types of research methods for phd

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15 kinds of research methodologies for phd. pupils, basic research.

Pure research or fundamental research or basic research zooms on enhancing scientific knowledge for the exhaustive understanding of a topic or certain natural phenomena, essentially in natural sciences; knowledge that is obtained for the purpose of knowledge it is called fundamental research.

1.Applied research

Research that covers real life applications of the natural sciences; aimed at offering an answer to particular practical issues and develops novel technologies

2.Fixed research versus flexible research

In fixed research, the design of the study is fixed prior to the main phase of data gathering; moreover, fixed designs are essentially theoretical. Variables that need to be controlled and measured need to be known in advance and they are measured quantitatively.

3.Quantitative research and qualitative research

Quantitative research denotes gauging phenomena in various grades; on the other hand, qualitative research sometimes deems Boolean measurements alone; solution can be studied qualitatively for its appropriateness. However, comparison between candidate solutions requires quantitative observation.

4.Experimental research and non-experimental research

In an experimental design , operationalize the variables to be measured; moreover, operationalize in the best manner. Consider the study expectations, outcome measurement, variable measurement, and the methods to answer research questions.

Think of the practical limitations such as the availability of data-sets and experimental set-ups that represent actual scenarios.

5.Exploratory research and confirmatory research

Confirmatory research tests a priori hypotheses—outcome predictions done prior to the measurement stage. Such a priori hypotheses are usually derived from a theory or the results of previous studies.

Exploratory research generates a posteriori hypotheses by investigating a data-set and ascertaining potential connection between variables.

6.Explanatory research or casual research

Causal research is also called explanatory research ; conducted to ascertain the extent and type of cause-effect relationships. Causal research are conducted to evaluate effects of specific changes on existing norms, various processes etc.

7.Descriptive research

Descriptive research is the available statement of affairs; researcher has no control over variable. Descriptive studies are characterised as simply an effort to ascertain, define or recognize.  Not “why it is that way” nor “how it came to be,” which is the objective of analytical research.

8.Historical research

Historical research explores and explains the meanings, phases and traits of a phenomena or process at a certain phase of time in the past; historical research is a research strategy from the research of history.

9.Casual comparative research

Also called as “ex-post facto” research (In Latin, implies “after the fact”); researchers determine the causes or consequences of differences that already exist between or among groups of individuals.

An effort to ascertain a causative relationship between an independent variable and a dependent variable; relationship between the independent variable and dependent variable are usually a suggested relationship (not proved yet) because you do not have complete control over the independent variable

10.Correlational research

Correlational research is a form of non-experimental research technique wherein a researcher measures 2 variables and assesses the statistical connection between them with no influence from any external variable.

The correlation between two variables is given through correlation coefficient, which is a statistical measure that calculates the strength of the relationship between two variables that is a value measured between -1 and +1.

11.Evaluation research method

Evaluation research technique is known as program evaluation and refers to a research purpose instead of a particular technique; objective is to assess the effect of social involvements such as new treatment techniques, innovations in services, etc.

A form of applied research to have some real-world effect. Methods such as surveys and experiments are used in evaluation research.

12.Formative and summative evaluation

While learning is in progress, formative assessment offers feedback and information; measures participant’s progress and also assess researcher’s own progress as well.

For example, when implementing a new program, you can determine whether or not the activity should be used again (or modified) with the help of observation and/or surveying.

Summative assessment happens after the learning has ended and offers info and feedback to sum up the process; essentially, no formal learning is happening at this phase other than incidental learning which might take place through the completion of program.

13.Diagnostic research

Descriptive research studies define the characteristics of a particular individual, or of a group.

Studies showing whether certain variables are linked are examples of diagnostic research.

Researcher defines what he or she wants to measure and finds adequate methods for measuring it along with a clear description of ‘population’.

Aim is to obtain complete and accurate information. And the researcher plans the procedure carefully.

14.Prognostic research

Prognostic research (specifically in clinical research) examines chosen predictive variables and risk factors; prognostic research assesses influence on the outcome of a disease. Clinicians have a better understanding of the history of the ailment.

This understanding facilitates clinical decision-making via providing apt treatment alternatives and helps to predict accurate disease outcomes.

Assessing prognostic studies involves ascertaining the internal validity of the study design and assessing the effects of bias or systemic errors.

15.Action research

A systematic inquiry for improving and/or honing researchers’ actions. Researchers find it an empowering experience.

Action research has positive result for various reasons; most important is that action research is pertinent to the research participants.

Relevance is assured because the aim of each research project is ascertained by the researchers, who are also the main beneficiaries of the research observations.

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15 Types of Research Methods

Research methods refer to the strategies, tools, and techniques used to gather and analyze data in a structured way in order to answer a research question or investigate a hypothesis (Hammond & Wellington, 2020).

Generally, we place research methods into two categories: quantitative and qualitative. Each has its own strengths and weaknesses, which we can summarize as:

• Quantitative research can achieve generalizability through scrupulous statistical analysis applied to large sample sizes.
• Qualitative research achieves deep, detailed, and nuance accounts of specific case studies, which are not generalizable.

Some researchers, with the aim of making the most of both quantitative and qualitative research, employ mixed methods, whereby they will apply both types of research methods in the one study, such as by conducting a statistical survey alongside in-depth interviews to add context to the quantitative findings.

Below, I’ll outline 15 common research methods, and include pros, cons, and examples of each .

Types of Research Methods

Research methods can be broadly categorized into two types: quantitative and qualitative.

• Quantitative methods involve systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques, providing an in-depth understanding of a specific concept or phenomenon (Schweigert, 2021). The strengths of this approach include its ability to produce reliable results that can be generalized to a larger population, although it can lack depth and detail.
• Qualitative methods encompass techniques that are designed to provide a deep understanding of a complex issue, often in a specific context, through collection of non-numerical data (Tracy, 2019). This approach often provides rich, detailed insights but can be time-consuming and its findings may not be generalizable.

These can be further broken down into a range of specific research methods and designs:

Combining the two methods above, mixed methods research mixes elements of both qualitative and quantitative research methods, providing a comprehensive understanding of the research problem . We can further break these down into:

• Sequential Explanatory Design (QUAN→QUAL): This methodology involves conducting quantitative analysis first, then supplementing it with a qualitative study.
• Sequential Exploratory Design (QUAL→QUAN): This methodology goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.

Let’s explore some methods and designs from both quantitative and qualitative traditions, starting with qualitative research methods.

Qualitative Research Methods

Qualitative research methods allow for the exploration of phenomena in their natural settings, providing detailed, descriptive responses and insights into individuals’ experiences and perceptions (Howitt, 2019).

These methods are useful when a detailed understanding of a phenomenon is sought.

1. Ethnographic Research

Ethnographic research emerged out of anthropological research, where anthropologists would enter into a setting for a sustained period of time, getting to know a cultural group and taking detailed observations.

Ethnographers would sometimes even act as participants in the group or culture, which many scholars argue is a weakness because it is a step away from achieving objectivity (Stokes & Wall, 2017).

In fact, at its most extreme version, ethnographers even conduct research on themselves, in a fascinating methodology call autoethnography .

The purpose is to understand the culture, social structure, and the behaviors of the group under study. It is often useful when researchers seek to understand shared cultural meanings and practices in their natural settings.

However, it can be time-consuming and may reflect researcher biases due to the immersion approach.

Example of Ethnography

Liquidated: An Ethnography of Wall Street  by Karen Ho involves an anthropologist who embeds herself with Wall Street firms to study the culture of Wall Street bankers and how this culture affects the broader economy and world.

2. Phenomenological Research

Phenomenological research is a qualitative method focused on the study of individual experiences from the participant’s perspective (Tracy, 2019).

It focuses specifically on people’s experiences in relation to a specific social phenomenon ( see here for examples of social phenomena ).

This method is valuable when the goal is to understand how individuals perceive, experience, and make meaning of particular phenomena. However, because it is subjective and dependent on participants’ self-reports, findings may not be generalizable, and are highly reliant on self-reported ‘thoughts and feelings’.

Example of Phenomenological Research

A phenomenological approach to experiences with technology  by Sebnem Cilesiz represents a good starting-point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

3. Historical Research

Historical research is a qualitative method involving the examination of past events to draw conclusions about the present or make predictions about the future (Stokes & Wall, 2017).

As you might expect, it’s common in the research branches of history departments in universities.

This approach is useful in studies that seek to understand the past to interpret present events or trends. However, it relies heavily on the availability and reliability of source materials, which may be limited.

Common data sources include cultural artifacts from both material and non-material culture , which are then examined, compared, contrasted, and contextualized to test hypotheses and generate theories.

Example of Historical Research

A historical research example might be a study examining the evolution of gender roles over the last century. This research might involve the analysis of historical newspapers, advertisements, letters, and company documents, as well as sociocultural contexts.

4. Content Analysis

Content analysis is a research method that involves systematic and objective coding and interpreting of text or media to identify patterns, themes, ideologies, or biases (Schweigert, 2021).

A content analysis is useful in analyzing communication patterns, helping to reveal how texts such as newspapers, movies, films, political speeches, and other types of ‘content’ contain narratives and biases.

However, interpretations can be very subjective, which often requires scholars to engage in practices such as cross-comparing their coding with peers or external researchers.

Content analysis can be further broken down in to other specific methodologies such as semiotic analysis, multimodal analysis , and discourse analysis .

Example of Content Analysis

How is Islam Portrayed in Western Media?  by Poorebrahim and Zarei (2013) employs a type of content analysis called critical discourse analysis (common in poststructuralist and critical theory research ). This study by Poorebrahum and Zarei combs through a corpus of western media texts to explore the language forms that are used in relation to Islam and Muslims, finding that they are overly stereotyped, which may represent anti-Islam bias or failure to understand the Islamic world.

5. Grounded Theory Research

Grounded theory involves developing a theory  during and after  data collection rather than beforehand.

This is in contrast to most academic research studies, which start with a hypothesis or theory and then testing of it through a study, where we might have a null hypothesis (disproving the theory) and an alternative hypothesis (supporting the theory).

Grounded Theory is useful because it keeps an open mind to what the data might reveal out of the research. It can be time-consuming and requires rigorous data analysis (Tracy, 2019).

Grounded Theory Example

Developing a Leadership Identity   by Komives et al (2005) employs a grounded theory approach to develop a thesis based on the data rather than testing a hypothesis. The researchers studied the leadership identity of 13 college students taking on leadership roles. Based on their interviews, the researchers theorized that the students’ leadership identities shifted from a hierarchical view of leadership to one that embraced leadership as a collaborative concept.

6. Action Research

Action research is an approach which aims to solve real-world problems and bring about change within a setting. The study is designed to solve a specific problem – or in other words, to take action (Patten, 2017).

This approach can involve mixed methods, but is generally qualitative because it usually involves the study of a specific case study wherein the researcher works, e.g. a teacher studying their own classroom practice to seek ways they can improve.

Action research is very common in fields like education and nursing where practitioners identify areas for improvement then implement a study in order to find paths forward.

Action Research Example

Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing   by Ellison and Drew was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

7. Natural Observational Research

Observational research can also be quantitative (see: experimental research), but in naturalistic settings for the social sciences, researchers tend to employ qualitative data collection methods like interviews and field notes to observe people in their day-to-day environments.

This approach involves the observation and detailed recording of behaviors in their natural settings (Howitt, 2019). It can provide rich, in-depth information, but the researcher’s presence might influence behavior.

While observational research has some overlaps with ethnography (especially in regard to data collection techniques), it tends not to be as sustained as ethnography, e.g. a researcher might do 5 observations, every second Monday, as opposed to being embedded in an environment.

Observational Research Example

A researcher might use qualitative observational research to study the behaviors and interactions of children at a playground. The researcher would document the behaviors observed, such as the types of games played, levels of cooperation , and instances of conflict.

