a research characteristic

Research: Definition, Characteristics, Goals, Approaches

Research is an original and systematic investigation undertaken to increase existing knowledge and understanding of the unknown to establish facts and principles.

Let’s understand research:

What is Research?

Research is a voyage of discovery of new knowledge. It comprises creating ideas and generating new knowledge that leads to new and improved insights and the development of new materials, devices, products, and processes.

It should have the potential to produce sufficiently relevant results to increase and synthesize existing knowledge or correct and integrate previous knowledge.

Good reflective research produces theories and hypotheses and benefits any intellectual attempt to analyze facts and phenomena.

Where did the word Research Come from?

The word ‘research’ perhaps originates from the old French word “recerchier” which meant to ‘ search again.’ It implicitly assumes that the earlier search was not exhaustive and complete; hence, a repeated search is called for.

In practice, ‘research’ refers to a scientific process of generating an unexplored horizon of knowledge, aiming at discovering or establishing facts, solving a problem, and reaching a decision. Keeping the above points in view, we arrive at the following definition of research:

Research Definition

Research is a scientific approach to answering a research question, solving a research problem, or generating new knowledge through a systematic and orderly collection, organization, and analysis of data to make research findings useful in decision-making.

When do we call research scientific? Any research endeavor is said to be scientific if

• It is based on empirical and measurable evidence subject to specific principles of reasoning;
• It consists of systematic observations, measurement, and experimentation;
• It relies on the application of scientific methods and harnessing of curiosity;
• It provides scientific information and theories for the explanation of nature;
• It makes practical applications possible, and
• It ensures adequate analysis of data employing rigorous statistical techniques.

The chief characteristic that distinguishes the scientific method from other methods of acquiring knowledge is that scientists seek to let reality speak for itself, supporting a theory when a theory’s predictions are confirmed and challenging a theory when its predictions prove false.

Scientific research has multidimensional functions, characteristics, and objectives.

Keeping these issues in view, we assert that research in any field or discipline:

• Attempts to solve a research problem;
• Involves gathering new data from primary or first-hand sources or using existing data for a new purpose;
• is based upon observable experiences or empirical evidence;
• Demands accurate observation and description;
• Employs carefully designed procedures and rigorous analysis;
• attempts to find an objective, unbiased solution to the problem and takes great pains to validate the methods employed;
• is a deliberate and unhurried activity that is directional but often refines the problem or questions as the research progresses.

Characteristics of Research

Keeping this in mind that research in any field of inquiry is undertaken to provide information to support decision-making in its respective area, we summarize some desirable characteristics of research:

• The research should focus on priority problems.
• The research should be systematic. It emphasizes that a researcher should employ a structured procedure.
• The research should be logical. Without manipulating ideas logically, the scientific researcher cannot make much progress in any investigation.
• The research should be reductive. This means that one researcher’s findings should be made available to other researchers to prevent them from repeating the same research.
• The research should be replicable. This asserts that there should be scope to confirm previous research findings in a new environment and different settings with a new group of subjects or at a different point in time.
• The research should be generative. This is one of the valuable characteristics of research because answering one question leads to generating many other new questions.
• The research should be action-oriented. In other words, it should be aimed at solving to implement its findings.
• The research should follow an integrated multidisciplinary approach, i.e., research approaches from more than one discipline are needed.
• The research should be participatory, involving all parties concerned (from policymakers down to community members) at all stages of the study.
• The research must be relatively simple, timely, and time-bound, employing a comparatively simple design.
• The research must be as much cost-effective as possible.
• The research results should be presented in formats most useful for administrators, decision-makers, business managers, or community members.

3 Basic Operations of Research

Scientific research in any field of inquiry involves three basic operations:

• Data collection;
• Data analysis;
• Report writing .

• Data collection refers to observing, measuring, and recording data or information.
• Data analysis, on the other hand, refers to arranging and organizing the collected data so that we may be able to find out what their significance is and generalize about them.
• Report writing is the ultimate step of the study . Its purpose is to convey the information contained in it to the readers or audience.

If you note down, for example, the reading habit of newspapers of a group of residents in a community, that would be your data collection.

If you then divide these residents into three categories, ‘regular,’ ‘occasional,’ and ‘never,’ you have performed a simple data analysis. Your findings may now be presented in a report form.

A reader of your report knows what percentage of the community people never read any newspaper and so on.

Here are some examples that demonstrate what research is:

• A farmer is planting two varieties of jute side by side to compare yields;
• A sociologist examines the causes and consequences of divorce;
• An economist is looking at the interdependence of inflation and foreign direct investment;
• A physician is experimenting with the effects of multiple uses of disposable insulin syringes in a hospital;
• A business enterprise is examining the effects of advertisement of their products on the volume of sales;
• An economist is doing a cost-benefit analysis of reducing the sales tax on essential commodities;
• The Bangladesh Bank is closely observing and monitoring the performance of nationalized and private banks;
• Based on some prior information, Bank Management plans to open new counters for female customers.
• Supermarket Management is assessing the satisfaction level of the customers with their products.

The above examples are all researching whether the instrument is an electronic microscope, hospital records, a microcomputer, a questionnaire, or a checklist.

Research Motivation – What makes one motivated to do research?

A person may be motivated to undertake research activities because

• He might have genuine interest and curiosity in the existing body of knowledge and understanding of the problem;
• He is looking for answers to questions that have remained unanswered so far and trying to unfold the truth;
• The existing tools and techniques are accessible to him, and others may need modification and change to suit the current needs.

One might research ensuring.

• Better livelihood;
• Better career development;
• Higher position, prestige, and dignity in society;
• Academic achievement leading to higher degrees;
• Self-gratification.

At the individual level, the results of the research are used by many:

• A villager is drinking water from an arsenic-free tube well;
• A rural woman is giving more green vegetables to her child than before;
• A cigarette smoker is actively considering quitting smoking;
• An old man is jogging for cardiovascular fitness;
• A sociologist is using newly suggested tools and techniques in poverty measurement.

The above activities are all outcomes of the research.

All involved in the above processes will benefit from the research results. There is hardly any action in everyday life that does not depend upon previous research.

Research in any field of inquiry provides us with the knowledge and skills to solve problems and meet the challenges of a fast-paced decision-making environment.

9 Qualities of Research

Good research generates dependable data. It is conducted by professionals and can be used reliably for decision-making. It is thus of crucial importance that research should be made acceptable to the audience for which research should possess some desirable qualities in terms of.

9 qualities of research are;

Purpose clearly defined

Research process detailed, research design planner, ethical issues considered, limitations revealed, adequate analysis ensured, findings unambiguously presented, conclusions and recommendations justified..

We enumerate below a few qualities that good research should possess.

Good research must have its purposes clearly and unambiguously defined.

The problem involved or the decision to be made should be sharply delineated as clearly as possible to demonstrate the credibility of the research.

The research procedures should be described in sufficient detail to permit other researchers to repeat the research later.

Failure to do so makes it difficult or impossible to estimate the validity and reliability of the results. This weakens the confidence of the readers.

Any recommendations from such research justifiably get little attention from the policymakers and implementation.

The procedural design of the research should be carefully planned to yield results that are as objective as possible.

In doing so, care must be taken so that the sample’s representativeness is ensured, relevant literature has been thoroughly searched, experimental controls, whenever necessary, have been followed, and the personal bias in selecting and recording data has been minimized.

A research design should always safeguard against causing mental and physical harm not only to the participants but also those who belong to their organizations.

Careful consideration must also be given to research situations when there is a possibility for exploitation, invasion of privacy, and loss of dignity of all those involved in the study.

The researcher should report with complete honesty and frankness any flaws in procedural design; he followed and provided estimates of their effects on the findings.

This enhances the readers’ confidence and makes the report acceptable to the audience. One can legitimately question the value of research where no limitations are reported.

Adequate analysis reveals the significance of the data and helps the researcher to check the reliability and validity of his estimates.

Data should, therefore, be analyzed with proper statistical rigor to assist the researcher in reaching firm conclusions.

When statistical methods have been employed, the probability of error should be estimated, and criteria of statistical significance applied.

The presentation of the results should be comprehensive, easily understood by the readers, and organized so that the readers can readily locate the critical and central findings.

Proper research always specifies the conditions under which the research conclusions seem valid.

Therefore, it is important that any conclusions drawn and recommendations made should be solely based on the findings of the study.

No inferences or generalizations should be made beyond the data. If this were not followed, the objectivity of the research would tend to decrease, resulting in confidence in the findings.

The researcher’s experiences were reflected.

The research report should contain information about the qualifications of the researchers .

If the researcher is experienced, has a good reputation in research, and is a person of integrity, his report is likely to be highly valued. The policymakers feel confident in implementing the recommendations made in such reports.

4 Goals of Research

The primary goal or purpose of research in any field of inquiry; is to add to what is known about the phenomenon under investigation by applying scientific methods. Though each research has its own specific goals, we may enumerate the following 4 broad goals of scientific research:

Exploration and Explorative Research

Description and descriptive research, causal explanation and causal research, prediction and predictive research.

The link between the 4 goals of research and the questions raised in reaching these goals.

Let’s try to understand the 4 goals of the research.

Exploration is finding out about some previously unexamined phenomenon. In other words, an explorative study structures and identifies new problems.

The explorative study aims to gain familiarity with a phenomenon or gain new insights into it.

Exploration is particularly useful when researchers lack a clear idea of the problems they meet during their study.

Through exploration, researchers attempt to

• Develop concepts more clearly;
• Establish priorities among several alternatives;
• Develop operational definitions of variables;
• Formulate research hypotheses and sharpen research objectives;
• Improve the methodology and modify (if needed) the research design .

Exploration is achieved through what we call exploratory research.

The end of an explorative study comes when the researchers are convinced that they have established the major dimensions of the research task.

Many research activities consist of gathering information on some topic of interest. The description refers to these data-based information-gathering activities. Descriptive studies portray precisely the characteristics of a particular individual, situation, or group.

Here, we attempt to describe situations and events through studies, which we refer to as descriptive research.

Such research is undertaken when much is known about the problem under investigation.

Descriptive studies try to discover answers to the questions of who, what, when, where, and sometimes how.

Such research studies may involve the collection of data and the creation of distribution of the number of times the researcher observes a single event or characteristic, known as a research variable.

A descriptive study may also involve the interaction of two or more variables and attempts to observe if there is any relationship between the variables under investigation .

Research that examines such a relationship is sometimes called a correlational study. It is correlational because it attempts to relate (i.e., co-relate) two or more variables.

A descriptive study may be feasible to answer the questions of the following types:

• What are the characteristics of the people who are involved in city crime? Are they young? Middle-aged? Poor? Muslim? Educated?
• Who are the potential buyers of the new product? Men or women? Urban people or rural people?
• Are rural women more likely to marry earlier than their urban counterparts?
• Does previous experience help an employee to get a higher initial salary?

Although the data description in descriptive research is factual, accurate, and systematic, the research cannot describe what caused a situation.

Thus, descriptive research cannot be used to create a causal relationship where one variable affects another.

In other words, descriptive research can be said to have a low requirement for internal validity. In sum, descriptive research deals with everything that can be counted and studied.

But there are always restrictions on that. All research must impact the lives of the people around us.

For example, finding the most frequent disease that affects the people of a community falls under descriptive research.

But the research readers will have the hunch to know why this has happened and what to do to prevent that disease so that more people will live healthy lives.

It dictates that we need a causal explanation of the situation under reference and a causal study vis-a-vis causal research .

Explanation reveals why and how something happens.

An explanatory study goes beyond description and attempts to establish a cause-and-effect relationship between variables. It explains the reason for the phenomenon that the descriptive study observed.

Thus, if a researcher finds that communities with larger family sizes have higher child deaths or that smoking correlates with lung cancer, he is performing a descriptive study.

If he explains why it is so and tries to establish a cause-and-effect relationship, he is performing explanatory or causal research . The researcher uses theories or at-least hypotheses to account for the factors that caused a certain phenomenon.

Look at the following examples that fit causal studies:

• Why are people involved in crime? Can we explain this as a consequence of the present job market crisis or lack of parental care?
• Will the buyers be motivated to purchase the new product in a new container ? Can an attractive advertisement motivate them to buy a new product?
• Why has the share market shown the steepest-ever fall in stock prices? Is it because of the IMF’s warnings and prescriptions on the commercial banks’ exposure to the stock market or because of an abundant increase in the supply of new shares?

Prediction seeks to answer when and in what situations will occur if we can provide a plausible explanation for the event in question.

However, the precise nature of the relationship between explanation and prediction has been a subject of debate.

One view is that explanation and prediction are the same phenomena, except that prediction precedes the event while the explanation takes place after the event has occurred.

Another view is that explanation and prediction are fundamentally different processes.

We need not be concerned with this debate here but can simply state that in addition to being able to explain an event after it has occurred, we would also be able to predict when it will occur.

Research Approaches

There are two main approaches to doing research.

The first is the basic approach, which mostly pertains to academic research. Many people view this as pure research or fundamental research.

The research implemented through the second approach is variously known as applied research, action research, operations research, or contract research.

Also, the third category of research, evaluative research, is important in many applications. All these approaches have different purposes influencing the nature of the respective research.

Lastly, precautions in research are required for thorough research.

So, 4 research approaches are;

• Basic Research .
• Applied Research .
• Evaluative Research .
• Precautions in Research.

Areas of Research

The most important fields or areas of research, among others, are;

• Social Research .
• Health Research .
• Population Research .
• Business Research .
• Marketing Research .
• Agricultural Research .
• Biomedical Research.
• Clinical Research .
• Outcomes Research.
• Internet Research.
• Archival Research.
• Empirical Research.
• Legal Research .
• Education Research .
• Engineering Research .
• Historical Research.

Check out our article describing all 16 areas of research .

Precautions in Research

Whether a researcher is doing applied or basic research or research of any other form, he or she must take necessary precautions to ensure that the research he or she is doing is relevant, timely, efficient, accurate, and ethical .

The research is considered relevant if it anticipates the kinds of information that decision-makers, scientists, or policymakers will require.

Timely research is completed in time to influence decisions.

• Research is efficient when it is of the best quality for the minimum expenditure and the study is appropriate to the research context.
• Research is considered accurate or valid when the interpretation can account for both consistencies and inconsistencies in the data.
• Research is ethical when it can promote trust, exercise care, ensure standards, and protect the rights of the participants in the research process.

What is the definition of research?

What are the characteristics of good research, what are the three basic operations involved in scientific research, what are the four broad goals of scientific research, what distinguishes the scientific method from other methods of acquiring knowledge, what is the origin of the word ‘research’, how is “research methodology” defined, how does research methodology ensure the appropriateness of a research method.

After discussing the research definition and knowing the characteristics, goals, and approaches, it’s time to delve into the research fundamentals. For a comprehensive understanding, refer to our detailed research and methodology concepts guide .

Research should be relevant, timely, efficient, accurate, and ethical. It should anticipate the information required by decision-makers, be completed in time to influence decisions, be of the best quality for the minimum expenditure, and protect the rights of participants in the research process.

The two main approaches to research are the basic approach, often viewed as pure or fundamental research, and the applied approach, which includes action research, operations research, and contract research.

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Qualitative Research: Characteristics, Design, Methods & Examples

Lauren McCall

MSc Health Psychology Graduate

MSc, Health Psychology, University of Nottingham

Lauren obtained an MSc in Health Psychology from The University of Nottingham with a distinction classification.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

“Not everything that can be counted counts, and not everything that counts can be counted“ (Albert Einstein)

Qualitative research is a process used for the systematic collection, analysis, and interpretation of non-numerical data (Punch, 2013). 

Qualitative research can be used to: (i) gain deep contextual understandings of the subjective social reality of individuals and (ii) to answer questions about experience and meaning from the participant’s perspective (Hammarberg et al., 2016).

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research focuses on thematic and contextual information.

Characteristics of Qualitative Research 

Reality is socially constructed.

Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013). 

The primacy of subject matter 

The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).

Variables are complex, interwoven, and difficult to measure

Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables , making them difficult to measure quantitatively.

However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013). 

Emic (insider’s point of view)

The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).

Emic is used to describe how participants interact, communicate, and behave in the context of the research setting (Scarduzio, 2017).

