/nmeth.2471 . [ PubMed ] [ Google Scholar ]
• NC3Rs. ARRIVE guidelines. [Accessed 20 Dec 2018]; 2018a https://www.nc3rs.org.uk/arrive-guidelines .
• NC3Rs. The experimental design assistant. [Accessed 20 Dec 2018]; 2018b https://eda.nc3rs.org.uk .
• NIH. Principles and guidelines for reporting preclinical research. [Accessed 20 Dec 2018]; 2018a https://www.nih.gov/research-training/rigor-reproducibility/principles-guidelines-reporting-preclinical-research .
• NIH. Rigor and reproducibility. [Accessed 20 Dec 2018]; 2018b https://www.nih.gov/research-training/rigor-reproducibility .
• O’Connor AM, Sargeant JM. Critical appraisal of studies using laboratory animal models. ILAR J. 2014; 55 :405–417. doi: 10.1093/ilar/ilu038. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Percie du Sert N, Bamsey I, Bate ST, Berdoy M, Clark RA, Cuthill I, Fry D, Karp NA, Macleod M, Moon L, Stanford SC, et al. The experimental design assistant. PLoS Biol. 2017; 15 :e2003779. doi: 10.1371/journal.pbio.2003779. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Reichlin TS, Vogt L, Wurbel H. The researchers’ view of scientific rigor-survey on the conduct and reporting of in vivo research. PLoS One. 2016; 11 :e0165999. doi: 10.1371/journal.pone.0165999. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rice ASC. Predicting analgesic efficacy from animal models of peripheral neuropathy and nerve injury: a critical view from the clinic. In: Mogil JS, editor. Pain 2010 – an updated review: refresher course syllabus. IASP Press; Seattle: 2010. pp. 1–12. [ Google Scholar ]
• Rice AS, Cimino-Brown D, Eisenach JC, Kontinen VK, Lacroix-Fralish ML, Machin I, Preclinical Pain C, Mogil JS, Stohr T. Animal models and the prediction of efficacy in clinical trials of analgesic drugs: a critical appraisal and call for uniform reporting standards. Pain. 2008; 139 :243–247. doi: 10.1016/j.pain.2008.08.017. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rooke ED, Vesterinen HM, Sena ES, Egan KJ, Macleod MR. Dopamine agonists in animal models of Parkinson’s disease: a systematic review and meta-analysis. Parkinsonism Relat Disord. 2011; 17 :313–320. doi: 10.1016/j.parkreldis.2011.02.010. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Saghaei M. Random allocation software for parallel group randomized trials. BMC Med Res Methodol. 2004; 4 :26. doi: 10.1186/1471-2288-4-26. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sena E, van der Worp HB, Howells D, Macleod M. How can we improve the pre-clinical development of drugs for stroke? Trends Neurosci. 2007; 30 :433–439. doi: 10.1016/j.tins.2007.06.009. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shineman DW, Basi GS, Bizon JL, Colton CA, Greenberg BD, Hollister BA, Lincecum J, Leblanc GG, Lee LB, Luo F, Morgan D, et al. Accelerating drug discovery for Alzheimer’s disease: best practices for preclinical animal studies. Alzheimers Res Ther. 2011; 3 :28. doi: 10.1186/alzrt90. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith AJ, Clutton RE, Lilley E, Hansen KEA, Brattelid T. PREPARE: guidelines for planning animal research and testing. Lab Anim. 2018; 52 :135–141. doi: 10.1177/0023677217724823. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• van der Worp HB, Howells DW, Sena ES, Porritt MJ, Rewell S, O’Collins V, Macleod MR. Can animal models of disease reliably inform human studies? PLoS Med. 2010; 7 :e1000245. doi: 10.1371/journal.pmed.1000245. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• van Hout GP, Jansen of Lorkeers SJ, Wever KE, Sena ES, Kouwenberg LH, van Solinge WW, Macleod MR, Doevendans PA, Pasterkamp G, Chamuleau SA, Hoefer IE. Translational failure of anti-inflammatory compounds for myocardial infarction: a meta-analysis of large animal models. Cardiovasc Res. 2016; 109 :240–248. doi: 10.1093/cvr/cvv239. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vesterinen HM, Sena ES, ffrench-Constant C, Williams A, Chandran S, Macleod MR. Improving the translational hit of experimental treatments in multiple sclerosis. Mult Scler. 2010; 16 :1044–1055. doi: 10.1177/1352458510379612. [ PubMed ] [ CrossRef ] [ Google Scholar ]

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Welcome to the Research Planning Guide

Step 5: devise a preliminary outline.

• Step 1: Understand the Assignment
• Step 2: Choose your Topic
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The preliminary outline can serve as your road map for research.

How do you create a preliminary outline? First, realize that all research papers will start with an introduction and end with a conclusion.  In between, there are usually three to five points that must be covered in order to answer the question sufficiently.