8. Case Study Research

Case study research is a qualitative method that involves a deep and thorough investigation of a single individual, group, or event in order to explore facets of that phenomenon that cannot be captured using other methods (Stokes & Wall, 2017).

Case study research is especially valuable in providing contextualized insights into specific issues, facilitating the application of abstract theories to real-world situations (Patten, 2017).

However, findings from a case study may not be generalizable due to the specific context and the limited number of cases studied (Walliman, 2021).

See More: Case Study Advantages and Disadvantages

Example of a Case Study

Scholars conduct a detailed exploration of the implementation of a new teaching method within a classroom setting. The study focuses on how the teacher and students adapt to the new method, the challenges encountered, and the outcomes on student performance and engagement. While the study provides specific and detailed insights of the teaching method in that classroom, it cannot be generalized to other classrooms, as statistical significance has not been established through this qualitative approach.

Quantitative Research Methods

Quantitative research methods involve the systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques (Pajo, 2022). The focus is on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

9. Experimental Research

Experimental research is a quantitative method where researchers manipulate one variable to determine its effect on another (Walliman, 2021).

This is common, for example, in high-school science labs, where students are asked to introduce a variable into a setting in order to examine its effect.

This type of research is useful in situations where researchers want to determine causal relationships between variables. However, experimental conditions may not reflect real-world conditions.

Example of Experimental Research

A researcher may conduct an experiment to determine the effects of a new educational approach on student learning outcomes. Students would be randomly assigned to either the control group (traditional teaching method) or the experimental group (new educational approach).

10. Surveys and Questionnaires

Surveys and questionnaires are quantitative methods that involve asking research participants structured and predefined questions to collect data about their attitudes, beliefs, behaviors, or characteristics (Patten, 2017).

Surveys are beneficial for collecting data from large samples, but they depend heavily on the honesty and accuracy of respondents.

They tend to be seen as more authoritative than their qualitative counterparts, semi-structured interviews, because the data is quantifiable (e.g. a questionnaire where information is presented on a scale from 1 to 10 can allow researchers to determine and compare statistical means, averages, and variations across sub-populations in the study).

Example of a Survey Study

A company might use a survey to gather data about employee job satisfaction across its offices worldwide. Employees would be asked to rate various aspects of their job satisfaction on a Likert scale. While this method provides a broad overview, it may lack the depth of understanding possible with other methods (Stokes & Wall, 2017).

11. Longitudinal Studies

Longitudinal studies involve repeated observations of the same variables over extended periods (Howitt, 2019). These studies are valuable for tracking development and change but can be costly and time-consuming.

With multiple data points collected over extended periods, it’s possible to examine continuous changes within things like population dynamics or consumer behavior. This makes a detailed analysis of change possible.

Perhaps the most relatable example of a longitudinal study is a national census, which is taken on the same day every few years, to gather comparative demographic data that can show how a nation is changing over time.

While longitudinal studies are commonly quantitative, there are also instances of qualitative ones as well, such as the famous 7 Up study from the UK, which studies 14 individuals every 7 years to explore their development over their lives.

Example of a Longitudinal Study

A national census, taken every few years, uses surveys to develop longitudinal data, which is then compared and analyzed to present accurate trends over time. Trends a census can reveal include changes in religiosity, values and attitudes on social issues, and much more.

12. Cross-Sectional Studies

Cross-sectional studies are a quantitative research method that involves analyzing data from a population at a specific point in time (Patten, 2017). They provide a snapshot of a situation but cannot determine causality.

This design is used to measure and compare the prevalence of certain characteristics or outcomes in different groups within the sampled population.

The major advantage of cross-sectional design is its ability to measure a wide range of variables simultaneously without needing to follow up with participants over time.

However, cross-sectional studies do have limitations . This design can only show if there are associations or correlations between different variables, but cannot prove cause and effect relationships, temporal sequence, changes, and trends over time.

Example of a Cross-Sectional Study

Our longitudinal study example of a national census also happens to contain cross-sectional design. One census is cross-sectional, displaying only data from one point in time. But when a census is taken once every few years, it becomes longitudinal, and so long as the data collection technique remains unchanged, identification of changes will be achievable, adding another time dimension on top of a basic cross-sectional study.

13. Correlational Research

Correlational research is a quantitative method that seeks to determine if and to what degree a relationship exists between two or more quantifiable variables (Schweigert, 2021).

This approach provides a fast and easy way to make initial hypotheses based on either positive or  negative correlation trends  that can be observed within dataset.

While correlational research can reveal relationships between variables, it cannot establish causality.

Methods used for data analysis may include statistical correlations such as Pearson’s or Spearman’s.

Example of Correlational Research

A team of researchers is interested in studying the relationship between the amount of time students spend studying and their academic performance. They gather data from a high school, measuring the number of hours each student studies per week and their grade point averages (GPAs) at the end of the semester. Upon analyzing the data, they find a positive correlation, suggesting that students who spend more time studying tend to have higher GPAs.

14. Quasi-Experimental Design Research

Quasi-experimental design research is a quantitative research method that is similar to experimental design but lacks the element of random assignment to treatment or control.

Instead, quasi-experimental designs typically rely on certain other methods to control for extraneous variables.

The term ‘quasi-experimental’ implies that the experiment resembles a true experiment, but it is not exactly the same because it doesn’t meet all the criteria for a ‘true’ experiment, specifically in terms of control and random assignment.

Quasi-experimental design is useful when researchers want to study a causal hypothesis or relationship, but practical or ethical considerations prevent them from manipulating variables and randomly assigning participants to conditions.

Example of Quasi-Experimental Design

A researcher wants to study the impact of a new math tutoring program on student performance. However, ethical and practical constraints prevent random assignment to the “tutoring” and “no tutoring” groups. Instead, the researcher compares students who chose to receive tutoring (experimental group) to similar students who did not choose to receive tutoring (control group), controlling for other variables like grade level and previous math performance.

Related: Examples and Types of Random Assignment in Research

15. Meta-Analysis Research

Meta-analysis statistically combines the results of multiple studies on a specific topic to yield a more precise estimate of the effect size. It’s the gold standard of secondary research .

Meta-analysis is particularly useful when there are numerous studies on a topic, and there is a need to integrate the findings to draw more reliable conclusions.

Some meta-analyses can identify flaws or gaps in a corpus of research, when can be highly influential in academic research, despite lack of primary data collection.

However, they tend only to be feasible when there is a sizable corpus of high-quality and reliable studies into a phenomenon.

Example of a Meta-Analysis

The power of feedback revisited (Wisniewski, Zierer & Hattie, 2020) is a meta-analysis that examines 435 empirical studies research on the effects of feedback on student learning. They use a random-effects model to ascertain whether there is a clear effect size across the literature. The authors find that feedback tends to impact cognitive and motor skill outcomes but has less of an effect on motivational and behavioral outcomes.

Choosing a research method requires a lot of consideration regarding what you want to achieve, your research paradigm, and the methodology that is most valuable for what you are studying. There are multiple types of research methods, many of which I haven’t been able to present here. Generally, it’s recommended that you work with an experienced researcher or research supervisor to identify a suitable research method for your study at hand.

Hammond, M., & Wellington, J. (2020). Research methods: The key concepts . New York: Routledge.

Howitt, D. (2019). Introduction to qualitative research methods in psychology . London: Pearson UK.

Pajo, B. (2022). Introduction to research methods: A hands-on approach . New York: Sage Publications.

Patten, M. L. (2017). Understanding research methods: An overview of the essentials . New York: Sage

Schweigert, W. A. (2021). Research methods in psychology: A handbook . Los Angeles: Waveland Press.

Stokes, P., & Wall, T. (2017). Research methods . New York: Bloomsbury Publishing.

Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact . London: John Wiley & Sons.

Walliman, N. (2021). Research methods: The basics. London: Routledge.

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 5 Top Tips for Succeeding at University
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• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 100 Consumer Goods Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 30 Globalization Pros and Cons

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How To Choose Your Research Methodology

Qualitative vs quantitative vs mixed methods.

By: Derek Jansen (MBA). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Without a doubt, one of the most common questions we receive at Grad Coach is “ How do I choose the right methodology for my research? ”. It’s easy to see why – with so many options on the research design table, it’s easy to get intimidated, especially with all the complex lingo!

In this post, we’ll explain the three overarching types of research – qualitative, quantitative and mixed methods – and how you can go about choosing the best methodological approach for your research.

Overview: Choosing Your Methodology

Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research

Choosing a research methodology – Nature of the research – Research area norms – Practicalities

1. Understanding the options

Before we jump into the question of how to choose a research methodology, it’s useful to take a step back to understand the three overarching types of research – qualitative , quantitative and mixed methods -based research. Each of these options takes a different methodological approach.

Qualitative research utilises data that is not numbers-based. In other words, qualitative research focuses on words , descriptions , concepts or ideas – while quantitative research makes use of numbers and statistics. Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them.

Importantly, qualitative research methods are typically used to explore and gain a deeper understanding of the complexity of a situation – to draw a rich picture . In contrast to this, quantitative methods are usually used to confirm or test hypotheses . In other words, they have distinctly different purposes. The table below highlights a few of the key differences between qualitative and quantitative research – you can learn more about the differences here.

• Uses an inductive approach
• Is used to build theories
• Takes a subjective approach
• Adopts an open and flexible approach
• The researcher is close to the respondents
• Interviews and focus groups are oftentimes used to collect word-based data.
• Generally, draws on small sample sizes
• Uses qualitative data analysis techniques (e.g. content analysis , thematic analysis , etc)
• Uses a deductive approach
• Is used to test theories
• Takes an objective approach
• Adopts a closed, highly planned approach
• The research is disconnected from respondents
• Surveys or laboratory equipment are often used to collect number-based data.
• Generally, requires large sample sizes
• Uses statistical analysis techniques to make sense of the data

Mixed methods -based research, as you’d expect, attempts to bring these two types of research together, drawing on both qualitative and quantitative data. Quite often, mixed methods-based studies will use qualitative research to explore a situation and develop a potential model of understanding (this is called a conceptual framework), and then go on to use quantitative methods to test that model empirically.

In other words, while qualitative and quantitative methods (and the philosophies that underpin them) are completely different, they are not at odds with each other. It’s not a competition of qualitative vs quantitative. On the contrary, they can be used together to develop a high-quality piece of research. Of course, this is easier said than done, so we usually recommend that first-time researchers stick to a single approach , unless the nature of their study truly warrants a mixed-methods approach.

The key takeaway here, and the reason we started by looking at the three options, is that it’s important to understand that each methodological approach has a different purpose – for example, to explore and understand situations (qualitative), to test and measure (quantitative) or to do both. They’re not simply alternative tools for the same job. 

Right – now that we’ve got that out of the way, let’s look at how you can go about choosing the right methodology for your research.

2. How to choose a research methodology

To choose the right research methodology for your dissertation or thesis, you need to consider three important factors . Based on these three factors, you can decide on your overarching approach – qualitative, quantitative or mixed methods. Once you’ve made that decision, you can flesh out the finer details of your methodology, such as the sampling , data collection methods and analysis techniques (we discuss these separately in other posts ).

The three factors you need to consider are:

• The nature of your research aims, objectives and research questions
• The methodological approaches taken in the existing literature
• Practicalities and constraints

Let’s take a look at each of these.

Factor #1: The nature of your research

As I mentioned earlier, each type of research (and therefore, research methodology), whether qualitative, quantitative or mixed, has a different purpose and helps solve a different type of question. So, it’s logical that the key deciding factor in terms of which research methodology you adopt is the nature of your research aims, objectives and research questions .

But, what types of research exist?

