Research Methods - Science method

With global research spending increasing rapidly, and a growing emphasis on international collaborations, professional management of research is essential. University leaders around the world are starting to see the value of building a sustainable research office, along with the research manager role. However, in many respects these roles are still highly adapted to their local research and funding environments.

To understand the growing trends in research management, and how the research manager’s role is changing in light of global challenges, we interviewed senior representatives of Research Management and Administration (RMA) organisations in Africa, Asia, Australasia, Europe, North and South America...

Hi Carlos Jimenez-Gallardo ,

precisely: two sides of the same coin.

Thank you so much, both of your answers were extremely helpful! I ultimately made the decision to run the analyses 3 times and then compare results - (1) full data set, (2) with n = 10 multivariate outliers removed, and (3) with n = 28 multivariate outliers removed. It was time-consuming, but luckily there were very few differences in results, so I didn't have to make any hard decisions.

I will have to look into the analyses David Morse suggested. In selecting my rating scale labels, I used research performed by Beckstead (2014). This study attempted to map the strength of adjectives that are typically attached to Likert scales - thus allowing researchers to select rating scale labels that are "spaced an equal distance apart," and thus to proceed with analyses under a crude assumption of interval scaling. But given this spacing assumption is based on single study, its hardly satisfactory.

Thank you again both of you!

While quantitative research is a way of studying and analyzing data using mathematical methods, it's important to note that not all research questions or studies can be effectively expressed in mathematical equations. However, if your research involves studying relationships between variables, you can express these relationships using mathematical equations.

For example, if you are studying the relationship between the amount of time students spend studying and their exam scores, you can express this relationship using a mathematical equation. Let X represent the amount of time students spend studying and let Y represent their exam scores. You can then create a simple linear regression model to analyze the relationship between X and Y:

Y = a + bX

where "a" is the y-intercept, or the predicted value of Y when X is 0, and "b" is the slope, or the change in Y for every one-unit increase in X. This equation can be used to predict the exam scores for a given amount of time spent studying (X). Other mathematical methods such as statistical analysis and correlation coefficients can also be used in quantitative research to measure relationships between variables and support conclusions.

A major potential limitation of a quantitative study (including the use of a quantitative study as the primary method in a sequential explanatory model) is the need for a large enough N to produce significant results. You can use the G*power program online to determine how large a sample you would need to have enough power to detect an effect.

In addition, you would need a clear set of measures for both your independent and dependent variables. For example, you would need to be able to measure "impact on mood."

Adding reversed questions is no longer recommended because research comparing two versions of the same scale, with and without reversed items, shows that using reversed items reduces the reliability of the scale.

I agree with Ian F Burgess you really can't equate pay to publish open access with bad journals - many excellent journals are open access only and have been so for some time. However, the majority of well-recognised journals in most fields still retain the traditional model of subscription access in which there is no fee to publish (although as mentioned, most of these now operate a hybrid model where open access is available for a fee). In terms of your specific topic - research methods in healthcare - there are lots but it depends on the type of methods article you have in mind. I'd suggest you look at the impact factor list for relevant disciplines (health sciences/services, nursing, etc,) and draw up a shortlist then do a bit of homework on the sort of articles each publishes. I can imagine that there is a tendency for methods-focused journals to be more likely to be open-access (only) but I am sure you will find one. This will help you avoid any predatory journals (but if you don't want to pay an APC you'll be avoiding them anyway)

In my opinion, qualitative content analysis and discourse analysis are so different that it is difficult to compare them at all. One key point is that there are at least two form of discourse analysis, one of which is more like conversation analysis, and the other of which is frequently called critical discourse analysis.

In terms of qualitative content analysis, the usual approach is build a codebook that allows you to mark and retrieve codes from the text. This differs from quantitative content analysis, which relies on counting codes.

I remember when I was at the US Energy Information Administration that there were something over 100 plants of a certain type, and someone called one to see how they were doing a certain accounting. He then called another and they were doing their accounting the same way. He then made the very, very rash assumption that that was the way it was done. It turned out that there were two ways to do this accounting, and the one he heard about was the less popular method. One should never rely on such a small "sample."