Suppose this is your research question: "Will stronger gun-control legislation protect lives?"  Your preliminary outline might look something like this:

I.     Introduction  

II.    Evidence that gun-control laws protect citizens

III.   Evidence that gun-control laws have no effect on civic safety

IV.   Analysis of effectiveness of current gun-control laws

V.    Conclusion

As you search for books and articles on your topic, you can look for items that will support the various parts of your outline.  You can even organize your research results by grouping items according to their usefulness for supporting the different points in your outline. 

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How to Write a Research Paper | A Beginner's Guide

A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research.

Research papers are similar to academic essays , but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research. Writing a research paper requires you to demonstrate a strong knowledge of your topic, engage with a variety of sources, and make an original contribution to the debate.

This step-by-step guide takes you through the entire writing process, from understanding your assignment to proofreading your final draft.

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Table of contents

Understand the assignment, choose a research paper topic, conduct preliminary research, develop a thesis statement, create a research paper outline, write a first draft of the research paper, write the introduction, write a compelling body of text, write the conclusion, the second draft, the revision process, research paper checklist, free lecture slides.

Completing a research paper successfully means accomplishing the specific tasks set out for you. Before you start, make sure you thoroughly understanding the assignment task sheet:

• Read it carefully, looking for anything confusing you might need to clarify with your professor.
• Identify the assignment goal, deadline, length specifications, formatting, and submission method.
• Make a bulleted list of the key points, then go back and cross completed items off as you’re writing.

Carefully consider your timeframe and word limit: be realistic, and plan enough time to research, write, and edit.

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There are many ways to generate an idea for a research paper, from brainstorming with pen and paper to talking it through with a fellow student or professor.

You can try free writing, which involves taking a broad topic and writing continuously for two or three minutes to identify absolutely anything relevant that could be interesting.

You can also gain inspiration from other research. The discussion or recommendations sections of research papers often include ideas for other specific topics that require further examination.

Once you have a broad subject area, narrow it down to choose a topic that interests you, m eets the criteria of your assignment, and i s possible to research. Aim for ideas that are both original and specific:

• A paper following the chronology of World War II would not be original or specific enough.
• A paper on the experience of Danish citizens living close to the German border during World War II would be specific and could be original enough.

Note any discussions that seem important to the topic, and try to find an issue that you can focus your paper around. Use a variety of sources , including journals, books, and reliable websites, to ensure you do not miss anything glaring.

Do not only verify the ideas you have in mind, but look for sources that contradict your point of view.

• Is there anything people seem to overlook in the sources you research?
• Are there any heated debates you can address?
• Do you have a unique take on your topic?
• Have there been some recent developments that build on the extant research?

In this stage, you might find it helpful to formulate some research questions to help guide you. To write research questions, try to finish the following sentence: “I want to know how/what/why…”

A thesis statement is a statement of your central argument — it establishes the purpose and position of your paper. If you started with a research question, the thesis statement should answer it. It should also show what evidence and reasoning you’ll use to support that answer.

The thesis statement should be concise, contentious, and coherent. That means it should briefly summarize your argument in a sentence or two, make a claim that requires further evidence or analysis, and make a coherent point that relates to every part of the paper.

You will probably revise and refine the thesis statement as you do more research, but it can serve as a guide throughout the writing process. Every paragraph should aim to support and develop this central claim.

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A research paper outline is essentially a list of the key topics, arguments, and evidence you want to include, divided into sections with headings so that you know roughly what the paper will look like before you start writing.

A structure outline can help make the writing process much more efficient, so it’s worth dedicating some time to create one.

Your first draft won’t be perfect — you can polish later on. Your priorities at this stage are as follows:

• Maintaining forward momentum — write now, perfect later.
• Paying attention to clear organization and logical ordering of paragraphs and sentences, which will help when you come to the second draft.
• Expressing your ideas as clearly as possible, so you know what you were trying to say when you come back to the text.

You do not need to start by writing the introduction. Begin where it feels most natural for you — some prefer to finish the most difficult sections first, while others choose to start with the easiest part. If you created an outline, use it as a map while you work.

Do not delete large sections of text. If you begin to dislike something you have written or find it doesn’t quite fit, move it to a different document, but don’t lose it completely — you never know if it might come in useful later.

Paragraph structure

Paragraphs are the basic building blocks of research papers. Each one should focus on a single claim or idea that helps to establish the overall argument or purpose of the paper.

Example paragraph

George Orwell’s 1946 essay “Politics and the English Language” has had an enduring impact on thought about the relationship between politics and language. This impact is particularly obvious in light of the various critical review articles that have recently referenced the essay. For example, consider Mark Falcoff’s 2009 article in The National Review Online, “The Perversion of Language; or, Orwell Revisited,” in which he analyzes several common words (“activist,” “civil-rights leader,” “diversity,” and more). Falcoff’s close analysis of the ambiguity built into political language intentionally mirrors Orwell’s own point-by-point analysis of the political language of his day. Even 63 years after its publication, Orwell’s essay is emulated by contemporary thinkers.

