a research characteristic

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.

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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:

Qualitative research is a type of research methodology that focuses on gathering and analyzing non-numerical data to gain a deeper understanding of human behavior, experiences, and perspectives.

It aims to explore the “why” and “how” of a phenomenon rather than the “what,” “where,” and “when” typically addressed by quantitative research.

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research involves researchers interpreting data to identify themes, patterns, and meanings.

Qualitative research can be used to:

• Gain deep contextual understandings of the subjective social reality of individuals
• To answer questions about experience and meaning from the participant’s perspective
• To design hypotheses, theory must be researched using qualitative methods to determine what is important before research can begin. 

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? 

Characteristics 

Naturalistic setting.

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

Naturalistic settings provide valuable contextual information to help researchers better understand and interpret the data they collect.

The environment, social interactions, and cultural factors can all influence behavior and experiences, and these elements are more easily observed in real-world settings.

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).

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). 

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 research setting (Scarduzio, 2017).

Interpretive analysis

In qualitative research, interpretive analysis is crucial in making sense of the collected data.

This process involves examining the raw data, such as interview transcripts, field notes, or documents, and identifying the underlying themes, patterns, and meanings that emerge from the participants’ experiences and perspectives.

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.

1. 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). 

2. 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). 

3. 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).

4. 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. 

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

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. 

While individual qualitative studies are often limited in their generalizability due to factors such as sample size and context, metasynthesis enables researchers to synthesize findings from multiple studies, potentially leading to more generalizable conclusions.

By integrating findings from studies conducted in diverse settings and with different populations, metasynthesis can provide broader insights into the phenomenon of interest.

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.

What is narrative analysis?

Narrative analysis is a qualitative research method used to understand how individuals create stories from their personal experiences.

There is an emphasis on understanding the context in which a narrative is constructed, recognizing the influence of historical, cultural, and social factors on storytelling.

Researchers can use different methods together to explore a research question.

Some narrative researchers focus on the content of what is said, using thematic narrative analysis, while others focus on the structure, such as holistic-form or categorical-form structural narrative analysis. Others focus on how the narrative is produced and performed.

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What is Research Design? Characteristics, Types, Process, & Examples

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Ever felt like a hamster on a research wheel fast, spinning with a million questions but going nowhere? You've got your topic; you're brimming with curiosity, but... what next? So, forget the research rut and get your papers! This ultimate guide to "what is research design?" will have you navigating your project like a pro, uncovering answers and avoiding dead ends. Know the features of good research design, what you mean by research design, elements of research design, and more.

What is Research Design?

Before starting with the topic, do you know what is research design? Research design is the structure of research methods and techniques selected to conduct a study. It refines the methods suited to the subject and ensures a successful setup. Defining a research topic clarifies the type of research (experimental, survey research, correlational, semi-experimental, review) and its sub-type (experimental design, research problem, descriptive case-study).

There are three main types of designs for research:

1. Data Collection

2. Measurement

3. Data Analysis

Elements of Research Design 

Now that you know what is research design, it is important to know the elements and components of research design. Impactful research minimises bias and enhances data accuracy. Designs with minimal error margins are ideal. Key elements include:

1. Accurate purpose statement

2. Techniques for data collection and analysis

3. Methods for data analysis

4. Type of research methodology

5. Probable objections to research

6. Research settings

7. Timeline

8. Measurement of analysis

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Characteristics of Research Design

Research design has several key characteristics that contribute to the validity, reliability, and overall success of a research study. To know the answer for what is research design, it is important to know the characteristics. These are-

1. Reliability

A reliable research design ensures that each study’s results are accurate and can be replicated. This means that if the research is conducted again under the same conditions, it should yield similar results.

2. Validity

A valid research design uses appropriate measuring tools to gauge the results according to the research objective. This ensures that the data collected and the conclusions drawn are relevant and accurately reflect the phenomenon being studied.

3. Neutrality

A neutral research design ensures that the assumptions made at the beginning of the research are free from bias. This means that the data collected throughout the research is based on these unbiased assumptions.

4. Generalizability

A good research design draws an outcome that can be applied to a large set of people and is not limited to the sample size or the research group.

Research Design Process

What is research design? A good research helps you do a really good study that gives fair, trustworthy, and useful results. But it's also good to have a bit of wiggle room for changes. If you’re wondering how to conduct a research in just 5 mins , here's a breakdown and examples to work even better.

1. Consider Aims and Approaches

Define the research questions and objectives, and establish the theoretical framework and methodology.

2. Choose a Type of Research Design

Select the suitable research design, such as experimental, correlational, survey, case study, or ethnographic, according to the research questions and objectives.

3. Identify Population and Sampling Method

Determine the target population and sample size, and select the sampling method, like random, stratified random sampling, or convenience sampling.

4. Choose Data Collection Methods

Decide on the data collection methods, such as surveys, interviews, observations, or experiments, and choose the appropriate instruments for data collection.

5. Plan Data Collection Procedures

Create a plan for data collection, detailing the timeframe, location, and personnel involved, while ensuring ethical considerations are met.

6. Decide on Data Analysis Strategies

Select the appropriate data analysis techniques, like statistical analysis, content analysis, or discourse analysis, and plan the interpretation of the results.

What are the Types of Research Design?

A researcher must grasp various types to decide which model to use for a study. There are different research designs that can be broadly classified into quantitative and qualitative.

Qualitative Research

Qualitative research identifies relationships between collected data and observations through mathematical calculations. Statistical methods validate or refute theories about natural phenomena. This research method answers "why" a theory exists and explores respondents' perspectives.

Quantitative Research

Quantitative research is essential when statistical conclusions are needed to gather actionable insights. Numbers provide clarity for critical business decisions. This method is crucial for organizational growth, with insights from complex numerical data guiding future business decisions.

Qualitative Research vs Quantitative Research

While researching, it is important to know the difference between qualitative and quantitative research. Here's a quick difference between the two:

The research types can be further divided into 5 categories:

1. Descriptive Research

Descriptive research design focuses on detailing a situation or case. It's a theory-driven method that involves gathering, analysing, and presenting data. This approach offers insights into the reasons and mechanisms behind a research subject, enhancing understanding of the research's importance. When the problem statement is unclear, exploratory research can be conducted.

2. Experimental Research

Experimental research design investigates cause-and-effect relationships. It’s a causal design where the impact of an independent variable on a dependent variable is observed. For example, the effect of price on customer satisfaction. This method efficiently addresses problems by manipulating independent variables to see their effect on dependent variables. Often used in social sciences, it involves analysing human behaviour by studying changes in one group's actions and their impact on another group.

3. Correlational Research

Correlational research design is a non-experimental technique that identifies relationships between closely linked variables. It uses statistical analysis to determine these relationships without assumptions. This method requires two different groups. A correlation coefficient between -1 and +1 indicates the strength and direction of the relationship, with +1 showing a positive correlation and -1 a negative correlation.

4. Diagnostic Research

Diagnostic research design aims to identify the underlying causes of specific issues. This method delves into factors creating problematic situations and has three phases: 

• Issue inception
• Issue diagnosis
• Issue resolution

5. Explanatory Research

Explanatory research design builds on a researcher’s ideas to explore theories further. It seeks to explain the unexplored aspects of a subject, addressing the what, how, and why of research questions.

Benefits of Research Design

After learning about what is research design and the process, it is important to know the key benefits of a well-structured research design:

1. Minimises Risk of Errors: A good research design minimises the risk of errors and reduces inaccuracy. It ensures that the study is carried out in the right direction and that all the team members are on the same page.

2. Efficient Use of Resources: It facilitates a concrete research plan for the efficient use of time and resources. It helps the researcher better complete all the tasks, even with limited resources.

3. Provides Direction: The purpose of the research design is to enable the researcher to proceed in the right direction without deviating from the tasks. It helps to identify the major and minor tasks of the study.

4. Ensures Validity and Reliability: A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. The main advantage of a good research design is that it provides accuracy, reliability, consistency, and legitimacy to the research.

5. Facilitates Problem-Solving: A researcher can easily frame the objectives of the research work based on the design of experiments (research design). A good research design helps the researcher find the best solution for the research problems.

6. Better Documentation: It helps in better documentation of the various activities while the project work is going on.

That's it! You've explored all the answers for what is research design in research? Remember, it's not just about picking a fancy method – it's about choosing the perfect tool to answer your burning questions. By carefully considering your goals and resources, you can design a research plan that gathers reliable information and helps you reach clear conclusions. 

Frequently Asked Questions

What are the key components of a research design, how can i choose the best research design for my study, what are some common pitfalls in research design, and how can they be avoided, how does research design impact the validity and reliability of a study, what ethical considerations should be taken into account in research design.

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

Bibliography

• 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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Research: Meaning and Purpose

• First Online: 27 October 2022

Cite this chapter

• Kazi Abusaleh 4 &
• Akib Bin Anwar 5  

3079 Accesses

The objective of the chapter is to provide the conceptual framework of the research and research process and draw the importance of research in social sciences. Various books and research papers were reviewed to write the chapter. The chapter defines ‘research’ as a deliberate and systematic scientific investigation into a phenomenon to explore, analyse, and predict about the issues or circumstances, and characterizes ‘research’ as a systematic and scientific mode of inquiry, a way to testify the existing knowledge and theories, and a well-designed process to answer questions in a reliable and unbiased way. This chapter, however, categorizes research into eight types under four headings, explains six steps to carry out a research work scientifically, and finally sketches the importance of research in social sciences.

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Research Design and Methodology

Research Questions and Research Design

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Abusaleh, K., Anwar, A.B. (2022). Research: Meaning and Purpose. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_2

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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
• According to Spaher and Swanson, a researcher must love his work, have abundant patience and perseverance, insist on authority and correctness of data, posses equity of consideration, thoughtfulness, and broadly responsible and always focused

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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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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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on September 5, 2024.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Nature of Research Meaning, Characteristics and Types

Table of contents:-, nature of research.

• Meaning of Research

Research Definition

• Characteristics of Research

Criteria for Good Research

• Qualities of a Good Research
• Types of Research

Need for Research

The basic nature of research is to advance knowledge and seek solutions to problems. To do this, we start with simple questions. For example, the fundamental questions in journalistic practice are: who, what, why, where, when and how. In research, these questions are addressed more systematically, reliably, testable, and replicable. In practice, all the questions are mixed, and it is difficult to isolate one from the other when dealing with human behaviour and social phenomena.

In research, these are isolated and studied in depth – separately and together. The basic premise is that any issue/event/phenomenon can be learned and subjected to appropriate systematic, objective scientific procedures, and conclusions can be arrived at that can preferably be generalised to the population. Such results and conclusions should also be amenable to replication as the search for knowledge is conducted with a defined set of rules and procedures commonly understood and shared by all sciences.

The following points can characterise the nature of research:

1) Systematic Activity

The research follows a systematic procedure to analyse a research problem in a better way. It is essential to avoid haphazard research methods and adhere to a well-structured approach for reliable outcomes. Researchers can proceed to the next step only after successfully concluding the previous one.

2) Logical Process

The basic tenet of research is “logic”. All the assumptions and analyses undertaken are based on certain logic. Research is a scientific, systematic, and planned investigation to understand the underlying problem.

3) Iterative Process

Research is an iterative process. Sometimes it becomes necessary for the researcher to review the work of earlier stages, which makes it cyclic. Often it becomes harder for the researcher to find out the starting and ending points.

4) Based on Empirical Evidence

Research studies are empirical. Researchers employ various scientific tools and techniques at every step of the research process. Accuracy and reliance on observable experiences or empirical evidence are verified in each research step. Therefore, quantitative research is easier to validate than qualitative research, which is more conceptual.