Data analysis in research methods refers to the process of transforming raw data into meaningful and interpretable information to answer research questions, test hypotheses, or conclude. It involves applying various statistical and analytical techniques to understand patterns, relationships, and trends within the data.

Data analysis plays a crucial role in the research process and helps researchers make sense of their collected data. It involves several steps, including data cleaning, exploration, transformation, modeling, and interpretation. Here are some key components of data analysis in research methods:

Data analysis in research methods allows researchers to uncover patterns, draw meaningful conclusions, and contribute to the overall knowledge in their field. It requires a combination of statistical knowledge, analytical skills, and domain expertise to conduct rigorous and insightful analyses.

Regenerate response

One issue here might be co-authoring a portion of dissertation. Some institutions insist that dissertations be the sole work of the doctoral student who is authoring them.

The objectives of research can vary depending on the field, topic, and context of the research project. However, there are several common objectives that researchers aim to achieve through their work. Some of these objectives include:

Overall, the objectives of research are to contribute new knowledge to a particular field, to test hypotheses or theories, and to provide evidence-based insights and recommendations for practice or policy. The choice of objectives will depend on the research question, the available resources, and the specific goals of the research project.

Dear Vladimir,

It's interesting to hear your perspective on the use of qualitative and quantitative research methods. I agree that qualitative analysis can provide a valuable foundation for quantitative research, particularly in terms of establishing a "coordinate system" for the data. However, I would argue that qualitative research also has its own unique strengths and can provide valuable insights that may be missed with a purely quantitative approach.

For example, qualitative research can allow for a more in-depth exploration of the attitudes, beliefs, and experiences of participants, which may not be fully captured by quantitative measures alone. Additionally, qualitative research can help identify new and unexpected research questions that may not have been initially considered in the design of a quantitative study.

With that said, I do agree that quantitative research is important for providing reproducible and generalizable results, particularly in fields where empirical evidence is crucial for informing policy or practice.

My question for you is: In what ways do you think researchers can balance the strengths of qualitative and quantitative research methods in order to maximize the benefits of both?

This is certainly not grounded theory because you have not alternated data collection and data analysis in the systematic fashion required by that method. To be mixed methods research you would need to integrate the results from qualitative and quantitative methods, but it sounds like your N would be too small to produce meaningful quantitative results.

That leaves exploratory research, would be a good match to a thematic analysis.

We may define research as analyzing/observing the results again and again and creating the data sets and then on the basis of this experimental proven data we can wind up the final results. For more detailed knowledge on this topic refer following lecture ->

#research #researchmethodology #meaningofresearch #researchmethods #researchmethodslectures

Investigating a method for elaborating a radical thinking of law would likely involve exploring alternative and non-mainstream approaches to legal theory and examining how these approaches might challenge and reshape traditional notions of law. Examining the consequences of the existence or absence of a research method for the elaboration and perception of political-legal problems marginalized in the Western tradition of the philosophy of law could involve analyzing the historical and cultural context of these marginalized problems and considering how different research methods and theoretical perspectives might impact our understanding and response to these issues.

Is there some reason why you think you need a paradigm, since that is a relatively old-fashioned approach to research. In particular, mixed methods research is more likely to emphasize the match between meaningful research questions and appropriate methods for addressing those questions -- rather than abstract issues of ontology and epistemology.

The classic triad in this regard is ontology, epistemology, and methodology. Ontology comes first because it involves the most basic assumptions -- about the nature of reality. After that, epistemology addresses what can be known, given a set of assumptions about reality. Finally, methodology concerns how one goes about producing knowledge, given epistemological assumptions about what can be known. (Note that methodology is more abstract than methods, which are simply mechanical means for collecting and analyzing data.)

This approach, based on the philosophy of science, was introduced into the social sciences by Lincoln and Guba (1985) in their book, Naturalistic Inquiry. It is now considered to be rather old fashioned, since it has been replaced by an emphasis on how one's research question determines one's choice of methods.

