Types of Data analysis

In the field of statistics , some people divide data analysis into descriptive statistical analysis, exploratory data analysis, and confirmatory data analysis; among them, exploratory data analysis focuses on discovering new features in the data, while confirmatory data analysis is Focus on the verification or falsification of existing assumptions.

Exploratory data analysis

Exploratory data analysis refers to a method of analyzing data in order to form a worthy hypothesis test, which is a supplement to traditional statistical hypothesis testing methods. This method is named by the famous American statistician John Tukey.

Qualitative data analysis

Qualitative data analysis, also known as “qualitative data analysis”, “qualitative research” or “qualitative research data analysis”, refers to the analysis of non-numerical data (or data) such as words, photos, and observations.

Offline data analysis

Offline data analysis is used for more complex and time-consuming data analysis and processing. It is generally built on cloud computing platforms, such as the open source HDFS file system and MapReduce computing framework. The Hadoop cluster consists of hundreds or even thousands of servers, storing several petabytes or even dozens of petabytes of data. Thousands of offline data analysis jobs are running every day, and each job handles hundreds of MB to hundreds of TB or more. The running time is minutes, hours, days or even longer.

Online data analysis

Online data analysis is also called online analysis and processing , which is used to process users’ online requests. It requires relatively high response time (usually no more than a few seconds). Compared with offline data analysis, online data analysis can process user requests in real time, allowing users to change the constraints and restrictions of analysis at any time. Compared with offline data analysis, the amount of data that can be processed by online data analysis is much smaller, but with the development of technology, current online analysis systems have been able to process tens of millions or even hundreds of millions of records in real time. The traditional online data analysis system is built on a data warehouse with a relational database as the core, while the online big data analysis system is built on the NoSQL system of the cloud-computing platform. If there is no online analysis and processing of big data, there will be no way to store and index a huge number of Internet webpages, there will not be today’s efficient search engines, nor will there be microblogs, blogs, and social networks based on big data processing. And so on.

What are the impact factors of academic journals and what are the determining factors?

The impact factor (IF) of journals is a quantitative indicator of the influence of journals. That is an average of each published papers are cited number, it is actually published in a certain number of times a year all of the source is cited journal published two years ago in the journal, with the previous two years the number of journal papers published full source Ratio.

Impact factor is not the most objective criterion for evaluating the influence of journals. Generally speaking, the higher the impact factor, the greater the influence of the journal.

For some comprehensive or major research fields, the citation rate is relatively high because of the wide range of research fields. For example, journals of biology and chemistry are generally more likely to have a higher influence.

Although the impact factor can characterize the academic quality to a certain extent, the relationship between the impact factor and academic quality is not linearly proportional . For example, it cannot be said that a journal with an impact factor of 5.0 is definitely better than a journal with an impact factor of 2.0. It does not have the function of accurately and quantitatively evaluating academic quality.

Some domestic scientific research institutions often use cumulative impact factors or individual impact factors as quantitative standards when conducting research performance evaluations. Some researchers may not be promoted to titles or award bonuses due to a 0.1 point difference in impact factors. This approach is absolutely absolute Is not desirable.

From the calculation formula, although the impact factor is only directly related to the number of citations and the number of papers, in fact, it is closely related to many factors. There are five main factors that determine the size of the impact factor:

(1) Thesis factor . Such as the publication time lag of the paper, the length of the paper, the type and the number of collaborators, etc. Publications with a shorter publication time lag are easier to obtain a higher impact factor. If the publication period of the publication is long, a considerable part of the citations are not counted due to the aging of the literature (more than 2 years), that is, they are not involved in the calculation of the impact factor, thereby reducing the impact factor.

A large amount of statistical data shows that if the published paper is a hot topic, and the length of the paper is short and published quickly, the citation rate will quickly reach a peak, which will cause the impact factor of the journal to rise rapidly, and then rapidly decline; publish complete research papers The journals have been cited for a long time, and the impact factor has been increased for a long time. There are also data showing that the average number of authors of a paper is significantly positively correlated with the total citation frequency of the paper.

(2) Journal factors. Such as journal size (number of papers published), type, etc. When calculating the impact factor, the number of published papers only counts the number of papers, newsletters, and reviews, but does not count reviews, letters, newsletters, and other frequently cited articles. Based on empirical judgment, the number of journals published is closely related to the impact factor and the total frequency of citations.