Citing sources

It’s also important to keep track of citations at this stage to avoid accidental plagiarism . Each time you use a source, make sure to take note of where the information came from.

You can use our free citation generators to automatically create citations and save your reference list as you go.

APA Citation Generator MLA Citation Generator

The research paper introduction should address three questions: What, why, and how? After finishing the introduction, the reader should know what the paper is about, why it is worth reading, and how you’ll build your arguments.

What? Be specific about the topic of the paper, introduce the background, and define key terms or concepts.

Why? This is the most important, but also the most difficult, part of the introduction. Try to provide brief answers to the following questions: What new material or insight are you offering? What important issues does your essay help define or answer?

How? To let the reader know what to expect from the rest of the paper, the introduction should include a “map” of what will be discussed, briefly presenting the key elements of the paper in chronological order.

The major struggle faced by most writers is how to organize the information presented in the paper, which is one reason an outline is so useful. However, remember that the outline is only a guide and, when writing, you can be flexible with the order in which the information and arguments are presented.

One way to stay on track is to use your thesis statement and topic sentences . Check:

• topic sentences against the thesis statement;
• topic sentences against each other, for similarities and logical ordering;
• and each sentence against the topic sentence of that paragraph.

Be aware of paragraphs that seem to cover the same things. If two paragraphs discuss something similar, they must approach that topic in different ways. Aim to create smooth transitions between sentences, paragraphs, and sections.

The research paper conclusion is designed to help your reader out of the paper’s argument, giving them a sense of finality.

Trace the course of the paper, emphasizing how it all comes together to prove your thesis statement. Give the paper a sense of finality by making sure the reader understands how you’ve settled the issues raised in the introduction.

You might also discuss the more general consequences of the argument, outline what the paper offers to future students of the topic, and suggest any questions the paper’s argument raises but cannot or does not try to answer.

You should not :

• Offer new arguments or essential information
• Take up any more space than necessary
• Begin with stock phrases that signal you are ending the paper (e.g. “In conclusion”)

There are four main considerations when it comes to the second draft.

• Check how your vision of the paper lines up with the first draft and, more importantly, that your paper still answers the assignment.
• Identify any assumptions that might require (more substantial) justification, keeping your reader’s perspective foremost in mind. Remove these points if you cannot substantiate them further.
• Be open to rearranging your ideas. Check whether any sections feel out of place and whether your ideas could be better organized.
• If you find that old ideas do not fit as well as you anticipated, you should cut them out or condense them. You might also find that new and well-suited ideas occurred to you during the writing of the first draft — now is the time to make them part of the paper.

The goal during the revision and proofreading process is to ensure you have completed all the necessary tasks and that the paper is as well-articulated as possible. You can speed up the proofreading process by using the AI proofreader .

Global concerns

• Confirm that your paper completes every task specified in your assignment sheet.
• Check for logical organization and flow of paragraphs.
• Check paragraphs against the introduction and thesis statement.

Fine-grained details

Check the content of each paragraph, making sure that:

• each sentence helps support the topic sentence.
• no unnecessary or irrelevant information is present.
• all technical terms your audience might not know are identified.

Next, think about sentence structure , grammatical errors, and formatting . Check that you have correctly used transition words and phrases to show the connections between your ideas. Look for typos, cut unnecessary words, and check for consistency in aspects such as heading formatting and spellings .

Finally, you need to make sure your paper is correctly formatted according to the rules of the citation style you are using. For example, you might need to include an MLA heading  or create an APA title page .

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Checklist: Research paper

I have followed all instructions in the assignment sheet.

My introduction presents my topic in an engaging way and provides necessary background information.

My introduction presents a clear, focused research problem and/or thesis statement .

My paper is logically organized using paragraphs and (if relevant) section headings .

Each paragraph is clearly focused on one central idea, expressed in a clear topic sentence .

Each paragraph is relevant to my research problem or thesis statement.

I have used appropriate transitions  to clarify the connections between sections, paragraphs, and sentences.

My conclusion provides a concise answer to the research question or emphasizes how the thesis has been supported.

My conclusion shows how my research has contributed to knowledge or understanding of my topic.

My conclusion does not present any new points or information essential to my argument.

I have provided an in-text citation every time I refer to ideas or information from a source.

I have included a reference list at the end of my paper, consistently formatted according to a specific citation style .

I have thoroughly revised my paper and addressed any feedback from my professor or supervisor.

I have followed all formatting guidelines (page numbers, headers, spacing, etc.).

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The Graduate School

University information technology (uit), main navigation, formatting requirements: preliminary pages.