5) Controlled in Nature

Researchers frequently manage variable effects by permitting the variation of selected variables for testing purposes. Due to this reason, controlling the variables in scientific research is much easier than controlling the factors in social research. Hence in research, it is essential to control the variables carefully.

Research Meaning

Research comprises two different words, “Re” and “Search”. ‘Re’ implies a repetitive or iterative process, whereas ‘search’ signifies conducting a comprehensive examination or looking over carefully to find something. Various researchers have defined research in different ways because of its expansive scope. In general, researchers define research as a scientific process that establishes and/or validates new facts, ideas, and theories across diverse domains of knowledge. The research aims at adding to the existing stock of knowledge for the betterment of the world.

According to Waltz and Bausell, “Research is a systematic, formal, rigorous and precise process employed to gain solutions to problems or to discover and interpret new facts and relationships”.

John Best states, “Research is a systematic activity directed towards discovery and the development of an organised body of knowledge.”

According to Clifford Woody, “Research comprises defining and redefining problems, formulating hypothesis or suggested solutions, collecting, organising and evaluating data. Making deductions and reaching conclusions to determine they fit the formulating hypothesis.”

Encyclopaedia of Social Science defines research as, “the manipulation of generalising to extend, connect or verify knowledge…” Manipulation incorporates experimentation adopted to arrive at generalisation.

Kerlinger (1973) defines “research as a systematic, controlled, empirical and critical investigation of hypothetical propositions about the presumed relationship about various phenomena.”

Burns (1994) also defines “research as a systematic investigation to find answers to a problem”.

Research involves scientific and systematic analysis of a specific area of study, culminating in the formulation of findings supported by sound reasoning.

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Characteristics of Good Research

A good research should qualify in the following essential criteria:

1) Ethically Conducted

A researcher should abide by the ethical standards laid down to conduct research accurately. Researchers must thoroughly examine, explain, and document both the research data and the limiting factors. This practice ensures transparency with the readers. The data should remain unaltered to accurately reflect the findings. The researchers must document the results of the study comprehensively.

2) Reliability

Reliability refers to the repeatability of a research, tool, procedure, or instrument. The degree of reliability of a research study depends on the consistency of its findings. Researchers determine the reliability of their work by observing consistent results under similar conditions and procedures. For example, a researcher may study the effect of a course written in English on the final grades of a group of students. To ensure the reliability of the study’s findings, researchers can replicate the study with a different group of students and achieve consistent results.

3) Clearly Defined Objectives

Researchers must clearly define the objectives of a research study. Well-defined research objectives provide researchers with a clear roadmap to follow. It helps the researchers to determine the type of data required to efficiently conduct the research.

4) Accuracy

Accurate research occurs when the research process, instruments, and tools interconnect seamlessly. It verifies that researchers are appropriately selecting their research tools. For example, Observation is the recommended data collection method when researching mental patients, as it helps overcome the challenge of potential inaccuracy in questionnaires or interviews .

5) Flexibility

Research involves re-examining the data till correct findings arrive. This is possible only if the research approach is flexible. There should always be scope to add on significant data or modify existing data as needed.

6) Generalisable Results

The degree to which the result of research can be applied to a bigger population is called generalisability. While carrying a research, the researcher selects a small sample from a target population. Hence, the sample and the research findings accurately reflect the characteristics of the target population. If the research results can be applied to other samples from a similar population, then the research findings can be considered generalisable.

7) Validity

Validity is a measure of the applicability of the research. It refers to the suitability and efficiency of the research instrument or procedure regarding the research problem. Validity measures the accuracy of an instrument in measuring the problem. It is a measurement of the applicability of the research. Validity is the basis of deciding whether a research conclusion, assumption, or proposition is true or false. The validity of research is maintained by clearly defining the concepts involved.

8) Credibility of Sources

Credibility means that the research data should be taken from trustworthy sources. Although the use of secondary data in research allows the researcher to complete the research within the timeframe, he loses credibility, as the secondary data are usually manipulated and hence relying exclusively on it can lead to erroneous and faulty research conclusions. A researcher should try to use primary data to the greatest extent feasible. If primary data is not available, then a specific amount of secondary data can be used. However, conducting research completely based on secondary data can harm the credibility of the research.

Objectives of Good Research

Research aims to uncover answers to questions by applying scientific procedures. The primary goal of research is to find hidden facts that have yet to be discovered. Although each research study has its specific purpose, research objectives can be broadly categorized into the following groups:

1. To test a hypothesis of a causal relationship between variables (such studies are known as hypothesis-testing research or experimental studies).

2. To gain familiarity with a phenomenon or achieve new insights into it (studies with this objective are termed exploratory research studies).

3. To determine the frequency with which something occurs or is associated with something else (studies with this objective are known as diagnostic research studies).

4. To accurately portray the characteristics of a particular individual, situation, or group (studies with this objective are known as descriptive research studies).

Research serves as a pool of knowledge. It is a vital source of guidelines for addressing various business, personal, professional, governmental, and social problems. It is a formal training ground, enabling individuals to understand new developments in their respective fields better.

The criteria for good research are outlined as follows:

1. The validity and reliability of the data should be examined.

2. The research report should be candid enough to assess the effects of the findings.

3. The research design should be carefully planned to generate results that maintain objectivity.

4. The purpose of the research should be clearly defined, and common concepts used should be operationally defined.

5. Data analysis in the research report should be adequate to reveal its significance, and the analysis method employed should be appropriate.

6. The research procedure must be precisely planned, focused, and appropriately described to enable other researchers to conduct further studies for advancement.

Qualities of Good Research

Good research possesses certain qualities, as outlined below:

1. Empirical

Conclusions are drawn based on hardcore evidence from real-life experiences and observations. This reliance on concrete information provides a foundation for external validation of research results.

2. Develop theories and Principles

Good research contributes to developing theories and principles, aiding in accurate predictions regarding the variables under study. Through the observation and analysis of samples, researchers can make sound generalizations about entire populations, extending beyond immediate situations, objects, or groups being investigated.

Research is guided by the rules of reasoning and logical processes, including induction (general to specific) and deduction (specific to public). Logical reasoning enhances the feasibility and meaningfulness of research in decision-making.

4. Replicable

The designs, procedures, and results of scientific research should be replicable, allowing anyone other than the original researcher to assess their validity. This ensures that one researcher can use or build upon the results obtained by another, making the procedures and results both replicable and transferrable.

5. Systematic

Research is structured according to a set of rules, following specific steps in a defined sequence. Systematic research encourages creative thinking, avoiding reliance on guessing and intuition to reach conclusions.

6. Valid and Verifiable

Research involves precise observation and accurate description. Researchers select reliable and valid instruments for data collection and utilize statistical measures to portray results accurately. The conclusions drawn are correct and can be verified by the researcher and others.

The research strives to achieve the following needs:

1) Describe the Features

The research seeks to describe the features of a particular phenomenon. It is one of the core activities of research where a researcher either observes the phenomenon and records its characteristic behaviour, conducts standardised tests to measure the behaviour or describes the change in attitude or opinion of the customers. For example, a researcher can describe the behaviour of smokers by either analysing or observing their behaviour by undergoing some standard tests, such as measuring per-day consumption, the level of resistance, etc.

2) Influence Activities

The research emphasises applying the existing theories and models instead of developing new theories, for influencing various facets of the environment. Most of the research conducted in social, behavioural and educational research falls under the area of influence.

3) Explore unknown facts

One of the prime objectives of research is to explore an unknown object or phenomenon. While exploring, a researcher tries to understand the details of the situation or phenomenon for developing preliminary hypotheses and generalisations. Exploring allows the researchers to develop theories and explain the questions of how and why a phenomenon operates in a particular way.

4) Explain a Phenomenon

Another objective of the research is to explain several facts. The research aims to explain why and how a phenomenon operates in a specific way. Researchers develop theories to explain the behaviour of a particular phenomenon, these theories are prepared by determining the factors that cause the change and identifying their effects on the phenomenon. Most scientific and educational researchers have this objective for their studies. For example, if a researcher is trying to know, “Do holiday trips for employee families improve work-life balance?”. Therefore, the cause is ‘holiday trips’ and the effect is ‘work-life balance’.

5) Predict Future Activities

Research is also conducted to predict future activities. Predictions can be made based on explanations regarding a phenomenon. Hence, for making forecasts adequate prior information is essential. Forecasting activity can also be performed on the research based on explanation. Here, predictions are made based on cause-and-effect relationships in a phenomenon. A good example of this objective is the research that analysts conduct during elections to predict the winning political party based on the information that they can gather from the voting polls.

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Home Market Research

Descriptive Research: Definition, Characteristics, Methods + Examples

Suppose an apparel brand wants to understand the fashion purchasing trends among New York’s buyers, then it must conduct a demographic survey of the specific region, gather population data, and then conduct descriptive research on this demographic segment.

The study will then uncover details on “what is the purchasing pattern of New York buyers,” but will not cover any investigative information about “ why ” the patterns exist. Because for the apparel brand trying to break into this market, understanding the nature of their market is the study’s main goal. Let’s talk about it.

What is descriptive research?

Descriptive research is a research method describing the characteristics of the population or phenomenon studied. This descriptive methodology focuses more on the “what” of the research subject than the “why” of the research subject.

The method primarily focuses on describing the nature of a demographic segment without focusing on “why” a particular phenomenon occurs. In other words, it “describes” the research subject without covering “why” it happens.

Characteristics of descriptive research

The term descriptive research then refers to research questions, the design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity.

Some distinctive characteristics of descriptive research are:

• Quantitative research: It is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature.
• Uncontrolled variables: In it, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher.
• Cross-sectional studies: It is generally a cross-sectional study where different sections belonging to the same group are studied.
• The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research.

Applications of descriptive research with examples

A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey , though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research? To understand the end objective of research goals, below are some ways organizations currently use descriptive research today:

• Define respondent characteristics: The aim of using close-ended questions is to draw concrete conclusions about the respondents. This could be the need to derive patterns, traits, and behaviors of the respondents. It could also be to understand from a respondent their attitude, or opinion about the phenomenon. For example, understand millennials and the hours per week they spend browsing the internet. All this information helps the organization researching to make informed business decisions.
• Measure data trends: Researchers measure data trends over time with a descriptive research design’s statistical capabilities. Consider if an apparel company researches different demographics like age groups from 24-35 and 36-45 on a new range launch of autumn wear. If one of those groups doesn’t take too well to the new launch, it provides insight into what clothes are like and what is not. The brand drops the clothes and apparel that customers don’t like.
• Conduct comparisons: Organizations also use a descriptive research design to understand how different groups respond to a specific product or service. For example, an apparel brand creates a survey asking general questions that measure the brand’s image. The same study also asks demographic questions like age, income, gender, geographical location, geographic segmentation , etc. This consumer research helps the organization understand what aspects of the brand appeal to the population and what aspects do not. It also helps make product or marketing fixes or even create a new product line to cater to high-growth potential groups.
• Validate existing conditions: Researchers widely use descriptive research to help ascertain the research object’s prevailing conditions and underlying patterns. Due to the non-invasive research method and the use of quantitative observation and some aspects of qualitative observation , researchers observe each variable and conduct an in-depth analysis . Researchers also use it to validate any existing conditions that may be prevalent in a population.
• Conduct research at different times: The analysis can be conducted at different periods to ascertain any similarities or differences. This also allows any number of variables to be evaluated. For verification, studies on prevailing conditions can also be repeated to draw trends.

Advantages of descriptive research

Some of the significant advantages of descriptive research are:

• Data collection: A researcher can conduct descriptive research using specific methods like observational method, case study method, and survey method. Between these three, all primary data collection methods are covered, which provides a lot of information. This can be used for future research or even for developing a hypothesis for your research object.
• Varied: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough.
• Natural environment: Descriptive research allows for the research to be conducted in the respondent’s natural environment, which ensures that high-quality and honest data is collected.
• Quick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive.