Why Conduct Qualitative Research? 

In order to gain a deeper understanding of how people experience the world, individuals are studied in their natural setting. This enables the researcher to understand a phenomenon close to how participants experience it. 

Qualitative research allows researchers to gain an in-depth understanding, which is difficult to attain using quantitative methods. 

An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022). 

This helps to further investigate and understand quantitative data by discovering reasons for the outcome of a study – answering the why question behind statistics. 

The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively (Busetto et al., 2020).

To design hypotheses, theory must be researched using qualitative methods to find out what is important in order to begin research. 

For example, by conducting interviews or focus groups with key stakeholders to discover what is important to them. 

Examples of qualitative research questions include: 

• How does stress influence young adults’ behavior?
• What factors influence students’ school attendance rates in developed countries?
• How do adults interpret binge drinking in the UK?
• What are the psychological impacts of cervical cancer screening in women?
• How can mental health lessons be integrated into the school curriculum? 

Collecting Qualitative Data

There are four main research design methods used to collect qualitative data: observations, interviews,  focus groups, and ethnography.

Observations

This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.

In participant observation, the researcher actively participates in the situation/events being observed.

In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020). 

Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).

However, awareness of an observer’s presence may influence participants’ behavior. 

Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013). 

Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.

Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias. 

Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).

The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias. 

Focus groups 

Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).

A shared narrative is built between participants to capture a group experience shaped by a shared context. 

The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.

Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.

Ethnography

Ethnography is a methodology used to study a group of people’s behaviors and social interactions in their environment (Reeves et al., 2008).

Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.

The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization. 

Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.

In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work. 

This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.

Qualitative Data Analysis Methods

Different methods can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study. 

The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007). 

Grounded theory

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).

 This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously. 

There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding. 

Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed. 

Content analysis

Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.

Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013). 

This research method can be used to analyze data in different formats, which can be written, oral, or visual. 

The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012). 

Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.

The following six steps provide a guideline for how to conduct qualitative content analysis.

• Define a Research Question : To start content analysis, a clear research question should be developed.
• Identify and Collect Data : Establish the inclusion criteria for your data. Find the relevant sources to analyze.
• Define the Unit or Theme of Analysis : Categorize the content into themes. Themes can be a word, phrase, or sentence.
• Develop Rules for Coding your Data : Define a set of coding rules to ensure that all data are coded consistently.
• Code the Data : Follow the coding rules to categorize data into themes.
• Analyze the Results and Draw Conclusions : Examine the data to identify patterns and draw conclusions in relation to your research question.

Discourse analysis

Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).

In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.

Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.

Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.

• Define the Research Question : Develop a relevant research question to frame the analysis.
• Gather Data and Establish the Context : Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
• Analyze the Content : Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
• Review the Results : Reflect on the findings to examine the function of the language, and the meaning and context of the discourse. 

Thematic analysis

Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts. 

Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).

Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question. 

In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns. 

This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.

Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive. 

An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.

Phases of Thematic Analysis

Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data. 

Template analysis

Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).

Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.

To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset. 

Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.

A guideline for the main procedural steps for conducting template analysis is outlined below.

• Familiarization with the Data : Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
• Preliminary Coding : Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
• Organize Themes : Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
• Produce an Initial Template : Develop an initial template. This may be based on a subset of the data.
• Apply and Develop the Template : Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes. 
• Finalize Template : Finalize the template, then apply it to the entire dataset. 

Frame analysis

Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.

Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.

Frame analysis encourages researchers to organize and manage their data using summarization.

This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns. 

Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.

Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.

• Familiarization with the Data : Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
• Develop a Theoretical Framework : Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
• Indexing : Apply the framework systematically to the entire study data.
• Summarize Data in Analytical Framework : Reduce the data into brief summaries of participants’ accounts.
• Mapping and Interpretation : Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.

Preventing Bias in Qualitative Research

To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).

The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking. 

Co-coding 

Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.

A common strategy must be defined at the beginning of the coding process  (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.

Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings. 

Member checking

Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).

Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.

Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).

This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000). 

Reflexivity in qualitative research

Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research. 

Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.

Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.

These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014). 

Reflexivity Example

Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking. I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic. My research aspirations are to help to develop interventions to help smokers quit.

Establishing Trustworthiness in Qualitative Research

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

Credibility in Qualitative Research

Credibility refers to how accurately the results represent the reality and viewpoints of the participants.

To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).

To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985). 

Transferability in Qualitative Research

Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).

Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017). 

Dependability in Qualitative Research

Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

Confirmability in Qualitative Research

Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.

Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).

This enables readers to understand the reasoning behind the decisions made. 

Audit Trails in Qualitative Research

An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. 

The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.

A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).

Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail. 

Discovery of unexpected data

Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.

This allows unexpected data to emerge, which can lead to further research into that topic. 

Flexibility

Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.

This enables researchers to investigate new opportunities while firmly maintaining their research goals. 

Naturalistic settings

The behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically. 

Limitations

Time-consuming .

Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.

Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes. 

Subjectivity 

The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.

Consequently, interpretations of data from another researcher may vary greatly. 

Limited generalizability

The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.

Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.

Therefore, results are only applicable to a small group of the population. 

Extraneous variables

Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:

• Situational variables : different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
• Participant characteristics : this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
• Experimenter effect : experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis. 

What sample size should qualitative research be?

The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).

Are surveys qualitative or quantitative?

Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.

The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.

In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.

What are the ethical considerations of qualitative research?

Before conducting a study, you must think about any risks that could occur and take steps to prevent them. Participant Protection : Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants. Transparency : Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data. Confidentiality : You need to consider how to maintain the confidentiality and anonymity of participants’ data.

What is triangulation in qualitative research?

Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings. 

Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).

Why is qualitative research important?

Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.

In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.

Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.

What is coding data in qualitative research?

Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.

These labels may be words or phrases which represent important (and recurring) patterns in the data.

This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.

What is the difference between qualitative and quantitative research?

Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.

This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.

In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.

Quantitative data may be collected using closed-ended questionnaires or experiments.

What is trustworthiness in qualitative research?

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability. 

Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.

Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).

What is data saturation in qualitative research?

Data saturation is a methodological principle used to guide the sample size of a qualitative research study.

Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis. 

The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.

Why is sampling in qualitative research important?

In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.

This is because quantitative research aims to provide generalizable conclusions that represent populations.

However, the aim of sampling in qualitative research is to gather data that will help the researcher understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.

Boeije, H. (2014). Analysis in qualitative research. Sage.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology , 3 (2), 77-101. https://doi.org/10.1191/1478088706qp063oa

Brooks, J., McCluskey, S., Turley, E., & King, N. (2014). The utility of template analysis in qualitative psychology research. Qualitative Research in Psychology , 12 (2), 202–222. https://doi.org/10.1080/14780887.2014.955224

Busetto, L., Wick, W., & Gumbinger, C. (2020). How to use and assess qualitative research methods. Neurological research and practice , 2 (1), 14-14. https://doi.org/10.1186/s42466-020-00059-z 

Carter, N., Bryant-Lukosius, D., DiCenso, A., Blythe, J., & Neville, A. J. (2014). The use of triangulation in qualitative research. Oncology nursing forum , 41 (5), 545–547. https://doi.org/10.1188/14.ONF.545-547

Critical Appraisal Skills Programme. (2018). CASP Checklist: 10 questions to help you make sense of a Qualitative research. https://casp-uk.net/images/checklist/documents/CASP-Qualitative-Studies-Checklist/CASP-Qualitative-Checklist-2018_fillable_form.pdf Accessed: March 15 2023

Clarke, V., & Braun, V. (2013). Successful qualitative research: A practical guide for beginners. Successful Qualitative Research , 1-400.

Denny, E., & Weckesser, A. (2022). How to do qualitative research?: Qualitative research methods. BJOG : an international journal of obstetrics and gynaecology , 129 (7), 1166-1167. https://doi.org/10.1111/1471-0528.17150 

Glaser, B. G., & Strauss, A. L. (2017). The discovery of grounded theory. The Discovery of Grounded Theory , 1–18. https://doi.org/10.4324/9780203793206-1

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18 (1), 59-82. doi:10.1177/1525822X05279903

Halpren, E. S. (1983). Auditing naturalistic inquiries: The development and application of a model (Unpublished doctoral dissertation). Indiana University, Bloomington.

Hammarberg, K., Kirkman, M., & de Lacey, S. (2016). Qualitative research methods: When to use them and how to judge them. Human Reproduction , 31 (3), 498–501. https://doi.org/10.1093/humrep/dev334

Koch, T. (1994). Establishing rigour in qualitative research: The decision trail. Journal of Advanced Nursing, 19, 976–986. doi:10.1111/ j.1365-2648.1994.tb01177.x

Lincoln, Y., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Mays, N., & Pope, C. (2000). Assessing quality in qualitative research. BMJ, 320(7226), 50–52.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods, 16 (1). https://doi.org/10.1177/1609406917733847

Petty, N. J., Thomson, O. P., & Stew, G. (2012). Ready for a paradigm shift? part 2: Introducing qualitative research methodologies and methods. Manual Therapy , 17 (5), 378–384. https://doi.org/10.1016/j.math.2012.03.004

Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches. London: Sage

Reeves, S., Kuper, A., & Hodges, B. D. (2008). Qualitative research methodologies: Ethnography. BMJ , 337 (aug07 3). https://doi.org/10.1136/bmj.a1020

Russell, C. K., & Gregory, D. M. (2003). Evaluation of qualitative research studies. Evidence Based Nursing, 6 (2), 36–40.

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018). Saturation in qualitative research: exploring its conceptualization and operationalization. Quality & quantity , 52 (4), 1893–1907. https://doi.org/10.1007/s11135-017-0574-8

Scarduzio, J. A. (2017). Emic approach to qualitative research. The International Encyclopedia of Communication Research Methods, 1–2 . https://doi.org/10.1002/9781118901731.iecrm0082

Schreier, M. (2012). Qualitative content analysis in practice / Margrit Schreier.

Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22 , 63–75.

Starks, H., & Trinidad, S. B. (2007). Choose your method: a comparison of phenomenology, discourse analysis, and grounded theory. Qualitative health research , 17 (10), 1372–1380. https://doi.org/10.1177/1049732307307031

Tenny, S., Brannan, J. M., & Brannan, G. D. (2022). Qualitative Study. In StatPearls. StatPearls Publishing.

Tobin, G. A., & Begley, C. M. (2004). Methodological rigour within a qualitative framework. Journal of Advanced Nursing, 48, 388–396. doi:10.1111/j.1365-2648.2004.03207.x

Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & health sciences , 15 (3), 398-405. https://doi.org/10.1111/nhs.12048

Wood L. A., Kroger R. O. (2000). Doing discourse analysis: Methods for studying action in talk and text. Sage.

Yilmaz, K. (2013). Comparison of Quantitative and Qualitative Research Traditions: epistemological, theoretical, and methodological differences. European journal of education , 48 (2), 311-325. https://doi.org/10.1111/ejed.12014

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Research Basics

• What Is Research?
• Types of Research
• Secondary Research | Literature Review
• Developing Your Topic
• Primary vs. Secondary Sources
• Evaluating Sources
• Responsible Conduct of Research
• Additional Help

A good working definition of research might be:

Research is the deliberate, purposeful, and systematic gathering of data, information, facts, and/or opinions for the advancement of personal, societal, or overall human knowledge.

Based on this definition, we all do research all the time. Most of this research is casual research. Asking friends what they think of different restaurants, looking up reviews of various products online, learning more about celebrities; these are all research.

Formal research includes the type of research most people think of when they hear the term “research”: scientists in white coats working in a fully equipped laboratory. But formal research is a much broader category that just this. Most people will never do laboratory research after graduating from college, but almost everybody will have to do some sort of formal research at some point in their careers.

So What Do We Mean By “Formal Research?”

Casual research is inward facing: it’s done to satisfy our own curiosity or meet our own needs, whether that’s choosing a reliable car or figuring out what to watch on TV. Formal research is outward facing. While it may satisfy our own curiosity, it’s primarily intended to be shared in order to achieve some purpose. That purpose could be anything: finding a cure for cancer, securing funding for a new business, improving some process at your workplace, proving the latest theory in quantum physics, or even just getting a good grade in your Humanities 200 class.

What sets formal research apart from casual research is the documentation of where you gathered your information from. This is done in the form of “citations” and “bibliographies.” Citing sources is covered in the section "Citing Your Sources."

Formal research also follows certain common patterns depending on what the research is trying to show or prove. These are covered in the section “Types of Research.”

• Next: Types of Research >>
• Last Updated: Dec 21, 2023 3:49 PM
• URL: https://guides.library.iit.edu/research_basics

Characteristics of research

• Empirical - based on observations and experimentation
• Systematic - follows orderly and sequential procedure.
• Controlled - all variables except those that are tested/experimented upon are kept constant.
• Employs hypothesis - guides the investigation process
• Analytical - There is critical analysis of all data used so that there is no error in their interpretation
• Objective, Unbiased, & Logical - all findings are logically based on empirical.
• Employs quantitative or statistical methods - data are transformed into numerical measures and are treated statistically.

See Also [ edit | edit source ]

• Thinking Scientifically
• Writing discipline specific research papers
• Wikipedia: Research
• Wikibooks: Research Methods

Bibliography [ edit | edit source ]

• Feigenbaum, Edward A.; McCorduck, Pamela (1983). The fifth generation: Artificial intelligence and Japan's computer challenge to the world . ISBN  978-0-201-11519-2 .  
• Kendal, Simon; Creen, Malcolm (2006-10-04). An Introduction to Knowledge Engineering . ISBN  978-1-84628-475-5 .  
• Russell, Stuart Jonathan; Norvig, Peter (1995). Artificial Intelligence: A Modern Approach . ISBN  0-13-103805-2 .  

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Qualities of Qualitative Research: Part I

Many important medical education research questions cry out for a qualitative research approach: How do teacher characteristics affect learning? Why do learners choose particular specialties? How is professionalism influenced by experiences, mentors, or the curriculum? The medical paradigm, the “hard” science most often taught in medical schools, usually employs quantitative approaches. 1 As a result, clinicians 5 be less familiar with qualitative research or its applicability to medical education questions. For these why types of questions, qualitative or mixed qualitative and quantitative approaches 5 be more appropriate and helpful. 2 Thus, we wish to encourage submissions to the Journal of Graduate Medical Education that are for qualitative purposes or use qualitative methods.

This editorial is the first in a series of two, and it will provide an introduction to qualitative approaches and compare features of quantitative and qualitative research. The second editorial will review in more detail the approaches for selecting participants, analyzing data, and ensuring rigor and study quality in qualitative research. The aims of the editorials are to enhance readers' understanding of articles using this approach and to encourage more researchers to explore qualitative approaches.

Theory and Methodology

Good research follows from a reasonable starting point, a theoretical concept or perspective. Quantitative research uses a positivist perspective in which evidence is objectively and systematically obtained to prove a causal model or hypothesis; what works is the focus. 3 Alternatively, qualitative approaches focus on how and why something works, to build understanding. 3 In the positivist model, study objects (eg, learners) are independent of the researchers, and knowledge or facts are determined through direct observations. Also, the context in which the observations occur is controlled or assumed to be stable. In contrast, in a qualitative paradigm researchers might interact with the study objects (learners) to collect observations, which are highly context specific. 3

Qualitative research has often been differentiated from quantitative as hypothesis generating rather than hypothesis testing . 4 Qualitative research methods “explore, describe, or generate theory, especially for uncertain and ‘immature’ concepts; sensitive and socially dependent concepts; and complex human intentions and motivations.” 4 In education, qualitative research strives to understand how learning occurs through close study of small numbers of learners and a focus on the individual. It attempts to explain a phenomenon or relationship. Typically, results from qualitative research have been assumed to apply only to the small groups studied, such that generalizability of the results to other populations is not expected. For this reason, qualitative research is considered to be hypothesis generating, although some experts dispute this limitation. 5 table 1 presents a comparison of qualitative and quantitative approaches.

