Her mother took care of her grandmother, and my mother took care of my grandmother and both took care of her mother, both had some help taking care of my dad when he was sick, and I know that it was inbred in me, not really inbred, but something I saw; you follow suit by example. SalTI, p. 9
Note . SES = socioeconomic status.
Reading from the left in Table 2 , codes were given a number and letter for use in marking sections of text. Next, the code name indicating a theme was entered in boldface type with a definition in the code immediately under it. The second column provided an exemplar of each code, along with a notation indicating where it was found in the data, so that coders could recognize instances of that particular code when they saw them.
The coding manual was tested against data gathered in a preliminary study and was revised as codes found to overlap or be missing entirely. We continued to revise it iteratively during the study as data collection and analysis proceeded and then used it to recode previously coded data. Using this procedure, it was used to revisit the data several times.
Level 1 Coding With Meaning Units.
Original text (meaning unit highlighted in relation to applied code) | Code(s) applied to meaning unit |
---|---|
I try to eat well. My wife seems to do a good job with that stuff and everything. I am fairly active around the house and stuff | |
I’ve recently become semi-retired, so even though retirement means like relaxation, it really hasn’t. It has just given me more work to do around the house and stuff, and again, having children of my own, basically, I not only have a honey-do list from wife, I have a honey-do list for my two charming daughters | |
Again too, I’d like to be around as long as possible. I enjoy life. I try to enjoy it to the fullest. I’d like to be—I want to live life. I don’t want survive, I guess is what I’d say. I’ve seen too many instances of this. My mother-in-law is a prime example. She is in an assisted-living facility, and I really think she’s just about, I don’t want to say given up and stuff, but she’s not living. She is surviving. I think that’s sad. I really do. I think you are going to get out of life what you put into life. I think if she would put a little more effort into life, her life would be a lot more fulfilling and rewarding to her and basically to people around her | |
Example of an analytic memo used in qualitative description analysis.
Data Matrix.
Case | CLOX-CG | CLOX-CR | CG Vigilance Scale | CG Strain | CG Gain |
---|---|---|---|---|---|
1 | 5 ( ) | 1 ( ) | 20 hr/wk ( ) | Moderate: fatigue and moderate anxiety | Moderate: Giving back to mom |
2 | 3 ( ) | 1 ( ) | 30 hr/wk ( ) | High: debilitating fatigue, high anxiety, feels depressed, and sleeplessness | Low: Unable to see positive aspects |
Note . The CLOX is an executive clock drawing task that tests cognition and was used in this study with the caregiver (CG) and the care recipient (CR). The CG Strain and the CG Gain scores were derived by the researcher through a qualitative content analysis ( Evans, Coon, & Belyea, 2006 ).
Many qualitative researchers do not provide enough information in their reports about the analytic strategies used to ensure verisimilitude or the “ring of truth” for the conclusions. Miles, Huberman, and Saldana (2014) outline 13 tactics for generating meaning from data and another 13 for testing or confirming findings. They also provide five standards for assessing the quality of conclusions. The techniques relied upon most heavily during a qualitative descriptive study ought to be addressed within the research report. It is important to establish “trustworthiness” and “authenticity” in qualitative research that are similar to the terms validity and reliability in quantitative research. The five standards (objectivity, dependability, credibility, transferability, and application) typically used in qualitative descriptive studies to assess quality and legitimacy (trustworthiness and authenticity) of the conclusions are discussed in the next sections ( Lincoln & Guba, 1985 ; Miles et al., 2014 ).
First, objectivity (confirmability) is conceptualized as relative neutrality and reasonable freedom from researcher bias and can be addressed by (a) describing the study’s methods and procedures in explicit detail, (b) sharing the sequence of data collection, analysis, and presentation methods to create an audit trail, (c) being aware of and reporting personal assumptions and potential bias, (d) retaining study data and making it available to collaborators for evaluation.
Second, dependability (reliability or auditability) can be fostered by consistency in procedures across participants over time through various methods, including the use of semistructured interview questions and an observation data collection worksheet. Quality control ( Miles et al., 2014 ) can be fostered by:
Third, credibility or verisimilitude (internal validity) is defined as the truth value of data: Do the findings of the study make sense ( Miles et al., 2014 , p. 312). Credibility in qualitative work promotes descriptive and evaluative understanding, which can be addressed by (a) providing context-rich “thick descriptions,” that is, the work of interpretation based on data ( Sandelowski, 2004 ), (b) checking with other practitioners or researchers that the findings “ring true,” (c) providing a comprehensive account, (d) using triangulation strategies, (e) searching for negative evidence, and (f) linking findings to a theoretical framework.
Fourth, transferability (external validity or “fittingness”) speaks to whether the findings of your study have larger import and application to other settings or studies. This includes a discussion of generalizability. Sample to population generalizability is important to quantitative researchers and less helpful to qualitative researchers who seek more of an analytic or case-to-case transfer ( Miles et al., 2014 ). Nonetheless, transferability can be aided by (a) describing the characteristics of the participants fully so that comparisons with other groups may be made, (b) adequately describing potential threats to generalizability through sample and setting sections, (c) using theoretical sampling, (d) presenting findings that are congruent with theory, and (e) suggesting ways that findings from your study could be tested further by other researchers.
Finally, Miles et al. (2014) speak to the utilization, application, or action orientation of the data. “Even if we know that a study’s findings are valid and transferable,” they write, “we still need to know what the study does for its participants and its consumers” ( Miles et al., 2014 , p. 314). To address application, findings of qualitative descriptive studies are typically made accessible to potential consumers of information through the publication of manuscripts, poster presentations, and summary reports written for consumers. In addition, qualitative descriptive study findings may stimulate further research, promote policy discussions, or suggest actual changes to a product or environment.
The qualitative description clarified and advocated by Sandelowski (2000 , 2010 ) is an excellent methodological choice for the healthcare environments designer, practitioner, or health sciences researcher because it provides rich descriptive content from the subjects’ perspective. Qualitative description allows the investigator to select from any number of theoretical frameworks, sampling strategies, and data collection techniques. The various content analysis strategies described in this paper serve to introduce the investigator to methods for data analysis that promote staying “close” to the data, thereby avoiding high-inference techniques likely challenging to the novice investigator. Finally, the devotion to thick description (interpretation based on data) and flexibility in the re-presentation of study findings is likely to produce meaningful information to designers and healthcare leaders. The practical, step-by-step nature of this article should serve as a starting guide to researchers interested in this technique as a way to answer their own burning questions.
