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Research Strategies and Methods

• First Online: 22 July 2021

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• Paul Johannesson 3 &
• Erik Perjons 3  

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Researchers have since centuries used research methods to support the creation of reliable knowledge based on empirical evidence and logical arguments. This chapter offers an overview of established research strategies and methods with a focus on empirical research in the social sciences. We discuss research strategies, such as experiment, survey, case study, ethnography, grounded theory, action research, and phenomenology. Research methods for data collection are also described, including questionnaires, interviews, focus groups, observations, and documents. Qualitative and quantitative methods for data analysis are discussed. Finally, the use of research strategies and methods within design science is investigated.

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Johannesson, P., Perjons, E. (2021). Research Strategies and Methods. In: An Introduction to Design Science. Springer, Cham. https://doi.org/10.1007/978-3-030-78132-3_3

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Conducting Online Research

Effective Online Research Strategies

• For effective online research:
• - know available search tools
• - understand how tools work
• - k now how to use tools
• - evaluate results found with tools

Online Research

• Characteristics of the Internet:
• large volumes of information
• doesn’t contain all information
• potentially frustrating

Web versus Print: Web

• anyone with web access can publish
• author/affiliations and qualifications may be unclear
• may not clearly identify external information
• may be biased/misleading
• publication info may not be listed

Web versus Print: Print

• extensive publication process
• clearly indicates author/affiliations
• clearly marks outside sources/quotations
• bias exists, but is reviewed
• only qualified manuscripts accepted for publication
• publication info clearly listed

Visible Web versus Invisible Web

• Visible Web:� content can be found using freely accessible search engines such as Google��
• Invisible Web:� content not found by general search engines

Invisible Web vs. Visible Web: Practice

• Write a topic on a piece of paper
• Exchange it with a partner
• Run one search in a general search engine such as Google
• Run another search using a library research tool, such as JSTOR, EBSCOHOST, or Megasearch
• Discuss the kinds of results each search turned up

Web Search Tools

• Search Engines
• - list results based on entered keywords
• Web Directories
• - offer categories for users to choose from
• Metasearch Engines
• - combine results from multiple search engines

Search Engine Results

• - site’s amount of information
• - number of linking sites
• - number of people who choose a link
• - length of time in search engine database
• - code of the site
• Different search engines might return different results in a different order
• Can include results from paying advertisers:

Search Engine Results: Practice

• Write a search topic on a piece of paper
• Exchange the piece of paper with a partner
• Enter the search term into three different search engines.
• Discuss the difference in results with your partner

Conducting a Search

• - keywords that apply
• - what kinds of information you need
• - multiple angles
• - keep notes

Using Search Terms

• Do multiple searches
• Try keyword variations
• - e.g. try “dining hall,” “cafeteria,” and “campus food service”
• Be specific as you learn more
• - e.g. change “dining hall” to “Midwest university dining hall”
• Boolean Operators: words added to a search to make it more specific

Defining a Search: Boolean Operators

• AND finds pages with all of the search terms used
• - e.g. “dining hall” AND “student workers”
• OR- finds pages with at least one of the search terms
• - e.g. “dining hall” OR “cafeteria” OR “campus food service”
• NOT - excludes pages that include the second term
• - e.g. Henry VII NOT Shakespeare

Defining a Search: Quotation Marks

• Return pages with exact matches
• enter dining hall
• Get: “As I was dining , I heard a noise coming from the hall ”
• enter “dining hall”
• Get : “ Dining hall food quality is assessed in this paper.”

Search Terms: Practice

• Write a general search term on a piece of paper
• Using the same search engine for the whole activity, run searches using:
• the original term
• synonyms of the term
• Boolean operators
• Discuss with your partner how the results of each search were different

Evaluating Search Results

• Some results won’t be helpful
• wrong topic
• not enough information
• incorrect or outdated information
• shallow or untrustworthy source
• wrong tone for your project (e.g. an opinionated article when you need a basic overview)
• Have a clear idea of type of content needed
• general overview
• different viewpoints in a debate
• in-depth explorations of a topic with numbers and statistics
• No precise formula
• who is responsible
• when last updated
• whether information is corroborated in other places

Some Clues to Determine a Site’s Purpose

• Tone and language used
• Assumptions/Generalizations
• Commercial/Non-commercial
• Advocating a particular opinion
• Copyright notice
• Links/Sources cited
• Domain name extensions
• anyone can register .com, .net, .org domain names
• not a great way to tell whether a source is “credible”
• .edu and .gov can only be used by educational institutions and governmental institutions
• still not necessarily reliable

Evaluating Search Results: Visuals

• Good design NOT an indicator of reliable information
• Bad design not an indicator of unreliable information
• might be more likely to indicate an outdated website or one run by an individual

Wikipedia Articles

• Often one of the first results listed
• “Web versus Print” slides apply
• Check for instructors’ policies
• Can be useful for:
• getting an overview
• generating new ideas
• pointing to other sources

Evaluating Sources: Using Wikipedia

• Example of sources and further reading in the Wikipedia Henry VIII article:

Resources for Online Research

• OWL Resource: Searching the World Wide Web
• OWL Resource: Evaluating Sources of Information
• OWL Resource: Copyright determine which content you are allowed to use
• http://www.vts.intute.ac.uk/detective/ (The Internet Detective—site with activities for developing critical online research and evaluation skills)

For More Information

• Contact the Purdue Writing Lab:
• Drop In: Heavilon 226
• Call: 765-494-3723
• Email: [email protected]
• On the web: http :// owl.english.purdue.edu

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

Introduction

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

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

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

General Structure and Writing Style

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

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

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

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

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

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

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

Action Research Design

Definition and Purpose

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

What do these studies tell you ?

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

What these studies don't tell you ?

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

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

Case Study Design

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

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

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

Causal Design

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

Conditions necessary for determining causality:

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

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

Cohort Design

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

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

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

Cross-Sectional Design

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

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

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

Descriptive Design

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

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

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

Experimental Design

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

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

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

Exploratory Design

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

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

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

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

Field Research Design

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

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

What these studies don't tell you

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

Historical Design

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

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

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

Longitudinal Design

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

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

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

Meta-Analysis Design

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

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

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

Mixed-Method Design

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

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

Observational Design

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

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

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

Philosophical Design

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

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

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

Sequential Design

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

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

Systematic Review

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

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

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Chapter 7: Research design - qualitative methods

2014, Doing Research in the Real World, 3rd edn

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Lecture Notes on Research Methodology

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Presentation on theme: "Lecture Notes on Research Methodology"— Presentation transcript:

Introduction to Research Methodology

Sabine Mendes Lima Moura Issues in Research Methodology PUC – November 2014.

Today Concepts underlying inferential statistics

Richard M. Jacobs, OSA, Ph.D.

Research Methodology Lecture 1.

Chapter 12 Inferential Statistics Gay, Mills, and Airasian

Sample Design.

Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey All rights reserved. John W. Creswell Educational Research: Planning,

Magister of Electrical Engineering Udayana University September 2011

Chapter 1: Introduction to Statistics

RESEARCH A systematic quest for undiscovered truth A way of thinking

Research Methodology.

Educational Research: Competencies for Analysis and Application, 9 th edition. Gay, Mills, & Airasian © 2009 Pearson Education, Inc. All rights reserved.