Broadly speaking, research can fall into one of three categories:

• Exploratory – getting a better understanding of an issue and potentially developing a theory regarding it
• Confirmatory – confirming a potential theory or hypothesis by testing it empirically
• A mix of both – building a potential theory or hypothesis and then testing it

As a rule of thumb, exploratory research tends to adopt a qualitative approach , whereas confirmatory research tends to use quantitative methods . This isn’t set in stone, but it’s a very useful heuristic. Naturally then, research that combines a mix of both, or is seeking to develop a theory from the ground up and then test that theory, would utilize a mixed-methods approach.

Let’s look at an example in action.

If your research aims were to understand the perspectives of war veterans regarding certain political matters, you’d likely adopt a qualitative methodology, making use of interviews to collect data and one or more qualitative data analysis methods to make sense of the data.

If, on the other hand, your research aims involved testing a set of hypotheses regarding the link between political leaning and income levels, you’d likely adopt a quantitative methodology, using numbers-based data from a survey to measure the links between variables and/or constructs .

So, the first (and most important thing) thing you need to consider when deciding which methodological approach to use for your research project is the nature of your research aims , objectives and research questions. Specifically, you need to assess whether your research leans in an exploratory or confirmatory direction or involves a mix of both.

The importance of achieving solid alignment between these three factors and your methodology can’t be overstated. If they’re misaligned, you’re going to be forcing a square peg into a round hole. In other words, you’ll be using the wrong tool for the job, and your research will become a disjointed mess.

If your research is a mix of both exploratory and confirmatory, but you have a tight word count limit, you may need to consider trimming down the scope a little and focusing on one or the other. One methodology executed well has a far better chance of earning marks than a poorly executed mixed methods approach. So, don’t try to be a hero, unless there is a very strong underpinning logic.

Need a helping hand?

Factor #2: The disciplinary norms

Choosing the right methodology for your research also involves looking at the approaches used by other researchers in the field, and studies with similar research aims and objectives to yours. Oftentimes, within a discipline, there is a common methodological approach (or set of approaches) used in studies. While this doesn’t mean you should follow the herd “just because”, you should at least consider these approaches and evaluate their merit within your context.

A major benefit of reviewing the research methodologies used by similar studies in your field is that you can often piggyback on the data collection techniques that other (more experienced) researchers have developed. For example, if you’re undertaking a quantitative study, you can often find tried and tested survey scales with high Cronbach’s alphas. These are usually included in the appendices of journal articles, so you don’t even have to contact the original authors. By using these, you’ll save a lot of time and ensure that your study stands on the proverbial “shoulders of giants” by using high-quality measurement instruments .

Of course, when reviewing existing literature, keep point #1 front of mind. In other words, your methodology needs to align with your research aims, objectives and questions. Don’t fall into the trap of adopting the methodological “norm” of other studies just because it’s popular. Only adopt that which is relevant to your research.

Factor #3: Practicalities

When choosing a research methodology, there will always be a tension between doing what’s theoretically best (i.e., the most scientifically rigorous research design ) and doing what’s practical , given your constraints . This is the nature of doing research and there are always trade-offs, as with anything else.

But what constraints, you ask?

When you’re evaluating your methodological options, you need to consider the following constraints:

• Data access
• Equipment and software
• Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

The first practical constraint you need to consider is your access to data . If you’re going to be undertaking primary research , you need to think critically about the sample of respondents you realistically have access to. For example, if you plan to use in-person interviews , you need to ask yourself how many people you’ll need to interview, whether they’ll be agreeable to being interviewed, where they’re located, and so on.

If you’re wanting to undertake a quantitative approach using surveys to collect data, you’ll need to consider how many responses you’ll require to achieve statistically significant results. For many statistical tests, a sample of a few hundred respondents is typically needed to develop convincing conclusions.

So, think carefully about what data you’ll need access to, how much data you’ll need and how you’ll collect it. The last thing you want is to spend a huge amount of time on your research only to find that you can’t get access to the required data.

Constraint #2: Time

The next constraint is time. If you’re undertaking research as part of a PhD, you may have a fairly open-ended time limit, but this is unlikely to be the case for undergrad and Masters-level projects. So, pay attention to your timeline, as the data collection and analysis components of different methodologies have a major impact on time requirements . Also, keep in mind that these stages of the research often take a lot longer than originally anticipated.

Another practical implication of time limits is that it will directly impact which time horizon you can use – i.e. longitudinal vs cross-sectional . For example, if you’ve got a 6-month limit for your entire research project, it’s quite unlikely that you’ll be able to adopt a longitudinal time horizon. 

Constraint #3: Money

As with so many things, money is another important constraint you’ll need to consider when deciding on your research methodology. While some research designs will cost near zero to execute, others may require a substantial budget .

Some of the costs that may arise include:

• Software costs – e.g. survey hosting services, analysis software, etc.
• Promotion costs – e.g. advertising a survey to attract respondents
• Incentive costs – e.g. providing a prize or cash payment incentive to attract respondents
• Equipment rental costs – e.g. recording equipment, lab equipment, etc.
• Travel costs
• Food & beverages

These are just a handful of costs that can creep into your research budget. Like most projects, the actual costs tend to be higher than the estimates, so be sure to err on the conservative side and expect the unexpected. It’s critically important that you’re honest with yourself about these costs, or you could end up getting stuck midway through your project because you’ve run out of money.

Constraint #4: Equipment & software

Another practical consideration is the hardware and/or software you’ll need in order to undertake your research. Of course, this variable will depend on the type of data you’re collecting and analysing. For example, you may need lab equipment to analyse substances, or you may need specific analysis software to analyse statistical data. So, be sure to think about what hardware and/or software you’ll need for each potential methodological approach, and whether you have access to these.

Constraint #5: Your knowledge and skillset

The final practical constraint is a big one. Naturally, the research process involves a lot of learning and development along the way, so you will accrue knowledge and skills as you progress. However, when considering your methodological options, you should still consider your current position on the ladder.

Some of the questions you should ask yourself are:

• Am I more of a “numbers person” or a “words person”?
• How much do I know about the analysis methods I’ll potentially use (e.g. statistical analysis)?
• How much do I know about the software and/or hardware that I’ll potentially use?
• How excited am I to learn new research skills and gain new knowledge?
• How much time do I have to learn the things I need to learn?

Answering these questions honestly will provide you with another set of criteria against which you can evaluate the research methodology options you’ve shortlisted.

So, as you can see, there is a wide range of practicalities and constraints that you need to take into account when you’re deciding on a research methodology. These practicalities create a tension between the “ideal” methodology and the methodology that you can realistically pull off. This is perfectly normal, and it’s your job to find the option that presents the best set of trade-offs.

Recap: Choosing a methodology

In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:

• Exploratory
• Confirmatory
• Combination
• Research area norms
• Hardware and software
• Your knowledge and skillset

If you have any questions, feel free to leave a comment below. If you’d like a helping hand with your research methodology, check out our 1-on-1 research coaching service , or book a free consultation with a friendly Grad Coach.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Very useful and informative especially for beginners

Nice article! I’m a beginner in the field of cybersecurity research. I am a Telecom and Network Engineer and Also aiming for PhD scholarship.

I find the article very informative especially for my decitation it has been helpful and an eye opener.

Hi I am Anna ,

I am a PHD candidate in the area of cyber security, maybe we can link up

The Examples shows by you, for sure they are really direct me and others to knows and practices the Research Design and prepration.

I found the post very informative and practical.

I’m the process of constructing my research design and I want to know if the data analysis I plan to present in my thesis defense proposal possibly change especially after I gathered the data already.

Thank you so much this site is such a life saver. How I wish 1-1 coaching is available in our country but sadly it’s not.

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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

Need to write a list of abbreviations for a thesis or dissertation? Read our post to find out where they go, what to include and how to format them.

The term monotonic relationship is a statistical definition that is used to describe the link between two variables.

Impostor Syndrome is a common phenomenon amongst PhD students, leading to self-doubt and fear of being exposed as a “fraud”. How can we overcome these feelings?

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The unit of analysis refers to the main parameter that you’re investigating in your research project or study.

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Raluca is a final year PhD student at Queen Mary University of London. Her research is on exploring the algorithms of rolling horizon evolutionary algorithms for general video game playing.

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• Capella University Blog
• PhD/Doctorate

What are acceptable dissertation research methods?

August 16, 2023

Reading time:  3–4 minutes

Doctoral research is the cornerstone of a PhD program .

In order to write a dissertation, you must complete extensive, detailed research. Depending on your area of study, different types of research methods will be appropriate to complete your work.

“The choice of research method depends on the questions you hope to answer with your research,” says Curtis Brant, PhD, Capella University dean of research and scholarship.

Once you’ve identified your research problem, you’ll employ the methodology best suited for solving the problem.

There are two primary dissertation research methods: qualitative and quantitative.

Qualitative

Qualitative research focuses on examining the topic via cultural phenomena, human behavior or belief systems. This type of research uses interviews, open-ended questions or focus groups to gain insight into people’s thoughts and beliefs around certain behaviors and systems.

Dr. Brant says there are several approaches to qualitative inquiry. The three most routinely used include:

Generic qualitative inquiry. The researcher focuses on people’s experiences or perceptions in the real world. This often includes, but is not limited to, subjective opinions, attitudes and beliefs .

Case study. The researcher performs an in-depth exploration of a program, event, activity or process with an emphasis on the experience of one or more individuals. The focus of this kind of inquiry must be defined and often includes more than one set of data, such as interviews and field notes, observations or other qualitative data.

Phenomenological. The researcher identifies lived experiences associated with how an individual encounters and engages with the real world .

Qualitative research questions seek to discover:

• A participant’s verbal descriptions of a phenomenon being investigated
•  A researcher’s observations of the phenomenon being investigated
• An integrated interpretation of participant’s descriptions and researchers observations

Quantitative

Quantitative research involves the empirical investigation of observable and measurable variables. It is used for theory testing, predicting outcomes or determining relationships between and among variables using statistical analysis.

According to Dr. Brant, there are two primary data sources for quantitative research.

Surveys: Surveys involve asking people a set of questions, usually testing for linear relationships, statistical differences or statistical independence. This approach is common in correlation research designs.

Archival research (secondary data analysis). Archival research involves using preexisting data to answer research questions instead of collecting data from active human participants.

Quantitative research questions seek to address:

• Descriptions of variables being investigated
• Measurements of relationships between (at least two) variables
• Differences between two or more groups’ scores on a variable or variables

Which method should you choose?

Choosing a qualitative or quantitative methodology for your research will be based on the nature of the questions you ask, the preferred method in your field, the feasibility of the approach and other factors. Many programs offer doctoral mentors and support teams that can help guide you throughout the process.

Capella University offers PhD and professional doctorate degree programs ranging from business to education and health to technology. Learn more about Capella doctoral programs and doctoral support.

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The Top 3 Types of Dissertation Research Explained

Preparing for your doctoral dissertation takes serious perseverance. You’ve endured years of studies and professional development to get to this point. After sleepless nights and labor-intensive research, you’re ready to present the culmination of all of your hard work. Even with a strong base knowledge, it can be difficult — even daunting — to decide how you will begin writing.

By taking a wide-lens view of the dissertation research process , you can best assess the work you have ahead of you and any gaps in your current research strategy. Subsequently, you’ll begin to develop a timeline so you can work efficiently and cross that finish line with your degree in hand.

What Is a Dissertation?

A dissertation is a published piece of research on a novel topic in your chosen field. Students complete a dissertation as part of a doctoral or PhD program. For most students, a dissertation is the first substantive piece of academic research they will write. 

Because a dissertation becomes a published piece of academic literature that other academics may cite, students must defend it in front of a board of experts consisting of peers in their field, including professors, their advisor, and other industry experts. 

For many students, a dissertation is the first piece of research in a long career full of research. As such, it’s important to choose a topic that’s interesting and engaging.

