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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On June 24, 2024

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

Also, see; Research methods, design, and analysis .

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Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

You May Also Like

How to write a hypothesis for dissertation,? A hypothesis is a statement that can be tested with the help of experimental or theoretical research.

This article is a step-by-step guide to how to write statement of a problem in research. The research problem will be half-solved by defining it correctly.

To help students organise their dissertation proposal paper correctly, we have put together detailed guidelines on how to structure a dissertation proposal.

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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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What is Research Design? Understand Types of Research Design, with Examples

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Are you unsure about the research design elements or which of the different types of research design best suit your study? Don’t worry! In this article, we’ve got you covered!   

Table of Contents

What is research design?  

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Don’t worry! In this article, we’ve got you covered!  

A research design is the plan or framework used to conduct a research study. It involves outlining the overall approach and methods that will be used to collect and analyze data in order to answer research questions or test hypotheses. A well-designed research study should have a clear and well-defined research question, a detailed plan for collecting data, and a method for analyzing and interpreting the results. A well-thought-out research design addresses all these features.  

Research design elements  

Research design elements include the following:  

• Clear purpose: The research question or hypothesis must be clearly defined and focused.  
• Sampling: This includes decisions about sample size, sampling method, and criteria for inclusion or exclusion. The approach varies for different research design types .  
• Data collection: This research design element involves the process of gathering data or information from the study participants or sources. It includes decisions about what data to collect, how to collect it, and the tools or instruments that will be used.  
• Data analysis: All research design types require analysis and interpretation of the data collected. This research design element includes decisions about the statistical tests or methods that will be used to analyze the data, as well as any potential confounding variables or biases that may need to be addressed.  
• Type of research methodology: This includes decisions about the overall approach for the study.  
• Time frame: An important research design element is the time frame, which includes decisions about the duration of the study, the timeline for data collection and analysis, and follow-up periods.  
• Ethical considerations: The research design must include decisions about ethical considerations such as informed consent, confidentiality, and participant protection.  
• Resources: A good research design takes into account decisions about the budget, staffing, and other resources needed to carry out the study.  

The elements of research design should be carefully planned and executed to ensure the validity and reliability of the study findings. Let’s go deeper into the concepts of research design .    

Characteristics of research design  

Some basic characteristics of research design are common to different research design types . These characteristics of research design are as follows:  

• Neutrality : Right from the study assumptions to setting up the study, a neutral stance must be maintained, free of pre-conceived notions. The researcher’s expectations or beliefs should not color the findings or interpretation of the findings. Accordingly, a good research design should address potential sources of bias and confounding factors to be able to yield unbiased and neutral results.   
•   Reliability : Reliability is one of the characteristics of research design that refers to consistency in measurement over repeated measures and fewer random errors. A reliable research design must allow for results to be consistent, with few errors due to chance.   
•   Validity : Validity refers to the minimization of nonrandom (systematic) errors. A good research design must employ measurement tools that ensure validity of the results.  
•   Generalizability: The outcome of the research design should be applicable to a larger population and not just a small sample . A generalized method means the study can be conducted on any part of a population with similar accuracy.   
•   Flexibility: A research design should allow for changes to be made to the research plan as needed, based on the data collected and the outcomes of the study  

A well-planned research design is critical for conducting a scientifically rigorous study that will generate neutral, reliable, valid, and generalizable results. At the same time, it should allow some level of flexibility.  

Different types of research design  

A research design is essential to systematically investigate, understand, and interpret phenomena of interest. Let’s look at different types of research design and research design examples .  

Broadly, research design types can be divided into qualitative and quantitative research.  

Qualitative research is subjective and exploratory. It determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc.  

Quantitative research is objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research is usually done using surveys and experiments.  

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

The following will familiarize you with the research design categories in qualitative research:  

• Grounded theory: This design is used to investigate research questions that have not previously been studied in depth. Also referred to as exploratory design , it creates sequential guidelines, offers strategies for inquiry, and makes data collection and analysis more efficient in qualitative research.   

Example: A researcher wants to study how people adopt a certain app. The researcher collects data through interviews and then analyzes the data to look for patterns. These patterns are used to develop a theory about how people adopt that app.  

•   Thematic analysis: This design is used to compare the data collected in past research to find similar themes in qualitative research.  

Example: A researcher examines an interview transcript to identify common themes, say, topics or patterns emerging repeatedly.  

• Discourse analysis : This research design deals with language or social contexts used in data gathering in qualitative research.   

Example: Identifying ideological frameworks and viewpoints of writers of a series of policies.  

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

• Descriptive research design : This quantitative research design is applied where the aim is to identify characteristics, frequencies, trends, and categories. It may not often begin with a hypothesis. The basis of this research type is a description of an identified variable. This research design type describes the “what,” “when,” “where,” or “how” of phenomena (but not the “why”).   

Example: A study on the different income levels of people who use nutritional supplements regularly.  

• Correlational research design : Correlation reflects the strength and/or direction of the relationship among variables. The direction of a correlation can be positive or negative. Correlational research design helps researchers establish a relationship between two variables without the researcher controlling any of them.  

Example : An example of correlational research design could be studying the correlation between time spent watching crime shows and aggressive behavior in teenagers.  

•   Diagnostic research design : In diagnostic design, the researcher aims to understand the underlying cause of a specific topic or phenomenon (usually an area of improvement) and find the most effective solution. In simpler terms, a researcher seeks an accurate “diagnosis” of a problem and identifies a solution.  

Example : A researcher analyzing customer feedback and reviews to identify areas where an app can be improved.    

• Explanatory research design : In explanatory research design , a researcher uses their ideas and thoughts on a topic to explore their theories in more depth. This design is used to explore a phenomenon when limited information is available. It can help increase current understanding of unexplored aspects of a subject. It is thus a kind of “starting point” for future research.  

Example : Formulating hypotheses to guide future studies on delaying school start times for better mental health in teenagers.  

•   Causal research design : This can be considered a type of explanatory research. Causal research design seeks to define a cause and effect in its data. The researcher does not use a randomly chosen control group but naturally or pre-existing groupings. Importantly, the researcher does not manipulate the independent variable.   

Example : Comparing school dropout levels and possible bullying events.  

•   Experimental research design : This research design is used to study causal relationships . One or more independent variables are manipulated, and their effect on one or more dependent variables is measured.  

Example: Determining the efficacy of a new vaccine plan for influenza.  

Benefits of research design  

 T here are numerous benefits of research design . These are as follows:  

• Clear direction: Among the benefits of research design , the main one is providing direction to the research and guiding the choice of clear objectives, which help the researcher to focus on the specific research questions or hypotheses they want to investigate.  
• Control: Through a proper research design , researchers can control variables, identify potential confounding factors, and use randomization to minimize bias and increase the reliability of their findings.
• Replication: Research designs provide the opportunity for replication. This helps to confirm the findings of a study and ensures that the results are not due to chance or other factors. Thus, a well-chosen research design also eliminates bias and errors.  
• Validity: A research design ensures the validity of the research, i.e., whether the results truly reflect the phenomenon being investigated.  
• Reliability: Benefits of research design also include reducing inaccuracies and ensuring the reliability of the research (i.e., consistency of the research results over time, across different samples, and under different conditions).  
• Efficiency: A strong research design helps increase the efficiency of the research process. Researchers can use a variety of designs to investigate their research questions, choose the most appropriate research design for their study, and use statistical analysis to make the most of their data. By effectively describing the data necessary for an adequate test of the hypotheses and explaining how such data will be obtained, research design saves a researcher’s time.   

Overall, an appropriately chosen and executed research design helps researchers to conduct high-quality research, draw meaningful conclusions, and contribute to the advancement of knowledge in their field.

Frequently Asked Questions (FAQ) on Research Design

Q: What are th e main types of research design?

Broadly speaking there are two basic types of research design –

qualitative and quantitative research. Qualitative research is subjective and exploratory; it determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc. Quantitative research , on the other hand, is more objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research design is usually done using surveys and experiments.