In most cases, journals with a small number of papers are likely to get high impact factors, and the impact factors of these journals will fluctuate greatly from year to year; while journals with a large number of papers and a long history of publication tend to get higher Total citation frequency.

In addition, it is also closely related to other citation indicators such as: the annual index, the half-life of journal citations, the number of regional distribution, the ratio of funded papers, and the scope and circulation of journals. The size and structure of journals will cause different impact factors of journals. Generally speaking, the larger the scale of the same type of journals, the larger the impact factors of these journals in general; the more “hot” topics or “hot” professional articles contained in the journals, the more they are always cited. The higher the frequency, the greater the impact factor of such journals.

(3) Subject factors. For example, the number of journals in different disciplines, the average number of references, the half-life of citations, etc. will all have an impact on the journal’s impact factor and total citation frequency. The impact factor and total citation frequency of journals are based on the relationship between the citations of the paper and the number of citations.

The number of citations in a subject depends on two main factors: the first is the development characteristics of each subject; the second is the proportion of the journals of the subject in the source journals of the database. Generally speaking, the more source journals of a discipline, the greater the total citation frequency and impact factor of the discipline journal. These two factors determine the imbalance of the subject impact factor and the total citation frequency distribution.

In addition, the impact factor is also affected by the professional social coverage of the subject involved in the journal. If a sci-tech journal has a very small professional social coverage and there are few similar journals, then its impact factor is not It may be very high.

Because different disciplines have different internal scientific research rules, the situations in which the scientific research results of others need to be cited are different when doing research. These differences will affect the size of the impact factor in at least two aspects. On the one hand, the difference is how much other people’s work needs to be cited, and the other is the time of cite others’ work. Since the impact factor is generally calculated only based on the literature cited in the journals in the past two years, it can be seen that the ranking results based on the distribution of citation years in the past two years are also consistent with the ranking results of the journals by using the impact factor method. It also shows that the impact factor cannot correctly reflect the influence of journals in different disciplines.

Due to historical reasons, the construction and development of different disciplines in a country are not balanced, even in different branches of the same discipline. Some disciplines are small in scale, but there are many researchers engaged in this discipline, and the capital investment in this discipline is also large, and there are more related discipline magazines, which will form a scale advantage. And often those smaller-scale disciplines do not have this scale advantage, so the impact factor and total citation frequency of such journals will not be high.

In terms of the development speed of the subject scale, different subjects are divided into “cold” and “hot”. “Hot” subjects have developed rapidly due to the needs of the times. Articles of this subject will have a high citation rate during the period of rapid development of their scale; while “unpopular” subjects are just the opposite. However, this distinction between “cold” and “hot” is often not due to the development needs of science itself, but is often caused by economic, social and other non-scientific factors.

There are also some disciplines that contain many popular topics. Although these “hot” topics have high citation rates, they do not have much scientific value. According to the above analysis, it can be considered that this kind of difference in the citation rate of papers caused only by the size of the subject and the speed of development, or the difference in the size of the impact factors of related journals and institutions, resulting in the importance of The difference in ranking is not caused by the development of the scientific wooden body, but by some other economic, historical and social non-scientific factors. Therefore, this kind of evaluation of small-scale or “cold” major disciplines is extremely What is unfair is also unreasonable.

(4) Retrieval system factors , Such as the sources of journals participating in the statistics, the statistical scope of citation entries, etc. For a specific publication, in the retrieval systems of China and foreign countries, due to the large differences in the composition of the journal groups included in it, the calculated impact factor values ​​are quite different, and the same publication has a large difference in the retrieval systems of different languages. Significantly different impact factors and total citations.

(5) The influence of celebrity effect . Celebrity effect is often manifested as: On the one hand, people often cite celebrity articles to increase the authority of their articles, even when there are other documents that are more suitable for their articles; On the one hand, articles signed with celebrity names or articles recommended by celebrities are easily published in so-called high-end magazines, and are therefore easily included in SCI or CSCD. Sometimes the articles do not have celebrity scientific research results, but in order to be able to The names of celebrities are signed in the publications, so excessive emphasis on the citation or inclusion situation will bring artificial bias to the total citation frequency and impact factor of the article.

Several journal categories by Chinese

Common types of journals generally include national journals, provincial journals, academic journals, non-academic journals, CN journals, ISSN journals, CSCD journals, scientific papers statistical source journals, SCI journals, etc. The following is a brief introduction.