Types of Dissertation Research

Dissertations can take on many forms, based on research and methods of presentation in front of a committee board of academics and experts in the field. Here, we’ll focus on the three main types of dissertation research to get you one step closer to earning your doctoral degree.

1. Qualitative

The first type of dissertation is known as a qualitative dissertation . A qualitative dissertation mirrors the qualitative research that a doctoral candidate would conduct throughout their studies. This type of research relies on non-numbers-based data collected through things like interviews, focus groups and participant observation. 

The decision to model your dissertation research according to the qualitative method will depend largely on the data itself that you are collecting. For example, dissertation research in the field of education or psychology may lend itself to a qualitative approach, depending on the essence of research. Within a qualitative dissertation research model, a candidate may pursue one or more of the following:

• Case study research
• Autoethnographies
• Narrative research 
• Grounded theory 

Although individual approaches may vary, qualitative dissertations usually include certain foundational characteristics. For example, the type of research conducted to develop a qualitative dissertation often follows an emergent design, meaning that the content and research strategy changes over time. Candidates also rely on research paradigms to further strategize how best to collect and relay their findings. These include critical theory, constructivism and interpretivism, to name a few. 

Because qualitative researchers integrate non-numerical data, their methods of collection often include unstructured interview, focus groups and participant observations. Of course, researchers still need rubrics from which to assess the quality of their findings, even though they won’t be numbers-based. To do so, they subject the data collected to the following criteria: dependability, transferability and validity. 

When it comes time to present their findings, doctoral candidates who produce qualitative dissertation research have several options. Some choose to include case studies, personal findings, narratives, observations and abstracts. Their presentation focuses on theoretical insights based on relevant data points. 

2. Quantitative

Quantitative dissertation research, on the other hand, focuses on the numbers. Candidates employ quantitative research methods to aggregate data that can be easily categorized and analyzed. In addition to traditional statistical analysis, quantitative research also hones specific research strategy based on the type of research questions. Quantitative candidates may also employ theory-driven research, replication-based studies and data-driven dissertations. 

When conducting research, some candidates who rely on quantitative measures focus their work on testing existing theories, while others create an original approach. To refine their approach, quantitative researchers focus on positivist or post-positivist research paradigms. Quantitative research designs focus on descriptive, experimental or relationship-based designs, to name a few. 

To collect the data itself, researchers focus on questionnaires and surveys, structured interviews and observations, data sets and laboratory-based methods. Then, once it’s time to assess the quality of the data, quantitative researchers measure their results against a set of criteria, including: reliability, internal/external validity and construct validity. Quantitative researchers have options when presenting their findings. Candidates convey their results using graphs, data, tables and analytical statements.

3. Mixed-Method

Many PhD candidates also use a hybrid model in which they employ both qualitative and quantitative methods of research. Mixed dissertation research models are fairly new and gaining traction. For a variety of reasons, a mixed-method approach offers candidates both versatility and credibility. It’s a more comprehensive strategy that allows for a wider capture of data with a wide range of presentation optimization. 

In the most common cases, candidates will first use quantitative methods to collect and categorize their data. Then, they’ll rely on qualitative methods to analyze that data and draw meaningful conclusions to relay to their committee panel. 

With a mixed-method approach, although you’re able to collect and analyze a more broad range of data, you run the risk of widening the scope of your dissertation research so much that you’re not able to reach succinct, sustainable conclusions. This is where it becomes critical to outline your research goals and strategy early on in the dissertation process so that the techniques you use to capture data have been thoroughly examined. 

How to Choose a Type of Dissertation Research That’s Right for You

After this overview of application and function, you may still be wondering how to go about choosing a dissertation type that’s right for you and your research proposition. In doing so, you’ll have a couple of things to consider: 

• What are your personal motivations? 
• What are your academic goals? 

It’s important to discern exactly what you hope to get out of your doctoral program . Of course, the presentation of your dissertation is, formally speaking, the pinnacle of your research. However, doctoral candidates must also consider:

• Which contributions they will make to the field
• Who they hope to collaborate with throughout their studies
• What they hope to take away from the experience personally, professionally and academically

Personal Considerations

To discern which type of dissertation research to choose, you have to take a closer look at your learning style, work ethic and even your personality. 

Quantitative research tends to be sequential and patterned-oriented. Steps move in a logical order, so it becomes clear what the next step should be at all times. For most candidates, this makes it easier to devise a timeline and stay on track. It also keeps you from getting overwhelmed by the magnitude of research involved. You’ll be able to assess your progress and make simple adjustments to stay on target. 

On the other hand, maybe you know that your research will involve many interviews and focus groups. You anticipate that you’ll have to coordinate participants’ schedules, and this will require some flexibility. Instead of creating a rigid schedule from the get-go, allowing your research to flow in a non-linear fashion may actually help you accomplish tasks more efficiently, albeit out of order. This also allows you the personal versatility of rerouting research strategy as you collect new data that leads you down other paths. 

After examining the research you need to conduct, consider more broadly: What type of student and researcher are you? In other words, What motivates you to do your best work? 

You’ll need to make sure that your methodology is conducive to the data you’re collecting, and you also need to make sure that it aligns with your work ethic so you set yourself up for success. If jumping from one task to another will cause you extra stress, but planning ahead puts you at ease, a quantitative research method may be best, assuming the type of research allows for this. 

Professional Considerations

The skills you master while working on your dissertation will serve you well beyond the day you earn your degree. Take into account the skills you’d like to develop for your academic and professional future. In addition to the hard skills you will develop in your area of expertise, you’ll also develop soft skills that are transferable to nearly any professional or academic setting. Perhaps you want to hone your ability to strategize a timeline, gather data efficiently or draw clear conclusions about the significance of your data collection. 

If you have considerable experience with quantitative analysis, but lack an extensive qualitative research portfolio, now may be your opportunity to explore — as long as you’re willing to put in the legwork to refine your skills or work closely with your mentor to develop a strategy together. 

Academic Considerations

For many doctoral candidates who hope to pursue a professional career in the world of academia, writing your dissertation is a practice in developing general research strategies that can be applied to any academic project. 

Candidates who are unsure which dissertation type best suits their research should consider whether they will take a philosophical or theoretical approach or come up with a thesis that addresses a specific problem or idea. Narrowing down this approach can sometimes happen even before the research begins. Other times, candidates begin to refine their methods once the data begins to tell a more concrete story.

Next Step: Structuring Your Dissertation Research Schedule

Once you’ve chosen which type of dissertation research you’ll pursue, you’ve already crossed the first hurdle. The next hurdle becomes when and where to fit dedicated research time and visits with your mentor into your schedule. The busyness of day-to-day life shouldn’t prevent you from making your academic dream a reality. In fact, search for programs that assist, not impede, your path to higher levels of academic success. 

Find out more about SNU’s online and on-campus education opportunities so that no matter where you are in life, you can choose the path that’s right for you.

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 3, 2023 3:14 PM
• URL: https://guides.lib.berkeley.edu/researchmethods

Public Health Doctoral Studies (PhD and DrPH): Types of Studies

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Study Definitions

Meta-Analysis

A quantitative method of combining the results of independent studies, which are drawn from the published literature, and synthesizing summaries and conclusions.

Systematic Review

A review which endeavors to consider all published and unpublished material on a specific question.  Studies that are judged methodologically sound are then combined quantitatively or qualitatively depending on their similarity.

Randomized Control Trial (RCT)

A  clinical trial involving one or more new treatments and at least one control treatment with specified outcome measures for evaluating the intervention.  The treatment may be a drug, device, or procedure. Controls are either placebo or an active treatment that is currently considered the "gold standard".  If patients are randomized via mathmatical techniques then the trial is designated as a randomized controlled trial.

Cohort Study

In cohort studies, groups of individuals, who are initially free of disease, are classified according to exposure or non-exposure to a risk factor and followed over time to determine the incidence of an outcome of interest.  In a prospective cohort study, the exposure information for the study subjects is collected at the start of the study and the new cases of disease are identified from that point on.  In a retrospective cohort study, the exposure status was measured in the past and disease identification has already begun. 

Case-Control Study

Studies that start by identifying persons with and without a disease of interest (cases and controls, respectively) and then look back in time to find differences in exposure to risk factors. 

Cross-Sectional Study

Studies in which the presence or absence of disease or other health-related variables are determined in each member of a population at one particular time. 

Levels of Evidence Pyramid

Levels of Evidence Pyramid created by Andy Puro, September 2014

Experimental vs. Observational Studies

An observational study is a study in which the investigator cannot control the assignment of treatment to subjects because the participants or conditions are not being directly assigned by the researcher.

• Examines predetermined treatments, interventions, policies, and their effects
• Four main types: case-series , case-control , cross-sectional , and cohort studies

In an experimental study , the investigators directly manipulate or assign participants to different interventions or environments.

• Controlled trials - studies in which the experimental drug or procedure is compared with another drug or procedure
• Uncontrolled trials - studies in which the investigators' experience with the experimental drug or procedure is described, but the treatment is not compared with another treatment

Formal Trials versus Observational Studies (Ravi Thadhani, Harvard Medical School)

Study Designs (Centre for Evidence Based Medicine, University of Oxford)

Learn about Clinical Studies (ClinicalTrials.gov, National Institutes of Health)

Definitions taken from: Dawson B, Trapp R.G. (2004). Chapter 2. Study Designs in Medical Research. In Dawson B, Trapp R.G. (Eds), Basic & Clinical Biostatistics, 4e Retrieved September 15, 2014 from http://accessmedicine.mhmedical.com/content.aspx?bookid=356&Sectionid=40086281.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

 Statistics

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

Research bias

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

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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.

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Topic selection & proposal development, enquire now, software implementation using matlab, questionnaire designing & data analysis, chapters writing & journal papers, 5 different types of research methodology for 2024.

Research Methodology refers to the systematic process used to conduct and analyze research. It involves a set of procedures and techniques employed to gather, organize, and interpret data. Various types of research methodology , such as qualitative and quantitative methods, form the foundation for investigating and understanding diverse phenomena. 

Diverse research methodology provide a spectrum of advantages in scientific exploration. Qualitative methodologies, such as interviews and observations, delve deep into understanding human behavior and motivations. Quantitative approaches, like surveys and experiments, offer precise numerical data for statistical analysis. Mixed-methods enable a comprehensive view by merging qualitative and quantitative strengths. Experimental methods establish cause-and-effect relationships, while case studies offer in-depth insights into specific instances. Each methodology caters to different research needs, fostering a nuanced understanding of complex phenomena and contributing to the richness and depth of scholarly inquiry.

This blog is your guide to the Top 5 types of research methods that haven’t been fully tapped into yet. We’re talking about different ways to do research, the kind that hasn’t been widely used or discovered. It’s crucial to stay on top of these categories of research methodology because as the world moves forward, so does the way we study and understand things. So, we’ll be checking out the latest and coolest Research methodology types , from new technologies to fresh ways of combining different fields of study. 

Different types of methodology in research

Research methodology encompasses a variety of approaches and techniques to gather and analyze data. Here are some Different types of methodology in research:

Qualitative Methodology:

In-depth exploration of attitudes, behaviors, and motivations.

Utilizes methods like interviews, focus groups, and content analysis.

Quantitative Methodology:

Focuses on numerical data and statistical analysis.

Involves surveys, experiments, and structured observations.

Mixed-Methods Approach:

Integrates both qualitative and quantitative methods.

Offers a thorough comprehension of the research problem.

Experimental Research:

Investigates cause-and-effect relationships.

Involves controlled experiments with manipulated variables.

Survey Research:

Gathers data from a selected group through structured questionnaires.

Examines trends, attitudes, and opinions.

Case Study Methodology:

In-depth analysis of a specific instance or case.

Offers detailed insights into complex phenomena.

Action Research:

Involves collaboration between researchers and practitioners.