Can you say more about how you would use AI to conduct your interviews? I have seen quite a few articles about using AI to assist in the analysis of qualitative data, but nothing on how it would work in collecting qualitative data.

For those who are not adequately familiar with statistics, it is better to avoid "facilitated books".

In these cases I recommend various tutorials, which are easily available on the net, specific to each individual topic.

There are several on youtube.

One of the key limitations is throughput. I can collect a sample but then I have to identify all of the pollen. If I want to detect a key (but rare) plant I might need to go through millions of grains just to find pollen from that species. What is the sample size I need to reliably estimate abundance of a species that makes up 0.000003% of the individuals (or biomass) in my area? Maybe AI could help analyze samples faster and more reliably.

KD-Consistency Analysis for formative scenario analysis.

It is important to note that KD-Consistency Analysis is a complex tool, and it may require some expertise to use effectively. You may want to consult with a specialist or seek additional training or guidance to ensure that you are using the tool correctly.

Memory bias.

I believe it is possible to do the same type of analysis using R. Could anyone suggest a script that would make this analysis possible?

For any network analysis, you need to define nodes and to set criteria for when a connection exists between a pair of nodes. What approach were you going to use to do this for qualitative data analysis.

In fact , choosing the method does not depend on your preference , you should justify your method in your work . Hence,I do believe that you have to think on what type of data you need in order to answer your research questions , with align to the intended scope of your study and its aims. Last but not least, consider the stipulated time and available resources.All the best , enjoy your study.

Thank you for your comment. I agree with the point that it differs from country to country and that it is less complicated when the languages belong to the same linguistic grouping. Although bilingualism in schools and families is a successful reality in many countries and cultures, it is important to consider the different aspects of bilingual education. It should also be pointed out that a bilingual education is not the same as a multicultural education. The point of reflection in this discussion is to understand how multilingualism is a component of the strategies embedded in national educational policies. Although the subject has already been well studied, I think that there are still many gaps which are reflected in a poverty of learning such as the one mentioned in the last World Bank report [https://www.worldbank.org/en/topic/education/brief/what-is-learning-poverty].

Ex post facto research, also known as retrospective research, is a type of research in which the researcher studies events that have already occurred. The term "ex post facto" is Latin for "after the fact," which refers to the fact that the research is conducted after the events in question have taken place. The term "post mortem" is used to refer to this type of research because it is similar to a medical examination of a dead body, in which the cause of death is determined after the fact. Both ex post facto research and a post mortem examination are used to understand and explain events that have already occurred.

If you're interested in thinking, but not necessarily a cookbook, try Claude Bernard's Introduction to the Study of Experimental Medicine. Bernard was a driving force in medicine's 1800s-era quest to become scientific, so you see him wrestling the the premises that will ultimately define acceptable methods. Think of this as a rigorous study in "why" rather than "how to."

Yes, you can think of a quasi-experimental design as a non-randomized controlled trial. Another name for this kind of design is a non-equivalent control group. There is quite a large literature on quasi-experimental designs.

David, I agree with you.

I think that if you present data stimulated by highly structured questions that are the same for all participants, percentages may provide an idea. For instance questions like "Do you find this intervention favorable?" may stimulate answers from all the participants involved and sometimes absence of an answer may matter. Reader can benefit from percentages at this point. But for a probe question which is not valid for many participants, percentages may not be very informative or may even be misleading. Depending on the nature of design and thus your questions you can decide whether or not to include percentages.

Your method depends on the sample matrix and expected concentration. Thus a 'Refractive Index' (RID) detector may be appropriate for the pure raw material, but a CAD/ELSD/MS may be appropriate for low concentration (ppb or ppt) in soil.

FEMALE ENTREPRENEUR

Hi there! This is a great question. I think the results of interviews conducted during a pilot study are certainly publishable, as long as it is identified as a pilot study, all human research protocols were followed, and the author states as part of an "implications for future study" type section what the author's intentions now are. Perhaps a very different sample from the original one might lead one to pursue further research here. Perhaps and results of from the "pilot study" are leading the researcher into another interesting area to pursue. As long as the author is clear in intent and communication intent, I think it's fine.