Q: How do I choose the appropriate research design for my study?

Choosing the appropriate research design for your study requires careful consideration of various factors. Start by clarifying your research objectives and the type of data you need to collect. Determine whether your study is exploratory, descriptive, or experimental in nature. Consider the availability of resources, time constraints, and the feasibility of implementing the different research designs. Review existing literature to identify similar studies and their research designs, which can serve as a guide. Ultimately, the chosen research design should align with your research questions, provide the necessary data to answer them, and be feasible given your own specific requirements/constraints.

Q: Can research design be modified during the course of a study?

Yes, research design can be modified during the course of a study based on emerging insights, practical constraints, or unforeseen circumstances. Research is an iterative process and, as new data is collected and analyzed, it may become necessary to adjust or refine the research design. However, any modifications should be made judiciously and with careful consideration of their impact on the study’s integrity and validity. It is advisable to document any changes made to the research design, along with a clear rationale for the modifications, in order to maintain transparency and allow for proper interpretation of the results.

Q: How can I ensure the validity and reliability of my research design?

Validity refers to the accuracy and meaningfulness of your study’s findings, while reliability relates to the consistency and stability of the measurements or observations. To enhance validity, carefully define your research variables, use established measurement scales or protocols, and collect data through appropriate methods. Consider conducting a pilot study to identify and address any potential issues before full implementation. To enhance reliability, use standardized procedures, conduct inter-rater or test-retest reliability checks, and employ appropriate statistical techniques for data analysis. It is also essential to document and report your methodology clearly, allowing for replication and scrutiny by other researchers.

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Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

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Qualitative research design and methods Synthesis Essay

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How qualitative and qualitative research approaches compare

Research questions that suit qualitative inquiry, popularity of qualitative methods in public administration.

Qualitative research differs from quantitative research because participants exist in their natural setting. Unlike quantitative research where an investigator manipulates variables or recreates the natural setting in the lab, qualitative research aims at assessing behaviours in it’s undisturbed from.

The investigator’s role also makes these research strategies quite divergent. In quantitative studies, examiners rely on external instruments, like questionnaires, as data collection instruments. However, in qualitative research, the researcher is the main instrument as he observes behaviour, conducts interviews and analyses documents.

Both research methods are similar because they may involve data collection from multiple sources. Data analysis in quantitative research is deductive in that it starts with hypotheses, then data collection, which are then analysed statistically. However, qualitative researchers conduct inductive data analysis by starting with the data and then working backwards to develop themes (Creswell, 2008).

This may involve continual interactions with the participants. Quantitative researchers often prescribe their research design and use it as a guide to determine how they will conduct their investigation. Conversely, qualitative research adheres to emergent design since phases and processes alter as the research progresses. Both research approaches rely on theoretical lenses.

Qualitative researchers use these lenses to view their subjects while quantitative researchers base their research questions on the same. Finally quantitative research involves giving a holistic account of a problem. Multiples factors and perspectives are involved. On the contrary, quantitative researchers usually narrow their areas of inquiry to one or two issues.

Qualitative research is appropriate for questions that lack effective models. They often start with why. For instance, “Why is the quasi market model unpopular in eastern local councils?”. Conversely, questions that start with what may also fall in this category if framed in a certain way. For instance if someone asks “What does public participation in health service provision mean to residents of Markenshire?”.

This question starts with what but it entails determining the personal experiences of people in this location. It raises a series of sub questions that are typical of qualitative research. Questions that start with how are appropriate for qualitative research.

They entail complex descriptions of findings, which are suitable for qualitative analyses. When questions do not involve subjective experiences and they commence with what, who or when, then quantitative methods are suitable.

Qualitative methods are limited in practical public administration research. It is likely that this limited popularity stems from the lack of research standards to guide these studies. Additionally, the methods of learning and practice are yet to be streamlined. Furthermore, some scholars simply classify all non quantitative studies as qualitative yet they could be non positivistic or interpretive.

Additionally, scholars stay away from this method because of questions of generalisability. It is likely that the discipline is more inclined towards objective analysis than subjective ones. While public administration falls in the field of humanities, it is largely managerial and also legalistic. Therefore, a reverence for objective work exists.

This implies that issues such as cost benefit analyses, and structures dominate practical research (Samier, 2005). Nonetheless, qualitative research in academic research still has its place. The human experience is an indispensable part of administrative work, so this holistic approach is tenable.

Furthermore, more researchers are finding new ways of addressing generalisability issues in case studies. Therefore, the mode of research holds a lot of promise in the future.

Creswell, J. (2008). Research design: Qualitative, quantitative and mixed method approaches . NY: Sage.

Samier, E. (2005). Toward public administration as a humanities discipline: A humanistic manifesto. Halduskultuur, 6(3), 6-59.

• Advantages of Fresnel Lenses
• The Role of Lenses in Optics
• Methodology of a Qualitative Inquiry
• Fundamentals of Scientific method
• A comparison of observations to measurement instruments
• Geometry, Space, Manipulative, and Technology
• Concepts of Research Methods
• Discrete Probability Distribution
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• Chicago (N-B)

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Research design

This refers to the plan, structure and format of any scientific or statistical work. It serves the purpose of guiding the researcher in his study and will set out the framework to be used. Research design will basically cover the data collection process, tools of collecting such data, how the tools will be used to collect data and how to analyze the collected data into a useful form (Gosling, 2014). A problem will be raised by researcher in which he will carry out his course study to draw an answer through collecting data (Meyer, et al, 2012). A research design is an essential component while planning to carry out a research on a particular subject or population. The characteristics of the subject determine the methods of data collection to be used in the research. Furthermore the instruments and the means of their deployment are determined during the research design. In this paper, we delve into the research methods in an educational institution. The research will take place at a local high school to determine the student’s preferences in accordance to meals offered by the school.

Characteristics of the Organization

This research will cover the Edgewood Senior High School, Ashtabula in Ohio. This is a very prominent school that excels in many activities that are offered in the school curriculum. The school was started in the early 1960’s to cater for the growing need for better education in the locality. Athletics make the backbone of the sporting calendar to the school. Therefore this research will take into consideration the effect of the meals given to the students on their performance mainly in the field. There has been a problem in the high school such that more than 40% of the students do not take their lunch portions. This makes a lot of food to go to waste thereby draining the resources offered by the federal government. The research is primarily aimed at finding and amicable solution to the problem and if not possible develops better ways of feeding the students at the school. The research organization being an educational institution also ensures that there will be a wide range of data that can be used for analysis. This therefore dictates the use of methods that will cover the school efficiently while at the same time using the fewest resources as possible.

Research Design Process

The research process is as follows: Statement of problem is identified; making a plan how to start actual research is determined; determining research type to use and stating methods to use. Below are some of the most significant research design methods to be used

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. Here a control group can be selected to be another high school within the locality. The high school should have a big number of the students taking their meals at the cafeteria. 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 (Jaksić, F. 1981).

Philosophical Design

This is empathized as more as a wide approach to studying a research problem than a methodological design, philosophic analytical review and argument is aimed to dispute deeply rooted, frequently unmanaged, assumptions laying an area of study (Jaksić, F. 1981).