Aims to solve real-world problems through iterative cycles of planning, acting, observing, and reflecting.

Ethnographic Research:

Immersive study of a specific group or culture.

Requires prolonged engagement and participant observation.

Methodology 1: Neurobiological Methodology

Neurobiological Methodology stands at the forefront of methodology in research paper, bridging the realms of neuroscience and traditional research methodologies. This is one of the Research methodology types which aims to unravel the intricacies of human cognition and behavior by integrating cutting-edge brain imaging techniques with established research methods.

Key Components:

Neuroimaging Technologies: Utilizes advanced technologies such as fMRI (functional Magnetic Resonance Imaging) and EEG (Electroencephalography) for all the research methodologies including exploratory research in research methodology. Enables real-time monitoring of brain activity, offering insights into cognitive processes during various tasks.

Biometric Data Integration: Incorporates biometric data, including heart rate variability and skin conductance, to supplement neurobiological findings. Provides a comprehensive understanding of emotional responses and physiological changes related to cognitive activities.

Experimental Designs with Neural Correlates: Designs experiments that correlate specific neural activities with behavioral responses. Allows researchers to identify neural markers associated with decision-making, memory, and learning.

Cross-Disciplinary Collaboration: Encourages collaboration between neuroscientists and researchers from diverse fields. Integrates expertise from psychology, sociology, and other disciplines to ensure a holistic approach.

Applications: Neurobiological Methodology, which is a descriptive methodology in research, holds immense potential across various research domains:

In Psychology: Unraveling the neural basis of psychological disorders, emotions, and cognitive functions.

In Marketing: Understanding consumer behavior by examining the neural responses to advertisements and product choices.

In Education: Enhancing learning methodologies by identifying neural patterns associated with effective teaching strategies.

Challenges and Future Directions: Despite its promises, Neurobiological Methodology faces challenges such as data complexity and ethical considerations. Future research should focus on refining methodologies, establishing ethical guidelines, and fostering interdisciplinary collaboration to unlock the full potential of this unexplored approach. Neurobiological Methodology emerges as a groundbreaking frontier, offering a novel lens through which researchers can delve into the intricacies of human cognition and behavior. As one of the different types of methodology in research, it holds the potential to reshape our understanding of the mind and pave the way for innovative solutions across diverse fields.

Methodology 2: Augmented Reality (AR) Research Methodology

Augmented Reality (AR) Research Methodology marks an unexplored frontier, intertwining cutting-edge AR technologies with traditional research methods. This is one of the types of methodology in research which seeks to create immersive environments for data collection, offering a unique perspective on human behavior and decision-making.

Constructs simulated environments using AR technology to observe and analyze real-time human behavior.

Enables researchers to study reactions and interactions in controlled yet dynamic settings.

Integrates AR-generated data collection points within physical spaces.

Facilitates the gathering of diverse data sets by embedding virtual elements in real-world contexts.

Utilizes AR interfaces to track user interactions and responses.

Enhances the understanding of user engagement and decision-making processes within augmented scenarios.

Combines AR experiences with traditional research methods such as exploratory research in research methodology for a comprehensive approach.

Allows researchers to triangulate findings by comparing results obtained from both virtual and non-virtual settings.

Applications: AR Research Methodology, which is also a descriptive methodology in research, holds promise across various research domains:

Simulating scenarios to observe human responses to environmental changes in descriptive qualitative research methodology.

Analyzing consumer behavior within augmented retail environments for product placement and advertising strategies.

Creating interactive learning experiences to study the impact of AR on knowledge retention.

Challenges and Future Directions: Challenges such as technological constraints and the need for standardized protocols highlight the evolving nature of this Research methodology types. Future endeavors should focus on refining AR applications, establishing ethical guidelines, and exploring collaborative opportunities with AR developers. Augmented Reality Research Methodology stands as an exciting avenue among the types of methodology in research, offering a transformative approach to understanding human behavior within virtual and augmented spaces. As technology continues to advance, this methodology holds the potential to redefine the landscape of research methodologies across diverse disciplines.

Methodology 3: Predictive Analytics in Social Sciences

Predictive Analytics in Social Sciences emerges as a groundbreaking methodology in research papers, introducing advanced statistical models and machine learning algorithms to forecast social trends and behaviors. This type of exploratory research methodologies harnesses the power of predictive analytics to offer a new dimension to traditional categories of research methodology.

Advanced Statistical Models:

Applies sophisticated statistical models, including regression analysis and time-series forecasting.

Enables researchers to identify patterns and relationships within social data.

Machine Learning Algorithms:

Integrates machine learning algorithms to predict future outcomes based on historical data.

Provides a dynamic and adaptive approach to understanding social phenomena in descriptive qualitative research methodology.

Big Data Utilization:

Harnesses large datasets from diverse sources, including social media, surveys, and public records.

Facilitates the identification of trends and correlations within complex social systems.

Real-Time Analysis:

Conducts real-time analysis of social data to generate instant predictions.

Allows for timely interventions and policy adjustments based on emerging social patterns.

Applications: Predictive Analytics in Social Sciences holds immense potential across various applications:

In Sociology: Forecasting demographic shifts, social movements, and cultural trends.

In Public Policy: Informing policy decisions by predicting the potential impact of interventions.

In Market Research: Anticipating consumer behavior and market trends for strategic planning.

Challenges and Future Directions: Despite its promises, integrating predictive analytics into social sciences faces challenges such as data privacy concerns and model interpretability, which is a type of exploratory research methodologies. Future research should focus on refining models, addressing ethical considerations, and enhancing the transparency of predictive analytics methodologies.

Predictive Analytics in Social Sciences stands as a dynamic methodology, extending beyond basic research methodology to offer foresight into the complex dynamics of human societies. As we embrace the era of big data, this approach holds the potential to revolutionize how we understand and respond to social changes in real time.

Methodology 4: Quantum Research Methodology

Quantum Research Methodology represents a paradigm shift, bridging the world of quantum physics with a basic research methodology. This unexplored approach challenges the traditional classification of research methodology by harnessing the principles of quantum mechanics for data analysis.

Quantum Computing for Data Processing: Utilizes quantum computing’s parallel processing capabilities for handling complex datasets. Offers a quantum leap in computational efficiency, enabling the analysis of vast amounts of information.

Quantum Entanglement in Data Relationships: Applies the concept of quantum entanglement to identify interconnected relationships within datasets. Provides a unique perspective on the interdependence of variables in comprehensive research methodology.

Superposition for Multifaceted Analysis: Exploits quantum superposition to analyze data simultaneously from multiple perspectives. Enhances researchers’ ability to examine complex phenomena from various angles.

Quantum Algorithms for Pattern Recognition: Develops quantum algorithms for advanced pattern recognition within datasets. Enables the identification of subtle patterns that may go unnoticed with classical algorithms.

Applications: Quantum Research Methodology holds potential classification of research methodology across diverse fields:

Exploring quantum phenomena and complex physical systems with enhanced computational power.

Analyzing intricate biological datasets to uncover hidden relationships and patterns.

Enhancing predictive modeling and risk analysis with quantum algorithms.

Challenges and Future Directions: The integration of quantum principles into research methodologies presents challenges such as the need for quantum expertise and the development of quantum-safe data encryption. Future research should focus on refining quantum algorithms, expanding interdisciplinary collaborations, and addressing ethical considerations. Quantum Research Methodology offers a novel and comprehensive approach that transcends traditional classifications of research methodology. As quantum technologies continue to advance, this unexplored frontier holds the promise of revolutionizing how we conduct research, analyze data, and gain insights into the underlying structures of complex systems.

Methodology 5: Bibliometric Research Methodology

Bibliometric research methodology is a quantitative approach that analyzes patterns and trends within academic literature, utilizing bibliographic data to gain insights into the scholarly landscape in the comprehensive research methodology.

Citation Analysis:

Examines the frequency and impact of citations to understand the influence of a publication.

Identifies seminal works and measures the academic impact of research.

Co-authorship Networks:

Maps collaborations among researchers through analysis of co-authored publications.

Unveils research communities and the dynamics of collaborative efforts.

Journal Impact Factors:

Evaluates the prestige and impact of academic journals based on citation patterns.

Assists researchers in identifying reputable outlets for publication.

Keyword Co-occurrence:

Identifies prevalent themes and topics within a field by analyzing the co-occurrence of keywords.

Facilitates trend analysis and the identification of emerging research areas.

Applications:

Research Evaluation:

Assessing the impact and productivity of researchers, institutions, or journals.

Informing funding agencies and policymakers in decision-making processes.

Trend Analysis:

Identifying emerging topics and research directions within a discipline.

Assisting researchers in staying abreast of the latest developments.

Collaboration Mapping:

Facilitating the identification of potential collaborators and research networks.

Enhancing interdisciplinary research initiatives.

Challenges:

Data Quality and Availability:

Limited availability and consistency of bibliographic data.

Challenges in obtaining accurate and comprehensive citation information.

Discipline-specific Differences:

Variability in citation practices across disciplines.

Difficulty in creating standardized metrics applicable to all fields.

Self-citation Bias:

Influence of self-citations on impact metrics.

Requires careful consideration to avoid skewing results.

Future Directions:

Integration with Altmetrics:

Incorporating alternative metrics like social media mentions to provide a more comprehensive assessment of research impact.

Open Science Initiatives:

Embracing open access principles to enhance data sharing and transparency.

Facilitating broader collaboration and increasing the accessibility of research outputs.

Machine Learning Applications:

Implementing machine learning algorithms for more sophisticated analysis.

Enhancing the automation of bibliometric processes and improving accuracy.

Final Thoughts

In wrapping up our exploration of the top 5 unexplored types of research methodology for 2024, it’s like we’ve discovered a treasure chest of new ideas. These methods are like a breath of fresh air in the world of research. From understanding how our brains work to creating virtual worlds with Augmented Reality, and predicting social trends, we’re on the brink of something big. 

Quantum research and Blockchain verification bring a touch of magic, making our data analysis smarter and more secure. These aren’t just fancy trends; they’re like keys to unlock a whole new era of learning. So, in 2024, researchers, buckle up and dive into these research methodology – the journey promises to be full of surprises, discoveries, and a whole lot of new knowledge!

Educba is a website that provides researchers with a comprehensive guide to different types of research methodologies. The website offers a wide range of courses and tutorials on research methodology, which can help researchers develop their research skills and knowledge. By taking these courses, researchers can learn about different research methods and techniques, such as surveys, case studies, and experiments. 

This knowledge can help researchers design and conduct their research more effectively and efficiently. Additionally, the website provides a platform for researchers to connect with other researchers and experts in their field. This can help researchers build their professional network and find new opportunities for research and collaboration. Overall, educba.com is a valuable resource for researchers who are looking to develop their research skills and knowledge and build their professional network.

Frequently Asked Questions

What is the research methodology?

Research methodology is the systematic process used to conduct and analyze research.

What is literature review in research methodology?

Literature review in research methodology involves reviewing and analyzing existing literature on a specific topic.

What is qualitative research methodology?

Qualitative research methodology involves exploring and understanding complex phenomena through non-numerical data.

What are qualitative methodologies?

Qualitative methodologies encompass various approaches like interviews, focus groups, and content analysis.

What are research methodology types?

Research methodology types include qualitative, quantitative, mixed methods, experimental, and survey research.

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Ph.D. in Qualitative and Quantitative Research Methodology

Qualitative and quantitative research methodology, (formerly ph.d. in inquiry methodology).

This unique program enables students to focus on quantitative research, qualitative research, or an integrated program of study.

The flexible curriculum enables you to delve deeply into your chosen area of interest, from statistical modeling to ethnography, from discourse and narrative analysis to psychometrics and assessment.

Yet our program is rigorous enough to ensure that all graduates are able to meaningfully contribute to the study of social and behavioral research.