Hello Melanie Zurba

The examples I immediately think of are:

1. The thalidomide story which caused a lot of babies to have malformations

2. The US Army used a lot of the soldiers to be there for when they were testing the Atomic Bomb so the authorities could find out what was the radioactive effect on humans

It is true the USA in the past as well as many other countries have used drainage ditches, tiles and other practices to drain lands, and many of these were basically wetland conversion. USA and probably some other countries have limits and compensatory mitigation plans if wetlands have to be drained, filled or otherwise converted. It is not always obvious that wetlands may offer unique habitats, and serve to accumulate and store organics, which helps bind carbon. Wetlands can be identified with soil, plant and hydrology indicators such as those published by the US Corps of Engineers in their Manual (1987) and other adjustment advice since then. But some select plants and trees do well in wetlands, but may not meet the specific desires for commercial crops. It might be worthwhile to consult your local wildlife and botanical specialists to get their opinion. Wetland also help promote water quality and other benefits in some circumstances, but some may have other items that could present problems to landowners.

Robert Yin's book on case studies specializes in comparative analysis and it contains a number of detailed example.

Is case study a methodology? Robert Stake (1998) argues: "As a form of research, case study is defined by interest in individual cases, not by the methods of inquiry used". I find it enlightening to regard case study methodology as a meta-methodology; the methodological recommendation is to combine the methodologies which are useful in the light of the issue at stake. A methodology is a recommendation to rely on certain data collection methods, methods to analyze and apply specific quality criteria. Action research procedures and purposes, Grounded Theory methodology, Hypothesis-testing approach (Yin) are examples of methodologies which can be used, alone or in combination, in a case study. In short: (1) Case study methodology is a combined methodology: a meta-methodology, (2) object of the study is a case, (3) triangulation is an overarching quality criterium, (4) a purposeful sampling procedure is used to select the case(s). As a consequence: in research it is more important to make clear how it is done, rather than what it is.

Best wishes

Rolf

To study this topic, I think that the comparative research method is useful in these topics

Braun and Clarke quite explicitly do not support comparing multiple coders for their own preferred version of Reflexive Thematic Analysis. However, they do allow for alternative approaches, such as a Codebook Thematic Analysis.

If I understand you are interested in interactions wouldn't a factorial design clean this up a lot. Just throwing stuff in is not a good experimental design. Best wishes David Booth

The book by Creswell and Poth (2018) could come in handy. Below is the full citation.

Creswell, J. W., & Poth, C., N. (2018). Qualitative inquiry & research design: Choosing among five approaches (4^th ed.). SAGE Publications. https://us.sagepub.com/en-us/nam/qualitative-inquiry-and-research-design/book246896

Good luck

I may be the first academic to use social cartography to map four traditional research paradigms in social and educational research.

If you look at my postmodern thoughts in the paper, you might find that decentering the lens of paradigms, we will find some connection points and re-orientations for comparing or placing four paradigms into some social cartographs.

As a historian of research paradigms in social and educational research, I raise further research questions are as follows:

1. What are the main axiological issues or key axiological features of the four traditional research paradigms when they are in comparison? Can we conceptualize their relationships among those features in some social cartographs or other alternatives?

2. What are the basic logial rules governing the four research paradigms throughout the process of uncovering them using social cartographs or other alternatives? To what extent can we construct the conceptual notion of 'cross-paradigm triangulation' with logical consistency? [c.f. Denzin devises the notions of cross-person/cross-data/cross-perspective/-cross-theory triangulation based on logical consistency.]

3. Could we have different results on natural science research using similar social cartographic methods of mapping the four paradigms or other alternatives?

I tend to answer the first and second research questions in social and ethical research in some upcoming international journal papers and refereed international conference proceedings in research methodology and methods. I leave the third one to other top-rank scientists or science researchers who have tremendous research interests in my research project as I cannot extend my discussion into science fields due to limitations of personal knowledge in empirical contexts.