 Generally speaking, “national” journals are journals sponsored by the Party Central Committee, the State Council and their affiliated departments, or by the Chinese Academy of Sciences, the Chinese Academy of Social Sciences, democratic parties and national people’s organizations, and the journals sponsored by national first-level professional societies. . In addition, publications clearly marked as “national journals” and “core journals” can also be regarded as national publications. For example, “Chinese Journal of Internal Medicine” is a national journal.

    So what are provincial periodicals? In fact, provincial periodicals are journals sponsored by provinces, autonomous regions, municipalities and their subordinate ministries, commissions, departments, and bureaus, as well as journals sponsored by various colleges and universities. For example, “Shanxi Medical Journal” is a provincial journal.

    There are two classification methods for journals, academic journals and non-academic journals. Academic journals publish mainly academic papers, while non-academic journals publish documents, reports, speeches, experiences, knowledge, etc. that can only be used as academic research materials rather than articles. Since the selection of “Summary” is based on a large number of articles, a large number of articles and a large number of citations, it does not emphasize the boundary between academic journals and non-academic journals, so naturally there is no strict distinction. “Chinese Journal of Preventive Medicine” is a relatively advanced academic journal.

    ISSN publications refer to publications registered outside the territory of China and issued to the public at home and abroad. The ISSN letter is marked before the issue number of this type of publication, which is also easy to identify.

    What are CN publications? The so-called CN publications refer to publications registered in China and publicly issued in China. The serial numbers of such publications are marked with CN letters, and people are used to calling them CN publications.

    Chinese Science Citation Database source journal, referred to as CSCD journal. The Chinese Science Citation Database is divided into a core library and an extended library. The source journals of the core library have undergone strict selection and are authoritative and representative core journals in various disciplines. The source journals of the extended library have also been selected on a large scale and are among the best journals in various disciplines in China. Core library journals: 669 types ; extended library journals: 378 types (dynamic).
The source journals of scientific paper statistics, also known as the core journals of China’s science and technology, are important scientific journals in various disciplines selected by the China Institute of Science and Technology Information after strict quantitative and qualitative analysis.

    SCI “Science Citation Index”, is a world-renowned journal document retrieval tool published by the American Institute of Scientific Information. Select journal sources through its strict selection criteria and evaluation procedures. SCI is divided into SCI and SCI-E from the number of source journals. Under normal circumstances, the journals indexed by SCI are of higher grade, but sometimes it is found that the impact factor of journals included by SCIE may be higher than that of SCI. SCI is a core journal, and all articles are included in SCI; SCI-E is an extended version of journals, not all articles are included in SCI.

7 Types Of Artificial Intelligence

Artificial intelligence is arguably the most amazing innovation to date. Although all kinds of artificial intelligence products have appeared on the market, this is only the tip of the iceberg. Artificial intelligence has been integrated into our lives, even if it is still in the early stages of development, it has brought a revolutionary impact to society.

With the rapid development of artificial intelligence and the continuous enhancement of its performance, people’s pursuit of artificial intelligence has reached a level of obsession. In addition, the changes that artificial intelligence has brought to different industries have made business leaders and the public mistakenly believe that we are about to reach the peak of artificial intelligence research and maximize the potential of artificial intelligence. But what is the future of artificial intelligence? On the long road of artificial intelligence research, in order not to blindly and not get lost, we need to understand clearly the types of artificial intelligence and what capabilities existing artificial intelligence have.

The purpose of artificial intelligence is to make machines imitate human functions, so the degree to which artificial intelligence systems can replicate human abilities is used as a criterion for judging the level/type of artificial intelligence. Artificial intelligence that can perform more human-like functions and has the same level of proficiency will be regarded as a more evolved type of artificial intelligence, while artificial intelligence with limited functions and performance will be regarded as a simpler and more advanced type Lower type.

Based on this standard, artificial intelligence usually has two classification methods. One is to classify based on the similarity of the thinking of machines and humans, that is, to imitate the ability of humans to “think” or even “feel”. According to this standard classification, there are four types of artificial intelligence: reaction machines, limited memory machines, intelligent artificial intelligence and autonomous artificial intelligence.

1. reaction machine

This is the most primitive artificial intelligence system, and their capabilities are extremely limited. They mimic the human brain’s response to various stimuli. These machines have no memory capabilities. This means that they cannot “accumulate” previously acquired experience to guide current operations, that is, these machines have no “learning” ability. These machines can only be used to automatically respond to one or more sets of inputs. A typical example of a reactive artificial intelligence machine is IBM’s Deep Blue, which defeated the chess master Garry Kasparov in 1997.