The author would like to recognize the other members of her dissertation committee for their contributions to the study: Gerri Lamb, Karen Dorman Marek, and Robert Greenes.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research assistance for data analysis and manuscript development was supported by training funds from the National Institutes of Health/National Institute on Nursing Research (NIH/NINR), award T32 1T32NR012718-01 Transdisciplinary Training in Health Disparities Science (C. Keller, P.I.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the NINR. This research was supported through the Hartford Center of Gerontological Nursing Excellence at Arizona State University College of Nursing & Health Innovation.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Descriptive research design is a powerful tool used by scientists and researchers to gather information about a particular group or phenomenon. This type of research provides a detailed and accurate picture of the characteristics and behaviors of a particular population or subject. By observing and collecting data on a given topic, descriptive research helps researchers gain a deeper understanding of a specific issue and provides valuable insights that can inform future studies.
In this blog, we will explore the definition, characteristics, and common flaws in descriptive research design, and provide tips on how to avoid these pitfalls to produce high-quality results. Whether you are a seasoned researcher or a student just starting, understanding the fundamentals of descriptive research design is essential to conducting successful scientific studies.
Table of Contents
The descriptive research design involves observing and collecting data on a given topic without attempting to infer cause-and-effect relationships. The goal of descriptive research is to provide a comprehensive and accurate picture of the population or phenomenon being studied and to describe the relationships, patterns, and trends that exist within the data.
Descriptive research methods can include surveys, observational studies , and case studies, and the data collected can be qualitative or quantitative . The findings from descriptive research provide valuable insights and inform future research, but do not establish cause-and-effect relationships.
1. understanding of a population or phenomenon.
Descriptive research provides a comprehensive picture of the characteristics and behaviors of a particular population or phenomenon, allowing researchers to gain a deeper understanding of the topic.
The information gathered through descriptive research can serve as a baseline for future research and provide a foundation for further studies.
Descriptive research can provide valuable information and insights into a particular topic, which can inform future research, policy decisions, and programs.
Descriptive research can be used to validate sampling methods and to help researchers determine the best approach for their study.
Descriptive research is often less expensive and less time-consuming than other research methods , making it a cost-effective way to gather information about a particular population or phenomenon.
Descriptive research is straightforward to replicate, making it a reliable way to gather and compare information from multiple sources.
The primary purpose of descriptive research is to describe the characteristics, behaviors, and attributes of a particular population or phenomenon.
Descriptive research studies a particular population or sample that is representative of the larger population being studied. Furthermore, sampling methods can include convenience, stratified, or random sampling.
Descriptive research typically involves the collection of both qualitative and quantitative data through methods such as surveys, observational studies, case studies, or focus groups.
Descriptive research data is analyzed to identify patterns, relationships, and trends within the data. Statistical techniques , such as frequency distributions and descriptive statistics, are commonly used to summarize and describe the data.
Descriptive research is focused on describing and summarizing the characteristics of a particular population or phenomenon. It does not make causal inferences.
Descriptive research is non-experimental, meaning that the researcher does not manipulate variables or control conditions. The researcher simply observes and collects data on the population or phenomenon being studied.
A researcher can conduct descriptive research in the following situations:
1. survey research.
Surveys are a type of descriptive research that involves collecting data through self-administered or interviewer-administered questionnaires. Additionally, they can be administered in-person, by mail, or online, and can collect both qualitative and quantitative data.
Observational research involves observing and collecting data on a particular population or phenomenon without manipulating variables or controlling conditions. It can be conducted in naturalistic settings or controlled laboratory settings.
Case study research is a type of descriptive research that focuses on a single individual, group, or event. It involves collecting detailed information on the subject through a variety of methods, including interviews, observations, and examination of documents.
Focus group research involves bringing together a small group of people to discuss a particular topic or product. Furthermore, the group is usually moderated by a researcher and the discussion is recorded for later analysis.
Ethnographic research involves conducting detailed observations of a particular culture or community. It is often used to gain a deep understanding of the beliefs, behaviors, and practices of a particular group.
1. provides a comprehensive understanding.
Descriptive research provides a comprehensive picture of the characteristics, behaviors, and attributes of a particular population or phenomenon, which can be useful in informing future research and policy decisions.
Descriptive research is non-invasive and does not manipulate variables or control conditions, making it a suitable method for sensitive or ethical concerns.
Descriptive research allows for a wide range of data collection methods , including surveys, observational studies, case studies, and focus groups, making it a flexible and versatile research method.
Descriptive research is often less expensive and less time-consuming than other research methods. Moreover, it gives a cost-effective option to many researchers.
Descriptive research is easy to replicate, making it a reliable way to gather and compare information from multiple sources.
The insights gained from a descriptive research can inform future research and inform policy decisions and programs.
1. limited scope.
Descriptive research only provides a snapshot of the current situation and cannot establish cause-and-effect relationships.
Descriptive research relies on existing data, which may not always be comprehensive or accurate.
Researchers have no control over the variables in descriptive research, which can limit the conclusions that can be drawn.
The researcher’s own biases and preconceptions can influence the interpretation of the data.
Descriptive research findings may not be applicable to other populations or situations.
Descriptive research provides a surface-level understanding of a phenomenon, rather than a deep understanding.
Descriptive research often requires a large amount of data collection and analysis, which can be time-consuming and resource-intensive.
A clearly defined research question is the foundation of any research study, and it is important to ensure that the question is both specific and relevant to the topic being studied.
Choosing the appropriate research design for a study is crucial to the success of the study. Moreover, researchers should choose a design that best fits the research question and the type of data needed to answer it.
Selecting a representative sample is important to ensure that the findings of the study are generalizable to the population being studied. Researchers should use a sampling method that provides a random and representative sample of the population.
Using valid and reliable data collection methods is important to ensure that the data collected is accurate and can be used to answer the research question. Researchers should choose methods that are appropriate for the study and that can be administered consistently and systematically.
Bias can significantly impact the validity and reliability of research findings. Furthermore, it is important to minimize bias in all aspects of the study, from the selection of participants to the analysis of data.
An adequate sample size is important to ensure that the results of the study are statistically significant and can be generalized to the population being studied.
The appropriate data analysis technique depends on the type of data collected and the research question being asked. Researchers should choose techniques that are appropriate for the data and the question being asked.
