CN110991924A - Structural equation model-based high-level thesis publication number influence factor evaluation method - Google Patents
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Abstract
The invention discloses a high-level thesis publication number influence factor evaluation method based on a structural equation model. The method realizes quantitative and systematic evaluation of the influence factors of the high-level publication number of doctor's living papers, so as to improve the publication number and quality of doctor's living papers and further provide suggestions for improving the doctor's living culture quality.
Description
Technical Field
The invention belongs to the technical field of high-level thesis publication quantity influence factor evaluation methods, and particularly relates to a high-level thesis publication quantity influence factor evaluation method based on a structural equation model.
Background
The doctor enrollment scale in China is enlarged year by year, 9.55 million people enroll in doctor students in 2018, and the increase is 13.8% compared with 2017. In quantity, China has possessed the most huge teams of doctors in the world for more than ten years. However, from the perspective of doctor's culture quality, a large gap still exists between the doctor and developed countries such as the United states, and as far as 3 months in 2017, the average number of citations of scientific research papers published by the university of the fifth highest ranking institute in China is 10.27-13.40, which is far lower than the average level of the university of the fifth highest ranking institute in the United states (about 28.02); the contribution rate of the top-ranked papers is around 12%, lower than 15% in the united states. Promote the conversion of the number of the doctor-living culture to high quality, and improve the quality of the doctor-living culture is an important challenge of the doctor-living culture at present.
The evaluation of the quality of doctor's live culture is a general social concern. The number and the level of published academic papers are important marks for testing the research level of the doctor life, and the published number of high-level papers becomes an important index for measuring the culture quality of the doctor life. The quality of doctor's culture is generally evaluated by the published number of the journal recorded in SCI/SSCI (Science circulation Index/Social Sciences circulation Index) in colleges and universities. High-level Chinese journals also play a significant role in academic evaluation of the humanistic society discipline.
Therefore, there is a need to find a method for quantitatively and systematically analyzing the influence factors of the number of doctor-born high-level papers published, and providing evaluation criteria for doctor-born culture quality.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a structural equation model-based high-level thesis publication number influence factor evaluation method, which realizes quantitative and systematic analysis of influence factors of the high-level thesis publication number of doctor students.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
a high-level thesis publication number influence factor evaluation method based on a structural equation model comprises the following steps:
s1, setting influencing factors influencing the publication number of high-level papers, and taking the influencing factors as initial variables of the structural equation model;
s2, collecting high-level paper-making person data corresponding to the initial variable in the step S1;
s3, calculating initial variables in the step S1 according to the data of the high-level paper publishers in the step S2;
s4, calculating a model fitting degree index according to the initial variable by using a structural equation model;
s5, setting a fitting degree judgment value, and judging whether the model fitting degree index calculated in the step S4 meets the fitting degree judgment value; if yes, judging influence factors influencing the publication number of the high-level papers according to the significance, and judging influence of the influence factors on the publication number of the high-level papers according to the signs of the normalization coefficients; otherwise, correcting the structural equation model according to the model correction value, and returning to the step S4;
and S6, deleting the unremarkable variables and paths, and analyzing the structural equation model again to obtain the evaluation result of each variable on the publication number of the high-level papers.
Further, the influencing factors influencing the publication quantity of the high-level papers in the step S1 include the instructor ' S scientific research ability, the instructor ' S social service ability, graduation conditions, the age of entrance, learning manner, categories of the master ' S school of employment, and the history of outbound/outbound cultivation; the social service capacity of the instructor is measured by three secondary indexes, namely the annual average high-level thesis quantity of the instructor, the annual average national level project quantity of the instructor and the annual average national level project amount of the instructor, and the social service capacity of the instructor is measured by the annual average horizontal project quantity of the instructor and the annual average horizontal project amount of the instructor.
Further, the high-level thesis publisher data in step S2 includes name, entrance age, graduation time, election mode, learning mode, school type of reading, outbound/outbound training experience, teacher name, thesis publication information, country-level project information of the teacher, horizontal project information and thesis publication information, and duration of the teacher in the school.