Sequential Design

This is research done that is deliberate in action, arranged approach. It is serial in nature. The stages follow each other in succession. After completion of one, the other will start. The former stage (output) will be the input of the new stage. This will take place until data extracted is enough for basing judgments on the theory. In this study, sample size is not determined. Researcher will analyze each sample and may accept the null hypothesis or accept the alternative hypothesis. He may also decide to select other pool of subjects and start carrying out the study again. Researcher can use a countless number of subjects before deciding whether to accept the alternative or null hypothesis. Using a quantitative model, a sequential study will utilize sampling and stratified techniques to collect data and apply statistical techniques to analyze collected data. Using a qualitative framework, sequential studies will utilize samples of group’s individuals [age brackets] and use qualitative techniques such as interviewing or observing, to collect information from each and every sample

Other main factors to consider

Exploiting all avenues of research environment (Exploratory study) This is a vital role in any research problem (Lawson, A. E. 2000). The researcher will define the study taking place. This is common in research studies where no other researcher has conducted any study on and it the environment of study is not known to research (Campana, P., & Varese, F. 2012). Such kind of study will lack any formal plan used in project study and is only meant to get a writer familiarized. Description in Study- This study seeks to provide an in depth answer to the problem posed in the form of question to the researcher. Such a study will give more information as compared to an exploratory study conducted (Robinson, 2004). The study is better compared to other research methods since the writer is able to give all details relating to the world and how it is. This is through study of possible trends and patterns followed by a certain variable and if there is any linkage to that effect. An example of descriptive question asked can be: "How often?", “What percentage", “What amount"," what proportion", "what is", “what are”. The following is a list of questions where descriptive study is brought out clearly; Question: What percentage of students takes lunch? Question: Which meals are the most popular among the students? Variable: Calories. Group: Students. In each of the descriptive questions we are quantifying the specific variables we require to ask (Sanchez Martin, et al., 2000). In our case above the descriptive questions seek to determine the frequency or the number. You may use descriptive questions to ask about percentage and counts involved.

Analytical study

As the name suggests the study carried out is explanatory in nature. Analytical studies will link the study of the cause to actual causes. The study usually will lead to an action. Analytical research is structured in form unlike exploration study. Exploratory study is used to provide qualitative data in research process (Xing, Q., Hulin, W., & Rui, H. 2013).A researcher will have to use his knowledge to determine how exploratory research should be and should not be used in his course work (Sartor, Maureen A., et Al).Exploratory research will involve the researcher asking people questions and taking note of the responds made during the study (Data analysis techniques). The researcher will ask questions will guide respondent but will be semi-structured and not formal in nature.

Exploratory Techniques to be used

Focus group interviews This is a small group of individuals usually six to a maximum of fifteen people and will include a moderator who will guide the group in discussing the agenda of the meeting (Singer, F. 2007).Researchers will ask the group specific questions related to what is being researched. Focus groups are selected randomly by the researcher and will be done so to achieve convenience of the researcher and respondents Brace, I. (2008). Focus groups will have a variety of advantages and disadvantages depending on the scenario at hand. This method provides an impromptu scenario where the data that is to be collected will be rarely influenced by anything. It also offers the researchers an opportunity to take information that is tailored specifically to the subject at hand. In this research the student groups will provide varied information on their preferences and even give reasons to why some don’t take their meals (Jaksić, F. 1981). Focus groups will be in different forms namely (Types of focus groups).Two-Way Focus Group, Moderating focus groups, moderator focus groups, Dueled moderating focus groups, Client focus participation group, Respondent driven moderator group, Small focus groups also known as mini groups, Teleconferenced focus groups and Online driven focus groups (Brace, I. 2008). Expert undertaken surveys: A researcher may decide to rely on expert survey information instead of undertaking a survey which he is not sure of. In expert surveys, a list of question is prepared by the researcher which is open ended structure. This will ensure that experts have a greater extent of freedom to place on answering questions asked (Tam, V. Y., Shen, L. Y., & Ochoa, J. 2013d). The expert will use their acquired skills and expertise to give detailed answers useful in the research process. In relation to this organization, Looking at previous instances can give the researchers an opportunity to have beforehand information regarding the subject matter. Looking at previous studies in other schools at the locality will enable the researchers to gather more data that can be used in critically analyzing the data collected in the research. This is a very significant component because it prepares the researchers for the obstacles that may be encountered in the research. Such obstacles may include non cooperation, inconclusive data and unreliable data (Jaksić, F. 1981). These surveys also need to have a specific subject to ensure that the jargon and other unwanted information are done away with. It will eventually save a lot of time and resources to be used in conducting the research. Conducting interviews: Here depth interview will be conducted. Depth interviews are somehow more or less the same to focus groups, but have a deeper need of acquiring information about feelings of customers and the general public about anything e.g. Product( Kluga, et al., 2012). For this study, this is the most effective method to carry out the research. The personal interviews should be carried out systematically and should be able to carry all the required information. The interviews should be divided to cover those who take the meals and those who don’t. For those who depend on the school for meals, the quality of the meals should be the most important area to concentrate on (Jaksić, F. 1981). The questions should also be formulated in such a way that the students easily understand them. Some of the questions may include, “Are the meals sufficient?”, “Is the quality of the food good?”” how many times in a week do you take the meals at the school?” For the students that do take meals, the questions should be more engaging than the above group. Furthermore, these interviews should be deeper since the students may have more concrete reasons to avoid taking meals at the school dining facility (Lawson, 2000). This group of students can provide a more detailed data set towards knowing what influence their choices. Some of the questions to ask this group include, “Why do you miss the meals at the dining hall?”, “Is the food offered at the institution up to standard?” Projective Techniques: This is the use of opinions, beliefs and attitudes of respondents to obtain research data (Lawson, 2000). This method is deployed to mine what is hidden by the interviewees. It enables the researchers to relate what the interviewees say and the information that the researchers may presume is being withheld. Regarding this research, the students who skip meals may deliberately holdback important information regarding the quality of food. It may be due to the fear of being in bad books with the schools administration. This technique can be useful in covering the students with diverse ethnic and cultural origins. Students with Asian backgrounds may find it hard to voice their opinions because of the model of the family that they are raised in. Their culture is bent on respecting the authorities above anyone else (Campana, P., & Varese, F. 2012). In order for this technique to be successful, the research group will have to source for some professionals who can explain certain behaviors during the interviews. Using open ended questions: This is similar to expert surveys in a way. It gives researcher’s ability to get views, comments, complaints, feelings, and attitude and ensure respondents have a forum to air their view of things (Guthery, F. 2007). The students will have a good opportunity to relay their feelings on the subject matter. It will enable them to give a much more detailed account on the quality of the food in the dining halls. Furthermore, it gives them a chance to feel free during the interview and thereby will provide much more relevant information to the researchers (Robinson, A. 2004).

Below are some examples;-

Research Design will take 2 forms mainly which includes, Generating of data from various sources: This includes using data collection methods to generate data. This can be through the use of questionnaires, doing of experiments, course studies undertaken and carrying out ethnographic studies (Robinson, A. 2004).. Analysis of existing and generated data testing of data will be in two main forms which are; Using numerical data analysis where the modeling of statistical data takes place and secondary data analysis. Using textual data to analyze which includes: discourse analysis, content analysis among other methods (Singer, F. 2007).

How the research will be conducted

Planning for the research is very essential. This will determine the quality of the data collected and the overall reliability of the answers given by the interviewees. The research will be divided into different portions to cover the whole school and capture the different perspectives pertaining the subject matter. (Campana, P., & Varese, F. 2012).

Survey on the Students

This is the most important segment of the research. They are directly involved in the matter and are the ones that consume the food provided by the institution. It is therefore paramount that the methods of interview are not threatening since many may give false information. Furthermore the questions will have one backbone and will essential aim at getting the reasons behind why some don’t consume the food at the dining hall. In this group, employing both qualitative and quantitative methods will help get a good set of data. Some of the research methods must involve direct communication with the students. The other ones will need observation especially on the ones who rarely take their food at the dining hall (Robinson, A. 2004).

Survey on the Catering staff

These researches will provide complementary data to the one gained from the students. The staff can provide more data on the amount of food that is received by the school and whether it is of acceptable quality. Furthermore, the staff at the dining hall may have greater information and may offer more conclusive data about the students’ feeding habits (Singer, F. 2007). The researchers are also supposed to take an impromptu visit to the cooking area. This should however be preplanned with the school administration. The researchers should be provided with passes to be able to access the kitchen. Once in the kitchen, observation and taking of notes should be done immediately to prevent the staff from changing the environment to suit their words (Campana, P., & Varese, F. 2012).