Have you worked on descriptive research designs? How was your experience creating a descriptive design? What challenges did you face? Do write to us or leave a comment below and share your insights on descriptive research designs!
extremely very educative
Indeed very educative and useful. Well explained. Thank you
Simple,easy to understand
Excellent and easy to understand queries and questions get answered easily. Its rather clear than any confusion. Thanks a million Shritika Sirisilla.
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Descriptive research studies.
Descriptive research is a type of research that is used to describe the characteristics of a population. It collects data that are used to answer a wide range of what, when, and how questions pertaining to a particular population or group. For example, descriptive studies might be used to answer questions such as: What percentage of Head Start teachers have a bachelor's degree or higher? What is the average reading ability of 5-year-olds when they first enter kindergarten? What kinds of math activities are used in early childhood programs? When do children first receive regular child care from someone other than their parents? When are children with developmental disabilities first diagnosed and when do they first receive services? What factors do programs consider when making decisions about the type of assessments that will be used to assess the skills of the children in their programs? How do the types of services children receive from their early childhood program change as children age?
Descriptive research does not answer questions about why a certain phenomenon occurs or what the causes are. Answers to such questions are best obtained from randomized and quasi-experimental studies . However, data from descriptive studies can be used to examine the relationships (correlations) among variables. While the findings from correlational analyses are not evidence of causality, they can help to distinguish variables that may be important in explaining a phenomenon from those that are not. Thus, descriptive research is often used to generate hypotheses that should be tested using more rigorous designs.
A variety of data collection methods may be used alone or in combination to answer the types of questions guiding descriptive research. Some of the more common methods include surveys, interviews, observations, case studies, and portfolios. The data collected through these methods can be either quantitative or qualitative. Quantitative data are typically analyzed and presenting using descriptive statistics . Using quantitative data, researchers may describe the characteristics of a sample or population in terms of percentages (e.g., percentage of population that belong to different racial/ethnic groups, percentage of low-income families that receive different government services) or averages (e.g., average household income, average scores of reading, mathematics and language assessments). Quantitative data, such as narrative data collected as part of a case study, may be used to organize, classify, and used to identify patterns of behaviors, attitudes, and other characteristics of groups.
Descriptive studies have an important role in early care and education research. Studies such as the National Survey of Early Care and Education and the National Household Education Surveys Program have greatly increased our knowledge of the supply of and demand for child care in the U.S. The Head Start Family and Child Experiences Survey and the Early Childhood Longitudinal Study Program have provided researchers, policy makers and practitioners with rich information about school readiness skills of children in the U.S.
Each of the methods used to collect descriptive data have their own strengths and limitations. The following are some of the strengths and limitations of descriptive research studies in general.
Study participants are questioned or observed in a natural setting (e.g., their homes, child care or educational settings).
Study data can be used to identify the prevalence of particular problems and the need for new or additional services to address these problems.
Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research."
Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples.
Limitations:
Descriptive studies cannot be used to establish cause and effect relationships.
Respondents may not be truthful when answering survey questions or may give socially desirable responses.
The choice and wording of questions on a questionnaire may influence the descriptive findings.
Depending on the type and size of sample, the findings may not be generalizable or produce an accurate description of the population of interest.
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Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on September 5, 2024.
Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .
It should include:
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How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.
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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .
It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.
You can start by introducing your overall approach to your research. You have two options here.
What research problem or question did you investigate?
And what type of data did you need to achieve this aim?
Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?
Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .
In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.
Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.
Surveys Describe where, when, and how the survey was conducted.
Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.
Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.
The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.
The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.
Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.
In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.
Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)
Interviews or focus groups Describe where, when, and how the interviews were conducted.
Participant observation Describe where, when, and how you conducted the observation or ethnography .
Existing data Explain how you selected case study materials for your analysis.
In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.
Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.
Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.
Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.
Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.
Professional editors proofread and edit your paper by focusing on:
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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.
In quantitative research , your analysis will be based on numbers. In your methods section, you can include:
In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).
Specific methods might include:
Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.
Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.
In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .
Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.
The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .
Your methodology can be strengthened by referencing existing research in your field. This can help you to:
Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.
Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.
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.
Methodology
Research bias
Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .
In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .
Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.
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.
Reliability and validity are both about how well a method measures something:
If you are doing experimental research, you also have to consider the internal and external validity of your experiment.
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
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Dave Cornell (PhD)
Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.
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Descriptive research involves gathering data to provide a detailed account or depiction of a phenomenon without manipulating variables or conducting experiments.
A scholarly definition is:
“Descriptive research is defined as a research approach that describes the characteristics of the population, sample or phenomenon studied. This method focuses more on the “what” rather than the “why” of the research subject.” (Matanda, 2022, p. 63)
The key feature of descriptive research is that it merely describes phenomena and does not attempt to manipulate variables nor determine cause and effect .
To determine cause and effect , a researcher would need to use an alternate methodology, such as experimental research design .
Common approaches to descriptive research include:
Methods that could be used include:
1. Understanding Autism Spectrum Disorder (Psychology): Researchers analyze various behavior patterns, cognitive skills, and social interaction abilities specific to children with Autism Spectrum Disorder to comprehensively describe the disorder’s symptom spectrum. This detailed description classifies it as descriptive research, rather than analytical or experimental, as it merely records what is observed without altering any variables or trying to establish causality.
2. Consumer Purchase Decision Process in E-commerce Marketplaces (Marketing): By documenting and describing all the factors that influence consumer decisions on online marketplaces, researchers don’t attempt to predict future behavior or establish causes—just describe observed behavior—making it descriptive research.
3. Impacts of Climate Change on Agricultural Practices (Environmental Studies): Descriptive research is seen as scientists outline how climate changes influence various agricultural practices by observing and then meticulously categorizing the impacts on crop variability, farming seasons, and pest infestations without manipulating any variables in real-time.
4. Work Environment and Employee Performance (Human Resources Management): A study of this nature, describing the correlation between various workplace elements and employee performance, falls under descriptive research as it merely narrates the observed patterns without altering any conditions or testing hypotheses.
5. Factors Influencing Student Performance (Education): Researchers describe various factors affecting students’ academic performance, such as studying techniques, parental involvement, and peer influence. The study is categorized as descriptive research because its principal aim is to depict facts as they stand without trying to infer causal relationships.