Further, the step S3 calculates the initial variables in the step S1 according to the data of the paper publishers in the step S2, specifically:
calculating the annual average national level project quantity of the instructor as the ratio of the national level project quantity of the instructor to the time length of the instructor in the period of the checking and giving work;
calculating the national level project sum of the annual average of the instructor as the ratio of the total sum of the national level project of the instructor to the time length of the instructor in the checking job;
calculating the annual average horizontal item quantity of the instructor as the ratio of the horizontal item quantity of the instructor to the time length of the instructor in the work of checking and giving;
calculating the annual average horizontal project sum of the instructor as the ratio of the total horizontal project sum of the instructor to the time length of the instructor in the checking job;
calculating the annual average high-level paper quantity of the instructor as the ratio of the quantity of SCI/SSCI recorded periodicals published by the instructor and named as a first author and a second author to the quantity of Chinese core periodicals to the length of the instructor at the position of the school and the hold;
the high-level paper quantity of the paper publication personnel is calculated as the quantity of the SCI/SSCI collection periodicals and the quantity of the Chinese core periodicals of the first author and the second author of the publication personnel.
Further, in the step S5, when the model fitting degree index calculated in the step S4 satisfies the fitting degree judgment value, the path relationships between the quality of the academic thesis and the scientific research ability of the instructor, the social service ability of the instructor, the election mode, the study age, the study mode, the category of the school for the master, and the outbound/outbound training experience are respectively obtained, the influence factors influencing the publication number of the high-level thesis are judged according to the significance, and the influence factors influencing the publication number of the high-level thesis of the doctor in the positive direction or the negative direction are judged according to the sign of the normalization coefficient.
Further, in step S5, the modifying the structural equation model according to the model modification value specifically includes: and sequentially establishing correlation relations of the model correction values according to the sequence from large to small, establishing a structural equation model correction sequence and a path, and sequentially correcting the structural equation model according to the correction sequence and the path.
The invention has the following beneficial effects: according to the method, the influence factors influencing the publication quantity of the high-level papers are set and serve as the initial variables of the structural equation model, the structural equation model is used for carrying out iterative analysis on the initial variables, and the unimportant variables and paths are deleted, so that the influence degree of different factors on the publication quantity of the high-level papers and the evaluation result of the positive and negative influence relation are finally obtained, the influence factors of the publication quantity of the high-level papers of doctor students are quantitatively and systematically evaluated, the publication quantity and quality of the doctor students are improved, and further, a suggestion is provided for improving the culture quality of the doctor students.
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FIG. 1 is a schematic flow chart of the method for evaluating influence factors of high-level paper publication quantity based on a structural equation model;
FIG. 2 is a schematic diagram of a structural equation model established in an embodiment of the present invention;
FIG. 3 is a diagram illustrating the results of estimating the parameters of the initial structural equation model according to an embodiment of the present invention;
FIG. 4 is a diagram of the final structure equation model parameter estimation result in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, an embodiment of the present invention provides a method for evaluating influence factors of high-level publication quantity of papers based on a structural equation model, including the following steps S1 to S6:
s1, setting influencing factors influencing the publication number of high-level papers, and taking the influencing factors as initial variables of the structural equation model;
in this embodiment, the doctor who issues high-level papers according to the present invention takes the doctor as an example, and the doctor respectively sets the factors such as the research ability of the teacher, the social service ability of the teacher, the graduation conditions, the entrance age, the learning manner, the category of the school visited by the master, and the outbound/outbound training experience as the influencing factors of the number of high-level papers issued by the doctor.
The present invention will be described in further detail below with respect to the various influencing factors set forth above:
the instructor's scientific research ability refers to the instructor's scientific research level, academic level and ability to obtain scientific research projects and scientific research funding, and can be measured by three secondary indexes, namely the instructor's annual average high-level thesis quantity, the instructor's annual average national project quantity and the instructor's annual average national project funding.
The lead social service ability refers to the ability of a lead to acquire lateral projects, which can be measured by the lead yearly lateral project quantity and the lead yearly lateral project cost.
Graduation conditions mean that the doctor reaches the quantity and quality requirements of published papers required for graduation.
The learning modes are divided into two modes, including a failure mode and an incumbent mode.
The category of the school for the master reading is the category of the school for the doctor during the period of the master reading, which is divided into two categories, including 985 colleges and non-985 colleges.
The outbound/outbound culture experience refers to whether the doctor lives in the period of reading and has the history of leaving the doctor (including hong Kong and Australia Taiwan) for study.
The horizontal project is a subject of entrustment to a business entity or an enterprise.
The national-level project refers to a subject on which a scientific research administration designated by a country represents government establishment.
The above-mentioned set influence factors are used as initial variables of the structural equation model.