Survey on the school administration

The school administration is supposed to cater for all the students in the school. However, failure of some of the students to consume the food provided may point to a disconnect within the school policy. The department involved should be assessed. School records can provide enough data about the amount spent and the type of food bought by the administration. Furthermore, it should be noted that there may be some obstacles in this section. Some of the staff involved may deliberately hide some information if they have a hand in the problem. To counter this, the research should also look at the documents of surrounding schools to get a general scenario. After that, the information gained should tally with others because the institution is government funded (Brace, I. 2008) .

The research design methods should be tailored to a specific subject. The characteristics of the subject matter should be studied extensively first. This will ensure that the type of questions formulated fit into the program. Furthermore, the methods and designs need to be determined before the research takes place.

List of References

Brace, I. (2008). Questionnaire Design : How to Plan, Structure and Write Survey Material for Effective Market Research. London: Kogan Page. Campana, P., & Varese, F. (2012). Listening to the wire: criteria and techniques for the quantitative Analysis of phone intercepts. Trends In Organized Crime, 15(1), 13-30. Gosling, E. (2014). New Science Museum Reseach Centre designs inspired by 'sitting under a tree on a summer's day'. Design Week (Online Edition), 7. 23-29. Guthery, F. S. (2007). Deductive and Inductive Methods of Accumulating Reliable Knowledge in Wildlife Science. Journal Of Wildlife Management, 71(1), 222-225. doi:10.2193.2006-276 Jaksić, F. M. (1981). Recognition of Morphological Adaptations in Animals: The Hypothetico- Deductive Method. Bioscience, 31(9), 667-670. Lawson, A. E. (2000). The Generality of Hypothetico-Deductive Reasoning: Making Scientific Thinking Explicit. American Biology Teacher (National Association Of Biology Teachers), 62(7), 482. Meyer, W., Caprioara-Buda, M., Jeynov, B., Corbisier, P., Trapmann, S., & Emons, H. (2012). The impactof analytical quality criteria and data evaluation on the quantification of genetically modified organisms. European Food Research & Technology, 235(4), 597-610. doi:10.1007/s00217-012- 1787-7. Robinson, A. (2004). Preserving correlation while modelling diameter distributions. Canadian Sartor, Maureen A., et al. "Genomewide Analysis Of Aryl Hydrocarbon Receptor Binding Targets Reveals An Extensive Array Of Gene Clusters That Control Morphogenetic And Developmental Programs." Environmental Health Perspectives 117.7 (2009): 1139-1146. GreenFILE. Web. 27 Nov. 2014. Sánchez-Martín, M. J., Sánchez-Camazano, M., & Lorenzo, L. F. (2000). Cadmium and Lead

Tam, V. Y., Shen, L. Y., & Ochoa, J. (2013). Design for Green Property Development in Developing Cities. Journal Of Professional Issues In Engineering Education & Practice, 139(4), 310-316. doi:10.1061/(ASCE)EI.1943-5541.0000161 Singer, F. (2007). Dualism, Science, and Statistics. Bioscience, 57(9), 778-782. doi:10.1641/B570910 Xing, Q., Hulin, W., & Rui, H. (2013). The impact of quantile and rank normalization procedure testing power of gene differential expression analysis. BMC Bioinformatics, 14(1), 1-10.

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Research Proposal Example/Sample

Detailed Walkthrough + Free Proposal Template

If you’re getting started crafting your research proposal and are looking for a few examples of research proposals , you’ve come to the right place.

In this video, we walk you through two successful (approved) research proposals , one for a Master’s-level project, and one for a PhD-level dissertation. We also start off by unpacking our free research proposal template and discussing the four core sections of a research proposal, so that you have a clear understanding of the basics before diving into the actual proposals.

• Research proposal example/sample – Master’s-level (PDF/Word)
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If you’re working on a research proposal for a dissertation or thesis, you may also find the following useful:

• Research Proposal Bootcamp : Learn how to write a research proposal as efficiently and effectively as possible
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PS – If you’re working on a dissertation, be sure to also check out our collection of dissertation and thesis examples here .

FAQ: Research Proposal Example

Research proposal example: frequently asked questions, are the sample proposals real.

Yes. The proposals are real and were approved by the respective universities.

Can I copy one of these proposals for my own research?

As we discuss in the video, every research proposal will be slightly different, depending on the university’s unique requirements, as well as the nature of the research itself. Therefore, you’ll need to tailor your research proposal to suit your specific context.

You can learn more about the basics of writing a research proposal here .

How do I get the research proposal template?

You can access our free proposal template here .

Is the proposal template really free?

Yes. There is no cost for the proposal template and you are free to use it as a foundation for your research proposal.

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

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

Methodology

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

 Statistics

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

Research bias

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

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Research Design and Mythology The research design and methodology of the paper will be a qualitative style of research combined with statistical data. The author will use secondary data to gather the five methods of research: observations, interviews, survey forms, focus groups and internal data. The hope is to combine these methods and research findings […]

Chapter III Research Design and Methodology Research Methodology This chapter deals with the method and procedure utilized by the researcher to enable the readers to find out how the study was conducted and on how the conclusions have been drawn. It includes the research design, research population, research instrumentation and sampling technique, data gathering procedure, […]

In the old chapter it was discussed the relevant literature, which is connected with research subject. Research methodological analysis every bit good as others methods are discussed exhaustively in this chapter. First, research worker explains research in general. After that research doctrine every bit good as attack has been discussed. Subsequently, research design has been […]

This investigation was concerned generally to see how new technologies come into the everyday lives of different people, and how In turn these people engage with these offerings: the way they are appropriated, Including adoption, learning and struggling, but also other strategies for non-adoption, or arms length appropriation. Particular issues include the influence of knowledge, […]

This project requires reading the Donates: Finding the New Pizza (Attached) case and answering the following discussion questions: 1. Map the research design used by Donation for new product development. 2. Evaluate the Wausau meetings as an exploratory methodology to help define the research question. 3. Evaluate the test marked Donates used. 4. What were […]

In order to assess the significance of information for managerial decision-making, appropriate evaluation techniques need to be employed by managers and researchers. Exploratory research can sometimes enable managers to make decisions, relying on secondary data sources such as encyclopedias, textbooks, handbooks, magazine and newspaper articles, and even most newscasts. These sources are deemed valuable. Nevertheless, […]

The research design also contains clear objectives, derived from research data, the sign technique(s) (survey), observation, experimental etc), the sampling methodology and procedures, the schedule and the budget. There should be clear justification with regard to the research design based on the research question and objectives. A research design is a framework or blueprint for […]

The research hypothesis is a statement that researchers create to speculate about the outcome of a research or experiment. It must be mathematically and statistically provable and serves as the foundation for the entire experiment’s design. This statement is vital in any authentic experimental design as it represents the ultimate goal of the experiment. Typically, […]

It is the core of whole research design. In the article, we use the “Research Onion” as symbolic word to describe relation between the core of research onion and outer layers on research onion. The situation (context) and limits within which data collection techniques and analysis procedures are selected depends on researcher’s understandings and decisions […]

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Research Designs, Essay Example

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In order to understand the advantages and disadvantages of different research study designs, this paper will take a look at two groups of 1200 Middle Eastern women (600 women from Egypt; 600 women from Saudi Arabia) sampled from rural areas.[1] In these experiments, the dependent variable being evaluated is attitude towards gender egalitarianism; the independent variables are the women’s nature of religious level (measured in a binary “orthodox” or “liberal” binary).  The scheduled intervention is a “women’s rights seminar” that will give a more nuanced (read: liberal) appraisal of women’s function in Islamic society.

For the pre-test/post-test control group design, the group of Egyptian women will be broken into two groups: 1) One group of 200 Egyptian women will be exposed to a “gender egalitarian” one hour long seminar talking about equal gender roles in an Islamic context with discussion after; one group of 200 Egyptian women will simply be put into a room for one hour meeting asking about their views towards life with no discussion afterwards.  Both of the groups will be given a pre-test and post-test assessing their views on gender roles in Islamic society. The same design, with the Saudi women split into two equal groups of 200, will also be performed. The pre-test/ post-test structure is typically used because there is a control group whose progress can be measured against the intervention- that is, measure the true impact of the intervention.