Quantitative Versus Qualitative Research

When Qualitative Studies Make Sense

Qualitative studies are helpful to understand why and how; quantitative studies focus on cause and effect, how much, and numeric correlations. Qualitative approaches are used when the potential answer to a question requires an explanation, not a straightforward yes/no. Generally, qualitative research is concerned with cases rather than variables, and understanding differences rather than calculating the mean of responses. 4 In-depth interviews, focus groups, case studies, and open-ended questions are often employed to find these answers. A qualitative study is concerned with the point of view of the individual under study. 6

For example, the changes in duty hours for residents in 2003 have generated many quantitative research articles, which have counted and correlated the changes in numbers of procedures, patient safety parameters, resident test results, and resident sleep hours. However, to determine why residents still sleep about the same number of hours since 2003, one could start from a qualitative framework in order to understand residents' decisions regarding sleep. Similarly, to understand how residents perceive the influence of resident work hour restrictions on aspects of professionalism, a qualitative study would start with the learners rather than by measuring and correlating scores on professionalism assessments. Because learning takes place in social environments characterized by complex interactions, the quantitative “cause and effect” model is often too simplistic. 7

A variety of ways to collect information are available to researchers, such as observation, field notes, reflexive journals, interviews, focus groups, and analysis of documents and materials; table 2 provides examples of these methods. Interviews and focus groups are usually audiorecorded and transcribed for analysis, whereas observations are recorded in field notes by the observer.

Potential Data Sources for Qualitative Research 8

After data collection, accepted methods are employed to interpret the data. Researchers review the observations and report their impressions in a structured format, with subsequent analysis also standardized. table 3 provides one example of an analysis plan. Strategies to ensure rigor in data collection and trustworthiness of the data and data analysis will be discussed in the second editorial in the series.

Iterative Team Process to Interpret Data 8

In contrast to quantitative methods, subjective responses are critical findings, both in participant responses and observer reactions. The unique or outlier response has value in contributing to understanding the experience of others, and thus individual responses are not lost in the aggregation of findings or in the development of research group consensus. 2 , 4 Qualitative methods acknowledge the “myth of objectivity” between researcher and subjects of study. 7 In fact, the researcher is unlikely to be a purely detached observer.

Ethical Issues

As qualitative researchers usually attempt to study subjects and interactions in their “natural settings,” ethical issues frequently arise. Because of the sensitive nature of some discussions as well as the relationship between researchers and participants, informed consent is often required. The very reason for doing qualitative research—to discover why and how, particularly for thorny topics—can lead to potential exposure of sensitive opinions, feelings, and personal information. Thus, consideration of how to protect participants from harm is essential from the very onset of the study.

Quality Assessment

Qualitative researchers need to show that their findings are credible. As with quantitative approaches, a strong research project starts with a basic review of existing knowledge: a solid literature search. However, in contrast to quantitative approaches, most qualitative paradigms do not look to find a single “truth,” but rather multiple views of a context-specific “reality.” The concepts of validity and reliability originally evolved from the quantitative tradition, and therefore their accepted definitions are considered inadequate for qualitative research. Instead, concepts of precision, credibility, and transferability are key aspects of evaluating a qualitative study. 9

Although some experts find that reliability has little relevance to qualitative studies, others propose the term “dependability” as the analogous metric for this type of research. Dependability is gained though consistency of data, which is evaluated through transparent research steps and research findings. 9 , 10 Trustworthiness and rigor are terms used to establish credible findings. One technique often used to enhance trustworthiness and rigor is triangulation, in which multiple data sources (eg, observation, interviews, and recordings), multiple analytic methods, or multiple researchers are used to study the question. 9 The overall goal is to minimize and understand potential bias while ensuring the researcher's “truthfulness” of interpretation. 9

A potentially helpful appraisal checklist for qualitative studies, developed by Coté and Turgeon, 11 is found in table 4 . This appraisal checklist has not been examined systematically. table 5 includes a list of terms commonly used in qualitative research. Approaches to ensure rigor and trustworthiness in qualitative research will be addressed in greater detail in Part 2.

Sample Quality Appraisal Checklist for Qualitative Studies 11 , a

Commonly Used Terms in Qualitative Research 8

Both quantitative and qualitative approaches have strengths and weaknesses; medical education research will benefit from each type of inquiry. The best approach will depend on the kind of question asked, and the best methods will be those most appropriate to the question. 4 To learn more about this topic, the references below are a useful start, as is talking to colleagues engaged in qualitative research at your institution or in your specialty.

Gail M. Sullivan, MD, MPH, is Editor-in-Chief, Journal of Graduate Medical Education; and Joan Sargeant, PhD, is Professor in the Division of Medical Education, Dalhousie University, Halifax, Nova Scotia, Canada.

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What is research- characteristics, importance, and objectives.

In this article, I will share the characteristics, importance, and objectives of the research…

Table of Contents

What is Research??

Research is a process through which an individual or the researcher helps to search the definite or useful information from the number of respondents to evaluate or solve the problem-related questions. In fact, research is an art of scientific investigation or technique.

In other words, some people say that research is a systematized effort to gain knowledge and it is a process of collecting, evaluating, and interpreting information to answer questions.

Characteristics of Research:

The characteristics of research include various points such as:-

1. Research should be controlled-

It should be controlled because of the relation between two or more variables are affected by each other (whether it is internal or external). If the research is not controllable, then it will not be able to design a particular research report .

2. Research should be rigorous-

It should be rigorous because it helps to follow the procedures to find out the answers related questions which are relevant and appropriate in nature. The research information consists of two types of sciences such as physical and social sciences. These two sciences are also varied from each other.

3. Research should be systematic-

Research should be systematic because if a researcher wants to do a perfect research design or process then it will have to evaluate or obtained the necessary information from the market in a systematic manner. It takes various steps to do a perfect or systematic research process and all the steps of procedures are interlinked to each other.

4. Research should be valid-

It means the information which is collected by the researcher can be the correct and verifiable by yourself (i.e,  researcher himself). If our collected information is fair or valid, then our research will also be ethical in nature.

5. Research should be empirical-

This means that any conclusion drawn is totally based upon ethical or hard evidence gathered information collected from observations and real-life experiences.

6. The foundation of knowledge-

Research is the foundation of knowledge for the purpose of knowledge and an important source for providing guidelines or norms for solving different social, business, or governmental problems. It is a variety of formal training which enables us to understand the new developments in one’s field in an efficient way.

 Importance or Objectives of the Research:

Research objectives help to identify the full purpose or attention of your research with the type of basic questions that will be noted. Explaining your research objectives means explaining what do I need to investigate and evaluate. The importance of research is also known as the objectives of the research . It includes various points such as:-

[ Q . What are the objectives of the research and What is the importance of research ??]…

1. To find out the real facts-

As we know, every type of research has its own object but the basic aim of the research is always to find out or obtained the information from the markets and societies and their number of respondents. A researcher evaluates or finds the real or exact information for our problem-related questions.

2. To achieve the new thoughts-

In this objective of the research , anybody can find new thoughts from the research. Research is the process of finding the exact information through proper observation, optimization, and experiments.

These are the scientific methods to find out or evaluate the information which is very necessary for evaluating the problem task.

3. To evaluate the information-

The first aim of the research is to find out the information and then evaluate them in an appropriate or efficient manner so that they can easily design the research problem and solve them also.

A researcher evaluates the information through various scientific approaches and methods, statistical analysis and procedures, and another type of tables and graphs.

4. To test a hypothesis-

In this objective of the research , the researcher does the causal relationship between the variables (it can also be said that the hypothesis testing research studies). The hypothesis testing study represents the number of actions like these terms:

(a) Making a formal statement,

(b) Selecting a significance level,

(c) Deciding the distribution use,

(d) Selecting a random sample and computing an appropriate value,

(e) Calculation of the probability,

(f) Comparing the probability.

5. To design or implement the research-

After the collection of all information, the researcher prepares the structure of a research design for the company so that they can easily describe or identify the structure of a particular research theme. The research designs can be broadcasted into two forms such as experimental designs and non-experimental designs.

After the structure of the research design, the researcher implements them in a problem and find out the optimum factor to solve them.

6. To improve the understanding-

In this objectives of the research , the researcher helps to improve the understanding of a particular topic by asking what else needs to be evidenced before the research is purposeful, or what knowledge could be assembled from a more focused investigation, or scrutiny of the existing findings.

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Literature Searching

Characteristics of a good research question

The first step in a literature search is to construct a well-defined question.  This helps in ensuring a comprehensive and efficient search of the available literature for relevant publications on your topic.  The well-constructed research question provides guidance for determining search terms and search strategy parameters.

A good or well-constructed research question is:

• Original and of interest to the researcher and the outside world
• It is clear and focused: it provides enough specifics that it is easy to understand its purpose and it is narrow enough that it can be answered. If the question is too broad it may not be possible to answer it thoroughly. If it is too narrow you may not find enough resources or information to develop a strong argument or research hypothesis.  
• The question concept is researchable in terms of time and access to a suitable amount of quality research resources.
• It is analytical rather than descriptive.  The research question should allow you to produce an analysis of an issue or problem rather than a simple description of it.  In other words, it is not answerable with a simple “yes” or “no” but requires a synthesis and analysis of ideas and sources.
• The results are potentially important and may change current ideas and/or practice
• And there is the potential to develop further projects with similar themes

The question you ask should be developed for the discipline you are studying. A question appropriate for Physical Therapy, for instance, is different from an appropriate one in Sociology, Political Science or Microbiology .

The well-constructed question provides guidance for determining search terms and search strategy parameters. The process of developing a good question to research involves taking your topic and breaking each aspect of it down into its component parts. 

One well-established way that can be used both for creating research questions and developing strategies is known as PICO(T). The PICO framework was designed primarily for questions that include clinical interventions and comparisons, however other types of questions may also be able to follow its principles.  If the PICO framework does not precisely fit your question, using its principles can help you to think about what you want to explore even if you do not end up with a true PICO question.

References/Additional Resources

Fandino W. (2019). Formulating a good research question: Pearls and pitfalls.   Indian journal of anaesthesia ,  63 (8), 611–616. 

Vandenbroucke, J. P., & Pearce, N. (2018). From ideas to studies: how to get ideas and sharpen them into research questions .  Clinical epidemiology ,  10 , 253–264.

Ratan, S. K., Anand, T., & Ratan, J. (2019). Formulation of Research Question - Stepwise Approach .  Journal of Indian Association of Pediatric Surgeons ,  24 (1), 15–20.

Lipowski, E.E. (2008). Developing great research questions. American Journal of Health-System Pharmacy, 65(17) , 1667–1670.

FINER Criteria

Another set of criteria for developing a research question was proposed by Hulley (2013) and is known as the FINER criteria. 

FINER stands for:

Feasible – Writing a feasible research question means that it CAN be answered under objective aspects like time, scope, resources, expertise, or funding. Good questions must be amenable to the formulation of clear hypotheses.

Interesting – The question or topic should be of interest to the researcher and the outside world. It should have a clinical and/or educational significance – the “so what?” factor. 

Novel – In scientific literature, novelty defines itself by being an answer to an existing gap in knowledge. Filling one of these gaps is highly rewarding for any researcher as it may represent a real difference in peoples’ lives.

Good research leads to new information. An investigation which simply reiterates what is previously proven is not worth the effort and cost. A question doesn’t have to be completely original. It may ask whether an earlier observation could be replicated, whether the results in one population also apply to others, or whether enhanced measurement methods can make clear the relationship between two variables.  

Ethical – In empirical research, ethics is an absolute MUST. Make sure that safety and confidentiality measures are addressed, and according to the necessary IRB protocols.

Relevant – An idea that is considered relevant in the healthcare community has better chances to be discussed upon by a larger number of researchers and recognized experts, leading to innovation and rapid information dissemination.

The results could potentially be important and may change current ideas and/or practice.

Cummings, S.R., Browner, W.S., & Hulley, S.B. (2013). Conceiving the research question and developing the study plan. In: Designing clinical research (Hulley, S. R. Cummings, W. S. Browner, D. Grady, & T. B. Newman, Eds.; Fourth edition.). Wolters Kluwer/Lippincott Williams & Wilkins. Pp. 14-22.    

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The Savvy Scientist

Experiences of a London PhD student and beyond

Characteristics of a Researcher

If you’re considering a career in research, or embarking on a PhD, you may be wondering what are the typical characteristics of a researcher?

Although people with many different personalities and backgrounds go into research, there are a few common traits which can help to ensure success.

In this post we’ll identify the key qualities of a good researcher and I’ll also provide a few suggestions everyone can use to become a better researcher.

Let’s go!

A note on how personalities are constructed

Our personalities are made up of character traits which are partly down to our genetics and partly down to the interactions our genes (us!) have had with our environments. This continual interaction means that our personalities also change and evolve over time.

The good news is that it’s entirely possible to build skills and habits in a character trait which doesn’t come naturally to you.

So if one of the characteristics isn’t something you recognise in yourself please don’t feel disheartened. I strongly believe research should be inclusive and that anyone can be a good researcher if they’re willing to put the work in.

If you’re interested in learning more about personality types I’d recommend checking out 16personalities.com which discusses the 16 Myers Briggs personality types. You can fill in a free 10 minute quiz to learn which of them you’re most closely aligned with.

The ‘Career Paths’ and ‘Workplace Habits’ sections under your personality type may help you to recognise if you’d personally find fulfillment from a career in research.

Now that we’ve covered how personalities are contructed, let’s discuss the key characteristics of a researcher.

What are the key characteristics of a researcher?

From my experience, successful researchers tend to share many of the following traits:

Good researchers are curious and inquisitive about the world around them. Typically a strong motivation for wanting to carry out research is to either better understand, or help to solve, unanswered problems.

Do you often question the world around you? If so you’re showing exactly the type of curiosity which is one of the main characteristics of a researcher. In fact, this curiosity is very common among all of us as children and sadly many of us seem to lose it once we mature and pursue traditional careers. This isn’t the case if you’re a researcher!

For example, researchers around the world ask questions such as:

• How do trees communicate?
• Could new materials improve our energy storage capabilities?
• Why are some corals more resilient to climate change than others?
• How can we help the human body to better regenerate itself?

The great thing with science is that you can keep delving deeper and curiosity can help to keep you pushing on to better understand the problem. Sometimes you’ll even uncover more questions than you resolve which can open up new branches of research. These could easily become research projects in their own right!

That’s why another one of the qualities of a good researcher is having…

Organisational Skills

To me, being organised means a couple of different things:

• Data management: Having a clear idea of the work I’ve already done. For example: If my supervisor asked to see the details of a certain experiment, can I quickly find them? This can come down to good note taking and organising data in a sensible way.
• Time management: So that I can do the most effective work in the present and future. This can include everything from smart scheduling of meetings, to making goals and staying accountable.

Let’s briefly dive into both:

Organisational skills: data management

Research projects typically involve many experiments followed by collating and analysing the associated data. A typical PhD project may involve dozens or even hundreds of experiments, each of which may comprise many samples. It can be easy to get lost in the sea of work.

If you’re not careful this can mean unncessarily needing to repeat experiments. Or even worse you could analyse existing results thinking they’re from a different experiment than the one they really are. It is critically important when you come to publish your research that you can provide details for all of your experiments and clearly know the relationship between samples.

For example, was an unusual data point collected in a different batch to the others? Was the batch of chemicals different? Reviewers can, and do, ask for details which you may not immediately think of being relevant at the time of the experiments.

Being organised is the best way to avoid this unnecessary stress, extra workload and potential bad science.

My suggestions:

• Take photos of experiments . Even if the experiment seems mundane, photos can be very useful. You may need to repeat the experiment in the future or include the photo in a presentation, report or publication.
• Track your experiments . In the past I’ve kept a physical lab book to document all my experiments and then used spreadsheets to keep track of all my experiments over time. The exact workflow which works for you will depend a lot on your own style and field but the crucial thing is: write down everything, particularly what changes between experiments. Small details you may not think are relevant at the time may become important later for your papers and thesis.
• Back everything up . Please have all of your data, including the photos you take, backed up. Bonus points for backing up to the cloud where you can then access the data from multiple computers and easily share folders with colleagues. Many universities have partnerships with places like Microsoft’s OneDrive so you can store all your data there seamlessly for free.

Organisational skills: time management

Beyond diligent note taking and experiment tracking to manage all the data you’re creating, being organised is critical to ensure you manage your time effectively.