Please help circulate my paper to any interested international colleagues you are familiar with. I look for fruitful answers to the above questions and / or co-research works in future.

From an ignorant Percy KWOK

Thank you Prof. Masud Ibn Afjal for the interesting question. Research methodology section is a must as this is a blue print of your research covering study area, sampling, data collection methods/tools/analysis etc. More articles can also help;

Documentation of ethnobotanical data is very important but if you can make practical implementation of any important use, then that would be great. Give benifit to the persons from whom you collected the data

Thanks, Fahimeh Hassani for the excellent question and professors for the valuable answers.

Sounds like grounded theory. Bernard Glaser stated: "All data is data." Observations are another piece of qualitative data which should be analyzed and presented in the results. Right now, consider methods of validity/reliability, if saturation is applicable, and triangulation.

A systematic review answers a defined research question by collecting all available studies related to that question and reviewing, analyzing their results, and summarizing all empirical evidence that fits pre-specified eligibility criteria.

A meta-analysis is the use of statistical methods to summarize the results of these studies. It uses statistical methods on estimates from two or more different studies to form a pooled estimate.

References:

1. Akhter, Shakib, Thierry Pauyo, and Moin Khan. "What is the difference between a systematic review and a meta-analysis?." Basic Methods Handbook for Clinical Orthopaedic Research (2019): 331-342. https://link.springer.com/chapter/10.1007/978-3-662-58254-1_37

2. Schmid, Christopher H., Ian R. White, and Theo Stijnen. "Introduction to Systematic Review and Meta-Analysis." Handbook of Meta-Analysis (2020): 1-18.

mixed method design is time and resource consuming, budget constraints is another limitation...on the other hand mixed method research design brings in depth insights to the addressed research problem

thanks Norkhairi Bin Ahmad

Dear Seun Bello,

In my opinion, significant research methods and quantitative data analysis in the area of ​​the analysis of the determinants of the pro-environmental transformation of the economy towards a sustainable, green, emission-free circular economy can be statistical analyzes of data collected and published by national statistics institutions, quantitative data from surveys and Big Data Analytics carried out as part of the sentiment analysis of data collected from entries, comments, posts, etc. posted on social media websites. I have described the methods of this analysis in some publications that I have posted on my profile of this Research Gate portal.

Best regards,

Dariusz

Here, the answer is mainly focused on the literature search what is commonly called literature review that can be used by the most of the researchers in the world.

Both Qualitative or Qualitative research need the proper literature review for the particular research undertaken by the researchers.

The peer review is the process of checking or selecting the most appropriate literatures are mainly pertaining to the research titles.

The researchers have never considered the peer review process while doing research. It is the duty of the publication office to monitor those mistakes before publication.

AA

Dear Fernando Rezende da Cunha Junior,

There are many books on Research Methods. A good number of articles are also there in Research Gate. You could find them browsing internet.

You can also search the following link:

Thank you.

Nice question and answers. All the best

It sounds to me like both your components are quantitative because they rely on descriptive measurements of pre-defined variables. In contrast, qualitative data would usually consist of more open ended interviews that would focus on issues like meaning and would use a more interpretative approach. Instead, I would think of this as a "multi-method" design with two quantitative sections.

One key issue that you are going to face is sample size. If you plan to do statistical analyses, you will need a large N (well over 100). Otherwise, you could select several particularly interesting firms for a series of case studies.

According to Fox et al. (2019) "Applications of Operations Research and Management Science for Military Decision Making," key elements for OAR applications: the formulation process, the solution process, the interpretation of the mathematical answer in context of the actual problem, and the sensitivity analysis. In my opinion there could be two questions to follow up on this: "Did you find appropriate techniques to obtain a solution?" If not, try optional techniques. Another question: "Did you over simplify the process?"

You can use covariance-based software such as AMOS and variance-based software such as PLS.

One way is to go through the schools. A common approach is to reach out to the superintendent of the district with your IRB approval, summary of study, and that you are seeking support/permission. Once granted, you would then reach out to individual principals of schools in those districts to get their support. At that point, you provide the students at the school where you have support an informed consent and permission letter to take home for parents. If the parent consents, you may even schedule a call to address any questions or concerns they might have. Once you have consent, you then can proceed. Good luck.