• Submission Procedure
• Policies for Theses and Dissertations
• Coauthored Theses and Dissertations
• Approval Requirements
• Publication Requirements

Copyright Page

Statement of thesis/dissertation approval, dedication, frontispiece, and epigraph, table of contents and list of figures/tables, acknowledgements.

• General Formatting Requirements
• Parts Composed of Related Chapters
• Headings and Subheadings
• Tables and Figures
• Footnote and Reference Citations
• Appendix or Appendices
• References or Selected Bibliography
• Documentation Styles
• Writing Styles
• Print Quality
• Accessibility in the PDF
• Electronic Version Submitted for Thesis Release
• Distribution of Theses and Dissertations
• Alternate Text
• Color Contrast
• Accessibility Issues in Table Construction
• Heading Space
• Double Space
• Single Space
• Previously Published, Accepted, and Submitted Articles as Chapters of a Dissertation
• Alternate Figure/Table Placement

Preliminary pages are, in order, the title page; copyright page; statement of thesis/dissertation approval; abstract; dedication (optional); frontispiece (optional); epigraph (optional); table of contents; lists of tables, figures, symbols, and abbreviations (necessary only in certain situations); and acknowledgments (optional). Table 2.1 lists all the possible preliminary sections in order and if they are required or not. 

The preliminary pages are counted in sequence (except the copyright page, which is neither counted nor numbered). Any page with a main heading on it (title page, abstract, table of contents, etc.) is counted, but no page number is typed on the page. Second pages to the abstract, table of contents, lists, and acknowledgments are numbered with lower case Roman numerals centered within the thesis margins and .5” from the bottom of the page. See the preliminary pages in this handbook for an example. 

Order of preliminary pages, indicating which are mandatory and where page numbers should be included.

Note : Page numbers in the preliminary pages appear centered on the bottom of the page in lower case Roman numerals. This differs from page numbers in the text, which appear on the top right of the page and use Arabic numerals.

SEE Sample Preliminary Pages

The title page is page i (Roman numeral) of the manuscript (page number not shown). 

The title of the thesis or dissertation is typed in all capital letters. The title should be placed in the same size and style of font as that used for major headings throughout the manuscript. If longer than 4 1/2 inches, the title should be double spaced and arranged so that it appears balanced on the page. The title should be a concise yet comprehensive description of the contents for cataloging and data retrieval purposes. Initials, abbreviations, acronyms, numerals, formulas, super/subscripts, and symbols should be used in the title with careful consideration of clarity and maximizing search results for future readers. Consult the manuscript editors if in doubt. 

The word “by” follows the title. The full legal name of the author as it appears in CIS follows after a double space. The name is not typed in all capital letters. These two lines of text are centered between the title and the statement described in the following paragraph. 

The statement “A thesis submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of” appears single spaced in the middle of the title page (see Figure 2.1). For doctoral candidates, the phrasing reads “A dissertation submitted. . . ” 

The appropriate degree follows the statement. The space between the statement and the degree should be the same size that is between the author’s name and the statement. In the event the name of the degree differs from the name of the department, e.g., Master of Science in Environmental Humanities, the words “Master of Science” are placed below the statement, followed by “in” and then the degree program; the lines of the degree name and program are double spaced (see Figure 2.2). Thus, a student receiving a doctorate in history need use only the words “Doctor of Philosophy.” A student receiving a doctorate in Geophysics must put “Doctor of Philosophy in Geophysics.” 

Below the degree field, the full name of the department is listed on the title page. “The University of Utah,” is listed a double space below the department name.

The date appears on the title page a double space below “The University of Utah.” Only the month and year appear, with no punctuation separating them. The month indicates the last month in the semester the degree is granted: fall semester, December; spring semester, May; summer semester, August. 

Again, the spaces below the title, the full legal name, the statement, and the degree should be of equal size. 

The second page is the copyright page, which is uncounted and unnumbered. A copyright notice appears in every copy of the thesis or dissertation. The notice, as illustrated in Figure 2.3, is centered within the side margins and the top and bottom margins of the page. 

Copyright © Student’s Full Legal Name 2022

All Rights Reserved 

There is a double space between the two lines. 

The statement of thesis/dissertation approval is page ii (Roman numeral) of the manuscript (page number not shown). This statement is prepared as shown in Figures 2.4 (for master’s students) and 2.5 (for doctoral students). 

The statement of thesis/dissertation approval signifies that the thesis or dissertation has been approved by the committee chair and a majority of the members of the committee and by the department chair and the dean of The Graduate School. The names of any committee members who did not approve or digitally sign the forms for the thesis or dissertation are not dated. The dates entered should match the date when you received notification that the committee member electronically signed the form. 

The full name of the student, as it appears on the title page and copyright page, must be used. 