Unlike short undergraduate projects, you really can’t leave all the work for your PhD (or postdoc) until right at the end!

As a researcher, trying to be as organised as possible can help in many ways, such as:

• Remembering to buy food and eat your lunch so that you can work effectively. Personally I choose to make food in advance which I figure is more healthy and definitely saved me money compared to outlets at university. You can read this short series I wrote about meal prep .
• Keeping an up to date calendar. If you do a lot of experiments in the lab, especially with equipment which needs booking, add this too for realistic amounts of time. This way you can ensure you’re not haphazardly rushing some lab work because you’re stressed and late for a meeting you should never have agreed to.
• Putting together a plan for future experiments, helping to ensure you make time for the most critical work.
• Making a clear plan of tasks you need to get done, and just as importantly: when they need to get done by. Experiments, undergraduate marking, editing reports, claiming expenses, submitting abstracts to conferences… researchers typically balance a lot of different tasks at once. Being organised can help you to prioritise whilst ensuring nothing gets missed.

Making changes such as these will make it much less likely that you’ll become overwhelmed by your research.

Everyone can become more organised!

Psst. Do you want an Experiment Checklist that breaks down the steps to ensure you’re staying organised before, during and after carrying out your research experiments? Click the button below.

Open Mindedness

On multiple levels it is so important that as a researcher you can remain both neutral and open minded about your research:

• When you’re carrying out your experiments sometimes there’ll be a certain outcome which you may be expecting or hoping to see. Tread carefully! Conducting research properly means being open to the results being different to what you initially expect. This may sound obvious but I’ve seen first hand how easy it is for people to see the results they want rather than the results which are really in front of them. This can lead researchers to continue down a direction which is potentially wasting money and resources and could potentially misrepresent the research to the wider scientific community.
• Taking a step back from the results of individual experiments, being open to changing the path of your research can be a very useful trait in getting the most from your time in research. Sometimes it will be useful to consider adding in different techniques, trying new materials or starting a new collaboration. In particular if you consult experts in other disciplines, take what they’re saying seriously and challenge your preconceptions. These things can both improve your research and enrich your research experience.

Determination & Persistence

Determination and persistence are critical characteristics of a researcher.

Doing a PhD can be a long slog and there will often be times when you’d rather not be doing work that day. By nature experiments are often very repetitive and frankly quite boring to carry out, therefore it’s important to have the persistence to stick with it and remember that eventually it will pay off.

Making progress in research doesn’t have to include working endless hours, sometimes working smart can go a long way to helping with persistence. For instance one way of making progress even if you’re not feeling up for intensive work, is to look at what else needs doing.

There are often boring but straightforward tasks which can fall to the bottom of the to do list. For example in the past I’ve worked on tasks like moving data, writing up notes or submitting expense claims. Some of which you may even be able to do while listening to music or watching Youtube. So if you’re not feeling up for doing ‘proper’ work, these tasks can be a good way to still achieve results but without requiring too much brain power.

However it’s also important to be pragmatic and sensible with your time. One of the perks of working in academic research is that you are in control of your own schedule, so do feel free to cut your losses and take time off. You can then come in the next day refreshed and with a better mindset.

Acting Logically

In an ideal world you’d come up with a research hypothesis, carry out a short series of clearly defined experiments and out pops an interesting result which you can go on to publish. This can happen but in reality research is usually not as clear-cut.

Typically you’ll carry out increasing numbers of experiments and be unsure where to draw the line for what to include in your analysis and publications. Other times you may not be able to replicate your initial findings or may need to add an extra experiment which helps test your hypothesis but also adds complexity:

In addition to your own web of experiments to untangle and make sense of, you may also have external pressures. In an effort to create a higher impact paper I’ve seen researchers be under pressure from supervisors to combine their work with other researchers from the group into a super-paper* which tells a larger story than just their own contributions.

*I just came up with this name a moment ago, I don’t expect you’ll see these types of papers described as such elsewhere on the web!

Example of where logic can be helpful

Being able to think logically about the best way to both present your work, and carry out upcoming experiments, is a very useful characteristic to pick up as a researcher.

For instance, say you’ve got a year or two of data from experiments under your belt with some potentially interesting findings.

Acting logically can help in two ways:

• Deciding how to divide your existing work up into logical bodies of work for publishing. Sometimes splitting work up into smaller papers makes sense so that you can provide a clear message for each paper. Otherwise you can be left with a muddled mess of work. Read my full guide to publishing your first paper . In particular the Deciding what to publish from your PhD work section is all about thinking logically.
• Determining a logical plan for future experiments to help you to achieve the aims of your project. Sometimes it may help to work backwards: once you’ve figured out what you’d like to achieve in your project, determine what experiments you’ll have to do to get you there and use this as a basis for planning your experiments. Often a project can go in many directions. This can be intimidating and you won’t realistically have time to explore all the options. Therefore being logical about the most useful experiments can be critical to ensuring that you make progress during your research career.

Experiments rarely go perfectly as planned (and when they do that can be worrying!). Sometimes equipment hasn’t been used in a while and doesn’t work properly, things break or the data your collecting seemingly makes no sense. Sometimes simply not having someone available who knows how to use the kit can be a frustrating roadblock to progress.

As a researcher you need patience to deal with these times you’re stuck debugging an issues or reading an instruction manual when you’d rather be doing actual research. Remind yourself that this is all a necessary step in making progress towards the goal of your research. You may not see the progress day by day, or month by month, but remember to take a step back every so often to gain perspective and remind yourself that your efforts are helping to advance knowledge.

Trustworthiness – acting with integrity

Being a trustworthy individual and acting with integrity as a researcher should go without saying.

Even so, depending on your discipline there may be elements of your research for which common sense can only take you so far. It may not be until someone else does something which turns out to be wrong that you realise the proper way to behave.

Potentially contentious areas of research

• Experimenting on certain biological samples such as human tissue or live animal models
• Being mindful about disclosing specifics of your work outside of the research group, especially if it involves access to secure facilities or themes which may be picked up in the media
• Accessing sensitive data, such as human patient information

All of these place you in a position of trust and it is critical that you act with integrity and abide by regulations and governance. Your university should have guides for it such as Imperial’s for animal work .

Ideally when you get started on your research your induction to the team and lab will include giving you the low down on the proper protocols. Always err on the side of caution, there are no silly questions and in fact any supervisor should be happy to see their researchers asking questions about ethics.

Other scenarios

Outside of these niche cases which only apply to relatively few researchers, more generally acting with integrity could include things such as :

• Owning up to breaking things in the lab
• Behaving properly around your lab mates and in particular if you’re involved with supervising more junior researchers

One main area across every discipline that trustworthiness and acting with integrity apply to is data analysis. While manipulating results is an obvious no-no, it’s also vitally important not to omit data you do not like as this could mislead future researchers. Oftentimes there is a grey area for where to draw the line for what results to include in a paper, so discuss it with your supervisor and they should be very supportive.

Your university should provide guidance on research ethics ( example webpages for Imperial ) so I suggest giving that a read as a starting point.

Self-reliance

During a PhD you are personally responsible for your project and you’ll be the one who has to do and defend the work being performed. To this end it’s critical that you are comfortable working on your own, managing your own time and making research decisions with minimal supervision.

In an ideal world you’ll have a supervisor that you meet up with regularly. So when you’re choosing a supervisor it’s a great help if you can speak to existing PhD students to find out the frequency of meet-ups and level of support. But ultimately you need to be comfortable driving the work and taking ownership of the progress.

That brings us on to our final characteristic of a researcher which is being…

Co-operative

You’ll get a lot more done during your research if you work well with others.

This could mean helping other people with their experiments when they need an extra pair of hands or politely asking for advice from someone who has expertise in something you want to try. Do be careful to ensure you’re not committing too much to doing other people’s work. These same people can also become your support network when things don’t go as planned and may help you to find a way through.

Starting new collaborations can be a highlight of a research project and I urge you to make the most of these types of opportunities. Just make sure to run it past your supervisor first. Not only could this be a learning experience, it could increase the quality and quantity of your own research output. I feel really thankful to have been involved with all the collaborations I’ve taken part in.

How to become a better researcher

Hopefully when reading through the various characteristics of a researcher you’ve been able to spot several traits which already match which your own personality.

But if there are any characteristics of a researcher you don’t recognise in yourself don’t despair! We can all work on ourselves and even if it makes you feel uncomfortable at times it’ll help you to grow as a person and researcher.

For instance, if you’re not naturally particularly organised, you can certainly develop this characteristic like skills in other parts of our lives.

This post has already becoming much longer than I first envisioned so I won’t go in depth on ways you could develop each quality. If you’d like to see a post on that let me know in the comments. Instead for now, here are a few suggestions for developing these characteristics to become a better researcher.

How to improve as a researcher

• Get organised. Becoming organised doesn’t just help in the obvious ways as in the section above. For instance it’ll help you to see your progress (good motivation to stay determined and act patiently), regulate effort to avoid burnout, see the bigger picture and therefore act both logically and hopefully become more open to new avenues of research and collaboration.
• Work on your weak points. If having read this post you’ve identified any characteristics of a researcher which you could do with some work on, don’t be afraid to address them. Operating outside of your comfort zone will help you to grow.
• Request feedback . During my PhD I had weekly meetings with my main supervisor and strongly suggest doing something similar. Not only was it a way to track progress and stay focussed but it can be an opportunity to ask for feedback on what you’re doing well and where you could improve. It can also be useful to speak openly with your peers to find what tips they have.
• Subscribe to this site! How could I resist adding this cheeky suggestion! There are dozens of posts about all aspects of research and PhDs and lots of new upcoming content on the way. As always if you have any suggestions for posts you’d find useful please do let me know.

I hope you’ve found this post about the characteristics of a researcher useful. Don’t worry if you don’t see all these traits in yourself just yet, they can be a work in progress which you’ll develop as a researcher.

If you have any thoughts on other essential characteristics of a researcher let me know in the comments section below.

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Peer-Reviewed, Refereed, Scholarly Publications

• Peer-Reviewed, Refereed, Scholarly Articles
• Databases - Find a Peer-Reviewed Article
• Characteristics

Characteristics of Scholarly Articles and Journals

The following characteristics list provides features of a Scholarly Article:

• Often have a formal appearance with tables, graphs, and diagrams
• Always have an abstract or summary paragraph above the text; may have sections decribing methodology
• Articles are written by an authority or expert in the field
• The language includes specialized terms and the jargon of the discipline
• Titles of scholarly journals often contain the word "Journal", "Review", "Bulletin", or "Research"
• Usually have a narrow or specific subject focus
• Contains original research, experimentation, or in-depth studies in the field
• Written for researchers, professors, or students in the field
• Often reviewed by the author's peers before publication (peer-reviewed or refereed)
• Advertising is minimal or none

[Excerpt from Mabee Library-Washburn University]

• Scholarly Peer-Reviewed Journals This research guide provides characteristics of scholarly, popular, trade and peer-reviewed articles. Created by Reference Librarian Cal Melick, Mabee Library-Washburn University.

Peer-Review/Refereed Journal Clues

• How to Recognize Peer-Reviewed (Refereed) Journals From Angelo State University Library

To see if a publication is peer-reviewed, check if the journal requires:

• a multiple-copy submission requirement
• an abstract
• literature review
• methodology

Scholarly, Popular and Trade Articles: What's the Difference?

• Scholarly vs Popular Articles A chart listing differences between scholarly and popular articles.
• << Previous: Databases - Find a Peer-Reviewed Article
• Last Updated: Sep 24, 2020 10:06 PM
• URL: https://libguides.lehman.edu/peer-review

What is Research? Types, Purpose, Characteristics, Process

• Post last modified: 26 August 2021
• Reading time: 13 mins read
• Post category: Research Methodology

• What is Research?

Research means a systematic and objective study to find facts which can be answers to questions and solutions to problems.

Social sciences Encyclopedia defines research as the manipulation of things, concepts or symbols for the purpose of generalizing to extend, correct as to verify knowledge, whether that knowledge aid in the construction of a theory or in the practice of art.

Table of Content

• 1 What is Research?
• 2.1 Basic or pure research
• 2.2 Applied or practical research
• 3 What is Social Research?
• 4 Purpose of Research
• 5 Characteristics of Research
• 6 Research process

In a different way effort to reach definiteness or certainty, to collect facts and ascertain truth constitute research. In research, we examine facts for truth. When facts are repeatedly examined and tested, truth is established. This leads to certainty and incorporates a generalization which is unique.

Types of Research

Basically, research is classified in two types.

Basic or pure research

Applied or practical research.

Basic or pure research explores broad, inclusive laws, rules, theories and tendencies with precise causation. Pure research is an intellectual response to great questions and seemingly difficult causal complexities.

Theory of gravity (Newton), a theory of relativity (Einstein), and birth of the universe theory (Hoyle and Naralikar theory) are examples of pure research. Such pure research may or may not be practical and socially useful immediately.

Applied or practical research aims at making existing, available knowledge useful in solving present problems of the society and individuals vis-a-vis production, distribution, consumption, and minimization of pain.

What is Social Research?

According to Pauline Young, social research is defined in the following words. “We may define social research as the systematic method of discovering new facts or verifying old facts, through sequence, interrelationship, causal explanations and the natural laws which cover them.

Prof. M. H. Copal, a senior Indian social scientist defined social research as the study of phenomena resulting from an interaction between different human groups in the process of their living together.

This study helps us in generalizing, theorizing and policy planning.

Social research is intrinsically dynamic and involves a large number of variables, some controllable some not so controllable.

As a result, social research involves a process of continuous revision of existing laws, theories, periodic refutation and/or modification of the same laws and theories. Freshly generated or collected data i.e. primary data give us new insights and evidence to arrive at new conclusions.

Purpose of Research

Purpose and functions of social research can be enumerated as below

• Search for truth
• Application of knowledge for better human life.
• Examining phenomena or events for identifying causes and establishing generalizations, and theories about human behaviour.
• Predicting the future on the basis of existing knowledge and study methods.
• Verifying, correlating or modifying existing generalizations or theories, differences of opinion and settling debates if any.

Characteristics of Research

Following are the essential characteristics of an ideal researcher.

• An unquenchable and strong desire to find out the truth
• Ability to identify similarity in diverse situations and diversity in similar Situations
• Curiosity, quest, doubt, patient, slow thinking, willingness to reexamine, discipline, no dogmatism are according to Francis Beacon, essential attributes of a researcher
• insistence for data
• caution in statements
• clear right/understanding
• awareness about multiplicity in varied social interrelations
• According to Carl Pearson, disciplined imagination is the distinguishable characteristics of an ideal researcher
• According to Sidney and Beatrice Web, a researcher must always avoid the influence of his personal biases
• A researcher, according to C. Luther Fry, must possess intellectual honesty and integrity
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Research process

To make your research efforts successful and socially meaningful, the whole approach has to be carefully planned and executed step by step in a scientific and logical way. It is, therefore, necessary to explain and present steps and design of any research work carefully.

Following are the steps in research process:

• Explain the objectives of research, present the problem and state the hypothesis/es.
• Elaborate on the research design mainly with reference to methodology of data collection and analysis.
• System of data collection with clear understanding of sampling techniques and/or census approach.
• Description, tabulation, coding, analysis of data and statement of analytical results/findings.
• Interpretation of these findings/results and reaching objective conclusions.
• Attempting reliable prediction.

Selection of the research topic/question is the first critically important step. Practical problems, emerging needs, scientific curiosity, intellectual quest values of life, life experiences are the main sources of research topics or questions.

Secondly, formation of the hypothesis is the next step. Before we start collecting, tabulating and analyzing data, it is necessary to have ‘a priori’ causal relationship which may explain the phenomenon under study, this is known as hypothesis/es.

A hypothesis/es explain the cause-effect relationship at a logical level. The hypothesis gives us basic concepts on the basis of which we collect data generate data, for empirical evidence.

In formulation of hypothesis, we in a way, organize our research question in a scientific way. The words hypothesis and concepts are explained elaborately in subsequent units.

In formulating research question and research design it is necessary that

• the researcher has advanced in-depth reading in related literature,
• he is fully aware of the current theories and research in related area
• he has close interaction with peers in the field and
• he must possess an inquisitive imaginative scientific mindset.