For the Solomon-Four-Group Design, a different group of 400 Egyptian and 400 Saudi women will each be broken into four different groups consisting of 100 women in each group: 1) The first group will be assigned to the pre-test regarding gender egalitarian issues, the intervention, and the post-test; 2) The second group will be exposed to the intervention but only with a post-test; 3) The third group will take the pre- and post-test on gender equality without the intervention; 4) The fourth group will just take the post-test.

The third test would be the “post-test” only group.  In these tests, the last group of 400 Egyptian and Saudi women will be broken down into two groups of 200.  The first group will be exposed to the seminar on gender equality and a post-test on views regarding gender equality; the second group will not be exposed to the intervention but will take a post-test regarding views on gender equality. The post-test experimental method is typically used if the investigator is worried about how the influence of testing may make subjects sensitive to the intervention, ultimately confounding results.

Overall, there are seven threats to internal validity (validity of putative relationship between dependent and independent variable) to analyze.  A majority of the threats to internal validity are mitigated through the use of randomized allocation and the use of a control group in experiments.  For example, the internal validity threats of maturation, testing, and regression are mitigated using a control group.  Maturation, the phenomenon by which changes in groups are a function of development (increased capacity) rather than the intervention, will be equally noticeable in both groups.  Regression to the mean is a similar threat: a control group will track the regression to the mean (mirroring a similar trend in the intervention group) indicating that the intervention is not the primary factor behind different results. Testing, the process by which participants may change answers due to multiple testing, is controlled for by a control group and is a main reason for the Solomon four group and post-test design. Randomizing subjects solves the internal validity problem of selection, in which the differing characteristics of subjects in different groups is confounding the analysis.

In the three different research designs, the internal validity threat of history is mitigated by the randomization of timing and experimenters giving the test.  The internal  validity threat of mortality is controlled for by having a placebo discussion session for the control group of Middle Eastern women.  Instrumentation is controlled for by giving the same test- there is little discretion left up to the experimenters for personal decision that could introduce bias.  Regarding the threat of attrition, the “intent-to-treat” analysis will be administered in the experiments leaving in data points even if they ultimately choose to exit the experiment.  The diffusion threat to internal validity will be controlled by keeping the women in different hotels and arranging their sessions individually and not as an entire group based on their random assignment before the experiment begins.  Finally, sequencing events should not be a foremost concern in any of these studies because there are no crossover designs.

[1] Note: defined as cities smaller than 25,000- at least 100 miles away from a city that is larger than 100,000).  Rural women are chosen because they are less likely to have been influenced to more liberal interpretations of Islam.

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Qualitative Research Skills In The Marketing Research Process

Published 25 Jun 2024

Introduction

The marketing research process helps analyze the problems involved in developing a specific marketing campaign related to an organization's specific product. A number of steps are involved in the marketing research process, and differences exist in the management and research problem. Those steps and differences are identified here, along with the evaluation of qualitative techniques used for primary data collection.

Steps involved in the marketing research process

The processes involved in marketing research are identifying the problem, setting the research aims and objectives, determining the design of the research, planning the sample, collecting the required data, analyzing those data, and formulating conclusions from the analysis.

Identification of the problem: In order to initiate the market research process, the identification of the problem experienced by the organization needs to be defined. The problem statement should be taken into consideration so that it can find a way to deal with the problems and resolve it properly (FLEACĂ, et al. 2016).

Identifying research aims and objectives: In the next step, research objectives and aims need to be defined so that the extent of the research problem can be determined. The research aim can illustrate the significance of the problem statement.

Research Design Planning: After defining the objectives and the problems related to the marketing research process, the research design needs to be determined. As the needed information is to be collected and analyzed, the specific procedure should be developed, and Research Design can be a master plan for it (Benzo et al. 2017).

Sample planning: It can be found that in order to collect the proper and first-hand information, the population or number of samples is to be defined. The target population and the right representation of samples need to be taken into consideration.

Data Collection process: Data collection is needed to resolve the problem regarding marketing research. Both primary and secondary data can be considered in developing market research methods (Kennedy, 2017). Whereas human interaction can be used as the primary data source, secondary data sources are comprised of reports, articles, journals, and other Publications.

Data analysis: In order to generate the research, the collected data needs to be analysed so that the right outcome can be generated properly. The respondents' responses need to be categorized properly so that the data transfer to the data storage media can be developed properly. Moreover, the right analytical technique needs to be chosen so that the outcome will be right.

Conclusion formulation: This is the final stage of the marketing research process, where the information is interpreted and conclusions drawn to be used for managerial decision-making. In fact, the Research report will be developed based on this segment.

Differences of the management and research problem in the given context

Management and research problems are dependent on action-oriented problems where research problem involves the Collection of data, sampling, and the analysis of collected data. The management research problem can be identified as one of the problems which are experienced by the management of a specific organisation (Birks, 2016). On the other hand, the research problem can be developed on many aspects unrelated to the organization. In the given context, thus, the research problem would be different as it is related to the problems of marketing research and its problems

Application and evaluation of qualitative techniques for primary data collection

Primary Data collection is one of the most effective techniques that can be helpful in terms of making sure that real-time data related to a particular subject is incorporated in the research. In this case, the researcher needs to make sure that he or she asks the right questions to the right stakeholders who will be considered as the research participants. However, there are several techniques related to primary qualitative data collection, such as the following.

Individual Interview—Interviews are the most popular technique for qualitative research. In this case, researchers can acquire more detailed information from individuals who are directly related to a topic or situation (Horrocks, 2018). Creating a list of questions and making appointments with the participants are activities related to this technique.

Focus Groups—This technique allows research to obtain information that can be easily generalized. Statements from individuals are critically analyzed, as different people in the same location can provide contrasting information. The main activities are gathering a group of participants and creating a list of questions.

Observations—Observation can be regarded as the easiest way of collecting qualitative data. In this case, the researcher sees a certain situation or event just to understand the changes or developments that are visible to the naked eye (Choi, 2018). However, there are no guarantees that the researcher will understand specific changes in a detailed manner.

The success of any business organisation largely depends on how the company and its management are able to align themselves with changes in the internal and external environment. However, understanding the environment is very important, and in this case, different research questions can be very helpful in understanding the alignment of the different business environments. The report effectively provided the opportunity to learn about such techniques.

Read also: Need marketing assignment help? Our professionals are ready to boost your grades.

Are there certain actions or behaviors during the activity that you did particularly well or that you would wish to improve?

It can be said that I was very excited when I got the opportunity to execute this assignment, and therefore, I decided to put my best foot forward in the execution of this assignment. However, during the execution phase, I understood that I had issues with my time management skills. In this case, I faced some difficulties in managing my other personal commitments while being aligned with my academic commitments, and therefore, I think that there is some room for improvement in terms of time management, and I must make timetables in order to accommodate my different commitments. I will try to make sure that I can cut down on my commitments so that the important activities are on top of my list in terms of allocating time.

In what ways do you think you could improve your own practice in relation to gathering data, analysing information, and using intelligence?

I think that the most effective way to improve my research skills is by ensuring I can access all effective online sources. In my opinion, as technology develops, all the literature sources will be available online; therefore, to effectively execute secondary data collection, the online sources must be accessed. It can be said that the internet is also helpful in terms of primary data, which is quantitative in nature. There are several websites and platforms in the internet that can be used for online chat and this will be helpful in gathering information from participants who live far from the researcher.

However, in order to be effective in gathering data from these online techniques, one has to be aware of the websites and how they access them from different browsers. In this case, I will also look to improve my research skills and make sure that I am able to surf the internet and learn about different platforms related to data collection.

• Benzo, R., Mohsen, M.G. and Fourali, C., 2017. Marketing research: planning, process, practice. Sage.
• Birks, D.F., 2016. Marketing research. In The Marketing Book (pp. 188-212). Routledge.
• FLEACĂ, E., FLEACĂ, B. and Maiduc, S., 2016. Fostering Organizational Innovation based on modeling the marketing research process through an event-driven process chain (EPC). TEM J, 5, pp.460-466.
• Kennedy, A.M., 2017. Macro-social marketing research: philosophy, methodology and methods. Journal of Macromarketing, 37(4), pp.347-355.
• King, N., Horrocks, C. and Brooks, J., 2018. Interviews in qualitative research. SAGE Publications Limited.
• Roulston, K. and Choi, M., 2018. Qualitative interviews. The SAGE handbook of qualitative data collection, pp.233-249.