2. Limited memory machine

In addition to the function of a reaction machine, a machine with limited memory (or limited memory) can also learn decisions from historical data. All current artificial intelligence systems, such as those that use deep learning, learn through a large amount of training data stored in memory, and finally form a reference model to solve future problems. For example, image recognition AI trains thousands of pictures and tags to recognize scanned objects. When the trained artificial intelligence model scans other images, it will label the new image according to the “learning experience”. As the training samples increase, the accuracy of AI recognition becomes higher and higher.

Almost all artificial intelligence applications today, from chatbots, virtual assistants and even self-driving cars, are based on artificial intelligence with limited memory.

3. Intelligent artificial intelligence

Today the first two types of artificial intelligence already exist and are widely used. Then the next stage of AI development will appear thinking consciousness. Although this is just a concept, researchers are innovating. Intelligent artificial intelligence is the higher level of artificial intelligence system. Thinking-conscious artificial intelligence can better interact with each other by identifying and understanding the needs, emotions, beliefs, and thinking processes of the other party. At present, the field of emotional intelligence has emerged, but to realize intelligent artificial intelligence, it is inseparable from the common development of related disciplines and interdisciplinary fields.

4. Autonomous artificial intelligence

This is the highest stage of artificial intelligence development. Self-aware AI, literally speaking, is an artificial intelligence that has evolved to be very similar to the human brain, and even developed autonomous consciousness. Perhaps the creation of this type of artificial intelligence, even if it does not take hundreds of years, will take decades to achieve. This will become the ultimate goal of all artificial intelligence research.

This type of artificial intelligence can not only understand and evoke the emotions of the people interacting with it, but also have its own emotions, needs, beliefs and potential desires. And this type of artificial intelligence is exactly what the doomsdayers worry about. The development of AI autonomous consciousness may promote the progress of human civilization, and may also bring disaster to society. Because once artificial intelligence has autonomous consciousness, it is very likely to easily acquire human wisdom and plan or even take over human beings by itself.

Another classification system is to divide artificial intelligence technology into artificial narrow intelligence (ANI), general artificial intelligence (AGI) and artificial super intelligence (ASI).

5. Artificial narrow intelligence

This type of artificial intelligence represents the existing artificial intelligence, including the most complex and capable artificial intelligence to date. Artificial narrow intelligence refers to an artificial intelligence system that can only use human-like functions to autonomously perform specific tasks. These machines can only do well-programmed things, so their capabilities are very limited. According to the former classification system, these systems correspond to all reactive and limited memory AIs. Even artificial intelligence that uses complex machine learning and deep learning belongs to ANI.

6. General Artificial Intelligence

General artificial intelligence refers to the ability of artificial intelligence to learn, perceive, understand and work exactly like humans. These systems can complete a variety of tasks independently, greatly reducing the time required for training. By copying various functions of human beings, artificial intelligence systems have the same capabilities as humans.

7. Artificial Super Intelligence

The development of artificial superintelligence may mark the pinnacle of artificial intelligence research, because AGI will become the most capable form of intelligence on earth to date. ASI has more powerful memory, faster data processing, analysis and decision-making capabilities. In addition to replicating the multi-faceted intelligence of humans, ASI will do everything very well (even beyond human level). The so-called “singularity” scene will appear in the development of AGI and ASI. Although the potential of artificial intelligence is very attractive, they may also threaten our survival and at least have a disruptive impact on our lifestyle.

If super artificial intelligence is really realized one day, what will our world look like? Artificial intelligence is still in its infancy, and there is still a long way to go to achieve this goal. For those who hold negative views, it is too early to worry about the emergence of “singularities”, and perhaps it will never happen-and there is still a long time to study the safety of artificial intelligence. For those who are optimistic about artificial intelligence, it can be said that “the road is long and I will search up and down”, and the future of artificial intelligence is even more exciting.

Qualitative research and quantitative research

Qualitative research and quantitative research Connections:

  (1) Both qualitative research and quantitative research belong to sociological methods. Qualitative research is mainly made cooked a expert noted the situation and the business based on personal intuition, experience, based on the study of past and present continuation of the situation and the latest information materials, the nature of the object of study, characteristics, changes of development to make judgments This method is to conduct research and judgment, put forward preliminary opinions, and then synthesize them as the main basis for predicting future conditions and development trends. Quantitative research refers to the use of modern mathematical methods to process relevant data, statistical data, establish various predictive models reflecting the regular relationship between relevant variables, and use mathematical models to calculate the various indicators and numerical values ​​of the research object a way.