Application Deadlines

Admission requirements.

The Graduate Studies Office will accept unofficial transcripts and self-reported test scores for admission reviews. Any admission made with these documents would be conditioned on receipt of official documents, which should be provided as soon as possible.

Note: If you are currently enrolled or have applied in the past year, you are eligible for a reduced application fee of $35. Learn more »

• Bachelor’s degree from an accredited institution
• Minimum undergraduate GPA of 2.75 out of 4.00
• Personal statement
• Resume (required from international students only)
• Three letters of recommendation
• Minimum 79 TOEFL score or minimum 6.5 IELTS score or minimum 115 Duolingo score (international students only)

Learn more about how to apply

Program Requirements

• Ph.D. in Qualitative and Quantitative Research Methodology – (formerly Ph.D. in Inquiry Methodology) Program Requirements

Per credit hour

*Does not include fees, which will vary depending on the number of credits enrolled. Find more information and calculate your expected costs at Student Central .

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• Active duty military, veterans, and military families should visit the Center for Veteran and Military Students to take full advantage of available financial assistance and educational benefits.

Qualifying Examination

At the completion of course work and before the dissertation, doctoral students specializing in Inquiry Methodology will need to pass a qualifying exam in the form of portfolio of work and an oral examination. This examination is tailored to the student's program of study. All students with a minor in education must also take a minor qualifying examination. Some departments outside of the School of Education waive the minor qualifying examination, under certain conditions.

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As a student you will have the opportunity to focus on methodology through theory and practice that cuts across a divide in qualitative and quantitative methods.

We are dedicated to advancing the understanding of social inquiry, especially with respect to the field of education, and we imagine these possibilities to be necessarily inclusive of methods typically disenfranchised from one another.

This 90-credit hour degree program requires students to spend at least two consecutive semesters on campus. Up to 30 credit hours may be transferred from another institution.

A 12-credit hour minor is also available to doctoral students majoring in other disciplines.

David Rutkowski ED 4234 drutkows@iu.edu (812) 856-8384

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PhD in Qualitative Research and Evaluation Methodologies

If you have a deep interest in the methodological, theoretical, and ethical procedures and challenges inherent to social science research and evaluation, this is a program for you. Our students contribute to the methodological and theoretical development of qualitative research and program evaluation.

Our program prepares research methodologists to study and develop theories and methods for conducting empirical and conceptual social science research and evaluation in education and other social science fields. Specifically, this program develops scholars and methodologists who are prepared to contribute to the advancement of innovative theories and methods used in qualitative research and program evaluation.

• Focus on qualitative methodologies with interdisciplinary topics
• Open to students with a variety of educational backgrounds and experiences
• Small cohorts support individual faculty attention and mentorship

Video: Overview of Programs Offered by the Qualitative Research Program

Video: Meet the Qualitative Research Faculty

The organization of the research and evaluation methods degree program recognizes the wide variety of specialties in which you might develop research agendas.

Our mission is to build your capacity to contribute methodological expertise to collaborative research efforts through real-world opportunities in which you develop and practice the skills needed in your area of emphasis. As a result of this experience, you will have a broad knowledge of research methods along with specific expertise in a focused methodology. You can use this knowledge to pursue careers as research methodologists and evaluation specialists in higher education, corporations, and non-profit agencies.

The Ph.D. degree is a 54-credit hour degree program in which students engage in advanced study of qualitative theories and methods, mixed methods, and approaches to evaluation.

• Core Coursework - 21 hours
• Research Seminar - 3 hours
• Elective Coursework - 18 hours
• Internship - 6 hours
• Doctoral hours Minimum of - 3 hours
• Dissertation Minimum of - 3 hours
• TOTAL - 54 hours

Part 1: Apply to the University of Georgia

The Graduate School handles admission for all graduate programs at the University of Georgia, including those in the College of Education. The Graduate School website contains important details about the application process, orientation, and many other useful links to guide you through the process of attending UGA at the graduate level.

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Part 2: Apply to the Ph.D. in Qualitative Research and Evaluation Methodologies

Please upload the following materials in your online application:

• Personal statement
• Curriculum vitae
• Writing sample (20-25 pages maximum)

Summary of Application Materials

Both the Graduate School and the academic department review application materials simultaneously. The Graduate School then reviews the department’s recommendation and makes the final determination on admission. As an applicant you will receive a formal letter regarding your admission from the Office of Graduate Admissions. You must be admitted to a program to be eligible to register for courses. Admission is granted for a specific semester and is validated by registration for that semester. Applicants must be admitted to the Graduate School before they are eligible to register. International applicants whose primary language is not English must submit scores from the TOEFL or IELTS tests in addition to a Certificate of Finances form. No application will be considered until all materials are received. To apply to the Ph.D. in Qualitative Research and Evaluation Methodologies, applicants must have completed a master’s degree. GRE scores are required for all applications.

Personal Statement

The program requires a personal statement, usually in the form of a letter of 2 to 3 pages, describing your background, work and research experience, interests in research and/or evaluation methodologies, and career aspirations. Specifically, you should be sure to address the following questions:

• What experience or background do you have in research methodologies?
• Why are you interested in the study of qualitative or evaluation methodologies?
• What research or evaluation questions do you hope to pursue, and why?
• In what way will the study of research or evaluation methodologies shape your career?

Writing Sample

You are required to submit a sample of formal writing (20-25 pages maximum). Scholarly or academic writing is preferred whether or not it has been published. If you have not published, recent course papers or work-related reports are appropriate.

Admissions Interviews

After a review of applications, selected applicants will be interviewed by the Ph.D. in Research and Evaluation Methodologies (REM) Admissions Committee early in the Spring semester for a subsequent Fall semester admission.

Deadline To Apply

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Additional Resources

Please use our online form if you have any questions for the department. Please be as specific as possible so that we may quickly assist you.

The College’s programs are taught by dedicated faculty who are experts in a range of areas and are passionate about helping students succeed both in their programs and professionally.

Meet the Faculty View Affiliated Faculty

Most graduate students at UGA are not assigned to a faculty advisor until after admittance. A close working relationship with your advisor is paramount to progressing through your program of study.

Almost all in-state students begin their studies at UGA paying limited tuition or fees. Please note that these amounts are subject to change and are meant to give prospective students an idea of the costs associated with a degree at the University of Georgia College of Education.

Students may qualify for a variety of assistantships, scholarships, and other financial awards to help offset the cost of tuition, housing, and other expenses.

Tuition Rates   Browse Financial Aid

In this program, you will take focused coursework with individual attention from faculty mentors .

Each semester, you will also take part in a seminar that brings together faculty in the qualitative research program to discuss topics of relevance to scholarship and teaching in qualitative research methodologies in higher education.

See for yourself how much UGA College of Education has to offer! Schedule a tour of campus to learn more about the UGA student experience.

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Useful Links

• Affiliate Faculty
• Course List
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• Summary of Materials Required by Area of Emphasis
• Projected QUAL Course Schedule
• Qualitative Research Program Community Practices

Testimonials

What first attracted me to the Qualitative Research and Evaluation Ph.D. program was the balance it offers between the philosophy of science and the practical application of research and evaluation. The program’s faculty members are amazing at supporting students’ learning, and at pushing us to be creative, deep thinking, and daring as we explore methodologies. Nuria Jaumot-Pascual, Doctoral Student

My path through the Qualitative Research and Evaluation Ph.D. program had me exploring the depths of qualitative research rarely visited by researchers in the health sciences but that have been essential in my current position as Senior Research Associate at Evidera. Now I find myself often referencing qualitative research theory and history when selecting strategies for new studies or when arranging trainings across my organization. Moreover, the breadth of interests across students in the program provided unique perspectives that enriched my journey. Most of all though, I am impressed with how well the faculty helped guide me through the program, making theory, history, and applied work relevant for my own career goals. Sean Halpin, Former Doctoral Student

I really discovered the world of qualitative research with the Qualitative Research and Evaluation Methodologies Ph.D. program, and the instructors made me adore it. Their support for the students’ learning was so impeccable. They had genial ways to encourage students to think outside the box and to be creative from every point of view, especially when it comes to methodologies. Bidossessi Mariano Ghislain Dossou Kpanou, Former Doctoral Student

Methodology Used for PhD Research

Common methods used in social science research.

Doctoral research is among the highest level of academic research conducted in universities and institutes throughout the United States. The methods used to conduct Ph.D. research should be sound and the data that results from the research must be credible. Scientific methodologies have been created to ensure the soundness and credibility of doctoral research and to secure continuity in research results. Among the most often employed methodologies are quantitative methods, qualitative methods, comparative methods and clinical trials.

Qualitative Research

Qualitative research methodologies are those scientific approaches that attempt to give meaning to certain experiences by describing cultural phenomena, human behavior or belief systems. Qualitative research is conducted by interviewing people, using a combination of closed and open-ended questions and analyzing the responses to draw conclusions about a pattern of behavior or social phenomena. One example of qualitative research in the field of cognitive learning is obtaining information about students' learning styles by listening to their own subjective descriptions of how they learn.

Quantitative Research

Quantitative research methodology is conducted by collecting data and creating statistics based on the evidence collected to prove or disprove a hypothesis. Quantitative research uses questionnaires or surveys of individuals and compiles the results into a chart, graph or other type of report. Quantitative research is useful in evaluating attitudes or views on certain topics. For example, doctoral research in the field of political science could survey whether people would support a government policy regarding taxes on luxury goods to shed light on the viability of such a proposal in a given community.

Comparative Research

The comparative research approach seeks to draw parallels and contrasts between two similar or competing systems of thought, among several cultures or within cross-cultural societies. Comparative research methodology is carried out by using a variety of tools, including surveys, personal observation and analysis of national data. Comparative research is useful for classifying shared social phenomena, placing cultural values in context and analyzing cultural differences. A doctoral project in criminal law could use comparative research, for example, to study how various countries in the European Union approach the rehabilitation of convicted criminals.

Clinical Trials

In the field of medicine and biological sciences, clinical trials are the methodology of choice. They are laboratory experiments carried out to test, confirm or disprove scientific theories or to measure the effect of medication or treatment on animals or humans. Clinical trials are conducted by observing the response of certain individuals to medical interventions and comparing those responses to individuals who have not received such medical interventions. In doctoral research in the field of physical therapy, clinical trials are an effective methodology for gauging the effects of therapeutic devices in patients.

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• University of Surrey: Comparative Research Methods

Trudie Longren began writing in 2008 for legal publications, including the "American Journal of Criminal Law." She has served as a classroom teacher and legal writing professor. Longren holds a bachelor's degree in international politics, a Juris Doctor and an LL.M. in human rights. She also speaks Spanish and French.

Introduction to Research Methods

Eric van Holm, PhD

1 Introduction to the Book

“The true path to wisdom can be identified … it has to have practical application in your life. Otherwise, wisdom becomes a useless thing and deteriorates, like a sword that is never used.” - Paulo Coelho, “The Pilgrimage”

This book is intended as a practical introduction to research methods in the social sciences. If you pursue research academically or professionally, it will probably not be the last book you need to read on the subject. This is intended as something of a gentle introduction with a focus on the applications of the information and examples.

There are a lot of terms that many such textbooks would include that are left out of this edition. I believe that learning is like pouring water into a cup. Once the cup is full, you can keep pouring but the cup wont hold more water. What you should do is drink the water before you refill it. The analogy gets a little bit stretched there, but “drinking the water” stands in for using the information. Once you actually understand the basics in this text, you’ll be ready to read a more advanced book that fills you back up.

So how did I decide what to leave out and what to include? I gave preference to the terms and information I need in my working life as someone who does research. Terms like inductive and deductive research are left out, not because they are unimportant, but because I rarely encounter them. Their definitions might be good material for a test on the terms in this book, but I don’t believe they are fundamentally important to your ability to engage with published research or conduct basic research yourself.