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Market Design in Regulated Health Insurance Markets: Risk Adjustment vs. Subsidies

Health insurance is increasingly provided through managed competition, in which subsidies for consumers and risk adjustment for insurers are key market design instruments. We illustrate that subsidies offer two advantages over risk adjustment in markets with adverse selection. They provide greater flexibility in tailoring premiums to heterogeneous buyers, and they produce equilibria with lower markups and greater enrollment. We assess these effects using demand and cost estimates from the California Affordable Care Act marketplace. Holding government spending fixed, we estimate that subsidies can increase enrollment by 16 percentage points (76%) over risk adjustment, while all consumers are weakly better off.

Einav and Finkelstein gratefully acknowledge support from the Sloan Foundation and from the Laura and John Arnold Foundation. Tebaldi acknowledges support from the Becker Friedman Institute. We thank Ben Handel, Mike Whinston, and many seminar participants for helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

I would like to disclose that I am an adviser to Nuna Health, a data analytics startup company, which specializes in analytics of health insurance claims. I am not being paid by them, but have received equity (nominal value is less than $1,000 the market value is hard to assess).

MARC RIS BibTeΧ

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Evaluation of entrepreneurship failure education in higher education from the perspective of the CIPP model and AHP-FCE methods

• Chaoyong Tang 1 ,  ,  , 
• Ruili Sun 1 , 
• Wanming Chen 2
• 1. School of economics and management, Hebei Agricultural University, Baoding, Hebei, China
• 2. School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
• Received: 27 February 2024 Revised: 06 June 2024 Accepted: 13 June 2024 Published: 25 June 2024
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• entrepreneurial failure education (EFE) ,
• FCE method ,
• CIPP model ,
• AHP technique ,
• education evaluation

Citation: Chaoyong Tang, Ruili Sun, Wanming Chen. Evaluation of entrepreneurship failure education in higher education from the perspective of the CIPP model and AHP-FCE methods[J]. AIMS Mathematics, 2024, 9(8): 20641-20661. doi: 10.3934/math.20241003

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• © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0 )

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沈阳化工大学材料科学与工程学院 沈阳 110142

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Uncanny Valley

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The term “Uncanny Valley” strikes a chord with our deepest unease, suggesting a realm where familiarity and strangeness converge. This concept, pivotal in robotics and animation, refers to the eerie sensation people often feel when humanoid objects closely resemble humans, yet miss the mark in crucial, subtle ways. Why does this phenomenon provoke discomfort, and what implications does it have for our interaction with increasingly lifelike technology? Join us as we unravel the psychological and ethical dimensions of the uncanny valley, shedding light on its impact across various fields.

What Is Uncanny Valley?

The uncanny valley is a phenomenon that occurs when a person views something that closely resembles a human being. A person often feels uncomfortable when viewing something that has human characteristics and an uncanny resemblance to something human-like. This often leads the person to find objects that exhibit an uncanny valley feeling to be creepy, which is heavily utilized in the horror genre.

Examples of Uncanny Valley

Humanoid Robots

• Hiroshi Ishiguro’s Geminoids : These robots are designed to look and act like their human counterparts, including replicating facial expressions and speech. The lifelike appearance, combined with slightly off movements or expressions, can evoke feelings typical of the uncanny valley.

Animated Characters and CGI

• Polar Express (2004) : This film used motion capture technology to animate its characters, which many viewers found unsettling due to their life-like yet simultaneously lifeless facial expressions.
• Beowulf (2007) : Similar to Polar Express, the use of advanced animation to create human-like characters resulted in a somewhat disturbing viewer experience due to the characters’ almost-real appearance.

Video Games

• LA Noire (2011) : This game was praised for its realistic facial animation technology, but it also left some players feeling uneasy because of the hyper-realistic face scans on characters, which still lacked some natural nuances in expression.

Further Humanoid Robots

• Sophia the Robot : Developed by Hanson Robotics, Sophia is known for her human-like appearance and behavior. Despite her advanced interaction capabilities, her expressions and movements can still feel eerily unnatural to some observers.
• Final Fantasy: The Spirits Within (2001) : This early attempt at creating photorealistic human characters in a feature film was noted for its high-quality graphics but criticized for the lifeless expressions of its characters, which disturbed some viewers.
• Cats (2019) : The film adaptation of the famous musical used CGI to create human-cat hybrids, which many found unsettling due to their strange, uncanny appearance that mixed human faces and feline features.
• Mass Effect: Andromeda (2017) : Upon its release, the game received attention for its awkward facial animations and character movements, which detracted from the realism and immersion, evoking the uncanny valley effect.
• Resident Evil: Code Veronica : This game featured early 3D character models that aimed for realism but ended up in the uncanny valley due to stiff animations and unnatural facial expressions, making the characters appear both eerie and artificial.

Wax Figures

• Madame Tussauds’ Wax Figures : While many wax figures at Madame Tussauds are impressively lifelike, some can fall into the uncanny valley when they mimic well-known faces with almost, but not quite, perfect accuracy, leading to a disconcerting experience for visitors.

Further Animated Characters and CGI

• Avatar (2009) : Although praised for its groundbreaking visuals, some viewers felt discomfort with the Na’vi characters. Their human-like features combined with alien attributes created an unsettling mix that approached the uncanny valley for some audience members.
• Tintin (2011) : “The Adventures of Tintin” used motion capture to create highly detailed and expressive characters. However, the almost-but-not-quite human appearance of the characters caused an uncanny feeling among some viewers, due to their exaggerated yet lifelike animations.
• Asimo by Honda : This humanoid robot, designed to assist with daily tasks, is known for its bipedal movement and ability to navigate environments. Despite its helpful design, Asimo’s human-like movements paired with its robot-like appearance can trigger uncanny valley sensations, as it closely imitates human motion but lacks other human-like features.

Examples of Uncanny Valley in Movies

• “The Polar Express” (2004) – The CGI animation style, especially the human characters’ eerily lifelike eyes and expressions, caused discomfort among viewers.
• “Beowulf” (2007) – This film utilized motion capture technology, which resulted in hyper-realistic animations of characters that many found unsettling.
• “Final Fantasy: The Spirits Within” (2001) – One of the first attempts at creating photorealistic human characters in animation, resulting in a mixed reaction due to their life-like yet odd appearance.
• “Cats” (2019) – The use of digital fur technology to create human-cat hybrids was widely criticized for being disconcerting and strange.
• “Rogue One: A Star Wars Story” (2016) – The digital resurrection of actor Peter Cushing to reprise his role as Moff Tarkin was both impressive and unsettling for many viewers.

Examples of Uncanny Valley in Real Life

• Sophia the Robot – A humanoid robot known for her lifelike appearance and expressions which some find deeply unsettling.
• RealDoll – These highly realistic silicone dolls intended for companionship evoke a sense of unease because of their human-like features yet inanimate nature.
• Actroid – A type of android developed in Japan that mimics human gestures and facial expressions closely, causing discomfort in some interactions.
• BINA48 – A robotic head-and-shoulders replica of a real person, designed to mimic thought and emotion processes in a way that can be disturbing due to its realism.
• Telenoid – A minimalistic humanoid telecommunication robot with stubby arms and a face, which is intended to appear both male and female, young and old, but its vague human features make it eerily disconcerting.