Thirdly, it is necessary to have a well planned research design. It helps in focussing work, precise explanation of events / questions and most importantly a research design helps in minimization of variance in the research system.

According to R. L. Ackoff there are two types of research design- Ideal Research Design – a design without practical limitation, the other research design is practical / feasible research design. In this, we consider limitations like time, resources availability of data and intellectual skills of the researcher.

Normally a practical research design has four important constituents.

• Sampling Design
• Statistical design
• Observational Design
• Operational Design

In preparing a practical research design, the researcher has to consider following aspects,

i. What is the primary research focus? ii. What is the data required for the research? iii. What are the exact objectives of the research? iv. Sources of data? v. Places to be visited for research vi. Time limits vii. A number of entities to be involved in the research viii. Criteria of sampling ix. Methods of data collection x. Methods of data coding classification and tabulation. xi. Material / financial resources available for research.

Broadly, there are five types of research design, according to Mac-Grant.

i. Controlled experiment ii. Study / case study iii. Survey sample / census iv. Investigation v. Action research

According to Seltiz and others, there are basically three types of research design,

i. Exploratory or formulative ii. Descriptive or diagnostic iii. Studies testing causal hypothesis.

Exploratory research relies heavily on review of literature, review of experience and entities/cases encouraging intuitions or inspiration. This depends heavily on the attitude of scientist, intensity of/or depth of his study/integrative powers of the researcher normally, reaction of indifferent individuals, behaviour of marginal individuals/groups, developmental transition, isolates, deviants and pathological cases and pure cases constitute factors which induce a researcher to explore.

In the case of many social sciences, majority of researchers collect and describe information regarding various groups, communities and sets of experiences consumption patterns, saving habits, investment, likes and dislikes, work culture, price responses, management decisions and practices, entrepreneurial behaviours, business leadership etc are such areas of research.

In the case of studies testing causal hypothesis the main objective of research is to verify an assumed causation, either positively or negatively. In such researches, experimental method is more frequently used.

However, with the passage of time and revolutionary changes in technology of analysis, experimental method is now used, as in natural sciences, in social sciences also. In a very formal way experiment is a way of organizing evidence so as to reach inference about the appropriateness of a hypothesis which essentially is a statement of relationship between a cause (set of causes) and a result (set of results).

In the case of experimental design two approaches are mainly practiced

• after only experiment
• before after experiment.

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Business Research Methodology pp 3–24 Cite as

The Nature of Research

• Sergey K. Aityan 2  
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Part of the book series: Classroom Companion: Business ((CCB))

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• Published: 15 April 2024

Experimental study on the interface characteristics of geogrid-reinforced gravelly soil based on pull-out tests

• Jie Liu 1 , 2 ,
• Jiadong Pan 1 , 2 ,
• Qi Liu 2 &

Scientific Reports volume  14 , Article number:  8669 ( 2024 ) Cite this article

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• Civil engineering

The factors influencing geogrid–soil interface characteristics are critical design parameters in some geotechnical designs. This study describes pull-out tests performed on gravelly soils commonly encountered in the Xinjiang region and reinforced with two types of geogrids. The factors affecting the geogrid–gravelly soil interface properties are investigated with different experimental loading methods (pull-out velocity, normal stress), geogrid types, and soil particle size distributions and water contents. The ultimate pull-out force increases with the normal stress and pull-out velocity. Furthermore, with increasing coarse particle content and water content, the ultimate pull-out force increases and then decreases sharply. Based on these research results, this paper provides reasonable parameters and recommendations for the design and pull-out testing of reinforced soil in engineering structures. In reinforced soil structure design, the grid depth should be increased appropriately, and the coarse particle content of the overlying soil should be between 30 and 40%. During construction, the gravelly soil should be compacted to the maximum compaction at the optimal water content, and the structure should have a reasonable waterproofing system. According to the calculation results of the interface strength parameters, the uniaxial geogrid–gravelly soil interface has a high cohesive force c sg , which should not be ignored in reinforced soil structure design.

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Introduction

Geosynthetics have been widely used in the past two decades to protect retaining walls, slopes, and embankments. The interaction between the geosynthetic materials and the soil body mainly reflects the reinforcement effect 1 . At the geogrid–soil interface, the resisting shear force mainly arises from the friction between the soil and the surface of the geogrid 2 . The interface characteristics between the soil and reinforcing material, especially the shear strength of the reinforcing soil interface, directly affect the safety and stability of reinforced soil structures. Therefore, the parameters of this interaction must be considered in design calculations 3 , 4 , 5 .

Many scholars have performed experimental research to understand the interface characteristics between the soil and reinforcing materials in reinforced soil. The corresponding tests are mainly interface direct shear and pull-out tests 3 , 6 , 7 . Comparing direct shear test and pull-out test results, Xu et al. 8 found that the direct shear strength τs, interfacial shear strength τds, and pull-out shear strength τp of a geogrid-compacted soil interface were similar. However, since the direct shear test is used to study the interfacial characteristics of reinforced soil, it can reflect only the interfacial strength of the reinforcing material and the soil and not the tensile strength of the reinforcing material or the strength of the whole soil body 9 . Furthermore, the direct shear test cannot fully simulate the double-sided sliding of the reinforcing material and soil and the large deformation characteristics of the soil when it is damaged. However, the pull-out test can consider various factors, such as soil expansion, crowding, and reinforcement slippage, influencing the performance of the reinforcement. This approach can simulate the working conditions of the geogrid inside the soil with simultaneous forces above and below. It can reflect the evolution of the reinforced soil structure during loading 10 . Therefore, the pull-out test is one of the standard methods used to study the reinforcement characteristics of geogrids in soil and to deduce the residual strength and peak strength.

Some domestic and foreign scholars have used this experimental method to investigate the mechanism of reinforcement and the factors influencing the interfacial characteristics of geogrids. Ochiai et al. 11 conducted field and laboratory pull-out tests to determine the parameters required to design and analyze geogrid reinforcement structures and elucidate their pull-out mechanisms. They noted that geogrids may fracture or elongate under normal stress. In addition, they recommended that pull-out tests be performed at a low normal stress. Li et al. 12 conducted a series of pull-out tests to investigate and compare the load‒displacement characteristics of tire belts and uniaxial and biaxial geogrid-reinforced sandy soils under different normal stresses. The damage to the tire belt-reinforced sand was progressive, with the shear strength of each part of the sand depending on that of various other parts of the sand. The interlocking effect and pull-out resistance between a tire belt and sandy soil are extreme and significantly greater than those between geogrid and sandy soil. Cardile et al. 13 investigated the stability of a geosynthetic–soil interface under cyclic loading. Under specific conditions, pull-out resistance parameters should be considered when designing geosynthetic-reinforced soil structures. Derksen et al. 14 designed a test instrument where the interface between the reinforcing materials and the soil could be observed to study the interfacial interactions occurring along the direction of the reinforcing materials. Three regions were identified based on different interaction patterns. Chen et al. 15 conducted a comprehensive study of extensive box pull-out tests using the discrete element method. Moreover, large-scale pull-out tests were conducted on embedded biaxial and triaxial geogrid ballast samples. A discrete element model that can reasonably predict the pull-out resistance of geogrid-reinforced soil was developed. Furthermore, the test results indicate that the effect of the geogrid aperture on the tensile strength of the grid is greater than the effect of the geogrid thickness. Perkins and Edens 16 combined pull-out tests with finite element numerical calculations to establish a numerical finite element model for pull-out tests and simulated pull-out tests with geosynthetic materials. By comparing the results of the finite element analysis and pull-out tests, it was demonstrated that the creep of the geogrid has a slight effect on the deformation of the geosynthetic material. Mosallanezhad et al. 17 investigated the performance of a new reinforcement system through large-scale pull-out tests and numerical analysis. In the new system, they used cubic units attached to the geogrid with elastic strips. The results showed that the pull-out interaction coefficient of the new system was 100% greater than that of typical geogrid systems. The successful design of geosynthetic reinforcement for geotechnical structures, especially geogrid reinforcement, requires information about the interaction of geogrid–geogrid interfaces. Hajitaheriha et al. 18 conducted a series of indoor tests and finite element modeling analyses to investigate the significant effects of parameters such as the number of geogrids, burial depth and effective trench depth on the bearing capacity ratio (BCR). The above study suggests that experimental research can not only establish a research model and verify the reasonableness of the model but also provide reasonable design parameters for engineering applications 15 . In addition, the successful design of geosynthetic reinforcement, especially geogrid reinforcement, of geotechnical structures requires information related to the interaction of the reinforcement–soil interfaces.

Factors affecting the characteristics of geogrid–soil interfaces are critical parameters in geotechnical design, so domestic and foreign scholars have studied this topic. Jing et al. 19 used the discrete element method to simulate the pull-out testing of geogrid-reinforced ballast to demonstrate the effects of particle shape, geogrid size and friction on a ballasted geogrid system. Du et al. 20 conducted direct shear and pull-out tests on tailings reinforced with geogrids of different grid sizes to explore reasonable grid sizes. The results show that the ratio of the geogrid–tailings interface area to the shear surface area should be controlled between 0.47 and 0.55, within which the embedding and occlusion function of the transverse ribs of the geogrid can be fully exploited so that the reinforcement effect of the geogrid can be optimized. Abdi et al. 21 designed and developed a sizeable pull-out test apparatus to evaluate the interaction between clay and thin sand layers and geogrids. The effects of factors such as geogrid geometry and soil grain size on pull-out resistance were investigated to facilitate the use of poor-quality soils in engineering. Zhao et al. 22 investigated the frictional characteristics of biaxial geogrid-reinforced soil at different pull-out velocities and embedment lengths on self-developed test equipment. The test results show that the pull-out velocity has little effect on the shear strength of reinforced soil. However, the pull-out force increases with increasing embedment length. The obtained results are of reference value for the design of biaxial geogrids in engineering. To test the pull-out performance of uniaxial polypropylene geogrids, Baykal and Dadasbilge 23 conducted pull-out testing to analyze the effect of the geogrid displacement velocity, load magnitude, and specimen width on the specimen behavior. The results show that the boundary effect of the pull-out box affects the peak value of the pull-out test.

An overview of the above research shows that the experimental study of the interface characteristics of reinforced soil is an essential element in the study of the functional properties, damage mode, and reinforcement mechanism of reinforced soil structures, which is of great significance for reducing engineering costs and engineering accidents. The factors influencing the characteristics of the geogrid–gravelly soil interface are critical to understand for predicting the reinforcement–soil interface properties and reinforcement mechanism. There are currently only a few studies on the geogrid–gravelly soil interface characteristics in Xinjiang. This study investigated three categories of gravelly soils in Xinjiang via pull-out tests with different normal stresses, pull-out velocities, and soil water contents. Sandy soil was used to artificially formulate two types of gravelly soil with five gradations each. The influences of the particle shape and gradation of the gravelly soil on the interfacial characteristics of the geogrid were investigated. On this basis, reasonable parameters and suggestions were given for the structural design of the reinforced soil project and pull-out test. The findings of this study will hopefully promote the application of geogrids in gravelly soil roadbeds in Xinjiang.

Pull-out testing of the geogrid

Test device.

The laboratory instrument used in this study was a YT140 pull-out tester for geosynthetics at the Wuhan University of Technology, College of Transportation (Fig.  1 ). The YT140 instrument can perform pull-out tests for geogrids, geomembranes, geotextiles, and other geosynthetic materials. The horizontal loading system consists of a displacement sensor and a pull-out force sensor, which can adjust the pull-out force and displacement during the test. A hydraulic device loads the normal stress at constant pressure. The instrument can record the data changes at each stage during the pull-out test in detail.

YT140 pull-out tester for geosynthetics: ( a ) test apparatus, ( b ) loading box, ( c ) schematic diagram of the device.

Test materials

The round gravelly soil, angular gravelly soil, and sandy soil studied in this experiment were collected from three different areas in Xinjiang (Fig.  2 ). The round gravelly soil was from the soil extraction site of the road construction project of the Shawan section of the S101 line in the Tacheng area, Xinjiang. It is a widely used roadbed filler for mountain highways in Xinjiang. The angular gravelly soil was from the soil quarry in Aketao County, Kechu, Xinjiang, and is a poorly graded gravel; it is a typical angular gravelly soil. The sandy soil was from the territory of the Xinjiang Hami region, and the site is located on a piedmont impact plain. The parent rock of this sandy soil is dominated by sandstone and siliceous rock. The three-phase proportion indices of the three gravelly soils derived from compaction testing are shown in Table 1 .

Soil sampling location: ( a ) round gravelly soil, ( b ) angular gravelly soil, ( c ) sandy soil.

Due to the limitation of the instrument size, particles greater than 60 mm were removed from the gravelly soils. To maintain the skeletal role of such coarse particles, the continuity of the coarse grain gradation, and a performance similar to that with a natural gradation, the equal mass substitution method was used to convert the content of extralarge particles. That is, all coarse materials are described as a proportion of equal replacement of extralarge particles (to allow the maximum particle size to correspond to the 5 mm particle size content) 24 , 25 , 26 . The scale-reduced gradation curves of the three gravelly soils for the test are shown in Fig.  3 .

The soil gradation curves.

The uniaxial geogrid used in the test was TGDG50HDPE (Fig.  4 ), with 46 longitudinal ribs per meter, and the maximum thickness of the horizontal ribs was 1.38 mm. The biaxial geogrid used for the test was a polypropylene biaxially oriented geogrid, model TGSG15-15 (Fig.  4 ). The geometric and strength characteristics of the geogrids are shown in Table 2 .

Schematic diagram of geogrids: ( a ) uniaxial geogrids and ( b ) biaxial geogrids.

The geogrid was cut according to the dimensions of the YT140 geosynthetic material pull-out tester loading box. The geogrid specimen used in this pull-out test contained eight vertical ribs along the width direction. The total length was 255 mm, the net length after deducting the distance inside the fixture was 237.5 mm, and the initial width of the geogrid buried in the soil was 100 mm.

Test design

Before the start of testing, the YT140-type geosynthetic pull-out instrument was calibrated. The standard calibrator fixed in the equipment recorded the measured value during tensile testing, which was compared to the standard value; then, the instrument was adjusted so that the error was within a reasonable range. Following the operation method stipulated in the “Test Methods of Geosynthetics for Highway Engineering” (JTG E50-2006), the geogrid was sampled across 75% of the width of the test box. To ensure that the reinforcing material could not be pulled out of the loading box and that a sufficient anchorage length was reserved, 300 mm was considered along the length direction. First, the lower box was filled in layers and compacted according to the set degree of compaction, and the loose soil particles on the surface were brushed off with a wire brush after each layer of filling to ensure a rough surface and a tight bond between the soil layers, with the top layer of the filling surface initially reaching slightly higher than the lower edge of the seam opening. After the lower box was filled, the specimen was pre-pressurized. The surface was cleaned after the prepressurization treatment so that the fill surface was flush with the lower edge of the seam opening. Then, the buried length of the 10–15 cm geogrid was centered and flatly laid on the soil surface of the lower box. The tensile end of the geogrid aligned with the seam opening between the upper and lower boxes and connected to a horizontally oriented fixture. A plate with a narrow slit of an adjustable height was inserted so that the positive lower edge was on the specimen's surface to fix the plate's position. Subsequently, the filling of the test box was continued in layers, and the layers were compacted until the compacted soil surface was flat and slightly below the top of the box. Finally, the pressurized plate was placed on top, and prepressure was applied for consolidation; the consolidation time was at least 15 min.

After the specimen preparation, a small amount of horizontal load was applied so that the horizontal loading device became taut, and the pull-out force of the instrument was set to zero. The pull-out velocity was set, a horizontal force was applied, pulling started, and after the pull-out force reached the peak, the test continued until it stabilized and then stopped. The pull-out force gradually pulled out the geogrid from the system. If no peak pull-out force occurred or the specimen was pulled out of the box as a whole, the length of the geogrid buried in the soil was shortened, and the test was repeated. The test program is shown in Table 3 .