As with the digital signature forms, full legal names of committee members must be listed. The full legal names of committee members and department chair or dean can be found on your CIS page under the Committee tab. Neither degrees nor titles should be listed with the names of faculty members. No signatures are required. 

Abstract Page

The abstract is page iii, unnumbered; if there is a second page, it is page iv, and a number appears on the page. The abstract is a concise, carefully composed summary of the contents of the thesis or dissertation. In the abstract, the author defines the problem, describes the research method or design, and reports the results and conclusions. No diagrams, illustrations, subheadings, or citations appear in the abstract. The abstract is limited to 350 words (approximately 1.5 double-spaced pages). A copy of the abstract of all doctoral candidates is published in Dissertation Abstracts International. The word ABSTRACT is placed 2 inches from the top of the page in all capital letters. Following a heading space, the abstract text begins, with the first line indented the same size space as for the paragraphs in the remainder of the manuscript. The text of the abstract must be double spaced. 

If a manuscript is written in a foreign language, the abstract is in the same language, but an English version (or translation) of the abstract must precede the foreign language abstract. The two abstracts are listed as one in the table of contents. The first page of each version is unnumbered but counted. If there is a second page to each version of the abstract, the page number (lower-case Roman numeral) is centered between the left and right margins and between the bottom of the page and the top of the bottom margin. 

The dedication is an optional entry; enumeration continues in sequence, but no page number appears on the page. It follows the abstract and precedes the table of contents. Often only one or two lines, it is centered within the top and bottom margins of the page and within the thesis margins. It is not labeled “Dedication” and is not listed in the table of contents. 

Frontispiece and Epigraph

These are infrequently used entries. The frontispiece is an illustration that alerts the reader to the major theme of the thesis or dissertation. An epigraph is a quotation of unusual aptness and relevance. 

Contents or Table of Contents

The table of contents follows the abstract (or dedication if one is used). The word CONTENTS (or TABLE OF CONTENTS) is placed 2 inches from the top of the page in all capital letters. Following a heading space, the table of contents begins. The table of contents, essentially an outline of the manuscript, lists the preliminary pages beginning with the abstract (page iii). It does not list a frontispiece, dedication, or epigraph if these are used, nor is the table of contents listed in the table of contents; these pages are, however, counted. The list of figures and list of tables, if used, are included (see the Table of Contents in this handbook for a sample using numbered chapters; see Figures 2.6, 2.7, and 2.8 for additional options). 

All chapters or main sections and all first-level subheadings of the manuscript are listed in the table of contents. No lower subheadings levels are to appear in the table of contents. Beginning page numbers of each chapter or section listed are lined up with each listing by a row of evenly spaced, aligned period leaders. The numbers, titles, and subheadings of chapters or sections used in the table of contents must agree exactly in wording and capitalization with the way they appear on the actual page. 

The table of contents reflects the relationship of the chapters and subheadings. Chapter titles appear in all capital letters, as do titles of appendices. First-level subheadings can be headline style or sentence style in capitalization. Subheadings are neither underlined nor italicized in the table of contents. If the table of contents continues to a second page, it begins 1 inch from the top of the page, and it is not labeled “Table of Contents Continued.” Main headings are followed by a double space in the table of contents; all subheadings are single spaced. The words “Chapters” and “Appendices” are used as referents only, printed above the list of entries. The word “Chapter” or “Appendix” is not repeated with each entry. 

List of Figures / List of Tables

The enumeration continues in sequence; no number appears on pages with main headings (those in all caps). A list of tables, a list of figures, a list of symbols, a list of abbreviations, or a glossary may be used. All lists follow the table of contents. The title is placed 2 inches from the top edge of the page in all capital letters: LIST OF TABLES. Following a heading space, the list begins. A list of tables or a list of figures is required if there are 5 to 25 entries. Lists with fewer than 5 entries or more than 25 are not included. It is not permissible to combine a list of tables and figures. The word “Table” or “Figure” is not repeated with each entry. 

As noted for entries in the table of contents, the listing of tables and figures must agree exactly in wording, capitalization, and punctuation with the table title or figure caption. (An exception to this rule occurs if the table title appears in all capital letters on the table itself; table titles in the list of tables are not typed in all capital letters.) Capitalization styles may not be mixed. In the case of long titles or captions, the first sentence must convey the essential description of the item. The first sentence alone then is used in the list. Long captions may not be summarized. 

The table or figure number begins at the left margin and is followed by the title or caption. The page on which each table or figure appears is at the right margin. As in the table of contents, the page numbers are lined up with each entry by a row of evenly spaced, aligned periods (period leaders). If a table or figure occupies more than one page, only the initial page number is listed. If the title or caption of a table or figure appears on a part-title page preceding the table or figure, the page number in the list refers to the number of the part-title page. 

If a list continues to a second page, the second page of text begins 1 inch from the top of the page. The second page is not labeled “List of Tables Continued” or “List of Figures Continued.” Individual entries are single-spaced with a double space between each entry. 