6. Technological Advances in Healthcare (Healthcare): This research describes and categorizes different technological advances (such as telemedicine, AI-enabled tools, digital collaboration) in healthcare without testing or modifying any parameters, making it an example of descriptive research.
7. Urbanization and Biodiversity Loss (Ecology): By describing the impact of rapid urban expansion on biodiversity loss, this study serves as a descriptive research example. It observes the ongoing situation without manipulating it, offering a comprehensive depiction of the existing scenario rather than investigating the cause-effect relationship.
8. Architectural Styles across Centuries (Art History): A study documenting and describing various architectural styles throughout centuries essentially represents descriptive research. It aims to narrate and categorize facts without exploring the underlying reasons or predicting future trends.
9. Media Usage Patterns among Teenagers (Sociology): When researchers document and describe the media consumption habits among teenagers, they are performing a descriptive research study. Their main intention is to observe and report the prevailing trends rather than establish causes or predict future behaviors.
10. Dietary Habits and Lifestyle Diseases (Nutrition Science): By describing the dietary patterns of different population groups and correlating them with the prevalence of lifestyle diseases, researchers perform descriptive research. They merely describe observed connections without altering any diet plans or lifestyles.
11. Shifts in Global Energy Consumption (Environmental Economics): When researchers describe the global patterns of energy consumption and how they’ve shifted over the years, they conduct descriptive research. The focus is on recording and portraying the current state without attempting to infer causes or predict the future.
12. Literacy and Employment Rates in Rural Areas (Sociology): A study aims at describing the literacy rates in rural areas and correlating it with employment levels. It falls under descriptive research because it maps the scenario without manipulating parameters or proving a hypothesis.
13. Women Representation in Tech Industry (Gender Studies): A detailed description of the presence and roles of women across various sectors of the tech industry is a typical case of descriptive research. It merely observes and records the status quo without establishing causality or making predictions.
14. Impact of Urban Green Spaces on Mental Health (Environmental Psychology): When researchers document and describe the influence of green urban spaces on residents’ mental health, they are undertaking descriptive research. They seek purely to understand the current state rather than exploring cause-effect relationships.
15. Trends in Smartphone usage among Elderly (Gerontology): Research describing how the elderly population utilizes smartphones, including popular features and challenges encountered, serves as descriptive research. Researcher’s aim is merely to capture what is happening without manipulating variables or posing predictions.
16. Shifts in Voter Preferences (Political Science): A study describing the shift in voter preferences during a particular electoral cycle is descriptive research. It simply records the preferences revealed without drawing causal inferences or suggesting future voting patterns.
17. Understanding Trust in Autonomous Vehicles (Transportation Psychology): This comprises research describing public attitudes and trust levels when it comes to autonomous vehicles. By merely depicting observed sentiments, without engineering any situations or offering predictions, it’s considered descriptive research.
18. The Impact of Social Media on Body Image (Psychology): Descriptive research to outline the experiences and perceptions of individuals relating to body image in the era of social media. Observing these elements without altering any variables qualifies it as descriptive research.
Descriptive research merely observes, records, and presents the actual state of affairs without manipulating any variables, while experimental research involves deliberately changing one or more variables to determine their effect on a particular outcome.
De Vaus (2001) succinctly explains that descriptive studies find out what is going on , but experimental research finds out why it’s going on /
Simple definitions are below:
Experimental designs often involve a control group and random assignment . While it can provide compelling evidence for cause and effect, its artificial setting might not perfectly mirror real-worldly conditions, potentially affecting the generalizability of its findings.
These two types of research are complementary, with descriptive studies often leading to hypotheses that are then tested experimentally (Devi, 2017; Zhao et al., 2021).
Parameter | Descriptive Research | Experimental Research |
---|---|---|
To describe and explore phenomena without influencing variables (Monsen & Van Horn, 2007). | To investigate cause-and-effect relationships by manipulating variables. | |
Observational and non-intrusive. | Manipulative and controlled. | |
Typically not aimed at testing a hypothesis. | Generally tests a hypothesis (Mukherjee, 2019). | |
No variables are manipulated (Erickson, 2017). | Involves manipulation of one or more variables (independent variables). | |
No control over variables and environment. | Strict control over variables and environment. | |
Does not establish causal relationships. | Aims to establish causal relationships. | |
Not focused on predicting outcomes. | Often seeks to predict outcomes based on variable manipulation (Zhao et al., 2021). | |
Uses surveys, observations, and case studies (Ivey, 2016). | Employs controlled experiments often with experimental and control groups. | |
Typically fewer ethical concerns due to non-interference. | Potential ethical considerations due to manipulation and intervention (Devi, 2017). |
Descriptive research offers several benefits: it allows researchers to gather a vast amount of data and present a complete picture of the situation or phenomenon under study, even within large groups or over long time periods.
It’s also flexible in terms of the variety of methods used, such as surveys, observations, and case studies, and it can be instrumental in identifying patterns or trends and generating hypotheses (Erickson, 2017).
However, it also has its limitations.
The primary drawback is that it can’t establish cause-effect relationships, as no variables are manipulated. This lack of control over variables also opens up possibilities for bias, as researchers might inadvertently influence responses during data collection (De Vaus, 2001).
Additionally, the findings of descriptive research are often not generalizable since they are heavily reliant on the chosen sample’s characteristics.
Provides a comprehensive and detailed profile of the subject or issue through rich data, offering a thorough understanding (Gresham, 2016). | Cannot or external factors, potentially influencing the accuracy and reliability of the data. |
Helps to identify patterns, trends, and variables for subsequent experimental or correlational research – Krishnaswamy et al. (2009) call it “fact finding” research, setting the groundwork for future experimental studies. | Cannot establish causal relationships due to its observational nature, limiting the explanatory power. |
See More Types of Research Design Here
De Vaus, D. A. (2001). Research Design in Social Research . SAGE Publications.
Devi, P. S. (2017). Research Methodology: A Handbook for Beginners . Notion Press.
Erickson, G. S. (2017). Descriptive research design. In New Methods of Market Research and Analysis (pp. 51-77). Edward Elgar Publishing.
Gresham, B. B. (2016). Concepts of Evidence-based Practice for the Physical Therapist Assistant . F.A. Davis Company.
Ivey, J. (2016). Is descriptive research worth doing?. Pediatric nursing , 42 (4), 189. ( Source )
Krishnaswamy, K. N., Sivakumar, A. I., & Mathirajan, M. (2009). Management Research Methodology: Integration of Principles, Methods and Techniques . Pearson Education.