In a pull-out test, the boundary effect of the sidewall of the pull-out box cannot be neglected. Figure  5 shows the pull-out curve of the uniaxial geogrid in the S1 soil sample when the normal stress is 100 kPa. After the pull-out test starts, the curve exhibits an obvious upward trend, after which the pull-out force decreases sharply. This is due to the increase in the pull-out displacement; geogrid mesh holes on the soil body of the embedded fixation effect lead to movement of the soil particles to the pull-out outlet, resulting in an increase in the density of the region near the pull-out outlet until the final geogrid becomes stuck in the pull-out outlet, resulting in a sharp increase in the pull-out force and ultimately in geogrid fracture. In contrast, this situation does not occur in actual projects because there are no fixed sidewall constraints. If the pull-out box is large enough, the geogrid can also break before it is pulled out. Therefore, the elimination of the boundary effect can only be performed by correcting the pull-out force–pull-out displacement curve. If the pull-out curve shows an upward section with a sharp increase in the pull-out force, this section is removed.

Force‒displacement curves of the S1 soil samples at a normal stress of 100 kPa.

Pull-out test results and analysis

Analysis of the force‒displacement curves of the pull-out tests under various normal stresses.

To study the influence of normal stress on the characteristics of the geogrid–soil interface in the pull-out test, a TGDG50HDPE uniaxial geogrid was used as the reinforcing material, and the S1 soil was used as the filler. A total of 7 groups of geogrid–gravelly soil pull-out tests with different normal stresses were carried out with a pull-out velocity of 1.0 mm/min. As shown in Fig.  6 , the curve between the pull-out displacement and force varies widely under different normal stresses. The pull-out force increases with increasing pull-out displacement, and the relationship between the pull-out force and pull-out displacement corresponds to strain hardening. At the beginning of the pull-out tests, the curves of the relationship between the pull-out displacement and force under different loads all have a linear segment for small pull-out displacements. This segment is the static friction stage, and its slope increases with normal stress. The pull-out displacement in this section mainly reflects the deformation of the geogrid. The greater the normal stress is, the longer the static friction stage, and the greater the pull-out force. After the linear static friction stage ends, the curve between the pull-out displacement and force increases linearly. The analysis shows that the curves of the relationship between the pull-out displacement and force under different normal stresses can be separated into two groups at this stage. The slope of the curve under 90–110 kPa of normal stress is significantly larger than that under 50–80 kPa of normal stress, and the pull-out force increases faster with increasing pull-out displacement. Afterward, the curves of the relationship between the pull-out displacement and force enter a nonlinearly increasing phase, in which the pull-out force of the geogrid increases at a slower velocity with increasing pull-out displacement. When the pull-out displacement reaches a certain level, the pull-out force peaks. Finally, the each curve of the relationship between the pull-out displacement and force ends with the peak pull-out force remaining stable or the geogrid fracturing.

Force‒displacement curves of pull-out tests of S1 soil under various vertical loads.

The relationship between the normal stress and the peak pull-out force was analyzed by comparing the geogrid force‒displacement curves of the pull-out tests under various vertical loads. When the normal stress is low, the force between the soil particles and the force between the soil and geogrid are small, the movement of the soil particles is easier to achieve, the embedment effect on the horizontal ribs of the geogrid is small, and the friction between the soil and geogrid is also very small. Hence, the pull-out force increases slowly with the pull-out displacement at the late stage of the test. This increase is mainly due to the resistance of the soil particles embedded in the mesh and the horizontal ribs before the crowding of the horizontal ribs 27 . When the normal stress is high, the force transmitted between the soil particles and the soil particles to the geogrid is large, and the friction force is high 28 . The more significant shear dilation effect at the interface between the soil particles and the geogrid makes the soil particles continuously compact. The embedment effect between the soil and the geogrid is more prominent. As a result, the pull-out resistance continues to increase, and the pull-out displacement corresponding to the peak strength increases.

The above analysis shows that the interface strength parameters should differ under the different normal stresses and peak pull-out forces on the geogrid in different layers. At present, the relevant specification does not consider this pattern. Applying a specific vertical load on the upper part of the geogrid or appropriately increasing the overburden thickness can improve the stability of the lower geogrid-reinforced soil.

Analysis of the force–displacement curves of the pull-out tests under various pull-out velocities

The TGDG50HDPE uniaxial geogrid was used as the reinforcing material, and the S1 soil and S2 soil were used as fillers to conduct pull-out tests at different pull-out velocities under 100 kPa of normal stress to study the effect of the change in pull-out velocity on the mechanical characteristics of the geogrid-reinforced gravelly soil. The pull-out velocity of the geogrid–soil pull-out test can be selected according to the site soil material and drainage conditions, as well as the consolidation rate of the soil samples. According to the “Test Methods of Geosynthetics for Highway Engineering” (JTG E50-2006), the corresponding range for this study is generally 0.2–3.0 mm/min. Thus, four different pull-out velocities of 1.0, 1.5, 2.0, and 3.0 mm/min were considered, and the force‒displacement curves of the pull-out tests were determined under a normal stress of σ = 100 kPa.

Figure  7 a, b show the geogrid force‒displacement curves of the pull-out tests for the S1 soil and S2 soil at different pull-out velocities. The overall curves of both soil samples exhibit strain hardening for all four pull-out velocities considered.

Force‒displacement curves of pull-out tests under various pull-out velocities: ( a ) S1 soil, ( b ) S2 soil.

Figure  7 a shows that the greater the pull-out velocity is, the greater the rate of increase in the pull-out force in the middle of the pull-out stage. Moreover, the difference between the peak pull-out force and the corresponding pull-out displacement is relatively small at the tested pull-out velocities. Because the coarse particles of round gravelly soil are spherical, the particles are more likely to move and rotate when subjected to a pull-out force, which is more likely to dissipate the dilatancy effect. Therefore, the round gravelly soil particles will be rearranged after a specific pull-out displacement.

Figure  7 b shows that the larger the pull-out velocity, the more pronounced the strain hardening phenomenon of the angular gravelly soil is, the faster the pull-out force increases with the pull-out displacement, and the larger the peak pull-out force is 22 . Compared to the peak pull-out force at a pull-out rate of 1 mm/min, the peak pull-out force at rates of 1.5 mm/min, 2 mm/min, and 3 mm/min increases by 30.7%, 70.6%, and 83.3%, respectively. This means that when the pull-out velocity is small, the relative displacement of the geogrid–gravelly soil interface is small per unit of time, and the geogrid has a long travel time to complete the displacement. The soil particles in the interface range have a stress concentration at the horizontal ribs that dissipates continuously with the rearrangement of soil particles. The stress of the reinforcing material should be evenly distributed, and the required pull-out force should be small. The larger the pull-out velocity is, the larger the relative displacement at the geogrid–soil interface per unit time. Additionally, the soil particles within a specific range above and below the geogrid–soil interface cannot readjust. Thus, the stress concentration at the horizontal ribs cannot dissipate, causing the soil near the geogrid to undergo shear dilation 29 . A significant interfacial frictional resistance is generated, increasing the peak pull-out force as the pull-out velocity increases.

The interaction mechanism between the geogrid and soil is more complex and closely related to the loading rate. In engineering applications, the mechanical performance index parameters should be determined through tests according to the actual conditions of the project. The geogrid–gravelly soil reinforcement structure takes some time to stabilize. Considering the safety of the structure, it is recommended to select a pulling speed of 1.5–2 mm/min for round gravelly soils and 1 mm/min for angular gravelly soils when selecting structural calculation parameters.

Analysis of the force‒displacement curves of the pull-out tests under various particle shapes and gradations

To study the effect of the gravelly soil particle shape and gradation on the geogrid–gravelly soil interface characteristics, coarse particles larger than 5 mm were sieved out of the S3 sandy soil, and the remaining fine particles were retained as the fine particle fraction of the test soil material. Compaction tests were performed on the fraction of fine particles less than 5 mm, yielding a maximum dry density of 1.61 g/cm 3 and an optimum water content of 6.1% for the fine particles. Crushed stone and pebble stone of 1 to 2 cm were used as the coarse grains of the gravelly soils and mixed with fine-grained soils in different proportions to make five gradations ranging from fine to coarse and a total of ten different gradations of angular gravelly and round gravelly soils. The compaction of these ten soil gradations was converted using the maximum dry density and optimum water content of the fine-grained fraction less than 5 mm to ensure that the compaction remained consistent. The grading scheme and physical properties are shown in Table 4 . The gradation curves of the five artificially formulated gravelly soils and the gradation curves of the sandy soil are shown in Fig.  8 .

The gradation curves of artificially prepared gravelly soil.

A TGSG15-15 biaxial geogrid was used as the reinforcing material for these tests. The force‒displacement curves of the pull-out tests of round gravelly soil and angular gravelly soil with different gradations were obtained under a 50 kPa normal stress and a 2.0 mm/min pull-out velocity, as shown in Fig.  9 a, b.

Force‒displacement curves of pull-out tests: ( a ) round gravelly soil, ( b ) angular gravelly soil.

Figure  9 a shows that the peak pull-out force of the sandy soil (0.51 kN) at a normal stress of 50 kPa is greater than the peak pull-out force of gradation 1 (0.44 kN) and gradation 2 (0.31 kN) with a lower content of round gravel. Because the surfaces of the pebble-like coarse particles used in this test are smooth, the friction coefficient is lower than that of the sandy soil, which reduces the internal friction angle φ of the mix. Therefore, the coarse particles distributed in the sandy soil are separated from each other when the coarse particle content is low, it is difficult to achieve mutual occlusion, and the pull-out force is smaller than that of pure sandy soil. The peak strength of gradation four among the five round gravelly soils is the largest at 0.88 kN, 1.73 times that of the sandy soil, 2.0 times that of gradation 1, and 2.73 times that of gradation 2. The difference in peak strength between gradation 3 and gradation 5 is minor, and the peak strengths are 0.73 kN and 0.66 kN, respectively.

Figure  9 b shows that the peak pull-out forces of the angular gravelly soil at a normal stress of 50 kPa are greater than the peak pull-out forces of the sandy soil. Among the five angular gravelly soils, the peak pull-out force of gradation 3 is 1.22 kN, which is 2.41 times that of the sandy soil, 1.91 times that of gradation 1, and 1.77 times that of gradation 2. The difference in the peak pull-out forces between gradation 4, 1.17 kN, and gradation 5, 1.11 kN, is small.

Figure  9 a, b show that the sandy soils of gradations 1 and 2 have similar trends for the curve segments after the peak pull-out force. Round gravelly soil with a higher coarse particle content (gradation 3 to gradation 5) exhibits strain softening. Gradation 3 to gradation 5, with higher coarse-grained contents, of angular gravelly soil show strain hardening characteristics in the curve's rising section after the pull-out force reaches its peak value. For gradation 3 to gradation 5, the angular gravelly soil and round gravelly soil, the pull-out forces developed faster and peaked earlier than those of the other three groups of tests. This indicates that the coarse grains are involved in the embedded fixation effect earlier and that only a very small pull-out displacement is required to achieve a specific strength. Meanwhile, comparing the force‒displacement curves of the pull-out tests of the angular gravel and round gravelly soils, it can be seen that the more coarse-grained material there is, the more pronounced the curve fluctuation, showing a clear step-like shape. When more coarse particles are present, the rotation, locking, and movement of soil particles affect the pull-out force of the geogrid more. In addition, with 1–2 cm coarse grains, the gravel material gradation is not uniform, and the large particle distribution dramatically influences the curve during the pull-out test. The large particles at the nodes and horizontal ribs of the geogrid are pushed as the geogrid is pulled. The adjustment of the misaligned large particles in the mesh increases the resistance of the large particles after realignment. Thus, the fluctuation in the force‒displacement curve of the pull-out test is more pronounced, showing an apparent step-like shape 30 .

Figure  10 shows that when the content of particles larger than 5 mm in the test material is 30%, the pull-out friction effect is substantially greater than that of general sandy soils. When the gravel content is between 30 and 40%, the fine particles in the gravelly soil fill the pores between the coarse particles, making the material denser. The responses of the coarse and fine particles in this case are coupled, and the contact area with the geogrid surface will reach a maximum. Conversely, when the gravel content exceeds 40%, the large particles play a skeletal role, and the fines are too small to fill the pores between the large particles. The frictional effect between the soil and reinforcement is then reduced 31 . This shows that appropriately increasing the content of coarse particles in gravelly soils can improve the shear strength of the reinforcement–soil contact surface and its residual shear strength 32 .

Curves of the peak shear stress of specimens with coarse particles.

The peak strength of the coarse grains of the irregular angular gravelly soil is generally significantly greater than that of the round gravelly soil with rounded coarse grains under the same working conditions in the pull-out test, indicating that the reinforcement effect of angular gravelly soil is greater than that of the round gravelly soil. Generally, in pull-out tests, angular gravelly soil has a peak strength that is 30% to 40% greater than that of round gravelly soil of the same gradation.

The above analysis shows that when the content of coarse particles with particle sizes greater than or equal to 1 cm is greater than 30%, the peak strength of the pull-out test is the greatest observed in this work. To ensure a good reinforcement effect, the content of coarse particles in geogrid-reinforced gravelly soil is recommended to be 30% to 40% for structure design, and it is recommended that gravelly soil with angular particles is used as roadbed filler.

Analysis of the force‒displacement curves of the pull-out tests under various water contents

In engineering practice, rainfall is the cause of damage to reinforced structures, and the water content is the main factor affecting the stability of reinforced structures 33 . To study the influence of the water content in the reinforced soil on the geogrid–soil interface characteristics, a pull-out test of the S3 soil sample under a 50 kPa normal stress and a 2.0 mm/min pull-out velocity was carried out by using a YT140 pull-out tester for geosynthetics with TGDG50 uniaxial geogrids under six groups of different water contents. The ultimate pull-out force of the geogrid was tested for different water contents.

Figure  11 shows the force‒displacement curves of sandy soils with different water contents from the pull-out tests. A clear differentiation in the curve shapes occurs at a water content of 6.4%. When the water content ranges from 2% to 6.4%, the force‒displacement curves of the pull-out test overlap at the beginning of the pull-out displacement, and the pull-out force increases faster with increasing displacement. When the water content ranges from 2 to 6.4%, the higher the water content, the earlier the peak pull-out force appears. The force‒displacement curves of the pull-out test show strain softening after the peak value, and the curve has a decreasing trend. When the water content is more significant than 6.4%, the peak pull-out force is lower, and the pull-out force increases more slowly with increasing pull-out displacement. The force‒displacement curves of the pull-out tests show a peak followed by a flat section, reflecting strain hardening. The displacement required to reach a specific pull-out force is greater at a higher water content.

Force‒displacement curves of the pull-out tests of S3 soil with different water contents.

Figure  12 shows the relationship between the ultimate pull-out force and the water content of gravelly soil. Clearly, the ultimate pull-out force increases and then decreases with increasing water content. The role of geogrids in reinforcing gravelly soil is mainly related to friction and embedded fixation. At low water contents, pseudocohesion occurs in gravelly soils due to capillary water action at the edges. The pseudocohesion will initially increase with increasing water content, and the resistance of the surrounding soil particles to movement when the geogrid is pulled increases. When the pseudocohesion reaches its maximum value, the pull-out resistance also peaks. Subsequently, the pseudocohesion decreases as the water content increases until it disappears, and the pull-out resistance decreases. When the pseudocohesion disappears, the water content then increases. At this point, the water acts as a lubricating fluid between the soil particles and at the contact surface between the geogrid and the soil particles. The higher the water content is, the more pronounced the lubrication effect will be such that the pull-out resistance will decrease sharply with increasing water content.

Relationship between the ultimate pull-out force and water content of gravelly soil.

In addition, the sandy soil used in this test is a typical cohesionless soil. When the water content in the soil changes, the friction coefficient between the soil particles and between the geogrid and soil particles decreases with increasing water content, which leads to a decrease in the friction between the soil and grids. Moreover, when the water content approaches the optimum water content, the compaction of the fill gradually increases, and the embedment effect of soil particles on the mesh becomes more pronounced 34 . The resistance of the transverse rib to the soil particles gradually dominates. As the water content continues to increase, the friction between the soil and the geogrid continues to decrease, the compaction of the soil decreases, the resistance of the horizontal rib to the soil particles gradually decreases, and the ultimate pull-out force appears to decay sharply 35 .