A list of symbols and abbreviations or a glossary does not replace defining terms, symbols, or abbreviations upon their first occurrence in the text. When introducing terms, always introduce terms upon their first usage in the document. 

The enumeration continues in sequence; no number appears on the first page. Acknowledgments are optional. If a preface is used, the acknowledgments are added to the end of the preface without a separate heading. The word ACKNOWLEDGMENTS is placed 2 inches from the top of the page in all capital letters. Following a heading space, the acknowledgments begin. The text of the acknowledgments must be double spaced. In the acknowledgments, students may wish to recognize special assistance from committee members, friends, or family members who may have helped in the research, writing, or technical aspects of the thesis or dissertation. Research funding, grants, and/or permission to reprint copyrighted materials should be acknowledged. Individuals employed to prepare the manuscript are not acknowledged. 

The enumeration continues in sequence; no number appears on the first page. This is an optional entry. The word PREFACE is placed 2 inches from the top of the page in all capital letters. Following a heading space, the preface begins. The text of the preface must be double spaced. A preface includes the reasons for undertaking the study, the methods and design of the researcher, and acknowledgments. Background data and historical or other information essential to the reader’s understanding of the subject are placed in the text as an introduction, not in the preface. Theses and dissertations generally do not contain a foreword (i.e., a statement about the work by someone other than the author). 

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Thesis / dissertation formatting manual (2024).

• Filing Fees and Student Status
• Submission Process Overview
• Electronic Thesis Submission
• Paper Thesis Submission
• Formatting Overview
• Fonts/Typeface
• Pagination, Margins, Spacing
• Paper Thesis Formatting
• Preliminary Pages Overview
• Copyright Page
• Dedication Page
• Table of Contents
• List of Figures (etc.)
• Acknowledgements
• Text and References Overview
• Figures and Illustrations
• Using Your Own Previously Published Materials
• Using Copyrighted Materials by Another Author
• Open Access and Embargoes
• Copyright and Creative Commons
• Ordering Print (Bound) Copies
• Tutorials and Assistance
• FAQ This link opens in a new window

Preliminary Pages

The Preliminary Pages require very specific wording, spacing, and layout. Templates and sample pages are provided for your reference.

Only the pages listed below may be included as part of the Preliminary Pages section, and they must appear in this order. No other pages are permitted. All pages are required except the Dedication Page. Lists of Symbols, Tables, Figures, and Illustrations are only required if applicable to the content of your manuscript. 

Note : A Signature Page is NOT a valid part of your manuscript and is not included in the submission of your thesis or dissertation. Committee signatures are now included on the “Ph.D. Form II/Signature Page” or the “Master’s Thesis/Signature Page” that you submit to the Graduate Division. 

Preliminary Pages Order

• Title Page (no page number)
• Copyright Page (no page number)
• Dedication Page (optional, page number ii if included)
• Table of Contents  (if Dedication Page is included, Table of Contents is page iii. If no Dedication, Table of Contents is page ii)
• List(s) of Figures/Illustrations/Formulae/Terms/etc.  (required, if applicable. Each new list should begin on a new page)
• Acknowledgements  (alt. spelling: Acknowledgments)
• Vita  (PhD dissertations ONLY. Should not be more than 3 pages)

Pagination - Preliminary Pages

Preliminary Pages are numbered with lowercase Roman numerals.

• The Title Page is counted in determining the total number of pages in this section but is NOT numbered.
• The Copyright Page is not counted or numbered.
• Your first numbered page will either be your Dedication Page if you have one, or your Table of Contents if you do not have a Dedication Page.
• There is no page i in the manuscript. 
• The subsequent pages are then numbered consecutively with lowercase Roman numerals through the end of the Abstract.
• Dashes, periods, underlining, letter suffixes, other text (including last names), and other stylizations are not permitted before, after, or under your page numbers.
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• DOI: 10.1201/9781420072068.AXG
• Corpus ID: 113898987

Preliminary Design Review (PDR)

• K. Pries , J. Quigley
• Published 22 October 2008
• Computer Science

One Citation

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Stage 2: Preliminary Design

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Once the design concepts have been developed and a final concept has been selected, the next stage in the design process is to develop the preliminary design of each of the system components. The key elements of most preliminary designs include an outline of the following items: the design’s systems, its basic requirements, and the high-level design features.

For our UAV example, we would start by creating a flowchart (or similar schematic) of the various systems, subsystems, and components of the UAV. For example, let’s say that we want to create a schematic of the flight system. Assuming we choose the quadcopter concept design, our top-level “block” in the flowchart would say “flight system.” Then, we would subdivide this system into its constituent subsystems, namely, each of the four rotor assemblies. These rotor assemblies consist of individual components, including the rotor blades, motors, wiring, and possibly the electronic speed controllers (ESCs). The motors and ESCs each have dozens of internal components, and the level of representation of these components is dependent on the requirements that must be levied on the system. 