Descriptive research methods

There are three distinctive methods to conduct descriptive research. They are:

Observational method

The observational method is the most effective method to conduct this research, and researchers make use of both quantitative and qualitative observations.

A quantitative observation is the objective collection of data primarily focused on numbers and values. It suggests “associated with, of or depicted in terms of a quantity.” Results of quantitative observation are derived using statistical and numerical analysis methods. It implies observation of any entity associated with a numeric value such as age, shape, weight, volume, scale, etc. For example, the researcher can track if current customers will refer the brand using a simple Net Promoter Score question .

Qualitative observation doesn’t involve measurements or numbers but instead just monitoring characteristics. In this case, the researcher observes the respondents from a distance. Since the respondents are in a comfortable environment, the characteristics observed are natural and effective. In a descriptive research design, the researcher can choose to be either a complete observer, an observer as a participant, a participant as an observer, or a full participant. For example, in a supermarket, a researcher can from afar monitor and track the customers’ selection and purchasing trends. This offers a more in-depth insight into the purchasing experience of the customer.

Case study method

Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they can’t make accurate predictions because there could be a bias on the researcher’s part. The other reason why case studies are not a reliable way of conducting descriptive research is that there could be an atypical respondent in the survey. Describing them leads to weak generalizations and moving away from external validity.

Survey research

In survey research, respondents answer through surveys or questionnaires or polls . They are a popular market research tool to collect feedback from respondents. A study to gather useful data should have the right survey questions. It should be a balanced mix of open-ended questions and close ended-questions . The survey method can be conducted online or offline, making it the go-to option for descriptive research where the sample size is enormous.

Examples of descriptive research

Some examples of descriptive research are:

• A specialty food group launching a new range of barbecue rubs would like to understand what flavors of rubs are favored by different people. To understand the preferred flavor palette, they conduct this type of research study using various methods like observational methods in supermarkets. By also surveying while collecting in-depth demographic information, offers insights about the preference of different markets. This can also help tailor make the rubs and spreads to various preferred meats in that demographic. Conducting this type of research helps the organization tweak their business model and amplify marketing in core markets.
• Another example of where this research can be used is if a school district wishes to evaluate teachers’ attitudes about using technology in the classroom. By conducting surveys and observing their comfortableness using technology through observational methods, the researcher can gauge what they can help understand if a full-fledged implementation can face an issue. This also helps in understanding if the students are impacted in any way with this change.

Some other research problems and research questions that can lead to descriptive research are:

• Market researchers want to observe the habits of consumers.
• A company wants to evaluate the morale of its staff.
• A school district wants to understand if students will access online lessons rather than textbooks.
• To understand if its wellness questionnaire programs enhance the overall health of the employees.

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Top 10 Qualities and Characteristics of a Good Researcher

Year after year, people with different personalities and backgrounds step into the field of research eager to develop the key qualities of a good researcher , only to find themselves faced with anxiety and self-doubt. Becoming a good researcher is a challenging task that requires a combination of skills and attributes as well as time, dedication, and a lot of hard work.   

So what are the qualities of a good researcher and how does one build these must-have characteristics? This article answers this by sharing the top 10 qualities of a good researcher that you must work to develop, strengthen, and apply on your journey to research success.   

Table of Contents

Top 10 qualities of a good researcher  

• Curiosity:  A curious mind and an ability to look at things from different perspectives is what makes a good researcher better. Good researchers are observant about the world around them and open to new ideas and possibilities; they are always asking questions and looking for answers. This ability to see the bigger picture while being curious about the smaller details is what makes a good researcher explore new ideas, test hypotheses, and make new discoveries.
• Critical thinking:  Successful researchers can think critically about the information they gather while reading about new developments in their own and related fields. This is an essential characteristic of a good researcher . Instead of simply accepting existing knowledge as fact, you need to have the ability to analyze and evaluate the validity and reliability of sources, consider alternative explanations for results you observe, and find connections between seemingly unrelated concepts.

• Creativity:  The qualities of a good researcher do not just include curiosity and critical thinking, but also thinking creatively when it comes to problem solving. Nurturing the ability to think outside the box and come up with novel and often unconventional solutions to challenges you face is how to become a better researcher. This allows you to come up with more ground-breaking research studies and results addressing issues that others might easily miss.
• Objectivity:  Nurturing preconceived notions is detrimental to research. Avoid temptations to make unconclusive statements or introduce personal biases into research, which will impact your research and standing in the long run. Remember, building essential qualities of a good researcher means consciously keeping aside personal preferences and biases and applying sound judgement to your work even when under pressure.
• Collaborative spirit:  An important characteristic of a good researcher is being able to work well with others. With a shift toward more collaborative research, successful researchers often connect with and work with peers to come up with innovative approaches to research problems. While sharing ideas and partnering with other researchers can lead to breakthroughs and boost your researcher reputation, it also opens the door for your work to reach and potentially benefit a wider audience.
• Communication skills:  An added strength of a good researcher is being able to communicate your findings clearly and effectively, which is a key contributor to your success. This is applicable when writing your manuscripts, presenting at conferences, as well as when seeking funding for your work. Good researchers can explain their research to both specialists and non-specialists to ensure their work is understood and appreciated by a wider audience.
• Attention to detail:  One of the key qualities of a good researcher is being meticulous in your work. Researchers need to pay attention to every detail, from the design of an experiment to the analysis of data, and further in writing and submitting their manuscript for publication. This crucial characteristic can help you ensure your research is accurate, testable, and reliable, and also gives your manuscripts a better chance of acceptance.
• Time management:  To understand what are the characteristics of a good researcher , first ask yourself if you manage your time well. Most successful researchers organize, prioritize, and optimize their time efficiently, allowing them to not only keep up with their responsibilities but also make time for personal tasks. If you’re being pulled in different directions or overwhelmed with trying to manage your research, stay updated on your research reading, or meeting your writing deadlines, consider honing this skill as a prerequisite to becoming a good researcher.
• Persistence & flexibility:  Research can be a long, difficult process with several hurdles and changes along the way. One of the key requirements to becoming a good researcher is being able to adapt to new technologies and changing circumstances and persevere despite setbacks and challenges that inevitably arise. Developing the qualities of a good researcher means anticipating problems, adjusting plans to tackle challenges head-on, and being patient while moving forward toward achieving your goals.
• Focus on self-care:  Anxiety, stress, and mental health issues are common among academics. Successful researchers are better equipped to manage this by adopting a healthy balanced lifestyle. Understanding what works for you can also improve your efficiency and productivity. Being aware of your strengths and weaknesses and using this to your advantage is key to becoming a good researcher.

In conclusion, perfecting the characteristics of a good researcher is not quick or easy, but by working consistently toward developing or strengthening these essential qualities, you will be well on your way to finding success as a well-established researcher.  

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10 Qualities of a Good Researcher: Quest for Excellence

• Post author By admin
• November 9, 2023

Discover the essential 10 qualities of a good researcher! Uncover the traits that drive success in the world of research. Learn what it takes to excel in the quest for knowledge and innovation

Suppose a vast landscape of knowledge, uncharted and waiting to be discovered. Research is the compass guiding us through this territory, and at the helm of every great exploration stands a good researcher.

But what sets them apart? It’s not just knowledge; it’s a unique set of qualities that propel them towards understanding.

In this journey, we’ll uncover the very essence of a good researcher. We’ll delve into the top 10 qualities that define them. From unquenchable curiosity to unwavering perseverance, these qualities are the secret sauce behind their success in academia and exploration.

Whether you’re already treading the path of research or gearing up for the adventure, understanding and embracing these qualities will transform you into a research dynamo. So, let’s embark on this quest to unravel what makes a good researcher tick.

Table of Contents

10 Qualities of a Good Researcher

Check out the 10 qualities of a good researcher:-

1. Inquisitiveness: The Craving for Knowledge

Think of a good researcher as that friend who’s always full of questions. They’re the eternal curious cats of the academic world, forever wondering, forever seeking, and forever hungry for knowledge. It’s like they have a built-in “Why?” button that never switches off.

A good researcher’s inquisitiveness is like the spark that lights up a dark room. It’s what pushes them to ask the questions no one else has thought of and venture into uncharted territories. They’re the ultimate seekers, the champions of “What if?” and “Why not?” It’s this insatiable curiosity that keeps their research fresh, exciting, and always on the hunt for more knowledge.

2. Patience: Sifting Through Data

Imagine a good researcher as a treasure hunter in the vast desert of data. Research can sometimes feel like slogging through quicksand – slow, meticulous, and demanding. But here’s the thing: good researchers have an incredible treasure map, and it’s called “patience.”

They understand that research isn’t a race; it’s a journey. It’s about sifting through tons of data, the way a prospector pans for gold. Every grain of information matters, and they’re willing to invest the time needed to collect, analyze, and interpret data accurately.

This patience isn’t about twiddling thumbs; it’s about meticulously building the puzzle of knowledge, piece by piece. They understand that no detail is too small to be overlooked, and in the end, it’s these small pieces that complete the big picture.

Good researchers don’t rush; they savor the journey, knowing that the best discoveries often lie in the details. They are the patient architects of knowledge, and it’s their patience that ensures that no gem of information goes undiscovered.

3. Attention to Detail: Devil in the Details

In research, it’s the little things that matter most. A good researcher understands this like no other. They’re the ones who spot the faintest footprints in the sand and the almost invisible fingerprints on the glass because they know that in research, the devil truly lies in the details.

For them, every piece of information is a precious puzzle piece. They’re like puzzle enthusiasts, and they’re determined to find and fit every piece perfectly. Because, in their world, even the tiniest detail holds the potential to make or break a study.

In a realm where precision reigns supreme, good researchers are the vigilant guardians of information. They’re the ones who make sure no stone is left unturned, no detail is too minor, and it’s this unwavering attention to detail that transforms their research into something truly extraordinary.

4. Critical Thinking: Questioning the Norm

Let’s picture a good researcher as the ultimate rebel of the research realm. They don’t just follow the herd; they’re the ones breaking the mold, challenging established theories, and stirring up the intellectual pot. Their secret weapon? It’s called critical thinking.

Critical thinking is like their sidekick, the Watson to their Holmes. It’s their power to look at information with a discerning eye, to cut through the noise, and make informed judgments. Good researchers? They’ve got critical thinking in their toolkit, and they’re not afraid to use it.

They’re not content with nodding along to the norm. No, they’re the ones who dare to ask, “Why?” and “What if?” They’re the Sherlock Holmes of academia, seeking the hidden clues that others might overlook. They’re the explorers who venture beyond the boundaries of convention.

For them, curiosity isn’t just a casual interest; it’s a full-blown investigation. They’re the skeptics, the truth-seekers, and the challengers of the status quo. Because they know that the road to enlightenment is paved with skepticism and paved with profound insights.

In a world where knowledge is the ultimate treasure, good researchers are the rebels with a cause. They’re the ones who question, challenge, and redefine the norm, making the pursuit of knowledge a thrilling adventure.

5. Organization: Chaos to Clarity

Let’s paint a mental picture of a good researcher as the master organizer of the research universe. Picture this: researchers often find themselves wading through mountains of data, like explorers in an information jungle.

But what sets good researchers apart is their exceptional skill in turning chaos into clarity through one magic word – organization.

These researchers are like the conductors of a grand symphony, where data plays the melodious tunes. They understand that without a meticulously organized score, the music may fall into chaos.

This is why they keep their work structured and well-organized. It’s like having a treasure map to navigate through the data wilderness.