According to the above analysis, during the construction of reinforced structures, attention should be given to selecting the two indicators: the water content and compaction degree of the fill soil. A suitable water content should be selected during the construction process and should be at most the optimum water content. The compaction degree should be as close as possible to the maximum for the fill material used. Reinforced structures should be designed to prevent rainwater immersion during the heavy rainfall flood season, as these processes reduce the strength of the structure and lead to structural instability.

On the basis of the above research results, the parameters of the likely cohesion ( c sg ) and likely interface friction angle ( φ sg ) are introduced for describing the strength of the geogrid–soil interface. For a given soil sample and a given grating, c sg and φ sg are constants, so c sg and φ sg are the recommended parameters for use in engineering design and testing. After determining the ultimate pull-out force under different normal stresses based on the pull-out curves of the uniaxial geogrids in three soil samples, the corresponding interface shear strength τ f is calculated from Eq. ( 1 ). On this basis, the interface strength indices c sg and φ sg can be obtained by plotting the τ f – σ relationship (Table 5 ).

where T d is the ultimate pull-out force (kN) and L 2 is the length of the part of the geogrid buried in the soil (m). B is the width of the geogrid specimen (m).

Table 5 shows that the interfacial friction angle is greater than 38° in all cases except for the case of S2 soil with a water content of 6.6%. The interfacial friction angle is not less than 40° at the optimum water content. The interface strength between the gravelly soil and the uniaxial grid is very high. Therefore, the water content at the time of rolling during roadbed construction is generally equal to or close to the optimum water content. In addition, the interfacial cohesion between uniaxial grids and gravelly soils is not equal to zero; instead, the interfacial cohesion is more than 10 kPa. Additionally, the c sg for S3 soil is even greater than 100 kPa (w = 6.4%) while the current specification 36 is ignored, which is on the conservative side. Because the cohesive force of the geogrid– gravelly soil interface is not between the two adhesive forces, the cohesive force c sg actually reflects the embedded fixation of the geogrid mesh and soil particles, particularly, that of the coarse particles in the gravelly soil and the geogrid holes and cross-ribs; this resistance is considerable, so ignoring it is too conservative.

This study used pull-out tests to study how four factors, namely, the normal stress, pull-out rate, particle shape and gradation, and water content, affect the geogrid–gravelly soil interface properties. According to the research results, reasonable parameters and suggestions are given for future engineering structure design and pull-out testing. In addition, in the design and construction of roadbeds, the type of geogrid should be selected based on the actual force and deformation of the roadbed. Uniaxial geogrids are suitable for resisting unidirectional forces, such as the reinforcement of high-fill roadbeds. Biaxial geogrids are suitable for resisting uneven settlement and deformation in weak roadbeds. However, this study investigated only the force‒displacement curves and peak pull-out forces of geogrid-reinforced coarse-grained soils under the above four factors, and the sample size was small. In the experimental design, not all three soil types were considered in the pull-out tests for each factor due to the limitations of the test conditions. For example, only the S1 soil was adopted in the tests that considered the effect of normal stress on the interfacial characteristics of the reinforced soil, and the S3 soil was not considered in the pull-out velocity tests. However, the patterns derived from these tests can be applied to other soils 3 , 37 , 38 . In the future, we will further expand our research on the factors influencing reinforced soils. Moreover, at the microscale, studies on the movement of gravelly soil particles under the action of different factors and changes in the influence zone of reinforcement have yet to be conducted. Using digital image correlation (DIC) or particle image velocimetry (PIV) to study the microscale motions at the reinforcement–soil interface allows for a better analysis of the evolution and distribution of the particle displacement field in the reinforcement influence zone of the soil 39 . At present, effective methods for studying the interfacial characteristics of reinforced soils include pull-out tests and direct shear tests. However, the two methods produce different test data, failure modes, and strength indices in practical tests 40 , 41 . This study used only the pull-out test to analyze geogrid–gravelly soil interface characteristics. In the future, the results of pull-out test under the same experimental conditions should be compared with the results of direct shear to explore the differences between and advantages and disadvantages of the two tests for studying the interfacial characteristics of reinforced soil.

In this study, a series of pull-out tests were conducted on geogrid-reinforced gravelly soils to determine the effects of different normal stresses, pull-out rates, soil particle shapes and gradations, and moisture content conditions on the interfacial properties of reinforced soils. Based on the pull-out test data, the interfacial strength parameters of the three types of soils reinforced by uniaxial geogrids were obtained for different normal stresses and water contents. Reasonable parameters and suggestions were given for the structural design of reinforced soil engineering and pull-out testing. The following conclusions were drawn:

The pull-out force increases with the pull-out displacement at each of the normal stresses tested, and the ultimate pull-out force increases continuously with the normal stress. Therefore, in the design and construction stage of reinforced soil structures, appropriately increasing the geogrid burial depth is helpful for improving the stability of the geogrid-reinforced soil.

The greater the pull-out velocity is, the more pronounced the strain-hardening behavior reflected in the force‒displacement curves of the pull-out tests. The faster the pull-out force continues to increase, the greater the peak pull-out force. Considering the safety of a structure, when choosing the structural calculation parameters, it is recommended to use a pull-out velocity of 1.5 –2 mm/min for the pull-out testing of geogrid-reinforced round gravelly soil and a pull-out velocity of 1 mm/min for the pull-out testing of angular gravelly soil.

Among the conditions tested, when the content of coarse particles with particle sizes greater than or equal to 1 cm is greater than 30%, the peak force of the pull-out test is the largest. To ensure a good reinforcement effect, the content of coarse particles in the geogrid–gravelly soil reinforcement structure design is recommended to be 30% to 40%, and it is recommended to prioritize gravelly soil with angular particles as roadbed filler.

When the water content in the sandy soil is less than the optimum, the trend of the force‒displacement curves and the peak pull-out forces of the pull-out tests are less different than when the water content is greater than the optimum. However, when the water content exceeds the optimum, the peak pull-out force decreases sharply. Therefore, in the design and construction of geogrid-reinforced soil engineering, special attention should be given to selecting and implementing the two indicators of the fill soil: the water content and compaction of the fill soil should be approximately the optimal water content and maximum compaction. During the construction and operation of geogrid-reinforced soil engineering structures, special attention should be given to the drainage system of the structure to avoid structural failure due to an excessive water content of the fill.

Uniaxial geogrid and gravelly soil interface cohesion c sg is larger; it is the grille cross rib end bearing resistance embodiment, is not a geogrid—soil interface viscous size of the reflection of the role of the actual engineering design to ignore the role of the c sg is unreasonable conservative practice.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Wang, J., Liu, F., Wang, P. & Cai, Y. Particle size effects on coarse soil-geogrid interface response in cyclic and post-cyclic direct shear tests. Geotext. Geomembr. 44 , 854–861. https://doi.org/10.1016/j.geotexmem.2016.06.011 (2016).

Article   Google Scholar  

Liu, C.-N., Ho, Y.-H. & Huang, J.-W. Large scale direct shear tests of soil/PET-yarn geogrid interfaces. Geotext. Geomembr. 27 , 19–30. https://doi.org/10.1016/j.geotexmem.2008.03.002 (2009).

Kayadelen, C., Önal, T. Ö. & Altay, G. Experimental study on pull-out response of geogrid embedded in sand. Measurement 117 , 390–396. https://doi.org/10.1016/j.measurement.2017.12.024 (2018).

Article   ADS   Google Scholar  

Moraci, N. et al. Soil geosynthetic interaction: Design parameters from experimental and theoretical analysis. Transport. Infrastruct. Geotechnol. 1 , 165–227. https://doi.org/10.1007/s40515-014-0007-2 (2014).

Karnamprabhakara, B. K., Chennarapu, H. & Balunaini, U. Modified axial pullout resistance factors of geostrip and metal strip reinforcements in sand considering transverse pull effects. Geotech. Geol. Eng. 41 , 3847–3858. https://doi.org/10.1007/s10706-023-02485-7 (2023).

Makkar, F. M., Chandrakaran, S. & Sankar, N. Performance of 3-D geogrid-reinforced sand under direct shear mode. Int. J. Geotech. Eng. 13 , 227–235. https://doi.org/10.1080/19386362.2017.1336297 (2019).

Article   CAS   Google Scholar  

Makkar, F. M., Chandrakaran, S. & Sankar, N. Experimental investigation of response of different granular soil-3D geogrid interfaces using large-scale direct shear tests. J. Mater. Civ. Eng. 31 , 04019012. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002645 (2019).

Xu, Y., Williams, D. J. & Serati, M. Measurement of shear strength and interface parameters by multi-stage large-scale direct/interface shear and pull-out tests. Meas. Sci. Technol. 29 , 085601. https://doi.org/10.1016/j.geotexmem.2016.06.011 (2018).

Hai-long, Z., Yi-chuan, X., Ai-jun, Z. & Shao-hong, Z. Experimental investigation on shear strength of reinforced coarse-grained soil. J. China Inst. Water Resour. Hydropower Res. 11 , 41–47 (2013).

Google Scholar  

Wen-bai, L. & Jian, Z. Experimental research on interface friction of geogrids and soil. Rock Soil Mech. 30 , 965–970 (2009).

Ochiai, H., Otani, J., Hayashic, S. & Hirai, T. The pull-out resistance of geogrids in reinforced soil. Geotextiles Geomembr. 14 , 19–42 (1996).

Li, L. H., Chen, Y. J., Ferreira, P. M. V., Liu, Y. & Xiao, H. L. Experimental investigations on the pull-out behavior of tire strips reinforced sands. Materials (Basel) https://doi.org/10.3390/ma10070707 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Cardile, G., Pisano, M. & Moraci, N. The influence of a cyclic loading history on soil-geogrid interaction under pullout condition. Geotextiles Geomembr. 47 , 552–565. https://doi.org/10.1016/j.geotexmem.2019.01.012 (2019).

Derksen, J., Ziegler, M. & Fuentes, R. Geogrid–soil interaction: A new conceptual model and testing apparatus. Geotextiles Geomembr. 49 , 1393–1406. https://doi.org/10.1016/j.geotexmem.2021.05.011 (2021).

Chen, C., McDowell, G. R. & Thom, N. A study of geogrid-reinforced ballast using laboratory pull-out tests and discrete element modelling. Geomech. Geoeng. 8 , 244–253. https://doi.org/10.1080/17486025.2013.805253 (2013).

Perkins, S. & Edens, M. Finite element modeling of a geosynthetic pullout test. Geotech. Geol. Eng. 21 , 357–375. https://doi.org/10.1023/b:gege.0000006053.77489.c5 (2003).

Mosallanezhad, M., Taghavi, S. H. S., Hataf, N. & Alfaro, M. C. Experimental and numerical studies of the performance of the new reinforcement system under pull-out conditions. Geotextiles Geomembr. 44 , 70–80. https://doi.org/10.1016/j.geotexmem.2015.07.006 (2016).

Hajitaheriha, M. M., Akbarimehr, D., Hasani Motlagh, A. & Damerchilou, H. Bearing capacity improvement of shallow foundations using a trench filled with granular materials and reinforced with geogrids. Arab. J. Geosci. https://doi.org/10.1007/s12517-021-07679-y (2021).

Jing, G. Q., Luo, X. H. & Wang, Z. J. Micro-analysis ballast-geogrid pull out tests interaction. Appl. Mech. Mater. 548–549 , 1716–1720. https://doi.org/10.4028/www.scientific.net/AMM.548-549.1716 (2014).

Du, C., Niu, B., Wang, L., Yi, F. & Liang, L. Experimental study of reasonable mesh size of geogrid reinforced tailings. Sci. Rep. 12 , 10037. https://doi.org/10.1038/s41598-022-13980-x (2022).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Abdi, M., Zandieh, A., Mirzaeifar, H. & Arjomand, M. Influence of geogrid type and coarse grain size on pull out behaviour of clays reinforced with geogrids embedded in thin granular layers. Eur. J. Environ. Civ. Eng. 25 , 2161–2180. https://doi.org/10.1080/19648189.2019.1619627 (2021).

Zhao, Y., Yang, G., Wang, Z. & Liang, X. Optimal configuration for the wind-solar complementary energy storage capacity based on improved harmony search algorithm J. Phys. Conf. Ser. 2598 , 012016 (2023) (IOP Publishing).

Baykal, G. & Dadasbilge, O. Geosynthetics in Civil and Environmental Engineering: Geosynthetics Asia 2008 Proceedings of the 4th Asian Regional Conference on Geosynthetics in Shanghai, China. 174–178 (Springer, 2008).

Hai-tao, L. & Xiao-hui, C. Discrete element analysis for size effects of coarse-grained soils. Rock Soil Mech. 30 , 287–292 (2009).

Tang, K., Xie, X. & Yang, L. Research on mechanical characteristics of gravel soil based on large-scale triaxial tests. Chin. J. Under Sp. Eng. 10 , 580–585 (2014).

Yong-zhen, Z., Wei, Z., Jia-jun, P. & Na, Z. Effects of gradation scale method on maximum dry density of coarse-grained soil. Rock Soil Mech. 36 , 417–422 (2015).

Ismail, M., Joohari, M., Habulat, A. & Azizan, F. Pull-out resistance of sand-geosynthetics reinforcement. Int. J. Integr. Eng. 13 , 87–93. https://doi.org/10.30880/ijie.2021.13.03.010 (2021).

Abdi, M. & Zandieh, A. Experimental and numerical analysis of large scale pull out tests conducted on clays reinforced with geogrids encapsulated with coarse material. Geotextiles Geomembr. 42 , 494–504. https://doi.org/10.1016/j.geotexmem.2014.07.008 (2014).

Liu, J., Wang, B. & Sun, Y. Mechanism and mesoscopic characteristics of indirectly reinforced gravelly soil by a geogrid. Adv. Mater. Sci. Eng. https://doi.org/10.1155/2022/8536258 (2022).

Guangqing, Y., Guangxin, L. & Baojian, Z. Experimental studies on interface friction characteristics of geogrids. Chin. J. Geotech. Eng. 28 , 948–952 (2006).

Shengyou, L. Theory and Technology of Modern Reinforced Soil . 75–81 (China Communication Press, 2006).

Jia-Quan, W., Biao, W. U., Hong, Z., Qi, Z. & Gang, K. E. Large direct shear test research of interface interaction characteristics of geogrid and coarse grained soil. J. Guangxi Univ. Sci. Technol. (2015).

Feng, X., Yang, Q., Li, S. & Luan, M. Influence of water content on pullout behavior of geogrid in red clay. Chin. J. Rock Mech. Eng. 28 , 4059–4064 (2009).

Altay, G., Kayadelen, C., Taşkıran, T. & Kaya, Y. Z. A laboratory study on pull-out resistance of geogrid in clay soil. Measurement 139 , 301–307. https://doi.org/10.1016/j.measurement.2019.02.065 (2019).

Chen, J.-N., Ren, X., Xu, H., Zhang, C. & Xia, L. Effects of grain size and moisture content on the strength of geogrid-reinforced sand in direct shear mode. Int. J. Geomech. 22 , 04022006. https://doi.org/10.1061/(ASCE)GM.1943-5622.0002309 (2022).

Giroud, J. P. From geotextiles to geosynthetics: A revolution in geotechnical engineering. Proc. 3rd Int. Conf. Geotextiles, Vienna, 1–18 (1986).

Cai, X. et al. Study on interface interaction between uniaxial geogrid reinforcement and soil based on tensile and pull-out tests. Sustainability. https://doi.org/10.3390/su141610386 (2022).

Zhao, Y., Yang, G., Wang, Z. & Yuan, S. Research on the effect of particle size on the interface friction between geogrid reinforcement and soil. Sustainability https://doi.org/10.3390/su142215443 (2022).

Liu, J. et al. Experimental study on the fine-scale characteristics of a geogrid-gravelly soil reinforcement influence zone. Front. Earth Sci. https://doi.org/10.3389/feart.2022.1053728 (2023).

Wang, Z., Jacobs, F. & Ziegler, M. Experimental and DEM investigation of geogrid–soil interaction under pullout loads. Geotextiles Geomembr. 44 , 230–246. https://doi.org/10.1016/j.geotexmem.2015.11.001 (2016).