S2, collecting high-level paper-making person data corresponding to the initial variable in the step S1;
in this embodiment, the high-level thesis publisher of the present invention takes the doctor student as an example, and the data to be collected includes name, age of entrance, graduation time, selection mode, learning mode, category of school attended by the master, exit/departure culture experience, name of the director, thesis publication information of the doctor, country-level project information of the director, horizontal project information and thesis publication information, and length of time of the director in the school and leave.
According to the method, 252-bit graduation doctor student data collected from a management system and a scientific research management department in the period of 2009-2019 are taken as an example, and 205 effective samples are finally obtained by deleting missing values and abnormal values.
S3, calculating initial variables in the step S1 according to the data of the high-level paper publishers in the step S2;
in this embodiment, the present invention calculates variables such as the number of senior high-level papers of the instructor, the number of national-level items of the senior average of the instructor, the amount of horizontal items of the senior average of the instructor, the number of horizontal items of the senior average of the instructor, and the number of senior high-level papers of the doctor according to the doctor data acquired in step S2.
Calculating the annual average national level project quantity of the instructor as the ratio of the national level project quantity of the instructor to the time length of the instructor in the period of the checking job;
calculating the national level project sum of the annual average of the instructor as the ratio of the total sum of the national level project of the instructor to the time length of the instructor in the checking job;
calculating the annual average horizontal item quantity of the instructor as the ratio of the horizontal item quantity of the instructor to the time length of the instructor in the work of checking and giving;
calculating the annual average horizontal project sum of the instructor as the ratio of the total horizontal project sum of the instructor to the time length of the instructor in the checking job;
calculating the annual average high-level paper quantity of the instructor as the ratio of the quantity of SCI/SSCI recorded periodicals published by the instructor and named as a first author and a second author to the quantity of Chinese core periodicals to the length of the instructor at the position of the school and the hold;
the high-level paper quantity of the paper publication personnel is calculated as the quantity of the SCI/SSCI collection periodicals and the quantity of the Chinese core periodicals of the first author and the second author of the publication personnel.
The invention adopts papers signed as the first and second authors on the periodical recorded by SCI/SSCI and the high-level Chinese periodical to measure the high-level papers.
S4, calculating the fitting degree of the model according to the initial variables by using a structural equation model;
in this embodiment, the method uses each set influence factor as an initial variable of the structural equation model, establishes an initial structural equation model as shown in fig. 2, analyzes according to the initial variable by using the structural equation model, and calculates to obtain a fitting degree index of the structural equation model. The fitting degree index calculation process can be realized by AMOS software, and the details are not repeated in the invention.
S5, setting a fitting degree judgment value, and judging whether the model fitting degree index calculated in the step S4 meets the fitting degree judgment value; if yes, judging influence factors influencing the publication number of the high-level papers according to the significance, and judging influence of the influence factors on the publication number of the high-level papers according to the signs of the normalization coefficients; otherwise, correcting the structural equation model according to the model correction value, and returning to the step S4;
in this embodiment, the fitting degree determination value of the structural equation model is preset, and the model fitting degree index calculated in step S4 is compared with the set fitting degree determination value to determine whether the fitting degree of the model, that is, the fitting degree index satisfies the fitting degree determination value.
If the fitness index meets the requirement of the fitness judgment value, observing the path relation between the quality of the doctor student's college papers and the teacher's scientific research ability, the teacher's social service ability, the election mode, the entrance age, the learning mode, the class of school taken by the master and the outbound/outbound culture experience, judging influence factors influencing the publication number of the high-level papers according to the significance, and judging whether the influence factors influence the publication number of the doctor student high-level papers in a positive or negative direction according to the sign of the standardization coefficient.
If the fitting degree index does not meet the requirement of the fitting degree judgment value, correcting the structural equation model according to the model correction value, specifically: and sequentially establishing correlation relations of the model correction values according to the sequence from large to small, wherein only one correlation relation is established each time. And should also satisfy: the index variables of the exogenous latent variables and the index variables of the endogenous latent variables cannot be directly related; the residual terms of the index variables and the latent variables cannot be added in a common variation relationship. Where latent variables are variables that cannot be observed directly but can be inferred or measured by other observed indicator variables (measurable variables); exogenous variables refer to variables that affect the system without being affected by the system; endogenous variables refer to variables determined by the model; the residual term refers to the total influence term of the exogenous variable and other random factors on the endogenous variable, which are not included in the model.
FIG. 3 is a schematic diagram of the estimation result of the initial structure equation model parameters; the initial structural equation model path analysis results are shown in table 1.