For them, organization isn’t just a preference; it’s a necessity. It ensures that every piece of data, every note in the symphony, can be easily accessed and referenced when needed. It’s the librarian’s skill of categorizing, labeling, and arranging knowledge in a way that makes sense.

In a world where data can be overwhelming, good researchers are the navigators who chart the course from chaos to clarity. They bring order to the information realm, making sure that every piece of data finds its place in the grand mosaic of knowledge.

6. Effective Communication: Sharing Insights

Imagine a good researcher as not just a discoverer of hidden treasures but also a gifted storyteller. Research isn’t merely about uncovering the unknown; it’s about sharing those discoveries with the world. Good researchers possess a unique superpower – effective communication.

They are the bards of academia, able to weave intricate tales of data and insight. It’s not enough to gather knowledge; they understand the importance of conveying it to their peers and the wider community. They’re like skilled translators, turning complex data into understandable narratives.

For them, research isn’t a solitary endeavor but a communal one. They can articulate their findings, transforming raw data into gems of wisdom. They speak not just to fellow researchers but to anyone who seeks understanding.

In a world where information is abundant but understanding can be scarce, good researchers are the bridges that connect data to meaning. They’re the ones who bring clarity to complexity, ensuring that their discoveries benefit not just themselves but all who thirst for knowledge.

7. Ethical Integrity: The Moral Compass

Picture a good researcher as a moral compass, always pointing in the direction of what’s right. In their world, there’s no room for ethical shortcuts; they’re the guardians of integrity, setting the highest standards.

Ethical conduct is their unwavering principle, not a mere guideline. These researchers tread the path of knowledge with profound respect for all beings, be it humans, animals, or the environment.

They understand that research isn’t just about facts and figures; it’s about the impact on the world.

They are the ethical warriors who ensure that every discovery is made with the utmost respect for boundaries. They’re the ones who hold the torch of integrity, even when the road gets dark and uncertain.

In a world where ethical dilemmas can cloud the way, good researchers are the beacons of moral clarity. They remind us that the pursuit of knowledge should always be illuminated by the light of ethics, leaving a positive and lasting legacy.

8. Adaptability: Rolling with Research’s Twists

Now, picture a good researcher as the ultimate research ninja. They know that in the world of research, surprises are the name of the game. What makes them exceptional? Their uncanny ability to adapt.

In their world, every research project is like a thrilling rollercoaster ride. They’re fully aware that not everything will go as planned.

But instead of dreading the unexpected, they welcome it with open arms. It’s not about dodging hurdles; it’s about using them as springboards for new discoveries.

Adaptability is their secret weapon. They don’t panic when faced with unexpected twists and turns; they thrive on them. They’re the daredevils of research, excited by the idea that every surprise brings a chance for a breakthrough.

They understand that research isn’t a linear path; it’s an expedition full of surprises. Good researchers approach each twist and turn as a new opportunity to learn, grow, and uncover the unknown.

9. Perseverance: Never Giving Up

Now, picture a good researcher as the indomitable hero of the research saga. The journey to groundbreaking discoveries is no walk in the park; it’s an epic adventure filled with obstacles and trials. What makes a good researcher extraordinary? Their unshakable perseverance.

In their world, setbacks are not dead ends; they are the very soil in which success takes root. They grasp that the path to pioneering research is not a sprint but a demanding marathon.

When confronted with challenges, they don’t retreat; they roll up their sleeves and forge ahead with unwavering resolve.

In their universe, perseverance is the North Star guiding them through the darkest nights of research. It’s the fire that keeps them warm when faced with the chilling winds of doubt.

They understand that every stumble is a lesson, every hurdle is an opportunity, and every fall is a chance to rise even higher.

In a realm where remarkable discoveries are born from sheer determination, good researchers are the embodiment of perseverance.

They don’t just weather the storms of research; they harness them to soar to new heights of understanding and innovation.

10. Problem-Solving Skills: Creative and Determined Issue Resolution

Think of a good researcher as a maverick in the world of problem-solving. They possess an innate ability to tackle research-related issues with a unique blend of creativity and unwavering determination. They’re not just issue-spotters; they’re issue-solvers.

In their realm, challenges aren’t roadblocks; they’re opportunities for innovation. Whether it’s deciphering a complex data conundrum, navigating unexpected research detours, or confronting formidable roadblocks, they approach each problem with a dash of unconventional thinking.

Their toolkit isn’t limited to traditional solutions; it includes a healthy dose of creativity. They know that sometimes the most extraordinary answers emerge from unconventional thinking.

When faced with adversity, they don’t back down; they dive headfirst into the challenge, armed with resourcefulness and an unyielding spirit.

In the world of research, where every obstacle conceals a chance for a groundbreaking discovery, these good researchers are the daring explorers.

They turn problems into springboards, propelling the journey of knowledge and unveiling new insights along the way.

What is the qualities of good researcher?

Exceptional researchers are a unique breed, possessing a blend of innate traits and developed skills that set them apart in the world of discovery. Here are the qualities that define an outstanding researcher:

Inherent Curiosity

Exceptional researchers are born with an insatiable curiosity about the world. They perpetually question, driven by an unrelenting thirst for knowledge. This curiosity fuels their exploration of new ideas and their deep dives into complex problems.

Independence and Initiative

They are fiercely independent, unafraid to challenge conventions and think outside the box. This independence empowers them to conduct research with rigor and objectivity, free from preconceived notions.

Critical Thinking

Exceptional researchers are expert critical thinkers. They scrutinize information, identifying biases and assumptions. This skill enables them to draw well-founded conclusions from their research, undeterred by misinformation.

Effective Communication

They are adept communicators, capable of presenting their findings clearly and concisely. Their ability to convey complex ideas is vital for sharing their discoveries with the broader scientific community.

Collaboration Prowess

Collaboration is second nature to them. Exceptional researchers seamlessly collaborate with others to achieve common research objectives. Their skill in teamwork is essential for handling large-scale research projects effectively.

Problem-Solving Expertise

Problem-solving is in their DNA. They spot issues, conceive and test solutions, and rigorously evaluate their effectiveness. This skill is the backbone of conducting thorough research.

In addition to these qualities, exceptional researchers boast an in-depth understanding of their chosen field. They stay abreast of the latest research findings and expertly apply this knowledge to their own work.

Furthermore, they adhere to ethical guidelines that govern research, conducting their inquiries responsibly and ethically.

Armed with these remarkable qualities, exceptional researchers not only expand our comprehension of the world but also contribute to solving critical problems and enhancing the quality of life for all.

What are the 7 major characteristics of research?

Research is a multifaceted endeavor, marked by seven pivotal characteristics that define its essence:

1, Empirical Foundation

At its core, research is grounded in empiricism. It shuns opinions, personal beliefs, and conjecture. Instead, it thrives on data and evidence drawn from real-world observations and experiments, bolstering its conclusions with solid support.

2. Systematic Approach

Research unfolds systematically, adhering to a meticulously designed process. It commences with defining the research question, identifying research methods, collecting data, rigorously analyzing it, and ultimately deriving well-founded conclusions. This systematic journey ensures both rigor and objectivity.

3. Logical Underpinning

Logic forms the backbone of research. It forges conclusions that harmonize seamlessly with the laws of logic, yielding findings that are not only profound but also reliable.

4. Cyclical Nature

Research possesses a cyclical essence. It commences with a question or problem, each exploration invariably begetting new inquiries. This continuous cycle propels researchers toward a deeper understanding of the ever-evolving world.

5. Analytical Rigor

Research demands meticulous data analysis. Researchers employ diverse analytical techniques to uncover patterns, trends, and relationships within the data. This scrutiny unveils the latent significance of the data, facilitating the derivation of meaningful conclusions.

6. Objective Stance

An unwavering objectivity characterizes research. Researchers diligently strive to avoid bias and partiality, ensuring that their personal beliefs or opinions exert no undue influence on their findings.

7. Replicability Standard

Research adheres to a replicability standard. Other researchers should be capable of replicating the study and achieving congruent results. This commitment to replicability bolsters the reliability and validity of research findings.

Incorporating these seven key characteristics, research emerges as a powerful tool for the exploration of the unknown, the validation of hypotheses, and the continuous advancement of knowledge.

What are the 3 important qualities of a good research?

When we delve into the world of outstanding research, we uncover the pillars that set it apart. Imagine these as the main characters in a compelling story:

1. Credibility

This is the unwavering foundation. Exceptional research is built on solid evidence and meticulous reasoning. It follows a rigorous and objective path, supported by thorough data and in-depth analysis.

2. Relevance

Consider this the heart of the matter. Exceptional research doesn’t shy away from addressing pressing questions and challenges.

It aims to contribute significantly to our understanding of the world and has the potential to solve crucial problems.

3. Originality

Think of this as the trailblazer, the innovator. Exceptional research ventures into uncharted territories, offering fresh and unique perspectives.

It doesn’t retrace well-worn paths; instead, it opens new doors to insights that haven’t been explored before.

These are the three pillars of remarkable research, igniting our quest to comprehend our world more deeply, confront significant challenges, and provide solutions that truly enhance our lives and the lives of those around us.

What are the 4 characteristics of a good research?

When we delve into the world of research, we discover the four cornerstones that define what makes research truly exceptional:

Imagine research as a sturdy ship navigating the vast sea of knowledge. What keeps it afloat? Credibility – the anchor of solid evidence and logical reasoning.

It’s about following a rigorous and objective methodology, with findings firmly supported by a wealth of data and meticulous analysis.

Good research is like a compass pointing to the critical questions and challenges that pique the curiosity of the research community and society.

It’s not just an exploration; it’s a journey with a purpose – to deepen our understanding of the world and unravel solutions to the most pressing problems.

Think of research as an explorer venturing into uncharted territory. It doesn’t follow the trodden paths; it forges its own.

Good research doesn’t echo what’s been said before; it blazes new trails, offering fresh insights and unique perspectives.

Effective research is a lighthouse, guiding others through the maze of complexity. Its findings are not buried in jargon or obscured by ambiguity.

They are presented with clarity and conciseness, ensuring that everyone can navigate the discoveries with ease.

These attributes, like the North Star, lead us in the pursuit of knowledge and understanding, casting light on the uncharted waters of research.

In the grand tapestry of knowledge, good researchers stand as the weavers of profound discovery. They embody a unique blend of qualities, shaping the course of understanding and change.

From the inquisitiveness that fuels their journey to the unwavering patience that carries them through the most intricate of labyrinths, these qualities are the compass, the guiding light.

The unquenchable curiosity of a good researcher keeps the embers of exploration burning bright. Patience, the steadfast companion, ensures that no detail remains in obscurity.

Their critical thinking propels them beyond the boundaries of convention, unraveling new layers of understanding.

In the chaos of data, they find serenity through organization, and in the midst of complexity, they wield the sword of effective communication.

Ethical integrity acts as their moral compass, while adaptability embraces the unpredictability of research’s twists.

But it’s perseverance, the indomitable spirit, that carries them through the darkest hours. They recognize that the path to groundbreaking research is often fraught with obstacles, but those obstacles serve as stepping stones to success.

These ten qualities, woven into the very fabric of their being, make good researchers the architects of transformation.

With every study they undertake, they draw closer to unraveling the mysteries of our world, bridging gaps in knowledge, and contributing to the betterment of humanity.

As we celebrate these qualities, we acknowledge the significance of their work. Through their endeavors, we glimpse the limitless potential of human exploration, and we are inspired to never cease questioning, exploring, and, above all, learning.

Frequently Asked Questions

Can anyone become a good researcher.

Yes, with dedication and a willingness to develop these qualities, anyone can become a good researcher.

Why is adaptability crucial for a researcher?

Research is unpredictable, and adaptability allows researchers to navigate unexpected challenges effectively.