Research is best learned through practice. Like many classes, it is hard to really understand the difference between the different terms and ideas unless you’re actually using them for yourself. This book is written with the understanding that the subject is hard to internalize, and so real world examples are offered where they can be. But that’s not enough to make you an expert. And in fact, it’s hard for anyone to be an expert - there are always new methods and techniques to learn that can improve the ability of researchers to answer important questions. But this book is a good place to start, or at least I hope it is.

With that, I have to give a warning. If you take research methods from another teacher in the future, things might be described differently. That means I’ve sometimes ignored esoteric terms that you wont encounter outside a research methods class. And sometimes I define the terms I use slightly differently than others would. The goal is for this book to be approachable for students who have no background in the subject, aren’t interested in methods, and don’t like statistics.

My primary goal is to impart some of my excitement for research and statistics to you. If I fail in that, I hope I can at least make you better able to engage with the copious amounts of research that will shape policy and your behaviors during your life.

This book is copyrighted under Creative Commons Attribution-NonCommercial 4.0 International Public License, which means that it’s free for you and anyone to use. The Fall 2020 edition is a substantial expansion of another edition written in the Fall of 2019, and will continue to be updated in the future.

1.1 The Plan for this book

This book encompasses 1) the development of a research project and 2) the analysis of the resulting data you might collect.

The first half of the book concentrates on the development of a research project. That half of the book is written for a student that has to write a class research paper or thesis and doesn’t know where to start. It’ll walk the student through the development and identification of an idea through the collection of data.

But just collecting data isn’t enough if you’re going to do your own research. And with the growth of data that is available online, it isn’t even a necessary place to start (although understanding the first half is important), So the second half is written with the idea that the data is collected, and now it must be analyzed. In those chapters the book will walk the reader through basic steps of presenting and analyzing data that are common to many research projects. In those chapters we’ll also learn how to do all of that in R, a free software that is commonly used in data science. We’ll talk about that more later though.

1.2 Acknowledgements

I’d like to thank several students that helped with the updating of this book: Theresa Anderson, Maria Andrade, Derek Brumfield, Diane Buckley, Ashley Felan, Britain Forsyth, Omar Garcia, Marcus Gibson, Heather Glass, Ashley Hebert, Kirstie Jiles, Alisha Large, Liz Mexwell, Todd McConnell, Tokolongo Mokuena, Ashley Paratore, Chad Populis, Ben Quimby, Sabrina Richardson, Stephanie Riegel, Frank Robertson, Michelle Rosamond, and Tosha Shanableh. Of course, any errors or mistakes are the authors alone.

I’d also like to thank Jesse Lecy for pushing me to learn R and inspiring me to put together this book.

Research Methods for Successful PhD

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• 02 April 2024

How can we make PhD training fit for the modern world? Broaden its philosophical foundations

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Indian Institute of Science Education and Research, Tirupati, India.

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You have highlighted how PhD training assessment has stagnated, despite evolving educational methodologies (see Nature 613 , 414 (2023) and Nature 627 , 244; 2024 ). In particular, you note the mismatch between the current PhD journey and the multifaceted demands of modern research and societal challenges.

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April 3, 2024 | Elaina Hancock - UConn Communications

UConn Researchers Closer to Near Real-Time Disaster Monitoring

Information that once could take weeks to gather now only takes four days with a new method

(Adobe stock)

When disaster hits, a quick and coordinated response is needed, and that requires data to assess the nature of the damage, the scale of response needed, and to plan safe evacuations. From the ground, this data collection can take days or weeks, but a team of UConn researchers has found a way to drastically cut the lag time for these assessments using remote sensing data and machine learning, bringing disturbance assessment closer to near real-time (NRT) monitoring. Their findings are published in Remote Sensing of Environment .

Su Ye , a post-doctoral researcher in UConn’s Global Environmental Remote Sensing Laboratory (GERS) and the paper’s first author, says he was inspired by methods used by biomedical researchers to study the earliest symptoms of infections.

“It’s a very intuitive idea,” says Ye. “For example, with COVID, the early symptoms can be very subtle, and you cannot tell it’s COVID until several weeks later when the symptoms become severe and then they confirm infection.”

Ye explains this method is called retrospective chart review (RCR) and it is especially helpful in learning more about infections that have a long latency period between initial exposure to the development of obvious infection.

“This research uses the same ideas. When we’re doing land disturbance monitoring of things like disasters or diseases in forests, for example, at the very beginning of our remote sensing observations, we may have very few or only one remote sensing image, so catching the symptoms early could be very beneficial,” says Ye.

Several days or weeks after a disturbance, researchers can confirm a change, and much like a patient diagnosed with COVID, Ye reasoned they could trace back and do a retrospective analysis to see if earlier signals could be found in the data and if those data could be used to construct a model for near real-time monitoring.

Ye explains that they have a wealth of data to work with – for example, Landsat data stretches back 50 years – so the team could perform a full retrospective analysis to help create an algorithm that can detect changes much faster than current methods which rely on a more manual approach.

“There is so much data and many good products but we have never taken full advantage of them to retrospectively analyze the symptoms for future analysis. We have never connected the past and the future, but this work is bringing these two together.”

Associate Professor in the Department of Natural Resources and the Environment and Director of the GERS Laboratory Zhe Zhu says they used the multitudes of data available and applied machine learning, along with physical barriers to pioneer a technique that pushes the boundary of near real-time detection to, at most, four days as opposed to a month or more.

Until now, early detection was more challenging, because it is harder to differentiate change in the early post-disturbance stages, says Zhu.

“These data contain a lot of noise caused by things like clouds , cloud shadows, smoke, aerosols, even the changing of the seasons, and accounting for these variations makes the interpretation of real change on the Earth’s surface difficult, especially when the goal is to detect those disturbances as soon as possible.”

A key point in developing the method is the open access to the most advanced data available at medium-resolution, says Ye.

“Scientists in the United States are in collaboration with European scientists, and we combine all four satellites, so we have built upon the work of many, many others. Satellite technologies like Landsat – I think that’s one of the greatest projects in human history.”

Beyond making the images open source, Zhu adds that the data set – NASA Harmonized Landsat and Sentinel-2 data (HLS) — was harmonized by a team at NASA, meaning the Landsat and Sentinel-2 data were all calibrated to the same resolution, which saves a lot of processing time and allows researchers to start working with the data directly,

“Without the NASA HLS data, we may spend months to just get the data ready.”

Ye explains they set thresholds based on empirical knowledge from what was seen in previous land disturbances. They look at signals in the data, called spectral change, and calculate the overall magnitude of change to help distinguish the noise from the early signals of disturbances. This approach ignores other relevant important disturbance-related information such as spectral change angle, patterns of seasonality, pre-disturbance land condition, says Ye.

“The new method lets the past data supervise us to find the real signals. For example, some disturbances occur in certain seasons, so similarity could be taken into account, and some disturbances have special spectral features that will increase at certain bands, but decrease in other bands. We can then use the data to build a model to better characterize the changes.”

On the other hand, we took advantage of numerous existing disturbance products that could be used as training data in machine learning and AI, says Zhu.

“Once this massive amount of training data is collected, there can be some wrong pixels, but this machine learning approach can further refine the results and provide better results. It’s as if the physical, statistical rules are talking to the machine learning approach and they work together to improve the results.”

Co-author and Postdoctoral Researcher Ji Won Suh says the team is eager to continue working on this method and to monitor land disturbances nationwide.

“For future directions, I hope we can help to tell the story about socio-economic impacts and what is going on in our earth system. If denser times series data are available, and more data storage is available, together with this algorithm, we can understand our system more intuitively. I’m very much looking forward to the future.”

Zhu says the approach is already attracting interest, and he expects the interest will grow. Their work is open source and Zhu says they are happy to help other groups adopt the method. The platform has already been used for near-real-time disaster monitoring. In the aftermath of Hurricane Ian, the team quickly employed this method to aid in the recovery efforts.

“I think it is extremely beneficial,” says Zhu. “If any kind of disaster happens, we can see the damage in the area quickly and determine the extent and the estimated cost for recovery. We’re hoping to have this comprehensive land disturbance monitoring system in near real-time to help people reduce the damage from those big disasters.”

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Call for 1 PhD scholarship under the project "SustInAfrica - Sustainable intensification of food production through resilient farming systems in West & North Africa”, H2020, type of action “Research and Innovation Action”

Job information, offer description.

Call for 1 PhD scholarship under the project "SustInAfrica - Sustainable intensification of food production through resilient farming systems in West & North Africa”, H2020, type of action “Research and Innovation Action”

Ref. SustInAfrica_ReasonPhD

Date: 02 nd April 2024

• Notice resume

The University of Lisbon (ULisboa) opens a call for the award of 1 (one) PhD Research Grant funded by the project SustInAfrica - Sustainable intensification of food production through resilient farming systems in West & North Africa https://www.sustinafrica.com/ through the H2020 Programme, funding source SFS-35-2019-2020, action type RIA-Research and Innovation Action.

The call is open to holders of a master’s degree in academic fields encompassing the scientific domains of Education, Agricultural Education or related areas. Availability to conduct short periods of field work in rural areas in W and N Africa, and enrolment in the sustainability science PhD at University of Lisbon https://csustentabilidade.ulisboa.pt/index-en.html , is mandatory.

2. Scientific Area:

Science Education

3. Admission requirements:

3.1 In accordance with art. 10 of the University of Lisbon Research Grants Regulations, national citizens, from other member states of the European Union and from third states, holding a valid residence title or beneficiaries of long-term resident status under the terms of Law n.0 23/2007 of July 4th, amended by Law n.0 28/2019, of March 29th, or with whom Portugal has entered into reciprocity agreements, may apply.

3.2 Academic qualifications: Master's degree in Education or Educational Management.

Applicants must be enrolled in the Sustainability Sciences PhD program at the University of Lisbon, with a view to consolidating their scientific training by developing research work leading to the award of the respective academic degree as part of an R&D project.

3.3 Preferential factors:

Preference will be given to candidates who possess:

Previous experience in multi- and transdisciplinary projects in training and development in the field of agriculture particularly in Africa and/or emerging or developing countries.

Experience in organizing and coordinating workshops focus group discussions, and seminars in institutions such as NGOs

Experience in designing and applying data collection methods via surveys, focus groups or interviews.

Experience in qualitative and quantitative data analysis.

Knowledge of agri-food systems.

Fluency in spoken and written English;

Applicants are expected to have the qualities of perseverance, initiative, very good organizational skills, able to multitask, good written and spoken communication skills and the ability to work as part of a team.

Skills for autonomous work and availability to travel abroad at different periods if required.

4. Deadline and form of application

4.1 The competition is open from 02nd to 16st April, 2024.

4.2 The call will be advertised through the FCT Scientific Employment Information Platform and also on the University of Lisbon Portal.

4.3 The application should be sent by email to: [email protected] ; indicating the reference of the grant for which you are applying.

4.4 The application form must include the following documents, under penalty of exclusion:

• Documents proving that the candidate meets the requirements for the respective type of scholarship, namely copies of the qualification certificates of the required academic degrees
• Curriculum vitae of the applicant
• Motivation letter
• A document proving the candidate's enrolment in the course leading to a higher education degree or diploma or the acceptance of the candidate by the institution awarding the degree or diploma
• Declaration under oath that there is no professional activity or rendering of services that would violate the duty of exclusive dedication;
• Copy of the civil identification document and, where applicable, a copy of a valid residence permit or another legally equivalent document. In the case of the identification document being the citizen card, the delivery of the copy can be replaced by the exhibition of the same, prior to the signing of the contract, in case the scholarship is awarded.