Examples of Uncanny Valley in Makeup

• Zombie Makeup – Hyper-realistic zombie makeup in films and television, especially when applied with prosthetics, can be unsettlingly lifelike.
• Drag Makeup – Although often vibrant and exaggerated, certain styles of drag makeup aim to transform features so drastically that they can provoke an uncanny sensation.
• Aging Makeup – When actors are made to look much older than they are, especially if the makeup is very detailed, it can create an eerie effect.
• Character Recreations – Makeup that meticulously recreates the features of well-known cartoon characters or icons on a real human face, leading to a bizarre blend of familiar and unfamiliar traits.
• Ultra Realistic Prosthetics – Use of prosthetics to create injuries, deformities, or other conditions can be so lifelike that they cross into the uncanny valley.

How to Design Something to Have Uncanny Valley

One of the best ways to obtain the attention of people is by respectfully showing them something that would make them somewhat uncomfortable, which is something an object with an uncanny valley will elicit from a person. The uncanny valley is related to an object’s ability to resemble something human-like, which will take some time and effort to do.

Step 1: Learn and Understand Design Elements that Match Human Realism

There are many design elements that will come allow the viewers to easily see something in the context desired by the artist. Begin by learning and understanding the various design elements that will come into play when it comes to making something feel realistic, like skin textures, skin color, eye textures, and more.

Step 2: Learn and Integrate Common Human Behavior. Attitude, and Mannerisms

Another thing one should have to take into consideration is the integration of human-like behavior, attitude, and mannerisms into the object. Uncanny valley occurs in the small space between absolute human-likeness and something human-like, which means the object will need to exhibit some occurrences of human behavior, attitude, and mannerisms.

Step 3: Ensure the Proportions Are Realistic and Human-Like

Lastly, you must ensure that the proportions you use are realistic and human-like. The proportion doesn’t necessarily need to match a hundred percent but should be similar enough to come off as realistic.

Uncanny Valley: Science or Pseudoscience?

The concept of the Uncanny Valley is grounded in several disciplines, including robotics, psychology, and neuroscience. Researchers in these fields investigate how factors such as aesthetics, emotion perception, and cognitive dissonance contribute to the uncanny valley phenomenon. For example, studies in psychology and neuroscience have explored how human brains react to faces and expressions that appear nearly, but not entirely, human.

These studies typically use methods such as brain imaging and behavioral tests to analyze reactions to different levels of humanoid likeness. They provide empirical evidence suggesting that the uncanny valley exists and can be measured, which supports its classification as a scientific concept.

Why does the uncanny valley exist?

• Visual and Behavioral Closeness : The uncanny valley effect occurs when a robot or an avatar is close to, but not perfectly, human-like in appearance or behavior. The slight deviations from human norms are perceived as strange or unsettling.
• Evolutionary Perspective : Some scientists suggest that this reaction could be an evolutionary response designed to protect us from potential threats. We are attuned to normal human behaviors and appearances, so anything that deviates from this norm can be perceived as a threat or sickness, eliciting a repulsion response.
• Familiarity and Empathy Gap : As objects appear more human-like, we tend to empathize more with them. However, if they fall short of full human likeness (but are very close), they can create a dissonance in our perception, leading to discomfort.
• Technological Limitations : Current technologies may not always capture the subtle nuances of human expression or movement perfectly, contributing to the uncanny effect. This can make these almost-human figures seem lifeless or zombie-like.
• Cultural Influences : Cultural background and individual experiences also play significant roles in how the uncanny valley is experienced. Different cultures may have different thresholds where these effects begin or are most pronounced.
• Application and Impact : Understanding and overcoming the uncanny valley is crucial in robotics and animation, where creating relatable and comfortable interactions with human-like beings is important.

History of the Uncanny Valley

• Concept Origin : The term “uncanny valley” was first coined by the Japanese roboticist Masahiro Mori in 1970. He used the term to describe his observations of human reactions to lifelike robots.
• Mori’s Hypothesis : Mori proposed that as robots become more human-like, people’s responses to them would become increasingly positive and empathetic, until a point where the likeness becomes too strong and the response suddenly turns to revulsion.
• Graphical Representation : Mori represented this concept through a graph: as the appearance of a robot becomes more human-like, the emotional response of humans increases positively, reaches a peak, then dips dramatically into negative territory—forming a valley.
• Publication and Impact : Although initially published in an energy company’s newsletter in Japanese, the concept was popularized in the 2000s when robotic technologies and digital graphics made significant advancements.
• Broader Application : Over time, the concept of the uncanny valley has been applied beyond robotics, extending into areas like computer-generated graphics in movies and video games, where overly realistic characters can cause discomfort among audiences.
• Psychological and Biological Insights : Studies have explored the psychological and biological bases for why humans may feel revulsion at entities that appear nearly, but not exactly, human. These include evolutionary mechanisms designed to help us avoid disease and select healthy mates.
• Contemporary Research : Recent research continues to explore how and why different cultures and individuals experience the uncanny valley differently, suggesting that familiarity, cultural background, and personal experiences play roles in how the phenomenon is perceived.
• Current Relevance : The uncanny valley remains a significant consideration in the fields of artificial intelligence, virtual reality, and robotics, where designers strive to create more comfortable and engaging interactions with human-like entities.

Implications of the Uncanny Valley

Robotics and artificial intelligence.

• Discomfort and mistrust in human-robot interaction, affecting the adoption and integration of humanoid robots in social settings.
• Challenges in designing robots that are close to, but not perfectly mimicking, human appearance and behavior.

Computer Graphics and Animation

• Difficulty in creating realistic human characters in movies and video games that do not provoke unease or rejection from viewers.
• Need for increased attention to subtleties in human expressions and movements to avoid the uncanny valley effect.

Virtual Reality

• Impact on user experience and immersion, where too realistic avatars might cause discomfort among users.
• Necessity to balance realism and stylization to maintain comfortable and engaging virtual interactions.

Healthcare and Therapeutics

• Potential issues in the effectiveness of humanoid robots used in therapy and patient care if they fall into the uncanny valley.
• Importance of designing therapeutic robots that foster trust and comfort among patients.

Marketing and Advertising

• Challenges in using virtual assistants and digital representatives that appear almost, but not fully, human.
• Risk of negative consumer reactions to marketing campaigns featuring lifelike humanoid figures.

Ethics and Society

• Philosophical and ethical implications regarding the creation of artificial beings that closely resemble humans.
• Debates about the emotional and psychological impacts of interacting with human-like machines.

Effects of the Uncanny Valley

• Emotional Discomfort : When robots or computer-generated figures closely resemble humans but are not quite lifelike, it often leads to feelings of eeriness or discomfort among human observers.
• Drop in Affinity : There’s a noticeable decrease in people’s affinity towards a robot or CGI character as it approaches human likeness but is perceptibly different. This drop in affinity recovers once the entity looks fully human.
• Impaired Trust and Interaction : The uncanny valley can negatively impact the willingness of humans to interact with or trust robots, particularly in roles that require close interaction like caregiving or customer service.
• Aesthetic and Design Challenges : Designers and developers face challenges in creating appealing and effective robotic or CGI characters due to the uncanny valley. It influences decisions in aesthetics to either make figures less human-like or perfectly human-like to avoid discomfort.
• Influence on Acceptance and Adoption : The phenomenon can influence the acceptance and adoption of robots and AI in society, particularly in fields where human interaction is prevalent.
• Impact on Psychological Perception : The uncanny valley may also affect how humans psychologically categorize entities, wavering between seeing them as objects or beings with agency, which can complicate emotional and social responses.

Research on the Uncanny Valley

• Perceptual and Cognitive Factors : Studies have shown that the Uncanny Valley effect is primarily driven by perceptual and cognitive dissonance when encountering near-human entities. The brain’s difficulty in categorizing these entities leads to discomfort and eeriness.
• Emotional Reactions : Research has demonstrated that human-like robots and avatars can evoke stronger emotional responses compared to less human-like ones. This is especially true when the entity’s appearance or behavior deviates slightly from human norms.
• Facial Realism : Experiments have found that even small imperfections in facial features or movements can trigger the Uncanny Valley effect. Highly realistic faces that fail to mimic natural expressions accurately can be perceived as unsettling.
• Movement and Animation : Smooth, human-like movements are crucial for avoiding the Uncanny Valley. Jerky or unnatural movements increase the likelihood of an uncanny response.
• Brain Imaging Studies : Functional MRI (fMRI) studies have revealed that areas of the brain involved in emotional processing, such as the amygdala, show increased activity when participants view entities that fall into the Uncanny Valley.
• Electrophysiological Measures : EEG studies have indicated that the Uncanny Valley effect is associated with specific patterns of brain waves, suggesting a neural basis for the discomfort experienced.