What role does ethics play in research?

Ethical integrity is vital in research to ensure the well-being of participants and the integrity of the study.

How do researchers maintain their inquisitiveness?

Researchers stay curious by continually seeking new questions and exploring uncharted territories in their field.

Is critical thinking a natural talent, or can it be developed?

Critical thinking can be developed through practice and a commitment to questioning and evaluating information.

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Other opioid–involved overdose deaths were opioid-involved deaths that did not involve buprenorphine. Thus, the buprenorphine-involved and other opioid-involved categories are mutually exclusive and together make up all opioid-involved overdose deaths. If date of death was missing, date pronounced dead was used. The 32 included jurisdictions were Alaska, Arizona, Colorado, Connecticut, Delaware, District of Columbia, Georgia, Illinois, Kansas, Kentucky, Maine, Massachusetts, Minnesota, Missouri, Montana, Nevada, New Hampshire, New Jersey, New Mexico, North Carolina, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Dakota, Tennessee, Utah, Vermont, Virginia, Washington, and West Virginia. Illinois, Missouri, and Washington reported deaths from counties that accounted for at least 75% of drug overdose deaths in the state in 2017, per the State Unintentional Drug Overdose Reporting System funding requirements; all other jurisdictions reported deaths from the full jurisdiction.

eTable 1. Jurisdictions Included in Each Analysis

eTable 2. Number of Buprenorphine and Other Opioid–Involved Overdose Deaths and Percentage of Opioid Overdose Deaths Involving Buprenorphine by Month of Death in 32 Jurisdictions From July 2019 to June 2021

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Tanz LJ , Jones CM , Davis NL, et al. Trends and Characteristics of Buprenorphine-Involved Overdose Deaths Prior to and During the COVID-19 Pandemic. JAMA Netw Open. 2023;6(1):e2251856. doi:10.1001/jamanetworkopen.2022.51856

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Trends and Characteristics of Buprenorphine-Involved Overdose Deaths Prior to and During the COVID-19 Pandemic

• 1 National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia
• 2 National Institute on Drug Abuse, National Institutes of Health, Bethesda, Maryland

Question   Did buprenorphine-involved overdose deaths change after implementing prescribing flexibilities during the COVID-19 pandemic?

Findings   In this cross-sectional study including 74 474 opioid-involved overdose deaths, buprenorphine was involved in 2.6% of opioid-involved overdose deaths during July 2019 to June 2021. Although monthly opioid-involved overdose deaths increased, the proportion involving buprenorphine fluctuated but did not increase.

Meaning   These findings suggest that actions to facilitate access to buprenorphine-based treatment for opioid use disorder during the COVID-19 pandemic were not associated with an increased proportion of overdose deaths involving buprenorphine; efforts are needed to expand more equitable and culturally competent access to and provision of buprenorphine-based treatment.

Importance   Buprenorphine remains underused in treating opioid use disorder, despite its effectiveness. During the onset of the COVID-19 pandemic, the US government implemented prescribing flexibilities to support continued access.

Objective   To determine whether buprenorphine-involved overdose deaths changed after implementing these policy changes and highlight characteristics and circumstances of these deaths.

Design, Setting, and Participants   This cross-sectional study used data from the State Unintentional Drug Overdose Reporting System (SUDORS) to assess overdose deaths in 46 states and the District of Columbia occurring July 2019 to June 2021. Data were analyzed from March 7, 2022, to June 30, 2022.

Main Outcomes and Measures   Buprenorphine-involved and other opioid-involved overdose deaths were examined. Monthly opioid-involved overdose deaths and the percentage involving buprenorphine were computed to assess trends. Proportions and exact 95% CIs of drug coinvolvement, demographics, and circumstances were calculated by group.

Results   During July 2019 to June 2021, 32 jurisdictions reported 89 111 total overdose deaths and 74 474 opioid-involved overdose deaths, including 1955 buprenorphine-involved overdose deaths, accounting for 2.2% of all drug overdose deaths and 2.6% of opioid-involved overdose deaths. Median (IQR) age was similar for buprenorphine-involved overdose deaths (41 [34-55] years) and other opioid–involved overdose deaths (40 [31-52] years). A higher proportion of buprenorphine-involved overdose decedents, compared with other opioid–involved decedents, were female (36.1% [95% CI, 34.2%-38.2%] vs 29.1% [95% CI, 28.8%-29.4%]), non-Hispanic White (86.1% [95% CI, 84.6%-87.6%] vs 69.4% [95% CI, 69.1%-69.7%]), and residing in rural areas (20.8% [95% CI, 19.1%-22.5%] vs 11.4% [95% CI, 11.2%-11.7%]). Although monthly opioid-involved overdose deaths increased, the proportion involving buprenorphine fluctuated but did not increase during July 2019 to June 2021. Nearly all (92.7% [95% CI, 91.5%-93.7%]) buprenorphine-involved overdose deaths involved at least 1 other drug; higher proportions involved other prescription medications compared with other opioid-involved overdose deaths (eg, anticonvulsants: 18.6% [95% CI, 17.0%-20.3%] vs 5.4% [95% CI, 5.2%-5.5%]) and a lower proportion involved illicitly manufactured fentanyls (50.2% [95% CI, 48.1%-52.3%] vs 85.3% [95% CI, 85.1%-85.5%]). Buprenorphine decedents were more likely to be receiving mental health treatment than other opioid–involved overdose decedents (31.4% [95% CI, 29.3%-33.5%] vs 13.3% [95% CI, 13.1%-13.6%]).

Conclusions and Relevance   The findings of this cross-sectional study suggest that actions to facilitate access to buprenorphine-based treatment for opioid use disorder during the COVID-19 pandemic were not associated with an increased proportion of overdose deaths involving buprenorphine. Efforts are needed to expand more equitable and culturally competent access to and provision of buprenorphine-based treatment.

The overdose crisis in the US continues to escalate, likely associated with the widespread availability of highly potent synthetic opioids, such as illicitly manufactured fentanyl and fentanyl analogs (IMFs) in the illicit drug supply. 1 , 2 Provisional data from the Centers for Disease Control and Prevention (CDC) estimate more than 107 000 overdose deaths in the US in the 12 months ending July 2022, with more than 81 000 deaths involving opioids. 3 Expanding access to medications for opioid use disorder (OUD) is a central component of the US response to the overdose crisis. 4

Buprenorphine is a partial mu-opioid receptor agonist with lower potential for misuse and overdose compared with the full mu-opioid receptor agonist methadone. 5 Despite buprenorphine being the most accessible form of medication for OUD in the US, under current federal law, it can only be prescribed in office-based settings by clinicians with a Drug Addiction Treatment Act waiver; clinicians are limited to prescribing up to 30, 100, or 275 patients at a given time, depending on waiver limit. 6 , 7 Therapeutic benefits of buprenorphine treatment include reduced illicit opioid use and prescription opioid misuse, decreased risk for injection-related infectious diseases, and decreased risk for fatal and nonfatal overdoses. 5 , 8 - 14 Yet, buprenorphine treatment remains substantially underused. 5

During the emergence of the COVID-19 pandemic, there were concerns for increased overdose risk among individuals with OUD from disruption to medications for OUD and other treatment access due to stay-at-home orders and temporary closures of medical and social services. 15 , 16 To facilitate continued access to care for individuals with OUD, the US federal government took actions following the declaration of the nationwide emergency on March 13, 2020. 17 , 18 In particular, on March 31, 2020, the Substance Abuse and Mental Health Services Administration and the Drug Enforcement Administration allowed Drug Addiction Treatment Act–waivered clinicians to remotely prescribe buprenorphine to new patients without conducting in-person examinations. 19 On March 27, 2020, the Centers for Medicare & Medicaid Services expanded payment for telehealth services and provided flexibility on accepted communication technologies (eg, audio-only) for clinical care of substance use disorders (SUD). 20 , 21

Recent studies have reported that clinicians have used these emergency authorizations to initiate and continue buprenorphine treatment during the COVID-19 pandemic and that patients have benefited. 22 - 24 However, questions remain about whether there was an increase in buprenorphine-involved overdose deaths following implementation of these new emergency authorizations that removed historical measures intended to reduce diversion and misuse of buprenorphine.

This study assessed trends in buprenorphine-involved overdose deaths before and during the period of COVID-19–related buprenorphine prescribing flexibilities. Additionally, given very limited research on characteristics and circumstances of buprenorphine-involved overdose deaths, this study examined differences in characteristics and circumstances between buprenorphine- and other opioid–involved overdose decedents. These findings could inform ongoing policy discussions about potential permanent adoption of COVID-19 emergency authorizations related to buprenorphine prescribing and inform strategies to prevent buprenorphine-involved overdose deaths.

This cross-sectional study was reviewed by the CDC and was deemed not to be human research under 45 CFR 46.102(l); therefore institutional review board oversight and informed consent were not required. This study was conducted consistent with applicable federal law and CDC policy. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

The CDC’s State Unintentional Drug Overdose Reporting System captures information on unintentional and undetermined intent drug overdose deaths from 47 states and the District of Columbia. 25 Jurisdictions abstract data from death certificates and medical examiner or coroner reports, including death scene investigations and postmortem toxicological findings. These sources capture drugs involved, decedent demographics, and overdose-specific circumstances.

Trend analyses included 32 jurisdictions (31 states and the District of Columbia; eTable 1 in Supplement 1 ) that reported unintentional and undetermined intent drug overdose deaths that occurred during July 2019 to June 2021, the 9 months before and 15 months after COVID-19 buprenorphine prescribing flexibilities were implemented. Twenty-nine jurisdictions reported all overdose deaths in their jurisdiction and 3 jurisdictions reported deaths from subsets of counties covering at least 75% of overdose deaths in the jurisdiction. Overdose deaths were restricted to those involving (ie, listed as a cause of death) at least 1 opioid, classified by whether buprenorphine was involved, and grouped by month using death date.

Analyses of drug coinvolvement, decedent demographics, and urbanicity included 47 jurisdictions with death certificate data available for at least one 6-month period during July 2019 to June 2021 (eTable 1 in Supplement 1 ). Among these, 10 jurisdictions reported deaths from counties that accounted for at least 75% of drug overdose deaths in the state for at least one 6-month period; all other jurisdictions reported deaths from the full jurisdiction. Overdose deaths were categorized into 2 mutually exclusive groups: buprenorphine-involved and other opioid–involved. To evaluate coinvolvement of other drugs, we classified deaths into the following nonmutually exclusive groups: any other drug, any other opioid, IMFs (includes fentanyl and fentanyl analogs classified using toxicological, scene, and witness evidence 26 ), cocaine, methamphetamine, prescription stimulants, benzodiazepines, antidepressants, anticonvulsants, cannabis, and alcohol. Additionally, sex, age, race and ethnicity, education, and county of residence 27 of decedents were examined. These variables were available from the death certificate and supplemented with information from medical examiner or coroner reports. Race and ethnicity were classified as American Indian or Alaska Native, non-Hispanic; Asian or other Pacific Islander, non-Hispanic; Black, non-Hispanic; Hispanic; multiple races, non-Hispanic; and White, non-Hispanic. Race and ethnicity data were included in analyses because proportions of overdose deaths and access to treatment for OUD often vary by race and ethnicity.

Circumstance analyses were restricted to 42 jurisdictions with medical examiner or coroner reports for at least 75% of decedents, as circumstance data come primarily from these reports, and to deaths with an available medical examiner or coroner report (eTable 1 in Supplement 1 ). Circumstances included events of the overdose (eg, naloxone administration, potential bystander presence); scene evidence (eg, route of drug use), evidence of history of drug use and treatment (eg, current treatment for SUD), and evidence of other circumstances (eg, homelessness or housing instability).