Research Seminars in IT in Education (MIT6003) Quantitative Educational Research Design 2 Dr Jacky Pow.

PROCESSING OF DATA The collected data in research is processed and analyzed to come to some conclusions or to verify the hypothesis made. Processing of.

Academic Research Academic Research Dr Kishor Bhanushali M

Question paper 1997.

Chapter 6: Analyzing and Interpreting Quantitative Data

Module III Multivariate Analysis Techniques- Framework, Factor Analysis, Cluster Analysis and Conjoint Analysis Research Report.

Chapter 7 Measuring of data Reliability of measuring instruments The reliability* of instrument is the consistency with which it measures the target attribute.

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Critical Reading Strategies: Overview of Research Process

Jan 03, 2020

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Critical Reading Strategies: Overview of Research Process. Week 3. Critical Thinking. A rational examination of ideas, inferences, principles, conclusions Uses criteria for critiquing Clarifies Considers content appropriateness Questions own thinking Uses skepticism. Critical Reading.

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Critical Reading Strategies: Overview of Research Process Week 3

Critical Thinking • A rational examination of ideas, inferences, principles, conclusions • Uses criteria for critiquing • Clarifies • Considers content appropriateness • Questions own thinking • Uses skepticism

Critical Reading • Active, intellectual process where the reader uses interpretation and objective assessment of written material • Identify concepts • Clarify concepts • Question assumptions and rationale • Determine supporting evidence

Four Levels of Critical Reading • Preliminary understanding: skimming • Title • Abstract • Identify main theme

Critical Reading Levels (cont) • Comprehensive understanding: • Variables and concepts • Definitions • Relationships • Terminology • Main idea or theme • Restate in own words

Critical Reading Levels (cont) • Analysis understanding: • Understand parts • Begin to critique • Summarize in own words each part • Synthesis understanding: • Put together • Explain relationships • Critique

Critiquing • A process of objectively and critically evaluate content for scientific merit and application to practice, theory, and education. • Uses criteria: research process • Find strengths as well as weaknesses

Abstract • Brief (50-250 words) • Comprehensive (succinct) • Research purpose • Research questions and/or hypotheses • Methodology • Results • Implications for practice or future research

Research Problem or Purpose • Researcher presents a picture of area researched • Abstract • Introduction • Aims • Conceptual framework

Purpose • Why study was done is defined • Introduction • End of Literature Review • End of Conceptual Framework

Literature Review and Framework • Presents main concepts investigated • Review of Literature • Literature Review • Theoretical Framework • Related Literature • Background • Conceptual Framework

Hypothesis, Problem or Question • Quantitative Research • Problems, hypotheses, questions • Qualitative Research • Questions and purposes • Found: • Under separate headings • Introduction • Background • Results or Findings

Design • Blueprint of study • How purpose and problem were studied • Differs by qualitative or quantitative • Found • Procedures • Methods

Sample • Who are the subjects and how were they selected? • Type • Size • Found: • Methods • Methodology • Subjects • Sample

Instruments: Measurement tools • Validity • Measurement instrument measures what it is supposed to measure • Reliability • Measurement instrument is consistent in measures.

Procedures and Data Collection • How data or information collected • How measures or instruments used • Found • Procedures • Data collection

Data Analysis and Results • Procedures for analyzing data • Statistical tests • Descriptive/inferential tests results • Procedures to analyze themes, concepts, observational data • Found • Results • Findings • Method

Discussion of Findings • Ties parts together for a whole • New findings • Unexpected findings • Found • Discussion

Implications, Limitations, and Recommendations • Based on findings • Suggest applications for practice and education • Self critique • Found • Conclusion and recommendation • Discussion • Implications

References • Resources used in research or article • Primary • Secondary • Support for material presented

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Top 20 Research and Innovation PPT Templates To Get A Competitive Edge

What pumps big brands like Apple and Google to feature in the top-most global ranking charts? How do they generate stellar revenues even after spending millions of dollars? Without an ounce of doubt, the answer lies in “Research and Innovation”. Yes! Research and innovation acts as a fuel for brands that often makes them world-class performers . It is the methodology that leverages business productivity and helps entrepreneurs ace the market. 

Organizations face changes now and then. So it is quintessential to research the latest market trends and apply innovative thinking to execute the expected business results. Certain factors guide why research and innovation are significant to run an organization. Let’s take a look at them:

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The key takeaways mentioned above will give you groundbreaking outcomes. Whether you are running an organization for a long-time or just a newbie who has opened a start-up, the idea to adapt research and innovation will never misfire. Be the Sherlock Holmes (we won't ignore his obsession with solving cases) of your business and tackle every challenge that comes your way before it pins you down. Take your time and re-analyze why you should move forward with these research and innovation bullet-points……! These ellipses and the exclamation mark were your thought-process countdown. Now that you have processed your mindmap , it's time for you to make another decision by grabbing our top 20 research and innovation PPT templates. Designed by industry experts, these templates will help you devise your research and innovation strategies effectively. The best advantage is that they are fully-customizable, which means you can alter their features and content conveniently. Have a doubt? Just click on the template, download it, and personalize it right away! And here we go!

Top 20 Research and Innovation PPT Templates

Research on business topics and share your innovative thoughts with fellow employees conveniently by utilizing our content-ready template. The deck comprises a total of 23 slides that have enough information pertaining to your business needs. Our designers have used an amazing background in this template that will enhance your subject. 

Take advantage of this template to present the best business ideas before your audience. In this, you can showcase your out-of-the-box thoughts that help your business grow. The deck includes attractive graphics and icons that make your presentation awe-inspiring.

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Research on the new project topic and bring to the table your creative ideas by using our readymade slide. The template comes with enough room for you to add and tweak the content as per your business requirements.

Take advantage of this template and convey your views to the audience. The deck showcases the image of a bulb that depicts the meaning of generating innovative thoughts. There are various high-grade icons present in the template that enhance the look and feel of your presentation.

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Research on various business topics and implement unique ideas to meet the target. With the aid of this deck, you can mention extraordinary and innovative ideas that help your business stand out from the competition.

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Update your social media handle by suggesting innovative ideas to the directors. This deck provides you content that helps your digital media channels grow effectively. Also, you can jot down the plan that enhances the traffic of your website. You can modify the color, font, and text present in this template according to your business requirements. 

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Employ this creatively-designed template to showcase your business ideation and planning. Grab this template to communicate your fresh strategies with the stakeholders. The icons and colors used in this slide will make your presentation more professional.

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Make your research and ideas recognizable by utilizing our topic-specific template. The deck helps you present your views towards a business subject carefully. By utilizing this template, you can share your thoughts with the readers and gain acknowledgment from them. 

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Research on sales enhancement and highlight your core ideas about the same in our professionally-designed template. The deck comprises a total of 10 slides that include relevant information about the innovation framework. So download the template right away and share your creative thoughts with business associates.

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Research on marketing tactics and gather ideas that help you launch your product successfully in the market. You can state your creative thoughts in this ready-to-use template and impress your bosses. 

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Incorporate this template to figure out the thoughtful ideas of your employees. The slide has been designed professionally by our experts for your convenience. The best part about this slide is that the images used in it do not pixelate. Hence, you can download and present it in any screen setting. 