TABLE 1 initial structural equation model Path analysis results
Wherein, χ2Is the chi-square value, df is the chi-square degree of freedom, χ2And/df is the chi-square degree of freedom ratio, RMSEA is the approximate error root mean square, CFI is the relative fitting index, TLI is the non-standard fitting index, IFI is the modified standard fitting index, and PCFI is the simple-to-efficient comparative fitting index.
As can be seen from Table 1, χ of the initial structural equation model25.186, RMSEA, CFI, TLI and IFI do not meet the standard, which indicates that the initial structure equation model is not well fitted with the data, so the initial structure equation model needs to be corrected.
Referring to fig. 3, the number of high-level papers per year of the instructor, the number of national-level projects per year of the instructor, and the amount of national-level projects per year of the instructor are used as the observation variables of the scientific research ability of the instructor, the number of horizontal projects per year of the instructor, the amount of horizontal projects per year of the instructor, and the amount of horizontal projects per year of the instructor are used as the observation variables of the social service ability of the instructor, the scientific research ability of the instructor, the social service ability of the instructor, graduation conditions, the age of entrance, learning manner, category of the master for reading colleges and universities, and whether going out/going out are cultured as exogenous latent variables, the number of published papers of high-level papers of the doctor student is endogenous latent variables, 0.64(p <0.01) indicates that the number of high-level papers per year of the instructor.
According to the correlation relationships sequentially established from large to small according to the model correction value, the structure equation model correction sequence and path are established, and are shown in table 2.
TABLE 2 route correction sequence Table
In order to increase the goodness of fit of the model, the order and the path are corrected according to the structural equation model, and the correlation between e3 and e5 is added firstly. The modified model fit index values are shown in table 3.
TABLE 3 structural equation model analysis fitting index after first correction
As can be seen from table 3, the fitting indexes of the model after the first modification are all improved, but the RMSEA, CFI, TLI and IFI still do not reach the ideal state, so further modification is needed.
After the structural equation model is corrected for multiple times according to the correction sequence and the path, the fitting index of the structural equation model reaches the requirement, and the final fitting condition is shown in table 4.
TABLE 4 Final correction analysis fitting index of structural equation model
As can be seen from Table 4, after multiple revisions, each fitting index of the structural equation model and the sample data meets the set fitting degree judgment value requirement.
After the model is corrected, judging which factors influence the number of the postings of the doctor's birth high-level papers according to the path coefficients and the significance, judging whether the influence of the factors on the number of the postings of the doctor's birth high-level papers is positive or negative according to the signs of the normalization coefficients, wherein the path coefficients and the significance among the variables are shown in a table 5.
TABLE 5, verification results
As can be seen from table 5, the mentor's ability to develop scientific results has a significant positive impact on the number of bosch high-level papers published (β ═ 0.22, p <0.01), the mentor's social service ability has a significant negative impact on the number of bosch high-level papers published (β ═ 0.12, p <0.05), the graduation conditions have no significant impact on the number of bosch high-level papers published, the age at school has a significant negative impact on the number of bosch high-level papers published (β ═ 0.16, p <0.05), the learning mode has a significant positive impact on the number of bosch high-level papers published (β ═ 0.16, p <0.05), the yield is better than the job, the master has no significant impact on the number of bosch high-level papers published in reading schools, and whether the doctor/border culture has a significant positive impact on the number of bosch high-level papers published (β ═ 0.17, p < 0.01).
And S6, deleting the unremarkable variables and paths, and analyzing the structural equation model again to obtain the evaluation result of each variable on the publication number of the high-level papers.
In this embodiment, according to the determination result of step S5, the insignificant variables and paths are deleted, and structural equation model analysis is performed again to obtain the evaluation result of each variable on the number of published high-level papers. The final structural equation model parameter estimates are shown in fig. 4.