Monthly opioid-involved overdose deaths and percentages of opioid-involved overdose deaths involving buprenorphine during July 2019 to June 2021 were computed. Descriptive analyses of drug coinvolvement, demographics, urbanicity, and circumstances were categorized by buprenorphine or other opioid involvement and reported as proportions and exact 95% CI for categorical variables or medians and IQRs for continuous variables. Complete case analysis was conducted and supported given limited missing data (<2% for 14 of 16 variables with missing data; <5% for 2 of 16 variables). Eleven circumstance variables were completed as checkboxes within the State Unintentional Drug Overdose Reporting System; lack of endorsement was considered lack of evidence of the circumstance and included in the denominator of proportion calculations.

Sensitivity analyses were conducted to examine whether inclusion of jurisdictions with less than 100% of death certificates (for trend, drug coinvolvement, demographics, and urbanicity analyses) or less than 90% of medical examiner or coroner reports (for circumstance analyses) changed conclusions. Additionally, to assess whether results differed before and during the COVID-19 pandemic, analyses were stratified into prepandemic (July 2019 to March 2020) and during COVID-19 (April 2020 to June 2021) time periods.

Analyses were conducted in SAS statistical software version 9.4 (SAS Institute). Data were analyzed from March 7, 2022, to June 30, 2022.

During July 2019 to June 2021, 32 jurisdictions reported 89 111 total overdose deaths and 74 474 opioid-involved overdose deaths, including 1955 buprenorphine-involved overdose deaths, accounting for 2.2% of all drug overdose deaths and 2.6% of opioid-involved overdose deaths. Although monthly opioid-involved overdose deaths increased starting in March 2020, corresponding with the COVID-19 pandemic, the proportion with buprenorphine-involvement fluctuated but did not increase between July 2019 (3.6%) and June 2021 (2.1%) ( Figure ; eTable 2 in Supplement 1 ). Median (IQR) monthly opioid-involved overdose deaths increased 35.7% from 2520 (2468-2633) deaths during July 2019 to March 2020 to 3419 (3054-3828) deaths during April 2020 to June 2021, an increase of approximately 899 deaths per month. Median (IQR) monthly buprenorphine-involved overdose deaths increased 26.9% from 67 (65-78) deaths to 85 (80-97) deaths during the same timeframe. Nearly all of the increase in median monthly buprenorphine-involved overdose deaths was in deaths that coinvolved IMFs, which increased from a median (IQR) of 31 (28-34) deaths per month during July 2019 to March 2020 to 45 (42-52) deaths per month during April 2020 to June 2021. In sensitivity analyses, excluding jurisdictions with less than 100% of death certificates did not meaningfully change results.

Among 2238 buprenorphine-involved overdose deaths reported by 47 jurisdictions during July 2019 to June 2021, 2202 (98.4%) were categorized as unintentional and 36 (1.6%) were categorized as undetermined intent. Similarly, among 93 128 other opioid–involved overdose deaths that did not involve buprenorphine, 89 205 (95.8%) were categorized as unintentional and 3923 (4.2%) were categorized as undetermined intent. Among buprenorphine-involved overdose deaths, 92.7% (95% CI, 91.5%-93.7%) involved at least 1 other drug; only 67.2% (95% CI, 66.9%-67.5%) of other opioid–involved overdose deaths involved another drug ( Table 1 ). The proportion of deaths involving IMFs was lower among buprenorphine-involved overdose deaths (50.2% [95% CI, 48.1%-52.3%]) compared with other opioid–involved overdose deaths (85.3% [95% CI, 85.1%-85.5%]). However, a higher proportion of buprenorphine-involved overdose deaths, compared with other opioid–involved deaths, coinvolved prescription stimulants (4.5% [95% CI, 3.7%-5.5%] vs 1.7% [95% CI, 1.6%-1.8%]), benzodiazepines (36.9% [95% CI, 34.9%-39.0%] vs 14.5% [95% CI, 14.3%-14.8%]), antidepressants (13.9% [95% CI, 12.5%-15.5%] vs 5.0% [95% CI, 4.8%-5.1%]), and anticonvulsants, primarily gabapentin and pregabalin (18.6% [95% CI, 17.0%-20.3%] vs 5.4% [95% CI, 5.2%-5.5%]).

A larger proportion of buprenorphine-involved overdose decedents were female compared with other opioid–involved overdose decedents (36.1% [95% CI, 34.2%-38.2%] vs 29.1% [95% CI, 28.8%-29.4%]); this was opposite for males (63.9% [95% CI, 61.8%-65.9%] vs 70.9% [95% CI, 70.6%-71.2%]) ( Table 1 ). Although median age at death was similar across groups, a higher proportion of buprenorphine-involved deaths, compared with other opioid–involved overdose deaths, occurred in the 35 to 44 years age group and a lower proportion occurred in the 18 to 35 years age groups. Additionally, 86.1% (95% CI, 84.6%-87.6%) of buprenorphine-involved overdose deaths occurred among White, non-Hispanic persons, significantly higher than the proportion for other opioid–involved overdose deaths (69.4% [95% CI, 69.1%-69.7%]). In contrast, lower proportions of buprenorphine overdose deaths occurred in Black, non-Hispanic (5.7% [95% CI, 4.8%-6.8%]) and Hispanic (5.5% [95% CI, 4.6%-6.5%]) persons compared with proportions of other opioid–involved overdose deaths (Black, non-Hispanic: 18.8% [95% CI, 18.5%-19.0%]; Hispanic: 9.4% [95% CI, 9.2%-9.6%]). The highest proportion of overdose decedents overall had a high school degree or equivalent, but no differences in education level were identified between buprenorphine-involved and other opioid–involved overdose deaths. A lower proportion of buprenorphine-involved overdose deaths occurred in decedents living in large central metropolitan areas (18.2% [95% CI, 16.6%-19.9%]) compared with other opioid–involved overdose deaths (30.6% [95% CI, 30.3%-30.9%]); a higher proportion of buprenorphine-involved overdose deaths occurred in less urban and more rural areas ( Table 1 ).

In sensitivity analyses, excluding jurisdictions with less than 100% of death certificates did not change conclusions on drug coinvolvement, demographics, and urbanicity. Additionally, results remained similar when stratified by whether they occurred before or during the COVID-19 pandemic.

More than 96% of overdose deaths in the 42 jurisdictions included in circumstance analyses had a medical examiner or coroner report. A higher proportion of buprenorphine-involved overdose deaths than other opioid–involved deaths occurred at home (72.0% [95% CI, 69.8%-74.1%] vs 65.2% [95% CI, 64.9%-65.6%]) and had documentation of no pulse at first responder arrival (62.2% [95% CI, 60.0%-64.4%] vs 56.3% [95% CI, 55.9%-56.7%]) ( Table 2 ). Among buprenorphine–involved and other opioid–involved deaths, proportions of whether the drug use leading to the fatal overdose was witnessed (7.0% [95% CI, 5.9%-8.3%] vs 8.7% [95% CI, 8.5%-8.9%]) and naloxone administration (23.1% [95% CI, 21.2%-25.0%] vs 21.4% [95% CI, 21.1%-21.7%]) were similarly low.

Although approximately half of decedents in each group had no reported route of drug use, a lower proportion of buprenorphine-involved overdose decedents had evidence of smoking (9.8% [95% CI, 8.5%-11.2%]) and snorting (9.5% [95% CI, 8.2%-10.9%]) compared with other opioid–involved decedents (smoking: 14.1% [95% CI, 13.9%-14.4%]; snorting: 15.2% [95% CI, 14.9%-15.4%]) ( Table 2 ). Evidence of illicit drugs on scene was lower among buprenorphine-involved deaths (28.4% [95% CI, 26.4%-30.5%]) than other opioid–involved deaths (38.5% [95% CI, 38.2%-38.9%]).

Less than a one-fourth of buprenorphine-involved overdose decedents were reportedly receiving treatment for SUD (22.5% [95% CI, 20.7%-24.5%]), with 20.2% (95% CI, 18.4%-22.1%) of decedents specifically receiving medications for OUD ( Table 2 ). In contrast, only 5.9% (95% CI, 5.7%-6.1%) of other opioid–involved overdose decedents were reportedly receiving treatment, with only 3.2% (95% CI, 3.1%-3.3%) receiving medications for OUD. Current SUD treatment results were similar when stratifying by urban and rural county of residence. Similarly, among buprenorphine-involved overdose deaths, 30.7% (95% CI, 28.7%-32.9%) were persons with a reported mental health diagnosis and 31.4% (95% CI, 29.3%-33.5%) were persons reportedly receiving mental health treatment. Proportions were lower among other opioid–involved overdose deaths, with 22.9% (95% CI, 22.6%-23.2%) of decedents having a reported mental health diagnosis and only 13.3% (95% CI, 13.1%-13.6%) of decedents receiving mental health treatment at the time of the fatal overdose.

In sensitivity analyses, excluding jurisdictions with medical examiner or coroner reports available for less than 90% of overdose deaths in their jurisdiction did not change conclusions. Similarly, stratifying analyses by before or during COVID-19 did not change conclusions.

This cross-sectional study found that buprenorphine was involved in a very small proportion of drug overdose deaths (2.2%) and opioid-involved overdose deaths (2.6%) in the US during July 2019 to June 2021. Importantly, the proportion of buprenorphine-involved overdose deaths fluctuated but did not increase during the 15 months from April 2020 to June 2021 when buprenorphine prescribing regulations were relaxed due to the COVID-19 pandemic. These findings have important policy implications when policy makers consider whether COVID-19–related buprenorphine prescribing flexibilities should be permanently adopted. Additionally, our findings are consistent with a 2022 study reporting no association between COVID-19–related prescribing flexibilities for methadone-based OUD treatment and methadone-involved overdose deaths. 28

Our data show that median monthly buprenorphine-involved overdose deaths increased less than opioid-involved overdose deaths from before the pandemic to during the pandemic, even with expanded access. Moreover, most of the increase was deaths that coinvolved IMFs. Given continued expansion of buprenorphine prescribing—2021 data show more than 1 million patients receiving buprenorphine from retail pharmacies in the US 29 —our findings suggest that expanded prescribing was not associated with a disproportionate number of deaths involving buprenorphine.

Characteristics of overdose deaths in this analysis provide important insights about potential ways to improve safety and clinical outcomes. First, nearly all (92.7%) buprenorphine-involved overdose deaths involved at least 1 other drug, reflecting the complex nature of polysubstance use and SUD. 30 Second, compared with other opioid–involved deaths, buprenorphine-involved overdose deaths were more likely to involve prescription medications (stimulants, benzodiazepines, antidepressants, and anticonvulsants) and less likely to involve IMFs. Buprenorphine-involved decedents were also more likely to be receiving mental health treatment and to die at home. Most overdose deaths, regardless of drugs involved, occurred without another person being present, a known risk factor for fatal overdose. 31 Together, these findings highlight the need to advance programmatic and clinical strategies that embrace the complexity of polysubstance use rather than single-drug approaches, address cooccurring mental health and SUD in a comprehensive and coordinated manner, and integrate provision of naloxone and overdose prevention education for both individuals at risk for overdose and family members, caregivers, or others who might be in a position to respond to overdoses.