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Throw light on your special set of opinions by employing our easily accessible template. The colorful icons shown in the slide will make your ideas more clear and recognizable. Make changes in this slide and apply to your presentation.

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Present your research and innovation techniques to the investors by utilizing our readily-available template. The slide includes different sections with which you can make your subject more interesting.

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Evidence-Based Guidance on Advancing Racial Equity Through Research

Built around ten steps that occur across the research lifecycle, this Roadmap may be used as a practical discussion guide to help broaden your team’s usual approaches to research and be more intentional about how you approach research culture change.

Roadmap for Researchers: Navigating the Research Process with an Equity Lens

The Roadmap is organized into ten steps that occur across the research lifecycle, from forming your team and developing your ideas to disseminating your findings and reflecting on what you learned. Each of the ten steps includes a contextual overview of the step, questions for your team to answer together, and references and resources for further study. An extensive glossary and additional resources are also included.

The Roadmap is a response to requests from the AcademyHealth Board of Directors and members, as well as our work partners and other colleagues, to provide actionable and evidence-based guidance on advancing racial equity perspectives in health systems and policy research. It builds on recommendations from an external advisory group report on Advancing Diversity, Equity, and Inclusion in the Health Services and Policy Research Workforce , which was released in August 2021.

The Roadmap reflects a thorough review and synthesis of available evidence and discussions with key members of the health systems and policy research communities. It includes the work and opinions of people who propose new research and cognitive frameworks and new language as well as emerging views about the value of lived experience in the research enterprise.

The Roadmap is not intended to be didactic, but rather to be used as a tool for planning and accountability. The questions in each section are not exhaustive and serve as a starting point to stimulate awareness, discussion, and action.

We are grateful for support and encouragement from The Robert Wood Johnson Foundation (RWJF). The Roadmap was created through grant support along with coaching and training sessions from the RWJF Equity Learning Lab.

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Title: injecting salesperson's dialogue strategies in large language models with chain-of-thought reasoning.

Abstract: Recent research in dialogue systems and corpora has focused on two main categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD systems help users accomplish specific tasks, while open-domain systems aim to create engaging conversations. However, in real-world scenarios, user intents are often revealed during interactions. A recent study introduced SalesBot, which simulates dialogues transitioning from chit-chat to task-oriented scenarios to train sales agents. Unfortunately, the initial data lacked smooth transitions and coherent long-turn dialogues, resulting in poor naturalness in sales-customer interactions. To address these issues, this paper presents SalesBot 2.0, an improved dataset. It leverages commonsense knowledge from large language models (LLMs) through strategic prompting. Additionally, we introduce a novel model called SalesAgent, trained on salesperson's interactions, using chain-of-thought (CoT) reasoning. This model excels in transitioning topics, understanding user intents, and selecting appropriate strategies. Experiments using diverse user simulations validate the effectiveness of our method in controlling dialogue strategies in LLMs. Furthermore, SalesBot 2.0 enhances coherence and reduces aggression, facilitating better model learning for sales-customer interactions.

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Lessons from Amazon’s Early Growth Strategy

If you’re interested in strategies for scaling start-ups, this episode is for you.

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So much has been written about Amazon’s outsized growth. But Harvard Business School professor Sunil Gupta says it’s the company’s unusual approach to strategy that has captured his scholarly attention. Gupta has spent years studying Amazon’s strategy and its founder and former CEO Jeff Bezos.

In this episode, Gupta shares how Amazon upended traditional corporate strategy by diversifying into multiple products serving many end users, instead of having a narrow focus.

He argues that some of Amazon’s simplest business strategies — like their obsession with customers and insistence on long-term thinking — are approaches that companies, big and small, can emulate.

Key episode topics include: strategy, innovation, leadership, scaling, Jeff Bezos, long-term thinking, customer focus.

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HANNAH BATES: Welcome to HBR On Strategy , case studies and conversations with the world’s top business and management experts, hand-selected to help you unlock new ways of doing business.

So much has been written about Amazon’s outsized growth. But Harvard Business School professor Sunil Gupta says it’s the company’s unusual approach to strategy that has captured his scholarly attention.

Gupta has spent years studying Amazon’s strategy and its founder and former CEO, Jeff Bezos.

In this episode, Gupta shares how Amazon upended traditional corporate strategy by diversifying into multiple products serving many end users instead of focusing more narrowly.

And he argues that some of their simplest business strategies – like their obsession with the customer and insistence on long-term thinking – are approaches that companies, big and small, should emulate.

If you’re interested in innovation strategy, this episode is for you. It originally aired on HBR IdeaCast in November 2020. Here it is.

ALISON BEARD:  Welcome to the HBR IdeaCast from Harvard Business Review.  I’m Alison Beard.

If you had to name the most successful business leader alive today, who would you say?  I can’t hear you from my basement podcasting room, but I would bet that for many of you, the answer is Jeff Bezos, CEO of Amazon.  This is a man who over the past 25 years turned his online bookstore startup into a diversified company currently valued at $1.6 trillion.

Amazon is a digital retailing juggernaut, it’s also a web services provider, media producer, and manufacturer of personal technology devices like Kindle and Echo.  Oh, and Bezos also owns the Washington Post and Blue Origin, a space exploration company.  Forbes tells us he is the richest person in the world.

How did he accomplish so much?  How did he change the business landscape?  What mistakes has he made along the way?  A new collection of Bezos’s own writing, which full disclosure, my colleagues at Harvard Business Review Press have published, offer some insights.  Here’s a clip from one speech that’s included.  The book is called Invent and Wander.

And our guest today, who has spent years studying both Amazon and Bezos, is here to talk with me about some of the key themes in it, including the broad drivers of both the company and the CEO’s success.  Sunil Gupta is a professor of business administration at Harvard Business School and cochair of its executive program, and cochair of its executive program on driving digital strategy, which is also the title of his book.  Sunil, thanks so much for being on the show.

SUNIL GUPTA:  Thank you for having me, Alison.

ALISON BEARD:  So Invent and Wander.  I get that Bezos is inventive.  You know, he created a new way for us to buy things – everything.  How is he also a wonderer?

SUNIL GUPTA:  So he’s full of experiments.  His company and his whole style is known for experimentation, and he says that in so many words that if you want big winners, then you have to be willing to have many failures.  And the argument is, one big winner will take care of a thousand failed experiments.  So I think that’s the wandering part.  But also his experiments are not aimless.  There is a certain thought and process behind what experiments to do and why they will connect to the old, old picture of what Amazon is today.

ALISON BEARD:  And your expertise is in digital strategy.  How does he break the traditional rules of strategy?

SUNIL GUPTA:  So for the longest time the way, at least I was taught in my MBA program and the way we teach to our MBA students and executives, is strategy is about focus.  But if you look at Amazon, Amazon certainly doesn’t look like it’s focusing on anything, so obviously Jeff Bezos missed that class, otherwise it’s a very, very different thing.

And then you’d say, why is it that so called lack of focus strategy seems to be working for Amazon?  And I think the fundamental underlying principle that he’s guiding his whole discussion of strategy is, he’s changed the rules of strategy.  So the old rules of strategy were, the way you gained competitive advantage is by being better or cheaper.  So if I am selling you a car, my car is better of cheaper.  But the inherent assumption in that strategy statement is, I’m selling one product to one customer.  And what Amazon is basically arguing is, the digital economy is all about connection.  We have got to connect products and connect customers.  Let me explain why that is so powerful.