For example, if the motors are designed in-house, it may make sense to further subdivide the motor into flowchart blocks that highlight its major components, as their design requirements may be tighter due to the ability to control each minute specification. However, if the motor is sourced from an external supplier, then the level of subdivision may be more coarse—i.e., limited to the options that can be specified when ordering from the supplier. The figure below shows an example of a preliminary design flowchart for our quadcopter’s flight system.

Quadcopter preliminary design flowchart

After creating flowcharts for each UAV system, the next step is to clearly define the system, subsystem, and component requirements. These requirements are often determined after consulting with the end users and developing mission use-cases. For example, let’s say that the farmers using the UAV want to be able to conduct at least 500-acre aerial sweeps in a single charge. In this case, the quadcopter must be able to aerodynamically support the weight of its components plus the imaging equipment while navigating the field area. It must also be capable of supplying adequate power to the imaging equipment and flight systems while accounting for the extra power demanded by anomalous factors such as wind gusts or changes in altitude to accommodate low or high-level imaging. 

The design features of each system are usually defined quantitatively, so high-level aerodynamic, structural, and electrical analyses should be carried out to obtain approximate numbers for each of the systems and subsystems. The minute details of each component and system will be defined later in the detailed design phase. Once the features are defined, they are analyzed to ensure that they meet the pre-specified design requirements. The text below shows a sample of what the requirements and design features of the quadcopter flight system might look like:

Requirements:  The quadcopter’s flight system must be capable of supporting 32 kg throughout the entire flight envelope. Nominal operating altitude will top out at 30 meters above ground level. The quadcopter must be capable of at least 10 m/s horizontal speed and at least 5 m/s vertical speed while overcoming vertical wind gusts of up to 2 m/s…

Design features:  The quadcopter rotors will consist of three blades with airfoils characterized by a high L/D ratio. The rotors will be fabricated out of glass fiber composite material. The motors will be brushless and powered by a Lithium-ion battery…

Note that this stage of the design process becomes an increasingly iterative process. This means that once a system’s preliminary design is completed, it should be reviewed and modified in order to generate a new design. This looped process saves time and resources by considering the effects of a particular system architecture and incorporating changes at the early stages of the design. Therefore, the preliminary design documentation may also include simulation and requirement verification reports that highlight the necessary design changes prior to developing the detailed designs of each system and component. 

For example, let’s say that the quadcopter rotors are initially designed to have three blades. However, a preliminary aerodynamic analysis of the rotors suggests that at least four blades will be necessary to generate enough lift to overcome the stipulated minimum wind gust requirement of 2 m/s. The best course of action is therefore to create a report that includes the quantitative results of this mission architecture simulation and a list of the requirements that were both met and not met. Finally, the necessary architecture/design changes should be clearly enumerated.

To summarize, the preliminary design document consists of the following elements:

• Flowchart (or other schematic) of each system 
• Clear definition of each system’s, subsystem’s, and component’s requirements
• High-level outline of design features that meet each of these requirements
• Requirement and system architecture verification reports

Preliminary Design Review

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The Preliminary Design Review (PDR) session helps you to make sure that the robustness diagrams, the domain model, and the use case text all match each other. This review is the “gateway” between the preliminary design and detailed design stages, for each package of use cases.

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Design Patterns: Applications and Open Issues

Modelling guidance in software engineering: a systematic literature review

The Relevance of Model-Driven Engineering Thirty Years from Now

Doug Rosenberg, Matt Stephens, and Mark Collins-Cope, Agile Development with ICONIX Process (Berkeley, CA: Apress, 2005).

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These steps are described in more detail in Chapter 6 of Applying Use Case Driven Object Modeling with UML by Doug Rosenberg and Kendall Scott (Addison-Wesley, 2001).

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© 2007 Doug Rosenberg and Matt Stephens

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(2007). Preliminary Design Review. In: Use Case Driven Object Modeling with UML. Apress. https://doi.org/10.1007/978-1-4302-0369-8_6

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What Is a Preliminary Research Design?

Lauren griffin.

Developing a research project involves a lot of planning and preparation. A preliminary research design describes the specifics of a planned project and should address the purpose of the proposed study, as well as details on how the study will be conducted.

Explore this article

• Introduction and Objectives
• Research Design and Methods
• Time Line and Budget

1 Introduction and Objectives

A preliminary research design must introduce the proposed study by stating what the study will be investigating, the hypothesis, and the significance of the subject. By completing a literature review, summarizing the main findings of these studies, and relating them to the current project, the researcher can explain how their study adds to the existing field of knowledge.