Ongoing Research and Future Directions

Cross-cultural studies.

• Researchers are investigating how cultural differences impact perceptions of the Uncanny Valley. Initial findings suggest that cultural background can influence the degree of discomfort experienced when encountering near-human entities.

Longitudinal Studies

• Long-term exposure studies are being conducted to understand whether repeated interactions with human-like robots and avatars can reduce the Uncanny Valley effect. These studies aim to determine if familiarity can mitigate initial discomfort.

Technological Advances

• AI and Machine Learning : Advances in AI and machine learning are being utilized to create more realistic and adaptive human-like robots and avatars. These technologies aim to improve the naturalness of interactions and reduce the Uncanny Valley effect.
• Virtual Reality (VR) and Augmented Reality (AR) : VR and AR platforms are being used to test and refine human-like avatars in immersive environments. These studies focus on enhancing the realism and acceptability of virtual characters.

How to Avoid the Uncanny Valley

• Maintain Simplicity in Design : Keep the design of robots or animated characters simple and stylized rather than striving for hyper-realism. This helps in keeping the characters approachable and less eerie.
• Focus on Consistency : Ensure consistency between the appearance and behavior of characters. Discrepancies between highly realistic visuals and less sophisticated motion can amplify uncanny feelings.
• Improve Motion Quality : Enhance the fluidity and naturalness of movements. Jerky or unnatural motions can be disturbing when paired with lifelike visuals.
• Use Familiar Yet Distinct Features : Incorporate familiar human traits, but avoid mimicking human appearance too closely. This can be achieved by altering proportions or features slightly to signal non-human status.
• Limit Detail in Certain Areas : Avoid high levels of detail in areas particularly sensitive to uncanniness, like eyes and facial expressions. Simplifying these elements can reduce discomfort.
• Test with Diverse Audiences : Regularly test designs with diverse groups of people to gather a wide range of perceptions and reactions. This feedback is crucial for identifying elements that might be contributing to the uncanny valley effect.
• Educate the Audience : Prepare the audience on what to expect regarding the realism of characters. This can help in mitigating shock or discomfort.
• Adjust Expectations : If high realism is necessary, set appropriate expectations for interaction. Make it clear that the character or robot is not human, which can recalibrate user expectations and reactions.

Criticisms of the Uncanny Valley

• Lack of Empirical Evidence Some researchers argue that the empirical evidence supporting the uncanny valley phenomenon is not robust or consistent. Studies have yielded mixed results, and the conditions under which the uncanny valley effect occurs are not well-defined.
• Individual Differences People’s reactions to human-like robots or avatars vary widely. Factors such as cultural background, personal experiences, and familiarity with technology can influence whether someone experiences the uncanny valley effect.
• Context Dependence The uncanny valley effect may be context-dependent, meaning that it is not a universal response. For example, a robot’s appearance might be unsettling in one situation but acceptable or even appealing in another.
• Overemphasis on Appearance Critics argue that the uncanny valley theory places too much emphasis on the appearance of robots or avatars, neglecting other factors such as behavior, functionality, and interaction quality that contribute to human-likeness and acceptance.
• Technological Development As technology advances, the boundaries of the uncanny valley may shift. What was once considered eerie may become acceptable as people become more accustomed to human-like machines.
• Ethical and Philosophical Concerns The focus on making robots and avatars more human-like raises ethical and philosophical questions about the nature of humanity, identity, and the role of machines in society. These concerns may overshadow practical considerations and hinder technological progress.
• Practical Implications The uncanny valley theory may not be practical for guiding the design and development of human-like robots and avatars. Designers and engineers need more concrete guidelines and empirical data to create effective and acceptable human-like machines.

Who coined the term “Uncanny Valley”?

The term “Uncanny Valley” was coined by roboticist Masahiro Mori in 1970.

Why do people feel uneasy in the Uncanny Valley?

People feel uneasy because almost-human entities trigger cognitive dissonance and an instinctive fear response due to their near-but-not-perfect resemblance to humans.

How is the Uncanny Valley relevant to robotics?

The Uncanny Valley is crucial in robotics, guiding the design of robots to ensure they evoke positive responses rather than discomfort.

What industries are affected by the Uncanny Valley?

The Uncanny Valley impacts industries like robotics, animation, virtual reality, and artificial intelligence, where creating human-like entities is common.

How can designers avoid the Uncanny Valley effect?

Designers can avoid the Uncanny Valley by either making robots distinctly non-human or achieving high levels of realism to avoid the eerie in-between.

What are examples of the Uncanny Valley in media?

Examples include lifelike CGI characters in movies, hyper-realistic video game avatars, and certain humanoid robots.

How does the Uncanny Valley impact user experience?

The Uncanny Valley can negatively affect user experience by making interactions with lifelike robots or animations unsettling and less enjoyable.

Can the Uncanny Valley be beneficial?

Understanding the Uncanny Valley helps improve the design of robots and animations, making them more acceptable and less likely to evoke discomfort.

What psychological theories explain the Uncanny Valley?

Psychological theories include cognitive dissonance, the mortality salience hypothesis, and evolutionary explanations regarding threat detection and survival instincts.

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Campus Community Honors Juneteenth

Published date.

Story by: Amanda Rubalcava  /  [email protected]

Photos by: Long Truong

Article Content

On June 19, 1865, enslaved African Americans in Texas were told they were free, marking the end of slavery in the United States following the Civil War. This day—now known as Juneteenth, Freedom Day, or Emancipation Day—continues to be celebrated across the United States as a federal holiday.

At UC San Diego, the campus commemorated this significant holiday with a vibrant afternoon celebration dedicated to honoring Black excellence. The event featured powerful speeches, live performances, an award ceremony and more, all aimed at celebrating the rich history and contributions of the Black community.

The event began with a dynamic performance by the Teye Sa Thiosanne African Drum and Dance Company, featuring traditional African procession, drum calls and libations. A common practice celebrated during Juneteenth commemorations, a libations ceremony is a way to honor one's ancestors.

Award-winning writer Johnnierenee Nia Nelson served as the event's Griot—the name given to the oral historians of West Africa who serve as storytellers, singers, musicians and oral historians. Also known as the "Kwanzaa poet," Nelson is a poet and teacher for both California Poets in the Schools and San Diego's Border Voices Project.

The event's opening remarks were provided by Executive Director of Student Health and Well-Being Dr. Edward P. Junkins, Jr and Black Staff Association co-chair Taura Gentry-Kelso. In his presentation, Dr. Junkins emphasized the importance of Juneteenth in American history. He explained that Juneteenth represents not only the end of physical slavery but also the ongoing fight for equality and justice. He urged everyone to reflect on Juneteenth's significance in our journey toward a more inclusive and equitable society, highlighting the need for continued education, dialogue and action to create lasting change.

For the fourth annual Juneteenth celebration on campus, the theme was "A Gospel Journey to Freedom." The Spirit of Oya performed uplifting gospel music, reminding attendees of the power of unity and hope.

"Celebrating this holiday on campus brings visibility to our cultural beginnings, cultural present and cultural presence of tomorrow.  It is important for the UC San Diego community to see how vibrant our community is and the impact we have in bridging all communities together through cultural programs like our Juneteenth celebration."

- Davyda Johnson, Black Staff Association 2021-24 Chair 

As part of the event's "Black Excellence Awards," both Ellen D. Nash & Professor Bennetta Jules-Rosette were honored for their achievements and contributions. Ellen D. Nash is the current chair of the San Diego chapter of the Black American Political Association of California. Dr. Bennetta Jules-Rosette is a distinguished professor of sociology and the Director of the African and African-American Studies Research Center at UC San Diego

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