So connecting products, here the idea is, I can sell you, this is a classic razor and blade strategy.  I can sell you a razor cheap in order to make money on the blade.  So I can sell you Kindle cheap in order to make money on the ebooks.  Now, at some level you might say, hey, razor and blade have been around forever.  What’s so unique today?  I think unique today is razor could be in one industry and blades could be in completely different industrys.

So for example, if you look at Amazon’s portfolio of businesses, you sort of say, not only Amazon is an e-commerce player, but also is making movies and TV shows, its own studio.  Well, why does it make sense for an e-commerce player, an online retailer to compete with Hollywood.  Well, Walmart doesn’t make movies.  Macy’s doesn’t make movies?  So why does it make sense for Amazon to make movies?

And I think once you dig into it, the answer becomes clear that the purpose of the movies is to keep and gain the Prime customers. Two day free shipping is fine, but if  you ask me to pay $99 or $119 for two day free shipping, I might start doing the math in my head, and say, OK, how many packages do I expect to get next year?  And is the Prime membership worth it or not?

But once you throw in, in addition to the two-day free shipping, you throw in some TV shows and movies that are uniquely found only on Amazon, I can’t do this math.  And why is Prime customers important to Amazon?  Because Prime customers are more loyal.  They buy three or four times more than the non-Prime customers, and they’re also less price sensitive.

And in fact, Jeff Bezos has said publicly that every time we win a Golden Globe Award for one of our shows, we sell more shoes.  So this is, and he said it in your book, Invent and Wander, also, that we might be the only company in the world which has figured out how winning Golden Globe Awards can actually translate into selling more products on the online commerce.

So this is a great example of the razor being in a very different industry and blade being in another industry.  Take another example.  Amazon has a lending business where they give loans to small and medium enterprises. If Amazon decides to compete with banks tomorrow, Amazon can decide to offer loans to the small merchants at such a low price that banks would never be able to compete.  And why would Amazon be able to do that?  Because Amazon can say, hey, I’m not going to make money on loans, as much money on loans, but I’ll make more money when these businesses, small businesses grow and do more transactions on my marketplace platform.  And I get more commissions.  So again, loan can become my razor in order to help the merchants grow and make money on the transaction and the commission that I get from that.  The moment I make somebody else’s, in this case the banks, core business my razor, they will make a very hard time competing.  So I think that’s the key change, the fundamental rules of strategy and competition in that direction.

The second part of connection is connecting customers, and this is the classic network effect.  So marketplace is a great example of network effects.  The more buyers I have, the more sellers I have.  The more sellers I have, the sellers I have, the more buyers I get, because the buyers can find all the items.  And that becomes flywheel effect, and it becomes a situation where it’s very hard for a new player to complete with Amazon.

ALISON BEARD:  In this diversification that Amazon has done, how have they managed to be good at all of those things?  Because they’re not focused.  You know, they’re not concentrated on an area of specific expertise.  So how have they succeeded when other companies might have failed because they lacked that expertise, or they were spreading themselves too thin?

SUNIL GUPTA:  So I think it depends on how you define focus.  Most of us, when we define focus, we sort of define focus by traditional industry boundaries, that I’m an online retailer, therefore going into some other business is lack of focus.  The way Amazon thinks about is focus on capabilities.

So if you look at it from that point of view, I would argue that Amazon had three fundamental core capabilities.  Number one, it’s highly customer focused, not only in its culture, but also in its capability in terms of how it can actually handle data and leverage data to get customer insight.  The second core capability of Amazon is logistics.  So it’s now a world class logistics player.  It uses really frontier technology, whether it’s key word, robotics, computer vision, in its warehouse to make it much more efficient.

And the third part of Amazon’s skill or the capability is its technology.  And a good example of that is Amazon Web Services, or AWS.  And I think if you look at these three core capabilities, customer focus and the data insight that it gets from that, the logistics capability, and the technology, everything that Amazon is doing is some way or the other connected to it.  In that sense, Amazon, and there’s no lack of focus, in my judgment on Amazon.

Now, if he starts doing, starts making cream cheese tomorrow or starts making airplane engines, then I would say, yes, it’s got a lack of focus.  But one of the other things that Jeff Bezos has said again and again is this notion of work backwards and scale forward.  And what that means is, because you’re customer obsessed, you sort of find ways to satisfy customers, and if that means developing new skills that we don’t have because we are working backwards from what the customer needs are, then we’ll build those skills.

So a good example of that is, when Amazon started building Kindle, Amazon was never in the hardware business.  It didn’t know how to build hardware.  But Bezos realized that as the industry moved, people are beginning to read more and more online, rather, or at least on their devices, rather than the physical paper copy of a book.  So as a result, he says, how do we make it easier for consumers to read it on an electronic version?  And they’re spending three years learning about this capability of hardware manufacturing.  And by the way, Kindle came out long before iPad came out.  And of course, that capability now has helped them launch Echo and many other devices.

ALISON BEARD:  Right.  So it’s the focus on the customer, plus a willingness to go outside your comfort zone, the wander part.

SUNIL GUPTA:  Exactly.

ALISON BEARD:  Yeah.  How would you describe Bezos’s leadership style?

SUNIL GUPTA:  So I think there are at least three parts to it.  One is, he said right from day one that he wants to be a long-term focus.  The second thing is being customer obsessed.  And many times he has said that he can imagine, in the meetings he wants people to imagine an empty chair.  That is basically for the customer. And he says, we are not competitor focused.  We are not product focused.  We are not technology focused.  We are customer focused.  And the third is, willingness to experiment.  And fail, and build that culture in the company that it’s OK to fail.

ALISON BEARD:  What about personally, though?  Is he a hard charger?  Is he an active listener?  What’s it like to be in a room with him?

SUNIL GUPTA:  Oh, he’s certainly a hard charger.  I mean, he’s also the kind of guy, when he hires people, he says, you can work long, hard, or smart.  But at Amazon, you can choose two out of three.  And I think this is similar to many other leaders.  If you look at Steve Jobs, he was also a very hard charging guy.  And I think some people find it exhilarating to work with these kind of leaders.  Some find it very tough.

ALISON BEARD:  Do you think that he communicates differently from other successful CEOs?

SUNIL GUPTA:  So the communication style that he has built in the company is the very famous now, there’s no PowerPoints.  So it’s a very thoughtful discussion.  You write six-page memos, which everybody, when their meeting starts, everybody sits down and actually reads the memo.

In fact, this was a very interesting experience that I had.  One of my students, who was in the executive program, works at Amazon in Germany.  And he is, he was at that point in time thinking of moving to another company and becoming a CEO of that company.  So he said, can I talk to you about this change of career path that I’m thinking about?  I said, sure.  So we set up a time, and five minutes before our call, he sends me an email with a six-page memo.  And I said, well, shouldn’t he have sent this to me before, so I could at least look at it?  He says, no, that’s the Amazon style.  We’ll sit in silence and read it together.  And so I read it together, because then you’re completely focused on it.  And then we can have a conversation.  But this discipline of writing a six-page memo, it’s a very, very unique experience, because you actually have to think through all your arguments.