2 Research Design and Methods

Preliminary research design must provide an overview of the study's methodology. This should include an explanation of what variables will be looked at and how they will be measured, where the study will take place, what tools or techniques will be used, and other information regarding how the study will be conducted.

3 Time Line and Budget

The researcher must propose a specific time line for her project in her preliminary research design, in which different stages of the study are allotted different amounts of time. Research designs also address the project's budget. It's best to identify specific expenditures and give an accurate view of precisely how money will be spent.

• 1 Agency for Healthcare Research and Quality: Essentials of the Research Plan
• 2 Unite for Sight: Preliminary Research Steps

About the Author

Lauren Griffin began writing professionally in 2010. Her articles appear on various websites, specializing in academics, food and other lifestyle topics. Griffin attended Columbia University and holds a Bachelor of Arts in psychology.

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ELECTRIC SPORTS CAR PRELIMINARY DESIGN (PERFORMANCE ENVELOPE)

Car design is a complex task because of how highly integrated system of systems it is. Fine?designed car models take years of design and optimization and are usually done by specialty teams who are dedicated to each sub-system. This thesis delves into designing a simplified electric race car from scratch with focus on the performance envelope of it. First, a 3D CAD model was done using SolidWorks. That section deals with spatial engineering and strategic placement of major car components for best performance. Having most of the parts in place gives a rough estimate of CoG (Center of Gravity) location, which is needed for vehicle dynamics analysis, which are discussed later in the report. The target for this project car is to have innovative aerodynamics features which might not have been used before because of bulky internal combustion engines restricting available space. One of them is an airfoil-like fascia which makes the center part of the car act as a one big wing. That is believed to give a significant reduction in drag loads on the car. The approach for aerodynamics design and analysis started with a model representing the car’s OML (Outer Mold line) which was simulated separately using Siemens StarCCM+. After understanding the car’s body aero behavior, a rear wing was added to provide extra rear downforce for better handling and stability. The rear wing design was explained in detail. Unfortunately, due to time restrictions as well as software access issues, the aerodynamic analysis of the full car with rear wing is left for future work. After having an estimate about aero loads acting on the car, vehicle dynamics analysis could start. The first subject studied in vehicle dynamics was front-view suspension geometry analysis. Taking the available packaging and geometry into consideration, a 2D model was done in SolidWorks to optimize camber gain. This analysis gave the motion ratio of the front and rear pushrod suspension system which was needed to analyze the performance of the one-eighth car model, ½ car pitch model, and ½ car roll model. These models gave insights into the decision-making process for spring and damping rates to reach a good balance between performance and comfort. This project acts as a hub for further development and studies related to car design.

Degree Type

• Master of Science in Engineering
• Mechanical Engineering

Campus location

• Indianapolis

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, usage metrics.

• Automotive engineering not elsewhere classified

A Design Approach to Assess Effects of Non-Contact Underwater Explosions on Naval Composite Vessels

• Mannacio, F.
• Di Marzo, F.
• Gaiotti, M.
• Rizzo, C. M.
• Venturini, M.

Despite the non-contact underwater explosion phenomena (UNDEX) have been studied for decades and several numerical methods have been proposed in literature, its effects on military structures, especially composite ones, are even nowadays matter of research. In early design phases, it is not always possible to verify the shock resistance of hull structures modelling the whole phenomenon, in which fluid, gas and solid properties must be properly set in a fully coupled fluid-structure interaction (FSI) numerical model. These ones are extremely complex to set, computationally demanding and certainly not suitable for everyday design practice. In this paper, a simplified finite element (FE) model, easy to use in an early design phase, is proposed. Both, the structure and the fluid are simulated. In this approximation, the fluid behaviour is simplified, using special finite elements, available in a commercial software environment. This choice reduces the computational time and numerical efforts avoiding the problem of combining computational fluid dynamics (CFD) and FE domains and equations in a fully coupled fluid-structure interaction model. A typical parallel body block of a minesweeper is modelled, using two-dimensional multi-layered shell elements to properly account for the composite materials behaviour. For the fluid instead, three dimensional volumetric elements, directly coupled to the structural elements, are placed. In addition, the same calculation is performed, modelling separately fluid in the CFD environment and structures in the finite element one. Thus, realizing a fully coupled fluid-structure interaction model. The results obtained by applying both numerical models are compared with the structural response measured on board of a composite ship during a full-scale shock test. The simplified proposed procedure provides results in satisfactory agreement with experiments, allowing the validation of the model. Approximations are discussed and differences with the real phenomenon and fully coupled CFD+FE method are shown, providing a better understanding of the phenomena. Eventually, the modelling strategy has been considered a valuable and cost-effective tool for the concept and preliminary design of composite structures subject to underwater explosions.

• Underwater explosions;
• Shock resistance;
• Composites;
• Fluid-structure interaction;
• Experimental analysis;
• Numerical simulation;
• Vulnerability;
• Preliminary design

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