Matanda, E. (2022). Research Methods and Statistics for Cross-Cutting Research: Handbook for Multidisciplinary Research . Langaa RPCIG.
Monsen, E. R., & Van Horn, L. (2007). Research: Successful Approaches . American Dietetic Association.
Mukherjee, S. P. (2019). A Guide to Research Methodology: An Overview of Research Problems, Tasks and Methods . CRC Press.
Siedlecki, S. L. (2020). Understanding descriptive research designs and methods. Clinical Nurse Specialist , 34 (1), 8-12. ( Source )
Zhao, P., Ross, K., Li, P., & Dennis, B. (2021). Making Sense of Social Research Methodology: A Student and Practitioner Centered Approach . SAGE Publications.
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Home » Descriptive Analytics – Methods, Tools and Examples
Table of Contents
Definition:
Descriptive analytics focused on describing or summarizing raw data and making it interpretable. This type of analytics provides insight into what has happened in the past. It involves the analysis of historical data to identify patterns, trends, and insights. Descriptive analytics often uses visualization tools to represent the data in a way that is easy to interpret.
Descriptive analytics plays a crucial role in research, helping investigators understand and describe the data collected in their studies. Here’s how descriptive analytics is typically used in a research setting:
Descriptive analytics involves a variety of techniques to summarize, interpret, and visualize historical data. Some commonly used techniques include:
This includes basic statistical methods like mean, median, mode (central tendency), standard deviation, variance (dispersion), correlation, and regression (relationships between variables).
It is the process of compiling and summarizing data to obtain a general perspective. It can involve methods like sum, count, average, min, max, etc., often applied to a group of data.
This involves analyzing large volumes of data to discover patterns, trends, and insights. Techniques used in data mining can include clustering (grouping similar data), classification (assigning data into categories), association rules (finding relationships between variables), and anomaly detection (identifying outliers).
This involves presenting data in a graphical or pictorial format to provide clear and easy understanding of the data patterns, trends, and insights. Common data visualization methods include bar charts, line graphs, pie charts, scatter plots, histograms, and more complex forms like heat maps and interactive dashboards.
This involves organizing data into informational summaries to monitor how different areas of a business are performing. Reports can be generated manually or automatically and can be presented in tables, graphs, or dashboards.
It involves displaying the relationship between two or more variables in a tabular form. It can provide a deeper understanding of the data by allowing comparisons and revealing patterns and correlations that may not be readily apparent in raw data.
Some techniques use complex algorithms to interpret data. Examples include decision tree analysis, which provides a graphical representation of decision-making situations, and neural networks, which are used to identify correlations and patterns in large data sets.
Some common Descriptive Analytics Tools are as follows:
Excel: Microsoft Excel is a widely used tool that can be used for simple descriptive analytics. It has powerful statistical and data visualization capabilities. Pivot tables are a particularly useful feature for summarizing and analyzing large data sets.
Tableau: Tableau is a data visualization tool that is used to represent data in a graphical or pictorial format. It can handle large data sets and allows for real-time data analysis.
Power BI: Power BI, another product from Microsoft, is a business analytics tool that provides interactive visualizations with self-service business intelligence capabilities.
QlikView: QlikView is a data visualization and discovery tool. It allows users to analyze data and use this data to support decision-making.
SAS: SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it.
SPSS: SPSS (Statistical Package for the Social Sciences) is a software package used for statistical analysis. It’s widely used in social sciences research but also in other industries.
Google Analytics: For web data, Google Analytics is a popular tool. It allows businesses to analyze in-depth detail about the visitors on their website, providing valuable insights that can help shape the success strategy of a business.
R and Python: Both are programming languages that have robust capabilities for statistical analysis and data visualization. With packages like pandas, matplotlib, seaborn in Python and ggplot2, dplyr in R, these languages are powerful tools for descriptive analytics.
Looker: Looker is a modern data platform that can take data from any database and let you start exploring and visualizing.
Descriptive analytics forms the base of the data analysis workflow and is typically the first step in understanding your business or organization’s data. Here are some situations when you might use descriptive analytics:
Understanding Past Behavior: Descriptive analytics is essential for understanding what has happened in the past. If you need to understand past sales trends, customer behavior, or operational performance, descriptive analytics is the tool you’d use.
Reporting Key Metrics: Descriptive analytics is used to establish and report key performance indicators (KPIs). It can help in tracking and presenting these KPIs in dashboards or regular reports.
Identifying Patterns and Trends: If you need to identify patterns or trends in your data, descriptive analytics can provide these insights. This might include identifying seasonality in sales data, understanding peak operational times, or spotting trends in customer behavior.
Informing Business Decisions: The insights provided by descriptive analytics can inform business strategy and decision-making. By understanding what has happened in the past, you can make more informed decisions about what steps to take in the future.
Benchmarking Performance: Descriptive analytics can be used to compare current performance against historical data. This can be used for benchmarking and setting performance goals.
Auditing and Regulatory Compliance: In sectors where compliance and auditing are essential, descriptive analytics can provide the necessary data and trends over specific periods.
Initial Data Exploration: When you first acquire a dataset, descriptive analytics is useful to understand the structure of the data, the relationships between variables, and any apparent anomalies or outliers.
Examples of Descriptive Analytics are as follows:
Retail Industry: A retail company might use descriptive analytics to analyze sales data from the past year. They could break down sales by month to identify any seasonality trends. For example, they might find that sales increase in November and December due to holiday shopping. They could also break down sales by product to identify which items are the most popular. This analysis could inform their purchasing and stocking decisions for the next year. Additionally, data on customer demographics could be analyzed to understand who their primary customers are, guiding their marketing strategies.
Healthcare Industry: In healthcare, descriptive analytics could be used to analyze patient data over time. For instance, a hospital might analyze data on patient admissions to identify trends in admission rates. They might find that admissions for certain conditions are higher at certain times of the year. This could help them allocate resources more effectively. Also, analyzing patient outcomes data can help identify the most effective treatments or highlight areas where improvement is needed.
Finance Industry: A financial firm might use descriptive analytics to analyze historical market data. They could look at trends in stock prices, trading volume, or economic indicators to inform their investment decisions. For example, analyzing the price-earnings ratios of stocks in a certain sector over time could reveal patterns that suggest whether the sector is currently overvalued or undervalued. Similarly, credit card companies can analyze transaction data to detect any unusual patterns, which could be signs of fraud.