Although a larger proportion of buprenorphine-involved decedents had evidence of current treatment for SUD compared with other opioid–involved decedents, most individuals in both groups (78% and 94%, respectively) had no evidence of current treatment. This stark finding highlights the need to expand access to evidence-based treatment, particularly medications for OUD; improve treatment retention; and support long-term recovery. Furthermore, the large percentage of buprenorphine-involved overdose decedents without evidence of treatment may reflect buprenorphine misuse to suppress withdrawal and self-treat OUD in the absence of formal treatment access. Prior research has shown that motivations for buprenorphine misuse are primarily associated with treatment outcomes (eg, suppression of withdrawal) rather than related to euphoria. 32 , 33 Finally, the finding that a larger proportion of buprenorphine-involved overdose deaths, compared with other opioid–involved overdose deaths, were White non-Hispanic persons, may reflect lower rates of buprenorphine treatment among Black and Hispanic individuals. 34 , 35 Disproportionate increases in overdose death rates have been reported among American Indian, Alaska Native, and Black persons compared with White persons in counties with higher SUD treatment availability. 36 This may reflect treatment access barriers, including mistrust in the health care system, stigma, transportation access, and insurance status. 36 , 37 Policy and structural interventions are needed for more equitable access to medications for OUD among people from racial and ethnic minority groups, such as American Indian, Alaska Native, and Black individuals. 36 , 37

This study has some limitations. Analyses were limited to states with data available for deaths during July 2019 to June 2021; therefore, results might not be generalizable to the entire country. Ten states submitted data on subsets of counties, which could have impacted results; however, sensitivity analyses excluding them did not yield meaningfully different results. Similarly, twelve jurisdictions did not have 100% of death certificates for drug coinvolvement, demographics, and urbanicity analyses, and 4 states had less than 90% of medical examiner or coroner reports for circumstances analyses; their exclusion did not change conclusions. Medical examiner and coroner reports also likely underestimate circumstances because death investigators may have limited information. The time-frame included in trend, drug coinvolvement, urbanicity, and circumstances analyses spanned the prepandemic and pandemic periods, and combining these timeframes may have masked differences over time. However, analyses stratified by time period did not identify significant differences. Despite these limitations, to our knowledge, this is the most extensive assessment of buprenorphine-involved overdose deaths in the US to date.

The findings of this cross-sectional study suggest that actions taken by the US federal government to facilitate access to buprenorphine-based medications for OUD during the pandemic were not associated with an increased proportion of overdose deaths involving buprenorphine, providing evidence to inform discussions on permanent adoption of COVID-19–related buprenorphine prescribing authorities. Nonetheless, although rare, overdose deaths involving buprenorphine highlight the importance of overdose prevention and support for those using buprenorphine both under medical supervision or outside of treatment for SUD or pain. Efforts to expand more equitable provision of medications for OUD and harm reduction strategies are needed to address the increasing overdose crisis.

Accepted for Publication: November 29, 2022.

Published: January 20, 2023. doi:10.1001/jamanetworkopen.2022.51856

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Tanz LJ et al. JAMA Network Open .

Corresponding Author: Lauren J. Tanz, ScD, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, MS 106-8, Atlanta, GA 30341 ( [email protected] ).

Author Contributions: Drs Tanz and Davis had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Tanz, Jones, Davis, Compton, Baldwin, Han.

Drafting of the manuscript: Tanz, Jones, Compton, Baldwin, Volkow.

Critical revision of the manuscript for important intellectual content: Tanz, Jones, Davis, Compton, Baldwin, Han.

Statistical analysis: Tanz, Davis.

Administrative, technical, or material support: Baldwin, Volkow.

Supervision: Jones, Davis, Baldwin, Volkow.

Conflict of Interest Disclosures: Dr Compton reported owning stock in General Electric, 3M, and Pfizer outside the submitted work. No other disclosures were reported.

Disclaimer: The findings and conclusions in this study are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention, the National Institute on Drug Abuse of the National Institutes of Health, or the US Department of Health and Human Services.

Data Sharing Statement: See Supplement 2 .

Additional Information: Stephanie Snodgrass, MPH (Strategic Innovative Solutions) assisted with analysis. She was not compensated outside of her normal salary.

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Research on dynamic modelling, characteristics and vibration reduction application of hot rolling mills considering the rolling process.

1. Introduction

• A torsional-vertical-horizontal coupled dynamic model of a rolling mill considering the rolling process is established and validated by simulation and field experiment.
• The influence rules of some typical parameters of the rolling process on the vibration of the rolling mill are revealed.
• Based on the theoretical findings, an application is performed for a hot continuous finishing mill on site to suppress its vibration.

2. Dynamic Model Establishment and Verification

2.1. coupled dynamic model, 2.2. model verification, 2.2.1. finite element simulation verification, 2.2.2. experimental verification, 3. analysis of dynamic response of the rolling mill system, 3.1. relationship between rolling excitations and rolling process parameters, 3.2. research on the influence of rolling process parameters, 4. on-site vibration reduction application in rolling mills, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Lu, Z.; Zhou, D.; Yu, D.; Xiao, H. Research on Dynamic Modelling, Characteristics and Vibration Reduction Application of Hot Rolling Mills Considering the Rolling Process. Machines 2024 , 12 , 629. https://doi.org/10.3390/machines12090629

Lu Z, Zhou D, Yu D, Xiao H. Research on Dynamic Modelling, Characteristics and Vibration Reduction Application of Hot Rolling Mills Considering the Rolling Process. Machines . 2024; 12(9):629. https://doi.org/10.3390/machines12090629

Lu, Zhiwen, Duolong Zhou, Danfeng Yu, and Han Xiao. 2024. "Research on Dynamic Modelling, Characteristics and Vibration Reduction Application of Hot Rolling Mills Considering the Rolling Process" Machines 12, no. 9: 629. https://doi.org/10.3390/machines12090629

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• Published: 02 September 2024

Research on prediction method of coal mining surface subsidence based on MMF optimization model

• Chunde Piao 1 ,
• Bin Zhu 1 , 2 ,
• Jianxin Jiang 1 , 3 &
• Qinghong Dong 1  

Scientific Reports volume  14 , Article number:  20316 ( 2024 ) Cite this article

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Coal seam mining causes fracture and movement of overlying strata in goaf, and endangers the safety of surface structures and underground pipelines. Based on the engineering geological conditions of 22,122 working face in Cuncaota No.2 Coal Mine of China Shenhua Shendong Coal Group Co., Ltd. a similar material model test of mining overburden rock was carried out. The subsidence of overburden rock was obtained through the full-section strain data of distributed optical fiber technology, and the characteristics of mining surface subsidence were studied. The Weibull model was used to adjust the mathematical form of the first half of the surface subsidence curve via the MMF function. On this basis, the prediction model of coal seam mining surface subsidence was established, and the parameters of the prediction model of surface subsidence were determined. The test results show that with the advancement of coal seam mining, the fit goodness of the surface subsidence prediction curve based on the MMF optimization model reaches 0.987. Compared with the measured values, the relative error of the surface subsidence prediction model is reduced to less than 10%. The model displays good prediction accuracy. The time required for settlement stability in the prediction model is positively correlated with parameter a and negatively correlated with parameter b. The research results can be further extended to the prediction of overburden “three zones” subsidence, and provide a scientific basis for the evaluation of surface subsidence compression potential in coal mine goaf.

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Introduction.

The underground mining of coal mine is accompanied by the initiation, expansion and penetration of the internal cracks in the overlying rock and soil mass, which leads to the movement, deformation and destruction of the rock strata to the goaf, causes the surface subsidence, and endangers the safety of the surface structures and underground pipelines 1 . The surface subsidence of coal mining is the result of deformation and failure of overlying strata from bottom to top. Through the monitoring of subsidence in the deformation evolution stage of overlying strata in coal seam mining, the prediction method of mining surface subsidence can be established, and effective prevention and control measures can be taken in advance to avoid and reduce the safety problems caused by surface subsidence 2 .

The settlement of overlying strata in goaf caused by underground coal mining is nonlinear and uncertain. It has become a new research direction to characterize the deformation and dynamic settlement of rock and soil mass through mathematical model. Combined with the field measured data to determine the time influence parameter C and the model order n, Zhang et al. 3 , 4 established an improved Knothe time function model by using the probability integral method, the two-medium method and the least square method. Based on actual geological conditions of a mine in East China, Ma et al. 5 , 6 t substituted he mechanical parameters into the Weibull composite subsidence prediction model (WCSPM) and the probability integral composite subsidence prediction model (PICSPM). Accordingly, they obtained the predictions of surface subsidence movement parameters. Chi et al. 7 introduced the multi-population genetic algorithm into the Boltzmann function and established a calculation model of mining subsidence parameters. Based on the dynamic model of improved probability integral method, Jiang et al. 8 established the observation condition equation of differential interferometric synthetic aperture radar (D-InSAR) monitoring mining subsidence, and proposed a three-dimensional deformation prediction method of mining subsidence. Sui et al. 9 established a mining subsidence prediction method combining D-InSAR technology and support vector machine regression algorithm. Oh HJ et al. 10 used logit boost meta-integrated machine learning model to predict land subsidence. Pal et al. 11 proposed a correction method for surface subsidence by using FNSE model and combining with the excavation parameters of long-arm mining face. The MMF (Morgan-Mercer-Flodin) model in the time function prediction model can well describe the development process of ground subsidence, and the model has a high consistency with the ground subsidence rate 12 , 13 , 14 . Surface subsidence is a dynamic problem caused by the voids in the goaf entering the surface along the overlying strata. The MMF model suffers reduced prediction accuracy when the geological conditions of rock strata are complex.

The advantage of distributed optical fiber sensing technology lies in using the optical fiber path to obtain the continuous distribution information of the measured field in time and space at the same time. It has been applied to the deformation monitoring of mining overburden 15 . Cuncaota No.2 Coal Mine of China Shenhua Shendong Coal Group Co., Ltd. is located in Yijinhuoluo Banner, Ordos City, Inner Mongolia Autonomous Region. Photovoltaic panels are installed on the surface above the 22,122 working face for power generation. Coal mining leads to the hidden danger of deformation and fracture of photovoltaic panels. This study set the 22,122 working face of Cuncaota No.2 Mine as the research object. Through the indoor similar material model test of mining overburden rock, the distributed optical fiber sensing technology was used to obtain the strain distribution of overburden rock in the process of coal seam mining, and the surface subsidence caused by mining was calculated. Aiming to solve the problem of insufficient accuracy in the prediction of surface subsidence caused by coal mining based on MMF model, Weibull model was introduced into the prediction method of surface subsidence based on MMF model, and MMF optimization model based on measured surface subsidence was established to determine the relevant parameters of the prediction model of surface subsidence. This study provides a theoretical basis for real-time control and treatment of surface subsidence above coal mine goaf.

Prediction model of surface subsidence induced by coal seam mining

Settlement prediction method based on mmf model.

The MMF model is a growth curve model with a 'S' type characteristic curve, and its model function form conforms to the evolution characteristics of surface subsidence caused by coal seam mining. The prediction expression of overburden subsidence related to MMF model is:

where \({S}_{t}\) denotes the surface settlement at time t; \({W}_{0}\) is the maximum settlement of the surface; a and b are parameters related to the geological conditions of overlying strata; and t represents the time of settlement.

The first and second derivatives of the MMF model of Eq. ( 1 ) are solved, and the expressions of surface subsidence velocity and acceleration are obtained.

Let \({W}_{0}\) = 1000mm, a = 1, b = 5, the relationship between surface subsidence value, settlement velocity and settlement acceleration based on MMF model is obtained, as shown in Fig.  1 .

Curves of surface subsidence, subsidence velocity and subsidence acceleration changing with mining time.

As can be seen from Fig.  1 , the surface subsidence curve of the MMF model exhibits obvious segmentation, and the settlement value of the initial stage and the stable stage of the settlement is small. Under the coal seam mining conditions in the underground coal mine, the strata in the caving zone first moves to the goaf and the subsidence develops rapidly, which is not consistent with the evolution characteristics of the initial stage of the MMF model.