  (2) Qualitative research is the basic premise of quantitative research, and quantitative research is the further deepening of qualitative research. It must be pointed out here that although qualitative research requires relatively low mathematical knowledge, there is no distinction between the two research methods, which is better and the other cannot be completely separated. In comparison, quantitative research is more scientific because it uses advanced mathematics knowledge, while qualitative research is a bit rougher, but this method has a wider application range and is suitable for general investors and economic workers because it is based on data. It is more suitable when the researcher’s mathematical knowledge is not sufficient or the researcher is relatively weak.

  The difference between qualitative research and quantitative research

  1. Different concepts

  (1) Qualitative research means that researchers use historical review, document analysis, interviews, observations, and participation in experience to obtain educational research materials, and use non-quantitative methods to analyze them , Methods of obtaining research conclusions.

  (2) The results of quantitative research are usually represented by a large amount of data. The research design is to enable researchers to make effective explanations through comparison and analysis of these data.

   Different theoretical foundations

  (1) Qualitative research is mainly a kind of value judgment, which is based on the humanistic methodology of hermeneutics, phenomenology and constructivist theories. The main point of view is that social phenomena are not dominated by causality like natural phenomena, and social phenomena are fundamentally different from natural phenomena.

  (2) Quantitative research is a factual judgment, which is based on the methodology of positivism. Positivism originates from empirical philosophy, and its main point is: social phenomena are objective reality that exist independently, and are not subject to human will. In the evaluation process, the subject and the object are entities that are isolated from each other, and there must be inherent logical causality within and between things. Quantitative evaluation is to find, determine and verify these quantitative relationships.

Importance of Spss

① The operation is simple, no programming

If you have not been exposed to statistical software before, SPSS is undoubtedly quite suitable. As one of the most widely used professional statistical software in the world, analysis results can be obtained only by menu operation. If you don’t have time to learn by yourself, SPSS can help you get started with data analysis in a short time.

② It has a wide range of applications.

For liberal arts majors, SPSS plays a role in many places, especially in the field of questionnaire analysis, SPSS is a unique existence. The entry of questionnaires, the reliability, validity test, T test, correlation analysis, multiple regression analysis, etc. in the questionnaire, SPSS is easy to grasp.

For science and engineering majors, SPSS can handle single-factor, multi-factor analysis of variance, multiple comparisons, and comparison of sample means involved in experimental design.

③ Powerful functions, advancing with the times

For decades, SPSS has been widely used in business intelligence, biomedicine, market research and other fields, providing users with functions such as data sorting, charting, and result display, which are flexible, clear and intuitive.

SPSS has strong compatibility and keeps pace with the times. STATA, SAS, R, Python, and databases are all available for docking. SPSS can call this function of Python, did you know?

Common Resume Mistakes

Biggest mistakes made in developing or submitting resumes

The most critical error made in writing resumes is to fail to mention specific accomplishments.

Resumes often include excellent job descriptions, but indicate little about how well the job was done. It is very important to include your accomplishments, using data to back them up if possible. It is not sufficient to merely describe a new initiative you introduced, but describe how it benefited the organization in cost savings, product/service improvement, or other tangible ways.

The second major mistake that seen frequently is the use of the functional resume format, where a list of accomplishments is given first. While that approach does highlight achievements, it leaves the employer guessing as to where and when your accomplishments took place. Employers will not spend the time trying to determine sequence and prefer a straightforward chronological approach so that they can see clearly the progression of your career.

Common mistakes to avoid in the resumes

  • Don’t download a resume from the internet or blindly copy anyone’s look. You will be restricted by someone else’s arrangement and not have a place or sufficient place to put in special items. Design your own resume and it should be neat, readable, not cute and gimmicky.
  • Sending your resume to any all jobs irrespective of the fit. When responding to job postings or ads you should only your resume if your background closely fits the description.
  • Making obvious that the recipient is part of a mass mailing.
  • Trying to go around the person designed to recruit for the position.
  • Being too pushy: calling too often, calling when posting says “No calls please.”
  • Mailing it instead of E-mailing it.