Descriptive analytics plays a vital role in the world of data analysis, providing numerous advantages:
While descriptive analytics offers numerous benefits, it also has certain limitations or disadvantages. Here are a few to consider:
Researcher, Academic Writer, Web developer
A research project always begins with selecting a topic. The next step is for researchers to identify the specific areas…
A research project always begins with selecting a topic. The next step is for researchers to identify the specific areas of interest. After that, they tackle the key component of any research problem: how to gather enough quality information. If we opt for a descriptive research design we have to ask the correct questions to access the right information.
For instance, researchers may choose to focus on why people invest in cryptocurrency, knowing how dynamic the market is rather than asking why the market is so shaky. These are completely different questions that require different research approaches. Adopting the descriptive method can help capitalize on trends the information reveals. Descriptive research examples show the thorough research involved in such a study.
Get to know more about descriptive research design .
Features of descriptive research design, types of descriptive research, descriptive research methods, applications of descriptive research, descriptive research examples.
A descriptive method of research is one that describes the characteristics of a phenomenon, situation or population. It uses quantitative and qualitative approaches to describe problems with little relevant information. Descriptive research accurately describes a research problem without asking why a particular event happened. By researching market patterns, the descriptive method answers how patterns change, what caused the change and when the change occurred, instead of dwelling on why the change happened.
Descriptive research refers to questions, study design and analysis of data conducted on a particular topic. It is a strictly observational research methodology with no influence on variables. Some distinctive features of descriptive research are:
To understand the descriptive research meaning , data collection methods, examples and application, we need a deeper understanding of its features.
Different ways of approaching the descriptive method help break it down further. Let’s look at the different types of descriptive research :
Descriptive normative survey, descriptive status.
This type of research quantitatively describes real-life situations. For example, to understand the relation between wages and performance, research on employee salaries and their respective performances can be conducted.
This technique analyzes a subject further. Once the relation between wages and performance has been established, an organization can further analyze employee performance by researching the output of those who work from an office with those who work from home.
Descriptive classification is mainly used in the field of biological science. It helps researchers classify species once they have studied the data collected from different search stations.
Comparing two variables can show if one is better than the other. Doing this through tests or surveys can reveal all the advantages and disadvantages associated with the two. For example, this technique can be used to find out if paper ballots are better than electronic voting devices.
The researcher has to effectively interpret the area of the problem and then decide the appropriate technique of descriptive research design .
A researcher can choose one of the following methods to solve research problems and meet research goals:
With this method, a researcher observes the behaviors, mannerisms and characteristics of the participants. It is widely used in psychology and market research and does not require the participants to be involved directly. It’s an effective method and can be both qualitative and quantitative for the sheer volume and variety of data that is generated.
It’s a popular method of data collection in research. It follows the principle of obtaining information quickly and directly from the main source. The idea is to use rigorous qualitative and quantitative research methods and ask crucial questions essential to the business for the short and long term.
Case studies tend to fall short in situations where researchers are dealing with highly diverse people or conditions. Surveys and observations are carried out effectively but the time of execution significantly differs between the two.
There are multiple applications of descriptive research design but executives must learn that it’s crucial to clearly define the research goals first. Here’s how organizations use descriptive research to meet their objectives:
Descriptive research is widely used due to its non-invasive nature. Quantitative observations allow in-depth analysis and a chance to validate any existing condition.
There are several different descriptive research examples that highlight the types, applications and uses of this research method. Let’s look at a few:
Descriptive research can be used by an organization to understand the spending patterns of customers as well as by a psychologist who has to deal with mentally ill patients. In both these professions, the individuals will require thorough analyses of their subjects and large amounts of crucial data to develop a plan of action.
Every method of descriptive research can provide information that is diverse, thorough and varied. This supports future research and hypotheses. But although they can be quick, cheap and easy to conduct in the participants’ natural environment, descriptive research design can be limited by the kind of information it provides, especially with case studies. Trying to generalize a larger population based on the data gathered from a smaller sample size can be futile. Similarly, a researcher can unknowingly influence the outcome of a research project due to their personal opinions and biases. In any case, a manager has to be prepared to collect important information in substantial quantities and have a balanced approach to prevent influencing the result.
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How to implement descriptive coding for qualitative data.
Home » How to Implement Descriptive Coding for Qualitative Data
Descriptive Coding Techniques provide a robust framework for organizing and interpreting qualitative data. These techniques begin by identifying and labeling key themes, insights, and patterns from interviews or open-ended survey responses. Each code serves as a shorthand, allowing researchers to categorize complex information efficiently, transforming raw data into actionable insights.
Implementing these techniques not only enhances the clarity of qualitative analysis but also encourages a deeper understanding of participants' experiences. By systematically coding data, analysts can uncover relationships among themes, ultimately supporting more informed decision-making and strategic development. This process empowers researchers to focus on significant findings, ensuring that essential insights are not overlooked during analysis.
Descriptive coding is a fundamental technique in qualitative research, serving to systematically categorize and organize data for analysis. This method allows researchers to distill complex information into manageable segments. By assigning descriptive codes to various themes or ideas, it becomes easier to identify patterns and draw insightful conclusions.
A practical approach to descriptive coding involves a few critical steps. First, review the qualitative data thoroughly to understand its nuances. Next, develop a coding scheme that reflects key concepts within the data. This can include pain points, desires, or any recurring themes identified during the review process. After that, systematically apply these codes to the data, ensuring consistency and clarity. Finally, analyze the coded data to uncover overarching narratives and insights, which can significantly influence decision-making and strategy. Mastering these descriptive coding techniques enhances the depth and reliability of qualitative research findings.
Descriptive coding techniques offer an efficient way to organize and interpret qualitative data. By employing these techniques, researchers can categorize large sets of textual information quickly and comprehensively. One of the primary reasons to choose descriptive coding is its ability to enhance clarity. This method creates a clearer understanding of underlying themes, enabling organizations to pinpoint key insights swiftly.
Another advantage is the improved collaboration it facilitates. Team members can easily access and share coded data, ensuring everyone is on the same page. Moreover, descriptive coding techniques support a systematic approach to identifying patterns and trends, which greatly aids in decision-making processes. By streamlining the analysis, researchers can translate insights into actionable strategies, thus enhancing overall productivity and efficacy in understanding customer feedback. In today's fast-paced environment, these techniques are indispensable for any organization aiming to excel in data-driven decision-making.