ALISON BEARD:  You also mentioned the long term focus, and that really stood out for me, too, this idea that he is not at all thinking of next year.  He’s thinking five years out, and sometimes even further.  But as a public company, how has Amazon been able to stick to that?  And is it replicable at other companies?

SUNIL GUPTA:  I think it is replicable.  It requires conviction, and it requires a way to articulate the vision to Wall Street that they can rally behind.  And it’s completely replicable.  There are other examples of companies who have followed a similar strategy.  I mean, Netflix is a good example.  Netflix hadn’t made money for a long period of time.  But they sold the vision of what the future will look like, and Wall Street bought that vision.

Mastercard is exactly the same thing.  Ajay Banga is giving three year guidance to Wall Street saying, this is my three-year plan, because things can change quarter to quarter.  I’m still responsible to tell you what we are doing this quarter, but my strategy will not be guided by what happens today.  It will be guided by the three-year plan that we have.

ALISON BEARD:  There are so many companies now that go public without turning any profit, whereas Amazon now is printing money, and thus able to reinvest and have this grand vision.  So at what point was Bezos able to say, right, we’re going to do it my way?

SUNIL GUPTA:  I think he said it right from day one, except that people probably didn’t believe it.  And in fact, one of the great examples of that was, when he was convinced about AWS, the Amazon Web Services, that was back in the early 2000s, when a majority of the Wall Street was not sure what Jeff Bezos was trying to do, because they say, hey, you are an online retailer.  You have no business being in web services.  That’s the business of IBM.  And that’s a B2B business.  You’re in a B2C business.  Why are you going in there?

And Bezos said, well, we have plenty of practice of being misunderstood.  And we will continue with our passion and vision, because we see the path.  And now he’s proven it again and again why his vision is correct, and I think that could give us more faith and conviction to the Wall Street investors.

SUNIL GUPTA:  Oh, absolutely.  And he’s one of the persons who has his opinion, and you always surround yourself with people better than you.

ALISON BEARD:  How has he managed to attract that talent when it is so fiercely competitive between Google, Facebook, all of these U.S. technology leaders?

SUNIL GUPTA:  So a couple of things I would say.  First of all, it’s always good fun to join a winning team.  And all of us want to join a winning team, so this certainly is on a trajectory which is phenomenal.  It’s like a rocket ship that is taking off and has been taking off for the last 25 years.  So I think that’s certainly attractive to many people, and certainly many hard charging people who want to be on a winning team.

And a second thing is, Amazon’s culture of experimentation and innovation.  That is energizing to a lot of people.  It’s not a bureaucracy where you get bogged down by the processes.  So the two type of decisions that we talked about, he gives you enough leeway to try different things, and is willing to invest hundreds of millions of dollars into things that may or may not succeed in the future.  And I think that’s very liberating to people who are willing to take on the ownership and build something.

ALISON BEARD:  But don’t all of the tech companies offer that?

SUNIL GUPTA:  They do, but if you think about many other tech companies, they’re much more narrow in focus.  So Facebook is primarily in social media.  Google is primarily in search advertising.  Yes, you have GoogleX, but that’s still a small part of what Google does.  Whereas if you ask yourself what business is Amazon in, there are much broader expansive areas that Amazon has gone into.  So I think the limits, I mean, Amazon does not have that many limits or boundaries as compared to many other businesses in Silicon Valley.

ALISON BEARD:  So let’s talk a little bit about Bezos’s acquisition strategy.  I think the most prominent is probably Whole Foods, but there are many others.  How does he think about the companies that he wants to bring in as opposed to grow organically?

SUNIL GUPTA:  So some acquisitions are areas where he thinks that he can actually benefit and accelerate the vision that he already has.  So for example, the acquisition of Kiva was to improve the efficiency and effectiveness of the systems that he already put in place in his warehouse.  And logistics and warehouse is a key component or key part of Amazon’s business, and he saw that Kiva already was ahead of the curve in technology that he probably wanted to have that in his own company.  So that was obvious acquisition, because that fits in the existing business.

Whole Foods is kind of a slightly different story, in my judgment, because I some ways, you can argue, why is Amazon, an online player, buying an offline retail store, Whole Foods?  And in fact, they bought it at 27% premium.  So that doesn’t make sense for an online retailer commerce to go to offline channels.  And I think, in fact, part of the reason in my judgment is, it’s not just Whole Foods, but it’s about the food business, per se.  And why is Amazon so interested in food?  In fact, Amazon has been trying this food business, online food delivery for a long period of time without much success.  And Whole Foods was one, another way to try and get access to that particular business.  And why is that so important to Amazon, even though you could argue, food is a low margin business?

And I would say, part of the reason is, food is something, grocery is something that you buy every week, perhaps twice a week.  And if I, as Amazon, can convince you to buy grocery online from Amazon, then I’m creating a habit for you to come onto Amazon every week, perhaps twice a week.  And once you are on Amazon, you will end up buying other products on Amazon.  Whereas if you are buying electronics, you may not come to Amazon every day.

So this is a habit creation activity, and again, it may not be a very high margin activity to sell you food.  But I’ve created a habit, just like Prime.  I’ve created a loyal customer where you think of nothing else but Amazon for your daily needs, and therefore you end up buying other things.

ALISON BEARD:  And Amazon isn’t without controversy.  You know, and we should talk about that, too.  First, there are questions about its treatment of warehouse employees, particularly during COVID.  And Bezos, as you said, has always been relentlessly focused on the customer.  But is Amazon employee centric, too?

SUNIL GUPTA:  So I think there is definitely some areas of concern, and you rightly said there is a significant concern about the, during the COVID, workers were complaining about safety, the right kind of equipment.  But even before COVID, there were a lot of concerns about whether the workers are being pushed too hard.  They barely have any breaks.  And they’re constantly on the go, because speed and efficiency become that much more important to make sure customers always get what they are promised.  And in fact, more than promised.

Clearly Amazon either hasn’t done a good job, or hasn’t at least done the public relations part of it that they have done a good job.  Now, if you ask Jeff Bezos, he will claim that, no, actually, they have done things.  For example, they offer something called carrier choice, where they give 95% tuition to the employees to learn new skills, whether they’re relevant to Amazon or not.  Pretty much like what Starbucks does for its baristas, for college education and other things.  But I think more than just giving money or tuition, it requires a bit of empathy and sense that you care for your employees, and perhaps that needs, that’s something that Amazon needs to work on.

ALISON BEARD:  And another challenge is the criticism that it has decimated mom and pop shops.  Even when someone sells through Amazon, the company will then see that it’s a popular category and create it itself and start selling it itself.  There’s environmental concerns about the fact that packages are being driven from warehouses to front doors all over America.  And boxes and packaging.  So how has Bezos, how has the company dealt with all of that criticism?

SUNIL GUPTA:  They haven’t.  And I think those are absolutely valid concerns on both counts, that the small sellers who grow to become reasonably big are always under the radar, and there are certainly anecdotal evidence there, small sellers have complained that Amazon had decided to sell exactly the same item that they were so successful in selling, and becoming too big is actually not good on Amazon, because Amazon can get into your business and wipe you away.  So that’s certainly a big concern, and I think that’s something that needs to be sorted out, and Amazon needs to clarify what its position on that area is, because it benefits from these small sellers on his platform.