Items never be listed on a resume

Personal information relating to physical characteristics, martial status, age, sex or religious affiliation has no place on a resume. Any thing that does not relate to your talent and experience only takes up valuable space-and possibly lessens your chances of getting in front of the interviewer.

Best way to organize a resume

There are two main methods of organizing a resume. These are referred to as the reverse chronological format and the functional format. The chronological format-which emphasizes career progression over time-is by far the most frequently used as it is the easiest for most readers to follow. In this format, a candidate’s work experience is listed in reverse chronological order, in other words with the most recent position first. Recent studies show that employers and executive recruiters continue to prefer this format to the functional style, because there is no guesswork required when it comes to identifying a person’s work history and career progression.

The functional format stresses the job seeker’s most marketable skills, but de-emphasizes career progression, job titles, and chronology. This approach works best for career changers with little or no direct experience in the field they are targeting or for individuals who have multiple gaps in their work history. For those pursuing a career change, however, it is critical that they effectively network to gain access to key contacts in their new target field and not simply rely on their resume. Ultimately, the decision regarding whether to use a functional format should always be weighted against the fact that most traditional employers and executive recruiters still prefer the chronological approach to resumes.

Whether you are a young scholar, a doctoral student or a graduate student preparing for a Ph.D., this article is a must-read classic blog post on your research road to share experience.

The first problem encountered when doing research is choosing the topic. We must first distinguish the difference between the topic and the question. Often students will ask me how to do a research, and I will ask him what question he wants to study? Students will list some key words, such as education, agriculture, etc. These keywords are not strictly a question for you to research. Keywords are just topics that you are researching, and they are still far away from specific questions that you are researching. After completing the distance from topic to question, you have taken the first step of the research and can really start a research. The following three aspects should be paid attention to when selecting the topic.

First, what are you interested in

If you are not interested in a problem, it is difficult for you to make excellent research. A master student once said to me, “Mr. Uzair, you have a lot of ideas, just give me one and I will do it.” I told him, I cannot help you, before you are interested in this issue. In the process of studying, students will certainly be interested in certain questions. Sometimes you will have this kind of experience. When you see an article, you feel excited, sometimes you do not. This is the difference in interest. A person’s interests are related to his accumulation, reading and personal experience.

On the way here, colleague said something to me, and I very much agree: “Great love can have great wisdom.” How do you understand this sentence? When you are doing a research, you must prove that your research is important. How to demonstrate the importance of the research question? It is the research on this issue that can improve human society and bring welfare to humankind. Continue to ask, where does such a problem come from? It depends on whether we can transcend our personal joy and lose our attention to the future and destiny of the entire society. This is the source of interest. A good economist should have a strong sense of humanistic care and social responsibility. The starting point of a good research is a good question, which is more than half of success. In this sense, being a human being and being a learned person are the same. If you do not pay attention to issues that are important to the general public, you will not be able to do excellent knowledge; if you want to fight for fame and gain every day, you will not be able to do excellent research, because the issues you care about are not relevant to most people.

Second, you have to understand the problem

Choose the aspects that you think are important. Mathematics cannot tell you, what is important. ‘What is important’ depends on your own understanding. After determining the research direction, you should focus on smaller aspects according to your own understanding. For example, in all aspects related to the three rural issues, if you feel that the land issue is the most critical, you have already taken a step forward. Next, you think that focusing on “how to make rural laborers lose their land and obtain social security after they move to cities in the process of industrialization and urbanization” is an important research topic. If you start from the three rural issues, narrow it down to the land issue, and then narrow it down to “how to use land for security”, you have already transitioned from topic to question.

Third, why do you concern about importance?

 It is mainly reflected in two aspects: theoretically important and practically important. The best research is both. I cannot rule out some outstanding articles that are important in theory but not important in practice, or research that is important in practice but not important in theory, especially those in economics that have pioneering work in methodology, often academic and theoretical value without direct social practical significance.

The above three aspects need to be explained one by one when you do a research or write a thesis. Many of our classmates understand doing research as constructing a mathematical or quantitative model and do not pay much attention to “writing”. You have completed the math work and the measurement work. I want to remind you that maybe your research work is only 30% completed, at most 30%. Because you haven’t told us why it is important.