Descriptive coding techniques play a vital role in organizing qualitative data and uncovering meaningful insights. By categorizing responses into manageable themes, researchers can streamline their data analysis and improve overall comprehension. This method simplifies the data review process, making it easier to identify patterns and trends within large datasets.
One of the key benefits of descriptive coding is enhancing collaboration among research teams. When data is clearly organized and categorized, team members can work more effectively together, as they can easily access specific information. Additionally, descriptive coding methods increase transparency in data interpretation, allowing stakeholders to understand how insights were gathered and conclusions were drawn. This leads to more reliable and actionable findings, enabling better decision-making and strategic planning for future projects. Overall, incorporating descriptive coding techniques fosters a structured approach that facilitates deeper understanding and implementation of qualitative data insights.
Implementing descriptive coding for qualitative data involves a systematic approach to organizing and interpreting textual information. Begin by carefully reviewing your qualitative data, which can include interviews, focus group discussions, or open-ended survey responses. Identify key themes and patterns that emerge from your data during this initial reading. This process helps you form a foundational understanding of the content, which is crucial for effective coding.
Once you have identified your themes, the next step is to develop descriptive coding techniques. Start with assigning codes to specific segments of text, capturing the essence of the information presented. Codes should be concise yet descriptive, offering insights into recurring topics or concepts. It can also be beneficial to create a codebook that lists and defines each code, ensuring consistency throughout the analysis. By following these steps, you can transform raw qualitative data into meaningful insights that drive informed decision-making.
Preparing your dataset for descriptive coding techniques is a crucial step in qualitative data analysis. Begin by ensuring that your data is well-organized and easily accessible. This might involve transcribing audio recordings or cleaning up text documents. A clear structure allows for smoother analysis and helps you focus on specific themes.
Once your data is organized, establish standard criteria for coding. This ensures consistency and reliability in your analysis. Consider creating a benchmark document that outlines your coding categories and questions. This reference will guide you in extracting meaningful insights from the data. Having these elements in place will not only streamline your coding process but also enhance the overall validity of your findings. By preparing your dataset thoughtfully, you set a strong foundation for effective descriptive coding techniques.
Descriptive coding techniques play a crucial role in organizing and interpreting qualitative data. To effectively implement this method, follow these essential steps. Firstly, familiarize yourself with the data by reading through your qualitative materials thoroughly. This initial review helps in understanding the context and nuances involved. Next, create a coding framework, which involves identifying potential themes or categories relevant to your research objectives.
Once the framework is established, begin coding the data by tagging relevant excerpts with descriptive labels. This process transforms raw data into meaningful segments, making it easier to analyze later. After coding, reflect on the results to ensure all significant themes are captured accurately. Finally, compile your findings into a coherent narrative, which will enhance the interpretation and applicability of your research insights. By following these steps, you can effectively apply descriptive coding techniques to your qualitative data analysis, leading to richer insights and thoughtful conclusions.
Mastering descriptive coding techniques is essential for effectively analyzing qualitative data. This process allows researchers to systematically categorize and retrieve valuable insights from diverse sources of information. By implementing these techniques, researchers can uncover meaningful patterns and themes within their data, which leads to enhanced understanding and actionable conclusions.
As you refine your skills in descriptive coding, the key lies in practice and adaptability. Understanding the nuances of your qualitative data will improve your coding efficiency and effectiveness. Ultimately, mastering these techniques will empower you to draw deeper insights, facilitating informed decision-making processes in your field. Embrace this methodology for a more robust qualitative analysis experience.
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In qualitative research, data triangulation means approaching a question from multiple perspectives.
It involves using more than one data source or method to investigate a theory or corroborate a finding.
For something with such a mathematical-sounding name, it’s a pretty simple concept. But it’s a powerful one, too. Ask a group of five boaters why their watercraft sank in the sea and you’ll get five slightly different stories. Each individual tale holds the bias of its teller. Taken together, though, the individual accounts form a deeper and more accurate picture of what went wrong.
This logic applies to qualitative research. Since qualitative data isn’t as cut-and-dry as quantitative data , you need more than one perspective, data type, and method to shore it up.
With triangulation, you give the results a stronger platform to stand on.
If you ask me, there’s never a bad time to do data triangulation when you’re working with qualitative data.
From a researcher’s point of view, triangulation can help you verify key details and strengthen your findings—and your argument.
And if you’re someone who uses qualitative data to inform your business decisions, gathering data from multiple sources is smart. It can help you make critical choices with a level of confidence you wouldn’t (and shouldn’t) have if you relied on just one source.
In a 2014 journal article published in the Oncology Nursing Forum titled, “ The use of triangulation in qualitative research ,” authors Nancy Carter, et al., lay out two views of data triangulation.
They argue that it’s both “the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena” and “a qualitative research strategy to test validity through the convergence of information from different sources.”
Put into simpler words, qualitative data triangulation helps us:
Well-rounded arguments benefit everyone, all the time.
So if you’ve been on the fence about whether to draw sources from more than one place or use more than one method in your research, consider this your sign.
Do it.
Before you begin running qualitative data triangulation, it’s helpful to know the four types, as outlined by Carter et al.:
Knowing which type of triangulation to focus on can be tricky.
We’ll explore each type in more detail and suggest questions to ask yourself when you’re tackling the beginning of the triangulation process.
Method triangulation means using a variety of research methods to study the same topic. In qualitative research, the most common data-gathering methods are:
So, in qualitative research, method triangulation means gathering data using at least two of these methods.
But method triangulation can also mean looping in non-qualitative forms of data collection, like demographic information or responses to closed-question (yes-or-no and/or multiple-choice) surveys. Since we’re focusing on qualitative data here, though, we’ll save mixed-method triangulation for another day.
Right now, our lens is firmly focused on qualitative research.
Use method triangulation when:
The goal of investigator triangulation is to have more than one researcher (or team of researchers) analyze the same set of data. Like a peer review for a scholarly journal article, investigator triangulation helps reduce bias. This, in turn, strengthens the credibility of your research.
But you have to be careful not to invite researchers with your same opinions and biases to participate in this type of triangulation. You don’t want them to confirm everything you’ve researched. You want them to read it line by line, grappling with the information and pushing you to see it in a new light.