And your second question about environmental issues is also absolutely on the money, because not only emission issues, but there’s so many boxes that pile in, certainly in my basement, from Amazon.  You sort of say, and it’s actually ironical that Millennials who are in love with Amazon are extremely environmentally friendly.  But at the same time, they would not hesitate to order something from Amazon and pile up all these boxes.  So I think Amazon needs to figure out a way to think about both those issues.

ALISON BEARD:  And at what point will it have to?  I mean, it seems to be rolling happily along.

SUNIL GUPTA:  Well, I think those issues are becoming bigger and bigger, and it’s certainly in the eye of the regulators, also, for some of these practices.  And not only because it’s too big, and there might be monopoly concerns, but these issues will become larger, and any time you become a large company, you become the center of attraction for broader issues than just providing shareholder value.

ALISON BEARD:  Yeah.  So those are weaknesses possibly for the company.  What are some of Bezos’s personal weaknesses that you’ve seen in studying him and the company?

SUNIL GUPTA:  So I think one thing that stands out to me, and at least in the public forums, I have not seen any empathy.  And it’s, I mean, we talk about that the leaders have, should have three qualities.  They should be competent.  They should have a good character.  And they should have compassion.  So he’s certainly very competent.  I mean, he’s brilliant in many aspects, right, from the computer vision and AI and machine learning, to the nuances of data analytics, to the Hollywood production, etc.  He also seems to have good character, at least I have not heard any personal scandals, apart from his other issues in his personal life, perhaps.

Those characteristics of competence and character make people respect you.  What makes people love you is when you show compassion, and at least I haven’t seen compassion or empathy that comes out of him.  I mean, he certainly comes across as a very hard charging, driven person, which probably is good for business.  But the question of empathy is perhaps something lacking right now.

ALISON BEARD:  Yeah.  The other issue is his just enormous wealth.  He did invent this colossally valuable company, but should anyone really be that rich?

SUNIL GUPTA:  Well, I guess that’s, you can say that’s the good or the bad thing about capitalism.  But I think, and again, my personal view is there’s nothing wrong in becoming rich, if you have been successful and done it with hard work and ingenuity.  But how you use your wealth is something that perhaps will define Jeff Bezos going forward.  I think Bill Gates is a great example how he actually has used his wealth and his influence and his expertise and his brilliance into some certain thing that actually is great for humanity.

Now, whether Jeff Bezos does that down the road, I don’t know, whether his space exploration provides that sort of outlet which is both his passion as well as good for humanity, I don’t know.  But at some point in time, I think it’s the responsibility of these leaders to sort of say, my goal is not simply to make money and make my shareholders rich, but also help humanity and help society.

ALISON BEARD:  If you’re talking to someone who’s running a startup, or even a manager of a team at a traditional company, what is the key lesson that you would say, this is what you can learn from Jeff Bezos?  This is what you can put to work in your own profession?

SUNIL GUPTA:  So I would say two things that at least I would take away if I were doing a startup.  One is customer obsession.  Now, every company says that, but honestly, not every company does it, because if you go to the management meetings, if you go to the quarterly meetings, you suddenly go focus on financials and competition and product.  But there’s rarely any conversation on customers.  And I think, as I mentioned earlier, that Jeff Bezos always tells his employee to think of the imaginary chair in which a customer is sitting, because that’s the person that we need to focus on.  Howard Shultz does the same thing at Starbucks, and that’s why Starbucks is so customer focused.

So I think that’s the first part.  And the argument that Bezos gives is, customers are never satisfied.  And that pushes us to innovate and move forward, so we need to innovate even before the rest of the world even sees that, because customers are the first ones to see what is missing in the offering that you have.

And the second I would say that I would take away from Jeff Bezos is the conviction and passion with what you do.  And many times that goes against the conventional wisdom.  And the Amazon Web Services is a great example of that.  The whole world, including the Wall Street Journal and the Wall Street analysts were saying, this is none of Amazon’s business to do web services.  But he was convinced that this is the right thing to do, and he went and did that.

And part of that conviction may come from experiments.  Part of that conviction comes from connecting the dots that he could see that many other people didn’t see.  I mean, that’s why he went, left his job, and went to Seattle to do the online bookstore, because he could see the macro trends as to what the Internet is likely to do.  So, I think that’s the vision that he had.  And once you have the conviction, then you follow your passion.

ALISON BEARD: Sunil, thanks so much for coming on the show.

SUNIL GUPTA:  Thank you for having me. Alison.

HANNAH BATES: That was Harvard Business School professor Sunil Gupta, in conversation with Alison Beard on the HBR IdeaCast .

We’ll be back next Wednesday with another hand-picked conversation about business strategy from Harvard Business Review. If you found this episode helpful, share it with your friends and colleagues, and follow our show on Apple Podcasts, Spotify, or wherever you get your podcasts. While you’re there, be sure to leave us a review.

And when you’re ready for more podcasts, articles, case studies, books, and videos with the world’s top business and management experts, find it all at HBR.org.

This episode was produced by Mary Dooe, Anne Saini, and me, Hannah Bates. Ian Fox is our editor. And special thanks to Maureen Hoch, Nicole Smith, Erica Truxler, Ramsey Khabbaz, Anne Bartholomew, and you – our listener. See you next week.

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2024 National Strategy for Suicide Prevention

Suicide is an urgent and growing public health crisis. More than 49,000 people in the United States died by suicide in 2022. That’s one death every 11 minutes.

National Strategy for Suicide Prevention

The 2024 National Strategy for Suicide Prevention is a bold new 10-year, comprehensive, whole-of-society approach to suicide prevention that provides concrete recommendations for addressing gaps in the suicide prevention field. This coordinated and comprehensive approach to suicide prevention at the national, state, tribal, local, and territorial levels relies upon critical partnerships across the public and private sectors. People with lived experience are critical to the success of this work. 

 The National Strategy seeks to prevent suicide risk in the first place; identify and support people with increased risk through treatment and crisis intervention; prevent reattempts; promote long-term recovery; and support survivors of suicide loss. 

Four strategic directions guide the National Strategy:

Strategic Direction 1: Community-Based Suicide Prevention

Goal 1: Establish effective, broad-based, collaborative, and sustainable suicide prevention partnerships.

Goal 2: Support upstream comprehensive community-based suicide prevention.

Goal 3: Reduce access to lethal means among people at risk of suicide.

Goal 4: Conduct postvention and support people with suicide-centered lived experience.

Goal 5: Integrate suicide prevention into the culture of the workplace and into other community settings.

Goal 6: Build and sustain suicide prevention infrastructure at the state, tribal, local, and territorial levels.

Goal 7: Implement research-informed suicide prevention communication activities in diverse populations using best practices from communication science.

Strategic Direction 2: Treatment and Crisis Services

Goal 8: Implement effective suicide prevention services as a core component of health care.

Goal 9: Improve the quality and accessibility of crisis care services across all communities.

Strategic Direction 3: Surveillance, Quality Improvement, and Research

Goal 10: Improve the quality, timeliness, scope, usefulness, and accessibility of data needed for suicide-related surveillance, research, evaluation, and quality improvement.

Goal 11: Promote and support research on suicide prevention.