The current division of labor in social sciences, especially economics, is very detailed. So if you get one hundred articles, you may not understand the content of the ninety-nine articles at all, so which of the remaining ninety-nine articles do you read? When we usually read an article, we first look at the abstract. It will tell you what he has studied and what contributions he has made. Then look at the introduction to answer the previous questions in more detail. Finally, look at the conclusion and see what creative content this research has. Finally, look at the main part of the article, the theoretical and empirical models. If your abstract and introduction are not well written, when others see 500 words, they will not read your article. If you don’t pay attention to these, your research may not produce the social value it should have, so you must pay attention to it, and it may even take 70% of the time to write the introduction.

Once you find a question that you find interesting, innovative, and meaningful, then you need to judge whether it is feasible? Whether it is feasible in theory, first of all depends on whether the starting point is correct. Mathematics cannot tell you whether the starting point is right. For the study of a problem, you can use either a static model or a dynamic model. You can adopt a multi-period model or a single-period model. You can adopt a model with or without government. It depends on your understanding of this problem.

A more common problem is evidence. Maybe you have a very good idea. Is there any data to consider? Is there enough financial capacity to obtain the data to support this research? Secondly, we must consider whether the variables needed for research are measurable, at least in theory, whether someone has proposed an excessive amount of method. It is also necessary to consider whether the data sample is large enough. For example, time series data requires at least 30 observation points, but China’s reform and opening up has only 28 years since 1978. Using annual data can only be a solution. These issues must be thought beforehand.

How to choose a suitable research topic?

1. Demand criteria. Determine the subject based on the development and demand analysis in the industry in the field, so that scientific research can better serve the decision plan of the administrative leadership of the industry, solve the existing problems in the industry, and provide assistance for the development of the industry.

2. Scientific guidelines. The selected topic must have factual and theoretical basis, and must have scientific value. Such as new discoveries in educational science, supplementation of blanks, correction of general explanations, supplements of previous explanations, etc.

3. Creativity criteria. It is the life of scientific research, which requires new inventions, new discoveries, and new creations in terms of research results.

4. Feasibility criteria. One is to see whether the selected topic has research ability and research conditions. The topic selection should not be too dense, too large, or too abstract. Especially for those who are new to education and scientific research, they should choose narrower and more detailed topics. The second is to see whether the effect of the research topic has the value of implementation.

When choosing a research topic, you should choose the topic level based on the industry you are engaged in, and according to the purpose of the reported topic. For example: Take the appraisal job as an example. You can choose a provincial-level topic for the middle-level job title. To evaluate senior professional titles, you need to declare national-level topics. In addition, I remind everyone: when applying for a project, you must do what you can, and it is basically based on your own strength to apply for research.

What is a Research Proposal?

The research proposal is an integral part to the PhD application process. It should provide an outline of your proposed topic and begin to develop the framework you will use to conduct your research. It will be the primary resource you are assessed by when you make your application, and thus, needs to make a powerful first impression.

There are three main things that you need to demonstrate in your proposal. These are:
1) You are capable of independent critical thinking and analysis.

2) You can communicate your ideas clearly.

3) You understand what a PhD involves.

How to Make It Better

Despite needing to demonstrate these things, it is important to remember that your proposal does not need to be perfect; you are not expected to be an expert before you begin your PhD. What is expected is that you have thought about your topic, identified a potential research area and have started to develop a plan of how you might undertake your research. None of these needs to be perfect. They just need to demonstrate that with a bit of guidance, you will be able to achieve a PhD.

There is no specific format to a PhD proposal and often the format will be dependent on the institute you apply to. However, a good proposal will most likely include the following:

1) A clear title / research question

2) Introduction What is the research question / problem? What is your hypothesis, research aims and objectives? Why is this research important? What difference will your research make?

3) Brief literature review / Background  Put your research proposal into context with published literature. Identify any gap in the knowledge or questions which have to be answered. What will your research add to the research field?

4) Research timetable and methodology a Research timetable – what are the main stages of the project and what methods will you use to carry out your research? b. Explain what you expect from each year of your PhD. Openly discuss challenges you expect to face and how you might overcome them.

5) Summary and conclusions provide your readers with the main points and conclusion of your proposal.

6) Bibliography Include a list of key references, which demonstrate you have read around your subject area.


More doesn’t always mean better. The research proposal doesn’t have to be long; 1000-2000 words is a good guideline. However, what is more important is that it articulates clearly, coherently and concisely what you want to achieve during your PhD and how you will go about achieving it. The proposal should leave the readers interested, excited and keen to find out more about you and your ideas. Do this and you will have a great chance of submitting an impressive application!