Reach out to people in different—but related—fields. Invite them to collaborate by analyzing your research and engaging with it from their own viewpoints. Listen carefully to what they have to say—don’t just dismiss it because you don’t see things the same way.
This is how you’ll get the most well-rounded analysis of your qualitative research.
Use investigator triangulation when:
With theory triangulation, you aren’t using different data collection methods or bringing in researchers with unique viewpoints.
Instead, you’re changing the lens through which you see the data.
This approach challenges researchers to set aside their original theories for analyzing information. It invites them to use at least one additional, theoretical perspective when they sit down to interpret the data.
Researchers usually use theory triangulation when their topic spans more than one discipline. If you were studying human grocery shopping behavior, for instance, you could analyze the results through three lenses:
Basically, theory triangulation pushes you to consider things from viewpoints you hadn’t before. And it can make the results a lot meatier than if you relied on a single theory.
Use theory triangulation when:
With data source triangulation, your goal is to gather data from at least two sources, but probably more than that.
What does this look like in qualitative research?
It might mean gathering data from:
The point of data source triangulation is to study one topic using these diverse data sources. (If you want to pull from quantitative data sources like web analytics and public databases, you can do that too.)
This is essentially another way to study your research question from multiple perspectives. But instead of a group of different researchers or a set of theories from multiple disciplines, those differing data sources are the other perspectives.
Use data source triangulation when:
You want to validate findings across existing qualitative data sources. Let’s say you’re studying stigmas on mental health issues. You’ve already used methodological triangulation to gather qualitative data from interviews and surveys. Now, you want to compare this data with themes from online forums, blog posts, and personal memoirs. The data found in these sources can help validate your findings—or bring up new questions and interesting discrepancies to explore.
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As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis. I nterpret results: Interpret your findings in light of your research question and objectives.
Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what, where, when and how questions, but not why questions. A descriptive research design can use a wide variety of research methods to investigate one or more variables. Unlike in experimental research, the researcher does ...
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 ...
Descriptive research is an exploratory research method.It enables researchers to precisely and methodically describe a population, circumstance, or phenomenon.. As the name suggests, descriptive research describes the characteristics of the group, situation, or phenomenon being studied without manipulating variables or testing hypotheses.This can be reported using surveys, observational ...
Some characteristics of descriptive research are: Quantitativeness. Descriptive research uses a quantitative research method by collecting quantifiable information to be used for statistical analysis of the population sample. This is very common when dealing with research in the physical sciences. Qualitativeness.
Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account ...
Definition of descriptive research. Descriptive research is defined as a research method that observes and describes the characteristics of a particular group, situation, or phenomenon. The goal is not to establish cause and effect relationships but rather to provide a detailed account of the situation.
Descriptive research methods. Descriptive research is usually defined as a type of quantitative research, though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable.. Surveys. Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages ...
Descriptive research design. Descriptive research design uses a range of both qualitative research and quantitative data (although quantitative research is the primary research method) to gather information to make accurate predictions about a particular problem or hypothesis. As a survey method, descriptive research designs will help ...
Definition: As its name says, descriptive research describes the characteristics of the problem, phenomenon, situation, or group under study. So the goal of all descriptive studies is to explore the background, details, and existing patterns in the problem to fully understand it. In other words, preliminary research.
INTRODUCTION. In our previous article in this series, [1] we introduced the concept of "study designs"- as "the set of methods and procedures used to collect and analyze data on variables specified in a particular research question.". Study designs are primarily of two types - observational and interventional, with the former being ...
Other interesting articles. 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. Statistics. Normal distribution. Skewness. Kurtosis. Degrees of freedom. Variance. Null hypothesis.
Descriptive research is distinct from correlational research, in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences ...
Table of contents. Step 1: Consider your aims and approach. Step 2: Choose a type of research design. Step 3: Identify your population and sampling method. Step 4: Choose your data collection methods. Step 5: Plan your data collection procedures. Step 6: Decide on your data analysis strategies.
Describing the Qualitative Descriptive Approach. In two seminal articles, Sandelowski promotes the mainstream use of qualitative description (Sandelowski, 2000, 2010) as a well-developed but unacknowledged method which provides a "comprehensive summary of an event in the every day terms of those events" (Sandelowski, 2000, p. 336).Such studies are characterized by lower levels of ...
Descriptive research can be used to validate sampling methods and to help researchers determine the best approach for their study. 5. Cost Effective. Descriptive research is often less expensive and less time-consuming than other research methods, making it a cost-effective way to gather information about a particular population or phenomenon. 6.
Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research." Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples. Limitations:
Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process. Step 4: Evaluate and justify the methodological choices you made. Above all, your methodology section should clearly make the case for why you chose the methods you did.
Qualitative Research Methodology. This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.
6. Technological Advances in Healthcare (Healthcare): This research describes and categorizes different technological advances (such as telemedicine, AI-enabled tools, digital collaboration) in healthcare without testing or modifying any parameters, making it an example of descriptive research. 7.
Types of descriptive research. Observational method. Case studies. Surveys. Recap. Descriptive research methods are used to define the who, what, and where of human behavior and other ...
According to Siedlecki [22], descriptive research design is a method that aims to describe and present the characteristics of a particular phenomenon or population without manipulating any ...
Descriptive Analytics. Definition: Descriptive analytics focused on describing or summarizing raw data and making it interpretable. This type of analytics provides insight into what has happened in the past. It involves the analysis of historical data to identify patterns, trends, and insights. Descriptive analytics often uses visualization ...
Descriptive Research Meaning. A descriptive method of research is one that describes the characteristics of a phenomenon, situation or population. It uses quantitative and qualitative approaches to describe problems with little relevant information. Descriptive research accurately describes a research problem without asking why a particular ...
The Essence of Descriptive Coding in Qualitative Research Descriptive coding is a fundamental technique in qualitative research, serving to systematically categorize and organize data for analysis. This method allows researchers to distill complex information into manageable segments.
Method triangulation means using a variety of research methods to study the same topic. In qualitative research, the most common data-gathering methods are: Interviews; Focus groups; Observation; Open-ended surveys or questionnaires; So, in qualitative research, method triangulation means gathering data using at least two of these methods.
Researchers have introduced a novel method for converting the greenhouse gas carbon dioxide (CO2) into ethanol, a sustainable fuel. This significant advancement could pave the way for more ...