Samantha Green, from what you have written in your question (much of which seems to be a bit of a jumble but also a mini lecture about things that are common, and very basic, knowledge), I suggest you try to examine the difference between replication in research versus test-retest reliability.

I hope that's helpful.

Dear Stefanie,

As far as I understood your question, a commonly used approach to analyse the trade-offs between systems is Multi-Criteria Decision Analysis (MCDA), one of the decision support systems used to select a course of action that explicitly accounts for the targeted ecosystem services by setting criteria.

Musse.

Here's a nice paper on ways to measure oxytocin levels in humans: https://www.nature.com/articles/s41598-017-17674-7

Unfortunately, it doesn't appear as if you can image oxytocin using MRS (which has pretty poor spatial and temporal resolution anyway) or any other neuroimaging measures as far as I can tell.

Hello Joseph Moses

Please check the courses tat fit your needs.

Also SAGE offers some content

I assume that you know the best Research Methods Books. If you need books, please leave a comment.

Good Luck

You can use AI/ML algorithms to automate parts of your research, do to that you have to have a passion to use research methodologies (a systematic and directing approach which leads you to get answer for your hypothesis/objectives) accordingly with research questions. It mus be scientific also, You can then apply AI/ML/DL approaches in that steps.

I was using "Smart Measure version 1.7.7" for measuring height of trees planted along streets within settlements. I am moderately satisfied with the precision. I found the optimal distance to carry out the measurements from a distance about 2-4 times of the tree height. In most cases, however, I was not able to measure from the optimal distance because buildings, parked cars, etc. made it impossible.

Creating a z score will result in a mean of zero and a SD of 1, but skewness will still be there and one skewed input variable (or indeed one with outliers) can have a big effect on the composite variable. You may be happy with this; if not then you could do a transformation that produces a more symmetric distribution and has mean of zero and SD of 1 - see https://en.wikipedia.org/wiki/Rankit and https://en.wikipedia.org/wiki/Normal_probability_plot

I will encourage you to apply interpretive approach.

There are a number of excellent references. Take for example:

Cohen, L., Manion, L., and Morrison, K. (2011, 7th edn.). Research Methods in Education (7th edn.), London: Routledge

Connolly, P. (2007). Quantitative Data Analysis in Education: a Critical Introduction Using SPSS, London; New York: Routledge.

Creswell, J. W. (2012). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research (4th edn.), Boston: Pearson.

Creswell, J. W. (2009) Research Design: Qualitative, Quantitative and mixed methods Approach. Los Angeles, Calif; London: Sage

Denscombe, M. (2014). The Good Research Guide: For Small Scare Social Research Projects (5th edn). McGraw-Hill.

Morgan, D. L. & Nica, A. (2020). Iterative Thematic Inquiry: A New Method for Analyzing Qualitative Data. (2020). International Journal of Qualitative Methods, 19, 1-11.

secondary sources become important if the study contribute to previous literature. It all depends on your research questions or objectives.

You are looking at the wrong end of the telescope. What are the research questions that you are trying to answer? Given that, what are the appropriate methods?

Data collection will be straightforward even for business research, as most people, customers, and managers are using online resources. Also, people are more familiar with the onoline tools, hence a higher chance to participate in survays, discussion, etc. The cost is also going to be reduced and from the participant perspective, most of the participants feel free to attend any meeting as they need not worry about the things they faced during a physical meeting

They are interrelated somehow.

I would agree with your view. I do think, however, it depends on the level of the student and the intent of the program. I have used it within our graduate level Exercise Science program where students are required to do research and strongly encouraged to publish.

The anti-establishment ethos of New Historicism was profoundly influenced by Foucault's theories of Power/Knowledge and Discourse. His primary concern has been with power's relationship to the discursive formations in society that make knowledge.

The theoretical framework is not something that is found readily available in the literature. You must review course readings and pertinent research literature for theories and analytic models that are relevant to the research problem you are investigating.