Kim, D. & Ha, S. Effects of particle size on the shear behavior of coarse grained soils reinforced with geogrid. Materials (Basel) 7 , 963–979. https://doi.org/10.3390/ma7020963 (2014).

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This study was funded by the Enterprise Commissioned Science and Technology Project of Xinjiang Traffic Design Institute Company (No. KY2022121902).

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Liu, J., Pan, J., Liu, Q. et al. Experimental study on the interface characteristics of geogrid-reinforced gravelly soil based on pull-out tests. Sci Rep 14 , 8669 (2024). https://doi.org/10.1038/s41598-024-59297-9

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Key facts about U.S. Latinos for National Hispanic Heritage Month

National Hispanic Heritage Month, which begins in the United States each year on Sept. 15, celebrates U.S. Latinos , their culture and their history. Started in 1968 by Congress as Hispanic Heritage Week, it was expanded to a month in 1988. The celebration begins in the middle of September to coincide with independence days in several Latin American countries: Guatemala, Honduras, El Salvador, Nicaragua and Costa Rica celebrate theirs on Sept. 15, followed by Mexico on Sept. 16, Chile on Sept. 18 and Belize on Sept. 21.

Here are some key facts about the U.S. Latino population by geography and by characteristics such as language use and origin group.

As part of our ongoing research about Hispanics in the United States, we analyzed how this group has changed over time using data from the U.S. Census Bureau. The decennial census ( PL94-171 census data ) provided some historical state and national population counts, and population estimates provided the latest data on total population, births and immigration.

We also examined characteristics of the U.S. Hispanic population using the American Community Survey (ACS), which provides data for states and the U.S. on Hispanic origin, language use, country of birth and educational attainment. Data from the 2022 ACS and some from the 2010 ACS are from tabulations released by U.S. Census Bureau . Some ACS and census data is from Integrated Public Use Microdata Series (IPUMS) of the University of Minnesota.

The U.S. Hispanic population reached 63.6 million in 2022, up from 50.5 million in 2010. The 26% increase in the Hispanic population was faster than the nation’s 8% growth rate but slower than the 34% increase in the Asian population. In 2022, Hispanics made up nearly one-in-five people in the U.S. (19%), up from 16% in 2010 and just 5% in 1970.

Hispanics have played a major role in U.S. population growth over the past decade. The U.S. population grew by 24.5 million from 2010 to 2022, and Hispanics accounted for 53% of this increase – a greater share than any other racial or ethnic group. The next closest group is non-Hispanic people who identify with two or more races. Their population grew by 8.4 million during this time, accounting for 34% of the overall increase.

The number of Latinos who say they are multiracial has increased dramatically. More than 27 million Latinos identified with more than one race in 2022, up from 3 million in 2010. The increase could be due to several factors, including changes to the census form that make it easier for people to select multiple races and growing racial diversity.

Growth in the number of multiracial Latinos comes primarily from those who identify as at least one specific race and “some other race” (i.e., those who write in a response). This population grew from 2.1 million to 24.9 million between 2010 and 2022 and now represents about 91% of multiracial Latinos. The increase was due almost entirely to growth in the number of people who identified as White and some other race, according to the 2020 census.

At the same time, the number of Latinos who identified as White and no other race declined from 26.7 million in 2010 to 10.7 million in 2022.

The roughly 37.4 million people of Mexican origin in the U.S. represented nearly 60% of the nation’s Hispanic population in 2022. Those of Puerto Rican origin are the next largest group, at 5.9 million, which does not include another roughly 3.2 million Puerto Ricans who lived on the island in 2022. The U.S. population of Puerto Rican origin has grown partly due to people moving from Puerto Rico to the 50 states and the District of Columbia.

Six other Hispanic origin groups in the U.S. each have 1 million or more people: Salvadorans, Cubans, Dominicans, Guatemalans, Colombians and Hondurans. In addition, in 2022, Spaniards accounted for nearly 1 million U.S. Latinos.

Puerto Rico’s population has declined by about 500,000 since 2010, from 3.7 million to 3.2 million. Puerto Rico has experienced a net population loss since at least 2005 , driven by low fertility rates and migration to the U.S. mainland. An ongoing economic recession and devastation from hurricanes Maria and Irma in 2017 have also contributed to the decline.

Venezuelans have seen the fastest population growth among U.S. Latinos. From 2010 to 2022, the Venezuelan-origin population in the U.S. increased by 236% to 815,000. Four other groups saw growth rates exceeding 50%: Hondurans increased by 67%, followed by Guatemalans (62%), Dominicans (59%) and Colombians (51%).

By contrast, the number of people of Mexican origin in the U.S. grew by only 14%, by far the slowest rate among the most populous origin groups.

Hispanics are the largest racial or ethnic group in California and Texas. This demographic milestone in California happened in 2014 and was a first for the state with the nation’s largest Hispanic population . Latinos accounted for 40% of California’s population in 2022, among the greatest shares in the country.

That year, there were about 15.7 million Hispanics in California, up from 14.0 million in 2010. The non-Hispanic White population, the next largest group, declined from 15.0 million to 13.2 million during this time, reflecting a broader national trend .

In Texas, the state with the next largest Latino population (12.1 million), Latinos also made up 40% of the population in 2022 and became the largest racial or ethnic group in 2021. In Florida, the state with the third-largest Latino population (6.0 million), Latinos made up 27% of residents.

Rounding out the top five states with the largest Hispanic populations were New York (3.9 million) and Arizona (2.4 million). Eight more states had 1 million or more Hispanics: Illinois, New Jersey, Colorado, Georgia, Pennsylvania, North Carolina, Washington and New Mexico.

Vermont had the nation’s smallest Latino population (15,000) in 2022, followed by Maine (29,000), West Virginia and North Dakota (34,000 each), and South Dakota (42,000).

In New Mexico, Hispanics have been a majority of the population since 2021 and the state’s largest racial or ethnic group since the early 2000s. In 2022, the state was home to 1.1 million Hispanics.

Three states’ Hispanic populations increased by more than 1 million from 2010 to 2022. Texas (2.5 million increase), Florida (1.8 million) and California (1.6 million) accounted for almost half of the growth nationwide since 2010. Arizona (480,000 increase), New Jersey (464,000) and New York (432,000) had the next-biggest increases. All 50 states and the District of Columbia have seen growth in their Hispanic populations since 2010.

North and South Dakota’s Hispanic populations have grown the fastest since 2010. The number of Hispanics in North and South Dakota more than doubled (146% and 107% increases, respectively) from 2010 to 2022. But even with that growth, these states each had fewer than 45,000 Hispanics in 2022, among the smallest populations in the country.

The slowest growth was in New Mexico (10% increase), California (12%), and Illinois and New York (13% each), all states with significant Hispanic populations.

The makeup of the U.S. Hispanic population varies widely across major metropolitan areas.  Most of the metro areas in the Midwest, West and South with the largest Hispanic populations are predominantly Mexican. About three-quarters of Hispanics in the Chicago (77%) and Los Angeles (75%) areas identify as Mexican, as do 67% in the Houston area.

Metro areas in the Northeast tend to have more diverse Hispanic origins. For example, no origin group makes up more than 30% of the New York and Boston metro areas’ Hispanic populations.

Metro areas in Florida and the nation’s capital have distinctive Hispanic enclaves. Puerto Ricans make up 43% of Hispanics in the Orlando area, while Cubans make up 39% of Hispanics in the Miami area. In the Washington, D.C., metro area, Salvadorans account for 30% of Hispanics.

Catholics remain the largest religious group among Latinos in the U.S., but they have become a smaller share of the Latino population over the past decade. In 2022, 43% of Latinos adults identify as Catholic, down from 67% in 2010. Meanwhile, 30% of Latinos are religiously unaffiliated (describing themselves as atheist, agnostic or “nothing in particular”), up from 10% in 2010. The share of Latinos who identify as Protestants – including evangelical Protestants – has been relatively stable.

Newborns, not immigrants, have driven the recent growth among U.S. Hispanics. During the 2010s, an average of 1 million Hispanic babies were born each year, slightly more than during the 2000s. At the same time, about 350,000 Hispanic immigrants arrived annually, down substantially from the previous two decades.

The recent predominance of new births over immigration as a source of Hispanic population growth is a reversal of historical trends. In the 1980s and 1990s, immigration drove Hispanic population growth.

From 2020 to 2022, average annual births among Hispanics were slightly below the previous decade, but immigration decreased considerably, from 350,000 per year to 270,000. Some of this decline can be attributed to immigration into the U.S. stopping almost entirely during the early stages of the COVID-19 pandemic. With the removal of pandemic-related restrictions , the contribution of immigration to Hispanic growth appears to be returning to early 2010s levels.

The share of Latinos in the U.S. who speak English proficiently is growing. In 2022, 72% of Latinos ages 5 and older spoke English proficiently, up from 59% in 2000. U.S.-born Latinos are driving this growth: The share of U.S.-born Latinos who speak English proficiently increased by 9 percentage points in that span, compared with a 5-point increase among Latino immigrants. All told, 42.3 million Latinos in the U.S. spoke English proficiently in 2022.

At the same time, the share of Latinos who speak Spanish at home declined from 78% in 2000 to 68% in 2022, and most of that decline was among the U.S. born.

Even though the share of Latinos who speak Spanish at home has declined, the number who do so has grown from 24.6 million in 2000 to 39.7 million in 2022 because of the overall growth in the Latino population.

The share of U.S. Hispanics with college experience has increased since 2010. About 45% of U.S. Hispanic adults ages 25 and older had at least some college experience in 2022, up from 36% in 2010. The share of Hispanics with a bachelor’s degree or more education also increased, from 13% to 20%. The share with a bachelor’s degree or higher increased more among Hispanic women (from 14% to 22%) than Hispanic men (12% to 18%).

The number of Latinos enrolled in college or postgraduate education also increased between 2010 and 2022, from 2.9 million to 4.2 million. Among all U.S. undergraduate and graduate students, the share of Latinos increased from 14% in 2010 to 20% in 2022, slightly higher than the Latino share of the total population.

Four-in-five Latinos are U.S. citizens. As of 2022, 81% of Latinos living in the country are U.S. citizens, up from 74% in 2010. This includes people born in the U.S. and its territories (including Puerto Rico), people born abroad to American parents, and immigrants who have become naturalized citizens. The Center recently published citizenship rates among Hispanic origin groups for 2021; this data is not yet available for 2022.

Note: This post has been regularly updated since it was originally published on Sept. 16, 2014.

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Dissociative Disorders

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Or text "HelpLine" to 62640

Dissociative disorders are characterized by an involuntary escape from reality characterized by a disconnection between thoughts, identity, consciousness and memory. People from all age groups and racial, ethnic and socioeconomic backgrounds can experience a dissociative disorder.

Up to 75% of people experience at least one depersonalization/derealization episode in their lives, with only 2% meeting the full criteria for chronic episodes. Women are more likely than men to be diagnosed with a dissociative disorder. 

The symptoms of a dissociative disorder usually first develop as a response to a traumatic event, such as abuse or military combat, to keep those memories under control. Stressful situations can worsen symptoms and cause problems with functioning in everyday activities. However, the symptoms a person experiences will depend on the type of dissociative disorder that a person has.

Treatment for dissociative disorders often involves psychotherapy and medication. Though finding an effective treatment plan can be difficult, many people are able to live healthy and productive lives.

Symptoms and signs of dissociative disorders include:

• Significant memory loss of specific times, people and events
• Out-of-body experiences, such as feeling as though you are watching a movie of yourself
• Mental health problems such as depression, anxiety and thoughts of suicide
• A sense of detachment from your emotions, or emotional numbness
• A lack of a sense of self-identity

The symptoms of dissociative disorders depend on the type of disorder that has been diagnosed. There are three types of dissociative disorders defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM):

• Dissociative Amnesia. The main symptom is difficulty remembering important information about one’s self. Dissociative amnesia may surround a particular event, such as combat or abuse, or more rarely, information about identity and life history. The onset for an amnesic episode is usually sudden, and an episode can last minutes, hours, days, or, rarely, months or years. There is no average for age onset or percentage, and a person may experience multiple episodes throughout their life.
• Depersonalization disorder. This disorder involves ongoing feelings of detachment from actions, feelings, thoughts and sensations as if they are watching a movie (depersonalization). Sometimes other people and things may feel like people and things in the world around them are unreal (derealization). A person may experience depersonalization, derealization or both. Symptoms can last just a matter of moments or return at times over the years. The average onset age is 16, although depersonalization episodes can start anywhere from early to mid childhood. Less than 20% of people with this disorder start experiencing episodes after the age of 20.
• Dissociative identity disorder.  Formerly known as multiple personality disorder, this disorder is characterized by alternating between multiple identities. A person may feel like one or more voices are trying to take control in their head. Often these identities may have unique names, characteristics, mannerisms and voices. People with DID will experience gaps in memory of every day events, personal information and trauma. Women are more likely to be diagnosed, as they more frequently present with acute dissociative symptoms. Men are more likely to deny symptoms and trauma histories, and commonly exhibit more violent behavior, rather than amnesia or fugue states. This can lead to elevated false negative diagnosis.

Dissociative disorders usually develop as a way of dealing with trauma. Dissociative disorders most often form in children exposed to long-term physical, sexual or emotional abuse. Natural disasters and combat can also cause dissociative disorders.

Doctors diagnose dissociative disorders based on a review of symptoms and personal history. A doctor may perform tests to rule out physical conditions that can cause symptoms such as memory loss and a sense of unreality (for example, head injury, brain lesions or tumors, sleep deprivation or intoxication). If physical causes are ruled out, a mental health specialist is often consulted to make an evaluation.

Many features of dissociative disorders can be influenced by a person’s cultural background. In the case of dissociative identity disorder and dissociative amnesia, patients may present with unexplained, non-epileptic seizures, paralyses or sensory loss. In settings where possession is part of cultural beliefs, the fragmented identities of a person who has DID may take the form of spirits, deities, demons or animals. Intercultural contact may also influence the characteristics of other identities. For example, a person in India exposed to Western culture may present with an “alter” who only speaks English. In cultures with highly restrictive social conditions, amnesia is frequently triggered by severe psychological stress such as conflict caused by oppression. Finally, voluntarily induced states of depersonalization can be a part of meditative practices prevalent in many religions and cultures, and should not be diagnosed as a disorder.

Dissociative disorders are managed through various therapies including:

• Psychotherapies such as cognitive behavioral therapy (CBT) and dialectical behavioral therapy (DBT)
• Eye movement desensitization and reprocessing (EMDR)
• ​ Medications such as antidepressants can treat symptoms of related conditions

Related Conditions

Because dissociative disorders appear on the trauma spectrum, many patients may have conditions associated with trauma, as well as additional trauma-based conditions.

• Posttraumatic stress disorder (PTSD)
• Borderline personality disorder (BPD)
• Substance use disorders / Dual Diagnosis

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6 Common Leadership Styles — and How to Decide Which to Use When

• Rebecca Knight

Being a great leader means recognizing that different circumstances call for different approaches.

Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business cycle. But what if you feel like you’re not equipped to take on a new and different leadership style — let alone more than one? In this article, the author outlines the six leadership styles Daniel Goleman first introduced in his 2000 HBR article, “Leadership That Gets Results,” and explains when to use each one. The good news is that personality is not destiny. Even if you’re naturally introverted or you tend to be driven by data and analysis rather than emotion, you can still learn how to adapt different leadership styles to organize, motivate, and direct your team.

Much has been written about common leadership styles and how to identify the right style for you, whether it’s transactional or transformational, bureaucratic or laissez-faire. But according to Daniel Goleman, a psychologist best known for his work on emotional intelligence, “Being a great leader means recognizing that different circumstances may call for different approaches.”

• RK Rebecca Knight is a journalist who writes about all things related to the changing nature of careers and the workplace. Her essays and reported stories have been featured in The Boston Globe, Business Insider, The New York Times, BBC, and The Christian Science Monitor. She was shortlisted as a Reuters Institute Fellow at Oxford University in 2023. Earlier in her career, she spent a decade as an editor and reporter at the Financial Times in New York, London, and Boston.

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