As can be seen from fig. 4, the scientific research ability, learning manner, and outbound/outbound training experience of the instructor positively influence the high-level thesis of the doctor's hair growth table. The scientific research capability of a unit of instructor leads to the change of the published quantity of 0.22 unit of doctor-birth high-level papers, the learning mode is that the quantity of doctor-birth high-level papers of the doctor-birth is 15% higher than that of the doctor-birth high-level papers of the doctor-birth of the worker, and the doctor-birth experienced in the culture of the doctor-birth/the exit is 17% higher than that of the doctor-birth high-level papers of the doctor-birth of the doctor who is not in the country/. The study age and social service ability of the instructor have negative influence on the high-level thesis of the hair growth table of the pharmacological doctor. The age of one unit of admission, and the ability of the lead to social services resulted in a variation in the number of-0.15 and-0.12 units of doctor's high-level papers published, respectively. Furthermore, graduation conditions and how the master took a school class for reading have no significant effect.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (6)
1. A high-level thesis publication number influence factor evaluation method based on a structural equation model is characterized by comprising the following steps of:
s1, setting influencing factors influencing the publication number of high-level papers, and taking the influencing factors as initial variables of the structural equation model;
s2, collecting high-level paper-making person data corresponding to the initial variable in the step S1;
s3, calculating initial variables in the step S1 according to the data of the high-level paper publishers in the step S2;
s4, calculating a model fitting degree index according to the initial variable by using a structural equation model;
s5, setting a fitting degree judgment value, and judging whether the model fitting degree index calculated in the step S4 meets the fitting degree judgment value; if yes, judging influence factors influencing the publication number of the high-level papers according to the significance, and judging influence of the influence factors on the publication number of the high-level papers according to the signs of the normalization coefficients; otherwise, correcting the structural equation model according to the model correction value, and returning to the step S4;
and S6, deleting the unremarkable variables and paths, and analyzing the structural equation model again to obtain the evaluation result of each variable on the publication number of the high-level papers.
2. The method for evaluating influence factors of high-level thesis publication numbers based on a structural equation model as recited in claim 1, wherein the influence factors influencing the publication numbers of the high-level thesis in step S1 include instructor ' S ability to develop scientific research, instructor ' S ability to serve social services, graduation conditions, age of entrance, learning style, category of student ' S school of employment, and history of outbound/outbound training; the social service capacity of the instructor is measured by three secondary indexes, namely the annual average high-level thesis quantity of the instructor, the annual average national level project quantity of the instructor and the annual average national level project amount of the instructor, and the social service capacity of the instructor is measured by the annual average horizontal project quantity of the instructor and the annual average horizontal project amount of the instructor.
3. The method as claimed in claim 2, wherein the high-level thesis presentation quantity influence factor evaluation method based on the structural equation model is characterized in that the high-level thesis presenter data in the step S2 includes name, entrance age, graduation time, election mode, learning mode, school category of reading, outbound/outbound training experience, instructor name, thesis presentation information, instructor 'S country-level project information, lateral project information and thesis presentation information, and instructor' S duration of school assignment.
4. The method for evaluating influence factors of high-level publication quantity of papers based on structural equation model as claimed in claim 3, wherein said step S3 is implemented by calculating initial variables in step S1 according to the data of the publication personnel in step S2:
calculating the annual average national level project quantity of the instructor as the ratio of the national level project quantity of the instructor to the time length of the instructor in the period of the checking and giving work;
calculating the national level project sum of the annual average of the instructor as the ratio of the total sum of the national level project of the instructor to the time length of the instructor in the checking job;
calculating the annual average horizontal item quantity of the instructor as the ratio of the horizontal item quantity of the instructor to the time length of the instructor in the work of checking and giving;
calculating the annual average horizontal project sum of the instructor as the ratio of the total horizontal project sum of the instructor to the time length of the instructor in the checking job;
calculating the annual average high-level paper quantity of the instructor as the ratio of the quantity of SCI/SSCI recorded periodicals published by the instructor and named as a first author and a second author to the quantity of Chinese core periodicals to the length of the instructor at the position of the school and the hold;
the high-level paper quantity of the paper publication personnel is calculated as the quantity of the SCI/SSCI collection periodicals and the quantity of the Chinese core periodicals of the first author and the second author of the publication personnel.
5. The method as claimed in claim 4, wherein in step S5, when the model fitness index calculated in step S4 satisfies the fitness judgment value, the method respectively obtains the academic paper quality and the path relationship between instructor scientific research ability, instructor social service ability, election mode, entrance age, learning mode, master category of school for reading, and outbound/outbound training experience, judges the influence factor affecting the publication number of high-level papers according to the significance, and judges whether the influence factor has a positive or negative influence on the publication number of high-level papers by doctor according to the sign of the normalization coefficient.
6. The method for evaluating the influence factors of the high-level publication quantity of papers based on the structural equation model as recited in claim 5, wherein in the step S5, the modifying the structural equation model according to the model modification value specifically comprises: and sequentially establishing correlation relations of the model correction values according to the sequence from large to small, establishing a structural equation model correction sequence and a path, and sequentially correcting the structural equation model according to the correction sequence and the path.
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