4.5 In relation to the qualification certificate, if the degree has been obtained in a foreign institution, it must be recognized by a Portuguese institution in accordance with Decree-Law n.0 66/2018, of 16th August and Ministerial Order n.0 33/2019, of 25th January, in their current wording, at the contractualization phase.

5. Work Plan

The applicant research activity leading to a PhD thesis will focus on:

• Education for sustainability particularly on the agro-food sector
• Community engagement and partnerships for sustainable agricultural practices
• Equity in the transfer of knowledge in education for sustainability
• Integration of indigenous knowledge on agro-food sector for sustainability to enhance smallholder incomes in some of the project countries namely Ghana, Niger and Tunisia.

In addition, it will:

• Support the team from ISEG with the management of the WP4 od project SustInAfrica and other WPs when needed. To collect more data on education and training matters in the study areas. Carry out qualitative analysis of qualitative data and quantitative analysis where necessary.
• To contribute to the design of a multi-scale and multidisciplinary monitoring approach for the agro-food systems under study (T1.3), in particular for training and education for sustainability assessment.
• Where necessary support in tasks related to project management and communication. This includes also contributing to the finalization of the following deliverables in the coming years: D1.8 - Mid-term synthesis; D5.3 - Monitoring metrics; D6.7 - Verification workshops in Africa; D6.8 - Synthesis workshops in Africa; D7.5 - Gender equality.

6. Work location and Scientific guidance

The work will be carried out at the School of Economics and Management of the University of Lisbon, under the scientific supervision of Prof. Idalina Dias Sardinha and Adam Standring.

7. Selection methods and respective evaluation

7.1 The selection method to be used: Curriculum Vitae evaluation (CA = 70%) and Motivation Letter (CM = 30%). The selection board reserves the right, should it prove necessary, to invite up to three candidates to an interview (E). In this case, for those candidates who pass the interview stage, the first evaluation component (AC = 50% + CM = 20%) will weigh 70% and the interview will weigh 30%.

7.2 In the interview (E), if it is held, the knowledge, technical competencies and behavioural aspects demonstrated during the interaction between the interviewer and the interviewee will be evaluated in an objective and systematic way, namely those related to the capacity for communication and interpersonal relationship, information analysis and critical sense, initiative and autonomy, planning and organizational capacity; capacity of integration and collaboration in work teams.

7.6 Each of the selection methods is eliminatory.

7.6.1 In the (CA+CM), candidates who do not obtain a classification equal to or higher than 14 in the CA and equal to or higher than 12 in the MCA, or who do not obtain a classification in the first 5 positions, will be excluded.

7.6.2 In (E), candidates who do not attend the interview or who obtain a mark lower than 9.5 in the interview will be excluded.

7.7 Candidates have access to the jury's minutes, which contain the evaluation parameters and respective weighting of each of the selection methods to be used, the classification grid and the final evaluation system of the method, only if requested by candidates.

8. Members of the Jury

President - Idalina Dias Sardinha (PhD)

Effective member - Daniela Mourão Craveiro (PhD)

Effective member - Adam Standring (PhD)

Substitute voting member – Amélia Branco (PhD)

Substitute voting member – Ines Faria (PhD)

9. Form of publication/notification of results

The list of candidates admitted and excluded from the competition, the results obtained in each of the stages and the final ranking list of the candidates that completed the procedure will be published on the University of Lisbon portal at https://www.iseg.ulisboa.pt/recursos-humanos/concursos/ .

9.1 Candidates admitted to the first selection method are invited to the next method by email to the email address communicated in their Curriculum Vitae.

9.2 Candidates who are excluded will be notified, by e-mail with receipt of delivery sent to the e-mail address communicated in their Curriculum Vitae, to hold a hearing of the interested parties under the terms of the Administrative Procedure Code.

9.3 The final ranking list will be notified to all candidates by e-mail with receipt of delivery.

9.4 Once the deadline for complaints against the final ranking list has passed, the selected candidate will be notified to, within a maximum of 10 working days, submit a written statement of acceptance of the grant, failing which, if no valid reason is invoked within the aforementioned deadline, this will be considered as a renunciation or withdrawal of the grant.

9.5 In case the selected candidate renounces or gives up the scholarship, the candidate classified in the subsequent place, if applicable, shall be notified, for the purposes of the provisions of the previous paragraph.

10. Duration of Scholarship and Amount of Monthly Maintenance Allowance

10.1 The fellowship will have a duration of 12 months, starting on May 1st, 2024, with the possibility of renewal for up to 1 year (total 2 years).

10.2 Scholarship renewal terms and conditions

Scholarships may be renewed for additional periods up to their maximum duration, provided that, on the renewal date, the conditions for their granting are verified, always depending on the request submitted, within 60 working days prior to the renewal start date, accompanied by the following documents:

• Detailed report of the work carried out, containing the URL addresses of communications, publications and scientific creations resulting from the activity developed, if any;
• Statement of the supervisors on the documents referred to in the previous paragraph;
• Work plan for the renewal period;
• Document proving renewal of enrolment in the PhD

10.3 The monthly stipend will be 1 259,64 accordingly with FCT.

10.4 The grant holder will have personal accident insurance and, if not covered by any social protection scheme, may ensure the exercise of the right to social security by joining the voluntary social insurance scheme, under the terms of the Social Security Contributions Code.

10.5 The selected grant holder will work as a grant holder with exclusive dedication under the terms foreseen in the Research Grant Holder Statute.

11. Applicable legislation and regulations

The Research Grant Holder Statute, approved by Law no. 40/2004, of 18 August, as amended by Decree-Law no. 123/2019, of 28 August. University of Lisbon Research Grants Regulations, order No. 6238/2020 published in the Official Gazette No. 113, Series II, of 12 June 2020.

Amounts of the monthly maintenance allowance applicable to grants covered by Regulation 234/2012. Monthly maintenance allowance (values updated with effect from 1 January 2024).

The President of the Jury,

Idalina Maria Dias Sardinha

Requirements

Additional information, work location(s), where to apply.

Sister Cells Reveal Cancer’s Fate

A new method traces treatment resistant cells and predicts drugs that can make them more susceptible to cancer therapy..

Aparna is a freelance science writer with a PhD in bioinformatics and genomics at Harvard University. Her writing has also appeared in The Philadelphia Inquirer, Popular Science, PBS NOVA, and more.

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Learn about our editorial policies.

ABOVE: Sister cells have similar molecular profiles, which researchers leveraged to measure different cellular traits in parallel. © iStock, Rost-9D

C ancer is notoriously hard to treat. In part, this is because the cells making up a tumor are heterogeneous, expressing different genes and molecules that determine their response to treatment. Even if a treatment kills most cancer cells, one survivor is enough for the cancer to persist.

As scientists struggled to find these treatment-resistant cells, they turned to an unexpected tool: sister cells. While human sisters may share clothes or toys, sister cells share their gene expression profiles, which could hint at whether the cells are treatment resistant.

In a study published in Nature Communications , researchers at the University of Helsinki presented a new method called ReSisTrace that utilizes sister cells to identify the molecular states driving treatment resistance in cancer cell lines. 1 Guided by these resistance signatures, the researchers devised a method to predict drugs that would sensitize the cells to treatment. 

“We can have data [on] both drug sensitivity and transcriptomics at the single-cell level,” said Jing Tang , a bioinformatician at the University of Helsinki and coauthor of the study. “This is unique and novel, and not available by using other techniques.”

Tang and Anna Vähärautio , a cancer biologist at the University of Helsinki and coauthor of the study, wanted to develop a method that combined lineage tracing—the process of tracking cell fate and offspring—with the ability to profile gene expression in individual cells. However, measuring gene expression in a cell typically destroys it, so scientists cannot trace its lineage at the same time. Enter sister cells: a way to achieve both goals in parallel.

Vähärautio's team devised a method to insert unique DNA barcodes into an ovarian cancer cell line using lentiviral transduction. Then, they allowed the cells to undergo a single division to each produce two sister cells, which they found had similar gene expression profiles. The researchers split the pool of cells in half: in one half, they measured gene expression by single cell RNA-sequencing (scRNA-seq) to construct a picture of each cell’s state, and in the other half, they tested whether the cells responded to certain common cancer treatments.

Using the treatment-resistant cells’ barcodes, the researchers matched them with their sister cells in the pre-treatment pool and analyzed their gene expression profiles. This comparison helped them identify genes that might have caused the cell to evade being killed. 

At first, the researchers tried to focus on individual genes, but they soon realized this approach might not be enough. “We don't know if [the genes] are really driving the resistance or if they are secondary effects,” Vähärautio said. This inspired the team to search the whole transcriptome for broader gene expression signatures of treatment sensitivity or resistance. Vähärautio and Tang suspected that these signatures could even help predict additional drugs that could sensitize the cells to a subsequent treatment.

Using published gene expression data collected from cell lines treated with a variety of compounds, Tang’s team identified potential drugs that could push treatment-resistant cells’ gene expression toward that of treatment-responsive cells. 2 By doing so, the added drug could prime the cells to respond to cancer treatment. Using computational models, the researchers predicted that administering pevonedistat—a drug that inhibits an enzyme involved in protein degradation—before carboplatin chemotherapy would make the cancer cell line that they were studying easier to kill. They tested their predictions and found that pevonedistat pretreatment, and many other predicted compounds, worked synergistically with common cancer therapies to kill the cancer cells. 

These findings came as a pleasant surprise to Vähärautio, and they convinced Tang that this could be a new approach for developing more effective cancer treatments to overcome drug resistance. 

Amy Brock , a bioengineer at the University of Texas at Austin who was not involved in this study, noted that the authors defined gene expression signatures by comparing all resistant cells to all sensitive cells, but that there might be even more patterns hidden in individual resistant cells. “It would be interesting to further examine whether sister cells become resistant via common or distinct mechanisms,” Brock said.

Brock hopes that, with a slew of similar methods to track cell lineages and single-cell gene expression , researchers will now focus on applying these tools to better understand how cells evade specific treatments. 3,4 Vähärautio and Tang are now applying their method to more sample types, including cancer organoids and acute myeloid leukemia cell lines. But Vähärautio thinks this method could even be useful for studying how cells’ states influence their fates in other contexts, such as development or responses to chemicals. With the computational models for drug prediction, ReSisTrace could even identify ways to change these fates.

“I think the method is really widely applicable and can be used to study many different cell state and fate connections,” Vähärautio said.

• Dai J, et al. Tracing back primed resistance in cancer via sister cells . Nat Commun . 2024;15(1):1158.
• Subramanian A, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles . Cell . 2017;171(6):1437-1452.
• Oren Y, et al. Cycling cancer persister cells arise from lineages with distinct programs . Nature . 2021;596(7873):576-582.
• Gutierrez C, et al. Multifunctional barcoding with ClonMapper enables high-resolution study of clonal dynamics during tumor evolution and treatment . Nat Cancer . 2021;2(7):758-772.

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• MyU : For Students, Faculty, and Staff

CSE DSI Machine Learning Seminar with Renbo Zhao

Frank-wolfe-type methods for minimizing log-homogenous self-concordant barriers.

We present and analyze a new Frank–Wolfe method for minimizing a theta-log-homogenous self-concordant barriers, with applications including positron emission tomography, D-optimal design, TV-regularized Poisson image de-blurring, quantum state tomography. The iteration complexity of our method is essentially O(\theta^2/\epsilon), which recovers that obtained by Khachiyan (1996) on the D-optimal design problem. In addition, we also present and analyze an away-step variant of our proposed Frank–Wolfe method, and we show the global linear convergence of this method. When specialized to the D-optimal design problem, this settles an open problem in Ahipasaoglu, Sun and Todd (2008).

Renbo Zhao is Assistant Professor of Business Analytics in the Tippie College of Business, University of Iowa. He received his PhD in PhD in Operations Research from MIT.

Keller Hall 3-180 and via Zoom .

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