Strategic Direction 4: Health Equity in Suicide Prevention

Goal 12: Embed health equity into all comprehensive suicide prevention activities.

Goal 13: Implement comprehensive suicide prevention strategies for populations disproportionately affected by suicide, with a focus on historically marginalized communities, persons with suicide-centered lived experience, and youth.

Goal 14: Create an equitable and diverse suicide prevention workforce that is equipped and supported to address the needs of the communities they serve.

Goal 15: Improve and expand effective suicide prevention programs for populations disproportionately impacted by suicide across the life span through improved data, research, and evaluation.

Federal Action Plan

The Federal Action Plan identifies more than 200 actions across the federal government to be taken over the next three years in support of those goals. These actions include:

• Evaluating promising community-based suicide prevention strategies
• Identifying ways to address substance use/overdose and suicide risk together in the clinical setting
• Funding a mobile crisis locator for use by 988 crisis centers
• Increasing support for survivors of suicide loss and others whose lives have been impacted by suicide

These actions will be monitored and evaluated regularly to determine progress and success, and to further identify barriers to suicide prevention.

Get Involved

Join the conversation. Everyone has a role to play in preventing the tragedy of suicide. Find social media material, templates, and other resources to support and participate in the shared effort.

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Breakthrough in brown fat research: Researchers have found brown fat's 'off-switch'

Brown fat, also known as brown adipose tissue (BAT), is a type of fat in our bodies that's different from the white fat around our belly and thighs that we are more familiar with. Brown fat has a special job -- it helps to burn calories from the foods that we eat into heat, which can be helpful, especially when we're exposed to cold temperatures like during winter swimming or cryotherapy. For a long time, scientists thought that only small animals like mice and newborns had brown fat. But new research shows that a certain number of adults maintain their brown fat throughout life. Because brown fat is so good at burning calories, scientists are trying to find ways to activate it safely using drugs that boost its heat-producing abilities.

A new study from the research groups of Prof. Jan-Wilhelm Kornfeld from the University of Southern Denmark/the Novo Nordisk Center for Adipocyte Signaling (Adiposign) and Dagmar Wachten from the University Hospital Bonn and the University of Bonn (Germany) has found that brown fat has a previously unknown built-in mechanism that switches it off shortly after being activated. This limits its effectiveness as treatment against obesity. According to first author of the study, Hande Topel, who is a Senior Postdoc at the University of Southern Denmark and the Novo Nordisk Center for Adipocyte Signaling (Adiposign), the team has now discovered a protein responsible for this switching-off process. It is called 'AC3-AT'.

Blocking the "off switch" opens up a new strategy

"Looking ahead, we think that finding ways to block AC3-AT could be a promising strategy for safely activating brown fat and tackling obesity and related health problems," Hande Topel says. The research team found the switch-off protein using advanced technology predicting unknown proteins. Hande Topel explains: "When we investigated mice that genetically didn't have AC3-AT, we found that they were protected from becoming obese, partly because their bodies were simply better at burning off calories and were able to increase their metabolic rates through activating brown fat."

Two groups of mice were fed a high-fat diet for 15 weeks, which rendered them obese. The group that had their AC3-AT protein removed, gained less weight than the control group and were metabolically healthier. "The mice that have no AC3-AT protein, also accumulated less fat in their body and increased their lean mass when compared to the control mice," says co-author, Ronja Kardinal, who is a PhD student at the University of Bonn in the lab of Dagmar Wachten at UKB, continuing: "As AC3-AT is found not only in mice but also in humans and other species, there are direct therapeutic implications for humans."

Hope for strategies that support weight loss

Although the prevalence of brown fat decreases as humans age, and despite grown-ups not having as much brown fat as newborns, it can still be activated, for instance by cold exposure. When it gets activated, it enhances the rate of metabolism of these individuals, which again may help to stabilize weight loss in conditions where calorie intake is (too) high.

Intriguingly, this study not only identified AC3-AT, which is a shorter, previously unknown form of the AC3protein. The researchers also identified other unknown protein/gene versions, that respond to cold exposure, similar to AC3-AT.

"However, further research is needed to elucidate the therapeutic impact of these alternative gene products and their regulatory mechanisms during BAT activation," says co-corresponding author Prof. Dagmar Wachten, Co-Director of the Institute of Innate Immunity at the UKB and member of the Cluster of Excellence ImmunoSensation2 and the Transdisciplinary Research Areas (TRA) "Modelling" and "Life & Health" at the University of Bonn.

"Understanding these kinds of molecular mechanisms not only sheds light on the regulation of brown fat but also holds promise for unraveling similar mechanisms in other cellular pathways. This knowledge can be instrumental in advancing our understanding of various diseases and in the development of novel treatments," says co-corresponding author Prof. Jan-Wilhelm Kornfeld, University of Southern Denmark.

This study was conducted in the context of the DFG Collaborative Research Center Transregio-SFB 333 "Brown and Beige Fat -- Organ Interactions, Signaling Pathways and Energy Balance (BATenergy)," which is pursuing a better understanding of the different types of adipose tissue and their role in metabolic diseases and the Novo Nordisk Foundation Center for Adipocyte Signaling (Adiposign) at University of Southern Denmark that aims to understand fat cell dysfunction in model organisms and obese patients.

• Diet and Weight Loss
• Triglycerides
• Dieting and Weight Control
• Nutrition Research
• Huntington's Disease
• Multiple Sclerosis
• Adipose tissue
• Anti-obesity drug
• Atkins Diet
• Saturated fat

Story Source:

Materials provided by University of Southern Denmark . Original written by Birgitte Svennevig. Note: Content may be edited for style and length.

Journal Reference :

• Sajjad Khani, Hande Topel, Ronja Kardinal, Ana Rita Tavanez, Ajeetha Josephrajan, Bjørk Ditlev Marcher Larsen, Michael James Gaudry, Philipp Leyendecker, Nadia Meincke Egedal, Aylin Seren Güller, Natasa Stanic, Phillip M. M. Ruppert, Isabella Gaziano, Nils Rouven Hansmeier, Elena Schmidt, Paul Klemm, Lara-Marie Vagliano, Rainer Stahl, Fraser Duthie, Jens-Henning Krause, Ana Bici, Christoph Andreas Engelhard, Sabrina Gohlke, Peter Frommolt, Thorsten Gnad, Alvaro Rada-Iglesias, Marta Pradas-Juni, Tim Julius Schulz, Frank Thomas Wunderlich, Alexander Pfeifer, Alexander Bartelt, Martin Jastroch, Dagmar Wachten, Jan-Wilhelm Kornfeld. Cold-induced expression of a truncated adenylyl cyclase 3 acts as rheostat to brown fat function . Nature Metabolism , 2024; DOI: 10.1038/s42255-024-01033-8

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Tesla’s Profit Fell 55%, Adding to Concerns About Its Strategy

The first-quarter results are likely to fuel worries that competitors will continue grabbing a bigger slice of a market dealing with slowing electric car sales.

By Jack Ewing

Tesla reported on Tuesday that it made significantly less money in the first three months of the year because of its tepid car sales, reinforcing concern among investors that the company led by Elon Musk is losing ground in the market for electric vehicles.

Profit fell 55 percent, to $1.1 billion, from the first quarter of 2023, the company said. And revenue fell 9 percent, to $21.3 billion.