Surface subsidence prediction method of MMF optimization model

In view of the difference between the predicted curve of MMF time function model and the actual subsidence curve in the early stage of coal seam mining, Weibull model is used to optimize and adjust the mathematical form of the first half of MMF function curve, so as to solve the problem that the overall prediction accuracy is reduced due to the small settlement value in the initial stage of settlement. The phased function form of the improved MMF model is established by using the time when the subsidence velocity of the surface monitoring point reaches the maximum \(\tau\) as the demarcation point, as shown in Eq. ( 2 ).

where u1 and u2 denote combined weights, which are non-negative numbers, and u1 + u2 = 1, which are determined according to Eq. ( 3 ); \(\tau\) refers to the maximum surface subsidence velocity moment; and t is the total time of surface subsidence deformation.

where \({e}_{i}\) is the settlement prediction error, and \({e}_{i}\) = predicted settlement value-actual settlement value.

Two methods are used to determine the maximum surface subsidence velocity time \(\tau\) .

Through the real-time monitoring of the whole section of the mining overburden, the second derivative of the fitting equation of the monitoring data is equal to zero, and the maximum settlement velocity moment \(\tau\) is obtained.

For the actual observation data missing conditions, according to the “Specification on Building, Water, Railway and Main Roadway Coal Pillar Setting and Coal Mining”, the total time of surface movement and deformation is calculated by Eq. ( 4 ):

where \({H}_{0}\) denotes the average mining depth of coal seam.

The time experienced by the active stage of ground surface subsidence deformation is 0.56 times of the total time T of the moving deformation, as shown in Eq. ( 5 ).

Similar material model test of mining overburden rock

Model monitoring and excavation scheme.

The 22,122 working face of Cuncaota No.2 Coal Mine of China Shenhua Shendong Coal Group Co., Ltd. is mined along the long arm of the coal seam. The length and width of the working face are 800 m and 340 m, respectively. According to the engineering geological conditions of the 22,122 working face of Cuncaota No.2 Coal Mine and the histogram of BK26 borehole, the average burial depth of the 2 –2 coal seam to be mined is 255 m, and the average mining thickness is 3 m. Given the mining conditions, the physical and mechanical properties of the overlying rock and the size of the model test, the geometric similarity ratio was determined to be 200, and the stress similarity ratio was 333.33. River sand, lime, gypsum and other materials were selected to configure similar materials according to the ratio number; and the test model of similar materials in mining area was made. In the model test, the uppermost Quaternary clay layer is replaced by weights of the same weight. The physical and mechanical parameters and ratio numbers of the prototype and model of Cuncaota No.2 Mine are shown in Table 1 16 .

In order to grasp the deformation and failure characteristics of overlying strata in the process of coal seam mining, four vertical tight sheath strain optical fibers were laid in the similar material test model, and BOTDR ( Brillouin optical-time-domain reflectometer ) was used to monitor the strain distribution of overlying strata. The laying of the sensing fiber in the model test is shown in Fig.  2 .

Sensor optical fiber layout and coal seam mining area diagram in the model.

As can be seen, the coal seam mining face is advanced from the left side of the 50 cm open-off cut. The excavation distance of the coal seam is 5cm each time. A total of 30 steps are mined. After each excavation, data acquisition and next mining are performed 30 min later when the rock layer is stable.

Analysis of strain distribution characteristics of overburden rock

Due to space limitation, this paper only analyzes the deformation characteristics of mining overburden according to the overburden strain distribution curve measured by A2 vertical sensing optical fiber during the mining process of 2 –2 coal seam. The strain distribution and deformation and failure characteristics of overlying strata during coal seam mining are shown in Fig.  3 .

Strain distribution and deformation failure diagram of mining overburden rock. ( a ) Overburden rock strain distribution curve and ( b ) Deformation and failure characteristics of overburden rock.

From the strain distribution curve of overburden rock in Fig.  3 , it can be seen that before the coal seam working face passes through the A2 monitoring hole, the compressive strain measured by the sensing optical fiber increases continuously with the advancement of the working face. When the coal seam working face advances from the open-off cut position to 60 cm, the optical fiber compressive strain reaches the extreme value, and the roof collapses for the first time. At this time, the roof failure height is 6 cm. When the coal seam is mined to 70 cm, the working face of the coal seam passes through the A2 sensing fiber monitoring hole. The strain measured by the A2 sensing fiber is gradually converted from compressive strain to tensile strain. The roof collapses for the second time, and the tensile strain concentration occurs at 12 cm from the roof of the coal seam. As the mining of the working face continues, the continuous collapse of the roof strata causes the stress release; the lower strain of the section measured by the optical fiber gradually decreases; and the strain value gradually shifts to the upper part of the monitoring hole. When the working face of the coal seam reaches the stop line, the tensile stress concentration occurs at 38 cm from the roof of the coal seam, and the tensile strain value of the A2 sensing fiber is 2200 με. Based on the measured strain, the mining fracture discrimination method 17 reveals that the development height of the caving zone is 12 cm, and that of the water-conducting fracture zone is 38 cm. It can be seen from Fig.  3 that there exists a good correspondence between the strain change measured by the sensing fiber and the deformation and failure characteristics of the overburden rock.

Prediction method of mining surface subsidence model

Calculation of subsidence of mining overburden rock.

There is a good coupling between the sensing fiber and the rock and soil mass around the monitoring hole. The settlement deformation of the mining rock and soil mass is calculated by the strain integral method 18 . The calculation formula of mining overburden subsidence is shown in Eq. ( 6 ).

where S denotes the settlement of overlying strata within the range of \({h}_{1}\) and \({h}_{2}\) from the roof of the coal seam, and the minimum spacing of BOTDR instrument is 0.05 m; and \(\bar{\varepsilon }\) refers to the average strain in the calculation area.

Mining surface subsidence prediction

According to the measured strain distribution and Eq. ( 6 ) displacement calculation formula, the surface subsidence during coal seam mining is obtained. Based on the MMF model and the Weibull model, the surface subsidence measured by the A2 fiber is fitted to obtain the surface subsidence prediction curve. The surface subsidence curve based on A2 optical fiber monitoring data and theoretical prediction model is shown in Fig.  4 .

Surface subsidence curve based on A2 optical fiber monitoring data and theoretical prediction model.

From the surface subsidence curve shown in Fig.  4 , it can be seen that the surface subsidence speed is accelerated after 14 times of coal seam mining, and the surface subsidence speed is reduced after 24 times of coal seam mining. The surface deformation is mainly affected by the compaction of the caving zone and the subsidence of the fracture zone and the bending subsidence zone. The subsidence deformation of the mining surface is 'S' type. When the MMF model is used to predict the mining surface subsidence, the error between the measured values is large when the surface subsidence velocity does not reach the maximum velocity. After the local surface subsidence reaches the maximum subsidence velocity, there is a high consistency between the prediction model and the measured values. The goodness of fit reaches 0.992, verifying a good calculation and prediction result. When the Weibull model is used to predict the surface settlement, the relative error between the measured value and the measured value is less than 5% before the settlement development reaches the maximum settlement speed. Compared with the MMF model, it boasts of higher accuracy. However, in the later stage of settlement development, the overall prediction accuracy and goodness of fit of the Weibull model display a downward trend, and the relative error is large.

According to the fitting curves of Eqs. ( 1 ), ( 2 ) and Fig.  4 , the parameters W0, a, b of MMF model and the parameters W0, c, k of Weibull model are determined respectively, and the theoretical calculation formula of surface subsidence is obtained, as shown in Eqs. ( 7 ) and ( 8 ).

According to Eqs. ( 7 ) and ( 8 ), the time when the surface subsidence reaches the maximum subsidence speed \(\tau\) of MMF model and Weibull model in the 14th mining of coal seam is obtained. According to Eq. ( 3 ), the error weights u1 and u2 in the MMF optimization model are 0.78 and 0.22, respectively.

The settlement prediction formula of the MMF optimization model is shown in Eq. ( 9 ):

The surface prediction curve of Eq. ( 9 ) is analyzed with the settlement based on the measured data of A2 fiber, and the comparison results are shown in Fig.  5 .

Settlement prediction curve and error analysis of MMF optimization model.

It can be seen from Fig.  5 that the overall accuracy of the prediction curve is high when the MMF optimization model is used to predict the surface subsidence. When the coal seam is mined for the fourth time, the relative error is less than 20%. With the advancement of mining, the prediction error is reduced to less than 10%, and the goodness of fit reaches 0.987, indicating that the overall prediction accuracy of the MMF optimization model meets the requirements.

The parameter evolution characteristics of the prediction model

It can be seen from Eq. ( 1 ) and Fig.  1 that the parameters a and b have a great influence on the function form and prediction accuracy of the MMF model. Figure  6 shows the function curves when W0 is 100mm with varying a and b. To be specific, when b is 2.5, a is 50, 100, 5000, 1000, 5000, 8000; when a is 500, b is 2, 2.5, 3, 5, 7, 10, respectively.

The influence of MMF model parameters on the predicted value. ( a ) The influence of the change of parameter a on the model and ( b ) The influence of the change of parameter b on the model.

It can be seen from Fig.  6 that the effects of MMF model parameters a and b on the predicted values are opposite. With the increase of parameter a, the settling velocity decreases obviously, and the time to reach the stable stage of settlement increases. With the increase of parameter b, the settling velocity increases obviously, and the time to reach the stable stage of settlement decreases. Therefore, according to the rock and soil properties of mining overburden and the mining conditions of coal seam, the surface subsidence velocity and stability time are analyzed. Combined with the field monitoring data, the parameters a and b in the MMF prediction model are determined to improve the prediction accuracy of surface subsidence.

The study has achieved the following research results.

According to the evolution characteristics of mining-induced surface subsidence in coal mines, the Weibull model is used to adjust the mathematical form of the first half of the MMF function curve, and the MMF optimization model for mining-induced surface subsidence prediction is established. The goodness of fit of the prediction curve based on the MMF optimization model is 0.987, the average residual sum is 0.25, and the prediction error is less than 10% with the coal seam mining. The model displays good prediction accuracy, indicating that the model is suitable for mining surface subsidence prediction.

In the MMF prediction model, the time required for the settlement stability stage increases with the increase of parameter a, and decreases with the increase of parameter b. The values of parameters a and b in the MMF prediction model should be determined according to the rock and soil properties of mining overburden, coal seam mining conditions and on-site monitoring data.

There is a close relationship between the subsidence evolution stage of overlying strata in coal mine goaf and the engineering properties of 'three zones', namely, overburden caving zone, fracture zone and bending subsidence zone. Based on the full-section strain distribution data of distributed optical fiber technology, the settlement of the three zones is calculated. In the next stage, the MMF optimization model is used to predict the subsidence evolution process of the 'three zones' of the overburden rock, which can provide a basis for the evaluation of the compression potential of the surface subsidence evolution stage of the coal mine goaf.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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This article was funded by National Natural Science Foundation of China, under Grant No. 42277159.

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Chunde Piao, Bin Zhu, Jianxin Jiang & Qinghong Dong

Qingdao West Coast New District Comprehensive Administrative Law Enforcement Bureau, Qingdao, China

Sichuan Zhongding Blasting Engineering Co, Ltd., Ya’an, China

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Piao. CD: Conceptualization, Methodology, Writing manuscript. Zhu. B: Data Curation, Revisions manuscript. Jiang. JX: Assistance for data acquisition and data analysis. Dong. QH: Provided theoretical insights, Funding acquisition.

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Piao, C., Zhu, B., Jiang, J. et al. Research on prediction method of coal mining surface subsidence based on MMF optimization model. Sci Rep 14 , 20316 (2024). https://doi.org/10.1038/s41598-024-71434-y

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DOI : https://doi.org/10.1038/s41598-024-71434-y

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