A slump in earnings was seen as inevitable after Tesla said this month that sales in the first quarter fell 8.5 percent from a year earlier, and after the company announced plans to lay off more than 10 percent of its employees worldwide, or about 14,000 people.

The job cuts, including more than 2,000 workers at the company’s factory in Fremont, Calif., and nearly 2,700 at a factory in Austin, Texas, were interpreted as a sign that Tesla was struggling to bring costs in line with sinking revenue.

In the first quarter of 2023, Tesla made $2.5 billion and had one of the best profit margins in the industry, the company said a year ago. But it has been forced to cut prices, including in a new round last week, lowering the amount it makes on each car it sells. For a while, that strategy seemed to help bolster the company’s sales, but Tesla now appears to be struggling to attract buyers even with lower prices.

Tesla’s operating profit margin last quarter was 5.5 percent, half as much as a year earlier and in line with how much other automakers tended to earn.

Tesla investors are increasingly worried that its falling sales and profit are a symptom of larger problems , possibly pointing to the company’s inability to effectively respond to increased competition from established automakers and new carmakers from China .

Mr. Musk signaled recently that Tesla would focus on autonomous driving technology and a vehicle he called the Robotaxi, sowing doubt about the company’s plans to develop a new, lower-priced model that could make electric cars affordable to a broader range of customers and people in more countries.

Self-driving cars have long been an obsession for Mr. Musk. In 2019, he said Tesla would have one million autonomous taxis on the road the next year; the company still has no such cabs.

“Tesla lived on the coolness of its car, the idea that the company was about to launch autonomous vehicles and investor confidence in Mr. Musk’s ability to do nearly impossible things,” Erik Gordon, an assistant professor at the University of Michigan’s Ross School of Business, said in an email. “Now its cars are old, the fleet of Robotaxis promised five years ago hasn’t arrived, and confidence in Mr. Musk is battered by disappointments and behavior that mystifies investors.”

Tesla said on Tuesday that it remained on track to start producing a lower-priced vehicle next year. But in a change designed to reduce upfront investment, the car will use some new components and some borrowed from existing vehicles. That strategy will allow Tesla to make new models without building new factories, the company said.

“This update may result in achieving less cost reduction than previously expected,” the company said in a presentation to investors.

Tesla’s share price, which had fallen by about 40 percent this year, surged in extended trading Tuesday after its first-quarter report. Investors appeared to be pleased that the company was still planning a more affordable model.

Mr. Musk has defended Tesla’s price cuts, saying all carmakers adjust prices, but usually through dealer incentives and other measures that are not quite as visible to buyers. Tesla sells cars directly to customers online rather than through franchised dealers.

“Tesla prices must change frequently in order to match production with demand,” he said.

Tesla attributed the sales decline to conflict in the Red Sea, which has disrupted global supply chains; a fire that halted production at the company’s factory near Berlin; and the ramp-up of an upgraded version of the Model 3 sedan in Fremont. Tesla also blamed a decision by other carmakers to sell more hybrid vehicles, which include a gasoline engine and batteries and electric motors, for putting pressure on sales of fully electric vehicles.

The second quarter “will be a lot better,” Mr. Musk said on a conference call to discuss the company’s results.

He postponed a planned trip on Monday to India, where he was expected to meet Prime Minister Narendra Modi and announce plans for a factory, citing “very heavy Tesla obligations.”

While the postponement may disappoint investors who had hoped India could be a new source of growth, it could also provide reassurance that Mr. Musk was addressing Tesla’s problems more urgently. The company’s models are unlikely to sell in large numbers in India, where most car buyers prefer smaller and more affordable vehicles.

Tesla’s newest vehicle is the Cybertruck, a pickup that the company began producing last year. But the company has sold only around 4,000, according to information that emerged in a recall last week, suggesting it will not be a significant source of growth.

The self-driving taxi is seen as a long shot, partly because even the most advanced autonomous systems available today sometimes make glaring mistakes. In addition, federal and state regulators will have to sign off before Tesla can put such taxis on the road. Tesla does not yet have a license to test driverless vehicles in California, where it would be expected to develop Robotaxi software.

“Elon Musk has promised Robotaxis since 2016,” said Jan Becker, chief executive of Apex.AI, a company that provides software used by autonomous driving systems. “I don’t see enough evidence of Tesla releasing a Robotaxi, at least in the short term.”

Mr. Musk said Tuesday that the technology was improving rapidly because of advances in artificial intelligence. Answering questions from analysts, he expressed impatience with anybody who viewed Tesla as primarily a car company.

“We should have been thought of as an A.I. and robotics company,” he said. Anyone who doesn’t have faith in Tesla’s ability to perfect autonomous driving, he added, “should not be an investor in the company.”

Until recently, Tesla was among very few carmakers making money on electric cars, but established carmakers are catching up. General Motors, which also reported earnings on Tuesday, has ironed out production difficulties in battery-pack manufacturing and is ramping up output, Paul Jacobson, the company’s chief financial officer, said in a conference call with reporters.

G.M. remains dependent on its gasoline-vehicle business, which was primarily responsible for a 24 percent jump in profits for the first three months of the year, to $3 billion. But the company expects to be selling electric vehicles profitably later this year, Mr. Jacobson said.

Focus on Tesla’s earnings report Tuesday was unusually intense after a series of recent events that raised questions about the company’s direction and Mr. Musk’s leadership.

Last week, Tesla’s board of directors disappointed investors who had hoped it would do more to get Mr. Musk to focus on the car business and spend less time on X, where his polarizing comments and affinity for right-wing conspiracy theories have alienated many potential customers.

The board took steps to reinstate a $47 billion pay package for Mr. Musk that a Delaware court had voided. The board also said it would ask shareholders to approve moving Tesla’s corporate domicile to Texas, a change Mr. Musk called for on the day the Delaware court struck down his pay package in January on the grounds that it was excessive and that shareholders were not properly informed when they approved it in 2018.

Neal E. Boudette contributed reporting.

Jack Ewing writes about the auto industry with an emphasis on electric vehicles. More about Jack Ewing

The World of Elon Musk

The billionaire’s portfolio includes the world’s most valuable automaker, an innovative rocket company and plenty of drama..

X: An Australian court extended an injunction ordering the social media platform X to remove videos depicting the recent stabbing of a bishop , setting the country’s judicial system up for a clash with Elon Musk, who has denounced the court’s order as censorship.

A $47 Billion Pay Deal: Despite   facing criticism that Tesla is overly beholden to Elon Musk , its board of directors said that the company would essentially give him everything he wanted, including the biggest pay package in corporate history.

Tesla: Tesla reported that it made significantly less money  in the first three months of the year because of its tepid car sales, reinforcing concern among investors that the company led by Elon Musk is losing ground  in the market for electric vehicles.

SpaceX: President Biden wants companies that use American airspace for rocket launches to start paying taxes into a federal fund  that finances the work of air traffic controllers.

Business With China : Tesla and China built a symbiotic relationship that made Elon Musk ultrarich. Now, his reliance on the country may give Beijing leverage .  

The Musk Foundation: After making billions in tax-deductible donations to his charity, Musk has failed recently to donate the minimum required to justify a tax break  — and what he did give often supported his interests.